```
# 1. Load required R packages
suppressPackageStartupMessages(library(ggpubr))
suppressPackageStartupMessages(library(rstatix))
# 2. Data preparation
df <- ToothGrowth
df$dose <- factor(df$dose)
# 3. Statistical tests
res.stats <- df %>%
group_by(dose) %>%
t_test(len ~ supp) %>%
adjust_pvalue() %>%
add_significance()
res.stats
```

```
## # A tibble: 3 x 11
## dose .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 0.5 len OJ VC 10 10 3.17 15.0 0.00636 0.0127 *
## 2 1 len OJ VC 10 10 4.03 15.4 0.00104 0.00312 **
## 3 2 len OJ VC 10 10 -0.0461 14.0 0.964 0.964 ns
```

```
# 4. Create a stacked bar plot, add "mean_se" error bars
p <- ggbarplot(
df, x = "dose", y = "len", add = "mean_se",
color = "supp", palette = "jco"
)
# 5. Add p-values to the bar plot using ggpubr verbs
p + stat_pvalue_manual(
res.stats, x = "dose", y.position = c(30, 45, 60),
label = "p.adj.signif"
)
```

```
# or use ggplot2 verbs
p + geom_text(
aes(x = dose, y = c(25, 42, 60), label = p.adj.signif),
data = res.stats
)
```

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