for loop in R - repeating a block of code iteratively

I have a block of code that extracts the top 50 values from a specific column in my dataframe, outputting a new dataframe named accordingly. I want to repeat this process for every column in my dataframe (50-100 columns), as follows. How can I automate this?

column1_top50 <- dataframe %>%
arrange(desc(column1)) %>%
slice_head(n = 50) %>%
select(sample_name, column1)

column2_top50 <- dataframe %>%
  arrange(desc(column2)) %>%
  slice_head(n = 50) %>%
  select(sample_name, column2)

column3_top50 <- dataframe %>%
  arrange(desc(column3)) %>%
  slice_head(n = 50) %>%
  select(sample_name, column3)

I'm unable to test without any sample data but here's an option using purrr::map_dfr. Where you "loop" through each col in the data.frame and return the top 50 numeric values.

library(dplyr)
library(purrr)

set.seed(100)

tmp = data.frame(
  col1 = rnorm(100),
  col2 = rnorm(100),
  col3 = rnorm(100)
)

tmp %>%
  map_dfr(~ head(sort(.x, decreasing = TRUE),50))

# A tibble: 50 × 3
    col1  col2  col3
   <dbl> <dbl> <dbl>
 1  2.58  2.17  2.73
 2  2.45  1.90  2.61
 3  2.31  1.65  2.55
 4  1.90  1.62  1.88
 5  1.82  1.58  1.79
 6  1.76  1.36  1.35
 7  1.73  1.35  1.35
 8  1.65  1.27  1.24
 9  1.43  1.24  1.23
10  1.40  1.03  1.14
# … with 40 more rows

I'm not sure a for loop would be the most efficient way to do this (purrr would probably be faster). I'll also note that creating a data frame within a for loop is generally frowned upon (if you make an empty placeholder df before the forloop it'd be much faster), but without any sample data I tried to generalize this for the 50-100 columns you said you have:

library(tidyverse)
df = data.frame(sample_name = rep("example", 100),
                x1 = rnorm(100),
                x2 = rnorm(100),
                x3 = rnorm(100)) 
 for(i in 1:ncol(df)) {
  df1 <- df %>% arrange(desc(.[[i]])) %>% 
    slice_head(n = 50)
  assign(paste0(colnames(df)[[i]], "_top50"), df1[,c(1, i)])
}