Why does stat_compare_means only show pairwise comparisons in one facet?

Question

I'm trying to do a pairwise comparison of grouped data. I've used stat_compare_means to do this successfully before, but for some reason this time it is only showing the comparison bars in one of the facet panels. I've tried, but can't seem to make it work. I've provided a simplified worked example below with just two conditions below. The real data has the same number of sets, but more conditions. Any help much appreciated!

data <- data.frame(sample = seq(1:83), 
condition = c("KO", "KO", "KO", "KO", "KO", "KO", "KO", "KO", "KO", "KO", 
"KO", "KO", "KO", "KO", "KO", "KO", "KO", "WT", "WT", "WT", "WT", 
"WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
"WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
"WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
"WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
"WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
"WT", "WT", "WT", "WT", "WT", "WT", "WT"),
set = c("di", "tet", "mono", "di", "tet", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
"tet", "mono"),
score = c(0, 0, 0, -5.95940584704586, 10.0932562947815, -3.16676604569923, 
4.46133814098881, -1.9888611720281, -7.08194974795108, 3.17097628218171, 
-1.83986980857496, -0.843268716519414, 1.40178526106758, -0.340339302553342, 
-3.76913603338144, 4.28943971347741, -3.20197704274428, -3.54168755452774, 
15.414510676737, -5.85333426479177, -1.87949902971026, 14.1424002410594, 
-2.14726619139082, -5.01378228438499, 11.1131227496058, 0.097013584879446, 
-6.55527134311774, 12.2234175105232, -2.55259067519978, -6.68392512983342, 
15.8358484731832, -2.27825891764331, -5.40451835097939, 11.2240240941934, 
-1.53075128507785, -4.80008896082703, 15.3667539728667, -4.81370852797055, 
-2.69976280917806, 21.9926791189896, 0.61798090190648, -8.68663007652496, 
13.8852585926079, -0.80329005076484, -16.8459570277756, 13.3500356549569, 
-5.59531483186873, -3.33602772657725, 11.338954967882, -2.12614700145763, 
-3.37418493461362, 11.5903340330526, -2.87224785160433, -14.7792521265679, 
7.7542705233175, -2.06876649679246, -1.31032740187699, 17.6666627835987, 
-0.824420163207606, -4.82659116096503, -0.0626028094479509, -1.90431942338018, 
-20.3180652520285, 6.83632028067972, -1.58570945276274, -2.77261532150261, 
2.98168865160908, -1.89922364414076, -5.80584721371712, 7.13599592922731, 
-1.23964885854847, -5.1236580504364, 14.5532078112838, -3.82017971494402, 
-4.39741499313133, 11.7342969461195, -2.5852194454582, -3.18458897513284, 
12.7762556862722, 1.1245622869403, -2.98430760077172, 13.981837061262, 
-1.19532384849617))

ggplot(data aes(x=condition, y=score, fill=set)) +
  stat_summary(fun=mean, geom="bar", colour="black", alpha=1) +
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.25) +
  geom_point(color="black", shape=21, show.legend=TRUE, size=3, stroke=1) +
  facet_wrap(~set, scales="free") +
  stat_compare_means(method = "t.test", comparisons=list(c("KO", "WT")), hide.ns=F) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))

score 3 · Answer 1 · answered Apr 22 '21 at 07:31

I think you should add group = condition to the aes in ggplot.

ggplot(temp, aes(x=condition, y=score, color = set,  fill=set, group = condition)) +
  stat_summary(fun=mean, geom="bar", colour="black", alpha=1) +
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.25) +
  geom_point(color="black", shape=21, show.legend=TRUE, size=3, stroke=1) +
  facet_wrap(~set, scales="free") +
  stat_compare_means(method = "t.test", comparisons=list(c("KO", "WT")), hide.ns=F) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))

score 2 · Accepted Answer · answered Apr 22 '21 at 07:38

You can also calculate the p-values manually and then place them on the plot using the ggsignif package. It's more work, but you can adjust the p-values for multiple comparisons and/or specify what symbols you want to use, so it might come in handy for more complicated plots e.g.

library(tidyverse)
library(ggsignif)
temp <- data.frame(sample = seq(1:83), 
                   condition = c("KO", "KO", "KO", "KO", "KO", "KO", "KO", "KO", "KO", "KO", 
                                 "KO", "KO", "KO", "KO", "KO", "KO", "KO", "WT", "WT", "WT", "WT", 
                                 "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
                                 "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
                                 "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
                                 "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
                                 "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", "WT", 
                                 "WT", "WT", "WT", "WT", "WT", "WT", "WT"),
                   set = c("di", "tet", "mono", "di", "tet", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono", "di", "tet", "mono", "di", "tet", "mono", "di", 
                           "tet", "mono"),
                   score = c(0, 0, 0, -5.95940584704586, 10.0932562947815, -3.16676604569923, 
                             4.46133814098881, -1.9888611720281, -7.08194974795108, 3.17097628218171, 
                             -1.83986980857496, -0.843268716519414, 1.40178526106758, -0.340339302553342, 
                             -3.76913603338144, 4.28943971347741, -3.20197704274428, -3.54168755452774, 
                             15.414510676737, -5.85333426479177, -1.87949902971026, 14.1424002410594, 
                             -2.14726619139082, -5.01378228438499, 11.1131227496058, 0.097013584879446, 
                             -6.55527134311774, 12.2234175105232, -2.55259067519978, -6.68392512983342, 
                             15.8358484731832, -2.27825891764331, -5.40451835097939, 11.2240240941934, 
                             -1.53075128507785, -4.80008896082703, 15.3667539728667, -4.81370852797055, 
                             -2.69976280917806, 21.9926791189896, 0.61798090190648, -8.68663007652496, 
                             13.8852585926079, -0.80329005076484, -16.8459570277756, 13.3500356549569, 
                             -5.59531483186873, -3.33602772657725, 11.338954967882, -2.12614700145763, 
                             -3.37418493461362, 11.5903340330526, -2.87224785160433, -14.7792521265679, 
                             7.7542705233175, -2.06876649679246, -1.31032740187699, 17.6666627835987, 
                             -0.824420163207606, -4.82659116096503, -0.0626028094479509, -1.90431942338018, 
                             -20.3180652520285, 6.83632028067972, -1.58570945276274, -2.77261532150261, 
                             2.98168865160908, -1.89922364414076, -5.80584721371712, 7.13599592922731, 
                             -1.23964885854847, -5.1236580504364, 14.5532078112838, -3.82017971494402, 
                             -4.39741499313133, 11.7342969461195, -2.5852194454582, -3.18458897513284, 
                             12.7762556862722, 1.1245622869403, -2.98430760077172, 13.981837061262, 
                             -1.19532384849617))

anno_df = compare_means(score ~ condition, group.by = "set", data = temp) %>%
  mutate(y_pos = c(3, 24, 2.5))

ggplot(temp, aes(x=condition, y=score, fill=set)) +
  stat_summary(fun=mean, geom="bar", colour="black", alpha=1) +
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.25) +
  geom_point(color="black", shape=21, show.legend=TRUE, size=3, stroke=1) +
  facet_wrap(~set, scales="free") +
  geom_signif(data = anno_df, aes(xmin=group1,
                                  xmax=group2,
                                  annotations=p.signif,
                                  y_position = y_pos),
              manual = TRUE) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(expand = expansion(mult = c(0.2, 0.2)))

@ricoderks answer is clearly the easier/better option though!

Why does stat_compare_means only show pairwise comparisons in one facet?

2 Answers2