Efficiently bind multiple data.frames by row and column

bind_cols(...)

bind_rows(..., .id = NULL)

Arguments

...

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 mutate_joins.

.id

character(1). data.frame identifier.

When .id is supplied, a new column of identifiers is created to link each row to its original data.frame. The labels are taken from the named arguments to bind_rows(). When a list of data.frames is supplied, the labels are taken from the names of the list. If no names are found a numeric sequence is used instead.

Examples

one <- mtcars[1:4, ] two <- mtcars[9:12, ] # You can supply data frames as arguments: bind_rows(one, two)
#> 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 #> 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 #> 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 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# The contents of lists are spliced automatically: bind_rows(list(one, two))
#> 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 #> 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 #> 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 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
bind_rows(split(mtcars, mtcars$cyl))
#> 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 #> 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 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 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 #> 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 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 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 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 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 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 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 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 #> 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 #> 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 #> 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
bind_rows(list(one, two), list(two, one))
#> 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 #> 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 #> 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 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 2301 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 2801 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C1 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE1 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Mazda RX41 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 7101 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive1 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 b #> 1 1 2 #> 2 3 4
# You can mix vectors and data frames: bind_rows( c(a = 1, b = 2), data.frame(a = 3:4, b = 5:6), c(a = 7, b = 8) )
#> a b #> 1 1 2 #> 2 3 5 #> 3 4 6 #> 4 7 8
# 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 #> Mazda RX4 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Merc 230 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 2 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
bind_rows(list(a = one, b = two), .id = "id")
#> id mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 a 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag a 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 a 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive a 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Merc 230 b 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 b 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C b 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE b 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
bind_rows("group 1" = one, "group 2" = two, .id = "groups")
#> groups mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 group 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag group 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 group 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive group 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Merc 230 group 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 group 2 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C group 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE group 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
if (FALSE) { # Rows need to match when column-binding bind_cols(data.frame(x = 1:3), data.frame(y = 1:2)) # even with 0 columns bind_cols(data.frame(x = 1:3), data.frame()) } bind_cols(one, two)
#> mpg cyl disp hp drat wt qsec vs am gear carb mpg cyl #> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 22.8 4 #> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 19.2 6 #> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 17.8 6 #> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 16.4 8 #> disp hp drat wt qsec vs am gear carb #> Mazda RX4 140.8 95 3.92 3.15 22.9 1 0 4 2 #> Mazda RX4 Wag 167.6 123 3.92 3.44 18.3 1 0 4 4 #> Datsun 710 167.6 123 3.92 3.44 18.9 1 0 4 4 #> Hornet 4 Drive 275.8 180 3.07 4.07 17.4 0 0 3 3
bind_cols(list(one, two))
#> mpg cyl disp hp drat wt qsec vs am gear carb mpg cyl #> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 22.8 4 #> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 19.2 6 #> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 17.8 6 #> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 16.4 8 #> disp hp drat wt qsec vs am gear carb #> Mazda RX4 140.8 95 3.92 3.15 22.9 1 0 4 2 #> Mazda RX4 Wag 167.6 123 3.92 3.44 18.3 1 0 4 4 #> Datsun 710 167.6 123 3.92 3.44 18.9 1 0 4 4 #> Hornet 4 Drive 275.8 180 3.07 4.07 17.4 0 0 3 3