library(tidyverse)
I'm attempting to use tidyverse tools to selectively bind a list of dataframes using dplyr::bind_rows(). I'll split the mtcars dataset to create a basic reproduction of my real data.
Df<-mtcars%>%
split(.$carb)%>%
head()
I can bind it together with bind_rows()...
Df<-Df%>%
bind_rows()
But how do I selectively bind elements of the list. What I want to do is create two lists - the first binds list elements 1,3,6 while the second binds 2,4,8.
I'm thinking something like...
Df<-Df%>%map(~bind_rows(.x,list(.$`1`,.$`3`,.$`6`),list(.$`2`,.$`4`,.$`8`)))
But this code is obviously not correct so I would appreciate some suggestions.
An easy approach is to just map with one fixed argument in an implicit function.
picker <- list(c('1', '3', '6'), c('2', '4', '8'))
my_out <- map(picker, ~'['(Df, .x) %>% bind_rows)
my_out %>% print
which based on the other answers is I think what you want now:
[[1]]
mpg cyl disp hp drat wt qsec vs am gear carb
1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
3 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
5 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
6 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
7 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
8 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
9 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
10 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
11 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
[[2]]
mpg cyl disp hp drat wt qsec vs am gear carb
1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
4 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
5 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
6 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
7 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
8 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
9 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
10 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
11 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
12 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
13 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
14 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
15 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
16 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
17 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
18 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
19 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
20 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
21 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Note: I originally was very confused by whether you wanted what I call picker to be indices of the list or you wanted to names of the list. This confusion was just an artifact of the way split() names lists and probably doesn't apply to your real data.
Ok, So I realised that OP has given this as just an example and originally, the starting point is from
Df<- mtcars%>% split(.$carb)
The original solution would still work, if we do
lst <- list(x = c(1, 3, 6), y = c(2, 4, 8))
Df %>%
bind_rows() %>%
split(.$carb %in% lst[[1]])
But is there a way we can bind them according to lst directly ?
I am not an expert in tidyverse but after going through through the documentation , I found a function invoke_map which can give what we want here.
invoke_map(list(
function(x){x %>% map(. %>% filter(carb %in% lst[[1]])) %>% map_df(c)},
function(x){x %>% map(. %>% filter(carb %in% lst[[2]])) %>% map_df(c)})
#[[1]]
# A tibble: 11 x 11
# mpg cyl disp hp drat wt qsec vs am gear carb
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# 2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# 3 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# 4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# 5 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# 6 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# 7 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# 8 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# 9 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#10 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#11 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#[[2]]
# A tibble: 21 x 11
# mpg cyl disp hp drat wt qsec vs am gear carb
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# 4 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# 5 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# 6 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# 7 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# 8 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# 9 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#10 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# ... with 11 more rows
gives us the expected output. There could be better ways to optimize this, I am not sure.
Original Answer :
Why not change your split step? Get the output without using bind_rows().
lst <- list(x = c(1, 3, 6), y = c(2, 4, 8))
mtcars %>%
split(.$carb %in% lst[[1]])
#$`FALSE`
# mpg cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#$`TRUE`
# mpg cyl disp hp drat wt qsec vs am gear carb
#Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
This could be another way. I tried to reflect your map() theme here. I used Map() in base R. If you want to use the purrr package, I think you can try map2().
foo <- mtcars %>% split(.$carb)
Map(function(x, y) bind_rows(foo[c(x, y)]), c(TRUE, FALSE), c(FALSE, TRUE))
map2(.x = c(TRUE, FALSE), .y = c(FALSE, TRUE), .f = ~ bind_rows(foo[c(.x, .y)]))
[[1]]
mpg cyl disp hp drat wt qsec vs am gear carb
1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
3 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
5 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
6 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
7 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
8 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
9 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
10 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
11 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
[[2]]
mpg cyl disp hp drat wt qsec vs am gear carb
1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
4 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
5 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
6 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
7 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
8 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
9 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
10 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
11 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
12 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
13 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
14 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
15 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
16 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
17 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
18 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
19 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
20 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
21 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
