{"id":15430,"date":"2020-03-22T08:09:34","date_gmt":"2020-03-22T07:09:34","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=15430"},"modified":"2020-03-22T08:13:26","modified_gmt":"2020-03-22T07:13:26","slug":"how-to-perform-t-test-for-multiple-variables-in-r-pairwise-group-comparisons","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/blog\/how-to-perform-t-test-for-multiple-variables-in-r-pairwise-group-comparisons\/","title":{"rendered":"How to Perform T-test for Multiple Variables in R: Pairwise Group Comparisons"},"content":{"rendered":"
\n

Prerequisites<\/h2>\n
# Load required R packages\r\nlibrary(tidyverse)\r\nlibrary(rstatix)\r\nlibrary(ggpubr)\r\n   \r\n# Prepare the data and inspect a random sample of the data\r\nmydata <- as_tibble(iris) \r\nmydata %>% sample_n(6)<\/code><\/pre>\n
## # A tibble: 6 x 5\r\n##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   \r\n##          <dbl>       <dbl>        <dbl>       <dbl> <fct>     \r\n## 1          7.7         2.6          6.9         2.3 virginica \r\n## 2          5           2            3.5         1   versicolor\r\n## 3          7.4         2.8          6.1         1.9 virginica \r\n## 4          6           3            4.8         1.8 virginica \r\n## 5          4.6         3.4          1.4         0.3 setosa    \r\n## 6          6.8         2.8          4.8         1.4 versicolor<\/code><\/pre>\n
# Transform the data into long format\r\n# Put all variables in the same column except `Species`, the grouping variable\r\nmydata.long <- mydata %>%\r\n  pivot_longer(-Species, names_to = \"variables\", values_to = \"value\")\r\nmydata.long %>% sample_n(6)<\/code><\/pre>\n
## # A tibble: 6 x 3\r\n##   Species    variables    value\r\n##   <fct>      <chr>        <dbl>\r\n## 1 virginica  Petal.Width    1.5\r\n## 2 versicolor Petal.Length   3.9\r\n## 3 versicolor Sepal.Width    2.2\r\n## 4 virginica  Petal.Width    1.9\r\n## 5 versicolor Petal.Length   4.1\r\n## 6 virginica  Petal.Width    2.5<\/code><\/pre>\n
Summary statistics<\/h2>\n
mydata.long %>%\r\n  group_by(variables, Species) %>%\r\n  summarise(\r\n    n = n(),\r\n    mean = mean(value),\r\n    sd = sd(value)\r\n  ) %>%\r\n  ungroup()<\/code><\/pre>\n
## # A tibble: 12 x 5\r\n##    variables    Species        n  mean    sd\r\n##    <chr>        <fct>      <int> <dbl> <dbl>\r\n##  1 Petal.Length setosa        50 1.46  0.174\r\n##  2 Petal.Length versicolor    50 4.26  0.470\r\n##  3 Petal.Length virginica     50 5.55  0.552\r\n##  4 Petal.Width  setosa        50 0.246 0.105\r\n##  5 Petal.Width  versicolor    50 1.33  0.198\r\n##  6 Petal.Width  virginica     50 2.03  0.275\r\n##  7 Sepal.Length setosa        50 5.01  0.352\r\n##  8 Sepal.Length versicolor    50 5.94  0.516\r\n##  9 Sepal.Length virginica     50 6.59  0.636\r\n## 10 Sepal.Width  setosa        50 3.43  0.379\r\n## 11 Sepal.Width  versicolor    50 2.77  0.314\r\n## 12 Sepal.Width  virginica     50 2.97  0.322<\/code><\/pre>\n
Run T-tests for multiple variables<\/h2>\n
    \n
  • Group the data by variables and compare Species groups. Multiple pairwise comparisons between groups are performed.<\/li>\n
  • Adjust the p-values and add significance levels<\/li>\n<\/ul>\n
    stat.test <- mydata.long %>%\r\n  group_by(variables) %>%\r\n  t_test(value ~ Species, p.adjust.method = \"bonferroni\")\r\n# Remove unnecessary columns and display the outputs\r\nstat.test %>% select(-.y., -statistic, -df)<\/code><\/pre>\n
    ## # A tibble: 12 x 8\r\n##    variables    group1     group2        n1    n2        p    p.adj p.adj.signif\r\n##  * <chr>        <chr>      <chr>      <int> <int>    <dbl>    <dbl> <chr>       \r\n##  1 Petal.Length setosa     versicolor    50    50 9.93e-46 2.98e-45 ****        \r\n##  2 Petal.Length setosa     virginica     50    50 9.27e-50 2.78e-49 ****        \r\n##  3 Petal.Length versicolor virginica     50    50 4.90e-22 1.47e-21 ****        \r\n##  4 Petal.Width  setosa     versicolor    50    50 2.72e-47 8.16e-47 ****        \r\n##  5 Petal.Width  setosa     virginica     50    50 2.44e-48 7.32e-48 ****        \r\n##  6 Petal.Width  versicolor virginica     50    50 2.11e-25 6.33e-25 ****        \r\n##  7 Sepal.Length setosa     versicolor    50    50 3.75e-17 1.12e-16 ****        \r\n##  8 Sepal.Length setosa     virginica     50    50 3.97e-25 1.19e-24 ****        \r\n##  9 Sepal.Length versicolor virginica     50    50 1.87e- 7 5.61e- 7 ****        \r\n## 10 Sepal.Width  setosa     versicolor    50    50 2.48e-15 7.44e-15 ****        \r\n## 11 Sepal.Width  setosa     virginica     50    50 4.57e- 9 1.37e- 8 ****        \r\n## 12 Sepal.Width  versicolor virginica     50    50 2.00e- 3 5.00e- 3 **<\/code><\/pre>\n
    Create multi-panel Boxplots with t-test p-values<\/h2>\n
    # Create the plot\r\nmyplot <- ggboxplot(\r\n  mydata.long, x = \"Species\", y = \"value\",\r\n  fill = \"Species\", palette = \"npg\", legend = \"none\",\r\n  ggtheme = theme_pubr(border = TRUE)\r\n  ) +\r\n  facet_wrap(~variables)\r\n# Add statistical test p-values\r\nstat.test <- stat.test %>% add_xy_position(x = \"Species\")\r\nmyplot + stat_pvalue_manual(stat.test, label = \"p.adj.signif\")<\/code><\/pre>\n
    \"plot<\/p>\n
    Create individual Box plots with t-test p-values<\/h2>\n
    # Group the data by variables and do a graph for each variable\r\ngraphs <- mydata.long %>%\r\n  group_by(variables) %>%\r\n  doo(\r\n    ~ggboxplot(\r\n      data =., x = \"Species\", y = \"value\",\r\n      fill = \"Species\", palette = \"npg\", legend = \"none\",\r\n      ggtheme = theme_pubr()\r\n      ), \r\n    result = \"plots\"\r\n  )\r\ngraphs<\/code><\/pre>\n
    ## # A tibble: 4 x 2\r\n##   variables    plots \r\n##   <chr>        <list>\r\n## 1 Petal.Length <gg>  \r\n## 2 Petal.Width  <gg>  \r\n## 3 Sepal.Length <gg>  \r\n## 4 Sepal.Width  <gg><\/code><\/pre>\n
    # Add statitistical tests to each corresponding plot\r\nvariables <- graphs$variables\r\nplots <- graphs$plots %>% set_names(variables)\r\nfor(variable in variables){\r\n  stat.test.i <- filter(stat.test, variables == variable) \r\n  graph.i <- plots[[variable]] + \r\n    labs(title = variable) +\r\n    stat_pvalue_manual(stat.test.i, label = \"p.adj.signif\")\r\n  print(graph.i)\r\n}<\/code><\/pre>\n
    \"plot\"plot\"plot\"plot<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
    Prerequisites # Load required R packages library(tidyverse) library(rstatix) library(ggpubr) # Prepare the data and inspect a random sample of the data mydata <- as_tibble(iris) mydata %>% sample_n(6) ## # A […]<\/p>\n","protected":false},"author":1,"featured_media":15427,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[341],"tags":[343,355],"class_list":["post-15430","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-faq","tag-ggpubr","tag-r-stats"],"yoast_head":"\nHow to Perform T-test for Multiple Variables in R: Pairwise Group Comparisons - Datanovia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.datanovia.com\/en\/blog\/how-to-perform-t-test-for-multiple-variables-in-r-pairwise-group-comparisons\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Perform T-test for Multiple Variables in R: Pairwise Group Comparisons - 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