T-Test Essentials: Definition, Formula and Calculation

Prerequisites

Make sure you have the following R packages:

• tidyverse for data manipulation and visualization
• ggpubr for creating easily publication ready plots
• rstatix provides pipe-friendly R functions for easy statistical analyses
• datarium: contains required data sets for this chapter

Load required R packages:

library(tidyverse)
library(ggpubr)
library(rstatix)

Pairwise t-test for independent groups

This section shows how to perform multiple independent t-tests between groups.

# Load and inspect the demo data
data("PlantGrowth")
set.seed(1234)
PlantGrowth %>% sample_n_by(group, size = 2)

## # A tibble: 6 x 2
##   weight group
##    <dbl> <fct>
## 1   5.58 ctrl 
## 2   4.61 ctrl 
## 3   6.03 trt1 
## 4   3.83 trt1 
## 5   5.8  trt2 
## 6   5.29 trt2

# Pairwise comparisons
pwc <- PlantGrowth %>% 
  pairwise_t_test(
    weight ~ group, pool.sd = FALSE,
    p.adjust.method = "bonferroni"
    )
pwc

## # A tibble: 3 x 10
##   .y.    group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <chr>  <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 weight ctrl   trt1      10    10      1.19  16.5 0.25  0.75  ns          
## 2 weight ctrl   trt2      10    10     -2.13  16.8 0.048 0.144 ns          
## 3 weight trt1   trt2      10    10     -3.01  14.1 0.009 0.028 *

# Visualization: box plots with p-values
pwc <- pwc %>% add_xy_position(x = "group", step.increase = 1)
ggboxplot(PlantGrowth, x = "group", y = "weight") +
  stat_pvalue_manual(pwc, hide.ns = TRUE) 

Pairwise t-test for paired groups

This section shows how to perform multiple paired t-tests between groups. We’ll use the self-esteem score dataset measured over three time points. The data is available in the datarium package.

# Data preparation: Wide format
data("selfesteem", package = "datarium")
head(selfesteem, 3)

## # A tibble: 3 x 4
##      id    t1    t2    t3
##   <int> <dbl> <dbl> <dbl>
## 1     1  4.01  5.18  7.11
## 2     2  2.56  6.91  6.31
## 3     3  3.24  4.44  9.78

# Gather columns t1, t2 and t3 into long format
# Convert id and time into factor variables
selfesteem <- selfesteem %>%
  gather(key = "time", value = "score", t1, t2, t3) %>%
  convert_as_factor(id, time)
head(selfesteem, 3)

## # A tibble: 3 x 3
##   id    time  score
##   <fct> <fct> <dbl>
## 1 1     t1     4.01
## 2 2     t1     2.56
## 3 3     t1     3.24

# Pairwise comparisons
pwc <- selfesteem %>%
  pairwise_t_test(
    score ~ time, paired = TRUE,
    p.adjust.method = "bonferroni"
    )
pwc

## # A tibble: 3 x 10
##   .y.   group1 group2    n1    n2 statistic    df           p    p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>       <dbl>    <dbl> <chr>       
## 1 score t1     t2        10    10     -4.97     9 0.000772    0.002    **          
## 2 score t1     t3        10    10    -13.2      9 0.000000334 0.000001 ****        
## 3 score t2     t3        10    10     -4.87     9 0.000886    0.003    **

# Visualization: box plots with p-values
pwc <- pwc %>% add_xy_position(x = "time")
ggboxplot(selfesteem, x = "time", y = "score", add = "point") +
  stat_pvalue_manual(pwc)

Related articles

• ANOVA in R
• Repeated Measures ANOVA in R