Overview: Here I’m 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. I will have four data frames at the end of this, one for each field site and its closest water station. China camp and paradise cay, EOS pier and point chauncy, Richardson Bay and brickyard park, and Horseshoe bay and fort point.
Possibly a cleaner way to do this but this works
lt= less than, gt=greater than
Steps for environmental data: filter between survey dates, then take mean. For events, I filter for certain values (ex. salinity <10) then subset by dates and take the mean. 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: salinty <20, 10, and 5. Dissolved oxygen <6. pH <6 and >8. Water temp >22
Are there other values I should be looking at? Not sure about the rational behind some of my choices and need to think it through a little more.
Notes to self:
-Look at factors at the monthly level: monthly avg density/juv/ect and monthly mean of salinity events (monthly average of the low salinity events selected). You need all the factors in the lm to be at the same level.
-Environmental terms: salinity, pH/DO, air temperature during low tides
-Interactive, additive terms. Pretty sure we wrote some sample code before so I’ll just search for that
-Before running the model I need a df with the factors at these monthly summaries. So I’ll have a column with survey dates, monthly mean density, monthly mean %R, monthly mean any field data, monthly mean max air temp, monthly min sal, monthly mean of the environmental events I’m looking at. This why I don’t need to introduce a lag term because I’ll calculate the envrionmental events the month before survey dates and then add it to the same row.
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)
field<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Final data/CB_field_data_plus.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)Starting with montly summaries of the field data
pc<-subset(field, field$site.old == "PC")
#monthly mean of fucus density
pc.cc<-aggregate(no.fuc.q ~ date, pc, mean, na.rm=TRUE)
pc.cc$date<-as.Date(pc.cc$date, format=c("%m/%d/%Y"))
#mean percent cover
pc.r<-aggregate(cover ~date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean of large fucus density
pc.r<-aggregate(no.large.fuc.q ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean small fucus density
pc.r<-aggregate(no.small.fuc.q ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#median reproductive cover class
pc.r<-aggregate(covcl.repro ~ date, pc, median, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean vegetative dry weight
pc.r<-aggregate(dw.veg ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean reproductive dry weight
pc.r<-aggregate(dw.repro ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean reproductive apices
pc.r<-aggregate(apices.repro ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean vegetative apices
pc.r<-aggregate(apices.veg ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean percent reproductive apices
pc.r<-aggregate(perc.ra ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean oogonia per conceptacle
pc.r<-aggregate(avg.oog ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
#mean percent reproductive dry weight
pc.r<-aggregate(perc.rdw ~ date, pc, mean, na.rm=TRUE)
pc.r$date<-as.Date(pc.r$date, format=c("%m/%d/%Y"))
pc.cc<-merge(pc.cc, pc.r, by="date")
rm(pc, pc.r)Now I need to add the summaries of the environmental data. I’ll take the mean between field surveys and 30days prior for the first field survey.
Salinity
cc_sal<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/cc_sal.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
cc_sal$date<-as.Date(cc_sal$date, format=c("%Y-%m-%d"))
#looking at survey dates
print(pc.cc)## date no.fuc.q cover no.large.fuc.q no.small.fuc.q covcl.repro dw.veg
## 1 2018-07-17 90.2 29.9 3.2 87.0 1.0 1.439800
## 2 2018-08-07 163.5 53.4 15.4 148.1 1.0 2.175400
## 3 2018-09-11 120.8 58.2 4.3 116.5 1.0 3.309800
## 4 2018-12-05 35.1 55.0 13.5 21.6 3.0 3.300500
## 5 2019-01-30 25.2 49.3 8.8 16.4 3.0 3.284600
## 6 2019-02-20 28.4 49.1 15.0 13.4 2.5 2.307556
## 7 2019-03-15 21.7 38.2 13.2 8.5 2.0 3.232600
## 8 2019-04-11 19.4 37.2 11.4 8.0 2.0 3.619200
## 9 2019-05-09 12.3 40.5 8.9 3.4 5.0 4.522300
## 10 2019-06-09 6.1 25.7 2.9 3.2 5.0 4.325100
## 11 2019-07-21 30.6 9.8 1.7 28.9 1.0 1.265500
## 12 2019-08-04 17.3 7.7 4.4 12.9 1.0 1.010800
## dw.repro apices.repro apices.veg perc.ra avg.oog perc.rdw
## 1 0.4870000 8.400000 24.900 9.677489 13.888889 8.027934
## 2 0.3992000 7.800000 28.900 13.324573 7.777778 9.162042
## 3 0.4294000 10.600000 47.100 18.573833 7.777778 11.125163
## 4 1.2959000 45.200000 58.900 33.636471 42.222222 23.061467
## 5 0.6915000 23.100000 56.400 19.856140 26.555556 12.042275
## 6 0.1604444 8.222222 48.375 9.447116 35.666667 5.055350
## 7 0.1099000 5.800000 75.300 5.439267 18.111111 2.280192
## 8 0.1755000 6.800000 96.200 6.819327 14.666667 4.055909
## 9 1.4546000 57.500000 92.700 33.104160 0.000000 21.665297
## 10 4.6780000 99.300000 57.600 59.168442 2.666667 47.385279
## 11 0.5457000 8.200000 21.800 12.346474 3.777778 10.412522
## 12 0.1000000 2.800000 19.200 4.743590 0.000000 2.409182#I'm going to subset the salinity data by dates and then get the mean and combine it into a df but I feel like there's a cleaner way of doing this were you calculate mean with set date ranges.
a1<- cc_sal[cc_sal$date >= "2018-05-14" & cc_sal$date < "2018-06-14",]
a2<- cc_sal[cc_sal$date >= "2018-06-14" & cc_sal$date < "2018-07-17",]
a3<- cc_sal[cc_sal$date >= "2018-07-17" & cc_sal$date < "2018-08-07",]
a4<- cc_sal[cc_sal$date >= "2018-08-07" & cc_sal$date < "2018-09-11",]
a5<- cc_sal[cc_sal$date >= "2018-09-11" & cc_sal$date < "2018-12-05",]
a6<- cc_sal[cc_sal$date >= "2018-12-05" & cc_sal$date < "2019-01-30",]
a7<- cc_sal[cc_sal$date >= "2019-01-30" & cc_sal$date < "2019-02-20",]
a8<- cc_sal[cc_sal$date >= "2019-02-20" & cc_sal$date < "2019-03-15",]
a9<- cc_sal[cc_sal$date >= "2019-03-15" & cc_sal$date < "2019-04-11",]
a10<- cc_sal[cc_sal$date >= "2019-04-11" & cc_sal$date < "2019-05-09",]
a11<- cc_sal[cc_sal$date >= "2019-05-09" & cc_sal$date < "2019-06-09",]
a12<- cc_sal[cc_sal$date >= "2019-06-09" & cc_sal$date < "2019-07-21",]
a13<- cc_sal[cc_sal$date >= "2019-07-21" & cc_sal$date < "2019-08-04",]
a14<- cc_sal[cc_sal$date >= "2019-08-04" & cc_sal$date < "2019-09-12",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(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$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.sal[,c("date", "salinity")], by="date")Now I’m going to summarize all the environmental data this way. Find and replace really helps with this.
Salinity events less than 20
cc_sal_lt20<-filter(cc_sal, salinity<20)
#subset dates
a1<- cc_sal_lt20[cc_sal_lt20$date >= "2018-05-14" & cc_sal_lt20$date < "2018-06-14",]
a2<- cc_sal_lt20[cc_sal_lt20$date >= "2018-06-14" & cc_sal_lt20$date < "2018-07-17",]
a3<- cc_sal_lt20[cc_sal_lt20$date >= "2018-07-17" & cc_sal_lt20$date < "2018-08-07",]
a4<- cc_sal_lt20[cc_sal_lt20$date >= "2018-08-07" & cc_sal_lt20$date < "2018-09-11",]
a5<- cc_sal_lt20[cc_sal_lt20$date >= "2018-09-11" & cc_sal_lt20$date < "2018-12-05",]
a6<- cc_sal_lt20[cc_sal_lt20$date >= "2018-12-05" & cc_sal_lt20$date < "2019-01-30",]
a7<- cc_sal_lt20[cc_sal_lt20$date >= "2019-01-30" & cc_sal_lt20$date < "2019-02-20",]
a8<- cc_sal_lt20[cc_sal_lt20$date >= "2019-02-20" & cc_sal_lt20$date < "2019-03-15",]
a9<- cc_sal_lt20[cc_sal_lt20$date >= "2019-03-15" & cc_sal_lt20$date < "2019-04-11",]
a10<- cc_sal_lt20[cc_sal_lt20$date >= "2019-04-11" & cc_sal_lt20$date < "2019-05-09",]
a11<- cc_sal_lt20[cc_sal_lt20$date >= "2019-05-09" & cc_sal_lt20$date < "2019-06-09",]
a12<- cc_sal_lt20[cc_sal_lt20$date >= "2019-06-09" & cc_sal_lt20$date < "2019-07-21",]
a13<- cc_sal_lt20[cc_sal_lt20$date >= "2019-07-21" & cc_sal_lt20$date < "2019-08-04",]
a14<- cc_sal_lt20[cc_sal_lt20$date >= "2019-08-04" & cc_sal_lt20$date < "2019-09-12",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
cc.mon.sal.lt20<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.sal.lt20<-as.data.frame(cc.mon.sal.lt20)
#change column names
names(cc.mon.sal.lt20)[1] <- "date"
names(cc.mon.sal.lt20)[2] <- "salinity.lt20"
cc.mon.sal.lt20$date<-as.Date(cc.mon.sal.lt20$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.sal.lt20[,c("date", "salinity.lt20")], by="date")Salinity events less than 10
cc_sal_lt10<-filter(cc_sal, salinity<10)
#subset dates
a1<- cc_sal_lt10[cc_sal_lt10$date >= "2018-05-14" & cc_sal_lt10$date < "2018-06-14",]
a2<- cc_sal_lt10[cc_sal_lt10$date >= "2018-06-14" & cc_sal_lt10$date < "2018-07-17",]
a3<- cc_sal_lt10[cc_sal_lt10$date >= "2018-07-17" & cc_sal_lt10$date < "2018-08-07",]
a4<- cc_sal_lt10[cc_sal_lt10$date >= "2018-08-07" & cc_sal_lt10$date < "2018-09-11",]
a5<- cc_sal_lt10[cc_sal_lt10$date >= "2018-09-11" & cc_sal_lt10$date < "2018-12-05",]
a6<- cc_sal_lt10[cc_sal_lt10$date >= "2018-12-05" & cc_sal_lt10$date < "2019-01-30",]
a7<- cc_sal_lt10[cc_sal_lt10$date >= "2019-01-30" & cc_sal_lt10$date < "2019-02-20",]
a8<- cc_sal_lt10[cc_sal_lt10$date >= "2019-02-20" & cc_sal_lt10$date < "2019-03-15",]
a9<- cc_sal_lt10[cc_sal_lt10$date >= "2019-03-15" & cc_sal_lt10$date < "2019-04-11",]
a10<- cc_sal_lt10[cc_sal_lt10$date >= "2019-04-11" & cc_sal_lt10$date < "2019-05-09",]
a11<- cc_sal_lt10[cc_sal_lt10$date >= "2019-05-09" & cc_sal_lt10$date < "2019-06-09",]
a12<- cc_sal_lt10[cc_sal_lt10$date >= "2019-06-09" & cc_sal_lt10$date < "2019-07-21",]
a13<- cc_sal_lt10[cc_sal_lt10$date >= "2019-07-21" & cc_sal_lt10$date < "2019-08-04",]
a14<- cc_sal_lt10[cc_sal_lt10$date >= "2019-08-04" & cc_sal_lt10$date < "2019-09-12",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
cc.mon.sal.lt10<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.sal.lt10<-as.data.frame(cc.mon.sal.lt10)
#change column names
names(cc.mon.sal.lt10)[1] <- "date"
names(cc.mon.sal.lt10)[2] <- "salinity.lt10"
cc.mon.sal.lt10$date<-as.Date(cc.mon.sal.lt10$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.sal.lt10[,c("date", "salinity.lt10")], by="date")Salinity events less than 5
cc_sal_lt5<-filter(cc_sal, salinity<5)
#subset dates
a1<- cc_sal_lt5[cc_sal_lt5$date >= "2018-05-14" & cc_sal_lt5$date < "2018-06-14",]
a2<- cc_sal_lt5[cc_sal_lt5$date >= "2018-06-14" & cc_sal_lt5$date < "2018-07-17",]
a3<- cc_sal_lt5[cc_sal_lt5$date >= "2018-07-17" & cc_sal_lt5$date < "2018-08-07",]
a4<- cc_sal_lt5[cc_sal_lt5$date >= "2018-08-07" & cc_sal_lt5$date < "2018-09-11",]
a5<- cc_sal_lt5[cc_sal_lt5$date >= "2018-09-11" & cc_sal_lt5$date < "2018-12-05",]
a6<- cc_sal_lt5[cc_sal_lt5$date >= "2018-12-05" & cc_sal_lt5$date < "2019-01-30",]
a7<- cc_sal_lt5[cc_sal_lt5$date >= "2019-01-30" & cc_sal_lt5$date < "2019-02-20",]
a8<- cc_sal_lt5[cc_sal_lt5$date >= "2019-02-20" & cc_sal_lt5$date < "2019-03-15",]
a9<- cc_sal_lt5[cc_sal_lt5$date >= "2019-03-15" & cc_sal_lt5$date < "2019-04-11",]
a10<- cc_sal_lt5[cc_sal_lt5$date >= "2019-04-11" & cc_sal_lt5$date < "2019-05-09",]
a11<- cc_sal_lt5[cc_sal_lt5$date >= "2019-05-09" & cc_sal_lt5$date < "2019-06-09",]
a12<- cc_sal_lt5[cc_sal_lt5$date >= "2019-06-09" & cc_sal_lt5$date < "2019-07-21",]
a13<- cc_sal_lt5[cc_sal_lt5$date >= "2019-07-21" & cc_sal_lt5$date < "2019-08-04",]
a14<- cc_sal_lt5[cc_sal_lt5$date >= "2019-08-04" & cc_sal_lt5$date < "2019-09-12",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
cc.mon.sal.lt5<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.sal.lt5<-as.data.frame(cc.mon.sal.lt5)
#change column names
names(cc.mon.sal.lt5)[1] <- "date"
names(cc.mon.sal.lt5)[2] <- "salinity.lt5"
cc.mon.sal.lt5$date<-as.Date(cc.mon.sal.lt5$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.sal.lt5[,c("date", "salinity.lt5")], by="date")Dissolved oxygen
cc_do<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/cc_do.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
a1<- cc_do[cc_do$date >= "2018-05-14" & cc_do$date < "2018-06-14",]
a2<- cc_do[cc_do$date >= "2018-06-14" & cc_do$date < "2018-07-17",]
a3<- cc_do[cc_do$date >= "2018-07-17" & cc_do$date < "2018-08-07",]
a4<- cc_do[cc_do$date >= "2018-08-07" & cc_do$date < "2018-09-11",]
a5<- cc_do[cc_do$date >= "2018-09-11" & cc_do$date < "2018-12-05",]
a6<- cc_do[cc_do$date >= "2018-12-05" & cc_do$date < "2019-01-30",]
a7<- cc_do[cc_do$date >= "2019-01-30" & cc_do$date < "2019-02-20",]
a8<- cc_do[cc_do$date >= "2019-02-20" & cc_do$date < "2019-03-15",]
a9<- cc_do[cc_do$date >= "2019-03-15" & cc_do$date < "2019-04-11",]
a10<- cc_do[cc_do$date >= "2019-04-11" & cc_do$date < "2019-05-09",]
a11<- cc_do[cc_do$date >= "2019-05-09" & cc_do$date < "2019-06-09",]
a12<- cc_do[cc_do$date >= "2019-06-09" & cc_do$date < "2019-07-21",]
a13<- cc_do[cc_do$date >= "2019-07-21" & cc_do$date < "2019-08-04",]
a14<- cc_do[cc_do$date >= "2019-08-04" & cc_do$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$do, na.rm=TRUE)
aa2<-mean(a2$do, na.rm=TRUE)
aa3<-mean(a3$do, na.rm=TRUE)
aa4<-mean(a4$do, na.rm=TRUE)
aa5<-mean(a5$do, na.rm=TRUE)
aa6<-mean(a6$do, na.rm=TRUE)
aa7<-mean(a7$do, na.rm=TRUE)
aa8<-mean(a8$do, na.rm=TRUE)
aa9<-mean(a9$do, na.rm=TRUE)
aa10<-mean(a10$do, na.rm=TRUE)
aa11<-mean(a11$do, na.rm=TRUE)
aa12<-mean(a12$do, na.rm=TRUE)
aa13<-mean(a13$do, na.rm=TRUE)
aa14<-mean(a14$do, na.rm=TRUE)
#string these values to a data frame
cc.mon.do<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.do<-as.data.frame(cc.mon.do)
#change column names
names(cc.mon.do)[1] <- "date"
names(cc.mon.do)[2] <- "dissolved.oxygen"
cc.mon.do$date<-as.Date(cc.mon.do$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.do, by="date")Dissolved oxygen less than 6
cc_do_lt6<-filter(cc_do, do<6)
a1<- cc_do_lt6[cc_do_lt6$date >= "2018-05-14" & cc_do_lt6$date < "2018-06-14",]
a2<- cc_do_lt6[cc_do_lt6$date >= "2018-06-14" & cc_do_lt6$date < "2018-07-17",]
a3<- cc_do_lt6[cc_do_lt6$date >= "2018-07-17" & cc_do_lt6$date < "2018-08-07",]
a4<- cc_do_lt6[cc_do_lt6$date >= "2018-08-07" & cc_do_lt6$date < "2018-09-11",]
a5<- cc_do_lt6[cc_do_lt6$date >= "2018-09-11" & cc_do_lt6$date < "2018-12-05",]
a6<- cc_do_lt6[cc_do_lt6$date >= "2018-12-05" & cc_do_lt6$date < "2019-01-30",]
a7<- cc_do_lt6[cc_do_lt6$date >= "2019-01-30" & cc_do_lt6$date < "2019-02-20",]
a8<- cc_do_lt6[cc_do_lt6$date >= "2019-02-20" & cc_do_lt6$date < "2019-03-15",]
a9<- cc_do_lt6[cc_do_lt6$date >= "2019-03-15" & cc_do_lt6$date < "2019-04-11",]
a10<- cc_do_lt6[cc_do_lt6$date >= "2019-04-11" & cc_do_lt6$date < "2019-05-09",]
a11<- cc_do_lt6[cc_do_lt6$date >= "2019-05-09" & cc_do_lt6$date < "2019-06-09",]
a12<- cc_do_lt6[cc_do_lt6$date >= "2019-06-09" & cc_do_lt6$date < "2019-07-21",]
a13<- cc_do_lt6[cc_do_lt6$date >= "2019-07-21" & cc_do_lt6$date < "2019-08-04",]
a14<- cc_do_lt6[cc_do_lt6$date >= "2019-08-04" & cc_do_lt6$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$do, na.rm=TRUE)
aa2<-mean(a2$do, na.rm=TRUE)
aa3<-mean(a3$do, na.rm=TRUE)
aa4<-mean(a4$do, na.rm=TRUE)
aa5<-mean(a5$do, na.rm=TRUE)
aa6<-mean(a6$do, na.rm=TRUE)
aa7<-mean(a7$do, na.rm=TRUE)
aa8<-mean(a8$do, na.rm=TRUE)
aa9<-mean(a9$do, na.rm=TRUE)
aa10<-mean(a10$do, na.rm=TRUE)
aa11<-mean(a11$do, na.rm=TRUE)
aa12<-mean(a12$do, na.rm=TRUE)
aa13<-mean(a13$do, na.rm=TRUE)
aa14<-mean(a14$do, na.rm=TRUE)
#string these values to a data frame
cc.mon.do.lt6<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.do.lt6<-as.data.frame(cc.mon.do.lt6)
#change column names
names(cc.mon.do.lt6)[1] <- "date"
names(cc.mon.do.lt6)[2] <- "dissolved.oxygen.lt6"
cc.mon.do.lt6$date<-as.Date(cc.mon.do.lt6$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.do.lt6, by="date")pH
cc_ph<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/cc_ph.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
a1<- cc_ph[cc_ph$date >= "2018-05-14" & cc_ph$date < "2018-06-14",]
a2<- cc_ph[cc_ph$date >= "2018-06-14" & cc_ph$date < "2018-07-17",]
a3<- cc_ph[cc_ph$date >= "2018-07-17" & cc_ph$date < "2018-08-07",]
a4<- cc_ph[cc_ph$date >= "2018-08-07" & cc_ph$date < "2018-09-11",]
a5<- cc_ph[cc_ph$date >= "2018-09-11" & cc_ph$date < "2018-12-05",]
a6<- cc_ph[cc_ph$date >= "2018-12-05" & cc_ph$date < "2019-01-30",]
a7<- cc_ph[cc_ph$date >= "2019-01-30" & cc_ph$date < "2019-02-20",]
a8<- cc_ph[cc_ph$date >= "2019-02-20" & cc_ph$date < "2019-03-15",]
a9<- cc_ph[cc_ph$date >= "2019-03-15" & cc_ph$date < "2019-04-11",]
a10<- cc_ph[cc_ph$date >= "2019-04-11" & cc_ph$date < "2019-05-09",]
a11<- cc_ph[cc_ph$date >= "2019-05-09" & cc_ph$date < "2019-06-09",]
a12<- cc_ph[cc_ph$date >= "2019-06-09" & cc_ph$date < "2019-07-21",]
a13<- cc_ph[cc_ph$date >= "2019-07-21" & cc_ph$date < "2019-08-04",]
a14<- cc_ph[cc_ph$date >= "2019-08-04" & cc_ph$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
aa14<-mean(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$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.ph, by="date")pH less than 6. None
cc_ph_lt6<-filter(cc_ph, ph<6)
a1<- cc_ph_lt6[cc_ph_lt6$date >= "2018-05-14" & cc_ph_lt6$date < "2018-06-14",]
a2<- cc_ph_lt6[cc_ph_lt6$date >= "2018-06-14" & cc_ph_lt6$date < "2018-07-17",]
a3<- cc_ph_lt6[cc_ph_lt6$date >= "2018-07-17" & cc_ph_lt6$date < "2018-08-07",]
a4<- cc_ph_lt6[cc_ph_lt6$date >= "2018-08-07" & cc_ph_lt6$date < "2018-09-11",]
a5<- cc_ph_lt6[cc_ph_lt6$date >= "2018-09-11" & cc_ph_lt6$date < "2018-12-05",]
a6<- cc_ph_lt6[cc_ph_lt6$date >= "2018-12-05" & cc_ph_lt6$date < "2019-01-30",]
a7<- cc_ph_lt6[cc_ph_lt6$date >= "2019-01-30" & cc_ph_lt6$date < "2019-02-20",]
a8<- cc_ph_lt6[cc_ph_lt6$date >= "2019-02-20" & cc_ph_lt6$date < "2019-03-15",]
a9<- cc_ph_lt6[cc_ph_lt6$date >= "2019-03-15" & cc_ph_lt6$date < "2019-04-11",]
a10<- cc_ph_lt6[cc_ph_lt6$date >= "2019-04-11" & cc_ph_lt6$date < "2019-05-09",]
a11<- cc_ph_lt6[cc_ph_lt6$date >= "2019-05-09" & cc_ph_lt6$date < "2019-06-09",]
a12<- cc_ph_lt6[cc_ph_lt6$date >= "2019-06-09" & cc_ph_lt6$date < "2019-07-21",]
a13<- cc_ph_lt6[cc_ph_lt6$date >= "2019-07-21" & cc_ph_lt6$date < "2019-08-04",]
a14<- cc_ph_lt6[cc_ph_lt6$date >= "2019-08-04" & cc_ph_lt6$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
aa14<-mean(a14$ph, na.rm=TRUE)
#string these values to a data frame
cc.mon.ph.lt6<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.ph.lt6<-as.data.frame(cc.mon.ph.lt6)
#change column names
names(cc.mon.ph.lt6)[1] <- "date"
names(cc.mon.ph.lt6)[2] <- "ph.lt6"
cc.mon.ph.lt6$date<-as.Date(cc.mon.ph.lt6$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.ph.lt6, by="date")pH greater than 8
cc_ph_gt8<-filter(cc_ph, ph>8)
a1<- cc_ph_gt8[cc_ph_gt8$date >= "2018-05-14" & cc_ph_gt8$date < "2018-06-14",]
a2<- cc_ph_gt8[cc_ph_gt8$date >= "2018-06-14" & cc_ph_gt8$date < "2018-07-17",]
a3<- cc_ph_gt8[cc_ph_gt8$date >= "2018-07-17" & cc_ph_gt8$date < "2018-08-07",]
a4<- cc_ph_gt8[cc_ph_gt8$date >= "2018-08-07" & cc_ph_gt8$date < "2018-09-11",]
a5<- cc_ph_gt8[cc_ph_gt8$date >= "2018-09-11" & cc_ph_gt8$date < "2018-12-05",]
a6<- cc_ph_gt8[cc_ph_gt8$date >= "2018-12-05" & cc_ph_gt8$date < "2019-01-30",]
a7<- cc_ph_gt8[cc_ph_gt8$date >= "2019-01-30" & cc_ph_gt8$date < "2019-02-20",]
a8<- cc_ph_gt8[cc_ph_gt8$date >= "2019-02-20" & cc_ph_gt8$date < "2019-03-15",]
a9<- cc_ph_gt8[cc_ph_gt8$date >= "2019-03-15" & cc_ph_gt8$date < "2019-04-11",]
a10<- cc_ph_gt8[cc_ph_gt8$date >= "2019-04-11" & cc_ph_gt8$date < "2019-05-09",]
a11<- cc_ph_gt8[cc_ph_gt8$date >= "2019-05-09" & cc_ph_gt8$date < "2019-06-09",]
a12<- cc_ph_gt8[cc_ph_gt8$date >= "2019-06-09" & cc_ph_gt8$date < "2019-07-21",]
a13<- cc_ph_gt8[cc_ph_gt8$date >= "2019-07-21" & cc_ph_gt8$date < "2019-08-04",]
a14<- cc_ph_gt8[cc_ph_gt8$date >= "2019-08-04" & cc_ph_gt8$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
aa14<-mean(a14$ph, na.rm=TRUE)
#string these values to a data frame
cc.mon.ph.gt8<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.ph.gt8<-as.data.frame(cc.mon.ph.gt8)
#change column names
names(cc.mon.ph.gt8)[1] <- "date"
names(cc.mon.ph.gt8)[2] <- "ph.gt8"
cc.mon.ph.gt8$date<-as.Date(cc.mon.ph.gt8$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.ph.gt8, by="date")Water temperature (wt for ease of coding)
cc_wt<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/cc_watertemp.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
cc_wt$date<-as.Date(cc_wt$date, format=c("%Y-%m-%d"))
a1<- cc_wt[cc_wt$date >= "2018-05-14" & cc_wt$date < "2018-06-14",]
a2<- cc_wt[cc_wt$date >= "2018-06-14" & cc_wt$date < "2018-07-17",]
a3<- cc_wt[cc_wt$date >= "2018-07-17" & cc_wt$date < "2018-08-07",]
a4<- cc_wt[cc_wt$date >= "2018-08-07" & cc_wt$date < "2018-09-11",]
a5<- cc_wt[cc_wt$date >= "2018-09-11" & cc_wt$date < "2018-12-05",]
a6<- cc_wt[cc_wt$date >= "2018-12-05" & cc_wt$date < "2019-01-30",]
a7<- cc_wt[cc_wt$date >= "2019-01-30" & cc_wt$date < "2019-02-20",]
a8<- cc_wt[cc_wt$date >= "2019-02-20" & cc_wt$date < "2019-03-15",]
a9<- cc_wt[cc_wt$date >= "2019-03-15" & cc_wt$date < "2019-04-11",]
a10<- cc_wt[cc_wt$date >= "2019-04-11" & cc_wt$date < "2019-05-09",]
a11<- cc_wt[cc_wt$date >= "2019-05-09" & cc_wt$date < "2019-06-09",]
a12<- cc_wt[cc_wt$date >= "2019-06-09" & cc_wt$date < "2019-07-21",]
a13<- cc_wt[cc_wt$date >= "2019-07-21" & cc_wt$date < "2019-08-04",]
a14<- cc_wt[cc_wt$date >= "2019-08-04" & cc_wt$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
aa14<-mean(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$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.wt, by="date")Water temperature greater than 22
cc_wt.gt22<-filter(cc_wt, water_temp>22)
a1<- cc_wt.gt22[cc_wt.gt22$date >= "2018-05-14" & cc_wt.gt22$date < "2018-06-14",]
a2<- cc_wt.gt22[cc_wt.gt22$date >= "2018-06-14" & cc_wt.gt22$date < "2018-07-17",]
a3<- cc_wt.gt22[cc_wt.gt22$date >= "2018-07-17" & cc_wt.gt22$date < "2018-08-07",]
a4<- cc_wt.gt22[cc_wt.gt22$date >= "2018-08-07" & cc_wt.gt22$date < "2018-09-11",]
a5<- cc_wt.gt22[cc_wt.gt22$date >= "2018-09-11" & cc_wt.gt22$date < "2018-12-05",]
a6<- cc_wt.gt22[cc_wt.gt22$date >= "2018-12-05" & cc_wt.gt22$date < "2019-01-30",]
a7<- cc_wt.gt22[cc_wt.gt22$date >= "2019-01-30" & cc_wt.gt22$date < "2019-02-20",]
a8<- cc_wt.gt22[cc_wt.gt22$date >= "2019-02-20" & cc_wt.gt22$date < "2019-03-15",]
a9<- cc_wt.gt22[cc_wt.gt22$date >= "2019-03-15" & cc_wt.gt22$date < "2019-04-11",]
a10<- cc_wt.gt22[cc_wt.gt22$date >= "2019-04-11" & cc_wt.gt22$date < "2019-05-09",]
a11<- cc_wt.gt22[cc_wt.gt22$date >= "2019-05-09" & cc_wt.gt22$date < "2019-06-09",]
a12<- cc_wt.gt22[cc_wt.gt22$date >= "2019-06-09" & cc_wt.gt22$date < "2019-07-21",]
a13<- cc_wt.gt22[cc_wt.gt22$date >= "2019-07-21" & cc_wt.gt22$date < "2019-08-04",]
a14<- cc_wt.gt22[cc_wt.gt22$date >= "2019-08-04" & cc_wt.gt22$date < "2019-09-12",]
#mean of these periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
aa14<-mean(a14$water_temp, na.rm=TRUE)
#string these values to a data frame
cc.mon.wt.gt22<-list(c('2018-06-14','2018-07-17','2018-08-07', '2018-09-11', '2018-12-05','2019-01-30','2019-02-20', '2019-03-15','2019-04-11','2019-05-09','2019-06-09','2019-07-21', '2019-08-04','2019-09-12'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
cc.mon.wt.gt22<-as.data.frame(cc.mon.wt.gt22)
#change column names
names(cc.mon.wt.gt22)[1] <- "date"
names(cc.mon.wt.gt22)[2] <- "water.temp.gt22"
cc.mon.wt.gt22$date<-as.Date(cc.mon.wt.gt22$date, format=c("%Y-%m-%d"))
#merge dfs
pc.cc<-merge(pc.cc, cc.mon.wt.gt22, by="date")Save csv
write.csv(pc.cc, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/pc.cc.csv")Starting with monthly summaries of the field data
nd<-subset(field, field$site.old == "ND")
#monthly mean of fucus density
nd.eos<-aggregate(no.fuc.q ~ date, nd, mean, na.rm=TRUE)
nd.eos$date<-as.Date(nd.eos$date, format=c("%m/%d/%Y"))
#mean percent cover
nd.r<-aggregate(cover ~date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean of large fucus density
nd.r<-aggregate(no.large.fuc.q ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean small fucus density
nd.r<-aggregate(no.small.fuc.q ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#median reproductive cover class
nd.r<-aggregate(covcl.repro ~ date, nd, median, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean vegetative dry weight
nd.r<-aggregate(dw.veg ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean reproductive dry weight
nd.r<-aggregate(dw.repro ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean reproductive apices
nd.r<-aggregate(apices.repro ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean vegetative apices
nd.r<-aggregate(apices.veg ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean percent reproductive apices
nd.r<-aggregate(perc.ra ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean oogonia per conceptacle
nd.r<-aggregate(avg.oog ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
#mean percent reproductive dry weight
nd.r<-aggregate(perc.rdw ~ date, nd, mean, na.rm=TRUE)
nd.r$date<-as.Date(nd.r$date, format=c("%m/%d/%Y"))
nd.eos<-merge(nd.eos, nd.r, by="date")
rm(nd, nd.r)Salinity
eos_sal<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/eos_sal.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
eos_sal$date<-as.Date(eos_sal$date, format=c("%Y-%m-%d"))
#looking at survey dates
print(nd.eos$date)## [1] "2018-07-17" "2018-08-07" "2018-09-11" "2018-10-10" "2018-12-05"
## [6] "2019-01-30" "2019-02-20" "2019-03-15" "2019-04-11" "2019-05-09"
## [11] "2019-06-09" "2019-07-21" "2019-08-04"#subset the salinity data by dates
a1<- eos_sal[eos_sal$date >= "2018-06-17" & eos_sal$date < "2018-07-17",]
a2<- eos_sal[eos_sal$date >= "2018-07-17" & eos_sal$date < "2018-08-07",]
a3<- eos_sal[eos_sal$date >= "2018-08-07" & eos_sal$date < "2018-09-11",]
a4<- eos_sal[eos_sal$date >= "2018-09-11" & eos_sal$date < "2018-10-10",]
a5<- eos_sal[eos_sal$date >= "2018-10-10" & eos_sal$date < "2018-12-05",]
a6<- eos_sal[eos_sal$date >= "2018-12-05" & eos_sal$date < "2019-01-30",]
a7<- eos_sal[eos_sal$date >= "2019-01-30" & eos_sal$date < "2019-02-20",]
a8<- eos_sal[eos_sal$date >= "2019-02-20" & eos_sal$date < "2019-03-15",]
a9<- eos_sal[eos_sal$date >= "2019-03-15" & eos_sal$date < "2019-04-11",]
a10<- eos_sal[eos_sal$date >= "2019-04-11" & eos_sal$date < "2019-05-09",]
a11<- eos_sal[eos_sal$date >= "2019-05-09" & eos_sal$date < "2019-06-09",]
a12<- eos_sal[eos_sal$date >= "2019-06-09" & eos_sal$date < "2019-07-21",]
a13<- eos_sal[eos_sal$date >= "2019-07-21" & eos_sal$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
#string these values to a data frame
eos.mon.sal<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
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")Salinity less than 20
eos_sal_lt20<-filter(eos_sal, salinity<20)
#subset the salinity data by dates
a1<- eos_sal_lt20[eos_sal_lt20$date >= "2018-06-17" & eos_sal_lt20$date < "2018-07-17",]
a2<- eos_sal_lt20[eos_sal_lt20$date >= "2018-07-17" & eos_sal_lt20$date < "2018-08-07",]
a3<- eos_sal_lt20[eos_sal_lt20$date >= "2018-08-07" & eos_sal_lt20$date < "2018-09-11",]
a4<- eos_sal_lt20[eos_sal_lt20$date >= "2018-09-11" & eos_sal_lt20$date < "2018-10-10",]
a5<- eos_sal_lt20[eos_sal_lt20$date >= "2018-10-10" & eos_sal_lt20$date < "2018-12-05",]
a6<- eos_sal_lt20[eos_sal_lt20$date >= "2018-12-05" & eos_sal_lt20$date < "2019-01-30",]
a7<- eos_sal_lt20[eos_sal_lt20$date >= "2019-01-30" & eos_sal_lt20$date < "2019-02-20",]
a8<- eos_sal_lt20[eos_sal_lt20$date >= "2019-02-20" & eos_sal_lt20$date < "2019-03-15",]
a9<- eos_sal_lt20[eos_sal_lt20$date >= "2019-03-15" & eos_sal_lt20$date < "2019-04-11",]
a10<- eos_sal_lt20[eos_sal_lt20$date >= "2019-04-11" & eos_sal_lt20$date < "2019-05-09",]
a11<- eos_sal_lt20[eos_sal_lt20$date >= "2019-05-09" & eos_sal_lt20$date < "2019-06-09",]
a12<- eos_sal_lt20[eos_sal_lt20$date >= "2019-06-09" & eos_sal_lt20$date < "2019-07-21",]
a13<- eos_sal_lt20[eos_sal_lt20$date >= "2019-07-21" & eos_sal_lt20$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
#string these values to a data frame
eos.mon.sal.lt20<-list(c('2018-07-17', '2018-08-07', '2018-09-11', '2018-10-10', '2018-12-05', '2019-01-30', '2019-02-20', '2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.sal.lt20<-as.data.frame(eos.mon.sal.lt20)
#change column names
names(eos.mon.sal.lt20)[1] <- "date"
names(eos.mon.sal.lt20)[2] <- "salinity.lt20"
eos.mon.sal.lt20$date<-as.Date(eos.mon.sal.lt20$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.sal.lt20[,c("date", "salinity.lt20")], by="date")Salinity less than 10
eos_sal_lt10<-filter(eos_sal, salinity<10)
#subset the salinity data by dates
a1<- eos_sal_lt10[eos_sal_lt10$date >= "2018-06-17" & eos_sal_lt10$date < "2018-07-17",]
a2<- eos_sal_lt10[eos_sal_lt10$date >= "2018-07-17" & eos_sal_lt10$date < "2018-08-07",]
a3<- eos_sal_lt10[eos_sal_lt10$date >= "2018-08-07" & eos_sal_lt10$date < "2018-09-11",]
a4<- eos_sal_lt10[eos_sal_lt10$date >= "2018-09-11" & eos_sal_lt10$date < "2018-10-10",]
a5<- eos_sal_lt10[eos_sal_lt10$date >= "2018-10-10" & eos_sal_lt10$date < "2018-12-05",]
a6<- eos_sal_lt10[eos_sal_lt10$date >= "2018-12-05" & eos_sal_lt10$date < "2019-01-30",]
a7<- eos_sal_lt10[eos_sal_lt10$date >= "2019-01-30" & eos_sal_lt10$date < "2019-02-20",]
a8<- eos_sal_lt10[eos_sal_lt10$date >= "2019-02-20" & eos_sal_lt10$date < "2019-03-15",]
a9<- eos_sal_lt10[eos_sal_lt10$date >= "2019-03-15" & eos_sal_lt10$date < "2019-04-11",]
a10<- eos_sal_lt10[eos_sal_lt10$date >= "2019-04-11" & eos_sal_lt10$date < "2019-05-09",]
a11<- eos_sal_lt10[eos_sal_lt10$date >= "2019-05-09" & eos_sal_lt10$date < "2019-06-09",]
a12<- eos_sal_lt10[eos_sal_lt10$date >= "2019-06-09" & eos_sal_lt10$date < "2019-07-21",]
a13<- eos_sal_lt10[eos_sal_lt10$date >= "2019-07-21" & eos_sal_lt10$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
#string these values to a data frame
eos.mon.sal.lt10<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.sal.lt10<-as.data.frame(eos.mon.sal.lt10)
#change column names
names(eos.mon.sal.lt10)[1] <- "date"
names(eos.mon.sal.lt10)[2] <- "salinity.lt10"
eos.mon.sal.lt10$date<-as.Date(eos.mon.sal.lt10$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.sal.lt10[,c("date", "salinity.lt10")], by="date")Salinity less than 5
eos_sal_lt5<-filter(eos_sal, salinity<5)
#subset the salinity data by dates
a1<- eos_sal_lt5[eos_sal_lt5$date >= "2018-06-17" & eos_sal_lt5$date < "2018-07-17",]
a2<- eos_sal_lt5[eos_sal_lt5$date >= "2018-07-17" & eos_sal_lt5$date < "2018-08-07",]
a3<- eos_sal_lt5[eos_sal_lt5$date >= "2018-08-07" & eos_sal_lt5$date < "2018-09-11",]
a4<- eos_sal_lt5[eos_sal_lt5$date >= "2018-09-11" & eos_sal_lt5$date < "2018-10-10",]
a5<- eos_sal_lt5[eos_sal_lt5$date >= "2018-10-10" & eos_sal_lt5$date < "2018-12-05",]
a6<- eos_sal_lt5[eos_sal_lt5$date >= "2018-12-05" & eos_sal_lt5$date < "2019-01-30",]
a7<- eos_sal_lt5[eos_sal_lt5$date >= "2019-01-30" & eos_sal_lt5$date < "2019-02-20",]
a8<- eos_sal_lt5[eos_sal_lt5$date >= "2019-02-20" & eos_sal_lt5$date < "2019-03-15",]
a9<- eos_sal_lt5[eos_sal_lt5$date >= "2019-03-15" & eos_sal_lt5$date < "2019-04-11",]
a10<- eos_sal_lt5[eos_sal_lt5$date >= "2019-04-11" & eos_sal_lt5$date < "2019-05-09",]
a11<- eos_sal_lt5[eos_sal_lt5$date >= "2019-05-09" & eos_sal_lt5$date < "2019-06-09",]
a12<- eos_sal_lt5[eos_sal_lt5$date >= "2019-06-09" & eos_sal_lt5$date < "2019-07-21",]
a13<- eos_sal_lt5[eos_sal_lt5$date >= "2019-07-21" & eos_sal_lt5$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
#string these values to a data frame
eos.mon.sal.lt5<-list(c('2018-07-17', '2018-08-07', '2018-09-11', '2018-10-10', '2018-12-05', '2019-01-30', '2019-02-20', '2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.sal.lt5<-as.data.frame(eos.mon.sal.lt5)
#change column names
names(eos.mon.sal.lt5)[1] <- "date"
names(eos.mon.sal.lt5)[2] <- "salinity.lt5"
eos.mon.sal.lt5$date<-as.Date(eos.mon.sal.lt5$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.sal.lt5[,c("date", "salinity.lt5")], by="date")Dissolved oxygen
eos_do<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/eos_do.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
names(eos_do)[3] <- "do"
#subsetting date
a1<- eos_do[eos_do$date >= "2018-06-17" & eos_do$date < "2018-07-17",]
a2<- eos_do[eos_do$date >= "2018-07-17" & eos_do$date < "2018-08-07",]
a3<- eos_do[eos_do$date >= "2018-08-07" & eos_do$date < "2018-09-11",]
a4<- eos_do[eos_do$date >= "2018-09-11" & eos_do$date < "2018-10-10",]
a5<- eos_do[eos_do$date >= "2018-10-10" & eos_do$date < "2018-12-05",]
a6<- eos_do[eos_do$date >= "2018-12-05" & eos_do$date < "2019-01-30",]
a7<- eos_do[eos_do$date >= "2019-01-30" & eos_do$date < "2019-02-20",]
a8<- eos_do[eos_do$date >= "2019-02-20" & eos_do$date < "2019-03-15",]
a9<- eos_do[eos_do$date >= "2019-03-15" & eos_do$date < "2019-04-11",]
a10<- eos_do[eos_do$date >= "2019-04-11" & eos_do$date < "2019-05-09",]
a11<- eos_do[eos_do$date >= "2019-05-09" & eos_do$date < "2019-06-09",]
a12<- eos_do[eos_do$date >= "2019-06-09" & eos_do$date < "2019-07-21",]
a13<- eos_do[eos_do$date >= "2019-07-21" & eos_do$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$do, na.rm=TRUE)
aa2<-mean(a2$do, na.rm=TRUE)
aa3<-mean(a3$do, na.rm=TRUE)
aa4<-mean(a4$do, na.rm=TRUE)
aa5<-mean(a5$do, na.rm=TRUE)
aa6<-mean(a6$do, na.rm=TRUE)
aa7<-mean(a7$do, na.rm=TRUE)
aa8<-mean(a8$do, na.rm=TRUE)
aa9<-mean(a9$do, na.rm=TRUE)
aa10<-mean(a10$do, na.rm=TRUE)
aa11<-mean(a11$do, na.rm=TRUE)
aa12<-mean(a12$do, na.rm=TRUE)
aa13<-mean(a13$do, na.rm=TRUE)
#string these values to a data frame
eos.mon.do<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.do<-as.data.frame(eos.mon.do)
#change column names
names(eos.mon.do)[1] <- "date"
names(eos.mon.do)[2] <- "dissolved.oxygen"
eos.mon.do$date<-as.Date(eos.mon.do$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.do[,c("date", "dissolved.oxygen")], by="date")Dissolved oxygen less than 6. None
eos_do.lt6<-filter(eos_do, do<6)
#subsetting date
a1<- eos_do.lt6[eos_do.lt6$date >= "2018-06-17" & eos_do.lt6$date < "2018-07-17",]
a2<- eos_do.lt6[eos_do.lt6$date >= "2018-07-17" & eos_do.lt6$date < "2018-08-07",]
a3<- eos_do.lt6[eos_do.lt6$date >= "2018-08-07" & eos_do.lt6$date < "2018-09-11",]
a4<- eos_do.lt6[eos_do.lt6$date >= "2018-09-11" & eos_do.lt6$date < "2018-10-10",]
a5<- eos_do.lt6[eos_do.lt6$date >= "2018-10-10" & eos_do.lt6$date < "2018-12-05",]
a6<- eos_do.lt6[eos_do.lt6$date >= "2018-12-05" & eos_do.lt6$date < "2019-01-30",]
a7<- eos_do.lt6[eos_do.lt6$date >= "2019-01-30" & eos_do.lt6$date < "2019-02-20",]
a8<- eos_do.lt6[eos_do.lt6$date >= "2019-02-20" & eos_do.lt6$date < "2019-03-15",]
a9<- eos_do.lt6[eos_do.lt6$date >= "2019-03-15" & eos_do.lt6$date < "2019-04-11",]
a10<- eos_do.lt6[eos_do.lt6$date >= "2019-04-11" & eos_do.lt6$date < "2019-05-09",]
a11<- eos_do.lt6[eos_do.lt6$date >= "2019-05-09" & eos_do.lt6$date < "2019-06-09",]
a12<- eos_do.lt6[eos_do.lt6$date >= "2019-06-09" & eos_do.lt6$date < "2019-07-21",]
a13<- eos_do.lt6[eos_do.lt6$date >= "2019-07-21" & eos_do.lt6$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$do.lt6, na.rm=TRUE)## Warning in mean.default(a1$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa2<-mean(a2$do.lt6, na.rm=TRUE)## Warning in mean.default(a2$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa3<-mean(a3$do.lt6, na.rm=TRUE)## Warning in mean.default(a3$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa4<-mean(a4$do.lt6, na.rm=TRUE)## Warning in mean.default(a4$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa5<-mean(a5$do.lt6, na.rm=TRUE)## Warning in mean.default(a5$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa6<-mean(a6$do.lt6, na.rm=TRUE)## Warning in mean.default(a6$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa7<-mean(a7$do.lt6, na.rm=TRUE)## Warning in mean.default(a7$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa8<-mean(a8$do.lt6, na.rm=TRUE)## Warning in mean.default(a8$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa9<-mean(a9$do.lt6, na.rm=TRUE)## Warning in mean.default(a9$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa10<-mean(a10$do.lt6, na.rm=TRUE)## Warning in mean.default(a10$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa11<-mean(a11$do.lt6, na.rm=TRUE)## Warning in mean.default(a11$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa12<-mean(a12$do.lt6, na.rm=TRUE)## Warning in mean.default(a12$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NAaa13<-mean(a13$do.lt6, na.rm=TRUE)## Warning in mean.default(a13$do.lt6, na.rm = TRUE): argument is not numeric or
## logical: returning NA#string these values to a data frame
eos.mon.do.lt6<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.do.lt6<-as.data.frame(eos.mon.do.lt6)
#change column names
names(eos.mon.do.lt6)[1] <- "date"
names(eos.mon.do.lt6)[2] <- "dissolved.oxygen.lt6"
eos.mon.do.lt6$date<-as.Date(eos.mon.do.lt6$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.do.lt6[,c("date", "dissolved.oxygen.lt6")], by="date")pH
eos_ph<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/eos_ph.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
a1<- eos_ph[eos_ph$date >= "2018-06-17" & eos_ph$date < "2018-07-17",]
a2<- eos_ph[eos_ph$date >= "2018-07-17" & eos_ph$date < "2018-08-07",]
a3<- eos_ph[eos_ph$date >= "2018-08-07" & eos_ph$date < "2018-09-11",]
a4<- eos_ph[eos_ph$date >= "2018-09-11" & eos_ph$date < "2018-10-10",]
a5<- eos_ph[eos_ph$date >= "2018-10-10" & eos_ph$date < "2018-12-05",]
a6<- eos_ph[eos_ph$date >= "2018-12-05" & eos_ph$date < "2019-01-30",]
a7<- eos_ph[eos_ph$date >= "2019-01-30" & eos_ph$date < "2019-02-20",]
a8<- eos_ph[eos_ph$date >= "2019-02-20" & eos_ph$date < "2019-03-15",]
a9<- eos_ph[eos_ph$date >= "2019-03-15" & eos_ph$date < "2019-04-11",]
a10<- eos_ph[eos_ph$date >= "2019-04-11" & eos_ph$date < "2019-05-09",]
a11<- eos_ph[eos_ph$date >= "2019-05-09" & eos_ph$date < "2019-06-09",]
a12<- eos_ph[eos_ph$date >= "2019-06-09" & eos_ph$date < "2019-07-21",]
a13<- eos_ph[eos_ph$date >= "2019-07-21" & eos_ph$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
#string these values to a data frame
eos.mon.ph<-list(c('2018-07-17', '2018-08-07', '2018-09-11', '2018-10-10', '2018-12-05', '2019-01-30', '2019-02-20', '2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.ph<-as.data.frame(eos.mon.ph)
#change column names
names(eos.mon.ph)[1] <- "date"
names(eos.mon.ph)[2] <- "ph"
eos.mon.ph$date<-as.Date(eos.mon.ph$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.ph[,c("date", "ph")], by="date")pH less than 6
eos_ph.lt6<-filter(eos_ph, ph>6)
a1<- eos_ph.lt6[eos_ph.lt6$date >= "2018-06-17" & eos_ph.lt6$date < "2018-07-17",]
a2<- eos_ph.lt6[eos_ph.lt6$date >= "2018-07-17" & eos_ph.lt6$date < "2018-08-07",]
a3<- eos_ph.lt6[eos_ph.lt6$date >= "2018-08-07" & eos_ph.lt6$date < "2018-09-11",]
a4<- eos_ph.lt6[eos_ph.lt6$date >= "2018-09-11" & eos_ph.lt6$date < "2018-10-10",]
a5<- eos_ph.lt6[eos_ph.lt6$date >= "2018-10-10" & eos_ph.lt6$date < "2018-12-05",]
a6<- eos_ph.lt6[eos_ph.lt6$date >= "2018-12-05" & eos_ph.lt6$date < "2019-01-30",]
a7<- eos_ph.lt6[eos_ph.lt6$date >= "2019-01-30" & eos_ph.lt6$date < "2019-02-20",]
a8<- eos_ph.lt6[eos_ph.lt6$date >= "2019-02-20" & eos_ph.lt6$date < "2019-03-15",]
a9<- eos_ph.lt6[eos_ph.lt6$date >= "2019-03-15" & eos_ph.lt6$date < "2019-04-11",]
a10<- eos_ph.lt6[eos_ph.lt6$date >= "2019-04-11" & eos_ph.lt6$date < "2019-05-09",]
a11<- eos_ph.lt6[eos_ph.lt6$date >= "2019-05-09" & eos_ph.lt6$date < "2019-06-09",]
a12<- eos_ph.lt6[eos_ph.lt6$date >= "2019-06-09" & eos_ph.lt6$date < "2019-07-21",]
a13<- eos_ph.lt6[eos_ph.lt6$date >= "2019-07-21" & eos_ph.lt6$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
#string these values to a data frame
eos.mon.ph.lt6<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.ph.lt6<-as.data.frame(eos.mon.ph.lt6)
#change column names
names(eos.mon.ph.lt6)[1] <- "date"
names(eos.mon.ph.lt6)[2] <- "ph.lt6"
eos.mon.ph.lt6$date<-as.Date(eos.mon.ph.lt6$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.ph.lt6[,c("date", "ph.lt6")], by="date")pH greater than 8. None
eos_ph.gt8<-filter(eos_ph, ph>8)
a1<- eos_ph.gt8[eos_ph.gt8$date >= "2018-06-17" & eos_ph.gt8$date < "2018-07-17",]
a2<- eos_ph.gt8[eos_ph.gt8$date >= "2018-07-17" & eos_ph.gt8$date < "2018-08-07",]
a3<- eos_ph.gt8[eos_ph.gt8$date >= "2018-08-07" & eos_ph.gt8$date < "2018-09-11",]
a4<- eos_ph.gt8[eos_ph.gt8$date >= "2018-09-11" & eos_ph.gt8$date < "2018-10-10",]
a5<- eos_ph.gt8[eos_ph.gt8$date >= "2018-10-10" & eos_ph.gt8$date < "2018-12-05",]
a6<- eos_ph.gt8[eos_ph.gt8$date >= "2018-12-05" & eos_ph.gt8$date < "2019-01-30",]
a7<- eos_ph.gt8[eos_ph.gt8$date >= "2019-01-30" & eos_ph.gt8$date < "2019-02-20",]
a8<- eos_ph.gt8[eos_ph.gt8$date >= "2019-02-20" & eos_ph.gt8$date < "2019-03-15",]
a9<- eos_ph.gt8[eos_ph.gt8$date >= "2019-03-15" & eos_ph.gt8$date < "2019-04-11",]
a10<- eos_ph.gt8[eos_ph.gt8$date >= "2019-04-11" & eos_ph.gt8$date < "2019-05-09",]
a11<- eos_ph.gt8[eos_ph.gt8$date >= "2019-05-09" & eos_ph.gt8$date < "2019-06-09",]
a12<- eos_ph.gt8[eos_ph.gt8$date >= "2019-06-09" & eos_ph.gt8$date < "2019-07-21",]
a13<- eos_ph.gt8[eos_ph.gt8$date >= "2019-07-21" & eos_ph.gt8$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
#string these values to a data frame
eos.mon.ph.gt8<-list(c('2018-07-17', '2018-08-07', '2018-09-11', '2018-10-10', '2018-12-05', '2019-01-30', '2019-02-20', '2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.ph.gt8<-as.data.frame(eos.mon.ph.gt8)
#change column names
names(eos.mon.ph.gt8)[1] <- "date"
names(eos.mon.ph.gt8)[2] <- "ph.gt8"
eos.mon.ph.gt8$date<-as.Date(eos.mon.ph.gt8$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.ph.gt8[,c("date", "ph.gt8")], by="date")Water temperature
eos_wt<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/eos_watertemp.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
eos_wt$date<-as.Date(eos_wt$date, format=c("%Y-%m-%d"))
names(eos_wt)[3] <- "water_temp"
a1<- eos_wt[eos_wt$date >= "2018-06-17" & eos_wt$date < "2018-07-17",]
a2<- eos_wt[eos_wt$date >= "2018-07-17" & eos_wt$date < "2018-08-07",]
a3<- eos_wt[eos_wt$date >= "2018-08-07" & eos_wt$date < "2018-09-11",]
a4<- eos_wt[eos_wt$date >= "2018-09-11" & eos_wt$date < "2018-10-10",]
a5<- eos_wt[eos_wt$date >= "2018-10-10" & eos_wt$date < "2018-12-05",]
a6<- eos_wt[eos_wt$date >= "2018-12-05" & eos_wt$date < "2019-01-30",]
a7<- eos_wt[eos_wt$date >= "2019-01-30" & eos_wt$date < "2019-02-20",]
a8<- eos_wt[eos_wt$date >= "2019-02-20" & eos_wt$date < "2019-03-15",]
a9<- eos_wt[eos_wt$date >= "2019-03-15" & eos_wt$date < "2019-04-11",]
a10<- eos_wt[eos_wt$date >= "2019-04-11" & eos_wt$date < "2019-05-09",]
a11<- eos_wt[eos_wt$date >= "2019-05-09" & eos_wt$date < "2019-06-09",]
a12<- eos_wt[eos_wt$date >= "2019-06-09" & eos_wt$date < "2019-07-21",]
a13<- eos_wt[eos_wt$date >= "2019-07-21" & eos_wt$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
#string these values to a data frame
eos.mon.wt<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
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")Water temperature greater than 22
eos_wt.gt22<-filter(eos_wt, water_temp>22)
a1<- eos_wt.gt22[eos_wt.gt22$date >= "2018-06-17" & eos_wt.gt22$date < "2018-07-17",]
a2<- eos_wt.gt22[eos_wt.gt22$date >= "2018-07-17" & eos_wt.gt22$date < "2018-08-07",]
a3<- eos_wt.gt22[eos_wt.gt22$date >= "2018-08-07" & eos_wt.gt22$date < "2018-09-11",]
a4<- eos_wt.gt22[eos_wt.gt22$date >= "2018-09-11" & eos_wt.gt22$date < "2018-10-10",]
a5<- eos_wt.gt22[eos_wt.gt22$date >= "2018-10-10" & eos_wt.gt22$date < "2018-12-05",]
a6<- eos_wt.gt22[eos_wt.gt22$date >= "2018-12-05" & eos_wt.gt22$date < "2019-01-30",]
a7<- eos_wt.gt22[eos_wt.gt22$date >= "2019-01-30" & eos_wt.gt22$date < "2019-02-20",]
a8<- eos_wt.gt22[eos_wt.gt22$date >= "2019-02-20" & eos_wt.gt22$date < "2019-03-15",]
a9<- eos_wt.gt22[eos_wt.gt22$date >= "2019-03-15" & eos_wt.gt22$date < "2019-04-11",]
a10<- eos_wt.gt22[eos_wt.gt22$date >= "2019-04-11" & eos_wt.gt22$date < "2019-05-09",]
a11<- eos_wt.gt22[eos_wt.gt22$date >= "2019-05-09" & eos_wt.gt22$date < "2019-06-09",]
a12<- eos_wt.gt22[eos_wt.gt22$date >= "2019-06-09" & eos_wt.gt22$date < "2019-07-21",]
a13<- eos_wt.gt22[eos_wt.gt22$date >= "2019-07-21" & eos_wt.gt22$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
#string these values to a data frame
eos.mon.wt.gt22<-list(c('2018-07-17', '2018-08-07', '2018-09-11', '2018-10-10', '2018-12-05', '2019-01-30', '2019-02-20', '2019-03-15', '2019-04-11', '2019-05-09', '2019-06-09', '2019-07-21', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13))
eos.mon.wt.gt22<-as.data.frame(eos.mon.wt.gt22)
#change column names
names(eos.mon.wt.gt22)[1] <- "date"
names(eos.mon.wt.gt22)[2] <- "water.temp.gt22"
eos.mon.wt.gt22$date<-as.Date(eos.mon.wt.gt22$date, format=c("%Y-%m-%d"))
#merge dfs
nd.eos<-merge(nd.eos, eos.mon.wt.gt22[,c("date", "water.temp.gt22")], by="date")Save csv
write.csv(nd.eos, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/nd.eos.csv")Monthly summaries of the field data
by<-subset(field, field$site.old == "BY")
#monthly mean of fucus density
by.rb<-aggregate(no.fuc.q ~ date, by, mean, na.rm=TRUE)
by.rb$date<-as.Date(by.rb$date, format=c("%m/%d/%Y"))
#mean percent cover
by.r<-aggregate(cover ~date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean of large fucus density
by.r<-aggregate(no.large.fuc.q ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean small fucus density
by.r<-aggregate(no.small.fuc.q ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#median reproductive cover class
by.r<-aggregate(covcl.repro ~ date, by, median, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean vegetative dry weight
by.r<-aggregate(dw.veg ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean reproductive dry weight
by.r<-aggregate(dw.repro ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean reproductive apices
by.r<-aggregate(apices.repro ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean vegetative apices
by.r<-aggregate(apices.veg ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean percent reproductive apices
by.r<-aggregate(perc.ra ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean oogonia per conceptacle
by.r<-aggregate(avg.oog ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
#mean percent reproductive dry weight
by.r<-aggregate(perc.rdw ~ date, by, mean, na.rm=TRUE)
by.r$date<-as.Date(by.r$date, format=c("%m/%d/%Y"))
by.rb<-merge(by.rb, by.r, by="date")
rm(by, by.r)Salinity
rb_sal<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/rb_sal.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
rb_sal$date<-as.Date(rb_sal$date, format=c("%Y-%m-%d"))
#looking at survey dates
print(by.rb$date)## [1] "2018-07-16" "2018-08-06" "2018-09-10" "2018-10-09" "2018-11-06"
## [6] "2018-12-04" "2019-01-31" "2019-02-21" "2019-03-14" "2019-04-09"
## [11] "2019-05-08" "2019-06-08" "2019-07-20" "2019-08-04"#subset the salinity data by dates
a1<- rb_sal[rb_sal$date >= "2018-06-16" & rb_sal$date < "2018-07-16",]
a2<- rb_sal[rb_sal$date >= "2018-07-16" & rb_sal$date < "2018-08-06",]
a3<- rb_sal[rb_sal$date >= "2018-08-06" & rb_sal$date < "2018-09-10",]
a4<- rb_sal[rb_sal$date >= "2018-09-10" & rb_sal$date < "2018-10-09",]
a5<- rb_sal[rb_sal$date >= "2018-10-09" & rb_sal$date < "2018-11-06",]
a6<- rb_sal[rb_sal$date >= "2018-11-06" & rb_sal$date < "2019-12-04",]
a7<- rb_sal[rb_sal$date >= "2018-12-04" & rb_sal$date < "2019-01-31",]
a8<- rb_sal[rb_sal$date >= "2019-01-31" & rb_sal$date < "2019-02-21",]
a9<- rb_sal[rb_sal$date >= "2019-02-21" & rb_sal$date < "2019-03-14",]
a10<- rb_sal[rb_sal$date >= "2019-03-14" & rb_sal$date < "2019-04-09",]
a11<- rb_sal[rb_sal$date >= "2019-04-09" & rb_sal$date < "2019-05-08",]
a12<- rb_sal[rb_sal$date >= "2019-05-08" & rb_sal$date < "2019-06-08",]
a13<- rb_sal[rb_sal$date >= "2019-06-08" & rb_sal$date < "2019-07-20",]
a14<- rb_sal[rb_sal$date >= "2019-07-20" & rb_sal$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
rb.mon.sal<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.sal<-as.data.frame(rb.mon.sal)
#change column names
names(rb.mon.sal)[1] <- "date"
names(rb.mon.sal)[2] <- "salinity"
rb.mon.sal$date<-as.Date(rb.mon.sal$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.sal[,c("date", "salinity")], by="date")Salinity less than 20
rb_sal.lt20<-filter(rb_sal, salinity<20)
#subset the salinity data by dates
a1<- rb_sal.lt20[rb_sal.lt20$date >= "2018-06-16" & rb_sal.lt20$date < "2018-07-16",]
a2<- rb_sal.lt20[rb_sal.lt20$date >= "2018-07-16" & rb_sal.lt20$date < "2018-08-06",]
a3<- rb_sal.lt20[rb_sal.lt20$date >= "2018-08-06" & rb_sal.lt20$date < "2018-09-10",]
a4<- rb_sal.lt20[rb_sal.lt20$date >= "2018-09-10" & rb_sal.lt20$date < "2018-10-09",]
a5<- rb_sal.lt20[rb_sal.lt20$date >= "2018-10-09" & rb_sal.lt20$date < "2018-11-06",]
a6<- rb_sal.lt20[rb_sal.lt20$date >= "2018-11-06" & rb_sal.lt20$date < "2019-12-04",]
a7<- rb_sal.lt20[rb_sal.lt20$date >= "2018-12-04" & rb_sal.lt20$date < "2019-01-31",]
a8<- rb_sal.lt20[rb_sal.lt20$date >= "2019-01-31" & rb_sal.lt20$date < "2019-02-21",]
a9<- rb_sal.lt20[rb_sal.lt20$date >= "2019-02-21" & rb_sal.lt20$date < "2019-03-14",]
a10<- rb_sal.lt20[rb_sal.lt20$date >= "2019-03-14" & rb_sal.lt20$date < "2019-04-09",]
a11<- rb_sal.lt20[rb_sal.lt20$date >= "2019-04-09" & rb_sal.lt20$date < "2019-05-08",]
a12<- rb_sal.lt20[rb_sal.lt20$date >= "2019-05-08" & rb_sal.lt20$date < "2019-06-08",]
a13<- rb_sal.lt20[rb_sal.lt20$date >= "2019-06-08" & rb_sal.lt20$date < "2019-07-20",]
a14<- rb_sal.lt20[rb_sal.lt20$date >= "2019-07-20" & rb_sal.lt20$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
rb.mon.sal.lt20<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21', '2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.sal.lt20<-as.data.frame(rb.mon.sal.lt20)
#change column names
names(rb.mon.sal.lt20)[1] <- "date"
names(rb.mon.sal.lt20)[2] <- "salinity.lt20"
rb.mon.sal.lt20$date<-as.Date(rb.mon.sal.lt20$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.sal.lt20[,c("date", "salinity.lt20")], by="date")Salinity less than 10
rb_sal.lt10<-filter(rb_sal, salinity<10)
#subset the salinity data by dates
a1<- rb_sal.lt10[rb_sal.lt10$date >= "2018-06-16" & rb_sal.lt10$date < "2018-07-16",]
a2<- rb_sal.lt10[rb_sal.lt10$date >= "2018-07-16" & rb_sal.lt10$date < "2018-08-06",]
a3<- rb_sal.lt10[rb_sal.lt10$date >= "2018-08-06" & rb_sal.lt10$date < "2018-09-10",]
a4<- rb_sal.lt10[rb_sal.lt10$date >= "2018-09-10" & rb_sal.lt10$date < "2018-10-09",]
a5<- rb_sal.lt10[rb_sal.lt10$date >= "2018-10-09" & rb_sal.lt10$date < "2018-11-06",]
a6<- rb_sal.lt10[rb_sal.lt10$date >= "2018-11-06" & rb_sal.lt10$date < "2019-12-04",]
a7<- rb_sal.lt10[rb_sal.lt10$date >= "2018-12-04" & rb_sal.lt10$date < "2019-01-31",]
a8<- rb_sal.lt10[rb_sal.lt10$date >= "2019-01-31" & rb_sal.lt10$date < "2019-02-21",]
a9<- rb_sal.lt10[rb_sal.lt10$date >= "2019-02-21" & rb_sal.lt10$date < "2019-03-14",]
a10<- rb_sal.lt10[rb_sal.lt10$date >= "2019-03-14" & rb_sal.lt10$date < "2019-04-09",]
a11<- rb_sal.lt10[rb_sal.lt10$date >= "2019-04-09" & rb_sal.lt10$date < "2019-05-08",]
a12<- rb_sal.lt10[rb_sal.lt10$date >= "2019-05-08" & rb_sal.lt10$date < "2019-06-08",]
a13<- rb_sal.lt10[rb_sal.lt10$date >= "2019-06-08" & rb_sal.lt10$date < "2019-07-20",]
a14<- rb_sal.lt10[rb_sal.lt10$date >= "2019-07-20" & rb_sal.lt10$date < "2019-08-04",]
#None during these periodsSince there was none <10 during survey dates I’m going to skip <5.
Dissolved oxygen
rb_do<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/rb_do.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
#subsetting date
a1<- rb_do[rb_do$date >= "2018-06-16" & rb_do$date < "2018-07-16",]
a2<- rb_do[rb_do$date >= "2018-07-16" & rb_do$date < "2018-08-06",]
a3<- rb_do[rb_do$date >= "2018-08-06" & rb_do$date < "2018-09-10",]
a4<- rb_do[rb_do$date >= "2018-09-10" & rb_do$date < "2018-10-09",]
a5<- rb_do[rb_do$date >= "2018-10-09" & rb_do$date < "2018-11-06",]
a6<- rb_do[rb_do$date >= "2018-11-06" & rb_do$date < "2019-12-04",]
a7<- rb_do[rb_do$date >= "2018-12-04" & rb_do$date < "2019-01-31",]
a8<- rb_do[rb_do$date >= "2019-01-31" & rb_do$date < "2019-02-21",]
a9<- rb_do[rb_do$date >= "2019-02-21" & rb_do$date < "2019-03-14",]
a10<- rb_do[rb_do$date >= "2019-03-14" & rb_do$date < "2019-04-09",]
a11<- rb_do[rb_do$date >= "2019-04-09" & rb_do$date < "2019-05-08",]
a12<- rb_do[rb_do$date >= "2019-05-08" & rb_do$date < "2019-06-08",]
a13<- rb_do[rb_do$date >= "2019-06-08" & rb_do$date < "2019-07-20",]
a14<- rb_do[rb_do$date >= "2019-07-20" & rb_do$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$do, na.rm=TRUE)
aa2<-mean(a2$do, na.rm=TRUE)
aa3<-mean(a3$do, na.rm=TRUE)
aa4<-mean(a4$do, na.rm=TRUE)
aa5<-mean(a5$do, na.rm=TRUE)
aa6<-mean(a6$do, na.rm=TRUE)
aa7<-mean(a7$do, na.rm=TRUE)
aa8<-mean(a8$do, na.rm=TRUE)
aa9<-mean(a9$do, na.rm=TRUE)
aa10<-mean(a10$do, na.rm=TRUE)
aa11<-mean(a11$do, na.rm=TRUE)
aa12<-mean(a12$do, na.rm=TRUE)
aa13<-mean(a13$do, na.rm=TRUE)
aa14<-mean(a14$do, na.rm=TRUE)
#string these values to a data frame
rb.mon.do<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.do<-as.data.frame(rb.mon.do)
#change column names
names(rb.mon.do)[1] <- "date"
names(rb.mon.do)[2] <- "dissolved.oxygen"
rb.mon.do$date<-as.Date(rb.mon.do$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.do[,c("date", "dissolved.oxygen")], by="date")Dissolved oxygen less than 6
rb_do.lt6<-filter(rb_do, do<6)
#subsetting date
a1<- rb_do.lt6[rb_do.lt6$date >= "2018-06-16" & rb_do.lt6$date < "2018-07-16",]
a2<- rb_do.lt6[rb_do.lt6$date >= "2018-07-16" & rb_do.lt6$date < "2018-08-06",]
a3<- rb_do.lt6[rb_do.lt6$date >= "2018-08-06" & rb_do.lt6$date < "2018-09-10",]
a4<- rb_do.lt6[rb_do.lt6$date >= "2018-09-10" & rb_do.lt6$date < "2018-10-09",]
a5<- rb_do.lt6[rb_do.lt6$date >= "2018-10-09" & rb_do.lt6$date < "2018-11-06",]
a6<- rb_do.lt6[rb_do.lt6$date >= "2018-11-06" & rb_do.lt6$date < "2019-12-04",]
a7<- rb_do.lt6[rb_do.lt6$date >= "2018-12-04" & rb_do.lt6$date < "2019-01-31",]
a8<- rb_do.lt6[rb_do.lt6$date >= "2019-01-31" & rb_do.lt6$date < "2019-02-21",]
a9<- rb_do.lt6[rb_do.lt6$date >= "2019-02-21" & rb_do.lt6$date < "2019-03-14",]
a10<- rb_do.lt6[rb_do.lt6$date >= "2019-03-14" & rb_do.lt6$date < "2019-04-09",]
a11<- rb_do.lt6[rb_do.lt6$date >= "2019-04-09" & rb_do.lt6$date < "2019-05-08",]
a12<- rb_do.lt6[rb_do.lt6$date >= "2019-05-08" & rb_do.lt6$date < "2019-06-08",]
a13<- rb_do.lt6[rb_do.lt6$date >= "2019-06-08" & rb_do.lt6$date < "2019-07-20",]
a14<- rb_do.lt6[rb_do.lt6$date >= "2019-07-20" & rb_do.lt6$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$do, na.rm=TRUE)
aa2<-mean(a2$do, na.rm=TRUE)
aa3<-mean(a3$do, na.rm=TRUE)
aa4<-mean(a4$do, na.rm=TRUE)
aa5<-mean(a5$do, na.rm=TRUE)
aa6<-mean(a6$do, na.rm=TRUE)
aa7<-mean(a7$do, na.rm=TRUE)
aa8<-mean(a8$do, na.rm=TRUE)
aa9<-mean(a9$do, na.rm=TRUE)
aa10<-mean(a10$do, na.rm=TRUE)
aa11<-mean(a11$do, na.rm=TRUE)
aa12<-mean(a12$do, na.rm=TRUE)
aa13<-mean(a13$do, na.rm=TRUE)
aa14<-mean(a14$do, na.rm=TRUE)
#string these values to a data frame
rb.mon.do.lt6<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.do.lt6<-as.data.frame(rb.mon.do.lt6)
#change column names
names(rb.mon.do.lt6)[1] <- "date"
names(rb.mon.do.lt6)[2] <- "dissolved.oxygen.lt6"
rb.mon.do.lt6$date<-as.Date(rb.mon.do.lt6$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.do.lt6[,c("date", "dissolved.oxygen.lt6")], by="date")pH
rb_ph<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/rb_ph.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
a1<- rb_ph[rb_ph$date >= "2018-06-16" & rb_ph$date < "2018-07-16",]
a2<- rb_ph[rb_ph$date >= "2018-07-16" & rb_ph$date < "2018-08-06",]
a3<- rb_ph[rb_ph$date >= "2018-08-06" & rb_ph$date < "2018-09-10",]
a4<- rb_ph[rb_ph$date >= "2018-09-10" & rb_ph$date < "2018-10-09",]
a5<- rb_ph[rb_ph$date >= "2018-10-09" & rb_ph$date < "2018-11-06",]
a6<- rb_ph[rb_ph$date >= "2018-11-06" & rb_ph$date < "2019-12-04",]
a7<- rb_ph[rb_ph$date >= "2018-12-04" & rb_ph$date < "2019-01-31",]
a8<- rb_ph[rb_ph$date >= "2019-01-31" & rb_ph$date < "2019-02-21",]
a9<- rb_ph[rb_ph$date >= "2019-02-21" & rb_ph$date < "2019-03-14",]
a10<- rb_ph[rb_ph$date >= "2019-03-14" & rb_ph$date < "2019-04-09",]
a11<- rb_ph[rb_ph$date >= "2019-04-09" & rb_ph$date < "2019-05-08",]
a12<- rb_ph[rb_ph$date >= "2019-05-08" & rb_ph$date < "2019-06-08",]
a13<- rb_ph[rb_ph$date >= "2019-06-08" & rb_ph$date < "2019-07-20",]
a14<- rb_ph[rb_ph$date >= "2019-07-20" & rb_ph$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
aa14<-mean(a14$ph, na.rm=TRUE)
#string these values to a data frame
rb.mon.ph<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.ph<-as.data.frame(rb.mon.ph)
#change column names
names(rb.mon.ph)[1] <- "date"
names(rb.mon.ph)[2] <- "ph"
rb.mon.ph$date<-as.Date(rb.mon.ph$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.ph[,c("date", "ph")], by="date")pH less than 6
rb_ph.lt6<-filter(rb_ph, ph<6)
#nonepH greater than 8
rb_ph.gt8<-filter(rb_ph, ph>8)
a1<- rb_ph.gt8[rb_ph.gt8$date >= "2018-06-16" & rb_ph.gt8$date < "2018-07-16",]
a2<- rb_ph.gt8[rb_ph.gt8$date >= "2018-07-16" & rb_ph.gt8$date < "2018-08-06",]
a3<- rb_ph.gt8[rb_ph.gt8$date >= "2018-08-06" & rb_ph.gt8$date < "2018-09-10",]
a4<- rb_ph.gt8[rb_ph.gt8$date >= "2018-09-10" & rb_ph.gt8$date < "2018-10-09",]
a5<- rb_ph.gt8[rb_ph.gt8$date >= "2018-10-09" & rb_ph.gt8$date < "2018-11-06",]
a6<- rb_ph.gt8[rb_ph.gt8$date >= "2018-11-06" & rb_ph.gt8$date < "2019-12-04",]
a7<- rb_ph.gt8[rb_ph.gt8$date >= "2018-12-04" & rb_ph.gt8$date < "2019-01-31",]
a8<- rb_ph.gt8[rb_ph.gt8$date >= "2019-01-31" & rb_ph.gt8$date < "2019-02-21",]
a9<- rb_ph.gt8[rb_ph.gt8$date >= "2019-02-21" & rb_ph.gt8$date < "2019-03-14",]
a10<- rb_ph.gt8[rb_ph.gt8$date >= "2019-03-14" & rb_ph.gt8$date < "2019-04-09",]
a11<- rb_ph.gt8[rb_ph.gt8$date >= "2019-04-09" & rb_ph.gt8$date < "2019-05-08",]
a12<- rb_ph.gt8[rb_ph.gt8$date >= "2019-05-08" & rb_ph.gt8$date < "2019-06-08",]
a13<- rb_ph.gt8[rb_ph.gt8$date >= "2019-06-08" & rb_ph.gt8$date < "2019-07-20",]
a14<- rb_ph.gt8[rb_ph.gt8$date >= "2019-07-20" & rb_ph.gt8$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$ph, na.rm=TRUE)
aa2<-mean(a2$ph, na.rm=TRUE)
aa3<-mean(a3$ph, na.rm=TRUE)
aa4<-mean(a4$ph, na.rm=TRUE)
aa5<-mean(a5$ph, na.rm=TRUE)
aa6<-mean(a6$ph, na.rm=TRUE)
aa7<-mean(a7$ph, na.rm=TRUE)
aa8<-mean(a8$ph, na.rm=TRUE)
aa9<-mean(a9$ph, na.rm=TRUE)
aa10<-mean(a10$ph, na.rm=TRUE)
aa11<-mean(a11$ph, na.rm=TRUE)
aa12<-mean(a12$ph, na.rm=TRUE)
aa13<-mean(a13$ph, na.rm=TRUE)
aa14<-mean(a14$ph, na.rm=TRUE)
#string these values to a data frame
rb.mon.ph.gt8<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.ph.gt8<-as.data.frame(rb.mon.ph.gt8)
#change column names
names(rb.mon.ph.gt8)[1] <- "date"
names(rb.mon.ph.gt8)[2] <- "ph.gt8"
rb.mon.ph.gt8$date<-as.Date(rb.mon.ph.gt8$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.ph.gt8[,c("date", "ph.gt8")], by="date")Water temperature
rb_wt<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/rb_watertemp.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
rb_wt$date<-as.Date(rb_wt$date, format=c("%Y-%m-%d"))
a1<- rb_wt[rb_wt$date >= "2018-06-16" & rb_wt$date < "2018-07-16",]
a2<- rb_wt[rb_wt$date >= "2018-07-16" & rb_wt$date < "2018-08-06",]
a3<- rb_wt[rb_wt$date >= "2018-08-06" & rb_wt$date < "2018-09-10",]
a4<- rb_wt[rb_wt$date >= "2018-09-10" & rb_wt$date < "2018-10-09",]
a5<- rb_wt[rb_wt$date >= "2018-10-09" & rb_wt$date < "2018-11-06",]
a6<- rb_wt[rb_wt$date >= "2018-11-06" & rb_wt$date < "2019-12-04",]
a7<- rb_wt[rb_wt$date >= "2018-12-04" & rb_wt$date < "2019-01-31",]
a8<- rb_wt[rb_wt$date >= "2019-01-31" & rb_wt$date < "2019-02-21",]
a9<- rb_wt[rb_wt$date >= "2019-02-21" & rb_wt$date < "2019-03-14",]
a10<- rb_wt[rb_wt$date >= "2019-03-14" & rb_wt$date < "2019-04-09",]
a11<- rb_wt[rb_wt$date >= "2019-04-09" & rb_wt$date < "2019-05-08",]
a12<- rb_wt[rb_wt$date >= "2019-05-08" & rb_wt$date < "2019-06-08",]
a13<- rb_wt[rb_wt$date >= "2019-06-08" & rb_wt$date < "2019-07-20",]
a14<- rb_wt[rb_wt$date >= "2019-07-20" & rb_wt$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
#string these values to a data frame
rb.mon.wt<-list(c('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'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
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")Water temperature greater than 22
rb_wt.gt22<-filter(rb_wt, water_temp>22)
a1<- rb_wt.gt22[rb_wt.gt22$date >= "2018-06-16" & rb_wt.gt22$date < "2018-07-16",]
a2<- rb_wt.gt22[rb_wt.gt22$date >= "2018-07-16" & rb_wt.gt22$date < "2018-08-06",]
a3<- rb_wt.gt22[rb_wt.gt22$date >= "2018-08-06" & rb_wt.gt22$date < "2018-09-10",]
a4<- rb_wt.gt22[rb_wt.gt22$date >= "2018-09-10" & rb_wt.gt22$date < "2018-10-09",]
a5<- rb_wt.gt22[rb_wt.gt22$date >= "2018-10-09" & rb_wt.gt22$date < "2018-11-06",]
a6<- rb_wt.gt22[rb_wt.gt22$date >= "2018-11-06" & rb_wt.gt22$date < "2019-12-04",]
a7<- rb_wt.gt22[rb_wt.gt22$date >= "2018-12-04" & rb_wt.gt22$date < "2019-01-31",]
a8<- rb_wt.gt22[rb_wt.gt22$date >= "2019-01-31" & rb_wt.gt22$date < "2019-02-21",]
a9<- rb_wt.gt22[rb_wt.gt22$date >= "2019-02-21" & rb_wt.gt22$date < "2019-03-14",]
a10<- rb_wt.gt22[rb_wt.gt22$date >= "2019-03-14" & rb_wt.gt22$date < "2019-04-09",]
a11<- rb_wt.gt22[rb_wt.gt22$date >= "2019-04-09" & rb_wt.gt22$date < "2019-05-08",]
a12<- rb_wt.gt22[rb_wt.gt22$date >= "2019-05-08" & rb_wt.gt22$date < "2019-06-08",]
a13<- rb_wt.gt22[rb_wt.gt22$date >= "2019-06-08" & rb_wt.gt22$date < "2019-07-20",]
a14<- rb_wt.gt22[rb_wt.gt22$date >= "2019-07-20" & rb_wt.gt22$date < "2019-08-04",]
#mean of these periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
#string these values to a data frame
rb.mon.wt.gt22<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
rb.mon.wt.gt22<-as.data.frame(rb.mon.wt.gt22)
#change column names
names(rb.mon.wt.gt22)[1] <- "date"
names(rb.mon.wt.gt22)[2] <- "water.temp.gt22"
rb.mon.wt.gt22$date<-as.Date(rb.mon.wt.gt22$date, format=c("%Y-%m-%d"))
#merge dfs
by.rb<-merge(by.rb, rb.mon.wt.gt22[,c("date", "water.temp.gt22")], by="date")Save csv
write.csv(by.rb, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/by.rb.csv")####Fort Point and Horseshoe Bay
Starting with montly summaries of the field data
hs<-subset(field, field$site.old == "HS")
#monthly mean of fucus density
hs.fp<-aggregate(no.fuc.q ~ date, hs, mean, na.rm=TRUE)
hs.fp$date<-as.Date(hs.fp$date, format=c("%m/%d/%Y"))
#mean percent cover
hs.r<-aggregate(cover ~date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean of large fucus density
hs.r<-aggregate(no.large.fuc.q ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean small fucus density
hs.r<-aggregate(no.small.fuc.q ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#median reproductive cover class
hs.r<-aggregate(covcl.repro ~ date, hs, median, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean vegetative dry weight
hs.r<-aggregate(dw.veg ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean reproductive dry weight
hs.r<-aggregate(dw.repro ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean reproductive apices
hs.r<-aggregate(apices.repro ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean vegetative apices
hs.r<-aggregate(apices.veg ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean percent reproductive apices
hs.r<-aggregate(perc.ra ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean oogonia per conceptacle
hs.r<-aggregate(avg.oog ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
#mean percent reproductive dry weight
hs.r<-aggregate(perc.rdw ~ date, hs, mean, na.rm=TRUE)
hs.r$date<-as.Date(hs.r$date, format=c("%m/%d/%Y"))
hs.fp<-merge(hs.fp, hs.r, by="date")
rm(hs, hs.r)Salinity
fp_sal<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/fp_sal.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
fp_sal$date<-as.Date(fp_sal$date, format=c("%Y-%m-%d"))
#looking at survey dates
print(hs.fp$date)## [1] "2018-07-16" "2018-08-06" "2018-09-10" "2018-10-09" "2018-11-06"
## [6] "2018-12-04" "2019-01-31" "2019-02-21" "2019-03-14" "2019-04-09"
## [11] "2019-05-08" "2019-06-08" "2019-07-20" "2019-08-04"#Subset the data by dates
a1<- fp_sal[fp_sal$date >= "2018-06-16" & fp_sal$date < "2018-07-16",]
a2<- fp_sal[fp_sal$date >= "2018-07-16" & fp_sal$date < "2018-08-06",]
a3<- fp_sal[fp_sal$date >= "2018-08-06" & fp_sal$date < "2018-09-10",]
a4<- fp_sal[fp_sal$date >= "2018-09-10" & fp_sal$date < "2018-10-09",]
a5<- fp_sal[fp_sal$date >= "2018-10-09" & fp_sal$date < "2018-11-06",]
a6<- fp_sal[fp_sal$date >= "2018-11-06" & fp_sal$date < "2019-12-04",]
a7<- fp_sal[fp_sal$date >= "2018-12-04" & fp_sal$date < "2019-01-31",]
a8<- fp_sal[fp_sal$date >= "2019-01-31" & fp_sal$date < "2019-02-21",]
a9<- fp_sal[fp_sal$date >= "2019-02-21" & fp_sal$date < "2019-03-14",]
a10<- fp_sal[fp_sal$date >= "2019-03-14" & fp_sal$date < "2019-04-09",]
a11<- fp_sal[fp_sal$date >= "2019-04-09" & fp_sal$date < "2019-05-08",]
a12<- fp_sal[fp_sal$date >= "2019-05-08" & fp_sal$date < "2019-06-08",]
a13<- fp_sal[fp_sal$date >= "2019-06-08" & fp_sal$date < "2019-07-20",]
a14<- fp_sal[fp_sal$date >= "2019-07-20" & fp_sal$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
fp.mon.sal<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
fp.mon.sal<-as.data.frame(fp.mon.sal)
#change column names
names(fp.mon.sal)[1] <- "date"
names(fp.mon.sal)[2] <- "salinity"
fp.mon.sal$date<-as.Date(fp.mon.sal$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.sal[,c("date", "salinity")], by="date")Salinity less than 20
fp_sal.lt20<-filter(fp_sal, salinity<20)
#Subset the data by dates
a1<- fp_sal.lt20[fp_sal.lt20$date >= "2018-06-16" & fp_sal.lt20$date < "2018-07-16",]
a2<- fp_sal.lt20[fp_sal.lt20$date >= "2018-07-16" & fp_sal.lt20$date < "2018-08-06",]
a3<- fp_sal.lt20[fp_sal.lt20$date >= "2018-08-06" & fp_sal.lt20$date < "2018-09-10",]
a4<- fp_sal.lt20[fp_sal.lt20$date >= "2018-09-10" & fp_sal.lt20$date < "2018-10-09",]
a5<- fp_sal.lt20[fp_sal.lt20$date >= "2018-10-09" & fp_sal.lt20$date < "2018-11-06",]
a6<- fp_sal.lt20[fp_sal.lt20$date >= "2018-11-06" & fp_sal.lt20$date < "2019-12-04",]
a7<- fp_sal.lt20[fp_sal.lt20$date >= "2018-12-04" & fp_sal.lt20$date < "2019-01-31",]
a8<- fp_sal.lt20[fp_sal.lt20$date >= "2019-01-31" & fp_sal.lt20$date < "2019-02-21",]
a9<- fp_sal.lt20[fp_sal.lt20$date >= "2019-02-21" & fp_sal.lt20$date < "2019-03-14",]
a10<- fp_sal.lt20[fp_sal.lt20$date >= "2019-03-14" & fp_sal.lt20$date < "2019-04-09",]
a11<- fp_sal.lt20[fp_sal.lt20$date >= "2019-04-09" & fp_sal.lt20$date < "2019-05-08",]
a12<- fp_sal.lt20[fp_sal.lt20$date >= "2019-05-08" & fp_sal.lt20$date < "2019-06-08",]
a13<- fp_sal.lt20[fp_sal.lt20$date >= "2019-06-08" & fp_sal.lt20$date < "2019-07-20",]
a14<- fp_sal.lt20[fp_sal.lt20$date >= "2019-07-20" & fp_sal.lt20$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
fp.mon.sal.lt20<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
fp.mon.sal.lt20<-as.data.frame(fp.mon.sal.lt20)
#change column names
names(fp.mon.sal.lt20)[1] <- "date"
names(fp.mon.sal.lt20)[2] <- "salinity.lt20"
fp.mon.sal.lt20$date<-as.Date(fp.mon.sal.lt20$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.sal.lt20[,c("date", "salinity.lt20")], by="date")Salinity less than 10
fp_sal.lt10<-filter(fp_sal, salinity<10)
#Subset the data by dates
a1<- fp_sal.lt10[fp_sal.lt10$date >= "2018-06-16" & fp_sal.lt10$date < "2018-07-16",]
a2<- fp_sal.lt10[fp_sal.lt10$date >= "2018-07-16" & fp_sal.lt10$date < "2018-08-06",]
a3<- fp_sal.lt10[fp_sal.lt10$date >= "2018-08-06" & fp_sal.lt10$date < "2018-09-10",]
a4<- fp_sal.lt10[fp_sal.lt10$date >= "2018-09-10" & fp_sal.lt10$date < "2018-10-09",]
a5<- fp_sal.lt10[fp_sal.lt10$date >= "2018-10-09" & fp_sal.lt10$date < "2018-11-06",]
a6<- fp_sal.lt10[fp_sal.lt10$date >= "2018-11-06" & fp_sal.lt10$date < "2019-12-04",]
a7<- fp_sal.lt10[fp_sal.lt10$date >= "2018-12-04" & fp_sal.lt10$date < "2019-01-31",]
a8<- fp_sal.lt10[fp_sal.lt10$date >= "2019-01-31" & fp_sal.lt10$date < "2019-02-21",]
a9<- fp_sal.lt10[fp_sal.lt10$date >= "2019-02-21" & fp_sal.lt10$date < "2019-03-14",]
a10<- fp_sal.lt10[fp_sal.lt10$date >= "2019-03-14" & fp_sal.lt10$date < "2019-04-09",]
a11<- fp_sal.lt10[fp_sal.lt10$date >= "2019-04-09" & fp_sal.lt10$date < "2019-05-08",]
a12<- fp_sal.lt10[fp_sal.lt10$date >= "2019-05-08" & fp_sal.lt10$date < "2019-06-08",]
a13<- fp_sal.lt10[fp_sal.lt10$date >= "2019-06-08" & fp_sal.lt10$date < "2019-07-20",]
a14<- fp_sal.lt10[fp_sal.lt10$date >= "2019-07-20" & fp_sal.lt10$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
fp.mon.sal.lt10<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
fp.mon.sal.lt10<-as.data.frame(fp.mon.sal.lt10)
#change column names
names(fp.mon.sal.lt10)[1] <- "date"
names(fp.mon.sal.lt10)[2] <- "salinity.lt10"
fp.mon.sal.lt10$date<-as.Date(fp.mon.sal.lt10$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.sal.lt10[,c("date", "salinity.lt10")], by="date")Salinity less than 5
fp_sal.lt5<-filter(fp_sal, salinity<5)
#Subset the data by dates
a1<- fp_sal.lt5[fp_sal.lt5$date >= "2018-06-16" & fp_sal.lt5$date < "2018-07-16",]
a2<- fp_sal.lt5[fp_sal.lt5$date >= "2018-07-16" & fp_sal.lt5$date < "2018-08-06",]
a3<- fp_sal.lt5[fp_sal.lt5$date >= "2018-08-06" & fp_sal.lt5$date < "2018-09-10",]
a4<- fp_sal.lt5[fp_sal.lt5$date >= "2018-09-10" & fp_sal.lt5$date < "2018-10-09",]
a5<- fp_sal.lt5[fp_sal.lt5$date >= "2018-10-09" & fp_sal.lt5$date < "2018-11-06",]
a6<- fp_sal.lt5[fp_sal.lt5$date >= "2018-11-06" & fp_sal.lt5$date < "2019-12-04",]
a7<- fp_sal.lt5[fp_sal.lt5$date >= "2018-12-04" & fp_sal.lt5$date < "2019-01-31",]
a8<- fp_sal.lt5[fp_sal.lt5$date >= "2019-01-31" & fp_sal.lt5$date < "2019-02-21",]
a9<- fp_sal.lt5[fp_sal.lt5$date >= "2019-02-21" & fp_sal.lt5$date < "2019-03-14",]
a10<- fp_sal.lt5[fp_sal.lt5$date >= "2019-03-14" & fp_sal.lt5$date < "2019-04-09",]
a11<- fp_sal.lt5[fp_sal.lt5$date >= "2019-04-09" & fp_sal.lt5$date < "2019-05-08",]
a12<- fp_sal.lt5[fp_sal.lt5$date >= "2019-05-08" & fp_sal.lt5$date < "2019-06-08",]
a13<- fp_sal.lt5[fp_sal.lt5$date >= "2019-06-08" & fp_sal.lt5$date < "2019-07-20",]
a14<- fp_sal.lt5[fp_sal.lt5$date >= "2019-07-20" & fp_sal.lt5$date < "2019-08-04",]
#mean of these salinity periods
aa1<-mean(a1$salinity, na.rm=TRUE)
aa2<-mean(a2$salinity, na.rm=TRUE)
aa3<-mean(a3$salinity, na.rm=TRUE)
aa4<-mean(a4$salinity, na.rm=TRUE)
aa5<-mean(a5$salinity, na.rm=TRUE)
aa6<-mean(a6$salinity, na.rm=TRUE)
aa7<-mean(a7$salinity, na.rm=TRUE)
aa8<-mean(a8$salinity, na.rm=TRUE)
aa9<-mean(a9$salinity, na.rm=TRUE)
aa10<-mean(a10$salinity, na.rm=TRUE)
aa11<-mean(a11$salinity, na.rm=TRUE)
aa12<-mean(a12$salinity, na.rm=TRUE)
aa13<-mean(a13$salinity, na.rm=TRUE)
aa14<-mean(a14$salinity, na.rm=TRUE)
#string these values to a data frame
fp.mon.sal.lt5<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
fp.mon.sal.lt5<-as.data.frame(fp.mon.sal.lt5)
#change column names
names(fp.mon.sal.lt5)[1] <- "date"
names(fp.mon.sal.lt5)[2] <- "salinity.lt5"
fp.mon.sal.lt5$date<-as.Date(fp.mon.sal.lt5$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.sal.lt5[,c("date", "salinity.lt5")], by="date")No dissolved oxygen or ph data for Fort Point.
Water temperature
fp_wt<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/fp_watertemp.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)
fp_wt$date<-as.Date(fp_wt$date, format=c("%Y-%m-%d"))
#Subset the data by dates
a1<- fp_wt[fp_wt$date >= "2018-06-16" & fp_wt$date < "2018-07-16",]
a2<- fp_wt[fp_wt$date >= "2018-07-16" & fp_wt$date < "2018-08-06",]
a3<- fp_wt[fp_wt$date >= "2018-08-06" & fp_wt$date < "2018-09-10",]
a4<- fp_wt[fp_wt$date >= "2018-09-10" & fp_wt$date < "2018-10-09",]
a5<- fp_wt[fp_wt$date >= "2018-10-09" & fp_wt$date < "2018-11-06",]
a6<- fp_wt[fp_wt$date >= "2018-11-06" & fp_wt$date < "2019-12-04",]
a7<- fp_wt[fp_wt$date >= "2018-12-04" & fp_wt$date < "2019-01-31",]
a8<- fp_wt[fp_wt$date >= "2019-01-31" & fp_wt$date < "2019-02-21",]
a9<- fp_wt[fp_wt$date >= "2019-02-21" & fp_wt$date < "2019-03-14",]
a10<- fp_wt[fp_wt$date >= "2019-03-14" & fp_wt$date < "2019-04-09",]
a11<- fp_wt[fp_wt$date >= "2019-04-09" & fp_wt$date < "2019-05-08",]
a12<- fp_wt[fp_wt$date >= "2019-05-08" & fp_wt$date < "2019-06-08",]
a13<- fp_wt[fp_wt$date >= "2019-06-08" & fp_wt$date < "2019-07-20",]
a14<- fp_wt[fp_wt$date >= "2019-07-20" & fp_wt$date < "2019-08-04",]
#mean of these water_temp periods
aa1<-mean(a1$water_temp, na.rm=TRUE)
aa2<-mean(a2$water_temp, na.rm=TRUE)
aa3<-mean(a3$water_temp, na.rm=TRUE)
aa4<-mean(a4$water_temp, na.rm=TRUE)
aa5<-mean(a5$water_temp, na.rm=TRUE)
aa6<-mean(a6$water_temp, na.rm=TRUE)
aa7<-mean(a7$water_temp, na.rm=TRUE)
aa8<-mean(a8$water_temp, na.rm=TRUE)
aa9<-mean(a9$water_temp, na.rm=TRUE)
aa10<-mean(a10$water_temp, na.rm=TRUE)
aa11<-mean(a11$water_temp, na.rm=TRUE)
aa12<-mean(a12$water_temp, na.rm=TRUE)
aa13<-mean(a13$water_temp, na.rm=TRUE)
aa14<-mean(a14$water_temp, na.rm=TRUE)
#string these values to a data frame
fp.mon.wt<-list(c('2018-07-16', '2018-08-06', '2018-09-10', '2018-10-09', '2018-11-06', '2018-12-04', '2019-01-31', '2019-02-21',
'2019-03-14', '2019-04-09', '2019-05-08', '2019-06-08', '2019-07-20', '2019-08-04'), c(aa1, aa2, aa3, aa4, aa5, aa6, aa7, aa8, aa9, aa10, aa11, aa12, aa13,aa14))
fp.mon.wt<-as.data.frame(fp.mon.wt)
#change column names
names(fp.mon.wt)[1] <- "date"
names(fp.mon.wt)[2] <- "water_temp"
fp.mon.wt$date<-as.Date(fp.mon.wt$date, format=c("%Y-%m-%d"))
#merge dfs
hs.fp<-merge(hs.fp, fp.mon.wt[,c("date", "water_temp")], by="date")Water temperature >22
fp_wt.gt22<-filter(fp_wt, water_temp>22)
#noneWrite csv
write.csv(hs.fp, "C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/hs.fp.csv")Looking at the final dataframes Paradise Cay and China Camp
print(pc.cc)## date no.fuc.q cover no.large.fuc.q no.small.fuc.q covcl.repro dw.veg
## 1 2018-07-17 90.2 29.9 3.2 87.0 1.0 1.439800
## 2 2018-08-07 163.5 53.4 15.4 148.1 1.0 2.175400
## 3 2018-09-11 120.8 58.2 4.3 116.5 1.0 3.309800
## 4 2018-12-05 35.1 55.0 13.5 21.6 3.0 3.300500
## 5 2019-01-30 25.2 49.3 8.8 16.4 3.0 3.284600
## 6 2019-02-20 28.4 49.1 15.0 13.4 2.5 2.307556
## 7 2019-03-15 21.7 38.2 13.2 8.5 2.0 3.232600
## 8 2019-04-11 19.4 37.2 11.4 8.0 2.0 3.619200
## 9 2019-05-09 12.3 40.5 8.9 3.4 5.0 4.522300
## 10 2019-06-09 6.1 25.7 2.9 3.2 5.0 4.325100
## 11 2019-07-21 30.6 9.8 1.7 28.9 1.0 1.265500
## 12 2019-08-04 17.3 7.7 4.4 12.9 1.0 1.010800
## dw.repro apices.repro apices.veg perc.ra avg.oog perc.rdw salinity
## 1 0.4870000 8.400000 24.900 9.677489 13.888889 8.027934 25.243002
## 2 0.3992000 7.800000 28.900 13.324573 7.777778 9.162042 26.272267
## 3 0.4294000 10.600000 47.100 18.573833 7.777778 11.125163 26.820053
## 4 1.2959000 45.200000 58.900 33.636471 42.222222 23.061467 26.448532
## 5 0.6915000 23.100000 56.400 19.856140 26.555556 12.042275 21.635561
## 6 0.1604444 8.222222 48.375 9.447116 35.666667 5.055350 13.603307
## 7 0.1099000 5.800000 75.300 5.439267 18.111111 2.280192 7.401316
## 8 0.1755000 6.800000 96.200 6.819327 14.666667 4.055909 NaN
## 9 1.4546000 57.500000 92.700 33.104160 0.000000 21.665297 10.906288
## 10 4.6780000 99.300000 57.600 59.168442 2.666667 47.385279 14.083926
## 11 0.5457000 8.200000 21.800 12.346474 3.777778 10.412522 19.212765
## 12 0.1000000 2.800000 19.200 4.743590 0.000000 2.409182 22.924359
## salinity.lt20 salinity.lt10 salinity.lt5 dissolved.oxygen
## 1 NaN NaN NaN 7.312386
## 2 NaN NaN NaN 7.619345
## 3 NaN NaN NaN 6.850087
## 4 NaN NaN NaN 7.483443
## 5 15.636061 9.950000 NaN 8.741842
## 6 12.875602 8.220476 4.010 9.357794
## 7 7.401316 6.322581 4.075 9.845810
## 8 NaN NaN NaN 9.293828
## 9 10.800543 8.211834 NaN 8.499625
## 10 13.739799 8.981159 NaN 7.981421
## 11 15.829290 9.225000 NaN 7.296673
## 12 NaN NaN NaN 6.896154
## dissolved.oxygen.lt6 ph ph.lt6 ph.gt8 water.temp water.temp.gt22
## 1 5.613889 7.755061 NaN 8.150000 19.97298 23.32012
## 2 5.740000 7.997277 NaN 8.238129 21.17093 23.00957
## 3 5.642708 7.867647 NaN 8.122549 19.55686 22.42333
## 4 5.262143 7.943074 NaN 8.130093 16.41865 NaN
## 5 NaN 7.797319 NaN NaN 11.24062 NaN
## 6 NaN 7.731982 NaN NaN 11.21163 NaN
## 7 NaN 7.846930 NaN NaN 11.17086 NaN
## 8 NaN 7.963867 NaN 8.152151 14.86948 NaN
## 9 NaN 7.965701 NaN 8.129130 17.34925 23.30556
## 10 2.700000 7.707294 NaN 8.200000 17.74049 23.52174
## 11 5.550000 7.677704 NaN NaN 20.17306 23.75616
## 12 5.750000 NaN NaN NaN 20.77500 22.86642Point Chauncy and EOS
print(nd.eos)## date no.fuc.q cover no.large.fuc.q no.small.fuc.q covcl.repro dw.veg
## 1 2018-07-17 95.0 41.0 12.4 82.6 3.0 4.7244
## 2 2018-08-07 114.9 40.7 22.9 92.0 3.0 2.3745
## 3 2018-09-11 70.2 36.7 5.2 65.0 2.0 1.3020
## 4 2018-10-10 39.6 36.4 3.0 36.6 1.0 2.4681
## 5 2018-12-05 18.9 27.0 5.2 13.7 2.5 1.2685
## 6 2019-01-30 21.6 28.3 12.1 9.5 3.0 1.3712
## 7 2019-02-20 18.9 28.9 2.7 16.2 2.0 2.5303
## 8 2019-03-15 16.9 28.3 4.6 12.3 3.0 2.2784
## 9 2019-04-11 23.0 22.1 7.9 15.1 3.0 2.0753
## 10 2019-05-09 38.7 35.7 8.9 29.8 2.0 2.1057
## 11 2019-06-09 19.3 21.2 5.2 14.1 3.0 2.6942
## 12 2019-07-21 20.8 15.0 2.6 18.2 3.0 1.6024
## 13 2019-08-04 19.0 17.4 2.7 16.3 2.0 1.8058
## dw.repro apices.repro apices.veg perc.ra avg.oog perc.rdw salinity
## 1 1.6321 45.4 79.5 40.11735 62.777778 29.543941 29.17588
## 2 1.0380 34.1 37.7 39.80308 14.444444 21.781957 29.92255
## 3 0.7757 13.3 22.8 25.86615 9.111111 22.433687 29.46621
## 4 2.8906 50.3 35.1 59.21805 50.777778 50.787790 29.89210
## 5 0.8268 26.6 21.8 43.32378 44.777778 32.418375 29.84593
## 6 0.5188 11.1 24.4 31.70662 25.222222 22.355161 23.90522
## 7 0.1787 5.8 46.9 16.64112 17.000000 4.758673 20.30732
## 8 0.5226 21.9 50.7 25.43207 17.000000 14.997014 11.70636
## 9 0.5320 12.4 42.8 16.78045 31.888889 14.768524 15.43527
## 10 1.2014 30.7 31.5 41.18681 3.444444 27.821887 18.62650
## 11 2.9833 64.6 31.3 59.72705 11.000000 45.581259 21.20664
## 12 2.0675 31.5 13.6 62.18129 25.333333 45.025064 25.22446
## 13 1.6752 31.2 16.6 62.94352 14.000000 43.661583 25.81161
## salinity.lt20 salinity.lt10 salinity.lt5 dissolved.oxygen
## 1 NaN NaN NaN 7.375743
## 2 NaN NaN NaN 7.158693
## 3 NaN NaN NaN 7.124946
## 4 NaN NaN NaN 7.103793
## 5 NaN NaN NaN 7.227150
## 6 14.683432 NaN NaN 7.198670
## 7 15.068021 8.308333 NaN 8.875000
## 8 9.405871 6.785515 4.255909 9.495736
## 9 12.438818 8.118447 4.015000 8.941466
## 10 14.280982 8.412375 NaN 8.336859
## 11 16.140194 9.540000 NaN 7.817849
## 12 17.059470 NaN NaN 7.435719
## 13 19.091667 NaN NaN 7.889420
## dissolved.oxygen.lt6 ph ph.lt6 ph.gt8 water.temp water.temp.gt22
## 1 NA 7.864582 7.864582 NaN 16.59540 NaN
## 2 NA 7.882556 7.882556 NaN 17.95631 NaN
## 3 NA 7.805481 7.805481 NaN 17.09885 NaN
## 4 NA 7.789097 7.789097 NaN 17.13193 NaN
## 5 NA 7.823692 7.823692 NaN 15.21397 NaN
## 6 NA 7.827860 7.827860 NaN 12.16397 NaN
## 7 NA 7.894970 7.894970 8.020000 11.81603 NaN
## 8 NA 7.935327 7.935327 8.030444 11.26282 NaN
## 9 NA 8.014051 8.014051 8.064614 13.80389 NaN
## 10 NA 8.036784 8.036784 8.072077 14.70829 NaN
## 11 NA 7.915725 7.915725 8.012273 15.49856 NaN
## 12 NA 7.728733 7.728733 8.143523 16.77642 NaN
## 13 NA 7.451456 7.451456 NaN 17.49810 NaNBrickyard Park and Richardson Bay
print(by.rb)## date no.fuc.q cover no.large.fuc.q no.small.fuc.q covcl.repro dw.veg
## 1 2018-07-16 128.2 52.7 10.9 117.3 2.0 2.5721
## 2 2018-08-06 119.9 38.4 12.9 107.0 4.0 3.4752
## 3 2018-09-10 102.4 52.4 4.8 97.6 1.0 4.2065
## 4 2018-10-09 49.9 47.5 8.9 41.0 1.0 9.2224
## 5 2018-11-06 33.2 61.5 10.2 23.0 2.0 7.3370
## 6 2018-12-04 26.4 53.5 8.1 18.3 2.0 3.6840
## 7 2019-01-31 19.6 42.3 4.0 15.6 2.0 2.8930
## 8 2019-02-21 17.4 39.1 7.2 10.2 2.0 3.3092
## 9 2019-03-14 18.1 44.1 10.3 7.8 2.0 4.0094
## 10 2019-04-09 17.0 49.1 7.9 9.1 2.0 5.9182
## 11 2019-05-08 44.2 43.2 6.2 38.0 2.5 5.2387
## 12 2019-06-08 10.5 23.9 3.4 7.1 2.5 7.6804
## 13 2019-07-20 45.2 6.8 0.7 44.5 1.0 1.9294
## 14 2019-08-04 60.0 8.1 0.6 59.4 1.0 1.2789
## dw.repro apices.repro apices.veg perc.ra avg.oog perc.rdw salinity
## 1 0.4581000 14.9 64.3 12.955427 4.666667 8.975621 31.23175
## 2 1.0841000 18.3 74.4 17.541093 8.444444 17.100184 31.17024
## 3 0.3563333 6.6 63.9 7.526545 11.222222 5.258867 31.92317
## 4 2.9664000 73.9 152.2 29.544340 23.222222 23.504467 31.69153
## 5 3.5210000 78.8 91.4 43.885052 25.777778 29.007451 31.67963
## 6 0.8919000 33.0 55.5 29.514662 47.000000 14.667327 25.89178
## 7 0.1291000 3.9 58.3 4.484666 55.111111 2.569262 28.77117
## 8 0.1633000 6.3 68.0 5.253587 28.111111 2.458637 22.87748
## 9 0.2845000 9.3 69.6 9.735095 29.222222 5.070444 16.85184
## 10 0.3883000 11.6 88.2 9.760565 23.333333 5.503680 18.50586
## 11 0.9579000 37.3 102.5 17.920402 1.777778 10.350939 20.97664
## 12 2.0539000 64.2 139.5 19.722508 9.111111 12.452450 24.44056
## 13 0.0880000 3.0 32.1 5.024392 3.333333 2.087947 26.76165
## 14 0.2807000 9.2 26.4 6.958359 12.833333 4.355524 29.90042
## salinity.lt20 dissolved.oxygen dissolved.oxygen.lt6 ph ph.gt8
## 1 NaN 7.450000 5.520000 7.786755 NaN
## 2 NaN 7.806840 NaN 7.933188 8.081818
## 3 NaN 7.590287 5.695455 7.885491 8.161321
## 4 NaN 7.731178 NaN 7.935821 8.090000
## 5 NaN 7.599777 NaN 7.924036 8.089286
## 6 17.56837 8.334902 5.238079 8.025594 8.221551
## 7 NaN 7.976488 NaN 7.873423 8.120000
## 8 18.81667 8.438406 NaN 7.937987 8.100000
## 9 16.75968 9.306367 NaN 7.998214 8.108654
## 10 17.94425 10.138141 NaN 8.195560 8.268895
## 11 18.29207 10.524569 5.017857 8.283513 8.360278
## 12 NaN 7.462366 5.446154 7.960261 8.167751
## 13 NaN 7.295314 5.100735 7.949878 8.186797
## 14 NaN 7.699409 5.700000 7.975410 8.097872
## water.temp water.temp.gt22
## 1 18.498462 22.681250
## 2 19.387824 22.166667
## 3 18.656157 22.125000
## 4 18.471480 NaN
## 5 16.986486 NaN
## 6 15.581987 22.608261
## 7 11.965666 NaN
## 8 11.796172 NaN
## 9 11.808124 NaN
## 10 14.557686 NaN
## 11 16.174891 NaN
## 12 17.078270 NaN
## 13 18.752193 22.536207
## 14 8.097872 8.097872Horseshoe Bay and Fort Point
print(hs.fp)## date no.fuc.q cover no.large.fuc.q no.small.fuc.q covcl.repro dw.veg
## 1 2018-07-16 18.7 15.7 4.0 14.7 3.0 1.839600
## 2 2018-08-06 25.5 22.9 11.2 14.3 4.0 2.123800
## 3 2018-09-10 15.6 25.0 6.8 8.8 1.0 1.912000
## 4 2018-10-09 26.4 37.0 5.2 21.2 1.5 3.930100
## 5 2018-11-06 12.2 23.9 5.3 6.9 2.0 3.227100
## 6 2018-12-04 7.2 15.9 2.9 4.3 1.5 1.469600
## 7 2019-01-31 23.5 35.7 10.2 13.3 2.5 3.450900
## 8 2019-02-21 19.2 24.7 4.4 14.8 2.0 2.911333
## 9 2019-03-14 20.2 38.5 7.9 12.3 2.0 3.429600
## 10 2019-04-09 25.6 37.5 8.9 16.7 2.5 3.438200
## 11 2019-05-08 19.1 44.0 12.5 6.6 5.0 4.265000
## 12 2019-06-08 13.1 25.0 7.7 5.4 5.0 3.884400
## 13 2019-07-20 12.1 24.5 6.7 5.4 5.0 5.783100
## 14 2019-08-04 13.9 22.3 3.6 10.3 2.5 2.493900
## dw.repro apices.repro apices.veg perc.ra avg.oog perc.rdw salinity
## 1 1.1900000 21.100000 20.30000 22.03452 30.33333 15.953240 31.05490
## 2 1.3822000 22.800000 20.20000 35.86905 16.11111 23.440101 NaN
## 3 0.4677500 17.600000 26.20000 22.03423 44.11111 13.304360 32.07884
## 4 2.3831000 32.300000 36.90000 43.30734 18.44444 33.692499 31.89282
## 5 2.3626000 38.900000 34.00000 40.30629 41.33333 30.187495 30.27281
## 6 0.6279000 9.700000 16.50000 31.94915 24.55556 23.695468 27.27799
## 7 0.4140000 14.700000 56.50000 15.44209 25.66667 8.250813 30.31083
## 8 0.1283333 3.666667 25.44444 11.16499 27.22222 4.199687 26.56695
## 9 0.3972000 11.900000 41.20000 19.82228 45.88889 10.629290 21.44457
## 10 1.8509000 39.300000 53.60000 35.39562 49.55556 27.207526 23.15754
## 11 4.9511000 122.600000 39.00000 70.96249 54.55556 49.586787 25.54409
## 12 4.6958000 98.500000 50.20000 64.90473 38.66667 52.439333 27.49928
## 13 2.2365000 50.700000 57.60000 37.46037 26.55556 22.522524 28.83675
## 14 1.3362000 32.400000 30.20000 38.41405 34.66667 25.723390 27.39783
## salinity.lt20 salinity.lt10 salinity.lt5 water_temp
## 1 0.4503333 0.4503333 0.4503333 14.96880
## 2 NaN NaN NaN NaN
## 3 NaN NaN NaN 16.08984
## 4 NaN NaN NaN 15.88770
## 5 NaN NaN NaN 15.75564
## 6 16.5819134 9.4005000 NaN 14.03912
## 7 NaN NaN NaN 12.66852
## 8 19.1119583 NaN NaN 12.29150
## 9 15.7903671 NaN NaN 11.87937
## 10 16.3141548 9.4005000 NaN 13.39822
## 11 18.4871500 NaN NaN 13.50294
## 12 19.1819500 NaN NaN 14.33122
## 13 NaN NaN NaN 15.03800
## 14 NaN NaN NaN 15.87216