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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_label()`: Create labels from user specified test results.
   
   
### 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](https://github.com/kassambara/rstatix) as follow: 
    
   
```{r, eval = FALSE}
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/rstatix")
```
     
     
- Or install from [CRAN](https://cran.r-project.org/package=ggpubr) as follow: 
    
     
```{r, eval = FALSE}
install.packages("rstatix")
```


- Loading packages
   
```{r}
library(rstatix)  
library(ggpubr)  # For easy data-visualization
```
  
  
## Descriptive statistics
    
```{r}
# Summary statistics of some selected variables
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>% 
  get_summary_stats(Sepal.Length, Sepal.Width, type = "common")

# Whole data frame
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>% get_summary_stats(type = "common")


# Grouped data
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>%
  group_by(Species) %>% 
  get_summary_stats(Sepal.Length, type = "mean_sd")
```

     
## 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:
   
```{r}
df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df)
```
   
   
### 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`).
    
    
```{r}
df %>% t_test(len ~ 1, mu = 0)
# One-sample test of each dose level
df %>% 
  group_by(dose) %>%
  t_test(len ~ 1, mu = 0)
```
   
   
### Compare two independent groups
   
   
- Create a simple box plot with p-values:
   
```{r unpaired-two-sample-t-test, fig.width=3.5, fig.height=4}
# T-test
stat.test <- df %>% 
  t_test(len ~ supp, paired = FALSE) 
stat.test

# 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)
```
    
    
- Customize labels using [glue expression](https://github.com/tidyverse/glue): 

  
```{r custoize-p-value-labels, fig.width=3.5, fig.height=4}
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"
   
```{r grouped-two-sample-t-test, fig.width=6, fig.height=4}
# Statistical test
stat.test <- df %>%
  group_by(dose) %>%
  t_test(len ~ supp) %>%
  adjust_pvalue() %>%
  add_significance("p.adj")
stat.test

# 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
    
    
```{r paired-t-test, fig.width=3.5, fig.height=4}
# T-test
stat.test <- df %>% 
  t_test(len ~ supp, paired = TRUE) 
stat.test

# 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.
   
   
```{r pairwise-comparisons, fig.width=3.5, fig.height=3}
# Pairwise t-test
pairwise.test <- df %>% t_test(len ~ dose)
pairwise.test
# 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
    
    
```{r comaprison-against-reference-group, fig.width=3.5, fig.height=3}
# 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
# 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.
   
   
```{r comparison-against-base-mean, fig.width=3.5, fig.height=3}
# T-test
stat.test <- df %>% t_test(len ~ dose, ref.group = "all")
stat.test
# 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
    
```{r}
# One-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>% anova_test(len ~ dose)

# Two-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>% anova_test(len ~ supp*dose)

# 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))

# Use model as arguments
#:::::::::::::::::::::::::::::::::::::::::
.my.model <- lm(yield ~ block + N*P*K, npk)
anova_test(.my.model)
```
   
   
## Correlation tests
   
```{r}
# Data preparation
mydata <- mtcars %>% 
  select(mpg, disp, hp, drat, wt, qsec)
head(mydata, 3)

# Correlation test between two variables
mydata %>% cor_test(wt, mpg, method = "pearson")

# Correlation of one variable against all
mydata %>% cor_test(mpg, method = "pearson")

# Pairwise correlation test between all variables
mydata %>% cor_test(method = "pearson")
```
   
   
## Correlation matrix
   
```{r, fig.width=4, fig.height=4}
# Compute correlation matrix
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat <- mydata %>% cor_mat()
cor.mat

# Show the significance levels
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>% cor_get_pval()

# Replacing correlation coefficients by symbols
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>%
  cor_as_symbols() %>%
  pull_lower_triangle()

# Mark significant correlations
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
cor.mat %>%
  cor_mark_significant()


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

    
## Related articles

- [How to Add P-Values onto Basic GGPLOTS](https://www.datanovia.com/en/blog/how-to-add-p-values-onto-basic-ggplots/)
- [How to Add Adjusted P-values to a Multi-Panel GGPlot](https://www.datanovia.com/en/blog/ggpubr-how-to-add-adjusted-p-values-to-a-multi-panel-ggplot/)
- [How to Add P-values to GGPLOT Facets](https://www.datanovia.com/en/blog/how-to-add-p-values-to-ggplot-facets/)
- [How to Add P-Values Generated Elsewhere to a GGPLOT](https://www.datanovia.com/en/blog/ggpubr-how-to-add-p-values-generated-elsewhere-to-a-ggplot/)
- [How to Add P-Values onto a Grouped GGPLOT using the GGPUBR R Package](https://www.datanovia.com/en/blog/how-to-add-p-values-onto-a-grouped-ggplot-using-the-ggpubr-r-package/)
- [How to Create Stacked Bar Plots with Error Bars and P-values](https://www.datanovia.com/en/blog/how-to-create-stacked-bar-plots-with-error-bars-and-p-values/)
- [How to Add P-Values onto Horizontal GGPLOTS](https://www.datanovia.com/en/blog/how-to-add-p-values-onto-horizontal-ggplots/)
- [Add P-values and Significance Levels to ggplots](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/76-add-p-values-and-significance-levels-to-ggplots/)
- [Comparing Means of Two Groups in R](https://www.datanovia.com/en/courses/comparing-means-of-two-groups-in-r/)
    - [T-test in R](https://www.datanovia.com/en/lessons/t-test-in-r/)
    - [Wilcoxon Test in R](https://www.datanovia.com/en/lessons/wilcoxon-test-in-r/)
    - [Sign Test in R](https://www.datanovia.com/en/lessons/sign-test-in-r/)
- [Comparing Multiple Means in R](https://www.datanovia.com/en/courses/comparing-multiple-means-in-r/)
    - [ANOVA in R](https://www.datanovia.com/en/lessons/anova-in-r/)
    - [Repeated Measures ANOVA in R](https://www.datanovia.com/en/lessons/repeated-measures-anova-in-r/)
    - [Mixed ANOVA in R](https://www.datanovia.com/en/lessons/mixed-anova-in-r/)
    - [ANCOVA in R](https://www.datanovia.com/en/lessons/ancova-in-r/)
    - [One-Way MANOVA in R](https://www.datanovia.com/en/lessons/one-way-manova-in-r/)
    - [Kruskal-Wallis Test in R](https://www.datanovia.com/en/lessons/kruskal-wallis-test-in-r/)
    - [Friedman Test in R](https://www.datanovia.com/en/lessons/friedman-test-in-r/)
