Following up on this question I posted some days ago, I want to perform something similar.
Given the following MWE:
##############################
##MWE
library(ggplot2)
library(ggsignif)
set.seed(1)
alpha.subA <- data.frame(Sample.ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000, 163, replace=FALSE), sep=''),
Group=c(rep('C',10),rep('FH',10),rep('I',19),rep('IF',42),rep('NA',14),rep('NF',42),rep('NI',15),rep('NS',10),rep('PGMC4',1)),
Value=rnorm(n=163))
alpha.subA$DB <- "DATABASE1"
set.seed(2)
alpha.subB <- data.frame(Sample.ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000, 163, replace=FALSE), sep=''),
Group=c(rep('C',10),rep('FH',10),rep('I',19),rep('IF',42),rep('NA',14),rep('NF',42),rep('NI',15),rep('NS',10),rep('PGMC4',1)),
Value=rnorm(n=163))
alpha.subB$DB <- "DATABASE2"
alpha.sub <- rbind(alpha.subA, alpha.subB)
alpha.sub$DB <- as.factor(alpha.sub$DB)
alpha.sub$both <- factor(paste(alpha.sub$Group, alpha.sub$DB), levels=paste(rep(levels(alpha.sub$Group), each=length(levels(alpha.sub$DB))), rep(levels(alpha.sub$DB), length(levels(alpha.sub$Group)))))
png(filename="test.png", height=1000, width=2000)
print(#or ggsave()
ggplot(alpha.sub, aes(x=both, y=Value, fill=Group)) + geom_boxplot() +
facet_grid(~Group, scales="free", space="free_x") +
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
# + geom_signif() ##HOW TO TEST EACH PAIR IN EACH FACET (DATABASE1 vs DATABASE2 PER GROUP)?
)
dev.off()
##############################
which produces:
I would like to make use of ggsignif's geom_signif() to compare DATABASE1 to DATABASE2 per Group (each pair in a facet) and show the significance level (* for p-value<0.05, ** for p-value<0.01) for each pairwise comparison.
Any help would be greatly appreciated. Thanks!!
EDIT!
I have managed to place the significance levels properly, but the moment I add facets, everything crumbles...
See my new MWE
##MWE
library(ggplot2)
library(ggsignif)
library(tidyverse)
library(broom)
set.seed(1)
alpha.subA <- data.frame(Sample.ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000, 163, replace=FALSE), sep=''),
Group=c(rep('C',10),rep('FH',10),rep('I',19),rep('IF',42),rep('NA',14),rep('NF',42),rep('NI',15),rep('NS',10),rep('PGMC4',1)),
Value=rnorm(n=163, mean=2.3, sd=0.45))
alpha.subA$DB <- "DATABASE1"
alpha.subB <- data.frame(Sample.ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000, 163, replace=FALSE), sep=''),
Group=c(rep('C',10),rep('FH',10),rep('I',19),rep('IF',42),rep('NA',14),rep('NF',42),rep('NI',15),rep('NS',10),rep('PGMC4',1)),
Value=rnorm(n=163, mean=2, sd=0.5))
alpha.subB$DB <- "DATABASE2"
alpha.sub <- rbind(alpha.subA, alpha.subB)
alpha.sub$DB <- as.factor(alpha.sub$DB)
alpha.sub$both <- factor(paste(alpha.sub$Group, alpha.sub$DB), levels=paste(rep(levels(alpha.sub$Group), each=length(levels(alpha.sub$DB))), rep(levels(alpha.sub$DB), length(levels(alpha.sub$Group)))))
v1 <- grep("DATABASE1", levels(alpha.sub$both), val=TRUE)[-9]
v2 <- grep("DATABASE2", levels(alpha.sub$both), val=TRUE)[-9]
CNb <- mapply(c, v1, v2, SIMPLIFY=FALSE)
CNb <- unname(CNb)
pv <- tidy(with(alpha.sub[ alpha.sub$Group != "PGMC4", ], pairwise.wilcox.test(Value, both, p.adjust.method = "BH")))#ADJUSTED PVALUES
# data preparation
CNb2 <- do.call(rbind.data.frame, CNb)
colnames(CNb2) <- colnames(pv)[-3]
# subset the pvalues, by merging the CN list
pv.final <- merge(CNb2, pv, by.x = c("group2", "group1"), by.y = c("group1", "group2"))
# fix ordering
pv.final <- pv.final[order(pv.final$group1), ]
# set signif level
pv.final$map.signif <- ifelse(pv.final$p.value > 0.05, "", ifelse(pv.final$p.value > 0.01,"*", "**"))
# subset
gr <- pv.final$p.value <= 0.05
CNb[gr]
# the plot
png(filename="test.png", height=1000, width=2000)
print(#or ggsave()
ggplot(alpha.sub, aes(x=both, y=Value, fill=Group)) + geom_boxplot() +
#facet_grid(~Group, scales="free", space="free_x") +
stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
geom_signif(comparisons=CNb[gr],
vjust=0.7,
annotation=pv.final$map.signif[gr],
textsize=10,
size=1)
)
dev.off()
which produces:
How can I reproduce this plot above, but with the facets in the first plot? Thanks!
I used the idea from here. In brief, plot the plot, get the underlying data, update the annotations and plot the final picture with facets.
# first save the plot in variable
myplot <- ggplot(alpha.sub, aes(x=DB, y=Value)) +
geom_boxplot(aes(fill=Group)) +
facet_grid(~Group) +
geom_signif(test="wilcox.test", comparisons = list(c("DATABASE1", "DATABASE2")), map_signif_level = F)
# a plot with all significance layer per facet group.
myplot
# build the plot, e.g. get a list of data frames
myplot2 <- ggplot_build(myplot)
# in list 2 you have access to each annotation
head(myplot2$data[[2]])
x xend y yend annotation group PANEL shape colour textsize angle hjust vjust alpha family
1 1 1 3.968526 4.063608 0.11 DATABASE1-DATABASE2-1 1 19 black 3.88 0 0.5 0 NA
2 1 2 4.063608 4.063608 0.11 DATABASE1-DATABASE2-1 1 19 black 3.88 0 0.5 0 NA
3 2 2 4.063608 3.968526 0.11 DATABASE1-DATABASE2-1 1 19 black 3.88 0 0.5 0 NA
4 1 1 3.968526 4.063608 0.035 DATABASE1-DATABASE2-1 2 19 black 3.88 0 0.5 0 NA
5 1 2 4.063608 4.063608 0.035 DATABASE1-DATABASE2-1 2 19 black 3.88 0 0.5 0 NA
6 2 2 4.063608 3.968526 0.035 DATABASE1-DATABASE2-1 2 19 black 3.88 0 0.5 0 NA
fontface lineheight linetype size
1 1 1.2 1 0.5
2 1 1.2 1 0.5
3 1 1.2 1 0.5
4 1 1.2 1 0.5
5 1 1.2 1 0.5
6 1 1.2 1 0.5
# now you can remove or update the annotation
# get all ADJUSTED PVALUES
pv <- tidy(with(alpha.sub, pairwise.wilcox.test(Value, interaction(Group,DB), p.adjust.method = "BH")))
# create final dataset using dplyr
pv_final <- pv %>%
separate(group1, c("g1_1", "g1_2")) %>%
separate(group2, c("g2_1", "g2_2")) %>%
filter(g1_1 == g2_1) %>%
mutate(p=ifelse(p.value > 0.05, "", ifelse(p.value > 0.01,"*", "**")))
# each pvalue is repeated three times in this dataset.
myplot2$data[[2]]$annotation <- rep(pv_final$p, each=3)
# remove non significants
myplot2$data[[2]] <- myplot2$data[[2]][myplot2$data[[2]]$annotation != "",]
# and the final plot
plot(ggplot_gtable(myplot2))
来源：https://stackoverflow.com/questions/45552715/r-ggplot2-perform-pairwise-tests-per-pair-in-a-facet-and-show-the-p-values-wit