Y <- which(names(dat) = "Sepal.Length" # names of variables to test X <- which(names(dat) = "Species") # name of grouping variable It also facilitates the creation of publication-ready plots for non-advanced statistical audiences.Īfter many refinements and modifications of the initial code (available in this article), I finally came up with a rather stable and robust process to perform t-tests and ANOVA for more than one variable at once, and more importantly, make the results concise and easily readable by anyone (statisticians or not).Ī graph is worth a thousand words, so here are the exact same tests than in the previous section, but this time with my new R routine: library(ggpubr)
This package allows to indicate the test used and the p-value of the test directly on a ggplot2-based graph. Draw boxplots illustrating the distributions by group (with the boxplot() function or thanks to the package.In the past, I used to do the analyses by following these 3 steps:
#How to do two way anova in excel 2007 how to#
These two tests are quite basic and have been extensively documented online and in statistical textbooks so the difficulty is not in how to perform these tests. To compare three groups or more, an ANOVA should be performed.
To compare two groups, a Student’s t-test should be used 1.As part of my teaching assistant position in a Belgian university, students often ask me for some help in their statistical analyses for their master’s thesis.Ī frequent question is how to compare groups of patients in terms of several quantitative continuous variables.
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