Apply dplyr functions on a single column across a list using piping

Question

I'm tring to filter something across a list of dataframes for a specific column. Typically across a single dataframe using dplyr I would use:

#creating dataframe
df <- data.frame(a = 0:10, d = 10:20)

# filtering column a for rows greater than 7
df %>% filter(a > 7)

I've tried doing this across a list using the following:

# creating list
x <- list(data.frame(a = 0:10, b = 10:20), 
data.frame(c = 11:20, d = 21:30), 
data.frame(e = 15:25, f = 35:45))

# selecting the appropriate column and trying to filter
# this is not working
x[1][[1]][1] %>% lapply(. %>% {filter(. > 2)})

# however, if I use the min() function it works
x[1][[1]][1] %>% lapply(. %>% {min(.)})

I find the %>% syntax quite easy to understand and carry out. However, in this case, selecting a specific column and doing something quite simple like filtering is not working. I'm guessing map could be equally useful. Any help is appreciated.

Answer 1

It seems you are interested in checking the condition on the first column of each dataframe in your list. One solution using dplyr would be

lapply(x, function(df) {df %>% filter_at(1, ~. > 7)})

The 1 in filter_at indicates that I want to check the condition on the first column (1 is a positional index) of each dataframe in the list.

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EDIT

After the discussion in the comments, I propose the following solution

lapply(x, function(df) {df %>% filter(a > 7) %>% select(a) %>% slice(1)})

Input data

x <- list(data.frame(a = 0:10, b = 10:20), 
      data.frame(a = 11:20, b = 21:30), 
      data.frame(a = 15:25, b = 35:45))

Output

[[1]]
  a
1 8

[[2]]
   a
1 11

[[3]]
   a
1 15