Pairwise comparisons between groups of the ToothGrowth data set, grouped according to supp. The boxplots and the brackets are automatically coloured according to supp. Three important points for this graph:

For grouped or faceted data you’ll also need a column which is named according to the grouping variable. See the Many more examples section for help with this/examples.

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You can easily change how the bracket and label looks. You can make the label a glue expression. You can also change the tip length of the bracket. Lastly, you can flip the label when using coord_flip().

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Pairwise comparisons within groups and between groups of the ToothGrowth data set, grouped according to supp. You can use bracket.nudge.y to slightly adjust the overall y position of the brackets instead of having to redefine df_p_val2.

Even if you don’t want brackets, add_pvalue() is also useful for adding significance text to plots with the correct/automatic positioning.

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This function is a re-written version of stat_pvalue_manual() from the ggpubr package, which itself is based on the geom_signif() function from the ggsignif package. Compared to stat_pvalue_manual(), the add_pvalue() function is: easier to use, more robust with less dependencies, and has more customisable brackets.

Now, compare the mean len for each dose to some arbitrary value, say 26 in this case. Error bars indicate 1 standard deviation from the mean.

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Facet according to some particular grouping variable called grp and dose, and then compare mean len between either supp. It is important that the p-value table must have columns with the same names as the two faceting variables (in this case "grp" and "dose").

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Next we’ll perform two t-tests and compare the means against dose = 0.5 as a reference group. Then we’ll correct the p-values for multiple testing.

ggprism includes the add_pvalue() function to add p-values with or without brackets to ggplots. This vignette will go through the many ways in which this function can be used.

And finally we’ll add this p-value to our plot. Because we have used the default column names (see above) in our p-value table we don’t necessarily have to specify any arguments of add_pvalue(). However, here we’ll do it for clarity’s sake. Additionally, if your p-value table has special column names, you will need to specify them in add_pvalue().

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To add significance brackets to a plot, you need a minimal data.frame with 4 columns and a number of rows corresponding to the number of brackets you want to add. The 4 columns should correspond to these 4 function arguments:

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In the example above, if you wanted the text but not the bracket, you can just use the remove.bracket argument. In this case, you must use the x argument to change the x position of the text.

With rstatix, you can perform the statistical test and create the p-value table with the appropriate x and y position automatically, in a single step.

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Compare mean len across the 3 different dose. Use the bracket.shorten argument to slightly shorten side-by-side brackets.

Now, compare overall mean len (base mean) to the mean len for each dose. Error bars indicate 1 standard deviation from the mean.

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We can now construct a p-value table. Note that in this case we don’t need to to specify a "group2" column for xmax. This is because text-only p-value annotations just have an x position (x) and not an x range (xmin and xmax).

Facet according to supp and then compare mean len between the three dose. It is important that the p-value table must have a column with the same name as the faceting variable (in this case "supp").

Arguably one of the most popular features of GraphPad Prism is adding p-values to plots. Indeed in Prism 9, GraphPad have added a feature to automatically perform pairwise comparisons and add the resulting p-values with brackets to the graph.

If you want the label number format to look nicer you can provide a column name to label with plotmath expressions and set parse = TRUE. This works with or without brackets.

Facet according to dose and then compare mean len between either supp. It is important that the p-value table must have a column with the same name as the faceting variable (in this case "dose").