This article describes how to compute and automatically **add p-values onto ggplot facets with different scales** using the *ggpubr* and the *rstatix* R packages. For multipanel plots with approximately similar y-axis scales on each panel, you can follow steps described in this article: How to Add P-values to GGPLOT Facets.

Here, we’ll We’ll use a demo data for creating panels of plots with very different y scales. You will learn how correctly auto-compute the y positions of the p-values when the facet scales is set to `free`

option. Examples are shown for box plots and bar plots.

Contents:

## Prerequisites

Read the following related article: How to Add P-values to GGPLOT Facets.

Make sure you have installed 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.

Start by loading the following required packages:

```
library(ggpubr)
library(rstatix)
```

## Data preparation

```
# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
df$group <- factor(rep(c("grp1", "grp2"), 30))
# Add some extremely high values in column 1 at rows c(1, 3, 5).
df[c(1, 3, 5), 1] <- c(500, 495, 505)
head(df, 3)
```

```
## len supp dose group
## 1 500.0 VC 0.5 grp1
## 2 11.5 VC 0.5 grp2
## 3 495.0 VC 0.5 grp1
```

## Statistical tests

Facet by the `supp`

and `group`

variables, and compare the levels of the `dose`

variable on the x-axis. The Tukey post hoc test is used for the pairwise comparisons.

```
stat.test <- df %>%
group_by(group, supp) %>%
tukey_hsd(len ~ dose)
stat.test
```

```
## # A tibble: 12 x 11
## supp group term group1 group2 null.value estimate conf.low conf.high p.adj p.adj.signif
## * <fct> <fct> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 OJ grp1 dose 0.5 1 0 6.32 0.275 12.4 0.0403 *
## 2 OJ grp1 dose 0.5 2 0 11.3 5.26 17.3 0.000852 ***
## 3 OJ grp1 dose 1 2 0 4.98 -1.06 11.0 0.112 ns
## 4 VC grp1 dose 0.5 1 0 -286. -548. -23.5 0.0328 *
## 5 VC grp1 dose 0.5 2 0 -276. -539. -14.0 0.0389 *
## 6 VC grp1 dose 1 2 0 9.46 -253. 272. 0.995 ns
## # … with 6 more rows
```

## Facet with fixed scales

```
# Create bar plots with significance levels
# Hide ns (non-significant)
# Add 15% space between labels and the plot top border
stat.test <- stat.test %>% add_xy_position(x = "dose", fun = "mean_se")
ggbarplot(
df, x = "dose", y = "len", fill = "#00AFBB",
add = "mean_se", facet = c("supp", "group")
) +
stat_pvalue_manual(stat.test, hide.ns = TRUE, tip.length = 0, step.increase = 0) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15)))
```

## Facet with free scales

### Facet wrap

You need to specify the option `scales = "free"`

in both the `add_xy_position()`

and in the `ggbarplot()`

functions.

```
stat.test <- stat.test %>%
add_xy_position(x = "dose", fun = "mean_se", scales = "free")
ggbarplot(
df, x = "dose", y = "len", fill = "#00AFBB",
add = "mean_se", facet.by = c("supp", "group")
) +
facet_wrap(vars(supp, group), scales = "free") +
stat_pvalue_manual(stat.test, hide.ns = TRUE, tip.length = 0) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15)))
```

### Facet grid

When the `scales = "free"`

argument is added in facet grid, plots on the same row cannot have different y-axis. Similarly, there can be only single x-axis for each column. Using `facet_wrap()`

, each plot is displayed independently, so it can “free” its x-axis and y-axis.

Facet grid is useful when you want to relatively compare the plots within a category, which can be accomplished by setting the same axis scales. Meanwhile, facet wrap is more useful for plots that are more independent between one another.

There are two possible solutions to customize the y position of significance levels.

#### Solution 1: Using the option step.increase

The default of the function `add_xy_position()`

is to automatically compute a global step increase value between brackets. This calculation assumes that the y scales of plot panels are fixed.

In the situation, where you want free scales, you can:

- Set the option
`step.increase`

to 0 when calling the function`add_xy_position()`

. - Specify only the option
`step.increase`

in the function`stat_pvalue_manual()`

. In this case, the step.increase will be adapted to each plot panel.

```
stat.test <- stat.test %>%
add_xy_position(x = "dose", fun = "mean_se", step.increase = 0)
bp <- ggbarplot(
df, x = "dose", y = "len", fill = "#00AFBB", add = "mean_se",
facet.by = c("supp", "group"), scales = "free"
)
bp +
stat_pvalue_manual(stat.test, hide.ns = TRUE, tip.length = 0, step.increase = 0.2) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15)))
```

#### Solution 2: Using the option scales = “free”.

In facet grid, the scales of the generated plot panels are not completely free. Consequently you will need more customization to adapt the look of the significance level position. You will have to play with the options `step.increase`

and `bracket.nudge.y`

in the `stat_pvalue_manual()`

function.

```
# Default plot
stat.test <- stat.test %>%
add_xy_position(x = "dose", fun = "mean_se", scales = "free")
bp <- ggbarplot(
df, x = "dose", y = "len", fill = "#00AFBB", add = "mean_se",
facet.by = c("supp", "group"), scales = "free"
)
bp +
stat_pvalue_manual(stat.test, hide.ns = TRUE, tip.length = 0) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15)))
```

```
# Increase the step between the brackets
bp +
stat_pvalue_manual(
stat.test, hide.ns = TRUE, tip.length = 0,
step.increase = 0.1
) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15)))
```

```
# Nudge down the brackets
# Specify the option bracket.nudge.y for each of the panel
# Should be of the same length as the number of comparisons
bracket.nudge.y <- c(
-2, -3, # Panel 1: grp1/OJ
-100, -160, # Panel 2: grp1/VC
-10, -11, # Panel 3: grp2/OJ
-250, -250, -250 # Panel 4: grp2/VC
)
bp +
stat_pvalue_manual(
stat.test, hide.ns = TRUE, tip.length = 0,
step.increase = 0.09, bracket.nudge.y = bracket.nudge.y
) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15)))
```

## Conclusion

This article describes how to add p-values onto ggplot facets with different y scales. See other related frequently questions: ggpubr FAQ.

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