I have a list object called Profile_list combining multiple df's, all with the same columns (but different number of rows):
> summary(Profile_list)
Length Class Mode
Profile_19 26 data.frame list
Profile_20 26 data.frame list
Profile_21 26 data.frame list
Profile_40 26 data.frame list
Profile_41 26 data.frame list
Profile_84 26 data.frame list
Profile_92 26 data.frame list
Profile_95 26 data.frame list
Profile_98 26 data.frame list
Profile_106 26 data.frame list
Profile_135 26 data.frame list
Profile_139 26 data.frame list
I want to be able to apply the dplyr::select function to select columns Col_A and Col_B, then to find unique combinations of these two extracted columns of each df, then assign these results to a new list with the same names of the dfs, Profile_list_unique_indicators. What would be the best wayof achieving this?
Here a solution with purrr, and the use of map (as long as the names of the columns are the same across all data.frames):
purrr::map(my_list, function(x) {
x %>% select(a, b) %>% group_by(a, b) %>% unique()
})
# [[1]]
# # A tibble: 3 x 2
# # Groups: a, b [3]
# a b
# <dbl> <int>
# 1 2 1
# 2 2 2
# 3 2 3
#
# [[2]]
# # A tibble: 3 x 2
# # Groups: a, b [3]
# a b
# <dbl> <int>
# 1 1 4
# 2 1 5
# 3 1 6
But I don't see the difference from simply use distinct:
purrr::map(my_list, function(x) {
x %>% select(a, b) %>% distinct(a, b)
})
# [[1]]
# a b
# 1 2 1
# 2 2 2
# 3 2 3
#
# [[2]]
# a b
# 1 1 4
# 2 1 5
# 3 1 6
Fake data:
data <- data.frame(a = rep(2, 4), b = rep(1:3, 4))
data2 <- data.frame(a = rep(1, 4), b = rep(4:6, 4))
my_list <- list(data, data2)
my_list
# [[1]]
# a b
# 1 2 1
# 2 2 2
# 3 2 3
# 4 2 1
# 5 2 2
# 6 2 3
# 7 2 1
# 8 2 2
# 9 2 3
# 10 2 1
# 11 2 2
# 12 2 3
#
# [[2]]
# a b
# 1 1 4
# 2 1 5
# 3 1 6
# 4 1 4
# 5 1 5
# 6 1 6
# 7 1 4
# 8 1 5
# 9 1 6
# 10 1 4
# 11 1 5
# 12 1 6