How to apply dplyr filter to list of data frames?

Question

I have a list() of dataframes. I want to apply dplyr's filter() to all of them.

Example code of what I have tried so far...

require(dplyr)
list.DFs <- list(df1,df2)
lapply(
  X = list.DFS,
  FUN = filter(Gold.fish.count=="Total")
)

But this gives an error: Object 'Gold.fish.count' not found.

Answer 1

Using purrr

library(purrr)
map(list.DFs, ~filter(.x, Gold.fish.count == "Total"))

Obviously, you can do exactly the same with lapply:

lapply(list.DFs, function(x) filter(x, Gold.fish.count == "Total"))

Answer 2

Without having example data it's hard to identify exactly what you're after (check out how to use dput function in future). But some variation of the following might help:

indices <- sapply(list.DFs, function(x) x$Gold.fish.count == "Total")
list.DFs[indices]

The first line creates a list of logicals (True/False) where your condition is met. These logicals are then used to subset your original list of data frames.

If Gold.fish.count is a column that contains "Total" for every row then you can use an indexing variation:

indices <- sapply(list.DFs, function(x) x$Gold.fish.count[1] == "Total")
list.DFs[indices]

(Notice the additional [1].)