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.

核心函数

Descriptive statistics

get_summary_stats(): 计算一个或多个数值变量的摘要统计信息。可以处理分组数据.
freq_table(): 计算分类变量频率表.
get_mode(): 计算向量的模式，这是最常见的值.
identify_outliers(): 用箱线图方法检测单变量异常值.
mahalanobis_distance(): 计算马氏距离和标志多元离群值.
shapiro_test() and mshapiro_test(): 单变量和多变量Shapiro-Wilk正态性检验.

均值比较

t_test(): 进行单样本、两样本和成对t检验
wilcox_test(): 进行单样本、两样本和成对的Wilcoxon测试
sign_test(): 进行符号测试, 以确定成对或匹配的观察值之间是否存在中位数差异.
anova_test(): 使用car::Anova()进行不同类型方差分析的易用包装, 包括__单次测量方差分析__, 重复测量方差分析__以及__混合方差分析.
get_anova_test_table(): 从anova_test()中提取方差表。可在受试者内（重复测量）设计中自动应用球度校正. - welch_anova_test(): 韦尔奇 (Welch) 单因素方差分析. A pipe-friendly wrapper around the base function stats::oneway.test(). 在方差齐性假设被违反的情况下，这是标准单因素方差分析的一种替代方法.
kruskal_test(): 进行kruskal-wallis秩和检验
friedman_test(): 提供了一个管道友好的框架来执行Friedman秩和检验，它是单向重复测量方差分析的非参数替代方法.
get_comparisons(): 创建组间可能的成对比较列表.
get_pvalue_position: 使用ggplot2自动计算绘制显著性的p值位置.

增强 R 的方差分析计算

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(). 结果包括方差分析表、广义效应大小和一些假设检验，如重复测量方差分析中的Mauchly球度检验.

事后 (post-hoc) 分析

tukey_hsd(): 执行tukey事后测试。可以处理不同的输入格式：aov，lm，formula.
dunn_test(): 根据Kruskal-Wallis检验计算多对比较.
games_howell_test(): 进行博弈豪厄尔检验，当方差齐性假设被违背时，用来比较所有可能的群体差异组合.
emmeans_test(): pipe-friendly wrapper arround emmeans function to perform pairwise comparisons of estimated marginal means. 用于ANOVA/ANCOVA测试后的事后分析.

比较比例数据

prop_test(), pairwise_prop_test() 和 row_wise_prop_test(). 执行一个样本和两个样本的比例z检验. 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(): 计数数据的Fisher精确检验. 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(): 进行卡方检验，包括拟合优度、同质性和独立性检验.
binom_test(), pairwise_binom_test(), pairwise_binom_test_against_p(): 执行精确的二项式检验和配对比较，然后进行显著的精确多项式检验. 替代卡方检验的拟合优度检验当样本.
multinom_test(): 进行精确的多项式检验. 当样本量较小时，可替代卡方检验的拟合优度检验.
mcnemar_test(): 执行McNemar卡方检验来比较配对比例. 提供多个组之间的成对比较.
cochran_qtest(): McNemar卡方检验用于比较两个以上配对比例的扩展.
prop_trend_test(): 按比例对趋势进行卡方检验。这种测试也被称为Cochran-Armitage趋势测试.

比较方差

levene_test(): 管道友好的框架，可以轻松地计算各组方差齐性的Levene检验。处理分组数据.
box_m(): 协方差矩阵齐性的Box M检验

效应量

cohens_d(): t检验中cohen的效应大小度量.
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.

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.

包的安装和加载

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

描述性统计

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


# Grouped data
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>%
  group_by(Species) %>% 
  get_summary_stats(Sepal.Length, type = "mean_sd")
#> # A tibble: 3 x 5
#>   Species    variable         n  mean    sd
#>   <fct>      <chr>        <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

两均值比较

要比较两组的平均值，可以使用函数t_test() (参数) 或wilcox_test() (非参). 在下面的例子中，将说明t检验.

数据

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

单样本检验

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 x 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 x 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

两组独立样本检验

创建带有p值的箱型图

# T-test
stat.test <- df %>% 
  t_test(len ~ supp, paired = FALSE) 
stat.test
#> # A tibble: 1 x 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)

Customize labels using glue expression:

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

数据分组: 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 x 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  
#> # … with 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)

成对样本检验

# T-test
stat.test <- df %>% 
  t_test(len ~ supp, paired = TRUE) 
stat.test
#> # A tibble: 1 x 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)

多重比较

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 x 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 x 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 x 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 8.70e-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 8.70e-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)

方差分析

# 单因素方差分析
#:::::::::::::::::::::::::::::::::::::::::
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

# 双因素方差分析
#:::::::::::::::::::::::::::::::::::::::::
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

# 双因素重复测量方差分析
#:::::::::::::::::::::::::::::::::::::::::
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

# 使用模型作为参数
#:::::::::::::::::::::::::::::::::::::::::
.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

Name		Name	Last commit message	Last commit date
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R		R
docs		docs
inst		inst
man		man
pkgdown		pkgdown
tests		tests
tools		tools
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
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rstatix.Rproj		rstatix.Rproj

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rstatix

核心函数

Descriptive statistics

均值比较

增强 R 的方差分析计算

事后 (post-hoc) 分析

比较比例数据

比较方差

效应量

相关性分析

Adjusting p-values, formatting and adding significance symbols

Extract information from statistical tests

Data manipulation helper functions

Others

包的安装和加载

描述性统计

两均值比较

数据

单样本检验

两组独立样本检验

成对样本检验

多重比较

方差分析

相关性分析

相关性矩阵

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rstatix

核心函数

Descriptive statistics

均值比较

增强 R 的方差分析计算

事后 (post-hoc) 分析

比较比例数据

比较方差

效应量

相关性分析

Adjusting p-values, formatting and adding significance symbols

Extract information from statistical tests

Data manipulation helper functions

Others

包的安装和加载

描述性统计

两均值比较

数据

单样本检验

两组独立样本检验

成对样本检验

多重比较

方差分析

相关性分析

相关性矩阵

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