CODEWITHSUNDEEP
rtidyverse
neves
neves

Reputation: 846

Apply purrr::map in dplyr functions into a list

I have this list:

mylist=structure(list(`1` = structure(list(var1 = c(33.1884311512113, 
34.3367128074169, 46.1769648082554, 65.8136370964348), var2 = c(95.4996081255376, 
88.2967706210911, 82.7290896326303, 95.4127290844917), kmeans = structure(c(1L, 
1L, 1L, 1L), .Label = c("1", "2", "3"), class = "factor")), .Names = c("var1", 
"var2", "kmeans"), row.names = c(NA, -4L), class = c("tbl_df", 
"tbl", "data.frame")), `2` = structure(list(var1 = c(41.0386378876865, 
51.3533580489457, 33.2395000942051, 30.9116746112704), var2 = c(62.3894362896681, 
64.3969475477934, 38.5772223211825, 43.388062492013), kmeans = structure(c(2L, 
2L, 2L, 2L), .Label = c("1", "2", "3"), class = "factor")), .Names = c("var1", 
"var2", "kmeans"), row.names = c(NA, -4L), class = c("tbl_df", 
"tbl", "data.frame")), `3` = structure(list(var1 = c(50.5543508939445, 
63.059715218842), var2 = c(35.3626389987767, 20.2830202504992
), kmeans = structure(c(3L, 3L), .Label = c("1", "2", "3"), class = "factor")), 
.Names = c("var1", 
"var2", "kmeans"), row.names = c(NA, -2L), class = c("tbl_df", 
"tbl", "data.frame"))), .Names = c("1", "2", "3"))

I try use purrr::map instead lapply with function(x) in dplyr functions. My codes:

mylist%>%
  lapply(function(x){
    group_by(x,kmeans)%>%
      mutate_if(is.numeric,sum)
  })

It's ok for lapply with function(x). But, in purrr::map doesn't work:

mylist%>%
  map(~group_by(.,kmeans))%>%
  mutate_if(is.numeric,sum)

What's wrong with purrr::map? *I need use this function without function(x).

Upvotes: 1

Views: 603

Answers (1)

Ronak Shah
Ronak Shah

Reputation: 389265

You could wrap everything in map

map(mylist, ~ group_by(.,kmeans)%>%
     mutate_if(is.numeric,sum))

# Groups:   kmeans [1]
#   var1  var2 kmeans
#  <dbl> <dbl> <fct> 
#1  180.  362. 1     
#2  180.  362. 1     
#3  180.  362. 1     
#4  180.  362. 1     

#$`2`
# A tibble: 4 x 3
# Groups:   kmeans [1]
#   var1  var2 kmeans
#  <dbl> <dbl> <fct> 
#1  157.  209. 2     
#2  157.  209. 2     
#3  157.  209. 2     
#4  157.  209. 2     

#$`3`
# A tibble: 2 x 3
# Groups:   kmeans [1]
#   var1  var2 kmeans
#  <dbl> <dbl> <fct> 
#1  114.  55.6 3     
#2  114.  55.6 3

Or with the formula call

mylist%>%
  map(~group_by(., kmeans) %>%
  mutate_if(is.numeric,sum))

Upvotes: 2

Related Questions