In this post, we will learn how to carry out repeated measures Analysis of Variance (ANOVA) in R and Python. Specifically, we will use the R package afex and the Python package pingouin to carry out one-way and two-way ANOVA for within-subjects design. The structure of the following data analysis tutorial is as follows; a brief introduction to (repeated measures) ANOVA, carrying out within-subjects ANOVA in R using afex and in Python using pingouin. In the end, there will be a comparison of the results and the pros and cons of using R or Python for data analysis (i.e., ANOVA).
What is ANOVA?
Before we go into how to carry out repeated measures ANOVA in R and Python, we will briefly learn what an ANOVA is. An ANOVA test is a parametrical method to determine whether the collected data results are significant. That is, this type of test will enable us to figure out whether we should reject the null hypothesis or accept the alternate hypothesis. In between-participants ANOVA we’re testing groups to see if there’s a statistical difference between them.
In this post, we are going to learn to do repeated measures ANOVA, however, using this method, we compare means across one or more variables that are based on repeated observations. These repeated observations can either be time points or different conditions. In the repeated ANOVA examples below, we use different conditions.
For more information about ANOVA:
Assumptions of Repeated Measures ANOVA
- First of all, the dependent variable should be measured at the continuous level (i.e., interval or ratio variables).
- Second, the independent variable should have at least two categorical levels (e.g., “distractors” or “no distractors”)
- Third, your data should not contain any significant outliers
- Fourth, the distribution of the dependent variable should be approximately normally distributed.
- Fifth, the variances of the differences between all combinations of factor levels (categorical groups) should be equal
Now, if your data is not normally distributed, you can try to transform the data in Python e.g. using the log, square root, or Box-Cox transformations. In the next section, we will look at some fake data that we can use to practice repeated measures ANOVA in Python.
In this repeated measures ANOVA example, we will use fake data (which can be downloaded here). This fake data is a sample of 60 adults responding as fast as possible to visual stimuli. The dependent variable (DV) is response time to the visual stimuli. While the subjects were categorizing visual stimuli, they were exposed to background noise or quiet (independent variable, iv1).
In the first example, we will use these two conditions (iv1) when we carry out a one-way ANOVA for repeated measures. Furthermore, the visual stimuli could either be presented in the upper part, lower part, or in the middle part of the computer screen (independent variable, iv2). A CSV file with the data used in this ANOVA tutorial can be downloaded here.
The variables in the data set:
- Sub_id = Subject ID #
- iv1 = Noise condition; quiet or noise
- iv2 = Location condition; upper, lower, middle
- DV = response time
In the next section, we will look at how we can carry out within-subjects ANOVA with the R-package afex. It is, of course, advised that you start by calculating descriptive statistics and visualize the data before you continue. Furthermore, if you need to clean your dataset a bit, it is also possible to remove columns in R with e.g. dplyr.
- How to Do the Brown-Forsythe Test in R: A Step-By-Step Example
- How to do a Kruskal-Wallis Test in R
- How to Make a Residual Plot in R & Interpret Them using ggplot2
Repeated Measures ANOVA in R
In this section, we will learn how to perform repeated measures ANOVA in R using afex. More specifically, we will learn how to carry out a one-way and two-way ANOVA using the aov_ez function. Note, working with aov_ez function, we need to have our data in long format.
First, we are going to install the needed package: afex. In the code chunk below, the package will only be installed if it’s not already installed.
list.of.packages <- c("afex", "emmeans") new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] if(length(new.packages)) install.packages(new.packages)Code language: R (r)
One-Way Repeated Measures ANOVA in R.
In the first example, we will carry out a one-way repeated measures ANOVA in R using aov_ez. Here we want to know whether there is any difference in response time with background noise compared to without background noise. To test this, we need to conduct a within-subjects ANOVA.
In the first code chunk below, we load the package, the data and print the first five rows using head
require(afex) df <- read.csv(file='./Python_ANOVA/rmAOV2way.csv', header=TRUE, sep=',') head(df)Code language: R (r)
Now, of course, you can also read data from a .xlsx file in R.
Example ANOVA for Within-Subjects Design:
Here’s how to carry out repeated measures ANOVA in R with afex:
aov <- aov_ez('Sub_id', 'rt', fun_aggregate = mean, df, within = 'iv1') print(aov)Code language: R (r)
Two-Way Repeated Measures ANOVA in R
In the second example, we are going to conduct a two-way repeated measures ANOVA in R. Here; we want to know whether there is any difference in response time during background noise compared to without background noise and whether there is a difference depending on where the visual stimuli are presented (up, down, middle). Finally, we are interested if there is an interaction between the noise and location conditions.
aov <- aov_ez('Sub_id', 'rt', fun_aggregate = mean, df, within = c('iv1', 'iv2')) print(aov)Code language: R (r)
Now, if you have categorical variables but still want to carry out regression analysis, creating dummy variables using R is advised before this might be possible.
Plotting an Interaction
The R package afex also has a function to plot an interaction. Now, before continuing with the Python ANOVA, we will use this function.
afex_plot(aov, x = "iv1", trace = "iv2", error = "within")Code language: R (r)
As can be seen and confirmed by the ANOVA table above, we see that there is no interaction. If we had an interaction, we could follow this up with pairwise comparisons using the package emmeans. Finally, before going on to the Python code, you can use the package dplyr to remove columns in R.
Here’s a Jupyter Notebook containing the above code examples.
Repeated Measures ANOVA in Python
Now that we know how to conduct a within-subjects ANOVA in R, we will carry out the same ANOVA in Python. In a previous post, we learned how to use the class AnovaRM from the Python package Statsmodels. In this post, however, we will use the package pingouin and the function anova_rm. Note that this function can handle a wide and long format data file.
Just a quick note: if your data is not following the normal distribution, the data is independent (i.e., two groups), you can carry out the Mann-Whitney U test in Python instead.
One-Way Repeated Measures ANOVA in Python
In the first example, we will conduct a one-way ANOVA for repeated measures using Python. We start by importing pandas as pd and pingoin as pg:
import pandas as pd import pingouin as pg df = pd.read_csv('./Python_ANOVA/rmAOV2way.csv') df.head()Code language: Python (python)
If we have data in Excel format, we can also import xlsx files into R dataframes.
Learn more about how to work with Pandas dataframe and load data from different file types:
- A Basic Pandas Dataframe Tutorial for Beginners
- Pandas Read CSV Tutorial
- Pandas Excel Tutorial: How to Read and Write Excel files
Now we can carry out our repeated measures ANOVA using Python:
aov = pg.rm_anova(dv='rt', within='iv1', subject='Sub_id', data=df, detailed=True) print(aov.round(2))Code language: Python (python)
Two-Way Repeated Measures ANOVA in Python
In the second example, we will carry out a two-way ANOVA for repeated measures using Python.
aov = pg.rm_anova(dv='rt', within=['iv1', 'iv2'], subject='Sub_id', data=df) print(aov.round(2))Code language: Python (python)
Interaction Plot in Python using Seaborn
For completeness, even though we didn’t have a significant interaction, we are going to create an interaction plot using Seaborn:
import seaborn as sns ax = sns.pointplot(x="iv1", y="rt", hue="iv2", data=df)Code language: Python (python)
Note that if we want to save plots in Python as high-resolution images, we can use matplotlib.pyplot and the savefig() method.
Learn more about data visualization in Python:
- How to Make a Scatter Plot in Python using Seaborn
- 9 Data Visualization Techniques You Should Learn in Python
Pingouin also comes with a function to carry out pairwise comparisons. If we had significant interaction, we could use it. See this post for an example of how to use this function.
Here’s a Jupyter Notebook containing the Python ANOVA examples above.
Conclusion: R vs Python
In this post, we have learned how to carry out one-way and two-way ANOVA for repeated measures using R and Python. We have used the r-package afex and the Python package pingouin. Both afex and pingouin are quite similar; they offer the Greenhouse-Geisser correction. In afex, however, you can get either partial eta-squared or general eta-squared effect sizes. Furthermore, as seen in the ANOVA tables, the results are the same.
In conclusion, the packages afex and pingouin offer an easy way to carry out ANOVA for within-subject designs in R and Python, respectively.
Here are some previous posts on how to carry out ANOVA in Python:
- Repeated Measures ANOVA in Python using Statsmodels
- Repeated measures ANOVA using Python
- Two-way ANOVA for repeated measures using Python
- Three ways to do a two-way ANOVA with Python
- Repeated Measures ANOVA using Statsmodels vs afex (YouTube)
The link to download the fake data isn’t there/working
Thanks for your comment. I’ve added a link to the fake data now,