Learn R Programming

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.

Related tutorial: Descriptive Statistics in R.

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.
  • ks_test(): perform two-sample and pairwise Kolmogorov-Smirnov tests comparing the empirical distributions of a numeric variable between groups.
  • 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_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.
  • add_xy_position(), get_y_position(): autocompute p-value positions for plotting significance using ggplot2.

Related tutorials: T-Test in R · Wilcoxon Test in R.

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.

Related tutorials: One-Way ANOVA in R · Repeated Measures ANOVA in R.

Post-hoc analyses

  • check_test_assumptions(): checks the normality and homogeneity-of-variance assumptions of a one-way, independent-groups design and returns the omnibus and post-hoc tests the data call for.
  • posthoc_test(): chooses and runs the post-hoc test appropriate to a one-way design, following the standard decision tree: Tukey HSD when the groups are normal with equal variances, Games-Howell when the variances differ, and Dunn’s test when the data are not normal.
  • 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.
  • conover_test(): compute Conover’s all-pairs rank comparison test, a more powerful alternative to Dunn’s test for post-hoc analysis following a significant Kruskal-Wallis test.
  • dunnett_test(): performs Dunnett’s many-to-one comparisons test, comparing each treatment group to a single control group following a one-way ANOVA.
  • friedman_conover_test() and friedman_nemenyi_test(): post-hoc pairwise comparisons (Conover and Nemenyi tests) following a significant Friedman 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.

Related tutorials: One-Way ANOVA in R · Kruskal-Wallis Test in R.

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.

Related tutorials: Chi-Square Test of Independence in R · Proportion Z-Test in R · Fisher’s Exact Test in R.

Comparing variances

  • levene_test(): Pipe-friendly framework to easily compute Levene’s test for homogeneity of variance across groups. Handles grouped data.
  • fligner_test(): Pipe-friendly wrapper around stats::fligner.test() to compute the Fligner-Killeen test, a non-parametric test for homogeneity of variances that is robust against departures from normality.
  • box_m(): Box’s M-test for homogeneity of covariance matrices

Related tutorial: Homogeneity of Variance Test in R.

Effect Size

  • cohens_d(): Compute cohen’s d measure of effect size for t-tests.
  • wilcox_effsize(): Compute Wilcoxon effect size (r) or the rank-biserial correlation.
  • cliff_delta(): Compute Cliff’s delta, a non-parametric effect size for the difference between two groups.
  • eta_squared() and partial_eta_squared(): Compute effect size for ANOVA, with optional confidence intervals.
  • omega_squared() and partial_omega_squared(): Compute the less-biased omega-squared effect size for a between-subjects 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, with optional confidence intervals.

Related tutorial: Cohen’s d Effect Size in R.

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.

Related tutorials: Correlation Test in R · Correlation Matrix in R.

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
  • add_cld(): add a compact letter display (CLD) column to a data frame of all-pairwise comparisons, grouping levels that are not significantly different under a shared letter.
  • 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() and create_test_label(): Extract or create labels from statistical tests, with an APA-7 in-text style (style = "apa").
  • tidy() and glance(): convert an rstatix test result into a plain tibble, so it flows into broom, gtsummary and gt.

Data manipulation helper functions

These functions are internally used in the rstatix and in the ggpubr R package to make it easy to program with tidyverse packages using non standard evaluation.

  • df_select(), df_arrange(), df_group_by(): wrappers arround dplyr functions for supporting standard and non standard evaluations.
  • df_nest_by(): Nest a tibble data frame using grouping specification. Supports standard and non standard evaluations.
  • df_split_by(): Split a data frame by groups into subsets or data panel. Very similar to the function df_nest_by(). The only difference is that, it adds labels to each data subset. Labels are the combination of the grouping variable levels.
  • df_unite(): Unite multiple columns into one.
  • df_unite_factors(): Unite factor columns. First, order factors levels then merge them into one column. The output column is a factor.
  • df_label_both(), df_label_value(): functions to label data frames rows by by one or multiple grouping variables.
  • df_get_var_names(): Returns user specified variable names. Supports standard and non standard evaluation.

Others

  • doo(): alternative to dplyr::do for doing anything. Technically it uses nest(...) %>% mutate(...) %>% map(...) to apply arbitrary computation to a grouped data frame.
  • sample_n_by(): sample n rows by group from a table
  • convert_as_factor(), set_ref_level(), reorder_levels(): Provides pipe-friendly functions to convert simultaneously multiple variables into a factor variable.
  • make_clean_names(): Pipe-friendly function to make syntactically valid column names (for input data frame) or names (for input vector).
  • counts_to_cases(): converts a contingency table or a data frame of counts into a data frame of individual observations.

Installation and loading

  • Install the latest developmental version from GitHub as follow:
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/rstatix")
  • Or install from CRAN as follow:
install.packages("rstatix")
  • Loading packages
library(rstatix)  
library(ggpubr)  # For easy data-visualization

Descriptive statistics

# Summary statistics of some selected variables
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>% 
  get_summary_stats(Sepal.Length, Sepal.Width, type = "common")
#> # A tibble: 2 × 10
#>   variable         n   min   max median   iqr  mean    sd    se    ci
#>   <fct>        <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal.Length   150   4.3   7.9    5.8   1.3  5.84 0.828 0.068 0.134
#> 2 Sepal.Width    150   2     4.4    3     0.5  3.06 0.436 0.036 0.07

# Whole data frame
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>% get_summary_stats(type = "common")
#> # A tibble: 4 × 10
#>   variable         n   min   max median   iqr  mean    sd    se    ci
#>   <fct>        <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal.Length   150   4.3   7.9   5.8    1.3  5.84 0.828 0.068 0.134
#> 2 Sepal.Width    150   2     4.4   3      0.5  3.06 0.436 0.036 0.07 
#> 3 Petal.Length   150   1     6.9   4.35   3.5  3.76 1.76  0.144 0.285
#> 4 Petal.Width    150   0.1   2.5   1.3    1.5  1.20 0.762 0.062 0.123


# Grouped data
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>%
  group_by(Species) %>% 
  get_summary_stats(Sepal.Length, type = "mean_sd")
#> # A tibble: 3 × 5
#>   Species    variable         n  mean    sd
#>   <fct>      <fct>        <dbl> <dbl> <dbl>
#> 1 setosa     Sepal.Length    50  5.01 0.352
#> 2 versicolor Sepal.Length    50  5.94 0.516
#> 3 virginica  Sepal.Length    50  6.59 0.636

Comparing two means

To compare the means of two groups, you can use either the function t_test() (parametric) or wilcox_test() (non-parametric). In the following example the t-test will be illustrated.

Data

Preparing the demo data set:

df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df)
#>    len supp dose
#> 1  4.2   VC  0.5
#> 2 11.5   VC  0.5
#> 3  7.3   VC  0.5
#> 4  5.8   VC  0.5
#> 5  6.4   VC  0.5
#> 6 10.0   VC  0.5

One-sample test

The one-sample test is used to compare the mean of one sample to a known standard (or theoretical / hypothetical) mean (mu).

df %>% t_test(len ~ 1, mu = 0)
#> # A tibble: 1 × 7
#>   .y.   group1 group2         n statistic    df        p
#> * <chr> <chr>  <chr>      <int>     <dbl> <dbl>    <dbl>
#> 1 len   1      null model    60      19.1    59 6.94e-27
# One-sample test of each dose level
df %>% 
  group_by(dose) %>%
  t_test(len ~ 1, mu = 0)
#> # A tibble: 3 × 8
#>   dose  .y.   group1 group2         n statistic    df        p
#> * <fct> <chr> <chr>  <chr>      <int>     <dbl> <dbl>    <dbl>
#> 1 0.5   len   1      null model    20      10.5    19 2.24e- 9
#> 2 1     len   1      null model    20      20.0    19 3.22e-14
#> 3 2     len   1      null model    20      30.9    19 1.03e-17

Compare two independent groups

  • Create a simple box plot with p-values:
# T-test
stat.test <- df %>% 
  t_test(len ~ supp, paired = FALSE) 
stat.test
#> # A tibble: 1 × 8
#>   .y.   group1 group2    n1    n2 statistic    df      p
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>  <dbl>
#> 1 len   OJ     VC        30    30      1.92  55.3 0.0606

# Create a box plot
p <- ggboxplot(
  df, x = "supp", y = "len", 
  color = "supp", palette = "jco", ylim = c(0,40)
  )
# Add the p-value manually
p + stat_pvalue_manual(stat.test, label = "p", y.position = 35)

p +stat_pvalue_manual(stat.test, label = "T-test, p = {p}", 
                      y.position = 36)

  • Grouped data: compare supp levels after grouping the data by “dose”
# Statistical test
stat.test <- df %>%
  group_by(dose) %>%
  t_test(len ~ supp) %>%
  adjust_pvalue() %>%
  add_significance("p.adj")
stat.test
#> # A tibble: 3 × 11
#>   dose  .y.   group1 group2    n1    n2 statistic    df       p   p.adj
#>   <fct> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl>
#> 1 0.5   len   OJ     VC        10    10    3.17    15.0 0.00636 0.0127 
#> 2 1     len   OJ     VC        10    10    4.03    15.4 0.00104 0.00312
#> 3 2     len   OJ     VC        10    10   -0.0461  14.0 0.964   0.964  
#> # ℹ 1 more variable: p.adj.signif <chr>

# Visualization
ggboxplot(
  df, x = "supp", y = "len",
  color = "supp", palette = "jco", facet.by = "dose",
  ylim = c(0, 40)
  ) +
  stat_pvalue_manual(stat.test, label = "p.adj", y.position = 35)

Compare paired samples

# T-test
stat.test <- df %>% 
  t_test(len ~ supp, paired = TRUE) 
stat.test
#> # A tibble: 1 × 8
#>   .y.   group1 group2    n1    n2 statistic    df       p
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>
#> 1 len   OJ     VC        30    30      3.30    29 0.00255

# Box plot
p <- ggpaired(
  df, x = "supp", y = "len", color = "supp", palette = "jco", 
  line.color = "gray", line.size = 0.4, ylim = c(0, 40)
  )
p + stat_pvalue_manual(stat.test, label = "p", y.position = 36)

Multiple pairwise comparisons

  • Pairwise comparisons: if the grouping variable contains more than two categories, a pairwise comparison is automatically performed.
# Pairwise t-test
pairwise.test <- df %>% t_test(len ~ dose)
pairwise.test
#> # A tibble: 3 × 10
#>   .y.   group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
#> 1 len   0.5    1         20    20     -6.48  38.0 1.27e- 7 2.54e- 7 ****        
#> 2 len   0.5    2         20    20    -11.8   36.9 4.40e-14 1.32e-13 ****        
#> 3 len   1      2         20    20     -4.90  37.1 1.91e- 5 1.91e- 5 ****
# Box plot
ggboxplot(df, x = "dose", y = "len")+
  stat_pvalue_manual(
    pairwise.test, label = "p.adj", 
    y.position = c(29, 35, 39)
    )

  • Multiple pairwise comparisons against reference group: each level is compared to the ref group
# Comparison against reference group
#::::::::::::::::::::::::::::::::::::::::
# T-test: each level is compared to the ref group
stat.test <- df %>% t_test(len ~ dose, ref.group = "0.5")
stat.test
#> # A tibble: 2 × 10
#>   .y.   group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
#> 1 len   0.5    1         20    20     -6.48  38.0 1.27e- 7 1.27e- 7 ****        
#> 2 len   0.5    2         20    20    -11.8   36.9 4.40e-14 8.80e-14 ****
# Box plot
ggboxplot(df, x = "dose", y = "len", ylim = c(0, 40)) +
  stat_pvalue_manual(
    stat.test, label = "p.adj.signif", 
    y.position = c(29, 35)
    )

# Remove bracket
ggboxplot(df, x = "dose", y = "len", ylim = c(0, 40)) +
  stat_pvalue_manual(
    stat.test, label = "p.adj.signif", 
    y.position = c(29, 35),
    remove.bracket = TRUE
    )

  • Multiple pairwise comparisons against all (base-mean): Comparison of each group against base-mean.
# T-test
stat.test <- df %>% t_test(len ~ dose, ref.group = "all")
stat.test
#> # A tibble: 3 × 10
#>   .y.   group1 group2    n1    n2 statistic    df         p   p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>     <dbl>   <dbl> <chr>       
#> 1 len   all    0.5       60    20     5.82   56.4   2.90e-7 8.69e-7 ****        
#> 2 len   all    1         60    20    -0.660  57.5   5.12e-1 5.12e-1 ns          
#> 3 len   all    2         60    20    -5.61   66.5   4.25e-7 8.69e-7 ****
# Box plot with horizontal mean line
ggboxplot(df, x = "dose", y = "len") +
  stat_pvalue_manual(
    stat.test, label = "p.adj.signif", 
    y.position = 35,
    remove.bracket = TRUE
    ) +
  geom_hline(yintercept = mean(df$len), linetype = 2)

ANOVA test

# One-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>% anova_test(len ~ dose)
#> ANOVA Table (type II tests)
#> 
#>   Effect DFn DFd      F        p p<.05   ges
#> 1   dose   2  57 67.416 9.53e-16     * 0.703

# Two-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>% anova_test(len ~ supp*dose)
#> ANOVA Table (type II tests)
#> 
#>      Effect DFn DFd      F        p p<.05   ges
#> 1      supp   1  54 15.572 2.31e-04     * 0.224
#> 2      dose   2  54 92.000 4.05e-18     * 0.773
#> 3 supp:dose   2  54  4.107 2.20e-02     * 0.132

# Two-way repeated measures ANOVA
#:::::::::::::::::::::::::::::::::::::::::
df$id <- rep(1:10, 6) # Add individuals id
# Use formula
# df %>% anova_test(len ~ supp*dose + Error(id/(supp*dose)))
# or use character vector
df %>% anova_test(dv = len, wid = id, within = c(supp, dose))
#> ANOVA Table (type III tests)
#> 
#> $ANOVA
#>      Effect DFn DFd       F        p p<.05   ges
#> 1      supp   1   9  34.866 2.28e-04     * 0.224
#> 2      dose   2  18 106.470 1.06e-10     * 0.773
#> 3 supp:dose   2  18   2.534 1.07e-01       0.132
#> 
#> $`Mauchly's Test for Sphericity`
#>      Effect     W     p p<.05
#> 1      dose 0.807 0.425      
#> 2 supp:dose 0.934 0.761      
#> 
#> $`Sphericity Corrections`
#>      Effect   GGe      DF[GG]    p[GG] p[GG]<.05   HFe      DF[HF]    p[HF]
#> 1      dose 0.838 1.68, 15.09 2.79e-09         * 1.008 2.02, 18.15 1.06e-10
#> 2 supp:dose 0.938 1.88, 16.88 1.12e-01           1.176 2.35, 21.17 1.07e-01
#>   p[HF]<.05
#> 1         *
#> 2

# Use model as arguments
#:::::::::::::::::::::::::::::::::::::::::
.my.model <- lm(yield ~ block + N*P*K, npk)
anova_test(.my.model)
#> ANOVA Table (type II tests)
#> 
#>   Effect DFn DFd      F     p p<.05   ges
#> 1  block   4  12  4.959 0.014     * 0.623
#> 2      N   1  12 12.259 0.004     * 0.505
#> 3      P   1  12  0.544 0.475       0.043
#> 4      K   1  12  6.166 0.029     * 0.339
#> 5    N:P   1  12  1.378 0.263       0.103
#> 6    N:K   1  12  2.146 0.169       0.152
#> 7    P:K   1  12  0.031 0.863       0.003
#> 8  N:P:K   0  12     NA    NA  <NA>    NA

Correlation tests

# Data preparation
mydata <- mtcars %>% 
  select(mpg, disp, hp, drat, wt, qsec)
head(mydata, 3)
#>                mpg disp  hp drat    wt  qsec
#> Mazda RX4     21.0  160 110 3.90 2.620 16.46
#> Mazda RX4 Wag 21.0  160 110 3.90 2.875 17.02
#> Datsun 710    22.8  108  93 3.85 2.320 18.61

# Correlation test between two variables
mydata %>% cor_test(wt, mpg, method = "pearson")
#> # A tibble: 1 × 9
#>   var1  var2    cor statistic    df        p conf.low conf.high method 
#>   <chr> <chr> <dbl>     <dbl> <int>    <dbl>    <dbl>     <dbl> <chr>  
#> 1 wt    mpg   -0.87     -9.56    30 1.29e-10   -0.934    -0.744 Pearson

# Correlation of one variable against all
mydata %>% cor_test(mpg, method = "pearson")
#> # A tibble: 5 × 9
#>   var1  var2    cor statistic    df        p conf.low conf.high method 
#>   <chr> <chr> <dbl>     <dbl> <int>    <dbl>    <dbl>     <dbl> <chr>  
#> 1 mpg   disp  -0.85     -8.75    30 9.38e-10  -0.923     -0.708 Pearson
#> 2 mpg   hp    -0.78     -6.74    30 1.79e- 7  -0.885     -0.586 Pearson
#> 3 mpg   drat   0.68      5.10    30 1.78e- 5   0.436      0.832 Pearson
#> 4 mpg   wt    -0.87     -9.56    30 1.29e-10  -0.934     -0.744 Pearson
#> 5 mpg   qsec   0.42      2.53    30 1.71e- 2   0.0820     0.670 Pearson

# Pairwise correlation test between all variables
mydata %>% cor_test(method = "pearson")
#> # A tibble: 36 × 9
#>    var1  var2    cor statistic    df        p conf.low conf.high method 
#>    <chr> <chr> <dbl>     <dbl> <int>    <dbl>    <dbl>     <dbl> <chr>  
#>  1 mpg   mpg    1       Inf       30 0          1          1     Pearson
#>  2 mpg   disp  -0.85     -8.75    30 9.38e-10  -0.923     -0.708 Pearson
#>  3 mpg   hp    -0.78     -6.74    30 1.79e- 7  -0.885     -0.586 Pearson
#>  4 mpg   drat   0.68      5.10    30 1.78e- 5   0.436      0.832 Pearson
#>  5 mpg   wt    -0.87     -9.56    30 1.29e-10  -0.934     -0.744 Pearson
#>  6 mpg   qsec   0.42      2.53    30 1.71e- 2   0.0820     0.670 Pearson
#>  7 disp  mpg   -0.85     -8.75    30 9.38e-10  -0.923     -0.708 Pearson
#>  8 disp  disp   1       Inf       30 0          1          1     Pearson
#>  9 disp  hp     0.79      7.08    30 7.14e- 8   0.611      0.893 Pearson
#> 10 disp  drat  -0.71     -5.53    30 5.28e- 6  -0.849     -0.481 Pearson
#> # ℹ 26 more rows

Correlation matrix

# Compute correlation matrix
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat <- mydata %>% cor_mat()
cor.mat
#> # A tibble: 6 × 7
#>   rowname   mpg  disp    hp   drat    wt   qsec
#> * <chr>   <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1 mpg      1    -0.85 -0.78  0.68  -0.87  0.42 
#> 2 disp    -0.85  1     0.79 -0.71   0.89 -0.43 
#> 3 hp      -0.78  0.79  1    -0.45   0.66 -0.71 
#> 4 drat     0.68 -0.71 -0.45  1     -0.71  0.091
#> 5 wt      -0.87  0.89  0.66 -0.71   1    -0.17 
#> 6 qsec     0.42 -0.43 -0.71  0.091 -0.17  1

# Show the significance levels
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>% cor_get_pval()
#> # A tibble: 6 × 7
#>   rowname      mpg     disp           hp      drat       wt       qsec
#>   <chr>      <dbl>    <dbl>        <dbl>     <dbl>    <dbl>      <dbl>
#> 1 mpg     0        9.38e-10 0.000000179  1.78e-  5 1.29e-10 0.0171    
#> 2 disp    9.38e-10 0        0.0000000714 5.28e-  6 1.22e-11 0.0131    
#> 3 hp      1.79e- 7 7.14e- 8 0            9.99e-  3 4.15e- 5 0.00000577
#> 4 drat    1.78e- 5 5.28e- 6 0.00999      7.44e-232 4.78e- 6 0.620     
#> 5 wt      1.29e-10 1.22e-11 0.0000415    4.78e-  6 0        0.339     
#> 6 qsec    1.71e- 2 1.31e- 2 0.00000577   6.20e-  1 3.39e- 1 0

# Replacing correlation coefficients by symbols
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>%
  cor_as_symbols() %>%
  pull_lower_triangle()
#>   rowname mpg disp hp drat wt qsec
#> 1     mpg                         
#> 2    disp   *                     
#> 3      hp   *    *                
#> 4    drat   +    +  .             
#> 5      wt   *    *  +    +        
#> 6    qsec   .    .  +

# Mark significant correlations
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>%
  cor_mark_significant()
#>   rowname       mpg      disp        hp      drat    wt qsec
#> 1     mpg                                                   
#> 2    disp -0.85****                                         
#> 3      hp -0.78****  0.79****                               
#> 4    drat  0.68**** -0.71****   -0.45**                     
#> 5      wt -0.87****  0.89****  0.66**** -0.71****           
#> 6    qsec     0.42*    -0.43* -0.71****     0.091 -0.17


# Draw correlogram using R base plot
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>%
  cor_reorder() %>%
  pull_lower_triangle() %>% 
  cor_plot()

Related articles

Copy Link

Version

Install

install.packages('rstatix')

Monthly Downloads

236,052

Version

1.1.0

License

GPL-2

Maintainer

Alboukadel Kassambara

Last Published

July 23rd, 2026

Functions in rstatix (1.1.0)

cochran_qtest

Cochran's Q Test
cliff_delta

Cliff's Delta Effect Size for Ordinal / Non-parametric Comparisons
cor_mat

Compute Correlation Matrix with P-values
cohens_d

Compute Cohen's d Measure of Effect Size
cor_reorder

Reorder Correlation Matrix
cor_as_symbols

Replace Correlation Coefficients by Symbols
cor_mark_significant

Add Significance Levels To a Correlation Matrix
chisq_test

Chi-squared Test for Count Data
conover_test

Conover's All-Pairs Rank Comparison Test
cor_plot

Visualize Correlation Matrix Using Base Plot
cor_select

Subset Correlation Matrix
df_label_both

Functions to Label Data Frames by Grouping Variables
df_get_var_names

Get User Specified Variable Names
counts_to_cases

Convert a Table of Counts into a Data Frame of cases
df_nest_by

Nest a Tibble By Groups
cramer_v

Compute Cramer's V
cor_gather

Reshape Correlation Data
df_group_by

Group a Data Frame by One or more Variables
df_arrange

Arrange Rows by Column Values
cor_test

Correlation Test
factorial_design

Build Factorial Designs for ANOVA
dunnett_test

Dunnett's Many-to-One Comparisons Test
eta_squared

Effect Size for ANOVA
dunn_test

Dunn's Test of Multiple Comparisons
df_select

Select Columns in a Data Frame
doo

Alternative to dplyr::do for Doing Anything
df_unite

Unite Multiple Columns into One
df_split_by

Split a Data Frame into Subset
emmeans_test

Pairwise Comparisons of Estimated Marginal Means
convert_as_factor

Factors
friedman_conover_test

Conover's All-Pairs Comparisons Test for Friedman Rank Sums
get_comparisons

Create a List of Possible Comparisons Between Groups
freq_table

Compute Frequency Table
fisher_test

Fisher's Exact Test for Count Data
fligner_test

Fligner-Killeen Test
friedman_test

Friedman Rank Sum Test
get_mode

Compute Mode
games_howell_test

Games Howell Post-hoc Tests
friedman_nemenyi_test

Nemenyi Post-Hoc Test for Friedman Rank Sums
friedman_effsize

Friedman Test Effect Size (Kendall's W Value)
kruskal_effsize

Kruskal-Wallis Effect Size
make_clean_names

Make Clean Names
get_summary_stats

Compute Summary Statistics
mahalanobis_distance

Compute Mahalanobis Distance and Flag Multivariate Outliers
kruskal_test

Kruskal-Wallis Test
mcnemar_test

McNemar's Chi-squared Test for Count Data
levene_test

Levene's Test
get_y_position

Autocompute P-value Positions For Plotting Significance
ks_test

Two-Sample Kolmogorov-Smirnov Test
get_pwc_label

Extract Label Information from Statistical Tests
posthoc_test

Choose and Run the Appropriate Post-Hoc Test
prop_trend_test

Test for Trend in Proportions
pull_triangle

Pull Lower and Upper Triangular Part of a Matrix
identify_outliers

Identify Univariate Outliers Using Boxplot Methods
prop_test

Proportion Test
multinom_test

Exact Multinomial Test
%>%

Pipe operator
omega_squared

Omega Squared for ANOVA
p_round

Rounding and Formatting p-values
reexports

Objects exported from other packages
sample_n_by

Sample n Rows By Group From a Table
tidy.rstatix_test

Tidy an rstatix Test Result
rstatix-references

References and related packages
replace_triangle

Replace Lower and Upper Triangular Part of a Matrix
sign_test

Sign Test
rstatix-programming

Programming with rstatix (tidy evaluation)
tukey_hsd

Tukey Honest Significant Differences
shapiro_test

Shapiro-Wilk Normality Test
remove_ns

Remove Non-Significant from Statistical Tests
t_test

T-test
wilcox_effsize

Wilcoxon Effect Size
welch_anova_test

Welch One-Way ANOVA Test
wilcox_test

Wilcoxon Tests
check_test_assumptions

Check One-Way Assumptions and Recommend the Test
Manova

Manova exported from car package
box_m

Box's M-test for Homogeneity of Covariance Matrices
binom_test

Exact Binomial Test
adjust_pvalue

Adjust P-values for Multiple Comparisons
add_cld

Compact Letter Display of All-Pairwise Comparisons
anova_summary

Create Nice Summary Tables of ANOVA Results
anova_test

Anova Test
as_cor_mat

Convert a Correlation Test Data Frame into a Correlation Matrix
add_significance

Add P-value Significance Symbols