Creating new dataframes inside a list from a single dataframe, using for loop

[PaulaV]

I have a dataframe with a character column and 4 numeric columns.
What I want to do is to apply filtering according to each column and create new data frames from each of them.

Here is an example data frame:

df <- data.frame(my_names=sample(LETTERS,4,replace=F),
                 column2=sample(1.3:100.3,4,replace=T),
                 column3=sample(1.3:100.3,4,replace=T),
                 column4=sample(1.3:100.3,4,replace=T),
                 column5=sample(1.3:100.3,4,replace=T))
> df
  my_names column2 column3 column4 column5
1        A     8.3     1.3    19.3    91.3
2        E    18.3    42.3     8.3    76.3
3        O     6.3    46.3    26.3    91.3
4   

What I want to get, for one column (here column2) is this:

top <- 2
top_column2 <- df %>% arrange(desc(column2)) %>% 
  select(my_names, column2) %>% 
  top_n(top, column2) %>% 
  mutate(column2=round(column2))
> top_column2
  my_names column2
1        O      76
2        E      75

This works perfectly fine.

Now I would like to iterate
-the naming of the data frames and
-the creation of the data frames
for columns 2:5

I first tried doing:

> for(i in 2:ncol(df)) {
+   paste("top", colnames(df)[i], sep="_") <- 
+     df %>% arrange(desc(i)) %>% select(my_names, i) %>% top_n(top, i) %>% mutate(i=round(i))
+ }
Error in paste("top", colnames(df)[i], sep = "_") <- df %>% arrange(desc(i)) %>%  : 
  target of assignment expands to non-language object

After googling, tried assign(paste(), functions):

for(i in 2:ncol(df)) {
  assign(paste("top", colnames(df)[i], sep="_"), 
    df %>% arrange(desc(i)) %>% select(my_names, i) %>% top_n(top, i) %>% mutate(i=round(i))) 
}
top_column4
> top_column4
  my_names column4 i
1        A    19.3 4
2        E     8.3 4
3        O    26.3 4
4        M    59.3 4

This creates 4 data frames with the right names, but the functions are not applied and a new column is created.

More googling, trying now with an empty list to then change its elements:

test_list <- list()
for(i in 2:ncol(df)) {
  test_list[[paste("top", colnames(df)[i], sep="_")]] <- 
    df %>% arrange(desc(i)) %>% select(my_names, i) %>% top_n(top, i) %>% mutate(i=round(i))
}
> top_column4
  my_names column4 i
1        A    19.3 4
2        E     8.3 4
3        O    26.3 4
4        M    59.3 4

But here's the same problem as with assign(), the data frames are created with the right names, but not the right content.

Could someone please help to solve this issue?

[pieterjanvc]

Hi,

Welcome to the RStudio community!

I'm not entirely sure if I understood what you were going for, but here is an example of my interpretation

library(dplyr) #Need version 1.0+

#Generate the data
set.seed(1)
df <- data.frame(my_names=sample(LETTERS,4,replace=F),
                 column2=sample(1.3:100.3,4,replace=T),
                 column3=sample(1.3:100.3,4,replace=T),
                 column4=sample(1.3:100.3,4,replace=T),
                 column5=sample(1.3:100.3,4,replace=T))

#Extract the data frames
top =  2
roundDecimals = 0

dfList = lapply(colnames(df)[-1], function(myCol){
  df %>% select(my_names, all_of(myCol)) %>%  
    arrange(across(all_of(myCol))) %>% slice(1:top) %>% 
    mutate(across(all_of(myCol), function(x) round(x, roundDecimals)))
})

dfList
#> [[1]]
#>   my_names column2
#> 1        A      14
#> 2        Y      34
#> 
#> [[2]]
#>   my_names column3
#> 1        G      51
#> 2        D      59
#> 
#> [[3]]
#>   my_names column4
#> 1        D      21
#> 2        G      54
#> 
#> [[4]]
#>   my_names column5
#> 1        Y       7
#> 2        D      73

^{Created on 2020-09-10 by the reprex package (v0.3.0)}

I used an lapply() function to iterate over all columns by name, then tried and implemented some of the new dplyr 1.0+ magic. I'm not very familiar with this yet, so there could be a more elegant solution, but in essence what I used was the new all_of() function to convert the name of a column into a dplyr variable of that same column. The across() functions allows you to perform a function across the selected columns.

I'm sorry for the weak explanation, but I had to fiddle with it myself as well to get it working, and am not sure yet I did everything the correct way, but it seems to produce results at least

Hope this helps,
PJ

[nirgrahamuk]

Here is an alternative,
I reuse more directly your original code for how to produce the table for column2, i functionised it and iterated it with purrr::map

library(tidyverse)
library(rlang)
df <- data.frame(my_names=sample(LETTERS,4,replace=F),
                      column2=sample(1.3:100.3,4,replace=T),
                      column3=sample(1.3:100.3,4,replace=T),
                      column4=sample(1.3:100.3,4,replace=T),
                      column5=sample(1.3:100.3,4,replace=T))
(cols <- setdiff(names(df),"my_names"))

results1<-purrr::map(.x=cols,
  ~{df %>% arrange(desc(!!sym(.x))) %>% 
  select(my_names, .x) %>% 
  top_n(2, !!sym(.x)) %>% 
  mutate(!!sym(.x):=round(!!sym(.x)))})

results1 %>% setNames(paste0("top_",cols))

# $top_column2
# my_names column2
# 1        H      79
# 2        K      36
# 
# $top_column3
# my_names column3
# 1        K      60
# 2        H      55
# 
# $top_column4
# my_names column4
# 1        P      76
# 2        K      58
# 
# $top_column5
# my_names column5
# 1        K      99
# 2        R      87