This is an efficient implementation of the common pattern of
do.call(rbind, dfs) or do.call(cbind, dfs) for binding many
data frames into one.
bind_rows(..., .id = NULL) bind_cols(...)
| ... | Data frames to combine. Each argument can either be a data frame, a list that could be a data frame, or a list of data frames. When row-binding, columns are matched by name, and any missing columns will be filled with NA. When column-binding, rows are matched by position, so all data frames must have the same number of rows. To match by value, not position, see join. |
|---|---|
| .id | Data frame identifier. When |
bind_rows() and bind_cols() return the same type as
the first input, either a data frame, tbl_df, or grouped_df.
The output of bind_rows() will contain a column if that column
appears in any of the inputs.
rbind_list() and rbind_all() have been deprecated. Instead use
bind_rows().
one <- mtcars[1:4, ] two <- mtcars[11:14, ] # You can supply data frames as arguments: bind_rows(one, two)#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3#> 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 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 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 5 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 6 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 8 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 9 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 10 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 11 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 12 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 13 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 14 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 15 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 16 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 17 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 18 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 19 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 20 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 21 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 22 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 23 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 24 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 25 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 26 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 27 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 28 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 29 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 30 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 31 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 32 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 9 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 10 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 11 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 12 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 13 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 14 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 15 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 16 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1# In addition to data frames, you can supply vectors. In the rows # direction, the vectors represent rows and should have inner # names: bind_rows( c(a = 1, b = 2), c(a = 3, b = 4) )#> # A tibble: 2 x 2 #> a b #> <dbl> <dbl> #> 1 1 2 #> 2 3 4# You can mix vectors and data frames: bind_rows( c(a = 1, b = 2), tibble(a = 3:4, b = 5:6), c(a = 7, b = 8) )#> # A tibble: 4 x 2 #> a b #> <dbl> <dbl> #> 1 1 2 #> 2 3 5 #> 3 4 6 #> 4 7 8# Note that for historical reasons, lists containing vectors are # always treated as data frames. Thus their vectors are treated as # columns rather than rows, and their inner names are ignored: ll <- list( a = c(A = 1, B = 2), b = c(A = 3, B = 4) ) bind_rows(ll)#> # A tibble: 2 x 2 #> a b #> <dbl> <dbl> #> 1 1 3 #> 2 2 4# You can circumvent that behaviour with explicit splicing: bind_rows(!!!ll)#> # A tibble: 2 x 2 #> A B #> <dbl> <dbl> #> 1 1 2 #> 2 3 4# When you supply a column name with the `.id` argument, a new # column is created to link each row to its original data frame bind_rows(list(one, two), .id = "id")#> id mpg cyl disp hp drat wt qsec vs am gear carb #> 1 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 7 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 8 2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3#> id mpg cyl disp hp drat wt qsec vs am gear carb #> 1 a 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 a 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 a 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 a 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 b 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 b 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 7 b 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 8 b 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3bind_rows("group 1" = one, "group 2" = two, .id = "groups")#> groups mpg cyl disp hp drat wt qsec vs am gear carb #> 1 group 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 group 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 group 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 group 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 group 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 group 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 7 group 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 8 group 2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3#> x y #> 1 1 NA #> 2 2 NA #> 3 3 NA #> 4 NA 1 #> 5 NA 2 #> 6 NA 3 #> 7 NA 4if (FALSE) { # Rows do need to match when column-binding bind_cols(data.frame(x = 1), data.frame(y = 1:2)) } bind_cols(one, two)#> mpg cyl disp hp drat wt qsec vs am gear carb mpg1 cyl1 disp1 hp1 drat1 #> 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 17.8 6 167.6 123 3.92 #> 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 16.4 8 275.8 180 3.07 #> 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 17.3 8 275.8 180 3.07 #> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 15.2 8 275.8 180 3.07 #> wt1 qsec1 vs1 am1 gear1 carb1 #> 1 3.44 18.9 1 0 4 4 #> 2 4.07 17.4 0 0 3 3 #> 3 3.73 17.6 0 0 3 3 #> 4 3.78 18.0 0 0 3 3#> mpg cyl disp hp drat wt qsec vs am gear carb mpg1 cyl1 disp1 hp1 drat1 #> 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 17.8 6 167.6 123 3.92 #> 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 16.4 8 275.8 180 3.07 #> 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 17.3 8 275.8 180 3.07 #> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 15.2 8 275.8 180 3.07 #> wt1 qsec1 vs1 am1 gear1 carb1 #> 1 3.44 18.9 1 0 4 4 #> 2 4.07 17.4 0 0 3 3 #> 3 3.73 17.6 0 0 3 3 #> 4 3.78 18.0 0 0 3 3