here we have 3 replicates for each accession and condition.

list.files(pattern = "csv")
## [1] "Root_Shoot_forR.csv"                  
## [2] "Root_Shoot_forR_0min_removed_forR.csv"
RS <- read.csv("Root_Shoot_forR_0min_removed_forR.csv")
RS
library(ggplot2)
library(ggpubr)

Crop_graph <- ggplot(data = RS, mapping = aes(x = treatment, y = TRE, colour = treatment)) 
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(Tissue + time ~ genotype)
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
#Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("TRE (a.u.)") + xlab("") + stat_compare_means(method="t.test", ref.group = "Mock", 
                                                              label = "p.signif", hide.ns = T, y.position = 80000)
## Warning in stat_compare_means(method = "t.test", ref.group = "Mock", label =
## "p.signif", : Ignoring unknown parameters: `y.position`
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph

Root <- subset(RS, RS$Tissue == "Root")
Shoot <- subset(RS, RS$Tissue == "Shoot")
my_comparisons <- list(c("Mock", "Salt"))


Crop_graph <- ggplot(data = Root, mapping = aes(x = treatment, y = TRE, colour = treatment)) 
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(genotype ~ time, scales = "free")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
#Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("TRE (a.u.)") + xlab("") + stat_compare_means(comparisons = my_comparisons)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
## Warning in wilcox.test.default(c(14, 5, 0), c(0, 2, 5), paired = FALSE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(41, 1, 0), c(922, 41, 119), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(4, 0, 0), c(7, 4, 0), paired = FALSE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(928, 0, 0), c(253, 400, 637), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(29, 42, 19), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(573, 285, 265), paired = FALSE):
## cannot compute exact p-value with ties

pdf("Mittlers_graph_Root.pdf", height = 10, width  = 5)
plot(Crop_graph)
## Warning in wilcox.test.default(c(14, 5, 0), c(0, 2, 5), paired = FALSE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(41, 1, 0), c(922, 41, 119), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(4, 0, 0), c(7, 4, 0), paired = FALSE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(928, 0, 0), c(253, 400, 637), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(29, 42, 19), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(573, 285, 265), paired = FALSE):
## cannot compute exact p-value with ties
dev.off()
## png 
##   2

OK let’s keep only genotypes we want to display in paper:

library(ggsci)
Root2 <- subset(Root, Root$genotype != "M058")
Crop_graph <- ggplot(data = Root2, mapping = aes(x = treatment, y = TRE, colour = treatment)) 
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(genotype ~ time, scales = "free")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","red"))
Crop_graph <- Crop_graph + ylab("TRE (a.u.)") + xlab("") + stat_compare_means(comparisons = my_comparisons)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend") #+ palette("jco")
Crop_graph
## Warning in wilcox.test.default(c(14, 5, 0), c(0, 2, 5), paired = FALSE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(41, 1, 0), c(922, 41, 119), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(29, 42, 19), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(573, 285, 265), paired = FALSE):
## cannot compute exact p-value with ties

pdf("Mittlers_graph_Root_publication.pdf", height = 7, width  = 5)
plot(Crop_graph)
## Warning in wilcox.test.default(c(14, 5, 0), c(0, 2, 5), paired = FALSE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(41, 1, 0), c(922, 41, 119), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(29, 42, 19), paired = FALSE):
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0), c(573, 285, 265), paired = FALSE):
## cannot compute exact p-value with ties
dev.off()
## png 
##   2
Shoot2 <- subset(Shoot, Shoot$genotype != "M058")
Crop_graph <- ggplot(data = Shoot2, mapping = aes(x = treatment, y = TRE, colour = treatment)) 
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(genotype ~ time, scales = "free")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","red"))
Crop_graph <- Crop_graph + ylab("TRE (a.u.)") + xlab("") + stat_compare_means(comparisons = my_comparisons)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend") #+ palette("jco")
Crop_graph

pdf("Mittlers_graph_Shoot_publication.pdf", height = 7, width  = 5)
plot(Crop_graph)
dev.off()
## png 
##   2

```