The Categorical Data series: analyse counts and contingency tables in R — the chi-square tests of independence and goodness-of-fit, Fisher’s exact test, McNemar and Cochran’s Q for paired data, Cochran-Mantel-Haenszel for stratified data, and the odds ratio & relative risk — with rstatix, base R, and on-plot p-values.
Learn › Biostatistics › Categorical Data Analysis
Categorical data are counts — how many observations fall into each category. This series analyses them the way the work happens: build a contingency table, test whether two categorical variables are associated (chi-square, Fisher), handle paired designs (McNemar, Cochran’s Q), control for a third variable (Cochran-Mantel-Haenszel), and quantify risk with the odds ratio & relative risk — with the tidy rstatix helpers, base R shown alongside, and the p-values on the plot.
The workhorse: does an outcome depend on a grouping variable? Cross-tabulate, then chisq.test():
xtab <- as.table(rbind(
Treated = c(improved = 21, none = 9),
Untreated = c(improved = 10, none = 20)
))
chisq.test(xtab)
Pearson's Chi-squared test with Yates' continuity correction
data: xtab
X-squared = 6.6741, df = 1, p-value = 0.009782A small p-value says the two variables are associated (here, improvement depends on treatment).
Ask Prova “which categorical test fits my data?” — it answers with the right test (chi-square, Fisher, McNemar, CMH, odds ratio) and rstatix code you can run on your own counts. The runtime is the judge. Ask Prova →
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