loop Tukey post hoc letters extraction - loops

Question

I need to apply a tukey post hoc test to a dataset with 80 columns/variables based on 3 groups/treatments. Is there any way to get a table with all variables in which common characters identify levels or groups that are not significantly different (based on p-values) in an automated way with a loop function?

Answer 1

Does this work for you?
# packages and function conflicts
library(conflicted)
library(emmeans)
library(multcomp)
library(multcompView)
library(tidyverse)
conflict_prefer("select", winner = "dplyr")
#> [conflicted] Will prefer dplyr::select over any other package
# Create example data
dat <- PlantGrowth %>%
transmute(
group = group,
y1 = weight,
y2 = weight * runif(30, 0.8, 1.2),
y3 = weight * runif(30, 0.8, 1.2)
) %>%
as_tibble()
dat
#> # A tibble: 30 x 4
#> group y1 y2 y3
#> <fct> <dbl> <dbl> <dbl>
#> 1 ctrl 4.17 4.22 3.53
#> 2 ctrl 5.58 6.19 6.46
#> 3 ctrl 5.18 5.95 5.66
#> 4 ctrl 6.11 5.36 7.19
#> 5 ctrl 4.5 4.41 5.11
#> 6 ctrl 4.61 3.93 4.89
#> 7 ctrl 5.17 4.36 4.67
#> 8 ctrl 4.53 4.53 4.72
#> 9 ctrl 5.33 4.64 5.86
#> 10 ctrl 5.14 5.34 4.89
#> # ... with 20 more rows
# Loop setup
var_names <- names(dat)[-1]
loop_out <- list()
# Loop
for (var_i in var_names) {
dat_i <- dat %>%
rename(y_i = !!var_i) %>%
select(group, y_i)
mod_i <- lm(y_i ~ group, data = dat_i)
emm_i <- emmeans(mod_i, "group") %>%
cld(Letters = letters)
loop_out[[var_i]] <- emm_i %>%
as_tibble() %>%
select(group, emmean, .group) %>%
rename_with(.cols = -group,
.fn = ~ paste(., var_i, sep = "_"))
}
# Join loop results
loop_out %>% reduce(full_join, by='group')
#> # A tibble: 3 x 7
#> group emmean_y1 .group_y1 emmean_y2 .group_y2 emmean_y3 .group_y3
#> <fct> <dbl> <chr> <dbl> <chr> <dbl> <chr>
#> 1 trt1 4.66 " a " 4.46 " a " 4.64 " a"
#> 2 ctrl 5.03 " ab" 4.89 " ab" 5.30 " a"
#> 3 trt2 5.53 " b" 5.74 " b" 5.39 " a"
Created on 2022-08-08 by the reprex package (v2.0.1)
Check out my summary on the compact letter display for more background.