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r dplyr aggregate summary t-test

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July 28, 2026 Score: 4 Rep: 26,843 Quality: Expert Completeness: 80%

I know of a few ways to calculate statistics for multiple variables in a table, but I'm not sure if I've understood your expected output for your example; does this approach using the rstatix package give you your desired outcome?

library(tidyverse)
library(rstatix)
stats %
  filter(cyl == 4 | cyl == 8) %>%
  groupby(cyl) %>%
  getsummarystats(type = "meansd") %>%
  pivotwider(namesfrom = cyl,
              valuesfrom = c(n, mean, sd), namesglue = "{.value} (cyl={cyl})")
stats
#> # A tibble: 10 × 7
#>    variable n (cyl=4) n (cyl=8) mean (cyl=4) mean (cyl=8) sd (cyl=4)
#>                                                 
#>  1 mpg               11          14         26.7           15.1          4.51 
#>  2 disp              11          14        105.           353.          26.9  
#>  3 hp                11          14         82.6          209.          20.9  
#>  4 drat              11          14          4.07           3.23         0.365
#>  5 wt                11          14          2.29           4.00         0.57 
#>  6 qsec              11          14         19.1           16.8          1.68 
#>  7 vs                11          14          0.909          0            0.302
#>  8 am                11          14          0.727          0.143        0.467
#>  9 gear              11          14          4.09           3.29         0.539
#> 10 carb              11          14          1.54           3.5          0.522
#> # ℹ 1 more variable: sd (cyl=8) 
pvals %
  filter(cyl == 4 | cyl == 8) %>%
  pivotlonger(-cyl) %>%
  groupby(name) %>%
  ttest(data = ., formula = value ~ cyl)
pvals
#> # A tibble: 10 × 9
#>    name  .y.   group1 group2    n1    n2 statistic    df        p
#>  *                  
#>  1 am    value 4      8         11    14      3.42  18.5 2.97e- 3
#>  2 carb  value 4      8         11    14     -4.39  16.5 4.2 e- 4
#>  3 disp  value 4      8         11    14    -12.5   17.8 3.03e-10
#>  4 drat  value 4      8         11    14      5.67  21.8 1.10e- 5
#>  5 gear  value 4      8         11    14      3.18  23.0 4.18e- 3
#>  6 hp    value 4      8         11    14     -8.43  18.1 1.11e- 7
#>  7 mpg   value 4      8         11    14      7.60  15.0 1.64e- 6
#>  8 qsec  value 4      8         11    14      3.94  17.4 1   e- 3
#>  9 vs    value 4      8         11    14     10     10   1.59e- 6
#> 10 wt    value 4      8         11    14     -6.44  23.0 1.42e- 6
combined %
  leftjoin(pvals, by = c("variable" = "name")) %>%
  select(-c(.y., group1, group2, n1, n2))
kableExtra::kable(combined)
variable n (cyl=4) n (cyl=8) mean (cyl=4) mean (cyl=8) sd (cyl=4) sd (cyl=8) statistic df p
mpg 11 14 26.664 15.100 4.510 2.560 7.596664 14.96675 1.60e-06
disp 11 14 105.136 353.100 26.872 67.771 -12.496797 17.79660 0.00e+00
hp 11 14 82.636 209.214 20.935 50.977 -8.429940 18.09582 1.00e-07
drat 11 14 4.071 3.229 0.365 0.372 5.668243 21.80614 1.10e-05
wt 11 14 2.286 3.999 0.570 0.759 -6.444974 22.97119 1.40e-06
qsec 11 14 19.137 16.772 1.682 1.196 3.944600 17.40676 1.00e-03
vs 11 14 0.909 0.000 0.302 0.000 10.000000 10.00000 1.60e-06
am 11 14 0.727 0.143 0.467 0.363 3.416883 18.53711 2.97e-03
gear 11 14 4.091 3.286 0.539 0.726 3.179761 22.95334 4.18e-03
carb 11 14 1.545 3.500 0.522 1.557 -4.393954 16.54979 4.20e-04

Created on 2026-07-28 with reprex v2.1.1.9000

July 28, 2026 Score: 3 Rep: 7,628 Quality: Medium Completeness: 80%

another approach:

library(dplyr)
library(tidyr)
mtcars |>
    filter(cyl %in% c(4, 8)) |>
    pivotlonger(cols = -cyl,
                 namesto = "variable"
                 ) |>
    mutate(p = t.test(value ~ cyl)$p.value,
           .by = c(variable)
           ) |>
    summarise(p = first(p),
              mean = mean(value),
              sd = sd(value),
              .by = c(variable, cyl)
              ) |>
    pivotwider(idcols = c(variable, p),
                namesfrom = cyl,
                valuesfrom = c(mean, sd)
                )

output:

# A tibble: 10 × 6
   variable        p  mean4  mean8   sd4   sd8
 1 mpg      1.64e- 6  26.7    15.1    4.51   2.56 
 2 disp     3.03e-10 105.    353.    26.9   67.8  
 3 hp       1.11e- 7  82.6   209.    20.9   51.0  
 4 drat     1.10e- 5   4.07    3.23   0.365  0.372
 5 wt       1.42e- 6   2.29    4.00   0.570  0.759
 6 qsec     1.00e- 3  19.1    16.8    1.68   1.20 
 7 vs       1.59e- 6   0.909   0      0.302  0    
 8 am       2.97e- 3   0.727   0.143  0.467  0.363
 9 gear     4.18e- 3   4.09    3.29   0.539  0.726
10 carb     4.20e- 4   1.55    3.5    0.522  1.56

If you need to summarize (incl. tests) and format output on a regular base, package gtsummary might be helpful.