Use Dplyr::Bind_Rows and Purrr to Selectively Bind

Question

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.

Answer 1

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

Answer 2

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

Answer 3

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.