Sarah Kezar Batch 02 Processing - PS2
A pipeline for evaluating the PS2 data from Sarah Kezar’s experiment This is a part of a series of experiments on Parthenium sp. from various locations.
PS2 data loading
OK - now let’s explore whats happening in the PS2 data!
Because the original output file is having some nonsense lines as we export it from the PS2 analyzer - we need to remove them:
PS2 <- read.csv("Exp_10190_Data_Analysis_MMJ.csv")
PS2For this specific experiment - in order to analyze the results per OVERALL canopy - let’s keep ONLY “all”
PS3 <- subset(PS2, PS2$Obj.No == "All")
PS3get only the columns that are informative biologically (for now):
colnames(PS3)## [1] "File" "Date" "Time" "Obj.No"
## [5] "nTmPam" "Obj.Size" "Obj.Xc" "Obj.Yc"
## [9] "Fv.Fm" "s.d." "Fq..Fm." "s.d..1"
## [13] "NPQ" "s.d..2" "F0." "s.d..3"
## [17] "qP" "s.d..4" "qN" "s.d..5"
## [21] "qL" "s.d..6" "qE" "s.d..7"
## [25] "qI" "s.d..8" "D.no" "s.d..9"
## [29] "D.npq" "s.d..10" "npq.t." "s.d..11"
## [33] "Red" "s.d..12" "Green" "s.d..13"
## [37] "Blue" "s.d..14" "Hue" "s.d..15"
## [41] "Saturation" "s.d..16" "Value" "s.d..17"
## [45] "SpcGrn" "s.d..18" "FarRed" "s.d..19"
## [49] "Nir" "s.d..20" "ChlIdx" "s.d..21"
## [53] "AriIdx" "s.d..22" "NDVI" "s.d..23"
## [57] "Border" "Mask.Border" "Points" "Area..CH."
## [61] "Mask.Area..CH." "X.Centre" "Y.Centre" "Radius"
## [65] "Area..MC." "Mask.Area..MC." "Width" "Height"
## [69] "Area..MR." "Mask.Area..MR." "Alpha" "Size..SK."
## [73] "Junction..SK." "Endpoint..SK." "Path..SK."PS4 <- PS3[,c(1:3, 6, 9, 11, 13, 17, 19, 21, 23, 25, 27, 29, 31, 51, 53, 55)]
PS4library(ggplot2)
library(ggpubr)
PS4$Date <- as.factor(PS4$Date)
lgraph <- ggplot(data=PS4, aes(x= Date, y=ChlIdx))
lgraph <- lgraph + geom_point(alpha = 0.7) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("days after stress")
lgraph
OK - so this dataset contains actually three experiments - batch 01 from
Sarah’s experiment from Dec. 2 to Dec. 16 - then Akhilesh’s dataset from
Dec. 18 to Jan. 02 - then batch 02 from Sarah’s experiment from Jan. 03
to Jan. 17th
We also have four trays that were used as “test” - let’s remove them too
Let’s isolate only the measurement from batch 01:
want <- c("20250103", "20250104", "20250105", "20250106", "20250107", "20250108", "20250109", "20250110", "20250111", "20250112", "20250113", "20250114", "20250116", "20250117")
PS4 <- subset(PS4, PS4$Date %in% want)
lgraph <- ggplot(data=PS4, aes(x= Date, y=ChlIdx))
lgraph <- lgraph + geom_point(alpha = 0.7) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("days after stress")
lgraph
There are still some odd measurements - but that’s probably because we
are including the “test” trays in there - which we can remove later.
This is great start, but lets isolate the measurement per tray ID - which is the third item in the File name (HDR_ExpID_TrayID_roundID.INF)
How do we get there?
strsplit(PS4$File[1], "_")[[1]][3]## [1] "39933"Now - lets isolate it for the entire data sheet:
for(i in 1:nrow(PS4)){
PS4$tray.ID[i] <- strsplit(PS4$File[i], "_")[[1]][3]
}
length(unique(PS4$tray.ID))## [1] 64unique(PS4$Date)## [1] 20250104 20250106 20250107 20250108 20250109 20250110 20250111 20250112
## [9] 20250113 20250114 20250116 20250117 20250105 20250103
## 47 Levels: 20241202 20241203 20241204 20241205 20241206 20241207 ... 20250117Let’s transfer date and time into TOE.
for(i in 1:nrow(PS4)){
PS4$month <- substr(PS4$Date, 5, 6)
PS4$day <- substr(PS4$Date, 7, 8)
PS4$hour <- substr(PS4$Time, 1, 2)
}
PS4Trays were loaded on Dec 2nd at around 10:00
PS4$TOE <- (as.numeric(as.character(PS4$day)) - 2)*24 + (as.numeric(as.character(PS4$hour)) - 10)
unique(PS4$TOE)## [1] 46 98 109 112 117 149 154 165 178 189 202 213 226 237 250 270 288 294 298
## [20] 334 361 96 111 269 360 97 47 99 100 295 67 101 110 118 150 155 166 179
## [39] 190 203 214 227 238 251 289 299 335 340 72 341 113 102 106 286 362 48 296
## [58] 287 29 119 263 342 30 267 151 290 156 167 180 191 204 215 239 300 228 268
## [77] 343 363 31 51 107 73 108 54 55 114 32 297PS4OK - then we have an issue with the FvFm being correct for one set of the data - and the other traits (FqFm and such) for other row. We need to correct it first:
# Get data containing right measurements into two separate datasets:
FvFm_data <- subset(PS4, PS4$Fv.Fm > 0)
notFvFm_data <- subset(PS4, PS4$Fv.Fm < 0)
# Get rid of collumns containing nonsense
FvFm_data <- FvFm_data[,c(1:5)]
notFvFm_data <- notFvFm_data[,c(1:4,6:23)]
FvFm_datanotFvFm_dataNow - let’s merge them together:
PS5 <- merge(FvFm_data, notFvFm_data, by =c("File", "Date", "Time", "Obj.Size"))
PS5Before moving forward - let’s decode the data:
decode <- read.csv("20250102_SarahK_Batch02.coding_MMJ.csv")
decodedecode <- decode[,c(7,2,5:6)]
colnames(decode)[1] <- "tray.ID"
decodeOK cool cool - now let’s fuse it with the PS2 data based on the tray.ID
unique(PS5$tray.ID)## [1] "39933" "39934" "39935" "39936" "39937" "39938" "39939" "39940" "39941"
## [10] "39942" "39943" "39944" "39945" "39946" "39947" "39948" "39949" "39950"
## [19] "39951" "39952" "39953" "39954" "39955" "39956" "39957" "39958" "39959"
## [28] "39960" "39961" "39962" "39963" "39964" "39965" "39966" "39967" "39968"
## [37] "39969" "39970" "39971" "39972" "39973" "39974" "39975" "39976" "39977"
## [46] "39978" "39979" "39980" "39981" "39982" "39983" "39984" "39985" "39986"
## [55] "39987" "39988" "39989" "39990" "39991" "39992"PS6 <- merge(PS5, decode, by= "tray.ID", all=T)
PS6Great! Now - lets remove all of the “Test” samples which are not part of this experiment:
PS6 <- subset(PS6, PS6$TrayInfo != "Test")Lets have a look at how the data looks like now:
lgraph <- ggplot(data=PS6, aes(x= TOE, y=Fv.Fm, group = tray.ID))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("Maximum Quantum Yield (Fv/Fm)") + xlab("Hours of Imaging")
lgraph
lets add more info to the graph:
FvFm_graph <- ggplot(data=PS6, aes(x= TOE, y=Fv.Fm, group = tray.ID, color = TrayInfo))
FvFm_graph <- FvFm_graph + geom_line(alpha = 0.7)
FvFm_graph <- FvFm_graph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= TrayInfo), alpha=0.3)
FvFm_graph <- FvFm_graph + stat_summary(fun=mean, aes(group= TrayInfo), size=0.7, geom="line", linetype = "dashed")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.FvFm_graph <- FvFm_graph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = TRUE)
FvFm_graph <- FvFm_graph + ylab("Fv/Fm (a.u.)") + xlab("Time of imaging (h)")
FvFm_graph## Warning: Computation failed in `stat_compare_means()`
## Caused by error in `mutate()`:
## ℹ In argument: `p = purrr::map(...)`.
## Caused by error in `purrr::map()`:
## ℹ In index: 28.
## ℹ With name: x.99.
## Caused by error in `t.test.default()`:
## ! not enough 'x' observations
As you can see - there are some lines that are not exactly the same timepoints - so we will get in trouble when calculating averages. Therefore - we need to do splines!
Splines for ALL PS2 traits
So - let’s first isolate one plant to establish our spline calculations
max(PS6$TOE)## [1] 363hours <- seq(0, 363, by = 12)
hours## [1] 0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216
## [20] 228 240 252 264 276 288 300 312 324 336 348 360length(hours)## [1] 31temp <- subset(PS6, PS6$tray.ID == unique(PS6$tray.ID)[1])
temp$TOE <- as.numeric(as.character(temp$TOE))
temp <- temp[order(temp$TOE, decreasing = F),]
temp$TOE## [1] 46 98 109 112 117 149 154 165 178 189 202 213 226 237 250 270 288 294 298
## [20] 334 361Fv/Fm
plot.spl <- with(temp, smooth.spline(TOE, Fv.Fm, df = 21))
plot(Fv.Fm ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
Fq/Fm
plot.spl <- with(temp, smooth.spline(TOE, Fq..Fm., df = 21))
plot(Fq..Fm. ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
NPQ
plot.spl <- with(temp, smooth.spline(TOE, NPQ, df = 21))
plot(NPQ ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
qP
plot.spl <- with(temp, smooth.spline(TOE, qP, df = 21))
plot(qP ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
qN
plot.spl <- with(temp, smooth.spline(TOE, qN, df = 21))
plot(qN ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
qL
plot.spl <- with(temp, smooth.spline(TOE, qL, df = 21))
plot(qL ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
qE
plot.spl <- with(temp, smooth.spline(TOE, qE, df = 21))
plot(qE ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
qI
plot.spl <- with(temp, smooth.spline(TOE, qI, df = 21))
plot(qI ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
фno
plot.spl <- with(temp, smooth.spline(TOE, D.no, df = 21))
plot(D.no ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
фnpq
plot.spl <- with(temp, smooth.spline(TOE, D.npq, df = 21))
plot(D.npq ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
npq(t)
plot.spl <- with(temp, smooth.spline(TOE, npq.t., df = 21))
plot(npq.t. ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
ChlIdx
plot.spl <- with(temp, smooth.spline(TOE, ChlIdx, df = 21))
plot(ChlIdx ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
AriIdx
plot.spl <- with(temp, smooth.spline(TOE, AriIdx, df = 21))
plot(AriIdx ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
NDVI
plot.spl <- with(temp, smooth.spline(TOE, NDVI, df = 21))
plot(NDVI ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")
Then - let’s save all of the information into one file
PS6# CHANGE last thing into trait name
names <- c(text = "tray.ID", "TOE", "Fv.Fm", "Fq.Fm", "NPQ", "qP", "qN", "qL", "qE", "qI", "phiNO", "phiNPQ", "npq.t", "ChlIdx", "AriIdx", "NDVI")
spline_data <- data.frame()
for (k in names) {
spline_data[[k]] <- as.character()}pred_temp <- predict(plot.spl, hours)
length(pred_temp$x)## [1] 31spline_data[1:31,1] <- temp$tray.ID[1]
spline_data[1:31,2] <- pred_temp$x
plot.spl <- with(temp, smooth.spline(TOE, Fv.Fm, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,3] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, Fq..Fm., df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,4] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, NPQ, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,5] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qP, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,6] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qN, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,7] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qL, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,8] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qE, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,9] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qI, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,10] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, D.no, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,11] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, D.npq, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,12] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, npq.t., df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,13] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, ChlIdx, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,14] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, AriIdx, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,15] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, NDVI, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,16] <- pred_temp$y
final_spline <- spline_data
final_splineall_plants <- unique(PS5$tray.ID)
all_plants## [1] "39933" "39934" "39935" "39936" "39937" "39938" "39939" "39940" "39941"
## [10] "39942" "39943" "39944" "39945" "39946" "39947" "39948" "39949" "39950"
## [19] "39951" "39952" "39953" "39954" "39955" "39956" "39957" "39958" "39959"
## [28] "39960" "39961" "39962" "39963" "39964" "39965" "39966" "39967" "39968"
## [37] "39969" "39970" "39971" "39972" "39973" "39974" "39975" "39976" "39977"
## [46] "39978" "39979" "39980" "39981" "39982" "39983" "39984" "39985" "39986"
## [55] "39987" "39988" "39989" "39990" "39991" "39992"for(i in 2:60){
temp <- subset(PS6, PS6$tray.ID == unique(PS6$tray.ID)[i])
if (dim(temp)[1] > 3) {
temp$TOE <- as.numeric(as.character(temp$TOE))
temp <- temp[order(temp$TOE, decreasing = F),]
spline_data[1:31,1] <- temp$tray.ID[1]
spline_data[1:31,2] <- hours
plot.spl <- with(temp, smooth.spline(TOE, Fv.Fm, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,3] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, Fq..Fm., df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,4] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, NPQ, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,5] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qP, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,6] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qN, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,7] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qL, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,8] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qE, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,9] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, qI, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,10] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, D.no, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,11] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, D.npq, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,12] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, npq.t., df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,13] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, ChlIdx, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,14] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, AriIdx, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,15] <- pred_temp$y
plot.spl <- with(temp, smooth.spline(TOE, NDVI, df = 21))
pred_temp <- predict(plot.spl, hours)
spline_data[1:31,16] <- pred_temp$y
final_spline <- rbind(final_spline, spline_data)
} else {
spline_data[1:31,1] <- temp$tray.ID[1]
spline_data[1:31,2] <- hours
spline_data[1:31,3] <- 0
spline_data[1:31,4] <- 0
spline_data[1:31,5] <- 0
spline_data[1:31,6] <- 0
spline_data[1:31,7] <- 0
spline_data[1:31,8] <- 0
spline_data[1:31,9] <- 0
spline_data[1:31,10] <- 0
spline_data[1:31,11] <- 0
spline_data[1:31,12] <- 0
spline_data[1:31,13] <- 0
spline_data[1:31,14] <- 0
spline_data[1:31,15] <- 0
spline_data[1:31,16] <- 0
final_spline <- rbind(final_spline, spline_data)
}}## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 18## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fv.Fm, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, Fq..Fm., df = 21): not using invalid df; must
## have 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NPQ, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qP, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qN, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qL, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qE, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, qI, df = 21): not using invalid df; must have 1 <
## df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.no, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, D.npq, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, npq.t., df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, ChlIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, AriIdx, df = 21): not using invalid df; must have
## 1 < df <= n := #{unique x} = 20## Warning in smooth.spline(TOE, NDVI, df = 21): not using invalid df; must have 1
## < df <= n := #{unique x} = 20final_splineLet’s change the collumns also to numeric - otherwise it will be tought to plot this:
final_spline2 <- final_spline
final_spline2[2:16] <- sapply(final_spline2[2:16],as.numeric)
final_spline2Decode
Now - let’s decode all of the information into individual experiments and TrayInfos:
final_spline3 <- merge(final_spline2, decode, by = "tray.ID")
final_spline3unique(final_spline3$TrayInfo)## [1] "Control" "Drought"unique(final_spline3$PlantID)## [1] "Tray01_Control_A1_Mexico" "Tray02_Control_A1_Pakistan"
## [3] "Tray03_Control_A1_Vietnam" "Tray04_Control_A1_Texas"
## [5] "Tray05_Control_A1_S.Africa" "Tray06_Control_A1_Australia"
## [7] "Tray07_Drought_A1_Texas" "Tray08_Drought_A1_Australia"
## [9] "Tray09_Control_A1_Vietnam" "Tray10_Control_A1_Mexico"
## [11] "Tray11_Drought_A1_S.Africa" "Tray12_Control_A1_Pakistan"
## [13] "Tray13_Drought_A1_Vietnam" "Tray14_Control_A1_Texas"
## [15] "Tray15_Drought_A1_Mexico" "Tray16_Control_A1_Vietnam"
## [17] "Tray17_Drought_A1_S.Africa" "Tray18_Drought_A1_Texas"
## [19] "Tray19_Drought_A1_Mexico" "Tray20_Drought_A1_Pakistan"
## [21] "Tray21_Drought_A1_Vietnam" "Tray22_Control_A1_S.Africa"
## [23] "Tray23_Control_A1_Pakistan" "Tray24_Drought_A1_Australia"
## [25] "Tray25_Control_A1_Texas" "Tray26_Control_A1_Mexico"
## [27] "Tray27_Drought_A1_S.Africa" "Tray28_Drought_A1_Australia"
## [29] "Tray29_Drought_A1_Mexico" "Tray30_Drought_A1_Pakistan"
## [31] "Tray31_Control_A1_Australia" "Tray32_Drought_A1_Australia"
## [33] "Tray33_Drought_A1_Vietnam" "Tray34_Drought_A1_Texas"
## [35] "Tray35_Control_A1_S.Africa" "Tray36_Control_A1_Mexico"
## [37] "Tray37_Control_A1_Australia" "Tray38_Drought_A1_Pakistan"
## [39] "Tray39_Drought_A1_S.Africa" "Tray40_Drought_A1_Vietnam"
## [41] "Tray41_Control_A1_S.Africa" "Tray42_Drought_A1_Mexico"
## [43] "Tray43_Drought_A1_Texas" "Tray44_Control_A1_Pakistan"
## [45] "Tray45_Drought_A1_S.Africa" "Tray46_Drought_A1_Pakistan"
## [47] "Tray47_Drought_A1_Australia" "Tray48_Drought_A1_Vietnam"
## [49] "Tray49_Control_A1_Texas" "Tray50_Control_A1_Vietnam"
## [51] "Tray51_Control_A1_Australia" "Tray52_Control_A1_Australia"
## [53] "Tray53_Drought_A1_Mexico" "Tray54_Control_A1_Texas"
## [55] "Tray55_Control_A1_Mexico" "Tray56_Drought_A1_Pakistan"
## [57] "Tray57_Control_A1_S.Africa" "Tray58_Control_A1_Vietnam"
## [59] "Tray59_Control_A1_Pakistan" "Tray60_Drought_A1_Texas"Also - since none of the recorded values are supposed to be negative - let’s clean it up:
#final_spline3 <- subset(final_spline3, final_spline3$Fv.Fm > 0)
#final_spline3 <- subset(final_spline3, final_spline3$Fq.Fm > 0)
#final_spline3 <- subset(final_spline3, final_spline3$NPQ > 0)
#final_spline3 <- subset(final_spline3, final_spline3$qP > 0)
#final_spline3 <- subset(final_spline3, final_spline3$qN > 0)
#final_spline3 <- subset(final_spline3, final_spline3$qL > 0)
#final_spline3 <- subset(final_spline3, final_spline3$qI > 0)
#final_spline3 <- subset(final_spline3, final_spline3$phiNO > 0)
#final_spline3 <- subset(final_spline3, final_spline3$phiNPQ > 0)
#final_spline3 <- subset(final_spline3, final_spline3$npq.t > 0)
#final_spline3 <- subset(final_spline3, final_spline3$ChlIdx > 0)
#final_spline3 <- subset(final_spline3, final_spline3$AriIdx > 0)
#final_spline3 <- subset(final_spline3, final_spline3$NDVI > 0)Plotting PS2 data
Fv/Fm
library(ggsci)
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = Fv.Fm, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "Fv/Fm (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Maximum Quantum Yield of Photosystem II")
C1_lgraph## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
final_spline3library("dplyr")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legendget_p_value <- function(final_spline3) {
t.test(Fv.Fm ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `p_value = get_p_value(cur_data())`.
## ℹ In group 1: `TOE = 0` and `PlantName = "Australia"`.
## Caused by warning:
## ! `cur_data()` was deprecated in dplyr 1.1.0.
## ℹ Please use `pick()` instead.## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_FvFm.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middledev.off()## quartz_off_screen
## 2Fq/Fm
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = Fq.Fm, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "Fq'/Fm'") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Quantum Yield of Photosystem II in light adapted state")
get_p_value <- function(final_spline3) {
t.test(Fq.Fm ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_FqFm.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middledev.off()## quartz_off_screen
## 2NPQ
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = NPQ, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "NPQ (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Non-photochemical Quenching [(Fm-Fm’)/Fm’]")
get_p_value <- function(final_spline3) {
t.test(NPQ ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_NPQ.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## 2qP
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = qP, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "qP (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Fraction of Open Reaction Centers (puddle mode) / photochemical quenching [(Fm’ – Fs’)/(Fm’ – F0’)]")
get_p_value <- function(final_spline3) {
t.test(qP ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_qP.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## 2qN
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = qN, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "qN (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Non-photochemical Quenching [1 – (Fm’ – F0’)/(Fm-F0)]")
get_p_value <- function(final_spline3) {
t.test(qN ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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pdf("Batch02_PS2_qN.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
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## 2qL
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = qL, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "qL (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Fraction of Open Reaction Centers (lake mode) / photochemical quenching (qP*F0’/Fs’)")
get_p_value <- function(final_spline3) {
t.test(qL ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
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## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_qL.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
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## Caused by error in `purrr::map()`:
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## Caused by error in `.check_npc_coord()`:
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## 2qE
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = qE, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "qE (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("fast relaxing component of NPQ (Fm*(Fm''-Fm’)/(Fm''*Fm’)")
get_p_value <- function(final_spline3) {
t.test(qE ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Removed 186 rows containing missing values (`geom_line()`).
pdf("Batch02_PS2_qE.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Removed 186 rows containing missing values (`geom_line()`).## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## (Fm*(Fm''-Fm’)/(Fm''*Fm’)' in 'mbcsToSbcs': dot substituted for <99>dev.off()## quartz_off_screen
## 2qI
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = qI, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "qI (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Slow relaxing component of NPQ [(Fm-Fm'')/Fm'']")
get_p_value <- function(final_spline3) {
t.test(qI ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_qI.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middledev.off()## quartz_off_screen
## 2PhiNO
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = phiNO, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "ɸNO (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Quantum Yield of non-regulated energy dissipation [1/(NPQ+1+qI*Fm/F0)]")
get_p_value <- function(final_spline3) {
t.test(phiNO ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_phiNO.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNO (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>dev.off()## quartz_off_screen
## 2PhiNPQ
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = phiNPQ, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "ɸNPQ (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Quantum Yield of NPQ (regulated energy dissipation) (1-Fq’/Fm’- ɸNO)")
get_p_value <- function(final_spline3) {
t.test(phiNPQ ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_phiNPQ.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNPQ (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## conversion failure on 'ɸNPQ (a.u.)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'ɸNPQ (a.u.)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <e2>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <80>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <99>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <e2>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <80>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <99>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <c9>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <b8>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
## (1-Fq’/Fm’- ɸNO)' in 'mbcsToSbcs': dot substituted for <e2>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on 'Quantum Yield of NPQ (regulated energy dissipation)
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## 2NPQ(t)
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = npq.t, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "NPQ(t) (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Fast NPQ 4.88/(Fm’/F0’-1)-1")
get_p_value <- function(final_spline3) {
# CHANGE
t.test(npq.t ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_NPQ(t).pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
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## 2ChlIdx
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = ChlIdx, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "ChlIdx (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Chlorophyll Index")
get_p_value <- function(final_spline3) {
t.test(ChlIdx ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_ChlIdx.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middledev.off()## quartz_off_screen
## 2AriIdx
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = AriIdx, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "AriIdx (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Anthocyanin Reflectance Index")
get_p_value <- function(final_spline3) {
t.test(AriIdx ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
# CHANGE
pdf("Batch02_PS2_AriIdx.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middledev.off()## quartz_off_screen
## 2NDVI
C1_lgraph <- ggplot(data = final_spline3, aes(x = TOE, y = NDVI, group = tray.ID, color = TrayInfo))
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ PlantName, ncol = 6)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
C1_lgraph <- C1_lgraph + labs(x = "", y = "NDVI (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Normalized Difference Vegetation Index")
get_p_value <- function(final_spline3) {
t.test(NDVI ~ TrayInfo, data = final_spline3)$p.value
}
p_values <- final_spline3 %>%
group_by(TOE, PlantName) %>%
summarise(p_value = get_p_value(cur_data())) %>%
ungroup()## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.p_values$LOD <- -log10(p_values$p_value)
C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD))
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ PlantName, ncol = 6)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)")
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank())
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middle
pdf("Batch02_PS2_NDVI.pdf", height = 5, width = 20)
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")## Warning: Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Computation failed in `stat_compare_means()`
## Caused by error in `purrr::map()`:
## ℹ In index: 1.
## Caused by error in `.check_npc_coord()`:
## ! '*.npc coord for x axis should be either a numeric value in [0-1] or a character strings including one of right, left, center, centre, middledev.off()## quartz_off_screen
## 2OK - that’s all - but just before we finish this - let’s save all of the data:
write.csv(final_spline3, "Batch.02.PS2.csv", row.names = F)