Custom annotations when using facets #23
Comments
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I found a solution to the problem by using a modified version of the StatSignif function from https://github.com/const-ae/ggsignif/blob/master/R/significance_annotation.R The input of the comparisons and annotations parameters of the stat_signif() function are the following: where l is the number of unique facets in the plot and comp.list is the required list of binary comparisons where annot.list has the same length as comparisons (l) and each element of the list is a character vector of p value annotations, the latter having the same legnth as the elements in comp.list Here is the modified version of the ggsignif StatSignif function: I hope this is of some help. best and thank you Michele |
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Could you provide an example just for the clarification?
On 3 Jul 2017 11:04 am, "MiKeLe" <notifications@github.com> wrote:
I found a solution to the problem by using a modified version of the
*StatSignif* function from https://github.com/const-ae/
ggsignif/blob/master/R/significance_annotation.R
The input of the *comparisons* and *annotations* parameters of the
*stat_signif()* function are the following:
comparisons = replicate(l, comp.list, FALSE)
where *l* is the number of unique facets in the plot and *comp.list* is the
required list of binary comparisons
annotations = annot.list
where annot.list has the same length as comparisons (*l*) and each element
of the list is a character vector of p value annotations, the latter having
the same legnth as the elements in *comp.list*
Here is the modified version of the ggsignif *StatSignif* function:
StatSignif <- ggplot2::ggproto("StatSignif", ggplot2::Stat,
required_aes = c("x", "y", "group"),
setup_params = function(data, params) {
# if(any(data$group == -1)||
any(data$group != data$x)){
if(any(data$group == -1)){
stop("Can only handle data with
groups that are plotted on the x-axis")
}
#
if (is.character(params$test))
params$test <- match.fun(params$test)
params$complete_data <- data
if(is.null(params$xmin) !=
is.null(params$xmax) || length(params$xmin) != length(params$xmax))
stop("If xmin or xmax is set, the
other one also needs to be set and they need to contain the same
number of values")
if(!is.null(params$xmin) &&
!is.null(params$comparisons))
stop("Set either the xmin, xmax
values or the comparisons")
if(!is.null(params$xmin) &&
is.null(params$y_position))
stop("If xmin, xmax are defined
also define y_position")
if(! is.null(params$y_position) &&
length(params$y_position) == 1)
params$y_position <-
rep(params$y_position, max(length(params$comparisons),
length(params$xmin)))
if(length(params$margin_top) == 1)
params$margin_top <- rep(params$margin_top,
max(length(params$comparisons),length(params$xmin)))
if(length(params$step_increase) == 1)
params$step_increase <- rep(params$step_increase,
max(length(params$comparisons),length(params$xmin)))
if(length(params$tip_length) == 1)
params$tip_length <- rep(params$tip_length,
max(length(params$comparisons),length(params$xmin)) * 2)
if(length(params$tip_length) ==
length(params$comparisons)) params$tip_length <-
rep(params$tip_length, each=2)
if(length(params$tip_length) ==
length(params$xmin)) params$tip_length <- rep(params$tip_length,
each=2)
if(! is.null(params$annotations) &&
length(params$annotations) == 1)
params$annotations <-
rep(params$annotations,
max(length(params$comparisons),length(params$xmin)))
if(! is.null(params$annotations) &&
length(params$annotations) !=
max(length(params$comparisons),length(params$xmin)))
stop(paste0("annotations contains a
different number of elements (", length(params$annotations),
") than comparisons or
xmin (", max(length(params$comparisons),length(params$xmin)), ")."))
if(all(params$map_signif_level == TRUE)){
params$map_signif_level <-
c("***"=0.001, "**"=0.01, "*"=0.05)
}else if(is.numeric(params$map_signif_level)){
if(is.null(names(params$map_signif_level)) ){
if(length(params$map_signif_level) <= 3){
names(params$map_signif_level)
<- tail(c("***", "**", "*"), n=length(params$map_signif_level))
}else{
stop('Cannot handle un-named
map for significance values, please provide in the following format:
c("***"=0.001, "**"=0.01, "*"=0.05)')
}
}
}
return(params)
},
compute_group = function(data, scales,
comparisons, test, test.args, complete_data,
annotations,
map_signif_level, y_position, xmax, xmin,
margin_top,
step_increase, tip_length) {
#
idx <- as.numeric(as.character(data$PANEL[1]))
if(! is.null(comparisons)){
#
comparisons2 <- comparisons[[idx]]
#
#- - - - - - - - - - #
# debug only
#
# print(str(comparisons2))
#- - - - - - - - - - #
#
i <- 0
#
result <- lapply(comparisons2,
function(comp){
#
i <<- i + 1
#
uno <- comp[[1]]
due <- comp[[2]]
#- - - - - - - - - - #
# debug only
#
# print(data$group[1])
# print(uno)
# print(due)
#- - - - - - - - - - #
#
# All entries in group should be the same
if(scales$x$map(uno) == data$group[1]){
#- - - - - - - - - - #
# debug only
#
# print("YES! Matched group mapping!")
#- - - - - - - - - - #
#
test_result <- if(is.null(annotations)){
#- - - - - - - - - - #
# debug only
#
# print("USING automatic annotations!")
#- - - - - - - - - - #
#
group_1 <-
complete_data$y[complete_data$x == scales$x$map(uno) &
complete_data$PANEL == data$PANEL[1]]
group_2 <-
complete_data$y[complete_data$x == scales$x$map(due) &
complete_data$PANEL == data$PANEL[1]]
p_value <- do.call(test,
c(list(group_1, group_2), test.args))$p.value
if(is.numeric(map_signif_level)){
temp_value <-
names(which.min(map_signif_level[which(map_signif_level > p_value)]))
if(is.null(temp_value)){
"NS."
}else{
temp_value
}
}else{
if(p_value < 2.2e-16){
"< 2.2e-16"
}else{
as.character(signif(p_value, digits=2))
}
}
}else{
#- - - - - - - - - - #
# debug only
#
# print("USING custom annotations!")
#- - - - - - - - - - #
#
valore <-
sapply(annotations[idx], "[[", i)
#- - - - - - - - - - #
# debug only
#
# print(valore)
#- - - - - - - - - - #
#
}
y_scale_range <-
(scales$y$range$range[2] - scales$y$range$range[1])
y_pos <- if(is.null(y_position)){
scales$y$range$range[2] +
y_scale_range * margin_top[i] + y_scale_range * step_increase[i] *
(i-1)
}else{
y_pos <- y_position[i]
}
data.frame(x=c(min(uno,due),
min(uno,due),
max(uno,due)),
xend=c(min(uno,due),
max(uno,due),
max(uno,due)),
y=c(y_pos -
y_scale_range*tip_length[(i-1)*2+1], y_pos, y_pos),
yend=c(y_pos, y_pos,
y_pos-y_scale_range*tip_length[(i-1)*2+2]),
annotation=test_result, group=paste(c(comp, i), collapse = "-"))
}
})
do.call(rbind, result)
}else{
if(data$x[1] ==
min(complete_data$x) & data$group[1] == min(complete_data$group)){
y_scale_range <-
(scales$y$range$range[2] - scales$y$range$range[1])
data.frame(x=c(xmin, xmin, xmax),
xend=c(xmin, xmax, xmax),
y=c(y_position -
y_scale_range*tip_length[seq_len(length(tip_length))%% 2 == 1],
y_position, y_position),
yend=c(y_position,
y_position, y_position-y_scale_range*tip_length[seq_len(length(tip_length))%%
2 == 0]),
annotation=rep(annotations, times=3), group=rep(seq_along(xmin),
times=3))
}
}
}
)
I hope this is of some help.
best and thank you
Michele
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Please try to run the file ggSignif_MS_code.R after loading the associated .RData Please note that the uploaded code is ready to produce a custom significance annotation plot and, in order for it to work, you SHOULD NOT LOAD the ggsignif library but rather source the stat_signif_modified.R (as specified in the script). Let me know if it works for you Best Michele |
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Hey Michele, sorry for my late response and thanks for your extensive description of the problem. #22 contains a discussion how to add labels that are specific for different facets, which might be useful for you. It would be really helpful to solve this issue if you could create a condensed version of the code (5-15 lines) using one of the pre-installed datasets (e.g. If you have any further questions, I will be happy to help you. Best, |
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Not a problem at all, thanks for your help anyways!
I agree with you, the nested list is not very intuitive since you should know ahead of doing the plot how many facets (n) you will get and make a list of exactly n elements.
I will try to build something reproducible and more “readable” with the mpg dataset and then get back to you again.
Thanks a lot for your support
Michele
Da: Constantin [mailto:notifications@github.com]
Inviato: venerdì 7 luglio 2017 00:58
A: const-ae/ggsignif <ggsignif@noreply.github.com>
Cc: Signore Michele <michele.signore@iss.it>; Author <author@noreply.github.com>
Oggetto: Re: [const-ae/ggsignif] Custom annotations when using facets (#23)
Hey Michele,
sorry for my late response and thanks for your extensive description of the problem.
I am able to reproduce the customly labeled facets with your code, but I am not sure that your suggested solution with a nested list for annot.list is the best way forward in general.
#22<#22> contains a discussion how to add labels that are specific for different facets, which might be useful for you.
It would be really helpful to solve this issue if you could create a condensed version of the code (5-15 lines) using one of the pre-installed datasets (e.g. mpg), because it takes quite a lot of effort to understand the ggSignif_MS_code.R example that you send .
If you have any further questions, I will be happy to help you.
Best,
Constantin
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Just created two minimal example script with both the mpg and the diamonds datasets. Different from the previously shared data and similar to many real-world examples, here the comparisons list is generated so as to account for the fact that grouping may differ in each different facet (see the mpg example). These examples work ONLY with the custom/modified functions and DO NOT work with the regular ggsignif package, at least in my hands. Again, I hope this could be of help Best Michele |
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Hi Michele, I have just pushed an update for the package to github that makes it possible to configure the Here is a simplified example of the code you send me with the respective output: library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag(): dplyr, stats
library(ggsignif)
diamonds
#> # A tibble: 53,940 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#> 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
#> 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
#> # ... with 53,930 more rows
annotation_df <- cross_d(list(clarity = unique(diamonds$clarity), cut = unique(diamonds$cut))) %>%
mutate(xmin = factor("Fair", levels = levels(cut)),
annotations = apply(combn(letters, 2), 2, paste0, collapse = "")[1:n()]) %>%
filter(cut != xmin) %>%
group_by(clarity) %>%
arrange(cut) %>%
mutate(y_position = 8000 + 2000 * row_number())
annotation_df
#> Source: local data frame [32 x 5]
#> Groups: clarity [8]
#>
#> # A tibble: 32 x 5
#> clarity cut xmin annotations y_position
#> <fctr> <fctr> <fctr> <chr> <dbl>
#> 1 SI2 Good Fair ar 10000
#> 2 SI1 Good Fair as 10000
#> 3 VS1 Good Fair at 10000
#> 4 VS2 Good Fair au 10000
#> 5 VVS2 Good Fair av 10000
#> 6 VVS1 Good Fair aw 10000
#> 7 I1 Good Fair ax 10000
#> 8 IF Good Fair ay 10000
#> 9 SI2 Very Good Fair az 12000
#> 10 SI1 Very Good Fair bc 12000
#> # ... with 22 more rows
highsd = function(x) {
return(mean(x, na.rm = T) + sd(x))
}
ggplot(data = diamonds, aes(x = cut, y = price)) +
stat_summary(fun.y = mean, fun.ymin = mean, fun.ymax = highsd,
width = 0.8, geom = "errorbar",
position = position_dodge(width = 0.9), colour = "black", size = 0.3) +
stat_summary(geom = "bar", fun.y = mean, aes(fill = cut),
width = 0.8, colour = "black",
position = position_dodge(width = 0.9), size = 0.4, show.legend = F) +
geom_signif(data = annotation_df,
aes(annotations = annotations, xmin = xmin, xmax = cut, y_position = y_position),
manual = TRUE) +
facet_wrap(~clarity, scales = "free") +
ylim(NA, 17000)
#> Warning: Ignoring unknown aesthetics: annotations, xmin, xmax, y_position
#> Warning: Removed 717 rows containing non-finite values (stat_summary).
#> Warning: Removed 717 rows containing non-finite values (stat_summary).You should easily be able to change the output by modifying the Best, |
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Thanks so much!!! I will try it as soon as I can.
Best
MICHELE
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Istituto Superiore di Sanità
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00161, Rome, Italy
Tel. +390649904453
Fax. +390649904461
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On Sat, Jul 15, 2017 at 2:10 PM +0200, "Constantin" <notifications@github.com<mailto:notifications@github.com>> wrote:
Hi Michele,
I have just pushed an update for the package to github that makes it possible to configure the geom_signif with a data.frame. The data frame has to contain the custom annotations and the start (xmin) and end (xmax) of the comparisons. In addition the data.frame can contain columns for the facet.
Here is a simplified example of the code you send me with the respective output:
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag(): dplyr, stats
library(ggsignif)
diamonds
#> # A tibble: 53,940 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#> 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
#> 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
#> # ... with 53,930 more rows
annotation_df <- cross_d(list(clarity = unique(diamonds$clarity), cut = unique(diamonds$cut))) %>%
mutate(xmin = factor("Fair", levels = levels(cut)),
annotations = apply(combn(letters, 2), 2, paste0, collapse = "")[1:n()]) %>%
filter(cut != xmin) %>%
group_by(clarity) %>%
arrange(cut) %>%
mutate(y_position = 8000 + 2000 * row_number())
annotation_df
#> Source: local data frame [32 x 5]
#> Groups: clarity [8]
#>
#> # A tibble: 32 x 5
#> clarity cut xmin annotations y_position
#> <fctr> <fctr> <fctr> <chr> <dbl>
#> 1 SI2 Good Fair ar 10000
#> 2 SI1 Good Fair as 10000
#> 3 VS1 Good Fair at 10000
#> 4 VS2 Good Fair au 10000
#> 5 VVS2 Good Fair av 10000
#> 6 VVS1 Good Fair aw 10000
#> 7 I1 Good Fair ax 10000
#> 8 IF Good Fair ay 10000
#> 9 SI2 Very Good Fair az 12000
#> 10 SI1 Very Good Fair bc 12000
#> # ... with 22 more rows
highsd = function(x) {
return(mean(x, na.rm = T) + sd(x))
}
ggplot(data = diamonds, aes(x = cut, y = price)) +
stat_summary(fun.y = mean, fun.ymin = mean, fun.ymax = highsd,
width = 0.8, geom = "errorbar",
position = position_dodge(width = 0.9), colour = "black", size = 0.3) +
stat_summary(geom = "bar", fun.y = mean, aes(fill = cut),
width = 0.8, colour = "black",
position = position_dodge(width = 0.9), size = 0.4, show.legend = F) +
geom_signif(data = annotation_df,
aes(annotations = annotations, xmin = xmin, xmax = cut, y_position = y_position),
manual = TRUE) +
facet_wrap(~clarity, scales = "free") +
ylim(NA, 17000)
#> Warning: Ignoring unknown aesthetics: annotations, xmin, xmax, y_position
#> Warning: Removed 717 rows containing non-finite values (stat_summary).
#> Warning: Removed 717 rows containing non-finite values (stat_summary).
[https://camo.githubusercontent.com/5a7a6ef9c53a5e87acd048cd2e3d248a997fe642/687474703a2f2f692e696d6775722e636f6d2f74647a686668542e706e67]<https://camo.githubusercontent.com/5a7a6ef9c53a5e87acd048cd2e3d248a997fe642/687474703a2f2f692e696d6775722e636f6d2f74647a686668542e706e67>
You should easily be able to change the output by modifying the anntation_df.
Best,
Constantin
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Dear Constantin,
thanks so much for your effort. I have re-installed ggsignif from GitHub using devtools to get the latest development version and I was able to generate a working ‘annotation_df’ data frame to be used via the geom_signif layer.
It seems to work nicely (I checked the correspondence of the p values) but:
-I need to manually adjust the height of the plot since I used the numbers in your suggested code example and my bars are all pushed down.
…-Although it works I get this error message: “Warning: Ignoring unknown aesthetics: annotations, xmin, xmax, tip_length, step_increase, margin_top, y_position”. Shall I ignore it?
Thanks a lot again
Best
Michele
Da: Constantin [mailto:notifications@github.com]
Inviato: sabato 15 luglio 2017 14:10
A: const-ae/ggsignif <ggsignif@noreply.github.com>
Cc: Signore Michele <michele.signore@iss.it>; Author <author@noreply.github.com>
Oggetto: Re: [const-ae/ggsignif] Custom annotations when using facets (#23)
Hi Michele,
I have just pushed an update for the package to github that makes it possible to configure the geom_signif with a data.frame. The data frame has to contain the custom annotations and the start (xmin) and end (xmax) of the comparisons. In addition the data.frame can contain columns for the facet.
Here is a simplified example of the code you send me with the respective output:
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag(): dplyr, stats
library(ggsignif)
diamonds
#> # A tibble: 53,940 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#> 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
#> 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
#> # ... with 53,930 more rows
annotation_df <- cross_d(list(clarity = unique(diamonds$clarity), cut = unique(diamonds$cut))) %>%
mutate(xmin = factor("Fair", levels = levels(cut)),
annotations = apply(combn(letters, 2), 2, paste0, collapse = "")[1:n()]) %>%
filter(cut != xmin) %>%
group_by(clarity) %>%
arrange(cut) %>%
mutate(y_position = 8000 + 2000 * row_number())
annotation_df
#> Source: local data frame [32 x 5]
#> Groups: clarity [8]
#>
#> # A tibble: 32 x 5
#> clarity cut xmin annotations y_position
#> <fctr> <fctr> <fctr> <chr> <dbl>
#> 1 SI2 Good Fair ar 10000
#> 2 SI1 Good Fair as 10000
#> 3 VS1 Good Fair at 10000
#> 4 VS2 Good Fair au 10000
#> 5 VVS2 Good Fair av 10000
#> 6 VVS1 Good Fair aw 10000
#> 7 I1 Good Fair ax 10000
#> 8 IF Good Fair ay 10000
#> 9 SI2 Very Good Fair az 12000
#> 10 SI1 Very Good Fair bc 12000
#> # ... with 22 more rows
highsd = function(x) {
return(mean(x, na.rm = T) + sd(x))
}
ggplot(data = diamonds, aes(x = cut, y = price)) +
stat_summary(fun.y = mean, fun.ymin = mean, fun.ymax = highsd,
width = 0.8, geom = "errorbar",
position = position_dodge(width = 0.9), colour = "black", size = 0.3) +
stat_summary(geom = "bar", fun.y = mean, aes(fill = cut),
width = 0.8, colour = "black",
position = position_dodge(width = 0.9), size = 0.4, show.legend = F) +
geom_signif(data = annotation_df,
aes(annotations = annotations, xmin = xmin, xmax = cut, y_position = y_position),
manual = TRUE) +
facet_wrap(~clarity, scales = "free") +
ylim(NA, 17000)
#> Warning: Ignoring unknown aesthetics: annotations, xmin, xmax, y_position
#> Warning: Removed 717 rows containing non-finite values (stat_summary).
#> Warning: Removed 717 rows containing non-finite values (stat_summary).
[https://camo.githubusercontent.com/5a7a6ef9c53a5e87acd048cd2e3d248a997fe642/687474703a2f2f692e696d6775722e636f6d2f74647a686668542e706e67]<https://camo.githubusercontent.com/5a7a6ef9c53a5e87acd048cd2e3d248a997fe642/687474703a2f2f692e696d6775722e636f6d2f74647a686668542e706e67>
You should easily be able to change the output by modifying the anntation_df.
Best,
Constantin
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Hello, I am trying to add custom annotations to ggplot2 faceted (facet_wrap) barplots and I am unable to make it work.
When I use the built-in tests in stat_signif() everything seems to work, from a technical standpoint, since I see the faceted barplots with all selected contrasts annotated by pvalues. Nonetheless I see strange things, like bars where there should clearly a statistically significant difference that are non-significant.
Therefore I decided to make tests separately and then add annotations in a custom way on the barplots.
I tried and it works nicely when I do a single plot, but when I add facets nothing works, I see just plain barplots without any annotation. I have tried to make it work by adding repeated lists to 'comparisons' and 'annotations' but the way it works is by piling each of the list elements on top of each other on every single plot of the facets rather than using one element per facet plot.
I hope I was able to explain my issue in a clear way.
Thanks for any suggestion
Michele
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