How to Perform Multiple T-test in R for Different Variables

Prerequisites

# Load required R packages
library(tidyverse)
library(rstatix)
library(ggpubr)
   
# Prepare the data and inspect a random sample of the data
mydata <- iris %>%
  filter(Species != "setosa") %>%
  as_tibble()
mydata %>% sample_n(6)

## # A tibble: 6 x 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>     
## 1          6.4         2.8          5.6         2.1 virginica 
## 2          5.5         2.6          4.4         1.2 versicolor
## 3          6.3         3.4          5.6         2.4 virginica 
## 4          6.9         3.1          5.1         2.3 virginica 
## 5          6.5         2.8          4.6         1.5 versicolor
## 6          6.3         3.3          6           2.5 virginica

# Transform the data into long format
# Put all variables in the same column except `Species`, the grouping variable
mydata.long <- mydata %>%
  pivot_longer(-Species, names_to = "variables", values_to = "value")
mydata.long %>% sample_n(6)

## # A tibble: 6 x 3
##   Species    variables    value
##   <fct>      <chr>        <dbl>
## 1 virginica  Sepal.Length   6.9
## 2 virginica  Sepal.Length   7.6
## 3 versicolor Sepal.Width    3  
## 4 virginica  Petal.Width    2.5
## 5 versicolor Petal.Width    1.3
## 6 virginica  Petal.Width    1.8

Run multiple T-tests

• Group the data by variables and compare Species groups
• Adjust the p-values and add significance levels

stat.test <- mydata.long %>%
  group_by(variables) %>%
  t_test(value ~ Species) %>%
  adjust_pvalue(method = "BH") %>%
  add_significance()
stat.test

## # A tibble: 4 x 11
##   variables    .y.   group1     group2       n1    n2 statistic    df        p    p.adj p.adj.signif
##   <chr>        <chr> <chr>      <chr>     <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
## 1 Petal.Length value versicolor virginica    50    50    -12.6   95.6 4.90e-22 9.80e-22 ****        
## 2 Petal.Width  value versicolor virginica    50    50    -14.6   89.0 2.11e-25 8.44e-25 ****        
## 3 Sepal.Length value versicolor virginica    50    50     -5.63  94.0 1.87e- 7 2.49e- 7 ****        
## 4 Sepal.Width  value versicolor virginica    50    50     -3.21  97.9 1.82e- 3 1.82e- 3 **

Create multi-panel Boxplots with t-test p-values

# Create the plot
myplot <- ggboxplot(
  mydata.long, x = "Species", y = "value",
  fill = "Species", palette = "npg", legend = "none",
  ggtheme = theme_pubr(border = TRUE)
  ) +
  facet_wrap(~variables)
# Add statistical test p-values
stat.test <- stat.test %>% add_xy_position(x = "Species")
myplot + stat_pvalue_manual(stat.test, label = "p.adj.signif")

Create individual Box plots with t-test p-values

# Group the data by variables and do a graph for each variable
graphs <- mydata.long %>%
  group_by(variables) %>%
  doo(
    ~ggboxplot(
      data =., x = "Species", y = "value",
      fill = "Species", palette = "npg", legend = "none",
      ggtheme = theme_pubr()
      ), 
    result = "plots"
  )
graphs

## # A tibble: 4 x 2
##   variables    plots 
##   <chr>        <list>
## 1 Petal.Length <gg>  
## 2 Petal.Width  <gg>  
## 3 Sepal.Length <gg>  
## 4 Sepal.Width  <gg>

# Add statitistical tests to each corresponding plot
variables <- graphs$variables
for(i in 1:length(variables)){
  graph.i <- graphs$plots[[i]] + 
    labs(title = variables[i]) +
    stat_pvalue_manual(stat.test[i, ], label = "p.adj.signif")
  print(graph.i)
}