## 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)
}
```

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Version: Français

# data

example.df <- data.frame(

genotype = sample(c("A", "B","C","D"), 50, replace = TRUE),

oxygen = sample(c("Yes", "No"), 50, replace = TRUE),

value1 = rnorm(50, 100, 5), value2=rnorm(50, 10,5), value3 = rnorm (50, 25, 5))

# plot

ggboxplot(example.df, x = "genotype", y = "value1", color = "oxygen", bxp.errorbar = TRUE, palette = "jco")

# stat test

stat.test %

group_by(oxygen) %>%

dunn_test(value1~genotype) %>%

adjust_pvalue(method = “bonferroni”) %>%

add_significance(“p.adj”)

If I wanted to use dunn test to perform all comparisons between the genotypes grouped by oxygen, for all numeric variables and generate plots with significance values for only certain comparisons, how could I accomplish that?

Thanks

Thanks for the brilliant tutorial. With my data I run into the problem of the y axes scales which vary strongly between variables and I cannot figure out how to adjust them. Can you give me a hint?