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rstatix

Provides a simple and intuitive pipe-friendly framework, coherent with the ‘tidyverse’ design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses.

The output of each test is automatically transformed into a tidy data frame to facilitate visualization.

Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely ‘within-Ss’ designs (repeated measures), purely ‘between-Ss’ designs, and mixed ‘within-and-between-Ss’ designs.

It’s also possible to compute several effect size metrics, including “eta squared” for ANOVA, “Cohen’s d” for t-test and “Cramer’s V” for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.

Key functions

Descriptive statistics

• get_summary_stats(): Compute summary statistics for one or multiple numeric variables. Can handle grouped data.
• freq_table(): Compute frequency table of categorical variables.
• get_mode(): Compute the mode of a vector, that is the most frequent values.
• identify_outliers(): Detect univariate outliers using boxplot methods.
• mahalanobis_distance(): Compute Mahalanobis Distance and Flag Multivariate Outliers.
• shapiro_test() and mshapiro_test(): Univariate and multivariate Shapiro-Wilk normality test.

Comparing means

• t_test(): perform one-sample, two-sample and pairwise t-tests
• wilcox_test(): perform one-sample, two-sample and pairwise Wilcoxon tests
• sign_test(): perform sign test to determine whether there is a median difference between paired or matched observations.
• anova_test(): an easy-to-use wrapper around car::Anova() to perform different types of ANOVA tests, including independent measures ANOVA, repeated measures ANOVA and mixed ANOVA.
• get_anova_test_table(): extract ANOVA table from anova_test() results. Can apply sphericity correction automatically in the case of within-subject (repeated measures) designs.
• welch_anova_test(): Welch one-Way ANOVA test. A pipe-friendly wrapper around the base function stats::oneway.test(). This is is an alternative to the standard one-way ANOVA in the situation where the homogeneity of variance assumption is violated.
• kruskal_test(): perform kruskal-wallis rank sum test
• friedman_test(): Provides a pipe-friendly framework to perform a Friedman rank sum test, which is the non-parametric alternative to the one-way repeated measures ANOVA test.
• get_comparisons(): Create a list of possible pairwise comparisons between groups.
• get_pvalue_position(): autocompute p-value positions for plotting significance using ggplot2.

Facilitating ANOVA computation in R

• factorial_design(): build factorial design for easily computing ANOVA using the car::Anova() function. This might be very useful for repeated measures ANOVA, which is hard to set up with the car package.
• anova_summary(): Create beautiful summary tables of ANOVA test results obtained from either car::Anova() or stats::aov(). The results include ANOVA table, generalized effect size and some assumption checks, such as Mauchly’s test for sphericity in the case of repeated measures ANOVA.

Post-hoc analyses

• tukey_hsd(): performs tukey post-hoc tests. Can handle different inputs formats: aov, lm, formula.
• dunn_test(): compute multiple pairwise comparisons following Kruskal-Wallis test.
• games_howell_test(): Performs Games-Howell test, which is used to compare all possible combinations of group differences when the assumption of homogeneity of variances is violated.
• emmeans_test(): pipe-friendly wrapper arround emmeans function to perform pairwise comparisons of estimated marginal means. Useful for post-hoc analyses following up ANOVA/ANCOVA tests.

Comparing proportions

• prop_test(), pairwise_prop_test() and row_wise_prop_test(). Performs one-sample and two-samples z-test of proportions. Wrappers around the R base function prop.test() but have the advantage of performing pairwise and row-wise z-test of two proportions, the post-hoc tests following a significant chi-square test of homogeneity for 2xc and rx2 contingency tables.
• fisher_test(), pairwise_fisher_test() and row_wise_fisher_test(): Fisher’s exact test for count data. Wrappers around the R base function fisher.test() but have the advantage of performing pairwise and row-wise fisher tests, the post-hoc tests following a significant chi-square test of homogeneity for 2xc and rx2 contingency tables.
• chisq_test(), pairwise_chisq_gof_test(), pairwise_chisq_test_against_p(): Performs chi-squared tests, including goodness-of-fit, homogeneity and independence tests.
• binom_test(), pairwise_binom_test(), pairwise_binom_test_against_p(): Performs exact binomial test and pairwise comparisons following a significant exact multinomial test. Alternative to the chi-square test of goodness-of-fit-test when the sample.
• multinom_test(): performs an exact multinomial test. Alternative to the chi-square test of goodness-of-fit-test when the sample size is small.
• mcnemar_test(): performs McNemar chi-squared test to compare paired proportions. Provides pairwise comparisons between multiple groups.
• cochran_qtest(): extension of the McNemar Chi-squared test for comparing more than two paired proportions.
• prop_trend_test(): Performs chi-squared test for trend in proportion. This test is also known as Cochran-Armitage trend test.

Comparing variances

• levene_test(): Pipe-friendly framework to easily compute Levene’s test for homogeneity of variance across groups. Handles grouped data.
• box_m(): Box’s M-test for homogeneity of covariance matrices

Effect Size

• cohens_d(): Compute cohen’s d measure of effect size for t-tests.
• wilcox_effsize(): Compute Wilcoxon effect size (r).
• eta_squared() and partial_eta_squared(): Compute effect size for ANOVA.
• kruskal_effsize(): Compute the effect size for Kruskal-Wallis test as the eta squared based on the H-statistic.
• friedman_effsize(): Compute the effect size of Friedman test using the Kendall’s W value.
• cramer_v(): Compute Cramer’s V, which measures the strength of the association between categorical variables.

Correlation analysis

Computing correlation:

• cor_test(): correlation test between two or more variables using Pearson, Spearman or Kendall methods.
• cor_mat(): compute correlation matrix with p-values. Returns a data frame containing the matrix of the correlation coefficients. The output has an attribute named “pvalue”, which contains the matrix of the correlation test p-values.
• cor_get_pval(): extract a correlation matrix p-values from an object of class cor_mat().
• cor_pmat(): compute the correlation matrix, but returns only the p-values of the correlation tests.
• as_cor_mat(): convert a cor_test object into a correlation matrix format.

Reshaping correlation matrix:

• cor_reorder(): reorder correlation matrix, according to the coefficients, using the hierarchical clustering method.
• cor_gather(): takes a correlation matrix and collapses (or melt) it into long format data frame (paired list)
• cor_spread(): spread a long correlation data frame into wide format (correlation matrix).

Subsetting correlation matrix:

• cor_select(): subset a correlation matrix by selecting variables of interest.
• pull_triangle(), pull_upper_triangle(), pull_lower_triangle(): pull upper and lower triangular parts of a (correlation) matrix.
• replace_triangle(), replace_upper_triangle(), replace_lower_triangle(): replace upper and lower triangular parts of a (correlation) matrix.

Visualizing correlation matrix:

• cor_as_symbols(): replaces the correlation coefficients, in a matrix, by symbols according to the value.
• cor_plot(): visualize correlation matrix using base plot.
• cor_mark_significant(): add significance levels to a correlation matrix.

Adjusting p-values, formatting and adding significance symbols

• adjust_pvalue(): add an adjusted p-values column to a data frame containing statistical test p-values
• add_significance(): add a column containing the p-value significance level
• p_round(), p_format(), p_mark_significant(): rounding and formatting p-values

Extract information from statistical tests

Extract information from statistical test results. Useful for labelling plots with test outputs.

• get_pwc_label(): Extract label from pairwise comparisons.
• get_test_label(): Extract label from statistical tests.
• `create_test_l