描述统计量

• 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.

比较均值

• 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.
• kruskal_test(): perform kruskal-wallis rank sum test
• 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.
• 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.
• get_comparisons(): Create a list of possible pairwise comparisons between groups.
• get_pvalue_position: autocompute p-value positions for plotting significance using ggplot2.

促进R的ANOVA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(). 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.

比较方差

• 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

效应值

• cohens_d(): Compute cohen’s d measure of effect size for t-tests.
• eta_squared() and partial_eta_squared(): Compute effect size for ANOVA.
• cramer_v(): Compute Cramer’s V, which measures the strength of the association between categorical variables.

相关分析

计算相关性:

• 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.

重塑相关矩阵:

• 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).

取子集:

• 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.

可视化相关矩阵:

• 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.

矫正p值和添加显著性标记

• 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

其他

• 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).

数据

导入样例数据集：

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

比较2个独立组别

• 创建一个带p值的箱线图

# 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)

• 使用 glue expression 自定义标签：

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

• 分组数据：在按照「dose」分组后比较 supp 水平：

# 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)

比较配对样本

# 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)

成对比较

• 成对比较：如果分组变量包含多于2个分类，会自动执行成对比较

# 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)
    )

• 基于参考组的成对比较

# 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
    )

• 基于总体水平的成对比较

# 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)