Here I produce a 3D graph with oogonia, salinity and pH. For oogonia, I’ll take the average of oogonia released per thalli. For pH I’ll correct the pH Hach readings based on our discrete samples (explained below). For salinity I’ll use the average salinity in the units measured by Hach.

For pH I will correct the value of the unit Hach values to the discrete values using the regressions line I made here: https://cmwegener.github.io/thesis/hach_vs_discrete.html pH_Hach=1.8+0.78*pH_discrete
pH_discrete=(pH_Hach - 1.8)/.78

Convert unit_ph_hach to discrete pH using equation.

exp$unit_ph_discrete<-(exp$unit_ph_hach-1.8)/.78

Convert to number

exp$oo_disc_1<-as.numeric(exp$oo_disc_1)
exp$oo_disc_2<-as.numeric(exp$oo_disc_2)
exp$oo_disc_3<-as.numeric(exp$oo_disc_3)
exp$oo_disc_4<-as.numeric(exp$oo_disc_4)

exp$unit_sal_hach<-as.numeric(exp$unit_sal_hach)
## Warning: NAs introduced by coercion

Average oogonia per thalli (average per unit)

exp$avg_oo<-rowMeans(exp[,c('oo_disc_1', 'oo_disc_2', 'oo_disc_3','oo_disc_4')], na.rm=TRUE)

Let’s subset the data per run

r1<-subset(exp, run=='1')
r2<-subset(exp, run=='2')
r3<-subset(exp,run=='3')

Average unit pH over the course of each experiment (using converted values)

r1_av_ph<-aggregate( unit_ph_discrete ~ thalli,r1, mean)

r2_av_ph<-aggregate( unit_ph_discrete ~ thalli,r2, mean)

r3_av_ph<-aggregate( unit_ph_discrete ~ thalli,r3, mean)

Average unit salinity over the course of each experiment (using Hach readings)

r1_av_sal<-aggregate( unit_sal_hach ~ thalli,r1, mean)

r2_av_sal<-aggregate( unit_sal_hach ~ thalli,r2, mean)

r3_av_sal<-aggregate( unit_sal_hach ~ thalli,r3, mean)

Combine pH and salinity to make new data frame

r1_ph_sal<-cbind(r1_av_ph, r1_av_sal)
r2_ph_sal<-cbind(r2_av_ph, r2_av_sal)
r3_ph_sal<-cbind(r3_av_ph, r3_av_sal)

Add run number

r1_ph_sal$run<-"1"
r2_ph_sal$run<-"2"
r3_ph_sal$run<-"3"

Change column names. Not needed. Just helps me check track

names(r1_ph_sal)[names(r1_ph_sal) == "unit_sal_hach"] <- "sal_graph"
names(r2_ph_sal)[names(r2_ph_sal) == "unit_sal_hach"] <- "sal_graph"
names(r3_ph_sal)[names(r3_ph_sal) == "unit_sal_hach"] <- "sal_graph"

names(r1_ph_sal)[names(r1_ph_sal) == "unit_ph_discrete"] <- "ph_graph"
names(r2_ph_sal)[names(r2_ph_sal) == "unit_ph_discrete"] <- "ph_graph"
names(r3_ph_sal)[names(r3_ph_sal) == "unit_ph_discrete"] <- "ph_graph"

Add average oogoina data

r1_all<-merge(r1, r1_ph_sal[, c("thalli", "sal_graph", "ph_graph")], by="thalli")

r2_all<-merge(r2, r2_ph_sal[, c("thalli", "sal_graph", "ph_graph")], by="thalli")

r3_all<-merge(r3, r3_ph_sal[, c("thalli", "sal_graph", "ph_graph")], by="thalli")

final<-rbind(r1_all, r2_all, r3_all)

final<-final[!is.na(final$avg_oo), ]

3D plot

plot_ly(final, x = ~ph_graph, y = ~sal_graph, z = ~avg_oo, color = ~treatment, colors = c('red', 'blue', "goldenrod1", 'green')) %>%
  layout(title="Interactive effect of salinity and pH on oogonia released")%>%
  add_markers(size=2) %>% 
  layout(scene = list(xaxis = list(title = 'pH'),
                      yaxis = list(title = 'Salinity'),
                      zaxis = list(title = 'Oogoinia')))
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Not colorblind friendly.

To add a surface I need to make a matrix

rm(list=setdiff(ls(), "final"))
final <- subset(final, select = c(avg_oo, ph_graph, sal_graph))

matrix<-data.matrix(final)

Now I can plot

plot_ly(final, x = ~ph_graph, y = ~sal_graph, z = matrix) %>%
  layout(title="Interactive effect of salinity and pH on oogonia released")%>%
  add_surface() %>% 
  layout(scene = list(xaxis = list(title = 'pH'),
                      yaxis = list(title = 'Salinity'),
                      zaxis = list(title = 'Oogoinia')))