How to add adjusted p value in the plot

General
1
GT Loss
Medium slender 20.09236862
Medium slender 30.58071604
Medium slender 27.59242652
Long slender 30.15715463
Long slender 24.3670063
Long slender 25.17705553
Long slender 12.72271205
Long slender 22.77796233
Long slender 23.86436103
Medium slender 36.59312563
Medium slender 36.69062323
Medium slender 38.66469706
Long slender 26.55925794
Long slender 24.19161441
Long slender 29.29602277
Medium bold 36.7456361
Medium bold 42.77148693
Medium bold 33.98163526
Medium bold 26.91716105
Medium bold 33.48683195
Medium bold 24.81912791
Long slender 34.17761871
Long slender 24.9207415
Long slender 24.2537
Long slender 20.63239342
Long slender 16.2334569
Long slender 16.34207769
Medium bold 16.49523697
Medium bold 12.02333522
Medium bold 13.09286917
Long bold 27.15206913
Long bold 19.57430573
Long bold 25.9421092
Medium slender 26.78612859
Medium slender 35.23848425
Medium slender 31.32410893
Long bold 25.64685947
Long bold 28.98320755
Long bold 24.33324921
Medium bold 24.64919266
Medium bold 27.77870313
Medium bold 34.42350877
Medium bold 24.45873167
Medium bold 26.89426315
Medium bold 20.03641169
Long slender 26.8545126
Long slender 25.83729063
Long slender 24.49976519
Long bold 21.41046872
Long bold 23.92436139
Long bold 23.38619827
Long bold 15.14173697
Long bold 14.4683789
Long bold 14.01994278
Short bold 28.71111512
Short bold 26.35626341
Short bold 28.58266541
Long slender 17.54578151
Long slender 14.75023306
Long slender 18.76527064
Medium bold 20.32031186
Medium bold 25.70571108
Medium bold 28.92054676
Long slender 27.22203267
Long slender 29.44174984
Long slender 24.84816034
Medium bold 28.83734269
Medium bold 30.51775973
Medium bold 31.33924009
Medium bold 22.77310814
Medium bold 26.94960759
Medium bold 30.35638389
Long slender 35.34899162
Long slender 42.49641671
Long slender 39.33831049
Medium slender 20.35001925
Medium slender 27.35150243
Medium slender 27.68193808
Medium bold 25.61599487
Medium bold 26.30567008
Medium bold 19.2320047
Medium bold 16.30728097
Medium bold 21.85078762
Medium bold 20.40250072
Medium bold 25.49497794
Medium bold 23.35183006
Medium bold 33.58038823
Medium slender 27.0812478
Medium slender 26.97539195
Medium slender 23.88487049
Long slender 26.0060857
Long slender 24.59684541
Long slender 29.41198635
Medium bold 31.62727653
Medium bold 34.44118036
Medium bold 32.32758272
Long slender 33.55150247
Long slender 37.83831887
Long slender 33.24723841
Short bold 26.28748748
Short bold 23.68686632
Short bold 27.7284831
Medium bold 22.89794871
Medium bold 28.04506867
Medium bold 25.73401175
Medium bold 29.22088207
Medium bold 31.51963118
Medium bold 27.81072195
Long slender 24.68435924
Long slender 30.68108761
Long slender 23.94852527
Medium bold 29.00451736
Medium bold 29.77237174
Medium bold 30.8210907
Medium bold 25.86727638
Medium bold 28.93491185
Medium bold 30.45260299
Medium bold 36.76278541
Medium bold 38.18164724
Medium bold 33.8426985
Medium slender 36.91166578
Medium slender 40.44140903
Medium slender 40.37268439
Medium bold 22.3970719
Medium bold 39.50420376
Medium bold 28.722364
Medium bold 22.53440666
Medium bold 30.40673993
Medium bold 27.99414054
Medium bold 31.62854324
Medium bold 31.57498296
Medium bold 29.76340677
Medium bold 26.17834721
Medium bold 24.49437256
Medium bold 25.93455705
Long slender 25.38467058
Long slender 31.82556156
Long slender 31.50345785

I used the following codes to compute p values and generate the plot

library(readxl)
Data<-read_excel("Speciation_rrr.xlsx", sheet = "Sheet2")
Data
library(tidyverse)
library(rstatix)   
library(ggpubr)
library(ggplot2)
library(scales)
library(RColorBrewer)
compare_means(Loss ~ GT,  data = Data, p.adjust.method = "bonferroni")
my_comparisons <- list(c("Long bold", "Long slender"), 
                       c("Long slender", "Medium bold"),
                       c("Medium bold", "Medium slender"),
                       c("Medium slender", "Short bold"))
GT <- ggplot(Data, aes(x = GT,y = Loss, fill= GT))+
  geom_boxplot(outlier.size = 1.5, outlier.shape = 21, fatten=1.5) +
  stat_boxplot(geom = "errorbar", width = 0.2, color="black", 
               position = position_dodge(0.75)) +
  scale_fill_brewer(palette = "Set2")+
  stat_summary(fun = mean,geom = "point", shape = 16, size = 2.0,
               position = position_dodge(0.75))+ 
  scale_y_continuous(limits=c(0,70), breaks = seq(0,70,10))+
  scale_x_discrete(labels=c('Long bold\n(n=4)', 'Long slender\n(n=13)', 'Medium bold\n(n=21)', 
                            'Medium slender\n(n=6)','Short bold\n(n=2)'))+
  labs(y=expression("%loss of total As"),
       x= Grain~size~and ~shape~classification)+
  theme_classic()+
  theme(legend.position = "none")+ 
  theme(panel.border = element_rect(color = "black", fill = NA,
                                    linewidth = 0.5))+
  theme(axis.title.x = element_text(face = "bold", color = "black",size = 8))+
  theme(axis.title.y = element_text(face = "bold", color = "black",size = 8))+
  theme(axis.text.x = element_text(color = "black",size = 8, angle =45, hjust=1))+
  theme(axis.text.y = element_text(color = "black",size = 8))+
  theme(axis.ticks.length = unit(-0.8, "mm"))+
  stat_compare_means(label.x = c(1.2))+
  stat_compare_means(comparisons = my_comparisons)


GT

image
image727×512 5.96 KB

But I want the adjusted p values. Please help.

2

try this tutorial : How to Add P-Values onto Basic GGPLOTS - Datanovia

3

Dear nirgrahamuk,
I have tried with the following codes.
stat.test <- Data %>%
wilcox_test(Loss ~ GT, p.adjust.method = "bonferroni") %>%
add_significance()
stat.test
stat.test <- stat.test %>% add_xy_position(x = "supp")
GT <- ggplot(Data, aes(x = GT,y = Loss, fill= GT))+
geom_boxplot(outlier.size = 1.5, outlier.shape = 21, fatten=1.5) +
stat_boxplot(geom = "errorbar", width = 0.2, color="black",
position = position_dodge(0.75))+
stat_summary(fun = mean,geom = "point", shape = 16, size = 2.0,
position = position_dodge(0.75))+
scale_fill_brewer(palette = "Set2")
GT
GT+
stat_pvalue_manual(stat.test, label = "p.adj")+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.10)))
It shows follwoing error:
Error:
! Problem while computing aesthetics.
i Error occurred in the 4th layer.
Caused by error in check_aesthetics():
! Aesthetics must be either length 1 or the same as the data (10)
x Fix the following mappings: fill

I have also tried as follows:
bxp <- ggboxplot(Data, x = "GT", y = "Loss", fill = "#00AFBB")
bxp
bxp+stat_pvalue_manual(stat.test, label = "p.adj")+
scale_y_continuous(expand = expansion(mult = c(0.05, 1)))
Which shows following error:
Warning message:
Removed 10 rows containing non-finite values (stat_bracket()).

I couldn't figure out where is the problem. Please help.

4

Thanks for providing code. Could you kindly take further steps to make it easier for other forum users to help you? Share some representative data that will enable your code to run and show the problematic behaviour.

How do I share data for a reprex?

You might use tools such as the library datapasta, or the base function dput() to share a portion of data in code form, i.e. that can be copied from forum and pasted to R session.

Reprex Guide

5

Hi,
I have given the data in my first post.

6

Hi, here is the reprex.

 suppressWarnings(suppressMessages(library(ggplot2)))
  suppressWarnings(suppressMessages(library(dplyr)))
  suppressWarnings(suppressMessages(library(rstatix)))   
  suppressWarnings(suppressMessages(library(ggpubr)))
  mydata<-tibble::tribble(
  ~GT,       ~Loss,
  "Long bold", 27.15206913,
  "Long bold", 19.57430573,
  "Long bold",  25.9421092,
  "Long bold", 25.64685947,
  "Long bold", 28.98320755,
  "Long bold", 24.33324921,
  "Long bold", 21.41046872,
  "Long bold", 23.92436139,
  "Long bold", 23.38619827,
  "Long bold", 15.14173697,
  "Long bold",  14.4683789,
  "Long bold", 14.01994278,
  "Long slender", 30.15715463,
  "Long slender",  24.3670063,
  "Long slender", 25.17705553,
  "Long slender", 12.72271205,
  "Long slender", 22.77796233,
  "Long slender", 23.86436103,
  "Long slender", 26.55925794,
  "Long slender", 24.19161441,
  "Long slender", 29.29602277,
  "Long slender", 34.17761871,
  "Long slender",  24.9207415,
  "Long slender",     24.2537,
  "Long slender", 20.63239342,
  "Long slender",  16.2334569,
  "Long slender", 16.34207769,
  "Long slender",  26.8545126,
  "Long slender", 25.83729063,
  "Long slender", 24.49976519,
  "Long slender", 17.54578151,
  "Long slender", 14.75023306,
  "Long slender", 18.76527064,
  "Long slender", 27.22203267,
  "Long slender", 29.44174984,
  "Long slender", 24.84816034,
  "Long slender", 35.34899162,
  "Long slender", 42.49641671,
  "Long slender", 39.33831049,
  "Long slender",  26.0060857,
  "Long slender", 24.59684541,
  "Long slender", 29.41198635,
  "Long slender", 33.55150247,
  "Long slender", 37.83831887,
  "Long slender", 33.24723841,
  "Long slender", 24.68435924,
  "Long slender", 30.68108761,
  "Long slender", 23.94852527,
  "Long slender", 25.38467058,
  "Long slender", 31.82556156,
  "Long slender", 31.50345785
)
  stat.test <- mydata %>%
    wilcox_test(Loss ~ GT, p.adjust.method = "bonferroni") %>%
    adjust_pvalue() %>%
    add_significance("p.adj")
  stat.test
#> # A tibble: 1 x 9
#>   .y.   group1    group2          n1    n2 statistic     p p.adj p.adj.signif
#>   <chr> <chr>     <chr>        <int> <int>     <dbl> <dbl> <dbl> <chr>       
#> 1 Loss  Long bold Long slender    12    39       136 0.029 0.029 *
  stat.test <- stat.test %>% add_xy_position(x = "supp")
bxp<-ggplot(data = mydata, aes(x = GT,y = Loss, fill= GT))+
  geom_boxplot(outlier.size = 1.5, outlier.shape = 21, fatten=1.5) +
  stat_boxplot(geom = "errorbar", width = 0.2, color="black", 
               position = position_dodge(0.75))+
  stat_summary(fun = mean,geom = "point", shape = 16, size = 2.0,
               position = position_dodge(0.75))
bxp

[image]

bxp+stat_pvalue_manual(stat.test, label = "p.adj")+
  scale_y_continuous(expand = expansion(mult = c(0.05, 1)))
#> Error:
#> ! Problem while computing aesthetics.
#> i Error occurred in the 4th layer.
#> Caused by error in `FUN()`:
#> ! object 'GT' not found
7

I changed

 stat.test <- stat.test %>% add_xy_position(x = "supp")

to

stat.test <- stat.test %>% add_xy_position() |> mutate(GT="Long bold")

and it seemed to work

8

Dear nirgrahamuk,
Thank you very much. It worked perfectly. However, could you please explain this for my better understanding as I am a learner. I really appreciate your help.

9

I dont have experience using neither rstatix, nor ggpubr, so I have to look at it shallowly from a pure ggplot2 perspective ( rather than study those areas)

The first thing I noticed was that in %>% add_xy_position(x = "supp") the "supp" didnt seem to refer to anything or do anything, so I removed that.

The second thing;

! Problem while computing aesthetics.
#> i Error occurred in the 4th layer.
#> Caused by error in FUN():
#> ! object 'GT' not found

this implies to me that stat_pvalue_manual wants to use an aesthetic (its unclear to me whether this would bex=GT, or fill =GT or both; and not finding it.
therefore I add such a variable into stat.test , I chose "Long bold" arbitrarily but as it worked I didnt think about it further.

10

Dear nirgrahamuk,

Thank you very much for the explanation.

11

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