This is the the same approach I’ve done before but instead of filtering environmental data between sites, I’m fitlering it one month (30 days) before field surveys.
Overview: Setting up my environmental and field data to run analysis. On the same row I’ll have monthly field variables (ex. mean density one month) and enviromental data since the last field survey or since 30 days prior. Field data will be linked with closest water station. China camp and paradise cay, EOS pier and point chauncy, Richardson Bay and brickyard park, and Horseshoe bay and fort point. I also have EOS air linked with Paradised Cay and Point Chauncy field data and Golden Gate air data with Horseshoe Bay and Richardson Bay field data. Possibly a cleaner way to do this but this works. I then merge all data to one large df that I will use for analysis.
Steps for environmental data: The environmental data here has been cleaned and is the filtered for tide hourly median dfs. First I filter environmental data one month before field survey dates, then take median. Next I did daily minimum, daily maximum, daily range, and daily median and saved these in a csv to explore more later. I then took the median of those values subseted between survey dates. I combine the data into one df per site/station. At the end I’ll export these as csvs which I could then use for analysis.
Events I’m interested in as define by my research questions and that will be used for model analysis:
- daily maximum salinity less than 10
- daily minimum salinity greater than 28
- daily maximum air temperature great than 26C (~80F)
- daily maximum pH greater than 8 - Adult density from the month prior. The first two values for this column will be “NA” since we didn’t count adult density the first month (first NA) and then there’s an NA for the adult density before surveys started. Doing this at the very end.
UPDATE: 7/8/2021 - I removed dissoloved oxygen from this markdown. I did not filter dissolved oxygen for tide. Not using dissolved oxygen in my analysis. I was orgionally interested in dissolved oxygen as a pH proxy.
- I also focused “extreme” events for my analysis (I had too many random ones before I wasn’t using)
Notes:
-another code that works for counting rows: length(which(a1$max.daily.sal < 10))
Field data: https://github.com/Cmwegener/thesis/tree/master/data/field
Environmental data:https://github.com/Cmwegener/thesis/tree/master/data/environmental Set up
rm(list=ls())
library(tidyverse)
library(ggpubr)
library(scales)
library(chron)
library(plotly)
library(taRifx)
library(aweek)
library(easypackages)
library(renv)
library(here)
library(ggthemes)
library(gridExtra)
library(patchwork)
library(tidyquant)
library(recipes)
library(cranlogs)
library(knitr)
library(openair)
library(data.table)Read in field data
field<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/field/CB_field_data_plus.csv",
header = TRUE
)Removing outlier
I have a note from my lab notebook about an extra large thalli I collected. It was heavier than the scale limit and no other thalli collected was that heavy. Additionally it has almost double the number of reproductive apices to the next highest on (401 RA while the next highest is 235). As such it is not a good represenative of the population and I am removing this outlier from analysis.
#outlier on row 515. Keeping field data but removing thalli specific data (weight, apices, ects). Since it wasn't selected as the subset for additional reproductive analysis (number of conceptacles, oogonica, ect) those paramaters are already "NA'
field$ww.veg<-replace(field$ww.veg, 515,NA)
field$ww.repro<-replace(field$ww.repro, 515,NA)
field$dw.veg<-replace(field$dw.veg, 515,NA)
field$dw.repro<-replace(field$dw.repro, 515,NA)
field$apices.repro<-replace(field$apices.repro, 515,NA)
field$apices.veg<-replace(field$apices.veg, 515,NA)####Paradise Cay and China Camp####
#subset site
pc<-subset(field, field$site.old == "PC")
pc$date<-as.Date(pc$date, format=c("%Y-%m-%d"))
#calculating the following parameters per survey/month
#mean of fucus density
pc.cc<-aggregate(no.fuc.q ~ date, pc, mean, na.rm=TRUE)
#mean percent cover
pc.r<-aggregate(cover ~date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=T)
#mean of large fucus density
pc.r<-aggregate(no.large.fuc.q ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean small fucus density
pc.r<-aggregate(no.small.fuc.q ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#median reproductive cover class
pc.r<-aggregate(covcl.repro ~ date, pc, median, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean vegetative dry weight
pc.r<-aggregate(dw.veg ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean reproductive dry weight
pc.r<-aggregate(dw.repro ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean reproductive apices
pc.r<-aggregate(apices.repro ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean vegetative apices
pc.r<-aggregate(apices.veg ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean percent reproductive apices
pc.r<-aggregate(perc.ra ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean oogonia per conceptacle
pc.r<-aggregate(oog.per.con ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean percent reproductive dry weight
pc.r<-aggregate(perc.rdw ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean oogonia per receptacle
pc$oog.recpt<-(pc$oog.per.con * pc$no.concept.recp)
pc.r<-aggregate(no.concept.recp ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean oogonia per thalli
#(note: theres 2 apices per receptical)
# oogonia/receptacle * (1 receptacle / 2 apices) * reproductive apices/thalli = oogonia/thalli
pc$oog.thalli <- (pc$oog.recpt * 0.5 * pc$apices.repro)
pc.r<-aggregate(oog.thalli ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)
#mean conceptacle per thalli
pc$con.thalli <- (pc$no.concept.recp * pc$apices.repro)
pc.r<-aggregate(con.thalli ~ date, pc, mean, na.rm=TRUE)
pc.cc<-merge(pc.cc, pc.r, by="date", all=TRUE)Now I need to add the summaries of the environmental data. Filter environmental data 30 days before field survey.
Salinity
#read and format data
cc.sal.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/cc.sal.tide.csv",
header = TRUE
)
cc.sal.tide$date<-as.Date(cc.sal.tide$date, format=c("%Y-%m-%d"))
#look at survey dates
print(pc.cc$date)## [1] "2018-06-14" "2018-07-17" "2018-08-07" "2018-09-11" "2018-12-05"
## [6] "2019-01-30" "2019-02-20" "2019-03-15" "2019-04-11" "2019-05-09"
## [11] "2019-06-09" "2019-07-21" "2019-08-04" "2019-09-12"#Subset data by dates
a1<- cc.sal.tide[cc.sal.tide$date >= "2018-05-15" & cc.sal.tide$date < "2018-06-14",]
a2<- cc.sal.tide[cc.sal.tide$date >= "2018-06-17" & cc.sal.tide$date < "2018-07-17",]
a3<- cc.sal.tide[cc.sal.tide$date >= "2018-07-08" & cc.sal.tide$date < "2018-08-07",]
a4<- cc.sal.tide[cc.sal.tide$date >= "2018-08-12" & cc.sal.tide$date < "2018-09-11",]
a5<- cc.sal.tide[cc.sal.tide$date >= "2018-11-05" & cc.sal.tide$date < "2018-12-05",]
a6<- cc.sal.tide[cc.sal.tide$date >= "2018-12-31" & cc.sal.tide$date < "2019-01-30",]
a7<- cc.sal.tide[cc.sal.tide$date >= "2019-01-21" & cc.sal.tide$date < "2019-02-20",]
a8<- cc.sal.tide[cc.sal.tide$date >= "2019-02-13" & cc.sal.tide$date < "2019-03-15",]
a9<- cc.sal.tide[cc.sal.tide$date >= "2019-03-12" & cc.sal.tide$date < "2019-04-11",]
a10<- cc.sal.tide[cc.sal.tide$date >= "2019-04-09" & cc.sal.tide$date < "2019-05-09",]
a11<- cc.sal.tide[cc.sal.tide$date >= "2019-05-10" & cc.sal.tide$date < "2019-06-09",]
a12<- cc.sal.tide[cc.sal.tide$date >= "2019-06-21" & cc.sal.tide$date < "2019-07-21",]
a13<- cc.sal.tide[cc.sal.tide$date >= "2019-07-05" & cc.sal.tide$date < "2019-08-04",]
a14<- cc.sal.tide[cc.sal.tide$date >= "2019-08-13" & cc.sal.tide$date < "2019-09-12",]
#median
aa1<-median(a1$salinity, na.rm=TRUE)
aa2<-median(a2$salinity, na.rm=TRUE)
aa3<-median(a3$salinity, na.rm=TRUE)
aa4<-median(a4$salinity, na.rm=TRUE)
aa5<-median(a5$salinity, na.rm=TRUE)
aa6<-median(a6$salinity, na.rm=TRUE)
aa7<-median(a7$salinity, na.rm=TRUE)
aa8<-median(a8$salinity, na.rm=TRUE)
aa9<-median(a9$salinity, na.rm=TRUE)
aa10<-median(a10$salinity, na.rm=TRUE)
aa11<-median(a11$salinity, na.rm=TRUE)
aa12<-median(a12$salinity, na.rm=TRUE)
aa13<-median(a13$salinity, na.rm=TRUE)
aa14<-median(a14$salinity, na.rm=TRUE)
#string these values to a data frame
cc.mon.sal<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.sal<-as.data.frame(cc.mon.sal)
#change column names
names(cc.mon.sal)[1] <- "date"
names(cc.mon.sal)[2] <- "salinity"
cc.mon.sal$date<-as.Date(cc.mon.sal$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.sal[,c("date", "salinity")], by="date")Daily median, range, minimum, and maximum salinity
#table summary saved as a df. Finds the daily median and daily max and min values bases on date column
#tried to do daily range here but it didn't seem to work. I'll just take the difference between the max and min columns
cc.daily.sum<-as.data.frame(setDT(cc.sal.tide)[, .(max.daily.sal = max(salinity), min.daily.sal = min(salinity), daily.med.sal=median(salinity)), .(date)])
#daily range
cc.daily.sum$daily.sal.range<-cc.daily.sum$max.daily.sal - cc.daily.sum$min.daily.sal
#Daily min
#subset dates
a1<- cc.daily.sum[cc.daily.sum$date >= "2018-05-15" & cc.daily.sum$date < "2018-06-14",]
a2<- cc.daily.sum[cc.daily.sum$date >= "2018-06-17" & cc.daily.sum$date < "2018-07-17",]
a3<- cc.daily.sum[cc.daily.sum$date >= "2018-07-08" & cc.daily.sum$date < "2018-08-07",]
a4<- cc.daily.sum[cc.daily.sum$date >= "2018-08-12" & cc.daily.sum$date < "2018-09-11",]
a5<- cc.daily.sum[cc.daily.sum$date >= "2018-11-05" & cc.daily.sum$date < "2018-12-05",]
a6<- cc.daily.sum[cc.daily.sum$date >= "2018-12-31" & cc.daily.sum$date < "2019-01-30",]
a7<- cc.daily.sum[cc.daily.sum$date >= "2019-01-21" & cc.daily.sum$date < "2019-02-20",]
a8<- cc.daily.sum[cc.daily.sum$date >= "2019-02-13" & cc.daily.sum$date < "2019-03-15",]
a9<- cc.daily.sum[cc.daily.sum$date >= "2019-03-12" & cc.daily.sum$date < "2019-04-11",]
a10<- cc.daily.sum[cc.daily.sum$date >= "2019-04-09" & cc.daily.sum$date < "2019-05-09",]
a11<- cc.daily.sum[cc.daily.sum$date >= "2019-05-10" & cc.daily.sum$date < "2019-06-09",]
a12<- cc.daily.sum[cc.daily.sum$date >= "2019-06-21" & cc.daily.sum$date < "2019-07-21",]
a13<- cc.daily.sum[cc.daily.sum$date >= "2019-07-05" & cc.daily.sum$date < "2019-08-04",]
a14<- cc.daily.sum[cc.daily.sum$date >= "2019-08-13" & cc.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.sal, na.rm=TRUE)
aa2<-median(a2$min.daily.sal, na.rm=TRUE)
aa3<-median(a3$min.daily.sal, na.rm=TRUE)
aa4<-median(a4$min.daily.sal, na.rm=TRUE)
aa5<-median(a5$min.daily.sal, na.rm=TRUE)
aa6<-median(a6$min.daily.sal, na.rm=TRUE)
aa7<-median(a7$min.daily.sal, na.rm=TRUE)
aa8<-median(a8$min.daily.sal, na.rm=TRUE)
aa9<-median(a9$min.daily.sal, na.rm=TRUE)
aa10<-median(a10$min.daily.sal, na.rm=TRUE)
aa11<-median(a11$min.daily.sal, na.rm=TRUE)
aa12<-median(a12$min.daily.sal, na.rm=TRUE)
aa13<-median(a13$min.daily.sal, na.rm=TRUE)
aa14<-median(a14$min.daily.sal, na.rm=TRUE)
#string these values to a data frame
cc.daily.min.sal<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.min.sal<-as.data.frame(cc.daily.min.sal)
#change column names
names(cc.daily.min.sal)[1] <- "date"
names(cc.daily.min.sal)[2] <- "daily.min.sal"
cc.daily.min.sal$date<-as.Date(cc.daily.min.sal$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.min.sal[,c("date", "daily.min.sal")], by="date")
#Same thing but for daily maximum values
#median
aa1<-median(a1$max.daily.sal, na.rm=TRUE)
aa2<-median(a2$max.daily.sal, na.rm=TRUE)
aa3<-median(a3$max.daily.sal, na.rm=TRUE)
aa4<-median(a4$max.daily.sal, na.rm=TRUE)
aa5<-median(a5$max.daily.sal, na.rm=TRUE)
aa6<-median(a6$max.daily.sal, na.rm=TRUE)
aa7<-median(a7$max.daily.sal, na.rm=TRUE)
aa8<-median(a8$max.daily.sal, na.rm=TRUE)
aa9<-median(a9$max.daily.sal, na.rm=TRUE)
aa10<-median(a10$max.daily.sal, na.rm=TRUE)
aa11<-median(a11$max.daily.sal, na.rm=TRUE)
aa12<-median(a12$max.daily.sal, na.rm=TRUE)
aa13<-median(a13$max.daily.sal, na.rm=TRUE)
aa14<-median(a14$max.daily.sal, na.rm=TRUE)
#string these values to a data frame
cc.daily.max.sal<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.max.sal<-as.data.frame(cc.daily.max.sal)
#change column names
names(cc.daily.max.sal)[1] <- "date"
names(cc.daily.max.sal)[2] <- "daily.max.sal"
cc.daily.max.sal$date<-as.Date(cc.daily.max.sal$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.max.sal[,c("date", "daily.max.sal")], by="date")
#daily range
#mean
aa1<-median(a1$daily.sal.range, na.rm=TRUE)
aa2<-median(a2$daily.sal.range, na.rm=TRUE)
aa3<-median(a3$daily.sal.range, na.rm=TRUE)
aa4<-median(a4$daily.sal.range, na.rm=TRUE)
aa5<-median(a5$daily.sal.range, na.rm=TRUE)
aa6<-median(a6$daily.sal.range, na.rm=TRUE)
aa7<-median(a7$daily.sal.range, na.rm=TRUE)
aa8<-median(a8$daily.sal.range, na.rm=TRUE)
aa9<-median(a9$daily.sal.range, na.rm=TRUE)
aa10<-median(a10$daily.sal.range, na.rm=TRUE)
aa11<-median(a11$daily.sal.range, na.rm=TRUE)
aa12<-median(a12$daily.sal.range, na.rm=TRUE)
aa13<-median(a13$daily.sal.range, na.rm=TRUE)
aa14<-median(a14$daily.sal.range, na.rm=TRUE)
#string these values to a data frame
cc.daily.sal.range<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.sal.range<-as.data.frame(cc.daily.sal.range)
#change column names
names(cc.daily.sal.range)[1] <- "date"
names(cc.daily.sal.range)[2] <- "daily.sal.range"
cc.daily.sal.range$date<-as.Date(cc.daily.sal.range$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.sal.range[,c("date", "daily.sal.range")], by="date")
#daily median
#mean of these salinity periods
aa1<-median(a1$daily.med.sal, na.rm=TRUE)
aa2<-median(a2$daily.med.sal, na.rm=TRUE)
aa3<-median(a3$daily.med.sal, na.rm=TRUE)
aa4<-median(a4$daily.med.sal, na.rm=TRUE)
aa5<-median(a5$daily.med.sal, na.rm=TRUE)
aa6<-median(a6$daily.med.sal, na.rm=TRUE)
aa7<-median(a7$daily.med.sal, na.rm=TRUE)
aa8<-median(a8$daily.med.sal, na.rm=TRUE)
aa9<-median(a9$daily.med.sal, na.rm=TRUE)
aa10<-median(a10$daily.med.sal, na.rm=TRUE)
aa11<-median(a11$daily.med.sal, na.rm=TRUE)
aa12<-median(a12$daily.med.sal, na.rm=TRUE)
aa13<-median(a13$daily.med.sal, na.rm=TRUE)
aa14<-median(a14$daily.med.sal, na.rm=TRUE)
#string these values to a data frame
cc.daily.med.sal<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.med.sal<-as.data.frame(cc.daily.med.sal)
#change column names
names(cc.daily.med.sal)[1] <- "date"
names(cc.daily.med.sal)[2] <- "daily.med.sal"
cc.daily.med.sal$date<-as.Date(cc.daily.med.sal$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.med.sal[,c("date", "daily.med.sal")], by="date")pH
#read in data
cc.ph.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/cc.ph.tide.csv",
header = TRUE
)
cc.ph.tide$date<-as.Date(cc.ph.tide$date, format=c("%Y-%m-%d"))
#subset
a1<- cc.ph.tide[cc.ph.tide$date >= "2018-05-15" & cc.ph.tide$date < "2018-06-14",]
a2<- cc.ph.tide[cc.ph.tide$date >= "2018-06-17" & cc.ph.tide$date < "2018-07-17",]
a3<- cc.ph.tide[cc.ph.tide$date >= "2018-07-08" & cc.ph.tide$date < "2018-08-07",]
a4<- cc.ph.tide[cc.ph.tide$date >= "2018-08-12" & cc.ph.tide$date < "2018-09-11",]
a5<- cc.ph.tide[cc.ph.tide$date >= "2018-11-05" & cc.ph.tide$date < "2018-12-05",]
a6<- cc.ph.tide[cc.ph.tide$date >= "2018-12-31" & cc.ph.tide$date < "2019-01-30",]
a7<- cc.ph.tide[cc.ph.tide$date >= "2019-01-21" & cc.ph.tide$date < "2019-02-20",]
a8<- cc.ph.tide[cc.ph.tide$date >= "2019-02-13" & cc.ph.tide$date < "2019-03-15",]
a9<- cc.ph.tide[cc.ph.tide$date >= "2019-03-12" & cc.ph.tide$date < "2019-04-11",]
a10<- cc.ph.tide[cc.ph.tide$date >= "2019-04-09" & cc.ph.tide$date < "2019-05-09",]
a11<- cc.ph.tide[cc.ph.tide$date >= "2019-05-10" & cc.ph.tide$date < "2019-06-09",]
a12<- cc.ph.tide[cc.ph.tide$date >= "2019-06-21" & cc.ph.tide$date < "2019-07-21",]
a13<- cc.ph.tide[cc.ph.tide$date >= "2019-07-05" & cc.ph.tide$date < "2019-08-04",]
a14<- cc.ph.tide[cc.ph.tide$date >= "2019-08-13" & cc.ph.tide$date < "2019-09-12",]
#median
aa2<-median(a2$ph, na.rm=TRUE)
aa3<-median(a3$ph, na.rm=TRUE)
aa4<-median(a4$ph, na.rm=TRUE)
aa5<-median(a5$ph, na.rm=TRUE)
aa6<-median(a6$ph, na.rm=TRUE)
aa7<-median(a7$ph, na.rm=TRUE)
aa8<-median(a8$ph, na.rm=TRUE)
aa9<-median(a9$ph, na.rm=TRUE)
aa10<-median(a10$ph, na.rm=TRUE)
aa11<-median(a11$ph, na.rm=TRUE)
aa12<-median(a12$ph, na.rm=TRUE)
aa13<-median(a13$ph, na.rm=TRUE)
aa14<-median(a14$ph, na.rm=TRUE)
#string these values to a data frame
cc.mon.ph<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.ph<-as.data.frame(cc.mon.ph)
#change column names
names(cc.mon.ph)[1] <- "date"
names(cc.mon.ph)[2] <- "ph"
cc.mon.ph$date<-as.Date(cc.mon.ph$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.ph, by="date")Daily median, range, minimum, and maximum pH
#table summary for daily max, min, med ph
cc.daily.sum.ph<-as.data.frame(setDT(cc.ph.tide)[, .(max.daily.ph = max(ph), min.daily.ph = min(ph), daily.med.ph=median(ph)), .(date)])
#daily range
cc.daily.sum.ph$daily.ph.range<-cc.daily.sum.ph$max.daily.ph - cc.daily.sum.ph$min.daily.ph
#merge
cc.daily.sum<-merge(cc.daily.sum, cc.daily.sum.ph, by="date", all=TRUE)
#Daily min
#subset dates
a1<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2018-05-15" & cc.daily.sum.ph$date < "2018-06-14",]
a2<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2018-06-17" & cc.daily.sum.ph$date < "2018-07-17",]
a3<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2018-07-08" & cc.daily.sum.ph$date < "2018-08-07",]
a4<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2018-08-12" & cc.daily.sum.ph$date < "2018-09-11",]
a5<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2018-11-05" & cc.daily.sum.ph$date < "2018-12-05",]
a6<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2018-12-31" & cc.daily.sum.ph$date < "2019-01-30",]
a7<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-01-21" & cc.daily.sum.ph$date < "2019-02-20",]
a8<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-02-13" & cc.daily.sum.ph$date < "2019-03-15",]
a9<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-03-12" & cc.daily.sum.ph$date < "2019-04-11",]
a10<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-04-09" & cc.daily.sum.ph$date < "2019-05-09",]
a11<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-05-10" & cc.daily.sum.ph$date < "2019-06-09",]
a12<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-06-21" & cc.daily.sum.ph$date < "2019-07-21",]
a13<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-07-05" & cc.daily.sum.ph$date < "2019-08-04",]
a14<- cc.daily.sum.ph[cc.daily.sum.ph$date >= "2019-08-13" & cc.daily.sum.ph$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.ph, na.rm=TRUE)
aa2<-median(a2$min.daily.ph, na.rm=TRUE)
aa3<-median(a3$min.daily.ph, na.rm=TRUE)
aa4<-median(a4$min.daily.ph, na.rm=TRUE)
aa5<-median(a5$min.daily.ph, na.rm=TRUE)
aa6<-median(a6$min.daily.ph, na.rm=TRUE)
aa7<-median(a7$min.daily.ph, na.rm=TRUE)
aa8<-median(a8$min.daily.ph, na.rm=TRUE)
aa9<-median(a9$min.daily.ph, na.rm=TRUE)
aa10<-median(a10$min.daily.ph, na.rm=TRUE)
aa11<-median(a11$min.daily.ph, na.rm=TRUE)
aa12<-median(a12$min.daily.ph, na.rm=TRUE)
aa13<-median(a13$min.daily.ph, na.rm=TRUE)
aa14<-median(a14$min.daily.ph, na.rm=TRUE)
#string these values to a data frame
cc.daily.min.ph<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.min.ph<-as.data.frame(cc.daily.min.ph)
#change column names
names(cc.daily.min.ph)[1] <- "date"
names(cc.daily.min.ph)[2] <- "daily.min.ph"
cc.daily.min.ph$date<-as.Date(cc.daily.min.ph$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.min.ph[,c("date", "daily.min.ph")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.ph, na.rm=TRUE)
aa2<-median(a2$max.daily.ph, na.rm=TRUE)
aa3<-median(a3$max.daily.ph, na.rm=TRUE)
aa4<-median(a4$max.daily.ph, na.rm=TRUE)
aa5<-median(a5$max.daily.ph, na.rm=TRUE)
aa6<-median(a6$max.daily.ph, na.rm=TRUE)
aa7<-median(a7$max.daily.ph, na.rm=TRUE)
aa8<-median(a8$max.daily.ph, na.rm=TRUE)
aa9<-median(a9$max.daily.ph, na.rm=TRUE)
aa10<-median(a10$max.daily.ph, na.rm=TRUE)
aa11<-median(a11$max.daily.ph, na.rm=TRUE)
aa12<-median(a12$max.daily.ph, na.rm=TRUE)
aa13<-median(a13$max.daily.ph, na.rm=TRUE)
aa14<-median(a14$max.daily.ph, na.rm=TRUE)
#string these values to a data frame
cc.daily.max.ph<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.max.ph<-as.data.frame(cc.daily.max.ph)
#change column names
names(cc.daily.max.ph)[1] <- "date"
names(cc.daily.max.ph)[2] <- "daily.max.ph"
cc.daily.max.ph$date<-as.Date(cc.daily.max.ph$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.max.ph[,c("date", "daily.max.ph")], by="date")
#daily range
#mean
aa1<-median(a1$daily.ph.range, na.rm=TRUE)
aa2<-median(a2$daily.ph.range, na.rm=TRUE)
aa3<-median(a3$daily.ph.range, na.rm=TRUE)
aa4<-median(a4$daily.ph.range, na.rm=TRUE)
aa5<-median(a5$daily.ph.range, na.rm=TRUE)
aa6<-median(a6$daily.ph.range, na.rm=TRUE)
aa7<-median(a7$daily.ph.range, na.rm=TRUE)
aa8<-median(a8$daily.ph.range, na.rm=TRUE)
aa9<-median(a9$daily.ph.range, na.rm=TRUE)
aa10<-median(a10$daily.ph.range, na.rm=TRUE)
aa11<-median(a11$daily.ph.range, na.rm=TRUE)
aa12<-median(a12$daily.ph.range, na.rm=TRUE)
aa13<-median(a13$daily.ph.range, na.rm=TRUE)
aa14<-median(a14$daily.ph.range, na.rm=TRUE)
#string these values to a data frame
cc.daily.ph.range<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.ph.range<-as.data.frame(cc.daily.ph.range)
#change column names
names(cc.daily.ph.range)[1] <- "date"
names(cc.daily.ph.range)[2] <- "daily.ph.range"
cc.daily.ph.range$date<-as.Date(cc.daily.ph.range$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.ph.range[,c("date", "daily.ph.range")], by="date")
#daily median
#mean of these phinity periods
aa1<-median(a1$daily.med.ph, na.rm=TRUE)
aa2<-median(a2$daily.med.ph, na.rm=TRUE)
aa3<-median(a3$daily.med.ph, na.rm=TRUE)
aa4<-median(a4$daily.med.ph, na.rm=TRUE)
aa5<-median(a5$daily.med.ph, na.rm=TRUE)
aa6<-median(a6$daily.med.ph, na.rm=TRUE)
aa7<-median(a7$daily.med.ph, na.rm=TRUE)
aa8<-median(a8$daily.med.ph, na.rm=TRUE)
aa9<-median(a9$daily.med.ph, na.rm=TRUE)
aa10<-median(a10$daily.med.ph, na.rm=TRUE)
aa11<-median(a11$daily.med.ph, na.rm=TRUE)
aa12<-median(a12$daily.med.ph, na.rm=TRUE)
aa13<-median(a13$daily.med.ph, na.rm=TRUE)
aa14<-median(a14$daily.med.ph, na.rm=TRUE)
#string these values to a data frame
cc.daily.med.ph<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.med.ph<-as.data.frame(cc.daily.med.ph)
#change column names
names(cc.daily.med.ph)[1] <- "date"
names(cc.daily.med.ph)[2] <- "daily.med.ph"
cc.daily.med.ph$date<-as.Date(cc.daily.med.ph$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.med.ph[,c("date", "daily.med.ph")], by="date")Water temperature
#read in data
cc.wtemp.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/cc.wtemp.tide.csv",
header = TRUE
)
cc.wtemp.tide$date<-as.Date(cc.wtemp.tide$date, format=c("%Y-%m-%d"))
#subset
a1<- cc.wtemp.tide[cc.wtemp.tide$date >= "2018-05-15" & cc.wtemp.tide$date < "2018-06-14",]
a2<- cc.wtemp.tide[cc.wtemp.tide$date >= "2018-06-17" & cc.wtemp.tide$date < "2018-07-17",]
a3<- cc.wtemp.tide[cc.wtemp.tide$date >= "2018-07-08" & cc.wtemp.tide$date < "2018-08-07",]
a4<- cc.wtemp.tide[cc.wtemp.tide$date >= "2018-08-12" & cc.wtemp.tide$date < "2018-09-11",]
a5<- cc.wtemp.tide[cc.wtemp.tide$date >= "2018-11-05" & cc.wtemp.tide$date < "2018-12-05",]
a6<- cc.wtemp.tide[cc.wtemp.tide$date >= "2018-12-31" & cc.wtemp.tide$date < "2019-01-30",]
a7<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-01-21" & cc.wtemp.tide$date < "2019-02-20",]
a8<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-02-13" & cc.wtemp.tide$date < "2019-03-15",]
a9<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-03-12" & cc.wtemp.tide$date < "2019-04-11",]
a10<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-04-09" & cc.wtemp.tide$date < "2019-05-09",]
a11<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-05-10" & cc.wtemp.tide$date < "2019-06-09",]
a12<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-06-21" & cc.wtemp.tide$date < "2019-07-21",]
a13<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-07-05" & cc.wtemp.tide$date < "2019-08-04",]
a14<- cc.wtemp.tide[cc.wtemp.tide$date >= "2019-08-13" & cc.wtemp.tide$date < "2019-09-12",]
#median
aa1<-median(a1$water_temp, na.rm=TRUE)
aa2<-median(a2$water_temp, na.rm=TRUE)
aa3<-median(a3$water_temp, na.rm=TRUE)
aa4<-median(a4$water_temp, na.rm=TRUE)
aa5<-median(a5$water_temp, na.rm=TRUE)
aa6<-median(a6$water_temp, na.rm=TRUE)
aa7<-median(a7$water_temp, na.rm=TRUE)
aa8<-median(a8$water_temp, na.rm=TRUE)
aa9<-median(a9$water_temp, na.rm=TRUE)
aa10<-median(a10$water_temp, na.rm=TRUE)
aa11<-median(a11$water_temp, na.rm=TRUE)
aa12<-median(a12$water_temp, na.rm=TRUE)
aa13<-median(a13$water_temp, na.rm=TRUE)
aa14<-median(a14$water_temp, na.rm=TRUE)
#string these values to a data frame
cc.mon.wt<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.wt<-as.data.frame(cc.mon.wt)
#change column names
names(cc.mon.wt)[1] <- "date"
names(cc.mon.wt)[2] <- "water.temp"
cc.mon.wt$date<-as.Date(cc.mon.wt$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.wt, by="date")Daily median, range, minimum, and maximum water temperature
#table summary for daily max, min, and median
cc.daily.sum.wt<-as.data.frame(setDT(cc.wtemp.tide)[, .(max.daily.wt = max(water_temp), min.daily.wt = min(water_temp), daily.med.wt=median(water_temp)), .(date)])
#daily range
cc.daily.sum.wt$daily.wt.range<-cc.daily.sum.wt$max.daily.wt - cc.daily.sum.wt$min.daily.wt
#merge
cc.daily.sum<-merge(cc.daily.sum, cc.daily.sum.wt, by="date", all=TRUE)
#daily min
#subset dates
a1<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2018-05-15" & cc.daily.sum.wt$date < "2018-06-14",]
a2<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2018-06-17" & cc.daily.sum.wt$date < "2018-07-17",]
a3<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2018-07-08" & cc.daily.sum.wt$date < "2018-08-07",]
a4<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2018-08-12" & cc.daily.sum.wt$date < "2018-09-11",]
a5<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2018-11-05" & cc.daily.sum.wt$date < "2018-12-05",]
a6<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2018-12-31" & cc.daily.sum.wt$date < "2019-01-30",]
a7<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-01-21" & cc.daily.sum.wt$date < "2019-02-20",]
a8<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-02-13" & cc.daily.sum.wt$date < "2019-03-15",]
a9<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-03-12" & cc.daily.sum.wt$date < "2019-04-11",]
a10<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-04-09" & cc.daily.sum.wt$date < "2019-05-09",]
a11<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-05-10" & cc.daily.sum.wt$date < "2019-06-09",]
a12<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-06-21" & cc.daily.sum.wt$date < "2019-07-21",]
a13<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-07-05" & cc.daily.sum.wt$date < "2019-08-04",]
a14<- cc.daily.sum.wt[cc.daily.sum.wt$date >= "2019-08-13" & cc.daily.sum.wt$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.wt, na.rm=TRUE)
aa2<-median(a2$min.daily.wt, na.rm=TRUE)
aa3<-median(a3$min.daily.wt, na.rm=TRUE)
aa4<-median(a4$min.daily.wt, na.rm=TRUE)
aa5<-median(a5$min.daily.wt, na.rm=TRUE)
aa6<-median(a6$min.daily.wt, na.rm=TRUE)
aa7<-median(a7$min.daily.wt, na.rm=TRUE)
aa8<-median(a8$min.daily.wt, na.rm=TRUE)
aa9<-median(a9$min.daily.wt, na.rm=TRUE)
aa10<-median(a10$min.daily.wt, na.rm=TRUE)
aa11<-median(a11$min.daily.wt, na.rm=TRUE)
aa12<-median(a12$min.daily.wt, na.rm=TRUE)
aa13<-median(a13$min.daily.wt, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
cc.daily.min.wt<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.min.wt<-as.data.frame(cc.daily.min.wt)
#change column names
names(cc.daily.min.wt)[1] <- "date"
names(cc.daily.min.wt)[2] <- "daily.min.wt"
cc.daily.min.wt$date<-as.Date(cc.daily.min.wt$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.min.wt[,c("date", "daily.min.wt")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.wt, na.rm=TRUE)
aa2<-median(a2$max.daily.wt, na.rm=TRUE)
aa3<-median(a3$max.daily.wt, na.rm=TRUE)
aa4<-median(a4$max.daily.wt, na.rm=TRUE)
aa5<-median(a5$max.daily.wt, na.rm=TRUE)
aa6<-median(a6$max.daily.wt, na.rm=TRUE)
aa7<-median(a7$max.daily.wt, na.rm=TRUE)
aa8<-median(a8$max.daily.wt, na.rm=TRUE)
aa9<-median(a9$max.daily.wt, na.rm=TRUE)
aa10<-median(a10$max.daily.wt, na.rm=TRUE)
aa11<-median(a11$max.daily.wt, na.rm=TRUE)
aa12<-median(a12$max.daily.wt, na.rm=TRUE)
aa13<-median(a13$max.daily.wt, na.rm=TRUE)
aa14<-median(a14$max.daily.wt, na.rm=TRUE)
#string these values to a data frame
cc.daily.max.wt<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.max.wt<-as.data.frame(cc.daily.max.wt)
#change column names
names(cc.daily.max.wt)[1] <- "date"
names(cc.daily.max.wt)[2] <- "daily.max.wt"
cc.daily.max.wt$date<-as.Date(cc.daily.max.wt$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.max.wt[,c("date", "daily.max.wt")], by="date")
#daily range
#mean
aa1<-median(a1$daily.wt.range, na.rm=TRUE)
aa2<-median(a2$daily.wt.range, na.rm=TRUE)
aa3<-median(a3$daily.wt.range, na.rm=TRUE)
aa4<-median(a4$daily.wt.range, na.rm=TRUE)
aa5<-median(a5$daily.wt.range, na.rm=TRUE)
aa6<-median(a6$daily.wt.range, na.rm=TRUE)
aa7<-median(a7$daily.wt.range, na.rm=TRUE)
aa8<-median(a8$daily.wt.range, na.rm=TRUE)
aa9<-median(a9$daily.wt.range, na.rm=TRUE)
aa10<-median(a10$daily.wt.range, na.rm=TRUE)
aa11<-median(a11$daily.wt.range, na.rm=TRUE)
aa12<-median(a12$daily.wt.range, na.rm=TRUE)
aa13<-median(a13$daily.wt.range, na.rm=TRUE)
aa14<-median(a14$daily.wt.range, na.rm=TRUE)
#string these values to a data frame
cc.daily.wt.range<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.wt.range<-as.data.frame(cc.daily.wt.range)
#change column names
names(cc.daily.wt.range)[1] <- "date"
names(cc.daily.wt.range)[2] <- "daily.wt.range"
cc.daily.wt.range$date<-as.Date(cc.daily.wt.range$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.wt.range[,c("date", "daily.wt.range")], by="date")
#daily median
#mean of these wtinity periods
aa1<-median(a1$daily.med.wt, na.rm=TRUE)
aa2<-median(a2$daily.med.wt, na.rm=TRUE)
aa3<-median(a3$daily.med.wt, na.rm=TRUE)
aa4<-median(a4$daily.med.wt, na.rm=TRUE)
aa5<-median(a5$daily.med.wt, na.rm=TRUE)
aa6<-median(a6$daily.med.wt, na.rm=TRUE)
aa7<-median(a7$daily.med.wt, na.rm=TRUE)
aa8<-median(a8$daily.med.wt, na.rm=TRUE)
aa9<-median(a9$daily.med.wt, na.rm=TRUE)
aa10<-median(a10$daily.med.wt, na.rm=TRUE)
aa11<-median(a11$daily.med.wt, na.rm=TRUE)
aa12<-median(a12$daily.med.wt, na.rm=TRUE)
aa13<-median(a13$daily.med.wt, na.rm=TRUE)
aa14<-median(a14$daily.med.wt, na.rm=TRUE)
#string these values to a data frame
cc.daily.med.wt<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.daily.med.wt<-as.data.frame(cc.daily.med.wt)
#change column names
names(cc.daily.med.wt)[1] <- "date"
names(cc.daily.med.wt)[2] <- "daily.med.wt"
cc.daily.med.wt$date<-as.Date(cc.daily.med.wt$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.daily.med.wt[,c("date", "daily.med.wt")], by="date")Now I’m coing to count “events” and adding them to the above dataframes. I’m having a hard time doing a loop or counting “3 consecutive days of salinity below x” so instead I’m going to just count “days with salinity below x” and use that for analysis. These counts will be subseted by survey dates as done above.
Subset by survey dates
a1<- cc.daily.sum[cc.daily.sum$date >= "2018-05-15" & cc.daily.sum$date < "2018-06-14",]
a2<- cc.daily.sum[cc.daily.sum$date >= "2018-06-17" & cc.daily.sum$date < "2018-07-17",]
a3<- cc.daily.sum[cc.daily.sum$date >= "2018-07-08" & cc.daily.sum$date < "2018-08-07",]
a4<- cc.daily.sum[cc.daily.sum$date >= "2018-08-12" & cc.daily.sum$date < "2018-09-11",]
a5<- cc.daily.sum[cc.daily.sum$date >= "2018-11-05" & cc.daily.sum$date < "2018-12-05",]
a6<- cc.daily.sum[cc.daily.sum$date >= "2018-12-31" & cc.daily.sum$date < "2019-01-30",]
a7<- cc.daily.sum[cc.daily.sum$date >= "2019-01-21" & cc.daily.sum$date < "2019-02-20",]
a8<- cc.daily.sum[cc.daily.sum$date >= "2019-02-13" & cc.daily.sum$date < "2019-03-15",]
a9<- cc.daily.sum[cc.daily.sum$date >= "2019-03-12" & cc.daily.sum$date < "2019-04-11",]
a10<- cc.daily.sum[cc.daily.sum$date >= "2019-04-09" & cc.daily.sum$date < "2019-05-09",]
a11<- cc.daily.sum[cc.daily.sum$date >= "2019-05-10" & cc.daily.sum$date < "2019-06-09",]
a12<- cc.daily.sum[cc.daily.sum$date >= "2019-06-21" & cc.daily.sum$date < "2019-07-21",]
a13<- cc.daily.sum[cc.daily.sum$date >= "2019-07-05" & cc.daily.sum$date < "2019-08-04",]
a14<- cc.daily.sum[cc.daily.sum$date >= "2019-08-13" & cc.daily.sum$date < "2019-09-12",]Number of days with daily maximum salinity less than 10
aa1<-nrow(a1[a1$max.daily.sal<10, ])
aa2<-nrow(a2[a2$max.daily.sal<10, ])
aa3<-nrow(a3[a3$max.daily.sal<10, ])
aa4<-nrow(a4[a4$max.daily.sal<10, ])
aa5<-nrow(a5[a5$max.daily.sal<10, ])
aa6<-nrow(a6[a6$max.daily.sal<10, ])
aa7<-nrow(a7[a7$max.daily.sal<10, ])
aa8<-nrow(a8[a8$max.daily.sal<10, ])
aa9<-nrow(a9[a9$max.daily.sal<10, ])
aa10<-nrow(a10[a10$max.daily.sal<10, ])
aa11<-nrow(a11[a11$max.daily.sal<10, ])
aa12<-nrow(a12[a12$max.daily.sal<10, ])
aa13<-nrow(a13[a13$max.daily.sal<10, ])
aa14<-nrow(a14[a14$max.daily.sal<10, ])
cc.dates<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.dates<-as.data.frame(cc.dates)
#change column names
names(cc.dates)[1] <- "date"
names(cc.dates)[2] <- "max.daily.sal.lt10"
cc.dates$date<-as.Date(cc.dates$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.dates[,c("date", "max.daily.sal.lt10")], by="date")Number of days with daily minimum salinity greater than 28
#already subsetted for between survey days so skipping to filtering
aa1<-nrow(a1[a1$min.daily.sal>28, ])
aa2<-nrow(a2[a2$min.daily.sal>28, ])
aa3<-nrow(a3[a3$min.daily.sal>28, ])
aa4<-nrow(a4[a4$min.daily.sal>28, ])
aa5<-nrow(a5[a5$min.daily.sal>28, ])
aa6<-nrow(a6[a6$min.daily.sal>28, ])
aa7<-nrow(a7[a7$min.daily.sal>28, ])
aa8<-nrow(a8[a8$min.daily.sal>28, ])
aa9<-nrow(a9[a9$min.daily.sal>28, ])
aa10<-nrow(a10[a10$min.daily.sal>28, ])
aa11<-nrow(a11[a11$min.daily.sal>28, ])
aa12<-nrow(a12[a12$min.daily.sal>28, ])
aa13<-nrow(a13[a13$min.daily.sal>28, ])
aa14<-nrow(a14[a14$min.daily.sal>28, ])
cc.dates<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.dates<-as.data.frame(cc.dates)
#change column names
names(cc.dates)[1] <- "date"
names(cc.dates)[2] <- "min.daily.sal.gt28"
cc.dates$date<-as.Date(cc.dates$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.dates[,c("date", "min.daily.sal.gt28")], by="date")pH Number of days where max.daily.ph is >8
aa1<-nrow(a1[a1$max.daily.ph>8, ])
aa2<-nrow(a2[a2$max.daily.ph>8, ])
aa3<-nrow(a3[a3$max.daily.ph>8, ])
aa4<-nrow(a4[a4$max.daily.ph>8, ])
aa5<-nrow(a5[a5$max.daily.ph>8, ])
aa6<-nrow(a6[a6$max.daily.ph>8, ])
aa7<-nrow(a7[a7$max.daily.ph>8, ])
aa8<-nrow(a8[a8$max.daily.ph>8, ])
aa9<-nrow(a9[a9$max.daily.ph>8, ])
aa10<-nrow(a10[a10$max.daily.ph>8, ])
aa11<-nrow(a11[a11$max.daily.ph>8, ])
aa12<-nrow(a12[a12$max.daily.ph>8, ])
aa13<-nrow(a13[a13$max.daily.ph>8, ])
aa14<-nrow(a14[a14$max.daily.ph>8, ])
cc.dates<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.dates<-as.data.frame(cc.dates)
#change column names
names(cc.dates)[1] <- "date"
names(cc.dates)[2] <- "max.daily.ph.gt8"
cc.dates$date<-as.Date(cc.dates$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.dates[,c("date", "max.daily.ph.gt8")], by="date")Save csvs
write.csv(pc.cc, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/one.month.lag.cc.daily.sum.csv")cc.daily sum: https://github.com/Cmwegener/thesis/blob/master/data/environmental/daily_summaries/
####Point Chauncy and EOS Tiburon#### Monthly summaries of the field data
#subset for site
nd<-subset(field, field$site.old == "ND")
nd$date<-as.Date(nd$date, format=c("%Y-%m-%d"))
#calculating the following parameters per survey/month
#mean fucus density
nd.eos<-aggregate(no.fuc.q ~ date, nd, mean, na.rm=TRUE)
#mean percent cover
nd.r<-aggregate(cover ~date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean of large fucus density
nd.r<-aggregate(no.large.fuc.q ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean small fucus density
nd.r<-aggregate(no.small.fuc.q ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#median reproductive cover class
nd.r<-aggregate(covcl.repro ~ date, nd, median, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean vegetative dry weight
nd.r<-aggregate(dw.veg ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean reproductive dry weight
nd.r<-aggregate(dw.repro ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean reproductive apices
nd.r<-aggregate(apices.repro ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean vegetative apices
nd.r<-aggregate(apices.veg ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean percent reproductive apices
nd.r<-aggregate(perc.ra ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean oogonia per conceptacle
nd.r<-aggregate(oog.per.con ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean percent reproductive dry weight
nd.r<-aggregate(perc.rdw ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean oogonia per receptacle
nd$oog.recpt<-(nd$oog.per.con * nd$no.concept.recp)
nd.r<-aggregate(no.concept.recp ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean oogonia per thalli
#(note: theres 2 apices per receptical)
# oogonia/receptacle * (1 receptacle / 2 apices) * reproductive apices/thalli = oogonia/thalli
nd$oog.thalli <- (nd$oog.recpt * 0.5 * nd$apices.repro)
nd.r<-aggregate(oog.thalli ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
#mean conceptacle per thalli
nd$con.thalli <- (nd$no.concept.recp * nd$apices.repro)
nd.r<-aggregate(con.thalli ~ date, nd, mean, na.rm=TRUE)
nd.eos<-merge(nd.eos, nd.r, by="date", all=TRUE)
rm(nd, nd.r)Salinity
#read in and format data
eos.sal.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/eos.sal.tide.csv",
header = TRUE
)
eos.sal.tide$date<-as.Date(eos.sal.tide$date, format=c("%Y-%m-%d"))
#look
print(nd.eos$date)## [1] "2018-06-14" "2018-07-17" "2018-08-07" "2018-09-11" "2018-10-10"
## [6] "2018-12-05" "2019-01-30" "2019-02-20" "2019-03-15" "2019-04-11"
## [11] "2019-05-09" "2019-06-09" "2019-07-21" "2019-08-04" "2019-09-12"#subset the salinity data by dates
a1<- eos.sal.tide[eos.sal.tide$date >= "2018-05-15" & eos.sal.tide$date < "2018-06-14",]
a2<- eos.sal.tide[eos.sal.tide$date >= "2018-06-17" & eos.sal.tide$date < "2018-07-17",]
a3<- eos.sal.tide[eos.sal.tide$date >= "2018-07-08" & eos.sal.tide$date < "2018-08-07",]
a4<- eos.sal.tide[eos.sal.tide$date >= "2018-08-12" & eos.sal.tide$date < "2018-09-11",]
a5<- eos.sal.tide[eos.sal.tide$date >= "2018-09-10" & eos.sal.tide$date < "2018-10-10",]
a6<- eos.sal.tide[eos.sal.tide$date >= "2018-11-05" & eos.sal.tide$date < "2018-12-05",]
a7<- eos.sal.tide[eos.sal.tide$date >= "2018-12-31" & eos.sal.tide$date < "2019-01-30",]
a8<- eos.sal.tide[eos.sal.tide$date >= "2019-01-21" & eos.sal.tide$date < "2019-02-20",]
a9<- eos.sal.tide[eos.sal.tide$date >= "2019-02-13" & eos.sal.tide$date < "2019-03-15",]
a10<- eos.sal.tide[eos.sal.tide$date >= "2019-03-12" & eos.sal.tide$date < "2019-04-11",]
a11<- eos.sal.tide[eos.sal.tide$date >= "2019-04-09" & eos.sal.tide$date < "2019-05-09",]
a12<- eos.sal.tide[eos.sal.tide$date >= "2019-05-10" & eos.sal.tide$date < "2019-06-09",]
a13<- eos.sal.tide[eos.sal.tide$date >= "2019-06-21" & eos.sal.tide$date < "2019-07-21",]
a14<- eos.sal.tide[eos.sal.tide$date >= "2019-07-05" & eos.sal.tide$date < "2019-08-04",]
a15<- eos.sal.tide[eos.sal.tide$date >= "2019-08-13" & eos.sal.tide$date < "2019-09-12",]
#median of these salinity periods
aa1<-median(a1$salinity, na.rm=TRUE)
aa2<-median(a2$salinity, na.rm=TRUE)
aa3<-median(a3$salinity, na.rm=TRUE)
aa4<-median(a4$salinity, na.rm=TRUE)
aa5<-median(a5$salinity, na.rm=TRUE)
aa6<-median(a6$salinity, na.rm=TRUE)
aa7<-median(a7$salinity, na.rm=TRUE)
aa8<-median(a8$salinity, na.rm=TRUE)
aa9<-median(a9$salinity, na.rm=TRUE)
aa10<-median(a10$salinity, na.rm=TRUE)
aa11<-median(a11$salinity, na.rm=TRUE)
aa12<-median(a12$salinity, na.rm=TRUE)
aa13<-median(a13$salinity, na.rm=TRUE)
aa14<-median(a14$salinity, na.rm=TRUE)
aa15<-median(a15$salinity, na.rm=TRUE)
#string these values to a data frame
eos.mon.sal<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.mon.sal<-as.data.frame(eos.mon.sal)
#change column names
names(eos.mon.sal)[1] <- "date"
names(eos.mon.sal)[2] <- "salinity"
eos.mon.sal$date<-as.Date(eos.mon.sal$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.sal[,c("date", "salinity")], by="date")Daily median, range, minimum, and maximum salinity
#table summary for daily max and min
eos.daily.sum<-as.data.frame(setDT(eos.sal.tide)[, .(max.daily.sal = max(salinity), min.daily.sal = min(salinity), daily.med.sal=median(salinity)), .(date)])
#daily range
eos.daily.sum$daily.sal.range<-eos.daily.sum$max.daily.sal - eos.daily.sum$min.daily.sal
#Daily min
#subset dates
a1<- eos.daily.sum[eos.daily.sum$date >= "2018-05-15" & eos.daily.sum$date < "2018-06-14",]
a2<- eos.daily.sum[eos.daily.sum$date >= "2018-06-17" & eos.daily.sum$date < "2018-07-17",]
a3<- eos.daily.sum[eos.daily.sum$date >= "2018-07-08" & eos.daily.sum$date < "2018-08-07",]
a4<- eos.daily.sum[eos.daily.sum$date >= "2018-08-12" & eos.daily.sum$date < "2018-09-11",]
a5<- eos.daily.sum[eos.daily.sum$date >= "2018-09-10" & eos.daily.sum$date < "2018-10-10",]
a6<- eos.daily.sum[eos.daily.sum$date >= "2018-11-05" & eos.daily.sum$date < "2018-12-05",]
a7<- eos.daily.sum[eos.daily.sum$date >= "2018-12-31" & eos.daily.sum$date < "2019-01-30",]
a8<- eos.daily.sum[eos.daily.sum$date >= "2019-01-21" & eos.daily.sum$date < "2019-02-20",]
a9<- eos.daily.sum[eos.daily.sum$date >= "2019-02-13" & eos.daily.sum$date < "2019-03-15",]
a10<- eos.daily.sum[eos.daily.sum$date >= "2019-03-12" & eos.daily.sum$date < "2019-04-11",]
a11<- eos.daily.sum[eos.daily.sum$date >= "2019-04-09" & eos.daily.sum$date < "2019-05-09",]
a12<- eos.daily.sum[eos.daily.sum$date >= "2019-05-10" & eos.daily.sum$date < "2019-06-09",]
a13<- eos.daily.sum[eos.daily.sum$date >= "2019-06-21" & eos.daily.sum$date < "2019-07-21",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-07-05" & eos.daily.sum$date < "2019-08-04",]
a15<- eos.daily.sum[eos.daily.sum$date >= "2019-08-13" & eos.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.sal, na.rm=TRUE)
aa2<-median(a2$min.daily.sal, na.rm=TRUE)
aa3<-median(a3$min.daily.sal, na.rm=TRUE)
aa4<-median(a4$min.daily.sal, na.rm=TRUE)
aa5<-median(a5$min.daily.sal, na.rm=TRUE)
aa6<-median(a6$min.daily.sal, na.rm=TRUE)
aa7<-median(a7$min.daily.sal, na.rm=TRUE)
aa8<-median(a8$min.daily.sal, na.rm=TRUE)
aa9<-median(a9$min.daily.sal, na.rm=TRUE)
aa10<-median(a10$min.daily.sal, na.rm=TRUE)
aa11<-median(a11$min.daily.sal, na.rm=TRUE)
aa12<-median(a12$min.daily.sal, na.rm=TRUE)
aa13<-median(a13$min.daily.sal, na.rm=TRUE)
aa14<-median(a14$min.daily.sal, na.rm=TRUE)
aa15<-median(a15$min.daily.sal, na.rm=TRUE)
#string these values to a data frame
eos.daily.min.sal<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.min.sal<-as.data.frame(eos.daily.min.sal)
#change column names
names(eos.daily.min.sal)[1] <- "date"
names(eos.daily.min.sal)[2] <- "daily.min.sal"
eos.daily.min.sal$date<-as.Date(eos.daily.min.sal$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.min.sal[,c("date", "daily.min.sal")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.sal, na.rm=TRUE)
aa2<-median(a2$max.daily.sal, na.rm=TRUE)
aa3<-median(a3$max.daily.sal, na.rm=TRUE)
aa4<-median(a4$max.daily.sal, na.rm=TRUE)
aa5<-median(a5$max.daily.sal, na.rm=TRUE)
aa6<-median(a6$max.daily.sal, na.rm=TRUE)
aa7<-median(a7$max.daily.sal, na.rm=TRUE)
aa8<-median(a8$max.daily.sal, na.rm=TRUE)
aa9<-median(a9$max.daily.sal, na.rm=TRUE)
aa10<-median(a10$max.daily.sal, na.rm=TRUE)
aa11<-median(a11$max.daily.sal, na.rm=TRUE)
aa12<-median(a12$max.daily.sal, na.rm=TRUE)
aa13<-median(a13$max.daily.sal, na.rm=TRUE)
aa14<-median(a14$max.daily.sal, na.rm=TRUE)
aa15<-median(a15$max.daily.sal, na.rm=TRUE)
#string these values to a data frame
eos.daily.max.sal<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.max.sal<-as.data.frame(eos.daily.max.sal)
#change column names
names(eos.daily.max.sal)[1] <- "date"
names(eos.daily.max.sal)[2] <- "daily.max.sal"
eos.daily.max.sal$date<-as.Date(eos.daily.max.sal$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.max.sal[,c("date", "daily.max.sal")], by="date")
#daily range
#mean
aa1<-median(a1$daily.sal.range, na.rm=TRUE)
aa2<-median(a2$daily.sal.range, na.rm=TRUE)
aa3<-median(a3$daily.sal.range, na.rm=TRUE)
aa4<-median(a4$daily.sal.range, na.rm=TRUE)
aa5<-median(a5$daily.sal.range, na.rm=TRUE)
aa6<-median(a6$daily.sal.range, na.rm=TRUE)
aa7<-median(a7$daily.sal.range, na.rm=TRUE)
aa8<-median(a8$daily.sal.range, na.rm=TRUE)
aa9<-median(a9$daily.sal.range, na.rm=TRUE)
aa10<-median(a10$daily.sal.range, na.rm=TRUE)
aa11<-median(a11$daily.sal.range, na.rm=TRUE)
aa12<-median(a12$daily.sal.range, na.rm=TRUE)
aa13<-median(a13$daily.sal.range, na.rm=TRUE)
aa14<-median(a14$daily.sal.range, na.rm=TRUE)
aa15<-median(a15$daily.sal.range, na.rm=TRUE)
#string these values to a data frame
eos.daily.sal.range<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.sal.range<-as.data.frame(eos.daily.sal.range)
#change column names
names(eos.daily.sal.range)[1] <- "date"
names(eos.daily.sal.range)[2] <- "daily.sal.range"
eos.daily.sal.range$date<-as.Date(eos.daily.sal.range$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.sal.range[,c("date", "daily.sal.range")], by="date")
#daily median
#mean of these salinity periods
aa1<-median(a1$daily.med.sal, na.rm=TRUE)
aa2<-median(a2$daily.med.sal, na.rm=TRUE)
aa3<-median(a3$daily.med.sal, na.rm=TRUE)
aa4<-median(a4$daily.med.sal, na.rm=TRUE)
aa5<-median(a5$daily.med.sal, na.rm=TRUE)
aa6<-median(a6$daily.med.sal, na.rm=TRUE)
aa7<-median(a7$daily.med.sal, na.rm=TRUE)
aa8<-median(a8$daily.med.sal, na.rm=TRUE)
aa9<-median(a9$daily.med.sal, na.rm=TRUE)
aa10<-median(a10$daily.med.sal, na.rm=TRUE)
aa11<-median(a11$daily.med.sal, na.rm=TRUE)
aa12<-median(a12$daily.med.sal, na.rm=TRUE)
aa13<-median(a13$daily.med.sal, na.rm=TRUE)
aa14<-median(a14$daily.med.sal, na.rm=TRUE)
aa15<-median(a15$daily.med.sal, na.rm=TRUE)
#string these values to a data frame
eos.daily.med.sal<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.med.sal<-as.data.frame(eos.daily.med.sal)
#change column names
names(eos.daily.med.sal)[1] <- "date"
names(eos.daily.med.sal)[2] <- "daily.med.sal"
eos.daily.med.sal$date<-as.Date(eos.daily.med.sal$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.med.sal[,c("date", "daily.med.sal")], by="date")pH
#read in data
eos.ph.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/eos.ph.tide.csv",
header = TRUE
)
eos.ph.tide$date<-as.Date(eos.ph.tide$date, format=c("%Y-%m-%d"))
#subset
a1<- eos.ph.tide[eos.ph.tide$date >= "2018-05-15" & eos.ph.tide$date < "2018-06-14",]
a2<- eos.ph.tide[eos.ph.tide$date >= "2018-06-17" & eos.ph.tide$date < "2018-07-17",]
a3<- eos.ph.tide[eos.ph.tide$date >= "2018-07-08" & eos.ph.tide$date < "2018-08-07",]
a4<- eos.ph.tide[eos.ph.tide$date >= "2018-08-12" & eos.ph.tide$date < "2018-09-11",]
a5<- eos.ph.tide[eos.ph.tide$date >= "2018-09-10" & eos.ph.tide$date < "2018-10-10",]
a6<- eos.ph.tide[eos.ph.tide$date >= "2018-11-05" & eos.ph.tide$date < "2018-12-05",]
a7<- eos.ph.tide[eos.ph.tide$date >= "2018-12-31" & eos.ph.tide$date < "2019-01-30",]
a8<- eos.ph.tide[eos.ph.tide$date >= "2019-01-21" & eos.ph.tide$date < "2019-02-20",]
a9<- eos.ph.tide[eos.ph.tide$date >= "2019-02-13" & eos.ph.tide$date < "2019-03-15",]
a10<- eos.ph.tide[eos.ph.tide$date >= "2019-03-12" & eos.ph.tide$date < "2019-04-11",]
a11<- eos.ph.tide[eos.ph.tide$date >= "2019-04-09" & eos.ph.tide$date < "2019-05-09",]
a12<- eos.ph.tide[eos.ph.tide$date >= "2019-05-10" & eos.ph.tide$date < "2019-06-09",]
a13<- eos.ph.tide[eos.ph.tide$date >= "2019-06-21" & eos.ph.tide$date < "2019-07-21",]
a14<- eos.ph.tide[eos.ph.tide$date >= "2019-07-05" & eos.ph.tide$date < "2019-08-04",]
a15<- eos.ph.tide[eos.ph.tide$date >= "2019-08-13" & eos.ph.tide$date < "2019-09-12",]
#median
aa1<-median(a1$ph, na.rm=TRUE)
aa2<-median(a2$ph, na.rm=TRUE)
aa3<-median(a3$ph, na.rm=TRUE)
aa4<-median(a4$ph, na.rm=TRUE)
aa5<-median(a5$ph, na.rm=TRUE)
aa6<-median(a6$ph, na.rm=TRUE)
aa7<-median(a7$ph, na.rm=TRUE)
aa8<-median(a8$ph, na.rm=TRUE)
aa9<-median(a9$ph, na.rm=TRUE)
aa10<-median(a10$ph, na.rm=TRUE)
aa11<-median(a11$ph, na.rm=TRUE)
aa12<-median(a12$ph, na.rm=TRUE)
aa13<-median(a13$ph, na.rm=TRUE)
aa14<-median(a14$ph, na.rm=TRUE)
aa15<-median(a15$ph, na.rm=TRUE)
#string these values to a data frame
eos.mon.ph<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.mon.ph<-as.data.frame(eos.mon.ph)
#change column names
names(eos.mon.ph)[1] <- "date"
names(eos.mon.ph)[2] <- "ph"
eos.mon.ph$date<-as.Date(eos.mon.ph$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.ph[,c("date", "ph")], by="date")Daily median, range, minimum, and maximum pH
#table summary for daily max and min
eos.daily.sum.ph<-as.data.frame(setDT(eos.ph.tide)[, .(max.daily.ph = max(ph), min.daily.ph = min(ph), daily.med.ph=median(ph)), .(date)])
#daily range
eos.daily.sum.ph$daily.ph.range<-eos.daily.sum.ph$max.daily.ph - eos.daily.sum.ph$min.daily.ph
#merge
eos.daily.sum<-merge(eos.daily.sum, eos.daily.sum.ph, by="date", all=TRUE)
#Daily min
#subset dates
a1<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-05-15" & eos.daily.sum.ph$date < "2018-06-14",]
a2<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-06-17" & eos.daily.sum.ph$date < "2018-07-17",]
a3<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-07-08" & eos.daily.sum.ph$date < "2018-08-07",]
a4<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-08-12" & eos.daily.sum.ph$date < "2018-09-11",]
a5<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-09-10" & eos.daily.sum.ph$date < "2018-10-10",]
a6<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-11-05" & eos.daily.sum.ph$date < "2018-12-05",]
a7<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2018-12-31" & eos.daily.sum.ph$date < "2019-01-30",]
a8<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-01-21" & eos.daily.sum.ph$date < "2019-02-20",]
a9<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-02-13" & eos.daily.sum.ph$date < "2019-03-15",]
a10<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-03-12" & eos.daily.sum.ph$date < "2019-04-11",]
a11<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-04-09" & eos.daily.sum.ph$date < "2019-05-09",]
a12<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-05-10" & eos.daily.sum.ph$date < "2019-06-09",]
a13<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-06-21" & eos.daily.sum.ph$date < "2019-07-21",]
a14<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-07-05" & eos.daily.sum.ph$date < "2019-08-04",]
a15<- eos.daily.sum.ph[eos.daily.sum.ph$date >= "2019-08-13" & eos.daily.sum.ph$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.ph, na.rm=TRUE)
aa2<-median(a2$min.daily.ph, na.rm=TRUE)
aa3<-median(a3$min.daily.ph, na.rm=TRUE)
aa4<-median(a4$min.daily.ph, na.rm=TRUE)
aa5<-median(a5$min.daily.ph, na.rm=TRUE)
aa6<-median(a6$min.daily.ph, na.rm=TRUE)
aa7<-median(a7$min.daily.ph, na.rm=TRUE)
aa8<-median(a8$min.daily.ph, na.rm=TRUE)
aa9<-median(a9$min.daily.ph, na.rm=TRUE)
aa10<-median(a10$min.daily.ph, na.rm=TRUE)
aa11<-median(a11$min.daily.ph, na.rm=TRUE)
aa12<-median(a12$min.daily.ph, na.rm=TRUE)
aa13<-median(a13$min.daily.ph, na.rm=TRUE)
aa14<-median(a14$min.daily.ph, na.rm=TRUE)
aa15<-median(a15$min.daily.ph, na.rm=TRUE)
#string these values to a data frame
eos.daily.min.ph<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.min.ph<-as.data.frame(eos.daily.min.ph)
#change column names
names(eos.daily.min.ph)[1] <- "date"
names(eos.daily.min.ph)[2] <- "daily.min.ph"
eos.daily.min.ph$date<-as.Date(eos.daily.min.ph$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.min.ph[,c("date", "daily.min.ph")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.ph, na.rm=TRUE)
aa2<-median(a2$max.daily.ph, na.rm=TRUE)
aa3<-median(a3$max.daily.ph, na.rm=TRUE)
aa4<-median(a4$max.daily.ph, na.rm=TRUE)
aa5<-median(a5$max.daily.ph, na.rm=TRUE)
aa6<-median(a6$max.daily.ph, na.rm=TRUE)
aa7<-median(a7$max.daily.ph, na.rm=TRUE)
aa8<-median(a8$max.daily.ph, na.rm=TRUE)
aa9<-median(a9$max.daily.ph, na.rm=TRUE)
aa10<-median(a10$max.daily.ph, na.rm=TRUE)
aa11<-median(a11$max.daily.ph, na.rm=TRUE)
aa12<-median(a12$max.daily.ph, na.rm=TRUE)
aa13<-median(a13$max.daily.ph, na.rm=TRUE)
aa14<-median(a14$min.daily.ph, na.rm=TRUE)
aa15<-median(a15$min.daily.ph, na.rm=TRUE)
#string these values to a data frame
eos.daily.max.ph<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.max.ph<-as.data.frame(eos.daily.max.ph)
#change column names
names(eos.daily.max.ph)[1] <- "date"
names(eos.daily.max.ph)[2] <- "daily.max.ph"
eos.daily.max.ph$date<-as.Date(eos.daily.max.ph$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.max.ph[,c("date", "daily.max.ph")], by="date")
#daily range
#mean
aa1<-median(a1$daily.ph.range, na.rm=TRUE)
aa2<-median(a2$daily.ph.range, na.rm=TRUE)
aa3<-median(a3$daily.ph.range, na.rm=TRUE)
aa4<-median(a4$daily.ph.range, na.rm=TRUE)
aa5<-median(a5$daily.ph.range, na.rm=TRUE)
aa6<-median(a6$daily.ph.range, na.rm=TRUE)
aa7<-median(a7$daily.ph.range, na.rm=TRUE)
aa8<-median(a8$daily.ph.range, na.rm=TRUE)
aa9<-median(a9$daily.ph.range, na.rm=TRUE)
aa10<-median(a10$daily.ph.range, na.rm=TRUE)
aa11<-median(a11$daily.ph.range, na.rm=TRUE)
aa12<-median(a12$daily.ph.range, na.rm=TRUE)
aa13<-median(a13$daily.ph.range, na.rm=TRUE)
aa14<-median(a14$min.daily.ph, na.rm=TRUE)
aa15<-median(a15$min.daily.ph, na.rm=TRUE)
#string these values to a data frame
eos.daily.ph.range<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.ph.range<-as.data.frame(eos.daily.ph.range)
#change column names
names(eos.daily.ph.range)[1] <- "date"
names(eos.daily.ph.range)[2] <- "daily.ph.range"
eos.daily.ph.range$date<-as.Date(eos.daily.ph.range$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.ph.range[,c("date", "daily.ph.range")], by="date")
#daily median
#mean of these ph periods
aa1<-median(a1$daily.med.ph, na.rm=TRUE)
aa2<-median(a2$daily.med.ph, na.rm=TRUE)
aa3<-median(a3$daily.med.ph, na.rm=TRUE)
aa4<-median(a4$daily.med.ph, na.rm=TRUE)
aa5<-median(a5$daily.med.ph, na.rm=TRUE)
aa6<-median(a6$daily.med.ph, na.rm=TRUE)
aa7<-median(a7$daily.med.ph, na.rm=TRUE)
aa8<-median(a8$daily.med.ph, na.rm=TRUE)
aa9<-median(a9$daily.med.ph, na.rm=TRUE)
aa10<-median(a10$daily.med.ph, na.rm=TRUE)
aa11<-median(a11$daily.med.ph, na.rm=TRUE)
aa12<-median(a12$daily.med.ph, na.rm=TRUE)
aa13<-median(a13$daily.med.ph, na.rm=TRUE)
aa14<-median(a14$min.daily.ph, na.rm=TRUE)
aa15<-median(a15$min.daily.ph, na.rm=TRUE)
#string these values to a data frame
eos.daily.med.ph<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.med.ph<-as.data.frame(eos.daily.med.ph)
#change column names
names(eos.daily.med.ph)[1] <- "date"
names(eos.daily.med.ph)[2] <- "daily.med.ph"
eos.daily.med.ph$date<-as.Date(eos.daily.med.ph$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.med.ph[,c("date", "daily.med.ph")], by="date")Water temperature
#read in data
eos.wtemp.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/eos.wtemp.tide.csv",
header = TRUE
)
eos.wtemp.tide$date<-as.Date(eos.wtemp.tide$date, format=c("%Y-%m-%d"))
#subset data
a1<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-05-15" & eos.wtemp.tide$date < "2018-06-14",]
a2<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-06-17" & eos.wtemp.tide$date < "2018-07-17",]
a3<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-07-08" & eos.wtemp.tide$date < "2018-08-07",]
a4<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-08-12" & eos.wtemp.tide$date < "2018-09-11",]
a5<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-09-10" & eos.wtemp.tide$date < "2018-10-10",]
a6<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-11-05" & eos.wtemp.tide$date < "2018-12-05",]
a7<- eos.wtemp.tide[eos.wtemp.tide$date >= "2018-12-31" & eos.wtemp.tide$date < "2019-01-30",]
a8<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-01-21" & eos.wtemp.tide$date < "2019-02-20",]
a9<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-02-13" & eos.wtemp.tide$date < "2019-03-15",]
a10<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-03-12" & eos.wtemp.tide$date < "2019-04-11",]
a11<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-04-09" & eos.wtemp.tide$date < "2019-05-09",]
a12<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-05-10" & eos.wtemp.tide$date < "2019-06-09",]
a13<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-06-21" & eos.wtemp.tide$date < "2019-07-21",]
a14<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-07-05" & eos.wtemp.tide$date < "2019-08-04",]
a15<- eos.wtemp.tide[eos.wtemp.tide$date >= "2019-08-13" & eos.wtemp.tide$date < "2019-09-12",]
#median
aa1<-median(a1$water_temp, na.rm=TRUE)
aa2<-median(a2$water_temp, na.rm=TRUE)
aa3<-median(a3$water_temp, na.rm=TRUE)
aa4<-median(a4$water_temp, na.rm=TRUE)
aa5<-median(a5$water_temp, na.rm=TRUE)
aa6<-median(a6$water_temp, na.rm=TRUE)
aa7<-median(a7$water_temp, na.rm=TRUE)
aa8<-median(a8$water_temp, na.rm=TRUE)
aa9<-median(a9$water_temp, na.rm=TRUE)
aa10<-median(a10$water_temp, na.rm=TRUE)
aa11<-median(a11$water_temp, na.rm=TRUE)
aa12<-median(a12$water_temp, na.rm=TRUE)
aa13<-median(a13$water_temp, na.rm=TRUE)
aa14<-median(a14$water_temp, na.rm=TRUE)
aa15<-median(a15$water_temp, na.rm=TRUE)
#string these values to a data frame
eos.mon.wt<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.mon.wt<-as.data.frame(eos.mon.wt)
#change column names
names(eos.mon.wt)[1] <- "date"
names(eos.mon.wt)[2] <- "water.temp"
eos.mon.wt$date<-as.Date(eos.mon.wt$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.wt[,c("date", "water.temp")], by="date")Daily median, range, minimum, and maximum water temperature
#table summary for daily max and min
eos.daily.sum.wt<-as.data.frame(setDT(eos.wtemp.tide)[, .(max.daily.wt = max(water_temp), min.daily.wt = min(water_temp), daily.med.wt=median(water_temp)), .(date)])
#daily range
eos.daily.sum.wt$daily.wt.range<-eos.daily.sum.wt$max.daily.wt - eos.daily.sum.wt$min.daily.wt
#merge
eos.daily.sum<-merge(eos.daily.sum, eos.daily.sum.wt, by="date", all=TRUE)
#Daily min
#subset dates
a1<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-05-15" & eos.daily.sum.wt$date < "2018-06-14",]
a2<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-06-17" & eos.daily.sum.wt$date < "2018-07-17",]
a3<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-07-08" & eos.daily.sum.wt$date < "2018-08-07",]
a4<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-08-12" & eos.daily.sum.wt$date < "2018-09-11",]
a5<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-09-10" & eos.daily.sum.wt$date < "2018-10-10",]
a6<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-11-05" & eos.daily.sum.wt$date < "2018-12-05",]
a7<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2018-12-31" & eos.daily.sum.wt$date < "2019-01-30",]
a8<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-01-21" & eos.daily.sum.wt$date < "2019-02-20",]
a9<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-02-13" & eos.daily.sum.wt$date < "2019-03-15",]
a10<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-03-12" & eos.daily.sum.wt$date < "2019-04-11",]
a11<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-04-09" & eos.daily.sum.wt$date < "2019-05-09",]
a12<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-05-10" & eos.daily.sum.wt$date < "2019-06-09",]
a13<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-06-21" & eos.daily.sum.wt$date < "2019-07-21",]
a14<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-07-05" & eos.daily.sum.wt$date < "2019-08-04",]
a15<- eos.daily.sum.wt[eos.daily.sum.wt$date >= "2019-08-13" & eos.daily.sum.wt$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.wt, na.rm=TRUE)
aa2<-median(a2$min.daily.wt, na.rm=TRUE)
aa3<-median(a3$min.daily.wt, na.rm=TRUE)
aa4<-median(a4$min.daily.wt, na.rm=TRUE)
aa5<-median(a5$min.daily.wt, na.rm=TRUE)
aa6<-median(a6$min.daily.wt, na.rm=TRUE)
aa7<-median(a7$min.daily.wt, na.rm=TRUE)
aa8<-median(a8$min.daily.wt, na.rm=TRUE)
aa9<-median(a9$min.daily.wt, na.rm=TRUE)
aa10<-median(a10$min.daily.wt, na.rm=TRUE)
aa11<-median(a11$min.daily.wt, na.rm=TRUE)
aa12<-median(a12$min.daily.wt, na.rm=TRUE)
aa13<-median(a13$min.daily.wt, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
aa15<-median(a15$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
eos.daily.min.wt<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.min.wt<-as.data.frame(eos.daily.min.wt)
#change column names
names(eos.daily.min.wt)[1] <- "date"
names(eos.daily.min.wt)[2] <- "daily.min.wt"
eos.daily.min.wt$date<-as.Date(eos.daily.min.wt$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.min.wt[,c("date", "daily.min.wt")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.wt, na.rm=TRUE)
aa2<-median(a2$max.daily.wt, na.rm=TRUE)
aa3<-median(a3$max.daily.wt, na.rm=TRUE)
aa4<-median(a4$max.daily.wt, na.rm=TRUE)
aa5<-median(a5$max.daily.wt, na.rm=TRUE)
aa6<-median(a6$max.daily.wt, na.rm=TRUE)
aa7<-median(a7$max.daily.wt, na.rm=TRUE)
aa8<-median(a8$max.daily.wt, na.rm=TRUE)
aa9<-median(a9$max.daily.wt, na.rm=TRUE)
aa10<-median(a10$max.daily.wt, na.rm=TRUE)
aa11<-median(a11$max.daily.wt, na.rm=TRUE)
aa12<-median(a12$max.daily.wt, na.rm=TRUE)
aa13<-median(a13$max.daily.wt, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
aa15<-median(a15$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
eos.daily.max.wt<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.max.wt<-as.data.frame(eos.daily.max.wt)
#change column names
names(eos.daily.max.wt)[1] <- "date"
names(eos.daily.max.wt)[2] <- "daily.max.wt"
eos.daily.max.wt$date<-as.Date(eos.daily.max.wt$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.max.wt[,c("date", "daily.max.wt")], by="date")
#daily range
#mean
aa1<-median(a1$daily.wt.range, na.rm=TRUE)
aa2<-median(a2$daily.wt.range, na.rm=TRUE)
aa3<-median(a3$daily.wt.range, na.rm=TRUE)
aa4<-median(a4$daily.wt.range, na.rm=TRUE)
aa5<-median(a5$daily.wt.range, na.rm=TRUE)
aa6<-median(a6$daily.wt.range, na.rm=TRUE)
aa7<-median(a7$daily.wt.range, na.rm=TRUE)
aa8<-median(a8$daily.wt.range, na.rm=TRUE)
aa9<-median(a9$daily.wt.range, na.rm=TRUE)
aa10<-median(a10$daily.wt.range, na.rm=TRUE)
aa11<-median(a11$daily.wt.range, na.rm=TRUE)
aa12<-median(a12$daily.wt.range, na.rm=TRUE)
aa13<-median(a13$daily.wt.range, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
aa15<-median(a15$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
eos.daily.wt.range<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.wt.range<-as.data.frame(eos.daily.wt.range)
#change column names
names(eos.daily.wt.range)[1] <- "date"
names(eos.daily.wt.range)[2] <- "daily.wt.range"
eos.daily.wt.range$date<-as.Date(eos.daily.wt.range$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.wt.range[,c("date", "daily.wt.range")], by="date")
#daily median
#mean of these wtinity periods
aa1<-median(a1$daily.med.wt, na.rm=TRUE)
aa2<-median(a2$daily.med.wt, na.rm=TRUE)
aa3<-median(a3$daily.med.wt, na.rm=TRUE)
aa4<-median(a4$daily.med.wt, na.rm=TRUE)
aa5<-median(a5$daily.med.wt, na.rm=TRUE)
aa6<-median(a6$daily.med.wt, na.rm=TRUE)
aa7<-median(a7$daily.med.wt, na.rm=TRUE)
aa8<-median(a8$daily.med.wt, na.rm=TRUE)
aa9<-median(a9$daily.med.wt, na.rm=TRUE)
aa10<-median(a10$daily.med.wt, na.rm=TRUE)
aa11<-median(a11$daily.med.wt, na.rm=TRUE)
aa12<-median(a12$daily.med.wt, na.rm=TRUE)
aa13<-median(a13$daily.med.wt, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
aa15<-median(a15$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
eos.daily.med.wt<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.med.wt<-as.data.frame(eos.daily.med.wt)
#change column names
names(eos.daily.med.wt)[1] <- "date"
names(eos.daily.med.wt)[2] <- "daily.med.wt"
eos.daily.med.wt$date<-as.Date(eos.daily.med.wt$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.med.wt[,c("date", "daily.med.wt")], by="date")Daily summaries and “monthly” summaries done. Time for occurances of events Subset by survey dates
a1<- eos.daily.sum[eos.daily.sum$date >= "2018-05-15" & eos.daily.sum$date < "2018-06-14",]
a2<- eos.daily.sum[eos.daily.sum$date >= "2018-06-17" & eos.daily.sum$date < "2018-07-17",]
a3<- eos.daily.sum[eos.daily.sum$date >= "2018-07-08" & eos.daily.sum$date < "2018-08-07",]
a4<- eos.daily.sum[eos.daily.sum$date >= "2018-08-12" & eos.daily.sum$date < "2018-09-11",]
a5<- eos.daily.sum[eos.daily.sum$date >= "2018-09-10" & eos.daily.sum$date < "2018-10-10",]
a6<- eos.daily.sum[eos.daily.sum$date >= "2018-11-05" & eos.daily.sum$date < "2018-12-05",]
a7<- eos.daily.sum[eos.daily.sum$date >= "2018-12-31" & eos.daily.sum$date < "2019-01-30",]
a8<- eos.daily.sum[eos.daily.sum$date >= "2019-01-21" & eos.daily.sum$date < "2019-02-20",]
a9<- eos.daily.sum[eos.daily.sum$date >= "2019-02-13" & eos.daily.sum$date < "2019-03-15",]
a10<- eos.daily.sum[eos.daily.sum$date >= "2019-03-12" & eos.daily.sum$date < "2019-04-11",]
a11<- eos.daily.sum[eos.daily.sum$date >= "2019-04-09" & eos.daily.sum$date < "2019-05-09",]
a12<- eos.daily.sum[eos.daily.sum$date >= "2019-05-10" & eos.daily.sum$date < "2019-06-09",]
a13<- eos.daily.sum[eos.daily.sum$date >= "2019-06-21" & eos.daily.sum$date < "2019-07-21",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-07-05" & eos.daily.sum$date < "2019-08-04",]
a15<- eos.daily.sum[eos.daily.sum$date >= "2019-08-13" & eos.daily.sum$date < "2019-09-12",]Daily maximum salinity less than 10
aa1<-nrow(a1[a1$max.daily.sal<10, ])
aa2<-nrow(a2[a2$max.daily.sal<10, ])
aa3<-nrow(a3[a3$max.daily.sal<10, ])
aa4<-nrow(a4[a4$max.daily.sal<10, ])
aa5<-nrow(a5[a5$max.daily.sal<10, ])
aa6<-nrow(a6[a6$max.daily.sal<10, ])
aa7<-nrow(a7[a7$max.daily.sal<10, ])
aa8<-nrow(a8[a8$max.daily.sal<10, ])
aa9<-nrow(a9[a9$max.daily.sal<10, ])
aa10<-nrow(a10[a10$max.daily.sal<10, ])
aa11<-nrow(a11[a11$max.daily.sal<10, ])
aa12<-nrow(a12[a12$max.daily.sal<10, ])
aa13<-nrow(a13[a13$max.daily.sal<10, ])
aa14<-nrow(a14[a14$max.daily.sal<10, ])
aa15<-nrow(a15[a15$max.daily.sal<10, ])
eos.dates<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.dates<-as.data.frame(eos.dates)
#change column names
names(eos.dates)[1] <- "date"
names(eos.dates)[2] <- "max.daily.sal.lt10"
eos.dates$date<-as.Date(eos.dates$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.dates[,c("date", "max.daily.sal.lt10")], by="date")Number of days with a daily minimum salinity greater than 28
#count
aa1<-nrow(a1[a1$min.daily.sal>28, ])
aa2<-nrow(a2[a2$min.daily.sal>28, ])
aa3<-nrow(a3[a3$min.daily.sal>28, ])
aa4<-nrow(a4[a4$min.daily.sal>28, ])
aa5<-nrow(a5[a5$min.daily.sal>28, ])
aa6<-nrow(a6[a6$min.daily.sal>28, ])
aa7<-nrow(a7[a7$min.daily.sal>28, ])
aa8<-nrow(a8[a8$min.daily.sal>28, ])
aa9<-nrow(a9[a9$min.daily.sal>28, ])
aa10<-nrow(a10[a10$min.daily.sal>28, ])
aa11<-nrow(a11[a11$min.daily.sal>28, ])
aa12<-nrow(a12[a12$min.daily.sal>28, ])
aa13<-nrow(a13[a13$min.daily.sal>28, ])
aa14<-nrow(a14[a14$min.daily.sal>28, ])
aa15<-nrow(a15[a15$min.daily.sal>28, ])
eos.dates<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.dates<-as.data.frame(eos.dates)
#change column names
names(eos.dates)[1] <- "date"
names(eos.dates)[2] <- "min.daily.sal.gt28"
eos.dates$date<-as.Date(eos.dates$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.dates[,c("date", "min.daily.sal.gt28")], by="date")pH
Number of days where max.daily.ph is >8
aa1<-nrow(a1[a1$max.daily.ph>8, ])
aa2<-nrow(a2[a2$max.daily.ph>8, ])
aa3<-nrow(a3[a3$max.daily.ph>8, ])
aa4<-nrow(a4[a4$max.daily.ph>8, ])
aa5<-nrow(a5[a5$max.daily.ph>8, ])
aa6<-nrow(a6[a6$max.daily.ph>8, ])
aa7<-nrow(a7[a7$max.daily.ph>8, ])
aa8<-nrow(a8[a8$max.daily.ph>8, ])
aa9<-nrow(a9[a9$max.daily.ph>8, ])
aa10<-nrow(a10[a10$max.daily.ph>8, ])
aa11<-nrow(a11[a11$max.daily.ph>8, ])
aa12<-nrow(a12[a12$max.daily.ph>8, ])
aa13<-nrow(a13[a13$max.daily.ph>8, ])
aa14<-nrow(a14[a14$max.daily.ph>8, ])
aa15<-nrow(a15[a15$max.daily.ph>8, ])
eos.mon.sal<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.mon.sal<-as.data.frame(eos.mon.sal)
#change column names
names(eos.dates)[1] <- "date"
names(eos.dates)[2] <- "max.daily.ph.gt8"
eos.dates$date<-as.Date(eos.dates$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.dates[,c("date", "max.daily.ph.gt8")], by="date")Save csv
write.csv(nd.eos, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/one.month.lag.eos.daily.sum.csv")eos.daily.sum: https://github.com/Cmwegener/thesis/blob/master/data/environmental/daily_summaries/
####Brickyard Park and Richardson Bay#### Monthly summaries of the field data
#subset site
by<-subset(field, field$site.old == "BY")
by$date<-as.Date(by$date, format=c("%Y-%m-%d"))
#mean of fucus density
by.rb<-aggregate(no.fuc.q ~ date, by, mean, na.rm=TRUE)
#mean percent cover
by.r<-aggregate(cover ~date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean of large fucus density
by.r<-aggregate(no.large.fuc.q ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean small fucus density
by.r<-aggregate(no.small.fuc.q ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#median reproductive cover class
by.r<-aggregate(covcl.repro ~ date, by, median, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean vegetative dry weight
by.r<-aggregate(dw.veg ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean reproductive dry weight
by.r<-aggregate(dw.repro ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean reproductive apices
by.r<-aggregate(apices.repro ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean vegetative apices
by.r<-aggregate(apices.veg ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean percent reproductive apices
by.r<-aggregate(perc.ra ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean oogonia per conceptacle
by.r<-aggregate(oog.per.con ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean percent reproductive dry weight
by.r<-aggregate(perc.rdw ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean oogonia per receptacle
by$oog.recpt<-(by$oog.per.con * by$no.concept.recp)
by.r<-aggregate(no.concept.recp ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean oogonia per thalli
#(note: theres 2 apices per receptical)
# oogonia/receptacle * (1 receptacle / 2 apices) * reproductive apices/thalli = oogonia/thalli
by$oog.thalli <- (by$oog.recpt * 0.5 * by$apices.repro)
by.r<-aggregate(oog.thalli ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
#mean conceptacle per thalli
by$con.thalli <- (by$no.concept.recp * by$apices.repro)
by.r<-aggregate(con.thalli ~ date, by, mean, na.rm=TRUE)
by.rb<-merge(by.rb, by.r, by="date", all=TRUE)
rm(by, by.r)Salinity
#read in an format data
rb.sal.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/rb.sal.tide.csv",
header = TRUE
)
rb.sal.tide$date<-as.Date(rb.sal.tide$date, format=c("%Y-%m-%d"))
#looking at survey dates
print(by.rb$date)## [1] "2018-06-15" "2018-07-16" "2018-08-06" "2018-09-10" "2018-10-09"
## [6] "2018-11-06" "2018-12-04" "2019-01-31" "2019-02-21" "2019-03-14"
## [11] "2019-04-09" "2019-05-08" "2019-06-08" "2019-07-20" "2019-08-04"
## [16] "2019-09-12"#subset the salinity data by dates
a1<- rb.sal.tide[rb.sal.tide$date >= "2018-05-16" & rb.sal.tide$date < "2018-06-15",]
a2<- rb.sal.tide[rb.sal.tide$date >= "2018-06-16" & rb.sal.tide$date < "2018-07-16",]
a3<- rb.sal.tide[rb.sal.tide$date >= "2018-07-07" & rb.sal.tide$date < "2018-08-06",]
a4<- rb.sal.tide[rb.sal.tide$date >= "2018-08-11" & rb.sal.tide$date < "2018-09-10",]
a5<- rb.sal.tide[rb.sal.tide$date >= "2018-09-09" & rb.sal.tide$date < "2018-10-09",]
a6<- rb.sal.tide[rb.sal.tide$date >= "2018-10-07" & rb.sal.tide$date < "2018-11-06",]
a7<- rb.sal.tide[rb.sal.tide$date >= "2018-11-04" & rb.sal.tide$date < "2018-12-04",]
a8<- rb.sal.tide[rb.sal.tide$date >= "2019-01-01" & rb.sal.tide$date < "2019-01-31",]
a9<- rb.sal.tide[rb.sal.tide$date >= "2019-01-22" & rb.sal.tide$date < "2019-02-21",]
a10<- rb.sal.tide[rb.sal.tide$date >= "2019-02-12" & rb.sal.tide$date < "2019-03-14",]
a11<- rb.sal.tide[rb.sal.tide$date >= "2019-03-10" & rb.sal.tide$date < "2019-04-09",]
a12<- rb.sal.tide[rb.sal.tide$date >= "2019-04-08" & rb.sal.tide$date < "2019-05-08",]
a13<- rb.sal.tide[rb.sal.tide$date >= "2019-05-09" & rb.sal.tide$date < "2019-06-08",]
a14<- rb.sal.tide[rb.sal.tide$date >= "2019-06-20" & rb.sal.tide$date < "2019-07-20",]
a15<- rb.sal.tide[rb.sal.tide$date >= "2019-07-05" & rb.sal.tide$date < "2019-08-04",]
a16<- rb.sal.tide[rb.sal.tide$date >= "2019-08-13" & rb.sal.tide$date < "2019-09-12",]
#median
aa1<-median(a1$salinity, na.rm=TRUE)
aa2<-median(a2$salinity, na.rm=TRUE)
aa3<-median(a3$salinity, na.rm=TRUE)
aa4<-median(a4$salinity, na.rm=TRUE)
aa5<-median(a5$salinity, na.rm=TRUE)
aa6<-median(a6$salinity, na.rm=TRUE)
aa7<-median(a7$salinity, na.rm=TRUE)
aa8<-median(a8$salinity, na.rm=TRUE)
aa9<-median(a9$salinity, na.rm=TRUE)
aa10<-median(a10$salinity, na.rm=TRUE)
aa11<-median(a11$salinity, na.rm=TRUE)
aa12<-median(a12$salinity, na.rm=TRUE)
aa13<-median(a13$salinity, na.rm=TRUE)
aa14<-median(a14$salinity, na.rm=TRUE)
aa15<-median(a15$salinity, na.rm=TRUE)
aa16<-median(a16$salinity, na.rm=TRUE)
#string these values to a data frame
rb.mon.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.mon.sal<-as.data.frame(rb.mon.sal)
#change column names
names(rb.mon.sal)[1] <- "date"
names(rb.mon.sal)[2] <- "salinity"
rb.mon.sal$date<-as.Date(rb.mon.sal$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.sal[,c("date", "salinity")], by="date")Daily median, range, minimum, and maximum salinity. Need to check this step. The df looks off
#table summary for daily max aby min
rb.daily.sum<-as.data.frame(setDT(rb.sal.tide)[, .(max.daily.sal = max(salinity), min.daily.sal = min(salinity), daily.med.sal=median(salinity)), .(date)])
#daily range
rb.daily.sum$daily.sal.range<-rb.daily.sum$max.daily.sal - rb.daily.sum$min.daily.sal
#Daily min
#subset dates
a1<- rb.daily.sum[rb.daily.sum$date >= "2018-05-16" & rb.daily.sum$date < "2018-06-15",]
a2<- rb.daily.sum[rb.daily.sum$date >= "2018-06-16" & rb.daily.sum$date < "2018-07-16",]
a3<- rb.daily.sum[rb.daily.sum$date >= "2018-07-07" & rb.daily.sum$date < "2018-08-06",]
a4<- rb.daily.sum[rb.daily.sum$date >= "2018-08-11" & rb.daily.sum$date < "2018-09-10",]
a5<- rb.daily.sum[rb.daily.sum$date >= "2018-09-09" & rb.daily.sum$date < "2018-10-09",]
a6<- rb.daily.sum[rb.daily.sum$date >= "2018-10-07" & rb.daily.sum$date < "2018-11-06",]
a7<- rb.daily.sum[rb.daily.sum$date >= "2018-11-04" & rb.daily.sum$date < "2018-12-04",]
a8<- rb.daily.sum[rb.daily.sum$date >= "2019-01-01" & rb.daily.sum$date < "2019-01-31",]
a9<- rb.daily.sum[rb.daily.sum$date >= "2019-01-22" & rb.daily.sum$date < "2019-02-21",]
a10<- rb.daily.sum[rb.daily.sum$date >= "2019-02-12" & rb.daily.sum$date < "2019-03-14",]
a11<- rb.daily.sum[rb.daily.sum$date >= "2019-03-10" & rb.daily.sum$date < "2019-04-09",]
a12<- rb.daily.sum[rb.daily.sum$date >= "2019-04-08" & rb.daily.sum$date < "2019-05-08",]
a13<- rb.daily.sum[rb.daily.sum$date >= "2019-05-09" & rb.daily.sum$date < "2019-06-08",]
a14<- rb.daily.sum[rb.daily.sum$date >= "2019-06-20" & rb.daily.sum$date < "2019-07-20",]
a15<- rb.daily.sum[rb.daily.sum$date >= "2019-07-05" & rb.daily.sum$date < "2019-08-04",]
a16<- rb.daily.sum[rb.daily.sum$date >= "2019-08-13" & rb.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.sal, na.rm=TRUE)
aa2<-median(a2$min.daily.sal, na.rm=TRUE)
aa3<-median(a3$min.daily.sal, na.rm=TRUE)
aa4<-median(a4$min.daily.sal, na.rm=TRUE)
aa5<-median(a5$min.daily.sal, na.rm=TRUE)
aa6<-median(a6$min.daily.sal, na.rm=TRUE)
aa7<-median(a7$min.daily.sal, na.rm=TRUE)
aa8<-median(a8$min.daily.sal, na.rm=TRUE)
aa9<-median(a9$min.daily.sal, na.rm=TRUE)
aa10<-median(a10$min.daily.sal, na.rm=TRUE)
aa11<-median(a11$min.daily.sal, na.rm=TRUE)
aa12<-median(a12$min.daily.sal, na.rm=TRUE)
aa13<-median(a13$min.daily.sal, na.rm=TRUE)
aa14<-median(a14$min.daily.sal, na.rm=TRUE)
aa15<-median(a15$min.daily.sal, na.rm=TRUE)
aa16<-median(a16$min.daily.sal, na.rm=TRUE)
#string these values to a data frame
rb.daily.min.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.min.sal<-as.data.frame(rb.daily.min.sal)
#change column names
names(rb.daily.min.sal)[1] <- "date"
names(rb.daily.min.sal)[2] <- "daily.min.sal"
rb.daily.min.sal$date<-as.Date(rb.daily.min.sal$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.min.sal[,c("date", "daily.min.sal")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.sal, na.rm=TRUE)
aa2<-median(a2$max.daily.sal, na.rm=TRUE)
aa3<-median(a3$max.daily.sal, na.rm=TRUE)
aa4<-median(a4$max.daily.sal, na.rm=TRUE)
aa5<-median(a5$max.daily.sal, na.rm=TRUE)
aa6<-median(a6$max.daily.sal, na.rm=TRUE)
aa7<-median(a7$max.daily.sal, na.rm=TRUE)
aa8<-median(a8$max.daily.sal, na.rm=TRUE)
aa9<-median(a9$max.daily.sal, na.rm=TRUE)
aa10<-median(a10$max.daily.sal, na.rm=TRUE)
aa11<-median(a11$max.daily.sal, na.rm=TRUE)
aa12<-median(a12$max.daily.sal, na.rm=TRUE)
aa13<-median(a13$max.daily.sal, na.rm=TRUE)
aa14<-median(a14$max.daily.sal, na.rm=TRUE)
aa15<-median(a15$max.daily.sal, na.rm=TRUE)
aa16<-median(a16$max.daily.sal, na.rm=TRUE)
#string these values to a data frame
rb.daily.max.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.max.sal<-as.data.frame(rb.daily.max.sal)
#change column names
names(rb.daily.max.sal)[1] <- "date"
names(rb.daily.max.sal)[2] <- "daily.max.sal"
rb.daily.max.sal$date<-as.Date(rb.daily.max.sal$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.max.sal[,c("date", "daily.max.sal")], by="date")
#daily range
#mean
aa1<-median(a1$daily.sal.range, na.rm=TRUE)
aa2<-median(a2$daily.sal.range, na.rm=TRUE)
aa3<-median(a3$daily.sal.range, na.rm=TRUE)
aa4<-median(a4$daily.sal.range, na.rm=TRUE)
aa5<-median(a5$daily.sal.range, na.rm=TRUE)
aa6<-median(a6$daily.sal.range, na.rm=TRUE)
aa7<-median(a7$daily.sal.range, na.rm=TRUE)
aa8<-median(a8$daily.sal.range, na.rm=TRUE)
aa9<-median(a9$daily.sal.range, na.rm=TRUE)
aa10<-median(a10$daily.sal.range, na.rm=TRUE)
aa11<-median(a11$daily.sal.range, na.rm=TRUE)
aa12<-median(a12$daily.sal.range, na.rm=TRUE)
aa13<-median(a13$daily.sal.range, na.rm=TRUE)
aa14<-median(a14$daily.sal.range, na.rm=TRUE)
aa15<-median(a15$daily.sal.range, na.rm=TRUE)
aa16<-median(a16$daily.sal.range, na.rm=TRUE)
#string these values to a data frame
rb.daily.sal.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.sal.range<-as.data.frame(rb.daily.sal.range)
#change column names
names(rb.daily.sal.range)[1] <- "date"
names(rb.daily.sal.range)[2] <- "daily.sal.range"
rb.daily.sal.range$date<-as.Date(rb.daily.sal.range$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.sal.range[,c("date", "daily.sal.range")], by="date")
#daily median
#mean of these salinity periods
aa1<-median(a1$daily.med.sal, na.rm=TRUE)
aa2<-median(a2$daily.med.sal, na.rm=TRUE)
aa3<-median(a3$daily.med.sal, na.rm=TRUE)
aa4<-median(a4$daily.med.sal, na.rm=TRUE)
aa5<-median(a5$daily.med.sal, na.rm=TRUE)
aa6<-median(a6$daily.med.sal, na.rm=TRUE)
aa7<-median(a7$daily.med.sal, na.rm=TRUE)
aa8<-median(a8$daily.med.sal, na.rm=TRUE)
aa9<-median(a9$daily.med.sal, na.rm=TRUE)
aa10<-median(a10$daily.med.sal, na.rm=TRUE)
aa11<-median(a11$daily.med.sal, na.rm=TRUE)
aa12<-median(a12$daily.med.sal, na.rm=TRUE)
aa13<-median(a13$daily.med.sal, na.rm=TRUE)
aa14<-median(a14$daily.med.sal, na.rm=TRUE)
aa15<-median(a15$daily.med.sal, na.rm=TRUE)
aa16<-median(a16$daily.med.sal, na.rm=TRUE)
#string these values to a data frame
rb.daily.med.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.med.sal<-as.data.frame(rb.daily.med.sal)
#change column names
names(rb.daily.med.sal)[1] <- "date"
names(rb.daily.med.sal)[2] <- "daily.med.sal"
rb.daily.med.sal$date<-as.Date(rb.daily.med.sal$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.med.sal[,c("date", "daily.med.sal")], by="date")pH
#read in data
rb.ph.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/rb.ph.tide.csv",
header = TRUE
)
rb.ph.tide$date<-as.Date(rb.ph.tide$date, format=c("%Y-%m-%d"))
#subset
#subset dates
a1<- rb.ph.tide[rb.ph.tide$date >= "2018-05-16" & rb.ph.tide$date < "2018-06-15",]
a2<- rb.ph.tide[rb.ph.tide$date >= "2018-06-16" & rb.ph.tide$date < "2018-07-16",]
a3<- rb.ph.tide[rb.ph.tide$date >= "2018-07-07" & rb.ph.tide$date < "2018-08-06",]
a4<- rb.ph.tide[rb.ph.tide$date >= "2018-08-11" & rb.ph.tide$date < "2018-09-10",]
a5<- rb.ph.tide[rb.ph.tide$date >= "2018-09-09" & rb.ph.tide$date < "2018-10-09",]
a6<- rb.ph.tide[rb.ph.tide$date >= "2018-10-07" & rb.ph.tide$date < "2018-11-06",]
a7<- rb.ph.tide[rb.ph.tide$date >= "2018-11-04" & rb.ph.tide$date < "2018-12-04",]
a8<- rb.ph.tide[rb.ph.tide$date >= "2019-01-01" & rb.ph.tide$date < "2019-01-31",]
a9<- rb.ph.tide[rb.ph.tide$date >= "2019-01-22" & rb.ph.tide$date < "2019-02-21",]
a10<- rb.ph.tide[rb.ph.tide$date >= "2019-02-12" & rb.ph.tide$date < "2019-03-14",]
a11<- rb.ph.tide[rb.ph.tide$date >= "2019-03-10" & rb.ph.tide$date < "2019-04-09",]
a12<- rb.ph.tide[rb.ph.tide$date >= "2019-04-08" & rb.ph.tide$date < "2019-05-08",]
a13<- rb.ph.tide[rb.ph.tide$date >= "2019-05-09" & rb.ph.tide$date < "2019-06-08",]
a14<- rb.ph.tide[rb.ph.tide$date >= "2019-06-20" & rb.ph.tide$date < "2019-07-20",]
a15<- rb.ph.tide[rb.ph.tide$date >= "2019-07-05" & rb.ph.tide$date < "2019-08-04",]
a16<- rb.ph.tide[rb.ph.tide$date >= "2019-08-13" & rb.ph.tide$date < "2019-09-12",]
#median
aa1<-median(a1$ph, na.rm=TRUE)
aa2<-median(a2$ph, na.rm=TRUE)
aa3<-median(a3$ph, na.rm=TRUE)
aa4<-median(a4$ph, na.rm=TRUE)
aa5<-median(a5$ph, na.rm=TRUE)
aa6<-median(a6$ph, na.rm=TRUE)
aa7<-median(a7$ph, na.rm=TRUE)
aa8<-median(a8$ph, na.rm=TRUE)
aa9<-median(a9$ph, na.rm=TRUE)
aa10<-median(a10$ph, na.rm=TRUE)
aa11<-median(a11$ph, na.rm=TRUE)
aa12<-median(a12$ph, na.rm=TRUE)
aa13<-median(a13$ph, na.rm=TRUE)
aa14<-median(a14$ph, na.rm=TRUE)
aa15<-median(a15$ph, na.rm=TRUE)
aa16<-median(a16$ph, na.rm=TRUE)
#string these values to a data frame
rb.mon.ph<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.mon.ph<-as.data.frame(rb.mon.ph)
#change column names
names(rb.mon.ph)[1] <- "date"
names(rb.mon.ph)[2] <- "ph"
rb.mon.ph$date<-as.Date(rb.mon.ph$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.ph[,c("date", "ph")], by="date")Daily median, range, minimum, and maximum pH
#table summary for daily max aby min
rb.daily.sum.ph<-as.data.frame(setDT(rb.ph.tide)[, .(max.daily.ph = max(ph), min.daily.ph = min(ph), daily.med.ph=median(ph)), .(date)])
#daily range
rb.daily.sum.ph$daily.ph.range<-rb.daily.sum.ph$max.daily.ph - rb.daily.sum.ph$min.daily.ph
#merge
rb.daily.sum<-merge(rb.daily.sum, rb.daily.sum.ph, by="date", all=TRUE)
#Daily min
#subset dates
a1<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-05-16" & rb.daily.sum.ph$date < "2018-06-15",]
a2<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-06-16" & rb.daily.sum.ph$date < "2018-07-16",]
a3<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-07-07" & rb.daily.sum.ph$date < "2018-08-06",]
a4<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-08-11" & rb.daily.sum.ph$date < "2018-09-10",]
a5<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-09-09" & rb.daily.sum.ph$date < "2018-10-09",]
a6<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-10-07" & rb.daily.sum.ph$date < "2018-11-06",]
a7<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2018-11-04" & rb.daily.sum.ph$date < "2018-12-04",]
a8<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-01-01" & rb.daily.sum.ph$date < "2019-01-31",]
a9<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-01-22" & rb.daily.sum.ph$date < "2019-02-21",]
a10<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-02-12" & rb.daily.sum.ph$date < "2019-03-14",]
a11<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-03-10" & rb.daily.sum.ph$date < "2019-04-09",]
a12<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-04-08" & rb.daily.sum.ph$date < "2019-05-08",]
a13<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-05-09" & rb.daily.sum.ph$date < "2019-06-08",]
a14<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-06-20" & rb.daily.sum.ph$date < "2019-07-20",]
a15<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-07-05" & rb.daily.sum.ph$date < "2019-08-04",]
a16<- rb.daily.sum.ph[rb.daily.sum.ph$date >= "2019-08-13" & rb.daily.sum.ph$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.ph, na.rm=TRUE)
aa2<-median(a2$min.daily.ph, na.rm=TRUE)
aa3<-median(a3$min.daily.ph, na.rm=TRUE)
aa4<-median(a4$min.daily.ph, na.rm=TRUE)
aa5<-median(a5$min.daily.ph, na.rm=TRUE)
aa6<-median(a6$min.daily.ph, na.rm=TRUE)
aa7<-median(a7$min.daily.ph, na.rm=TRUE)
aa8<-median(a8$min.daily.ph, na.rm=TRUE)
aa9<-median(a9$min.daily.ph, na.rm=TRUE)
aa10<-median(a10$min.daily.ph, na.rm=TRUE)
aa11<-median(a11$min.daily.ph, na.rm=TRUE)
aa12<-median(a12$min.daily.ph, na.rm=TRUE)
aa13<-median(a13$min.daily.ph, na.rm=TRUE)
aa14<-median(a14$min.daily.ph, na.rm=TRUE)
aa15<-median(a15$min.daily.ph, na.rm=TRUE)
aa16<-median(a16$min.daily.ph, na.rm=TRUE)
#string these values to a data frame
rb.daily.min.ph<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.min.ph<-as.data.frame(rb.daily.min.ph)
#change column names
names(rb.daily.min.ph)[1] <- "date"
names(rb.daily.min.ph)[2] <- "daily.min.ph"
rb.daily.min.ph$date<-as.Date(rb.daily.min.ph$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.min.ph[,c("date", "daily.min.ph")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.ph, na.rm=TRUE)
aa2<-median(a2$max.daily.ph, na.rm=TRUE)
aa3<-median(a3$max.daily.ph, na.rm=TRUE)
aa4<-median(a4$max.daily.ph, na.rm=TRUE)
aa5<-median(a5$max.daily.ph, na.rm=TRUE)
aa6<-median(a6$max.daily.ph, na.rm=TRUE)
aa7<-median(a7$max.daily.ph, na.rm=TRUE)
aa8<-median(a8$max.daily.ph, na.rm=TRUE)
aa9<-median(a9$max.daily.ph, na.rm=TRUE)
aa10<-median(a10$max.daily.ph, na.rm=TRUE)
aa11<-median(a11$max.daily.ph, na.rm=TRUE)
aa12<-median(a12$max.daily.ph, na.rm=TRUE)
aa13<-median(a13$max.daily.ph, na.rm=TRUE)
aa14<-median(a14$max.daily.ph, na.rm=TRUE)
aa15<-median(a15$max.daily.ph, na.rm=TRUE)
aa16<-median(a16$max.daily.ph, na.rm=TRUE)
#string these values to a data frame
rb.daily.max.ph<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.max.ph<-as.data.frame(rb.daily.max.ph)
#change column names
names(rb.daily.max.ph)[1] <- "date"
names(rb.daily.max.ph)[2] <- "daily.max.ph"
rb.daily.max.ph$date<-as.Date(rb.daily.max.ph$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.max.ph[,c("date", "daily.max.ph")], by="date")
#daily range
#mean
aa1<-median(a1$daily.ph.range, na.rm=TRUE)
aa2<-median(a2$daily.ph.range, na.rm=TRUE)
aa3<-median(a3$daily.ph.range, na.rm=TRUE)
aa4<-median(a4$daily.ph.range, na.rm=TRUE)
aa5<-median(a5$daily.ph.range, na.rm=TRUE)
aa6<-median(a6$daily.ph.range, na.rm=TRUE)
aa7<-median(a7$daily.ph.range, na.rm=TRUE)
aa8<-median(a8$daily.ph.range, na.rm=TRUE)
aa9<-median(a9$daily.ph.range, na.rm=TRUE)
aa10<-median(a10$daily.ph.range, na.rm=TRUE)
aa11<-median(a11$daily.ph.range, na.rm=TRUE)
aa12<-median(a12$daily.ph.range, na.rm=TRUE)
aa13<-median(a13$daily.ph.range, na.rm=TRUE)
aa14<-median(a14$daily.ph.range, na.rm=TRUE)
aa15<-median(a15$daily.ph.range, na.rm=TRUE)
aa16<-median(a16$daily.ph.range, na.rm=TRUE)
#string these values to a data frame
rb.daily.ph.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.ph.range<-as.data.frame(rb.daily.ph.range)
#change column names
names(rb.daily.ph.range)[1] <- "date"
names(rb.daily.ph.range)[2] <- "daily.ph.range"
rb.daily.ph.range$date<-as.Date(rb.daily.ph.range$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.ph.range[,c("date", "daily.ph.range")], by="date")
#daily median
#mean of these phinity periods
aa1<-median(a1$daily.med.ph, na.rm=TRUE)
aa2<-median(a2$daily.med.ph, na.rm=TRUE)
aa3<-median(a3$daily.med.ph, na.rm=TRUE)
aa4<-median(a4$daily.med.ph, na.rm=TRUE)
aa5<-median(a5$daily.med.ph, na.rm=TRUE)
aa6<-median(a6$daily.med.ph, na.rm=TRUE)
aa7<-median(a7$daily.med.ph, na.rm=TRUE)
aa8<-median(a8$daily.med.ph, na.rm=TRUE)
aa9<-median(a9$daily.med.ph, na.rm=TRUE)
aa10<-median(a10$daily.med.ph, na.rm=TRUE)
aa11<-median(a11$daily.med.ph, na.rm=TRUE)
aa12<-median(a12$daily.med.ph, na.rm=TRUE)
aa13<-median(a13$daily.med.ph, na.rm=TRUE)
aa14<-median(a14$daily.med.ph, na.rm=TRUE)
aa15<-median(a15$daily.med.ph, na.rm=TRUE)
aa16<-median(a16$daily.med.ph, na.rm=TRUE)
#string these values to a data frame
rb.daily.med.ph<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.med.ph<-as.data.frame(rb.daily.med.ph)
#change column names
names(rb.daily.med.ph)[1] <- "date"
names(rb.daily.med.ph)[2] <- "daily.med.ph"
rb.daily.med.ph$date<-as.Date(rb.daily.med.ph$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.med.ph[,c("date", "daily.med.ph")], by="date")Water temperature
#read in data
rb.wtemp.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/rb.wtemp.tide.csv",
header = TRUE
)
rb.wtemp.tide$date<-as.Date(rb.wtemp.tide$date, format=c("%Y-%m-%d"))
#subset
a1<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-05-16" & rb.wtemp.tide$date < "2018-06-15",]
a2<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-06-16" & rb.wtemp.tide$date < "2018-07-16",]
a3<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-07-07" & rb.wtemp.tide$date < "2018-08-06",]
a4<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-08-11" & rb.wtemp.tide$date < "2018-09-10",]
a5<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-09-09" & rb.wtemp.tide$date < "2018-10-09",]
a6<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-10-07" & rb.wtemp.tide$date < "2018-11-06",]
a7<- rb.wtemp.tide[rb.wtemp.tide$date >= "2018-11-04" & rb.wtemp.tide$date < "2018-12-04",]
a8<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-01-01" & rb.wtemp.tide$date < "2019-01-31",]
a9<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-01-22" & rb.wtemp.tide$date < "2019-02-21",]
a10<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-02-12" & rb.wtemp.tide$date < "2019-03-14",]
a11<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-03-10" & rb.wtemp.tide$date < "2019-04-09",]
a12<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-04-08" & rb.wtemp.tide$date < "2019-05-08",]
a13<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-05-09" & rb.wtemp.tide$date < "2019-06-08",]
a14<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-06-20" & rb.wtemp.tide$date < "2019-07-20",]
a15<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-07-05" & rb.wtemp.tide$date < "2019-08-04",]
a16<- rb.wtemp.tide[rb.wtemp.tide$date >= "2019-08-13" & rb.wtemp.tide$date < "2019-09-12",]
#median
aa1<-median(a1$water_temp, na.rm=TRUE)
aa2<-median(a2$water_temp, na.rm=TRUE)
aa3<-median(a3$water_temp, na.rm=TRUE)
aa4<-median(a4$water_temp, na.rm=TRUE)
aa5<-median(a5$water_temp, na.rm=TRUE)
aa6<-median(a6$water_temp, na.rm=TRUE)
aa7<-median(a7$water_temp, na.rm=TRUE)
aa8<-median(a8$water_temp, na.rm=TRUE)
aa9<-median(a9$water_temp, na.rm=TRUE)
aa10<-median(a10$water_temp, na.rm=TRUE)
aa11<-median(a11$water_temp, na.rm=TRUE)
aa12<-median(a12$water_temp, na.rm=TRUE)
aa13<-median(a13$water_temp, na.rm=TRUE)
aa14<-median(a14$water_temp, na.rm=TRUE)
aa15<-median(a15$water_temp, na.rm=TRUE)
aa16<-median(a16$water_temp, na.rm=TRUE)
#string these values to a data frame
rb.mon.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.mon.wt<-as.data.frame(rb.mon.wt)
#change column names
names(rb.mon.wt)[1] <- "date"
names(rb.mon.wt)[2] <- "water.temp"
rb.mon.wt$date<-as.Date(rb.mon.wt$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.wt[,c("date", "water.temp")], by="date")Daily median, range, minimum, and maximum water temperature
#table summary for daily med, max, min
rb.daily.sum.wt<-as.data.frame(setDT(rb.wtemp.tide)[, .(max.daily.wt = max(water_temp), min.daily.wt = min(water_temp), daily.med.wt=median(water_temp)), .(date)])
#daily range
rb.daily.sum.wt$daily.wt.range<-rb.daily.sum.wt$max.daily.wt - rb.daily.sum.wt$min.daily.wt
#merge
rb.daily.sum<-merge(rb.daily.sum, rb.daily.sum.wt, by="date", all=TRUE)
#Daily min
#subset dates
a1<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-05-16" & rb.daily.sum.wt$date < "2018-06-15",]
a2<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-06-16" & rb.daily.sum.wt$date < "2018-07-16",]
a3<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-07-07" & rb.daily.sum.wt$date < "2018-08-06",]
a4<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-08-11" & rb.daily.sum.wt$date < "2018-09-10",]
a5<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-09-09" & rb.daily.sum.wt$date < "2018-10-09",]
a6<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-10-07" & rb.daily.sum.wt$date < "2018-11-06",]
a7<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2018-11-04" & rb.daily.sum.wt$date < "2018-12-04",]
a8<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-01-01" & rb.daily.sum.wt$date < "2019-01-31",]
a9<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-01-22" & rb.daily.sum.wt$date < "2019-02-21",]
a10<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-02-12" & rb.daily.sum.wt$date < "2019-03-14",]
a11<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-03-10" & rb.daily.sum.wt$date < "2019-04-09",]
a12<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-04-08" & rb.daily.sum.wt$date < "2019-05-08",]
a13<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-05-09" & rb.daily.sum.wt$date < "2019-06-08",]
a14<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-06-20" & rb.daily.sum.wt$date < "2019-07-20",]
a15<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-07-05" & rb.daily.sum.wt$date < "2019-08-04",]
a16<- rb.daily.sum.wt[rb.daily.sum.wt$date >= "2019-08-13" & rb.daily.sum.wt$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.wt, na.rm=TRUE)
aa2<-median(a2$min.daily.wt, na.rm=TRUE)
aa3<-median(a3$min.daily.wt, na.rm=TRUE)
aa4<-median(a4$min.daily.wt, na.rm=TRUE)
aa5<-median(a5$min.daily.wt, na.rm=TRUE)
aa6<-median(a6$min.daily.wt, na.rm=TRUE)
aa7<-median(a7$min.daily.wt, na.rm=TRUE)
aa8<-median(a8$min.daily.wt, na.rm=TRUE)
aa9<-median(a9$min.daily.wt, na.rm=TRUE)
aa10<-median(a10$min.daily.wt, na.rm=TRUE)
aa11<-median(a11$min.daily.wt, na.rm=TRUE)
aa12<-median(a12$min.daily.wt, na.rm=TRUE)
aa13<-median(a13$min.daily.wt, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
aa15<-median(a15$min.daily.wt, na.rm=TRUE)
aa16<-median(a16$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
rb.daily.min.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.min.wt<-as.data.frame(rb.daily.min.wt)
#change column names
names(rb.daily.min.wt)[1] <- "date"
names(rb.daily.min.wt)[2] <- "daily.min.wt"
rb.daily.min.wt$date<-as.Date(rb.daily.min.wt$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.min.wt[,c("date", "daily.min.wt")], by="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.wt, na.rm=TRUE)
aa2<-median(a2$max.daily.wt, na.rm=TRUE)
aa3<-median(a3$max.daily.wt, na.rm=TRUE)
aa4<-median(a4$max.daily.wt, na.rm=TRUE)
aa5<-median(a5$max.daily.wt, na.rm=TRUE)
aa6<-median(a6$max.daily.wt, na.rm=TRUE)
aa7<-median(a7$max.daily.wt, na.rm=TRUE)
aa8<-median(a8$max.daily.wt, na.rm=TRUE)
aa9<-median(a9$max.daily.wt, na.rm=TRUE)
aa10<-median(a10$max.daily.wt, na.rm=TRUE)
aa11<-median(a11$max.daily.wt, na.rm=TRUE)
aa12<-median(a12$max.daily.wt, na.rm=TRUE)
aa13<-median(a13$max.daily.wt, na.rm=TRUE)
aa14<-median(a14$max.daily.wt, na.rm=TRUE)
aa15<-median(a15$max.daily.wt, na.rm=TRUE)
aa16<-median(a16$max.daily.wt, na.rm=TRUE)
#string these values to a data frame
rb.daily.max.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.max.wt<-as.data.frame(rb.daily.max.wt)
#change column names
names(rb.daily.max.wt)[1] <- "date"
names(rb.daily.max.wt)[2] <- "daily.max.wt"
rb.daily.max.wt$date<-as.Date(rb.daily.max.wt$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.max.wt[,c("date", "daily.max.wt")], by="date")
#daily range
#mean
aa1<-median(a1$daily.wt.range, na.rm=TRUE)
aa2<-median(a2$daily.wt.range, na.rm=TRUE)
aa3<-median(a3$daily.wt.range, na.rm=TRUE)
aa4<-median(a4$daily.wt.range, na.rm=TRUE)
aa5<-median(a5$daily.wt.range, na.rm=TRUE)
aa6<-median(a6$daily.wt.range, na.rm=TRUE)
aa7<-median(a7$daily.wt.range, na.rm=TRUE)
aa8<-median(a8$daily.wt.range, na.rm=TRUE)
aa9<-median(a9$daily.wt.range, na.rm=TRUE)
aa10<-median(a10$daily.wt.range, na.rm=TRUE)
aa11<-median(a11$daily.wt.range, na.rm=TRUE)
aa12<-median(a12$daily.wt.range, na.rm=TRUE)
aa13<-median(a13$daily.wt.range, na.rm=TRUE)
aa14<-median(a14$daily.wt.range, na.rm=TRUE)
aa15<-median(a15$daily.wt.range, na.rm=TRUE)
aa16<-median(a16$daily.wt.range, na.rm=TRUE)
#string these values to a data frame
rb.daily.wt.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.wt.range<-as.data.frame(rb.daily.wt.range)
#change column names
names(rb.daily.wt.range)[1] <- "date"
names(rb.daily.wt.range)[2] <- "daily.wt.range"
rb.daily.wt.range$date<-as.Date(rb.daily.wt.range$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.wt.range[,c("date", "daily.wt.range")], by="date")
#daily median
#mean of these wtinity periods
aa1<-median(a1$daily.med.wt, na.rm=TRUE)
aa2<-median(a2$daily.med.wt, na.rm=TRUE)
aa3<-median(a3$daily.med.wt, na.rm=TRUE)
aa4<-median(a4$daily.med.wt, na.rm=TRUE)
aa5<-median(a5$daily.med.wt, na.rm=TRUE)
aa6<-median(a6$daily.med.wt, na.rm=TRUE)
aa7<-median(a7$daily.med.wt, na.rm=TRUE)
aa8<-median(a8$daily.med.wt, na.rm=TRUE)
aa9<-median(a9$daily.med.wt, na.rm=TRUE)
aa10<-median(a10$daily.med.wt, na.rm=TRUE)
aa11<-median(a11$daily.med.wt, na.rm=TRUE)
aa12<-median(a12$daily.med.wt, na.rm=TRUE)
aa13<-median(a13$daily.med.wt, na.rm=TRUE)
aa14<-median(a14$daily.med.wt, na.rm=TRUE)
aa15<-median(a15$daily.med.wt, na.rm=TRUE)
aa16<-median(a16$daily.med.wt, na.rm=TRUE)
#string these values to a data frame
rb.daily.med.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.daily.med.wt<-as.data.frame(rb.daily.med.wt)
#change column names
names(rb.daily.med.wt)[1] <- "date"
names(rb.daily.med.wt)[2] <- "daily.med.wt"
rb.daily.med.wt$date<-as.Date(rb.daily.med.wt$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.daily.med.wt[,c("date", "daily.med.wt")], by="date")Daily summary
Subset by survey dates
a1<- rb.daily.sum[rb.daily.sum$date >= "2018-05-16" & rb.daily.sum$date < "2018-06-15",]
a2<- rb.daily.sum[rb.daily.sum$date >= "2018-06-16" & rb.daily.sum$date < "2018-07-16",]
a3<- rb.daily.sum[rb.daily.sum$date >= "2018-07-07" & rb.daily.sum$date < "2018-08-06",]
a4<- rb.daily.sum[rb.daily.sum$date >= "2018-08-11" & rb.daily.sum$date < "2018-09-10",]
a5<- rb.daily.sum[rb.daily.sum$date >= "2018-09-09" & rb.daily.sum$date < "2018-10-09",]
a6<- rb.daily.sum[rb.daily.sum$date >= "2018-10-07" & rb.daily.sum$date < "2018-11-06",]
a7<- rb.daily.sum[rb.daily.sum$date >= "2018-11-04" & rb.daily.sum$date < "2018-12-04",]
a8<- rb.daily.sum[rb.daily.sum$date >= "2019-01-01" & rb.daily.sum$date < "2019-01-31",]
a9<- rb.daily.sum[rb.daily.sum$date >= "2019-01-22" & rb.daily.sum$date < "2019-02-21",]
a10<- rb.daily.sum[rb.daily.sum$date >= "2019-02-12" & rb.daily.sum$date < "2019-03-14",]
a11<- rb.daily.sum[rb.daily.sum$date >= "2019-03-10" & rb.daily.sum$date < "2019-04-09",]
a12<- rb.daily.sum[rb.daily.sum$date >= "2019-04-08" & rb.daily.sum$date < "2019-05-08",]
a13<- rb.daily.sum[rb.daily.sum$date >= "2019-05-09" & rb.daily.sum$date < "2019-06-08",]
a14<- rb.daily.sum[rb.daily.sum$date >= "2019-06-20" & rb.daily.sum$date < "2019-07-20",]
a15<- rb.daily.sum[rb.daily.sum$date >= "2019-07-05" & rb.daily.sum$date < "2019-08-04",]
a16<- rb.daily.sum[rb.daily.sum$date >= "2019-08-13" & rb.daily.sum$date < "2019-09-12",]Daily maximum salinity less than 10
aa1<-nrow(a1[a1$max.daily.sal<10, ])
aa2<-nrow(a2[a2$max.daily.sal<10, ])
aa3<-nrow(a3[a3$max.daily.sal<10, ])
aa4<-nrow(a4[a4$max.daily.sal<10, ])
aa5<-nrow(a5[a5$max.daily.sal<10, ])
aa6<-nrow(a6[a6$max.daily.sal<10, ])
aa7<-nrow(a7[a7$max.daily.sal<10, ])
aa8<-nrow(a8[a8$max.daily.sal<10, ])
aa9<-nrow(a9[a9$max.daily.sal<10, ])
aa10<-nrow(a10[a10$max.daily.sal<10, ])
aa11<-nrow(a11[a11$max.daily.sal<10, ])
aa12<-nrow(a12[a12$max.daily.sal<10, ])
aa13<-nrow(a13[a13$max.daily.sal<10, ])
aa14<-nrow(a14[a14$max.daily.sal<10, ])
aa15<-nrow(a15[a15$max.daily.sal<10, ])
aa16<-nrow(a16[a16$max.daily.sal<10, ])
rb.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.dates<-as.data.frame(rb.dates)
#change column names
names(rb.dates)[1] <- "date"
names(rb.dates)[2] <- "max.daily.sal.lt10"
rb.dates$date<-as.Date(rb.dates$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.dates[,c("date", "max.daily.sal.lt10")], by="date")Daily min salinity greater than 28
#count
aa1<-nrow(a1[a1$min.daily.sal>28, ])
aa2<-nrow(a2[a2$min.daily.sal>28, ])
aa3<-nrow(a3[a3$min.daily.sal>28, ])
aa4<-nrow(a4[a4$min.daily.sal>28, ])
aa5<-nrow(a5[a5$min.daily.sal>28, ])
aa6<-nrow(a6[a6$min.daily.sal>28, ])
aa7<-nrow(a7[a7$min.daily.sal>28, ])
aa8<-nrow(a8[a8$min.daily.sal>28, ])
aa9<-nrow(a9[a9$min.daily.sal>28, ])
aa10<-nrow(a10[a10$min.daily.sal>28, ])
aa11<-nrow(a11[a11$min.daily.sal>28, ])
aa12<-nrow(a12[a12$min.daily.sal>28, ])
aa13<-nrow(a13[a13$min.daily.sal>28, ])
aa14<-nrow(a14[a14$min.daily.sal>28, ])
aa15<-nrow(a15[a15$min.daily.sal>28, ])
aa16<-nrow(a16[a16$min.daily.sal>28, ])
rb.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.dates<-as.data.frame(rb.dates)
#change column names
names(rb.dates)[1] <- "date"
names(rb.dates)[2] <- "min.daily.sal.gt28"
rb.dates$date<-as.Date(rb.dates$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.dates[,c("date", "min.daily.sal.gt28")], by="date")pH
Number of days where max.daily.ph is >8
aa1<-nrow(a1[a1$max.daily.ph>8, ])
aa2<-nrow(a2[a2$max.daily.ph>8, ])
aa3<-nrow(a3[a3$max.daily.ph>8, ])
aa4<-nrow(a4[a4$max.daily.ph>8, ])
aa5<-nrow(a5[a5$max.daily.ph>8, ])
aa6<-nrow(a6[a6$max.daily.ph>8, ])
aa7<-nrow(a7[a7$max.daily.ph>8, ])
aa8<-nrow(a8[a8$max.daily.ph>8, ])
aa9<-nrow(a9[a9$max.daily.ph>8, ])
aa10<-nrow(a10[a10$max.daily.ph>8, ])
aa11<-nrow(a11[a11$max.daily.ph>8, ])
aa12<-nrow(a12[a12$max.daily.ph>8, ])
aa13<-nrow(a13[a13$max.daily.ph>8, ])
aa14<-nrow(a14[a14$max.daily.ph>8, ])
aa15<-nrow(a15[a15$max.daily.ph>8, ])
aa16<-nrow(a16[a16$max.daily.ph>8, ])
rb.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.dates<-as.data.frame(rb.dates)
#change column names
names(rb.dates)[1] <- "date"
names(rb.dates)[2] <- "max.daily.ph.gt8"
rb.dates$date<-as.Date(rb.dates$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.dates[,c("date", "max.daily.ph.gt8")], by="date")Save csv
write.csv(by.rb, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/one.month.lag.rb.daily.sum.csv")rb.daily.sum: https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/daily_summaries/
####Fort Point and Horseshoe Bay
Monthly summaries of the field data
hs<-subset(field, field$site.old == "HS")
hs$date<-as.Date(hs$date, format=c("%Y-%m-%d"))
#monthly mean of fucus density
hs.fp<-aggregate(no.fuc.q ~ date, hs, mean, na.rm=TRUE)
#mean percent cover
hs.r<-aggregate(cover ~date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean of large fucus density
hs.r<-aggregate(no.large.fuc.q ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean small fucus density
hs.r<-aggregate(no.small.fuc.q ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#median reproductive cover class
hs.r<-aggregate(covcl.repro ~ date, hs, median, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean vegetative dry weight
hs.r<-aggregate(dw.veg ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean reproductive dry weight
hs.r<-aggregate(dw.repro ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean reproductive apices
hs.r<-aggregate(apices.repro ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean vegetative apices
hs.r<-aggregate(apices.veg ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean percent reproductive apices
hs.r<-aggregate(perc.ra ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean oogonia per conceptacle
hs.r<-aggregate(oog.per.con ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean percent reproductive dry weight
hs.r<-aggregate(perc.rdw ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean oogonia per receptacle
hs$oog.recpt<-(hs$oog.per.con * hs$no.concept.recp)
hs.r<-aggregate(no.concept.recp ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean oogonia per thalli
#(note: theres 2 apices per receptical)
# oogonia/receptacle * (1 receptacle / 2 apices) * reproductive apices/thalli = oogonia/thalli
hs$oog.thalli <- (hs$oog.recpt * 0.5 * hs$apices.repro)
hs.r<-aggregate(oog.thalli ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
#mean conceptacle per thalli
hs$con.thalli <- (hs$no.concept.recp * hs$apices.repro)
hs.r<-aggregate(con.thalli ~ date, hs, mean, na.rm=TRUE)
hs.fp<-merge(hs.fp, hs.r, by="date", all=TRUE)
rm(hs, hs.r)Salinity
#read in data
fp.sal.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/fp.sal.tide.csv",
header = TRUE
)
fp.sal.tide$date<-as.Date(fp.sal.tide$date, format=c("%Y-%m-%d"))
#looking at survey dates
print(hs.fp$date)## [1] "2018-06-15" "2018-07-16" "2018-08-06" "2018-09-10" "2018-10-09"
## [6] "2018-11-06" "2018-12-04" "2019-01-31" "2019-02-21" "2019-03-14"
## [11] "2019-04-09" "2019-05-08" "2019-06-08" "2019-07-20" "2019-08-04"
## [16] "2019-09-12"#Subset the data by dates
a1<-fp.sal.tide[fp.sal.tide$date >= "2018-05-16" & fp.sal.tide$date < "2018-06-15",]
a2<- fp.sal.tide[fp.sal.tide$date >= "2018-06-16" & fp.sal.tide$date < "2018-07-16",]
a3<- fp.sal.tide[fp.sal.tide$date >= "2018-07-07" & fp.sal.tide$date < "2018-08-06",]
a4<- fp.sal.tide[fp.sal.tide$date >= "2018-08-11" & fp.sal.tide$date < "2018-09-10",]
a5<- fp.sal.tide[fp.sal.tide$date >= "2018-09-09" & fp.sal.tide$date < "2018-10-09",]
a6<- fp.sal.tide[fp.sal.tide$date >= "2018-10-07" & fp.sal.tide$date < "2018-11-06",]
a7<- fp.sal.tide[fp.sal.tide$date >= "2018-11-04" & fp.sal.tide$date < "2018-12-04",]
a8<- fp.sal.tide[fp.sal.tide$date >= "2019-01-01" & fp.sal.tide$date < "2019-01-31",]
a9<- fp.sal.tide[fp.sal.tide$date >= "2019-01-22" & fp.sal.tide$date < "2019-02-21",]
a10<- fp.sal.tide[fp.sal.tide$date >= "2019-02-12" & fp.sal.tide$date < "2019-03-14",]
a11<- fp.sal.tide[fp.sal.tide$date >= "2019-03-10" & fp.sal.tide$date < "2019-04-09",]
a12<- fp.sal.tide[fp.sal.tide$date >= "2019-04-08" & fp.sal.tide$date < "2019-05-08",]
a13<- fp.sal.tide[fp.sal.tide$date >= "2019-05-09" & fp.sal.tide$date < "2019-06-08",]
a14<- fp.sal.tide[fp.sal.tide$date >= "2019-06-20" & fp.sal.tide$date < "2019-07-20",]
a15<- fp.sal.tide[fp.sal.tide$date >= "2019-07-05" & fp.sal.tide$date < "2019-08-04",]
a16<-fp.sal.tide[fp.sal.tide$date >= "2019-08-13" & fp.sal.tide$date < "2019-09-12",]
#median
aa1<-median(a1$salinity, na.rm=TRUE)
aa2<-median(a2$salinity, na.rm=TRUE)
aa3<-median(a3$salinity, na.rm=TRUE)
aa4<-median(a4$salinity, na.rm=TRUE)
aa5<-median(a5$salinity, na.rm=TRUE)
aa6<-median(a6$salinity, na.rm=TRUE)
aa7<-median(a7$salinity, na.rm=TRUE)
aa8<-median(a8$salinity, na.rm=TRUE)
aa9<-median(a9$salinity, na.rm=TRUE)
aa10<-median(a10$salinity, na.rm=TRUE)
aa11<-median(a11$salinity, na.rm=TRUE)
aa12<-median(a12$salinity, na.rm=TRUE)
aa13<-median(a13$salinity, na.rm=TRUE)
aa14<-median(a14$salinity, na.rm=TRUE)
aa15<-median(a15$salinity, na.rm=TRUE)
aa16<-median(a16$salinity, na.rm=TRUE)
#string these values to a data frame
fp.mon.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.mon.sal<-as.data.frame(fp.mon.sal)
#change column names
names(fp.mon.sal)[1] <- "date"
names(fp.mon.sal)[2] <- "salinity"
fp.mon.sal$date<-as.Date(fp.mon.sal$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.sal[,c("date", "salinity")], by="date")CHECK LAST DATE
Daily median, range, minimum, and maximum salinity
#table summary for daily max, min, median
fp.daily.sum<-as.data.frame(setDT(fp.sal.tide)[, .(max.daily.sal = max(salinity), min.daily.sal = min(salinity), daily.med.sal=median(salinity)), .(date)])
#daily range
fp.daily.sum$daily.sal.range<-fp.daily.sum$max.daily.sal - fp.daily.sum$min.daily.sal
#Daily min
#subset dates
a1<-fp.daily.sum[fp.daily.sum$date >= "2018-05-16" & fp.daily.sum$date < "2018-06-15",]
a2<- fp.daily.sum[fp.daily.sum$date >= "2018-06-16" & fp.daily.sum$date < "2018-07-16",]
a3<- fp.daily.sum[fp.daily.sum$date >= "2018-07-07" & fp.daily.sum$date < "2018-08-06",]
a4<- fp.daily.sum[fp.daily.sum$date >= "2018-08-11" & fp.daily.sum$date < "2018-09-10",]
a5<- fp.daily.sum[fp.daily.sum$date >= "2018-09-09" & fp.daily.sum$date < "2018-10-09",]
a6<- fp.daily.sum[fp.daily.sum$date >= "2018-10-07" & fp.daily.sum$date < "2018-11-06",]
a7<- fp.daily.sum[fp.daily.sum$date >= "2018-11-04" & fp.daily.sum$date < "2018-12-04",]
a8<- fp.daily.sum[fp.daily.sum$date >= "2019-01-01" & fp.daily.sum$date < "2019-01-31",]
a9<- fp.daily.sum[fp.daily.sum$date >= "2019-01-22" & fp.daily.sum$date < "2019-02-21",]
a10<- fp.daily.sum[fp.daily.sum$date >= "2019-02-12" & fp.daily.sum$date < "2019-03-14",]
a11<- fp.daily.sum[fp.daily.sum$date >= "2019-03-10" & fp.daily.sum$date < "2019-04-09",]
a12<- fp.daily.sum[fp.daily.sum$date >= "2019-04-08" & fp.daily.sum$date < "2019-05-08",]
a13<- fp.daily.sum[fp.daily.sum$date >= "2019-05-09" & fp.daily.sum$date < "2019-06-08",]
a14<- fp.daily.sum[fp.daily.sum$date >= "2019-06-20" & fp.daily.sum$date < "2019-07-20",]
a15<- fp.daily.sum[fp.daily.sum$date >= "2019-07-05" & fp.daily.sum$date < "2019-08-04",]
a16<-fp.daily.sum[fp.daily.sum$date >= "2019-08-13" & fp.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.sal, na.rm=TRUE)
aa2<-median(a2$min.daily.sal, na.rm=TRUE)
aa3<-median(a3$min.daily.sal, na.rm=TRUE)
aa4<-median(a4$min.daily.sal, na.rm=TRUE)
aa5<-median(a5$min.daily.sal, na.rm=TRUE)
aa6<-median(a6$min.daily.sal, na.rm=TRUE)
aa7<-median(a7$min.daily.sal, na.rm=TRUE)
aa8<-median(a8$min.daily.sal, na.rm=TRUE)
aa9<-median(a9$min.daily.sal, na.rm=TRUE)
aa10<-median(a10$min.daily.sal, na.rm=TRUE)
aa11<-median(a11$min.daily.sal, na.rm=TRUE)
aa12<-median(a12$min.daily.sal, na.rm=TRUE)
aa13<-median(a13$min.daily.sal, na.rm=TRUE)
aa14<-median(a14$min.daily.sal, na.rm=TRUE)
aa15<-median(a15$min.daily.sal, na.rm=TRUE)
aa16<-median(a16$min.daily.sal, na.rm=TRUE)
#string these values to a data frame
fp.daily.min.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.daily.min.sal<-as.data.frame(fp.daily.min.sal)
#change column names
names(fp.daily.min.sal)[1] <- "date"
names(fp.daily.min.sal)[2] <- "daily.min.sal"
fp.daily.min.sal$date<-as.Date(fp.daily.min.sal$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.min.sal, hs="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.sal, na.rm=TRUE)
aa2<-median(a2$max.daily.sal, na.rm=TRUE)
aa3<-median(a3$max.daily.sal, na.rm=TRUE)
aa4<-median(a4$max.daily.sal, na.rm=TRUE)
aa5<-median(a5$max.daily.sal, na.rm=TRUE)
aa6<-median(a6$max.daily.sal, na.rm=TRUE)
aa7<-median(a7$max.daily.sal, na.rm=TRUE)
aa8<-median(a8$max.daily.sal, na.rm=TRUE)
aa9<-median(a9$max.daily.sal, na.rm=TRUE)
aa10<-median(a10$max.daily.sal, na.rm=TRUE)
aa11<-median(a11$max.daily.sal, na.rm=TRUE)
aa12<-median(a12$max.daily.sal, na.rm=TRUE)
aa13<-median(a13$max.daily.sal, na.rm=TRUE)
aa14<-median(a14$max.daily.sal, na.rm=TRUE)
aa15<-median(a15$max.daily.sal, na.rm=TRUE)
aa16<-median(a16$max.daily.sal, na.rm=TRUE)
#string these values to a data frame
fp.daily.max.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15, aa16))
fp.daily.max.sal<-as.data.frame(fp.daily.max.sal)
#change column names
names(fp.daily.max.sal)[1] <- "date"
names(fp.daily.max.sal)[2] <- "daily.max.sal"
fp.daily.max.sal$date<-as.Date(fp.daily.max.sal$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.max.sal, hs="date")
#daily range
#mean
aa1<-median(a1$daily.sal.range, na.rm=TRUE)
aa2<-median(a2$daily.sal.range, na.rm=TRUE)
aa3<-median(a3$daily.sal.range, na.rm=TRUE)
aa4<-median(a4$daily.sal.range, na.rm=TRUE)
aa5<-median(a5$daily.sal.range, na.rm=TRUE)
aa6<-median(a6$daily.sal.range, na.rm=TRUE)
aa7<-median(a7$daily.sal.range, na.rm=TRUE)
aa8<-median(a8$daily.sal.range, na.rm=TRUE)
aa9<-median(a9$daily.sal.range, na.rm=TRUE)
aa10<-median(a10$daily.sal.range, na.rm=TRUE)
aa11<-median(a11$daily.sal.range, na.rm=TRUE)
aa12<-median(a12$daily.sal.range, na.rm=TRUE)
aa13<-median(a13$daily.sal.range, na.rm=TRUE)
aa14<-median(a14$daily.sal.range, na.rm=TRUE)
aa15<-median(a15$daily.sal.range, na.rm=TRUE)
aa16<-median(a16$daily.sal.range, na.rm=TRUE)
#string these values to a data frame
fp.daily.sal.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.daily.sal.range<-as.data.frame(fp.daily.sal.range)
#change column names
names(fp.daily.sal.range)[1] <- "date"
names(fp.daily.sal.range)[2] <- "daily.sal.range"
fp.daily.sal.range$date<-as.Date(fp.daily.sal.range$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.sal.range, hs="date")
#daily median
#median of these salinity periods
aa1<-median(a1$daily.med.sal, na.rm=TRUE)
aa2<-median(a2$daily.med.sal, na.rm=TRUE)
aa3<-median(a3$daily.med.sal, na.rm=TRUE)
aa4<-median(a4$daily.med.sal, na.rm=TRUE)
aa5<-median(a5$daily.med.sal, na.rm=TRUE)
aa6<-median(a6$daily.med.sal, na.rm=TRUE)
aa7<-median(a7$daily.med.sal, na.rm=TRUE)
aa8<-median(a8$daily.med.sal, na.rm=TRUE)
aa9<-median(a9$daily.med.sal, na.rm=TRUE)
aa10<-median(a10$daily.med.sal, na.rm=TRUE)
aa11<-median(a11$daily.med.sal, na.rm=TRUE)
aa12<-median(a12$daily.med.sal, na.rm=TRUE)
aa13<-median(a13$daily.med.sal, na.rm=TRUE)
aa14<-median(a14$daily.med.sal, na.rm=TRUE)
aa15<-median(a15$daily.med.sal, na.rm=TRUE)
aa16<-median(a16$daily.med.sal, na.rm=TRUE)
#string these values to a data frame
fp.daily.med.sal<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.daily.med.sal<-as.data.frame(fp.daily.med.sal)
#change column names
names(fp.daily.med.sal)[1] <- "date"
names(fp.daily.med.sal)[2] <- "daily.med.sal"
fp.daily.med.sal$date<-as.Date(fp.daily.med.sal$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.med.sal)No ph data for Fort Point Making a NA/empty fillers so that it’s the same size as the other site dfs and will be easier to merge at the end
#string these values to a data frame
fp.filler<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'))
fp.filler<-as.data.frame(fp.filler)
#change column names
names(fp.filler)[1] <- "date"
fp.filler$date<-as.Date(fp.filler$date, format=c("%Y-%m-%d"))
#empty columns
fp.filler$ph<-NA
fp.filler$daily.min.ph<-NA
fp.filler$daily.max.ph<-NA
fp.filler$daily.med.ph<-NA
fp.filler$daily.ph.range<-NA
#merge dfs
hs.fp<-merge(hs.fp, fp.filler, by="date")Water temperature
#read in data
fp.wtemp.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/fp.wtemp.tide.csv",
header = TRUE
)
fp.wtemp.tide$date<-as.Date(fp.wtemp.tide$date, format=c("%Y-%m-%d"))
#Subset the data by dates
a1<-fp.wtemp.tide[fp.wtemp.tide$date >= "2018-05-16" & fp.wtemp.tide$date < "2018-06-15",]
a2<- fp.wtemp.tide[fp.wtemp.tide$date >= "2018-06-16" & fp.wtemp.tide$date < "2018-07-16",]
a3<- fp.wtemp.tide[fp.wtemp.tide$date >= "2018-07-07" & fp.wtemp.tide$date < "2018-08-06",]
a4<- fp.wtemp.tide[fp.wtemp.tide$date >= "2018-08-11" & fp.wtemp.tide$date < "2018-09-10",]
a5<- fp.wtemp.tide[fp.wtemp.tide$date >= "2018-09-09" & fp.wtemp.tide$date < "2018-10-09",]
a6<- fp.wtemp.tide[fp.wtemp.tide$date >= "2018-10-07" & fp.wtemp.tide$date < "2018-11-06",]
a7<- fp.wtemp.tide[fp.wtemp.tide$date >= "2018-11-04" & fp.wtemp.tide$date < "2018-12-04",]
a8<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-01-01" & fp.wtemp.tide$date < "2019-01-31",]
a9<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-01-22" & fp.wtemp.tide$date < "2019-02-21",]
a10<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-02-12" & fp.wtemp.tide$date < "2019-03-14",]
a11<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-03-10" & fp.wtemp.tide$date < "2019-04-09",]
a12<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-04-08" & fp.wtemp.tide$date < "2019-05-08",]
a13<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-05-09" & fp.wtemp.tide$date < "2019-06-08",]
a14<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-06-20" & fp.wtemp.tide$date < "2019-07-20",]
a15<- fp.wtemp.tide[fp.wtemp.tide$date >= "2019-07-05" & fp.wtemp.tide$date < "2019-08-04",]
a16<-fp.wtemp.tide[fp.wtemp.tide$date >= "2019-08-13" & fp.wtemp.tide$date < "2019-09-12",]
#median
aa1<-median(a1$water_temp, na.rm=TRUE)
aa2<-median(a2$water_temp, na.rm=TRUE)
aa3<-median(a3$water_temp, na.rm=TRUE)
aa4<-median(a4$water_temp, na.rm=TRUE)
aa5<-median(a5$water_temp, na.rm=TRUE)
aa6<-median(a6$water_temp, na.rm=TRUE)
aa7<-median(a7$water_temp, na.rm=TRUE)
aa8<-median(a8$water_temp, na.rm=TRUE)
aa9<-median(a9$water_temp, na.rm=TRUE)
aa10<-median(a10$water_temp, na.rm=TRUE)
aa11<-median(a11$water_temp, na.rm=TRUE)
aa12<-median(a12$water_temp, na.rm=TRUE)
aa13<-median(a13$water_temp, na.rm=TRUE)
aa14<-median(a14$water_temp, na.rm=TRUE)
aa15<-median(a15$water_temp, na.rm=TRUE)
aa16<-median(a16$water_temp, na.rm=TRUE)
#string these values to a data frame
fp.mon.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.mon.wt<-as.data.frame(fp.mon.wt)
#change column names
names(fp.mon.wt)[1] <- "date"
names(fp.mon.wt)[2] <- "water.temp"
fp.mon.wt$date<-as.Date(fp.mon.wt$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.wt, by="date")Daily median, range, minimum, and maximum water temperature
#table summary for daily max, min, median
fp.daily.sum.wt<-as.data.frame(setDT(fp.wtemp.tide)[, .(max.daily.wt = max(water_temp), min.daily.wt = min(water_temp), daily.med.wt=median(water_temp)), .(date)])
#daily range
fp.daily.sum.wt$daily.wt.range<-fp.daily.sum.wt$max.daily.wt - fp.daily.sum.wt$min.daily.wt
#merge
fp.daily.sum<-merge(fp.daily.sum, fp.daily.sum.wt, by="date", all=TRUE)
#Daily min
#subset dates
a1<-fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-05-16" & fp.daily.sum.wt$date < "2018-06-15",]
a2<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-06-16" & fp.daily.sum.wt$date < "2018-07-16",]
a3<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-07-07" & fp.daily.sum.wt$date < "2018-08-06",]
a4<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-08-11" & fp.daily.sum.wt$date < "2018-09-10",]
a5<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-09-09" & fp.daily.sum.wt$date < "2018-10-09",]
a6<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-10-07" & fp.daily.sum.wt$date < "2018-11-06",]
a7<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2018-11-04" & fp.daily.sum.wt$date < "2018-12-04",]
a8<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-01-01" & fp.daily.sum.wt$date < "2019-01-31",]
a9<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-01-22" & fp.daily.sum.wt$date < "2019-02-21",]
a10<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-02-12" & fp.daily.sum.wt$date < "2019-03-14",]
a11<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-03-10" & fp.daily.sum.wt$date < "2019-04-09",]
a12<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-04-08" & fp.daily.sum.wt$date < "2019-05-08",]
a13<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-05-09" & fp.daily.sum.wt$date < "2019-06-08",]
a14<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-06-20" & fp.daily.sum.wt$date < "2019-07-20",]
a15<- fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-07-05" & fp.daily.sum.wt$date < "2019-08-04",]
a16<-fp.daily.sum.wt[fp.daily.sum.wt$date >= "2019-08-13" & fp.daily.sum.wt$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.wt, na.rm=TRUE)
aa2<-median(a2$min.daily.wt, na.rm=TRUE)
aa3<-median(a3$min.daily.wt, na.rm=TRUE)
aa4<-median(a4$min.daily.wt, na.rm=TRUE)
aa5<-median(a5$min.daily.wt, na.rm=TRUE)
aa6<-median(a6$min.daily.wt, na.rm=TRUE)
aa7<-median(a7$min.daily.wt, na.rm=TRUE)
aa8<-median(a8$min.daily.wt, na.rm=TRUE)
aa9<-median(a9$min.daily.wt, na.rm=TRUE)
aa10<-median(a10$min.daily.wt, na.rm=TRUE)
aa11<-median(a11$min.daily.wt, na.rm=TRUE)
aa12<-median(a12$min.daily.wt, na.rm=TRUE)
aa13<-median(a13$min.daily.wt, na.rm=TRUE)
aa14<-median(a14$min.daily.wt, na.rm=TRUE)
aa15<-median(a15$min.daily.wt, na.rm=TRUE)
aa16<-median(a16$min.daily.wt, na.rm=TRUE)
#string these values to a data frame
fp.daily.min.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15, aa16))
fp.daily.min.wt<-as.data.frame(fp.daily.min.wt)
#change column names
names(fp.daily.min.wt)[1] <- "date"
names(fp.daily.min.wt)[2] <- "daily.min.wt"
fp.daily.min.wt$date<-as.Date(fp.daily.min.wt$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.min.wt, hs="date")
#daily maximum values
#median
aa1<-median(a1$max.daily.wt, na.rm=TRUE)
aa2<-median(a2$max.daily.wt, na.rm=TRUE)
aa3<-median(a3$max.daily.wt, na.rm=TRUE)
aa4<-median(a4$max.daily.wt, na.rm=TRUE)
aa5<-median(a5$max.daily.wt, na.rm=TRUE)
aa6<-median(a6$max.daily.wt, na.rm=TRUE)
aa7<-median(a7$max.daily.wt, na.rm=TRUE)
aa8<-median(a8$max.daily.wt, na.rm=TRUE)
aa9<-median(a9$max.daily.wt, na.rm=TRUE)
aa10<-median(a10$max.daily.wt, na.rm=TRUE)
aa11<-median(a11$max.daily.wt, na.rm=TRUE)
aa12<-median(a12$max.daily.wt, na.rm=TRUE)
aa13<-median(a13$max.daily.wt, na.rm=TRUE)
aa14<-median(a14$max.daily.wt, na.rm=TRUE)
aa15<-median(a15$max.daily.wt, na.rm=TRUE)
aa16<-median(a16$max.daily.wt, na.rm=TRUE)
#string these values to a data frame
fp.daily.max.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15, aa16))
fp.daily.max.wt<-as.data.frame(fp.daily.max.wt)
#change column names
names(fp.daily.max.wt)[1] <- "date"
names(fp.daily.max.wt)[2] <- "daily.max.wt"
fp.daily.max.wt$date<-as.Date(fp.daily.max.wt$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.max.wt, hs="date")
#daily range
#mean
aa1<-median(a1$daily.wt.range, na.rm=TRUE)
aa2<-median(a2$daily.wt.range, na.rm=TRUE)
aa3<-median(a3$daily.wt.range, na.rm=TRUE)
aa4<-median(a4$daily.wt.range, na.rm=TRUE)
aa5<-median(a5$daily.wt.range, na.rm=TRUE)
aa6<-median(a6$daily.wt.range, na.rm=TRUE)
aa7<-median(a7$daily.wt.range, na.rm=TRUE)
aa8<-median(a8$daily.wt.range, na.rm=TRUE)
aa9<-median(a9$daily.wt.range, na.rm=TRUE)
aa10<-median(a10$daily.wt.range, na.rm=TRUE)
aa11<-median(a11$daily.wt.range, na.rm=TRUE)
aa12<-median(a12$daily.wt.range, na.rm=TRUE)
aa13<-median(a13$daily.wt.range, na.rm=TRUE)
aa14<-median(a14$daily.wt.range, na.rm=TRUE)
aa15<-median(a15$daily.wt.range, na.rm=TRUE)
aa16<-median(a16$daily.wt.range, na.rm=TRUE)
#string these values to a data frame
fp.daily.wt.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15, aa16))
fp.daily.wt.range<-as.data.frame(fp.daily.wt.range)
#change column names
names(fp.daily.wt.range)[1] <- "date"
names(fp.daily.wt.range)[2] <- "daily.wt.range"
fp.daily.wt.range$date<-as.Date(fp.daily.wt.range$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.wt.range, hs="date")
#daily median
#mean of these wtinity periods
aa1<-median(a1$daily.med.wt, na.rm=TRUE)
aa2<-median(a2$daily.med.wt, na.rm=TRUE)
aa3<-median(a3$daily.med.wt, na.rm=TRUE)
aa4<-median(a4$daily.med.wt, na.rm=TRUE)
aa5<-median(a5$daily.med.wt, na.rm=TRUE)
aa6<-median(a6$daily.med.wt, na.rm=TRUE)
aa7<-median(a7$daily.med.wt, na.rm=TRUE)
aa8<-median(a8$daily.med.wt, na.rm=TRUE)
aa9<-median(a9$daily.med.wt, na.rm=TRUE)
aa10<-median(a10$daily.med.wt, na.rm=TRUE)
aa11<-median(a11$daily.med.wt, na.rm=TRUE)
aa12<-median(a12$daily.med.wt, na.rm=TRUE)
aa13<-median(a13$daily.med.wt, na.rm=TRUE)
aa14<-median(a14$daily.med.wt, na.rm=TRUE)
aa15<-median(a15$daily.med.wt, na.rm=TRUE)
aa16<-median(a16$daily.med.wt, na.rm=TRUE)
#string these values to a data frame
fp.daily.med.wt<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.daily.med.wt<-as.data.frame(fp.daily.med.wt)
#change column names
names(fp.daily.med.wt)[1] <- "date"
names(fp.daily.med.wt)[2] <- "daily.med.wt"
fp.daily.med.wt$date<-as.Date(fp.daily.med.wt$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.daily.med.wt, hs="date")Daily summary Subset hs survey dates
a1<-fp.daily.sum[fp.daily.sum$date >= "2018-05-16" & fp.daily.sum$date < "2018-06-15",]
a2<- fp.daily.sum[fp.daily.sum$date >= "2018-06-16" & fp.daily.sum$date < "2018-07-16",]
a3<- fp.daily.sum[fp.daily.sum$date >= "2018-07-07" & fp.daily.sum$date < "2018-08-06",]
a4<- fp.daily.sum[fp.daily.sum$date >= "2018-08-11" & fp.daily.sum$date < "2018-09-10",]
a5<- fp.daily.sum[fp.daily.sum$date >= "2018-09-09" & fp.daily.sum$date < "2018-10-09",]
a6<- fp.daily.sum[fp.daily.sum$date >= "2018-10-07" & fp.daily.sum$date < "2018-11-06",]
a7<- fp.daily.sum[fp.daily.sum$date >= "2018-11-04" & fp.daily.sum$date < "2018-12-04",]
a8<- fp.daily.sum[fp.daily.sum$date >= "2019-01-01" & fp.daily.sum$date < "2019-01-31",]
a9<- fp.daily.sum[fp.daily.sum$date >= "2019-01-22" & fp.daily.sum$date < "2019-02-21",]
a10<- fp.daily.sum[fp.daily.sum$date >= "2019-02-12" & fp.daily.sum$date < "2019-03-14",]
a11<- fp.daily.sum[fp.daily.sum$date >= "2019-03-10" & fp.daily.sum$date < "2019-04-09",]
a12<- fp.daily.sum[fp.daily.sum$date >= "2019-04-08" & fp.daily.sum$date < "2019-05-08",]
a13<- fp.daily.sum[fp.daily.sum$date >= "2019-05-09" & fp.daily.sum$date < "2019-06-08",]
a14<- fp.daily.sum[fp.daily.sum$date >= "2019-06-20" & fp.daily.sum$date < "2019-07-20",]
a15<- fp.daily.sum[fp.daily.sum$date >= "2019-07-05" & fp.daily.sum$date < "2019-08-04",]
a16<-fp.daily.sum[fp.daily.sum$date >= "2019-08-13" & fp.daily.sum$date < "2019-09-12",]Daily maximum salinity less than 10
aa1<-nrow(a1[a1$max.daily.sal<10, ])
aa2<-nrow(a2[a2$max.daily.sal<10, ])
aa3<-nrow(a3[a3$max.daily.sal<10, ])
aa4<-nrow(a4[a4$max.daily.sal<10, ])
aa5<-nrow(a5[a5$max.daily.sal<10, ])
aa6<-nrow(a6[a6$max.daily.sal<10, ])
aa7<-nrow(a7[a7$max.daily.sal<10, ])
aa8<-nrow(a8[a8$max.daily.sal<10, ])
aa9<-nrow(a9[a9$max.daily.sal<10, ])
aa10<-nrow(a10[a10$max.daily.sal<10, ])
aa11<-nrow(a11[a11$max.daily.sal<10, ])
aa12<-nrow(a12[a12$max.daily.sal<10, ])
aa13<-nrow(a13[a13$max.daily.sal<10, ])
aa14<-nrow(a14[a14$max.daily.sal<10, ])
aa15<-nrow(a15[a15$max.daily.sal<10, ])
aa16<-nrow(a16[a16$max.daily.sal<10, ])
fp.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.dates<-as.data.frame(fp.dates)
#change column names
names(fp.dates)[1] <- "date"
names(fp.dates)[2] <- "max.daily.sal.lt10"
fp.dates$date<-as.Date(fp.dates$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.dates[,c("date", "max.daily.sal.lt10")], hs="date")daily min salinity greater than 28
#counts the number of rows with a min.daily.sal less than 10, already subsetted for between survey days
aa1<-nrow(a1[a1$min.daily.sal>28, ])
aa2<-nrow(a2[a2$min.daily.sal>28, ])
aa3<-nrow(a3[a3$min.daily.sal>28, ])
aa4<-nrow(a4[a4$min.daily.sal>28, ])
aa5<-nrow(a5[a5$min.daily.sal>28, ])
aa6<-nrow(a6[a6$min.daily.sal>28, ])
aa7<-nrow(a7[a7$min.daily.sal>28, ])
aa8<-nrow(a8[a8$min.daily.sal>28, ])
aa9<-nrow(a9[a9$min.daily.sal>28, ])
aa10<-nrow(a10[a10$min.daily.sal>28, ])
aa11<-nrow(a11[a11$min.daily.sal>28, ])
aa12<-nrow(a12[a12$min.daily.sal>28, ])
aa13<-nrow(a13[a13$min.daily.sal>28, ])
aa14<-nrow(a14[a14$min.daily.sal>28, ])
aa15<-nrow(a15[a15$min.daily.sal>28, ])
aa16<-nrow(a16[a16$min.daily.sal>28, ])
fp.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.dates<-as.data.frame(fp.dates)
#change column names
names(fp.dates)[1] <- "date"
names(fp.dates)[2] <- "min.daily.sal.gt28"
fp.dates$date<-as.Date(fp.dates$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.dates[,c("date", "min.daily.sal.gt28")], hs="date")Write csv
write.csv(hs.fp, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/one.month.lag.fp.daily.sum.csv")fp.daily.sum: https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/daily_summaries/
Linking air temperature to field work. Linking EOS air data with Paradise Cay and Point Chauncy field data and Golden Gate Bridge air data with Horseshoe Bay and Richardson Bay field data.
Environment is getting messy so I’m going to merge and clean up the dfs I have now and then add air to this. Not neccessary but it’s feeling messy and I want to keep track of what I’m doing. ####Combine dfs####
#adding site name
pc.cc$site<-"pc.cc"
nd.eos$site<-"nd.eos"
by.rb$site<-"by.rb"
hs.fp$site<-"hs.fp"
#combine
field<-dplyr::bind_rows(pc.cc,nd.eos,by.rb,hs.fp)
#remove everything except for this merged df
rm(list=setdiff(ls(), "field"))Median air temperature
#read in eos air data
eos.air.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/eos.air.tide.csv",
header = TRUE
)
eos.air.tide$date<-as.Date(eos.air.tide$date, format=c("%Y-%m-%d"))
#subset site
pc<-subset(field, field$site == "pc.cc")
pc$date<-as.Date(pc$date, format=c("%Y-%m-%d"))
#Subset air data by dates
a1<- eos.air.tide[eos.air.tide$date >= "2018-05-15" & eos.air.tide$date < "2018-06-14",]
a2<- eos.air.tide[eos.air.tide$date >= "2018-06-17" & eos.air.tide$date < "2018-07-17",]
a3<- eos.air.tide[eos.air.tide$date >= "2018-07-08" & eos.air.tide$date < "2018-08-07",]
a4<- eos.air.tide[eos.air.tide$date >= "2018-08-12" & eos.air.tide$date < "2018-09-11",]
a5<- eos.air.tide[eos.air.tide$date >= "2018-11-05" & eos.air.tide$date < "2018-12-05",]
a6<- eos.air.tide[eos.air.tide$date >= "2018-12-31" & eos.air.tide$date < "2019-01-30",]
a7<- eos.air.tide[eos.air.tide$date >= "2019-01-21" & eos.air.tide$date < "2019-02-20",]
a8<- eos.air.tide[eos.air.tide$date >= "2019-02-13" & eos.air.tide$date < "2019-03-15",]
a9<- eos.air.tide[eos.air.tide$date >= "2019-03-12" & eos.air.tide$date < "2019-04-11",]
a10<- eos.air.tide[eos.air.tide$date >= "2019-04-09" & eos.air.tide$date < "2019-05-09",]
a11<- eos.air.tide[eos.air.tide$date >= "2019-05-10" & eos.air.tide$date < "2019-06-09",]
a12<- eos.air.tide[eos.air.tide$date >= "2019-06-21" & eos.air.tide$date < "2019-07-21",]
a13<- eos.air.tide[eos.air.tide$date >= "2019-07-05" & eos.air.tide$date < "2019-08-04",]
a14<- eos.air.tide[eos.air.tide$date >= "2019-08-13" & eos.air.tide$date < "2019-09-12",]
#median air temp between field surveys
aa1<-median(a1$air_temperature, na.rm=TRUE)
aa2<-median(a2$air_temperature, na.rm=TRUE)
aa3<-median(a3$air_temperature, na.rm=TRUE)
aa4<-median(a4$air_temperature, na.rm=TRUE)
aa5<-median(a5$air_temperature, na.rm=TRUE)
aa6<-median(a6$air_temperature, na.rm=TRUE)
aa7<-median(a7$air_temperature, na.rm=TRUE)
aa8<-median(a8$air_temperature, na.rm=TRUE)
aa9<-median(a9$air_temperature, na.rm=TRUE)
aa10<-median(a10$air_temperature, na.rm=TRUE)
aa11<-median(a11$air_temperature, na.rm=TRUE)
aa12<-median(a12$air_temperature, na.rm=TRUE)
aa13<-median(a13$air_temperature, na.rm=TRUE)
aa14<-median(a14$air_temperature, na.rm=TRUE)
#string these values to a data frame
pc.mon.air<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
pc.mon.air<-as.data.frame(pc.mon.air)
#change column names
names(pc.mon.air)[1] <- "date"
names(pc.mon.air)[2] <- "air.temp"
pc.mon.air$date<-as.Date(pc.mon.air$date, format=c("%Y-%m-%d"))
#merge dfs
pc.eos<-merge(pc, pc.mon.air[,c("date", "air.temp")], by="date")Daily median, range, minimum, and maximum air temperaure
#table summary saved as a df. Finds the daily median and daily max and min values bases on date column
#tried to do daily range here but it didn't seem to work. I'll just take the difference between the max and min columns
eos.daily.sum<-as.data.frame(setDT(eos.air.tide)[, .(max.daily.air = max(air_temperature), min.daily.air = min(air_temperature), daily.med.air=median(air_temperature)), .(date)])
#daily range
eos.daily.sum$daily.air.range<-eos.daily.sum$max.daily.air - eos.daily.sum$min.daily.air
#Daily min
#subset dates
a1<- eos.daily.sum[eos.daily.sum$date >= "2018-05-15" & eos.daily.sum$date < "2018-06-14",]
a2<- eos.daily.sum[eos.daily.sum$date >= "2018-06-17" & eos.daily.sum$date < "2018-07-17",]
a3<- eos.daily.sum[eos.daily.sum$date >= "2018-07-08" & eos.daily.sum$date < "2018-08-07",]
a4<- eos.daily.sum[eos.daily.sum$date >= "2018-08-12" & eos.daily.sum$date < "2018-09-11",]
a5<- eos.daily.sum[eos.daily.sum$date >= "2018-11-05" & eos.daily.sum$date < "2018-12-05",]
a6<- eos.daily.sum[eos.daily.sum$date >= "2018-12-31" & eos.daily.sum$date < "2019-01-30",]
a7<- eos.daily.sum[eos.daily.sum$date >= "2019-01-21" & eos.daily.sum$date < "2019-02-20",]
a8<- eos.daily.sum[eos.daily.sum$date >= "2019-02-13" & eos.daily.sum$date < "2019-03-15",]
a9<- eos.daily.sum[eos.daily.sum$date >= "2019-03-12" & eos.daily.sum$date < "2019-04-11",]
a10<- eos.daily.sum[eos.daily.sum$date >= "2019-04-09" & eos.daily.sum$date < "2019-05-09",]
a11<- eos.daily.sum[eos.daily.sum$date >= "2019-05-10" & eos.daily.sum$date < "2019-06-09",]
a12<- eos.daily.sum[eos.daily.sum$date >= "2019-06-21" & eos.daily.sum$date < "2019-07-21",]
a13<- eos.daily.sum[eos.daily.sum$date >= "2019-07-05" & eos.daily.sum$date < "2019-08-04",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-08-13" & eos.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.air, na.rm=TRUE)
aa2<-median(a2$min.daily.air, na.rm=TRUE)
aa3<-median(a3$min.daily.air, na.rm=TRUE)
aa4<-median(a4$min.daily.air, na.rm=TRUE)
aa5<-median(a5$min.daily.air, na.rm=TRUE)
aa6<-median(a6$min.daily.air, na.rm=TRUE)
aa7<-median(a7$min.daily.air, na.rm=TRUE)
aa8<-median(a8$min.daily.air, na.rm=TRUE)
aa9<-median(a9$min.daily.air, na.rm=TRUE)
aa10<-median(a10$min.daily.air, na.rm=TRUE)
aa11<-median(a11$min.daily.air, na.rm=TRUE)
aa12<-median(a12$min.daily.air, na.rm=TRUE)
aa13<-median(a13$min.daily.air, na.rm=TRUE)
aa14<-median(a14$min.daily.air, na.rm=TRUE)
#string these values to a data frame
eos.daily.min.air<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
eos.daily.min.air<-as.data.frame(eos.daily.min.air)
#change column names
names(eos.daily.min.air)[1] <- "date"
names(eos.daily.min.air)[2] <- "daily.min.air"
eos.daily.min.air$date<-as.Date(eos.daily.min.air$date, format=c("%Y-%m-%d"))
#merge dfs
pc.eos<-merge(pc.eos, eos.daily.min.air[,c("date", "daily.min.air")], by="date")
#Same thing but for daily maximum values
#median
aa1<-median(a1$max.daily.air, na.rm=TRUE)
aa2<-median(a2$max.daily.air, na.rm=TRUE)
aa3<-median(a3$max.daily.air, na.rm=TRUE)
aa4<-median(a4$max.daily.air, na.rm=TRUE)
aa5<-median(a5$max.daily.air, na.rm=TRUE)
aa6<-median(a6$max.daily.air, na.rm=TRUE)
aa7<-median(a7$max.daily.air, na.rm=TRUE)
aa8<-median(a8$max.daily.air, na.rm=TRUE)
aa9<-median(a9$max.daily.air, na.rm=TRUE)
aa10<-median(a10$max.daily.air, na.rm=TRUE)
aa11<-median(a11$max.daily.air, na.rm=TRUE)
aa12<-median(a12$max.daily.air, na.rm=TRUE)
aa13<-median(a13$max.daily.air, na.rm=TRUE)
aa14<-median(a14$max.daily.air, na.rm=TRUE)
#string these values to a data frame
eos.daily.max.air<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
eos.daily.max.air<-as.data.frame(eos.daily.max.air)
#change column names
names(eos.daily.max.air)[1] <- "date"
names(eos.daily.max.air)[2] <- "daily.max.air"
eos.daily.max.air$date<-as.Date(eos.daily.max.air$date, format=c("%Y-%m-%d"))
#merge dfs
pc.eos<-merge(pc.eos, eos.daily.max.air[,c("date", "daily.max.air")], by="date")
#daily range
#mean
aa1<-median(a1$daily.air.range, na.rm=TRUE)
aa2<-median(a2$daily.air.range, na.rm=TRUE)
aa3<-median(a3$daily.air.range, na.rm=TRUE)
aa4<-median(a4$daily.air.range, na.rm=TRUE)
aa5<-median(a5$daily.air.range, na.rm=TRUE)
aa6<-median(a6$daily.air.range, na.rm=TRUE)
aa7<-median(a7$daily.air.range, na.rm=TRUE)
aa8<-median(a8$daily.air.range, na.rm=TRUE)
aa9<-median(a9$daily.air.range, na.rm=TRUE)
aa10<-median(a10$daily.air.range, na.rm=TRUE)
aa11<-median(a11$daily.air.range, na.rm=TRUE)
aa12<-median(a12$daily.air.range, na.rm=TRUE)
aa13<-median(a13$daily.air.range, na.rm=TRUE)
aa14<-median(a14$daily.air.range, na.rm=TRUE)
#string these values to a data frame
eos.daily.air.range<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
eos.daily.air.range<-as.data.frame(eos.daily.air.range)
#change column names
names(eos.daily.air.range)[1] <- "date"
names(eos.daily.air.range)[2] <- "daily.air.range"
eos.daily.air.range$date<-as.Date(eos.daily.air.range$date, format=c("%Y-%m-%d"))
#merge dfs
pc.eos<-merge(pc.eos, eos.daily.air.range[,c("date", "daily.air.range")], by="date")
#daily median
#mean of these periods (mean of daily means)
aa1<-median(a1$daily.med.air, na.rm=TRUE)
aa2<-median(a2$daily.med.air, na.rm=TRUE)
aa3<-median(a3$daily.med.air, na.rm=TRUE)
aa4<-median(a4$daily.med.air, na.rm=TRUE)
aa5<-median(a5$daily.med.air, na.rm=TRUE)
aa6<-median(a6$daily.med.air, na.rm=TRUE)
aa7<-median(a7$daily.med.air, na.rm=TRUE)
aa8<-median(a8$daily.med.air, na.rm=TRUE)
aa9<-median(a9$daily.med.air, na.rm=TRUE)
aa10<-median(a10$daily.med.air, na.rm=TRUE)
aa11<-median(a11$daily.med.air, na.rm=TRUE)
aa12<-median(a12$daily.med.air, na.rm=TRUE)
aa13<-median(a13$daily.med.air, na.rm=TRUE)
aa14<-median(a14$daily.med.air, na.rm=TRUE)
#string these values to a data frame
eos.daily.med.air<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
eos.daily.med.air<-as.data.frame(eos.daily.med.air)
#change column names
names(eos.daily.med.air)[1] <- "date"
names(eos.daily.med.air)[2] <- "daily.med.air"
eos.daily.med.air$date<-as.Date(eos.daily.med.air$date, format=c("%Y-%m-%d"))
#merge dfs
pc.eos<-merge(pc.eos, eos.daily.med.air[,c("date", "daily.med.air")], by="date")Subset by survey dates
a1<- eos.daily.sum[eos.daily.sum$date >= "2018-05-15" & eos.daily.sum$date < "2018-06-14",]
a2<- eos.daily.sum[eos.daily.sum$date >= "2018-06-17" & eos.daily.sum$date < "2018-07-17",]
a3<- eos.daily.sum[eos.daily.sum$date >= "2018-07-08" & eos.daily.sum$date < "2018-08-07",]
a4<- eos.daily.sum[eos.daily.sum$date >= "2018-08-12" & eos.daily.sum$date < "2018-09-11",]
a5<- eos.daily.sum[eos.daily.sum$date >= "2018-11-05" & eos.daily.sum$date < "2018-12-05",]
a6<- eos.daily.sum[eos.daily.sum$date >= "2018-12-31" & eos.daily.sum$date < "2019-01-30",]
a7<- eos.daily.sum[eos.daily.sum$date >= "2019-01-21" & eos.daily.sum$date < "2019-02-20",]
a8<- eos.daily.sum[eos.daily.sum$date >= "2019-02-13" & eos.daily.sum$date < "2019-03-15",]
a9<- eos.daily.sum[eos.daily.sum$date >= "2019-03-12" & eos.daily.sum$date < "2019-04-11",]
a10<- eos.daily.sum[eos.daily.sum$date >= "2019-04-09" & eos.daily.sum$date < "2019-05-09",]
a11<- eos.daily.sum[eos.daily.sum$date >= "2019-05-10" & eos.daily.sum$date < "2019-06-09",]
a12<- eos.daily.sum[eos.daily.sum$date >= "2019-06-21" & eos.daily.sum$date < "2019-07-21",]
a13<- eos.daily.sum[eos.daily.sum$date >= "2019-07-05" & eos.daily.sum$date < "2019-08-04",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-08-13" & eos.daily.sum$date < "2019-09-12",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-08-04" & eos.daily.sum$date < "2019-09-12",]Number of days with daily maximum air temperature greater than 26C
aa1<-nrow(a1[a1$max.daily.air>26, ])
aa2<-nrow(a2[a2$max.daily.air>26, ])
aa3<-nrow(a3[a3$max.daily.air>26, ])
aa4<-nrow(a4[a4$max.daily.air>26, ])
aa5<-nrow(a5[a5$max.daily.air>26, ])
aa6<-nrow(a6[a6$max.daily.air>26, ])
aa7<-nrow(a7[a7$max.daily.air>26, ])
aa8<-nrow(a8[a8$max.daily.air>26, ])
aa9<-nrow(a9[a9$max.daily.air>26, ])
aa10<-nrow(a10[a10$max.daily.air>26, ])
aa11<-nrow(a11[a11$max.daily.air>26, ])
aa12<-nrow(a12[a12$max.daily.air>26, ])
aa13<-nrow(a13[a13$max.daily.air>26, ])
aa14<-nrow(a14[a14$max.daily.air>26, ])
cc.dates<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.dates<-as.data.frame(cc.dates)
#change column names
names(cc.dates)[1] <- "date"
names(cc.dates)[2] <- "max.daily.air.gt26"
cc.dates$date<-as.Date(cc.dates$date, format=c("%Y-%m-%d"))
#merge dfs
pc.eos<-merge(pc.eos, cc.dates[,c("date", "max.daily.air.gt26")], by="date")Median air temperature between field surveys
#read in eos air data #this is redundant
eos.air.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/eos.air.tide.csv",
header = TRUE
)
eos.air.tide$date<-as.Date(eos.air.tide$date, format=c("%Y-%m-%d"))
#subset site
nd<-subset(field, field$site == "nd.eos")
nd$date<-as.Date(nd$date, format=c("%Y-%m-%d"))
#Subset air data by dates between surveys
a1<- eos.air.tide[eos.air.tide$date >= "2018-05-15" & eos.air.tide$date < "2018-06-14",]
a2<- eos.air.tide[eos.air.tide$date >= "2018-06-17" & eos.air.tide$date < "2018-07-17",]
a3<- eos.air.tide[eos.air.tide$date >= "2018-07-08" & eos.air.tide$date < "2018-08-07",]
a4<- eos.air.tide[eos.air.tide$date >= "2018-08-12" & eos.air.tide$date < "2018-09-11",]
a5<- eos.air.tide[eos.air.tide$date >= "2018-09-10" & eos.air.tide$date < "2018-10-10",]
a6<- eos.air.tide[eos.air.tide$date >= "2018-11-05" & eos.air.tide$date < "2018-12-05",]
a7<- eos.air.tide[eos.air.tide$date >= "2018-12-31" & eos.air.tide$date < "2019-01-30",]
a8<- eos.air.tide[eos.air.tide$date >= "2019-01-21" & eos.air.tide$date < "2019-02-20",]
a9<- eos.air.tide[eos.air.tide$date >= "2019-02-13" & eos.air.tide$date < "2019-03-15",]
a10<- eos.air.tide[eos.air.tide$date >= "2019-03-12" & eos.air.tide$date < "2019-04-11",]
a11<- eos.air.tide[eos.air.tide$date >= "2019-04-09" & eos.air.tide$date < "2019-05-09",]
a12<- eos.air.tide[eos.air.tide$date >= "2019-05-10" & eos.air.tide$date < "2019-06-09",]
a13<- eos.air.tide[eos.air.tide$date >= "2019-06-21" & eos.air.tide$date < "2019-07-21",]
a14<- eos.air.tide[eos.air.tide$date >= "2019-07-05" & eos.air.tide$date < "2019-08-04",]
a15<- eos.air.tide[eos.air.tide$date >= "2019-08-13" & eos.air.tide$date < "2019-09-12",]
#median air temp between field surveys
aa1<-median(a1$air_temperature, na.rm=TRUE)
aa2<-median(a2$air_temperature, na.rm=TRUE)
aa3<-median(a3$air_temperature, na.rm=TRUE)
aa4<-median(a4$air_temperature, na.rm=TRUE)
aa5<-median(a5$air_temperature, na.rm=TRUE)
aa6<-median(a6$air_temperature, na.rm=TRUE)
aa7<-median(a7$air_temperature, na.rm=TRUE)
aa8<-median(a8$air_temperature, na.rm=TRUE)
aa9<-median(a9$air_temperature, na.rm=TRUE)
aa10<-median(a10$air_temperature, na.rm=TRUE)
aa11<-median(a11$air_temperature, na.rm=TRUE)
aa12<-median(a12$air_temperature, na.rm=TRUE)
aa13<-median(a13$air_temperature, na.rm=TRUE)
aa14<-median(a14$air_temperature, na.rm=TRUE)
aa15<-median(a15$air_temperature, na.rm=TRUE)
#string these values to a data frame
nd.mon.air<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
nd.mon.air<-as.data.frame(nd.mon.air)
#change column names
names(nd.mon.air)[1] <- "date"
names(nd.mon.air)[2] <- "air.temp"
nd.mon.air$date<-as.Date(nd.mon.air$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd, nd.mon.air[,c("date", "air.temp")], by="date")Daily median, range, minimum, and maximum air temperaure
#table summary saved as a df. Finds the daily median and daily max and min values bases on date column
#tried to do daily range here but it didn't seem to work. I'll just take the difference between the max and min columns
eos.daily.sum<-as.data.frame(setDT(eos.air.tide)[, .(max.daily.air = max(air_temperature), min.daily.air = min(air_temperature), daily.med.air=median(air_temperature)), .(date)])
#daily range
eos.daily.sum$daily.air.range<-eos.daily.sum$max.daily.air - eos.daily.sum$min.daily.air
#Daily min
#subset dates
a1<- eos.daily.sum[eos.daily.sum$date >= "2018-05-15" & eos.daily.sum$date < "2018-06-14",]
a2<- eos.daily.sum[eos.daily.sum$date >= "2018-06-17" & eos.daily.sum$date < "2018-07-17",]
a3<- eos.daily.sum[eos.daily.sum$date >= "2018-07-08" & eos.daily.sum$date < "2018-08-07",]
a4<- eos.daily.sum[eos.daily.sum$date >= "2018-08-12" & eos.daily.sum$date < "2018-09-11",]
a5<- eos.daily.sum[eos.daily.sum$date >= "2018-09-10" & eos.daily.sum$date < "2018-10-10",]
a6<- eos.daily.sum[eos.daily.sum$date >= "2018-11-05" & eos.daily.sum$date < "2018-12-05",]
a7<- eos.daily.sum[eos.daily.sum$date >= "2018-12-31" & eos.daily.sum$date < "2019-01-30",]
a8<- eos.daily.sum[eos.daily.sum$date >= "2019-01-21" & eos.daily.sum$date < "2019-02-20",]
a9<- eos.daily.sum[eos.daily.sum$date >= "2019-02-13" & eos.daily.sum$date < "2019-03-15",]
a10<- eos.daily.sum[eos.daily.sum$date >= "2019-03-12" & eos.daily.sum$date < "2019-04-11",]
a11<- eos.daily.sum[eos.daily.sum$date >= "2019-04-09" & eos.daily.sum$date < "2019-05-09",]
a12<- eos.daily.sum[eos.daily.sum$date >= "2019-05-10" & eos.daily.sum$date < "2019-06-09",]
a13<- eos.daily.sum[eos.daily.sum$date >= "2019-06-21" & eos.daily.sum$date < "2019-07-21",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-07-05" & eos.daily.sum$date < "2019-08-04",]
a15<- eos.daily.sum[eos.daily.sum$date >= "2019-08-13" & eos.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.air, na.rm=TRUE)
aa2<-median(a2$min.daily.air, na.rm=TRUE)
aa3<-median(a3$min.daily.air, na.rm=TRUE)
aa4<-median(a4$min.daily.air, na.rm=TRUE)
aa5<-median(a5$min.daily.air, na.rm=TRUE)
aa6<-median(a6$min.daily.air, na.rm=TRUE)
aa7<-median(a7$min.daily.air, na.rm=TRUE)
aa8<-median(a8$min.daily.air, na.rm=TRUE)
aa9<-median(a9$min.daily.air, na.rm=TRUE)
aa10<-median(a10$min.daily.air, na.rm=TRUE)
aa11<-median(a11$min.daily.air, na.rm=TRUE)
aa12<-median(a12$min.daily.air, na.rm=TRUE)
aa13<-median(a13$min.daily.air, na.rm=TRUE)
aa14<-median(a14$min.daily.air, na.rm=TRUE)
aa15<-median(a15$min.daily.air, na.rm=TRUE)
#string these values to a data frame
eos.daily.min.air<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.min.air<-as.data.frame(eos.daily.min.air)
#change column names
names(eos.daily.min.air)[1] <- "date"
names(eos.daily.min.air)[2] <- "daily.min.air"
eos.daily.min.air$date<-as.Date(eos.daily.min.air$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.min.air[,c("date", "daily.min.air")], by="date")
#Same thing but for daily maximum values
#median
aa1<-median(a1$max.daily.air, na.rm=TRUE)
aa2<-median(a2$max.daily.air, na.rm=TRUE)
aa3<-median(a3$max.daily.air, na.rm=TRUE)
aa4<-median(a4$max.daily.air, na.rm=TRUE)
aa5<-median(a5$max.daily.air, na.rm=TRUE)
aa6<-median(a6$max.daily.air, na.rm=TRUE)
aa7<-median(a7$max.daily.air, na.rm=TRUE)
aa8<-median(a8$max.daily.air, na.rm=TRUE)
aa9<-median(a9$max.daily.air, na.rm=TRUE)
aa10<-median(a10$max.daily.air, na.rm=TRUE)
aa11<-median(a11$max.daily.air, na.rm=TRUE)
aa12<-median(a12$max.daily.air, na.rm=TRUE)
aa13<-median(a13$max.daily.air, na.rm=TRUE)
aa14<-median(a14$max.daily.air, na.rm=TRUE)
aa15<-median(a15$max.daily.air, na.rm=TRUE)
#string these values to a data frame
eos.daily.max.air<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.max.air<-as.data.frame(eos.daily.max.air)
#change column names
names(eos.daily.max.air)[1] <- "date"
names(eos.daily.max.air)[2] <- "daily.max.air"
eos.daily.max.air$date<-as.Date(eos.daily.max.air$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.max.air[,c("date", "daily.max.air")], by="date")
#daily range
#mean
aa1<-median(a1$daily.air.range, na.rm=TRUE)
aa2<-median(a2$daily.air.range, na.rm=TRUE)
aa3<-median(a3$daily.air.range, na.rm=TRUE)
aa4<-median(a4$daily.air.range, na.rm=TRUE)
aa5<-median(a5$daily.air.range, na.rm=TRUE)
aa6<-median(a6$daily.air.range, na.rm=TRUE)
aa7<-median(a7$daily.air.range, na.rm=TRUE)
aa8<-median(a8$daily.air.range, na.rm=TRUE)
aa9<-median(a9$daily.air.range, na.rm=TRUE)
aa10<-median(a10$daily.air.range, na.rm=TRUE)
aa11<-median(a11$daily.air.range, na.rm=TRUE)
aa12<-median(a12$daily.air.range, na.rm=TRUE)
aa13<-median(a13$daily.air.range, na.rm=TRUE)
aa14<-median(a14$daily.air.range, na.rm=TRUE)
aa15<-median(a15$daily.air.range, na.rm=TRUE)
#string these values to a data frame
eos.daily.air.range<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.air.range<-as.data.frame(eos.daily.air.range)
#change column names
names(eos.daily.air.range)[1] <- "date"
names(eos.daily.air.range)[2] <- "daily.air.range"
eos.daily.air.range$date<-as.Date(eos.daily.air.range$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.air.range[,c("date", "daily.air.range")], by="date")
#daily median
#mean of these periods (mean of daily means)
aa1<-median(a1$daily.med.air, na.rm=TRUE)
aa2<-median(a2$daily.med.air, na.rm=TRUE)
aa3<-median(a3$daily.med.air, na.rm=TRUE)
aa4<-median(a4$daily.med.air, na.rm=TRUE)
aa5<-median(a5$daily.med.air, na.rm=TRUE)
aa6<-median(a6$daily.med.air, na.rm=TRUE)
aa7<-median(a7$daily.med.air, na.rm=TRUE)
aa8<-median(a8$daily.med.air, na.rm=TRUE)
aa9<-median(a9$daily.med.air, na.rm=TRUE)
aa10<-median(a10$daily.med.air, na.rm=TRUE)
aa11<-median(a11$daily.med.air, na.rm=TRUE)
aa12<-median(a12$daily.med.air, na.rm=TRUE)
aa13<-median(a13$daily.med.air, na.rm=TRUE)
aa14<-median(a14$daily.med.air, na.rm=TRUE)
aa15<-median(a15$daily.air.range, na.rm=TRUE)
#string these values to a data frame
eos.daily.med.air<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.daily.med.air<-as.data.frame(eos.daily.med.air)
#change column names
names(eos.daily.med.air)[1] <- "date"
names(eos.daily.med.air)[2] <- "daily.med.air"
eos.daily.med.air$date<-as.Date(eos.daily.med.air$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.daily.med.air[,c("date", "daily.med.air")], by="date")Subset by survey dates
a1<- eos.daily.sum[eos.daily.sum$date >= "2018-05-15" & eos.daily.sum$date < "2018-06-14",]
a2<- eos.daily.sum[eos.daily.sum$date >= "2018-06-17" & eos.daily.sum$date < "2018-07-17",]
a3<- eos.daily.sum[eos.daily.sum$date >= "2018-07-08" & eos.daily.sum$date < "2018-08-07",]
a4<- eos.daily.sum[eos.daily.sum$date >= "2018-08-12" & eos.daily.sum$date < "2018-09-11",]
a5<- eos.daily.sum[eos.daily.sum$date >= "2018-09-10" & eos.daily.sum$date < "2018-10-10",]
a6<- eos.daily.sum[eos.daily.sum$date >= "2018-11-05" & eos.daily.sum$date < "2018-12-05",]
a7<- eos.daily.sum[eos.daily.sum$date >= "2018-12-31" & eos.daily.sum$date < "2019-01-30",]
a8<- eos.daily.sum[eos.daily.sum$date >= "2019-01-21" & eos.daily.sum$date < "2019-02-20",]
a9<- eos.daily.sum[eos.daily.sum$date >= "2019-02-13" & eos.daily.sum$date < "2019-03-15",]
a10<- eos.daily.sum[eos.daily.sum$date >= "2019-03-12" & eos.daily.sum$date < "2019-04-11",]
a11<- eos.daily.sum[eos.daily.sum$date >= "2019-04-09" & eos.daily.sum$date < "2019-05-09",]
a12<- eos.daily.sum[eos.daily.sum$date >= "2019-05-10" & eos.daily.sum$date < "2019-06-09",]
a13<- eos.daily.sum[eos.daily.sum$date >= "2019-06-21" & eos.daily.sum$date < "2019-07-21",]
a14<- eos.daily.sum[eos.daily.sum$date >= "2019-07-05" & eos.daily.sum$date < "2019-08-04",]
a15<- eos.daily.sum[eos.daily.sum$date >= "2019-08-13" & eos.daily.sum$date < "2019-09-12",]Daily maximum air temperature greater than 26C
aa1<-nrow(a1[a1$max.daily.air>26, ])
aa2<-nrow(a2[a2$max.daily.air>26, ])
aa3<-nrow(a3[a3$max.daily.air>26, ])
aa4<-nrow(a4[a4$max.daily.air>26, ])
aa5<-nrow(a5[a5$max.daily.air>26, ])
aa6<-nrow(a6[a6$max.daily.air>26, ])
aa7<-nrow(a7[a7$max.daily.air>26, ])
aa8<-nrow(a8[a8$max.daily.air>26, ])
aa9<-nrow(a9[a9$max.daily.air>26, ])
aa10<-nrow(a10[a10$max.daily.air>26, ])
aa11<-nrow(a11[a11$max.daily.air>26, ])
aa12<-nrow(a12[a12$max.daily.air>26, ])
aa13<-nrow(a13[a13$max.daily.air>26, ])
aa14<-nrow(a14[a14$max.daily.air>26, ])
aa15<-nrow(a15[a15$max.daily.air>26, ])
eos.dates<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
eos.dates<-as.data.frame(eos.dates)
#change column names
names(eos.dates)[1] <- "date"
names(eos.dates)[2] <- "max.daily.air.gt26"
eos.dates$date<-as.Date(eos.dates$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.dates[,c("date", "max.daily.air.gt26")], by="date")Median air temperature between field surveys
#read in golen gate air data
gg.air.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/gg.air.tide.csv",
header = TRUE
)
gg.air.tide$date<-as.Date(gg.air.tide$date, format=c("%Y-%m-%d"))
#subset site
by<-subset(field, field$site == "by.rb")
by$date<-as.Date(by$date, format=c("%Y-%m-%d"))
#Subset air data by dates between surveys
a1<- gg.air.tide[gg.air.tide$date >= "2018-05-16" & gg.air.tide$date < "2018-06-15",]
a2<- gg.air.tide[gg.air.tide$date >= "2018-06-16" & gg.air.tide$date < "2018-07-16",]
a3<- gg.air.tide[gg.air.tide$date >= "2018-07-07" & gg.air.tide$date < "2018-08-06",]
a4<- gg.air.tide[gg.air.tide$date >= "2018-08-11" & gg.air.tide$date < "2018-09-10",]
a5<- gg.air.tide[gg.air.tide$date >= "2018-09-09" & gg.air.tide$date < "2018-10-09",]
a6<- gg.air.tide[gg.air.tide$date >= "2018-10-07" & gg.air.tide$date < "2018-11-06",]
a7<- gg.air.tide[gg.air.tide$date >= "2018-11-04" & gg.air.tide$date < "2018-12-04",]
a8<- gg.air.tide[gg.air.tide$date >= "2019-01-01" & gg.air.tide$date < "2019-01-31",]
a9<- gg.air.tide[gg.air.tide$date >= "2019-01-22" & gg.air.tide$date < "2019-02-21",]
a10<- gg.air.tide[gg.air.tide$date >= "2019-02-12" & gg.air.tide$date < "2019-03-14",]
a11<- gg.air.tide[gg.air.tide$date >= "2019-03-10" & gg.air.tide$date < "2019-04-09",]
a12<- gg.air.tide[gg.air.tide$date >= "2019-04-08" & gg.air.tide$date < "2019-05-08",]
a13<- gg.air.tide[gg.air.tide$date >= "2019-05-09" & gg.air.tide$date < "2019-06-08",]
a14<- gg.air.tide[gg.air.tide$date >= "2019-06-20" & gg.air.tide$date < "2019-07-20",]
a15<- gg.air.tide[gg.air.tide$date >= "2019-07-05" & gg.air.tide$date < "2019-08-04",]
a16<- gg.air.tide[gg.air.tide$date >= "2019-08-13" & gg.air.tide$date < "2019-09-12",]
#median air temp between field surveys
aa1<-median(a1$air_temp, na.rm=TRUE)
aa2<-median(a2$air_temp, na.rm=TRUE)
aa3<-median(a3$air_temp, na.rm=TRUE)
aa4<-median(a4$air_temp, na.rm=TRUE)
aa5<-median(a5$air_temp, na.rm=TRUE)
aa6<-median(a6$air_temp, na.rm=TRUE)
aa7<-median(a7$air_temp, na.rm=TRUE)
aa8<-median(a8$air_temp, na.rm=TRUE)
aa9<-median(a9$air_temp, na.rm=TRUE)
aa10<-median(a10$air_temp, na.rm=TRUE)
aa11<-median(a11$air_temp, na.rm=TRUE)
aa12<-median(a12$air_temp, na.rm=TRUE)
aa13<-median(a13$air_temp, na.rm=TRUE)
aa14<-median(a14$air_temp, na.rm=TRUE)
aa15<-median(a15$air_temp, na.rm=TRUE)
aa16<-median(a16$air_temp, na.rm=TRUE)
#string these values to a data frame
by.mon.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
by.mon.air<-as.data.frame(by.mon.air)
#change column names
names(by.mon.air)[1] <- "date"
names(by.mon.air)[2] <- "air.temp"
by.mon.air$date<-as.Date(by.mon.air$date, format=c("%Y-%m-%d"))
#merge dfs
by.gg<-merge(by, by.mon.air[,c("date", "air.temp")], by="date")Daily median, range, minimum, aby maximum air temperaure
#table summary saved as a df. Fibys the daily median aby daily max aby min values bases on date column
#tried to do daily range here but it didn't seem to work. I'll just take the difference between the max aby min columns
gg.daily.sum<-as.data.frame(setDT(gg.air.tide)[, .(max.daily.air = max(air_temp), min.daily.air = min(air_temp), daily.med.air=median(air_temp)), .(date)])
#daily range
gg.daily.sum$daily.air.range<-gg.daily.sum$max.daily.air - gg.daily.sum$min.daily.air
#Daily min
#subset dates
a1<- gg.daily.sum[gg.daily.sum$date >= "2018-05-16" & gg.daily.sum$date < "2018-06-15",]
a2<- gg.daily.sum[gg.daily.sum$date >= "2018-06-16" & gg.daily.sum$date < "2018-07-16",]
a3<- gg.daily.sum[gg.daily.sum$date >= "2018-07-07" & gg.daily.sum$date < "2018-08-06",]
a4<- gg.daily.sum[gg.daily.sum$date >= "2018-08-11" & gg.daily.sum$date < "2018-09-10",]
a5<- gg.daily.sum[gg.daily.sum$date >= "2018-09-09" & gg.daily.sum$date < "2018-10-09",]
a6<- gg.daily.sum[gg.daily.sum$date >= "2018-10-07" & gg.daily.sum$date < "2018-11-06",]
a7<- gg.daily.sum[gg.daily.sum$date >= "2018-11-04" & gg.daily.sum$date < "2018-12-04",]
a8<- gg.daily.sum[gg.daily.sum$date >= "2019-01-01" & gg.daily.sum$date < "2019-01-31",]
a9<- gg.daily.sum[gg.daily.sum$date >= "2019-01-22" & gg.daily.sum$date < "2019-02-21",]
a10<- gg.daily.sum[gg.daily.sum$date >= "2019-02-12" & gg.daily.sum$date < "2019-03-14",]
a11<- gg.daily.sum[gg.daily.sum$date >= "2019-03-10" & gg.daily.sum$date < "2019-04-09",]
a12<- gg.daily.sum[gg.daily.sum$date >= "2019-04-08" & gg.daily.sum$date < "2019-05-08",]
a13<- gg.daily.sum[gg.daily.sum$date >= "2019-05-09" & gg.daily.sum$date < "2019-06-08",]
a14<- gg.daily.sum[gg.daily.sum$date >= "2019-06-20" & gg.daily.sum$date < "2019-07-20",]
a15<- gg.daily.sum[gg.daily.sum$date >= "2019-07-05" & gg.daily.sum$date < "2019-08-04",]
a16<- gg.daily.sum[gg.daily.sum$date >= "2019-08-13" & gg.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.air, na.rm=TRUE)
aa2<-median(a2$min.daily.air, na.rm=TRUE)
aa3<-median(a3$min.daily.air, na.rm=TRUE)
aa4<-median(a4$min.daily.air, na.rm=TRUE)
aa5<-median(a5$min.daily.air, na.rm=TRUE)
aa6<-median(a6$min.daily.air, na.rm=TRUE)
aa7<-median(a7$min.daily.air, na.rm=TRUE)
aa8<-median(a8$min.daily.air, na.rm=TRUE)
aa9<-median(a9$min.daily.air, na.rm=TRUE)
aa10<-median(a10$min.daily.air, na.rm=TRUE)
aa11<-median(a11$min.daily.air, na.rm=TRUE)
aa12<-median(a12$min.daily.air, na.rm=TRUE)
aa13<-median(a13$min.daily.air, na.rm=TRUE)
aa14<-median(a14$min.daily.air, na.rm=TRUE)
aa15<-median(a15$min.daily.air, na.rm=TRUE)
aa16<-median(a16$min.daily.air, na.rm=TRUE)
#string these values to a data frame
gg.daily.min.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.min.air<-as.data.frame(gg.daily.min.air)
#change column names
names(gg.daily.min.air)[1] <- "date"
names(gg.daily.min.air)[2] <- "daily.min.air"
gg.daily.min.air$date<-as.Date(gg.daily.min.air$date, format=c("%Y-%m-%d"))
#merge dfs
by.gg<-merge(by.gg, gg.daily.min.air[,c("date", "daily.min.air")], by="date")
#Same thing but for daily maximum values
#median
aa1<-median(a1$max.daily.air, na.rm=TRUE)
aa2<-median(a2$max.daily.air, na.rm=TRUE)
aa3<-median(a3$max.daily.air, na.rm=TRUE)
aa4<-median(a4$max.daily.air, na.rm=TRUE)
aa5<-median(a5$max.daily.air, na.rm=TRUE)
aa6<-median(a6$max.daily.air, na.rm=TRUE)
aa7<-median(a7$max.daily.air, na.rm=TRUE)
aa8<-median(a8$max.daily.air, na.rm=TRUE)
aa9<-median(a9$max.daily.air, na.rm=TRUE)
aa10<-median(a10$max.daily.air, na.rm=TRUE)
aa11<-median(a11$max.daily.air, na.rm=TRUE)
aa12<-median(a12$max.daily.air, na.rm=TRUE)
aa13<-median(a13$max.daily.air, na.rm=TRUE)
aa14<-median(a14$max.daily.air, na.rm=TRUE)
aa15<-median(a15$max.daily.air, na.rm=TRUE)
aa16<-median(a16$max.daily.air, na.rm=TRUE)
#string these values to a data frame
gg.daily.max.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.max.air<-as.data.frame(gg.daily.max.air)
#change column names
names(gg.daily.max.air)[1] <- "date"
names(gg.daily.max.air)[2] <- "daily.max.air"
gg.daily.max.air$date<-as.Date(gg.daily.max.air$date, format=c("%Y-%m-%d"))
#merge dfs
by.gg<-merge(by.gg, gg.daily.max.air[,c("date", "daily.max.air")], by="date")
#daily range
#mean
aa1<-median(a1$daily.air.range, na.rm=TRUE)
aa2<-median(a2$daily.air.range, na.rm=TRUE)
aa3<-median(a3$daily.air.range, na.rm=TRUE)
aa4<-median(a4$daily.air.range, na.rm=TRUE)
aa5<-median(a5$daily.air.range, na.rm=TRUE)
aa6<-median(a6$daily.air.range, na.rm=TRUE)
aa7<-median(a7$daily.air.range, na.rm=TRUE)
aa8<-median(a8$daily.air.range, na.rm=TRUE)
aa9<-median(a9$daily.air.range, na.rm=TRUE)
aa10<-median(a10$daily.air.range, na.rm=TRUE)
aa11<-median(a11$daily.air.range, na.rm=TRUE)
aa12<-median(a12$daily.air.range, na.rm=TRUE)
aa13<-median(a13$daily.air.range, na.rm=TRUE)
aa14<-median(a14$daily.air.range, na.rm=TRUE)
aa15<-median(a15$daily.air.range, na.rm=TRUE)
aa16<-median(a16$daily.air.range, na.rm=TRUE)
#string these values to a data frame
gg.daily.air.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.air.range<-as.data.frame(gg.daily.air.range)
#change column names
names(gg.daily.air.range)[1] <- "date"
names(gg.daily.air.range)[2] <- "daily.air.range"
gg.daily.air.range$date<-as.Date(gg.daily.air.range$date, format=c("%Y-%m-%d"))
#merge dfs
by.gg<-merge(by.gg, gg.daily.air.range[,c("date", "daily.air.range")], by="date")
#daily median
#mean of these periods (mean of daily means)
aa1<-median(a1$daily.med.air, na.rm=TRUE)
aa2<-median(a2$daily.med.air, na.rm=TRUE)
aa3<-median(a3$daily.med.air, na.rm=TRUE)
aa4<-median(a4$daily.med.air, na.rm=TRUE)
aa5<-median(a5$daily.med.air, na.rm=TRUE)
aa6<-median(a6$daily.med.air, na.rm=TRUE)
aa7<-median(a7$daily.med.air, na.rm=TRUE)
aa8<-median(a8$daily.med.air, na.rm=TRUE)
aa9<-median(a9$daily.med.air, na.rm=TRUE)
aa10<-median(a10$daily.med.air, na.rm=TRUE)
aa11<-median(a11$daily.med.air, na.rm=TRUE)
aa12<-median(a12$daily.med.air, na.rm=TRUE)
aa13<-median(a13$daily.med.air, na.rm=TRUE)
aa14<-median(a14$daily.med.air, na.rm=TRUE)
aa15<-median(a15$daily.air.range, na.rm=TRUE)
aa16<-median(a16$daily.air.range, na.rm=TRUE)
#string these values to a data frame
gg.daily.med.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.med.air<-as.data.frame(gg.daily.med.air)
#change column names
names(gg.daily.med.air)[1] <- "date"
names(gg.daily.med.air)[2] <- "daily.med.air"
gg.daily.med.air$date<-as.Date(gg.daily.med.air$date, format=c("%Y-%m-%d"))
#merge dfs
by.gg<-merge(by.gg, gg.daily.med.air[,c("date", "daily.med.air")], by="date")Subset by survey dates
a1<- gg.daily.sum[gg.daily.sum$date >= "2018-05-16" & gg.daily.sum$date < "2018-06-15",]
a2<- gg.daily.sum[gg.daily.sum$date >= "2018-06-16" & gg.daily.sum$date < "2018-07-16",]
a3<- gg.daily.sum[gg.daily.sum$date >= "2018-07-07" & gg.daily.sum$date < "2018-08-06",]
a4<- gg.daily.sum[gg.daily.sum$date >= "2018-08-11" & gg.daily.sum$date < "2018-09-10",]
a5<- gg.daily.sum[gg.daily.sum$date >= "2018-09-09" & gg.daily.sum$date < "2018-10-09",]
a6<- gg.daily.sum[gg.daily.sum$date >= "2018-10-07" & gg.daily.sum$date < "2018-11-06",]
a7<- gg.daily.sum[gg.daily.sum$date >= "2018-11-04" & gg.daily.sum$date < "2018-12-04",]
a8<- gg.daily.sum[gg.daily.sum$date >= "2019-01-01" & gg.daily.sum$date < "2019-01-31",]
a9<- gg.daily.sum[gg.daily.sum$date >= "2019-01-22" & gg.daily.sum$date < "2019-02-21",]
a10<- gg.daily.sum[gg.daily.sum$date >= "2019-02-12" & gg.daily.sum$date < "2019-03-14",]
a11<- gg.daily.sum[gg.daily.sum$date >= "2019-03-10" & gg.daily.sum$date < "2019-04-09",]
a12<- gg.daily.sum[gg.daily.sum$date >= "2019-04-08" & gg.daily.sum$date < "2019-05-08",]
a13<- gg.daily.sum[gg.daily.sum$date >= "2019-05-09" & gg.daily.sum$date < "2019-06-08",]
a14<- gg.daily.sum[gg.daily.sum$date >= "2019-06-20" & gg.daily.sum$date < "2019-07-20",]
a15<- gg.daily.sum[gg.daily.sum$date >= "2019-07-05" & gg.daily.sum$date < "2019-08-04",]
a16<- gg.daily.sum[gg.daily.sum$date >= "2019-08-13" & gg.daily.sum$date < "2019-09-12",]Number of days with daily maximum air temperature greater than 26C
aa1<-nrow(a1[a1$max.daily.air>26, ])
aa2<-nrow(a2[a2$max.daily.air>26, ])
aa3<-nrow(a3[a3$max.daily.air>26, ])
aa4<-nrow(a4[a4$max.daily.air>26, ])
aa5<-nrow(a5[a5$max.daily.air>26, ])
aa6<-nrow(a6[a6$max.daily.air>26, ])
aa7<-nrow(a7[a7$max.daily.air>26, ])
aa8<-nrow(a8[a8$max.daily.air>26, ])
aa9<-nrow(a9[a9$max.daily.air>26, ])
aa10<-nrow(a10[a10$max.daily.air>26, ])
aa11<-nrow(a11[a11$max.daily.air>26, ])
aa12<-nrow(a12[a12$max.daily.air>26, ])
aa13<-nrow(a13[a13$max.daily.air>26, ])
aa14<-nrow(a14[a14$max.daily.air>26, ])
aa15<-nrow(a15[a15$max.daily.air>26, ])
aa16<-nrow(a16[a16$max.daily.air>26, ])
rb.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
rb.dates<-as.data.frame(rb.dates)
#change column names
names(rb.dates)[1] <- "date"
names(rb.dates)[2] <- "max.daily.air.gt26"
rb.dates$date<-as.Date(rb.dates$date, format=c("%Y-%m-%d"))
#merge dfs
by.gg<-merge(by.gg, rb.dates[,c("date", "max.daily.air.gt26")], by="date")####Horseshoe Bay field data and Golden Gate Bridge air #### Median air temperature between field surveys
#read in golen gate air data #redundant
gg.air.tide<-read.csv(
"https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environmental/filtered_for_tides/gg.air.tide.csv",
header = TRUE
)
gg.air.tide$date<-as.Date(gg.air.tide$date, format=c("%Y-%m-%d"))
#subset site
hs<-subset(field, field$site == "hs.fp")
hs$date<-as.Date(hs$date, format=c("%Y-%m-%d"))
#Subset air data hs dates between surveys
a1<-gg.air.tide[gg.air.tide$date >= "2018-05-16" & gg.air.tide$date < "2018-06-15",]
a2<- gg.air.tide[gg.air.tide$date >= "2018-06-16" & gg.air.tide$date < "2018-07-16",]
a3<- gg.air.tide[gg.air.tide$date >= "2018-07-07" & gg.air.tide$date < "2018-08-06",]
a4<- gg.air.tide[gg.air.tide$date >= "2018-08-11" & gg.air.tide$date < "2018-09-10",]
a5<- gg.air.tide[gg.air.tide$date >= "2018-09-09" & gg.air.tide$date < "2018-10-09",]
a6<- gg.air.tide[gg.air.tide$date >= "2018-10-07" & gg.air.tide$date < "2018-11-06",]
a7<- gg.air.tide[gg.air.tide$date >= "2018-11-04" & gg.air.tide$date < "2018-12-04",]
a8<- gg.air.tide[gg.air.tide$date >= "2019-01-01" & gg.air.tide$date < "2019-01-31",]
a9<- gg.air.tide[gg.air.tide$date >= "2019-01-22" & gg.air.tide$date < "2019-02-21",]
a10<- gg.air.tide[gg.air.tide$date >= "2019-02-12" & gg.air.tide$date < "2019-03-14",]
a11<- gg.air.tide[gg.air.tide$date >= "2019-03-10" & gg.air.tide$date < "2019-04-09",]
a12<- gg.air.tide[gg.air.tide$date >= "2019-04-08" & gg.air.tide$date < "2019-05-08",]
a13<- gg.air.tide[gg.air.tide$date >= "2019-05-09" & gg.air.tide$date < "2019-06-08",]
a14<- gg.air.tide[gg.air.tide$date >= "2019-06-20" & gg.air.tide$date < "2019-07-20",]
a15<- gg.air.tide[gg.air.tide$date >= "2019-07-05" & gg.air.tide$date < "2019-08-04",]
a16<-gg.air.tide[gg.air.tide$date >= "2019-08-13" & gg.air.tide$date < "2019-09-12",]
#median air temp between field surveys
aa1<-median(a1$air_temp, na.rm=TRUE)
aa2<-median(a2$air_temp, na.rm=TRUE)
aa3<-median(a3$air_temp, na.rm=TRUE)
aa4<-median(a4$air_temp, na.rm=TRUE)
aa5<-median(a5$air_temp, na.rm=TRUE)
aa6<-median(a6$air_temp, na.rm=TRUE)
aa7<-median(a7$air_temp, na.rm=TRUE)
aa8<-median(a8$air_temp, na.rm=TRUE)
aa9<-median(a9$air_temp, na.rm=TRUE)
aa10<-median(a10$air_temp, na.rm=TRUE)
aa11<-median(a11$air_temp, na.rm=TRUE)
aa12<-median(a12$air_temp, na.rm=TRUE)
aa13<-median(a13$air_temp, na.rm=TRUE)
aa14<-median(a14$air_temp, na.rm=TRUE)
aa15<-median(a15$air_temp, na.rm=TRUE)
aa16<-median(a16$air_temp, na.rm=TRUE)
#string these values to a data frame
hs.mon.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
hs.mon.air<-as.data.frame(hs.mon.air)
#change column names
names(hs.mon.air)[1] <- "date"
names(hs.mon.air)[2] <- "air.temp"
hs.mon.air$date<-as.Date(hs.mon.air$date, format=c("%Y-%m-%d"))
#merge dfs
hs.gg<-merge(hs, hs.mon.air[,c("date", "air.temp")], hs="date")Daily median, range, minimum, and maximum air temperaure
#table summary saved as a df. Fihss the daily median ahs daily max ahs min values bases on date column
#tried to do daily range here but it didn't seem to work. I'll just take the difference between the max ahs min columns
gg.daily.sum<-as.data.frame(setDT(gg.air.tide)[, .(max.daily.air = max(air_temp), min.daily.air = min(air_temp), daily.med.air=median(air_temp)), .(date)])
#daily range
gg.daily.sum$daily.air.range<-gg.daily.sum$max.daily.air - gg.daily.sum$min.daily.air
#Daily min
#subset dates
a1<-gg.daily.sum[gg.daily.sum$date >= "2018-05-16" & gg.daily.sum$date < "2018-06-15",]
a2<- gg.daily.sum[gg.daily.sum$date >= "2018-06-16" & gg.daily.sum$date < "2018-07-16",]
a3<- gg.daily.sum[gg.daily.sum$date >= "2018-07-07" & gg.daily.sum$date < "2018-08-06",]
a4<- gg.daily.sum[gg.daily.sum$date >= "2018-08-11" & gg.daily.sum$date < "2018-09-10",]
a5<- gg.daily.sum[gg.daily.sum$date >= "2018-09-09" & gg.daily.sum$date < "2018-10-09",]
a6<- gg.daily.sum[gg.daily.sum$date >= "2018-10-07" & gg.daily.sum$date < "2018-11-06",]
a7<- gg.daily.sum[gg.daily.sum$date >= "2018-11-04" & gg.daily.sum$date < "2018-12-04",]
a8<- gg.daily.sum[gg.daily.sum$date >= "2019-01-01" & gg.daily.sum$date < "2019-01-31",]
a9<- gg.daily.sum[gg.daily.sum$date >= "2019-01-22" & gg.daily.sum$date < "2019-02-21",]
a10<- gg.daily.sum[gg.daily.sum$date >= "2019-02-12" & gg.daily.sum$date < "2019-03-14",]
a11<- gg.daily.sum[gg.daily.sum$date >= "2019-03-10" & gg.daily.sum$date < "2019-04-09",]
a12<- gg.daily.sum[gg.daily.sum$date >= "2019-04-08" & gg.daily.sum$date < "2019-05-08",]
a13<- gg.daily.sum[gg.daily.sum$date >= "2019-05-09" & gg.daily.sum$date < "2019-06-08",]
a14<- gg.daily.sum[gg.daily.sum$date >= "2019-06-20" & gg.daily.sum$date < "2019-07-20",]
a15<- gg.daily.sum[gg.daily.sum$date >= "2019-07-05" & gg.daily.sum$date < "2019-08-04",]
a16<-gg.daily.sum[gg.daily.sum$date >= "2019-08-13" & gg.daily.sum$date < "2019-09-12",]
#median
aa1<-median(a1$min.daily.air, na.rm=TRUE)
aa2<-median(a2$min.daily.air, na.rm=TRUE)
aa3<-median(a3$min.daily.air, na.rm=TRUE)
aa4<-median(a4$min.daily.air, na.rm=TRUE)
aa5<-median(a5$min.daily.air, na.rm=TRUE)
aa6<-median(a6$min.daily.air, na.rm=TRUE)
aa7<-median(a7$min.daily.air, na.rm=TRUE)
aa8<-median(a8$min.daily.air, na.rm=TRUE)
aa9<-median(a9$min.daily.air, na.rm=TRUE)
aa10<-median(a10$min.daily.air, na.rm=TRUE)
aa11<-median(a11$min.daily.air, na.rm=TRUE)
aa12<-median(a12$min.daily.air, na.rm=TRUE)
aa13<-median(a13$min.daily.air, na.rm=TRUE)
aa14<-median(a14$min.daily.air, na.rm=TRUE)
aa15<-median(a15$min.daily.air, na.rm=TRUE)
aa16<-median(a16$min.daily.air, na.rm=TRUE)
#string these values to a data frame
gg.daily.min.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.min.air<-as.data.frame(gg.daily.min.air)
#change column names
names(gg.daily.min.air)[1] <- "date"
names(gg.daily.min.air)[2] <- "daily.min.air"
gg.daily.min.air$date<-as.Date(gg.daily.min.air$date, format=c("%Y-%m-%d"))
#merge dfs
hs.gg<-merge(hs.gg, gg.daily.min.air[,c("date", "daily.min.air")], hs="date")
#Same thing but for daily maximum values
#median
aa1<-median(a1$max.daily.air, na.rm=TRUE)
aa2<-median(a2$max.daily.air, na.rm=TRUE)
aa3<-median(a3$max.daily.air, na.rm=TRUE)
aa4<-median(a4$max.daily.air, na.rm=TRUE)
aa5<-median(a5$max.daily.air, na.rm=TRUE)
aa6<-median(a6$max.daily.air, na.rm=TRUE)
aa7<-median(a7$max.daily.air, na.rm=TRUE)
aa8<-median(a8$max.daily.air, na.rm=TRUE)
aa9<-median(a9$max.daily.air, na.rm=TRUE)
aa10<-median(a10$max.daily.air, na.rm=TRUE)
aa11<-median(a11$max.daily.air, na.rm=TRUE)
aa12<-median(a12$max.daily.air, na.rm=TRUE)
aa13<-median(a13$max.daily.air, na.rm=TRUE)
aa14<-median(a14$max.daily.air, na.rm=TRUE)
aa15<-median(a15$max.daily.air, na.rm=TRUE)
aa16<-median(a16$max.daily.air, na.rm=TRUE)
#string these values to a data frame
gg.daily.max.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.max.air<-as.data.frame(gg.daily.max.air)
#change column names
names(gg.daily.max.air)[1] <- "date"
names(gg.daily.max.air)[2] <- "daily.max.air"
gg.daily.max.air$date<-as.Date(gg.daily.max.air$date, format=c("%Y-%m-%d"))
#merge dfs
hs.gg<-merge(hs.gg, gg.daily.max.air[,c("date", "daily.max.air")], hs="date")
#daily range
#mean
aa1<-median(a1$daily.air.range, na.rm=TRUE)
aa2<-median(a2$daily.air.range, na.rm=TRUE)
aa3<-median(a3$daily.air.range, na.rm=TRUE)
aa4<-median(a4$daily.air.range, na.rm=TRUE)
aa5<-median(a5$daily.air.range, na.rm=TRUE)
aa6<-median(a6$daily.air.range, na.rm=TRUE)
aa7<-median(a7$daily.air.range, na.rm=TRUE)
aa8<-median(a8$daily.air.range, na.rm=TRUE)
aa9<-median(a9$daily.air.range, na.rm=TRUE)
aa10<-median(a10$daily.air.range, na.rm=TRUE)
aa11<-median(a11$daily.air.range, na.rm=TRUE)
aa12<-median(a12$daily.air.range, na.rm=TRUE)
aa13<-median(a13$daily.air.range, na.rm=TRUE)
aa14<-median(a14$daily.air.range, na.rm=TRUE)
aa15<-median(a15$daily.air.range, na.rm=TRUE)
aa16<-median(a16$daily.air.range, na.rm=TRUE)
#string these values to a data frame
gg.daily.air.range<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.air.range<-as.data.frame(gg.daily.air.range)
#change column names
names(gg.daily.air.range)[1] <- "date"
names(gg.daily.air.range)[2] <- "daily.air.range"
gg.daily.air.range$date<-as.Date(gg.daily.air.range$date, format=c("%Y-%m-%d"))
#merge dfs
hs.gg<-merge(hs.gg, gg.daily.air.range[,c("date", "daily.air.range")], hs="date")
#daily median
#mean of these periods (mean of daily means)
aa1<-median(a1$daily.med.air, na.rm=TRUE)
aa2<-median(a2$daily.med.air, na.rm=TRUE)
aa3<-median(a3$daily.med.air, na.rm=TRUE)
aa4<-median(a4$daily.med.air, na.rm=TRUE)
aa5<-median(a5$daily.med.air, na.rm=TRUE)
aa6<-median(a6$daily.med.air, na.rm=TRUE)
aa7<-median(a7$daily.med.air, na.rm=TRUE)
aa8<-median(a8$daily.med.air, na.rm=TRUE)
aa9<-median(a9$daily.med.air, na.rm=TRUE)
aa10<-median(a10$daily.med.air, na.rm=TRUE)
aa11<-median(a11$daily.med.air, na.rm=TRUE)
aa12<-median(a12$daily.med.air, na.rm=TRUE)
aa13<-median(a13$daily.med.air, na.rm=TRUE)
aa14<-median(a14$daily.med.air, na.rm=TRUE)
aa15<-median(a15$daily.air.range, na.rm=TRUE)
aa16<-median(a16$daily.air.range, na.rm=TRUE)
#string these values to a data frame
gg.daily.med.air<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
gg.daily.med.air<-as.data.frame(gg.daily.med.air)
#change column names
names(gg.daily.med.air)[1] <- "date"
names(gg.daily.med.air)[2] <- "daily.med.air"
gg.daily.med.air$date<-as.Date(gg.daily.med.air$date, format=c("%Y-%m-%d"))
#merge dfs
hs.gg<-merge(hs.gg, gg.daily.med.air[,c("date", "daily.med.air")], hs="date")Subset hs survey dates
a1<-gg.daily.sum[gg.daily.sum$date >= "2018-05-16" & gg.daily.sum$date < "2018-06-15",]
a2<- gg.daily.sum[gg.daily.sum$date >= "2018-06-16" & gg.daily.sum$date < "2018-07-16",]
a3<- gg.daily.sum[gg.daily.sum$date >= "2018-07-07" & gg.daily.sum$date < "2018-08-06",]
a4<- gg.daily.sum[gg.daily.sum$date >= "2018-08-11" & gg.daily.sum$date < "2018-09-10",]
a5<- gg.daily.sum[gg.daily.sum$date >= "2018-09-09" & gg.daily.sum$date < "2018-10-09",]
a6<- gg.daily.sum[gg.daily.sum$date >= "2018-10-07" & gg.daily.sum$date < "2018-11-06",]
a7<- gg.daily.sum[gg.daily.sum$date >= "2018-11-04" & gg.daily.sum$date < "2018-12-04",]
a8<- gg.daily.sum[gg.daily.sum$date >= "2019-01-01" & gg.daily.sum$date < "2019-01-31",]
a9<- gg.daily.sum[gg.daily.sum$date >= "2019-01-22" & gg.daily.sum$date < "2019-02-21",]
a10<- gg.daily.sum[gg.daily.sum$date >= "2019-02-12" & gg.daily.sum$date < "2019-03-14",]
a11<- gg.daily.sum[gg.daily.sum$date >= "2019-03-10" & gg.daily.sum$date < "2019-04-09",]
a12<- gg.daily.sum[gg.daily.sum$date >= "2019-04-08" & gg.daily.sum$date < "2019-05-08",]
a13<- gg.daily.sum[gg.daily.sum$date >= "2019-05-09" & gg.daily.sum$date < "2019-06-08",]
a14<- gg.daily.sum[gg.daily.sum$date >= "2019-06-20" & gg.daily.sum$date < "2019-07-20",]
a15<- gg.daily.sum[gg.daily.sum$date >= "2019-07-05" & gg.daily.sum$date < "2019-08-04",]
a16<-gg.daily.sum[gg.daily.sum$date >= "2019-08-13" & gg.daily.sum$date < "2019-09-12",]Daily maximum air temperature greater than 26C
aa1<-nrow(a1[a1$max.daily.air>26, ])
aa2<-nrow(a2[a2$max.daily.air>26, ])
aa3<-nrow(a3[a3$max.daily.air>26, ])
aa4<-nrow(a4[a4$max.daily.air>26, ])
aa5<-nrow(a5[a5$max.daily.air>26, ])
aa6<-nrow(a6[a6$max.daily.air>26, ])
aa7<-nrow(a7[a7$max.daily.air>26, ])
aa8<-nrow(a8[a8$max.daily.air>26, ])
aa9<-nrow(a9[a9$max.daily.air>26, ])
aa10<-nrow(a10[a10$max.daily.air>26, ])
aa11<-nrow(a11[a11$max.daily.air>26, ])
aa12<-nrow(a12[a12$max.daily.air>26, ])
aa13<-nrow(a13[a13$max.daily.air>26, ])
aa14<-nrow(a14[a14$max.daily.air>26, ])
aa15<-nrow(a15[a15$max.daily.air>26, ])
aa16<-nrow(a16[a16$max.daily.air>26, ])
fp.dates<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
fp.dates<-as.data.frame(fp.dates)
#change column names
names(fp.dates)[1] <- "date"
names(fp.dates)[2] <- "max.daily.air.gt26"
fp.dates$date<-as.Date(fp.dates$date, format=c("%Y-%m-%d"))
#merge dfs
hs.gg<-merge(hs.gg, fp.dates[,c("date", "max.daily.air.gt26")], hs="date")####Combine dfs####
#adding site name for air df. Using the water station data names instead of the air data names to line them all up together. (ex. China Camp water data, EOS air data, and Paradise Cay field data will be on the same line)
pc.eos$site<-"pc.cc"
nd.eos$site<-"nd.eos"
by.gg$site<-"by.rb"
hs.gg$site<-"hs.fp"
#combine
all<-dplyr::bind_rows(pc.eos, nd.eos, by.gg, hs.gg)Probably a cleaner way of doing this but this works. It’s not a large data set so this is doable.
#remove everthing except for "all" df
rm(list=setdiff(ls(), "all"))
#subset by site
pc<-subset(all, all$site == "pc.cc")
nd<-subset(all, all$site == "nd.eos")
by<-subset(all, all$site == "by.rb")
hs<-subset(all, all$site == "hs.fp")Paradise Cay
#print adult density
print(pc$no.large.fuc.q)## [1] NA 3.2 15.4 4.3 13.5 8.8 15.0 13.2 11.4 8.9 2.9 1.7 4.4 3.1#Copy and list printed values lagging the values by one month
aa1<-"NA"
aa2<-"NA"
aa3<-"3.2"
aa4<-"15.4"
aa5<-"4.3"
aa6<-"13.5"
aa7<-"8.8"
aa8<-"15.0"
aa9<-"13.2"
aa10<-"11.4"
aa11<-"8.9"
aa12<-"2.9"
aa13<-"1.7"
aa14<-"4.4"
#string these values to a data frame. Using field surveys dates for this site
pc.den<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
pc.den<-as.data.frame(pc.den)
#change column names
names(pc.den)[1] <- "date"
names(pc.den)[2] <- "lag.no.large.fuc.q"
pc.den$date<-as.Date(pc.den$date, format=c("%Y-%m-%d"))
#merge dfs
pc<-merge(pc, pc.den[,c("date", "lag.no.large.fuc.q")], by="date")
#checking that it worked before doing the same for the other field sites
check<-pc %>% select(date, no.large.fuc.q, lag.no.large.fuc.q)Perfect! Now doing the same for all other sites.
Point Chauncy
#print adult density
print(nd$no.large.fuc.q)## [1] NA 12.4 22.9 5.2 3.0 5.2 12.1 2.7 4.6 7.9 8.9 5.2 2.6 2.7 1.9#Copy and list printed values lagging the values by one month
aa1<-"NA"
aa2<-"NA"
aa3<-"12.4"
aa4<-"22.9"
aa5<-"5.2"
aa6<-"3.0"
aa7<-"5.2"
aa8<-"12.1"
aa9<-"2.7"
aa10<-"4.6"
aa11<-"7.9"
aa12<-"8.9"
aa13<-"5.2"
aa14<-"2.6"
aa15<-"2.7"
#string these values to a data frame. Using field surveys dates for this site
nd.den<-list(c('2018-06-14','2018-07-17','2018-08-07','2018-09-11','2018-10-10','2018-12-05','2019-01-30','2019-02-20','2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04', '2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13, aa14, aa15))
nd.den<-as.data.frame(nd.den)
nd.den<-as.data.frame(nd.den)
#change column names
names(nd.den)[1] <- "date"
names(nd.den)[2] <- "lag.no.large.fuc.q"
nd.den$date<-as.Date(nd.den$date, format=c("%Y-%m-%d"))
#merge dfs
nd<-merge(nd, nd.den[,c("date", "lag.no.large.fuc.q")], by="date")
#check
check<-nd %>% select(date, no.large.fuc.q, lag.no.large.fuc.q)Brickyard Park
#print adult density
print(by$no.large.fuc.q)## [1] NA 10.9 12.9 4.8 8.9 10.2 8.1 4.0 7.2 10.3 7.9 6.2 3.4 0.7 0.6
## [16] 1.2#Copy and list printed values lagging the values by one month
aa1<-"NA"
aa2<-"NA"
aa3<-"10.9"
aa4<-"12.9"
aa5<-"4.8"
aa6<-"8.9"
aa7<-"10.2"
aa8<-"8.1"
aa9<-"4.0"
aa10<-"7.2"
aa11<-"10.3"
aa12<-"7.9"
aa13<-"6.2"
aa14<-"3.4"
aa15<-"0.7"
aa16<-"0.6"
#string these values to a data frame. Using field surveys dates for this site
by.den<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
by.den<-as.data.frame(by.den)
by.den<-as.data.frame(by.den)
#change column names
names(by.den)[1] <- "date"
names(by.den)[2] <- "lag.no.large.fuc.q"
by.den$date<-as.Date(by.den$date, format=c("%Y-%m-%d"))
#merge dfs
by<-merge(by, by.den[,c("date", "lag.no.large.fuc.q")], by="date")
#check
check<-by %>% select(date, no.large.fuc.q, lag.no.large.fuc.q)Horseshoe Bay
#print adult density
print(hs$no.large.fuc.q)## [1] NA 4.0 11.2 6.8 5.2 5.3 2.9 10.2 4.4 7.9 8.9 12.5 7.7 6.7 3.6
## [16] 3.3#Copy and list printed values lagging the values by one month
aa1<-"NA"
aa2<-"NA"
aa3<-"4.0"
aa4<-"11.2"
aa5<-"6.8"
aa6<-"5.2"
aa7<-"5.3"
aa8<-"2.9"
aa9<-"10.2"
aa10<-"4.4"
aa11<-"7.9"
aa12<-"8.9"
aa13<-"12.5"
aa14<-"7.7"
aa15<-"6.7"
aa16<-"3.6"
#string these values to a data frame. Using field surveys dates for this site
hs.den<-list(c('2018-06-15','2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14,aa15,aa16))
hs.den<-as.data.frame(hs.den)
#change column names
names(hs.den)[1] <- "date"
names(hs.den)[2] <- "lag.no.large.fuc.q"
hs.den$date<-as.Date(hs.den$date, format=c("%Y-%m-%d"))
#merge dfs
hs<-merge(hs, hs.den[,c("date", "lag.no.large.fuc.q")], by="date")
#check
check<-hs %>% select(date, no.large.fuc.q, lag.no.large.fuc.q)####Merge and save####
#merge
all<-dplyr::bind_rows(pc, nd, by, hs)
#save csv
write.csv(all, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/envi.field.all.one.month.lag.csv")final combined df for analysis:https://github.com/Cmwegener/thesis/blob/master/data/environment_field