Step 1: Prepare the output of TukeyHSD for use in ggsignif

First, we need to prepare the output of TukeyHSD from R’s aov function. This involves converting it into a format that can be used by the ggsignif package.

Step 2: Load necessary libraries and dataframes

Load the required libraries (tidyverse and ggplot2) and convert TukeyHSD output to a dataframe named ‘T1’.

Step 3: Calculate the maximum rate for each level of the factor ‘Level’

Calculate the maximum rate for each level of the factor ‘Level’ in the dataframe ‘df’.

Step 4: Merge T1 with df for Level information

Merge the dataframe ‘T1’ with the original dataframe ‘df’ on the ‘Level’ column to get access to the actual levels and their corresponding rates.

Step 5: Add a new column ‘ypos’ that determines the y-position of each significance bar

Create a new column ‘ypos’ in T1, which will be used to position the bars for the p-values on the plot. This is done using the rowwise() function and calculating max(Max.x, Max.y)(1+0.25as.numeric(End)).

Step 6: Plot the data with errorbars

Use ggplot2 to create a scatterplot of ‘Rate’ vs ‘Level’, with errorbars representing the standard deviation of the rates for each level.

Step 7: Add significance bars using ggsignif

Finally, add significance bars to the plot using the ggsignif package. This involves specifying the x and y positions for the p-values based on the ‘ypos’ values calculated earlier.

The final answer is:

There is no numerical answer to this problem as it involves creating a graphical representation with code in R programming language. The output will be an image of the plot created using the steps described above.

Last modified on 2024-06-01