rowbind.Rdcollapse's version of data.table::rbindlist and rbind.data.frame. The core code is copied from data.table, which deserves all credit for the implementation. rowbind only binds lists/data.frame's. For a more flexible recursive version see unlist2d. To combine lists column-wise see add_vars or ftransform (with replacement).
rowbind(..., idcol = NULL, row.names = FALSE,
use.names = TRUE, fill = FALSE, id.factor = "auto",
return = c("as.first", "data.frame", "data.table", "tibble", "list"))a single list of list-like objects (data.frames) or comma separated objects (internally assembled using list(...)). Names can be supplied if !is.null(idcol).
character. The name of an id-column to be generated identifying the source of rows in the final object. Using idcol = TRUE will set the name to ".id". If the input list has names, these will form the content of the id column, otherwise integers are used. To save memory, it is advised to keep id.factor = TRUE.
TRUE extracts row names from all the objects in l and adds them to the output in a column named "row.names". Alternatively, a column name i.e. row.names = "variable" can be supplied.
logical. TRUE binds by matching column name, FALSE by position.
logical. TRUE fills missing columns with NAs. When TRUE, use.names is set to TRUE.
if TRUE and !isFALSE(idcols), create id column as factor instead of character or integer vector. It is also possible to specify "ordered" to generate an ordered factor id. "auto" uses TRUE if !is.null(names(l)) where l is the input list (because factors are much more memory efficient than character vectors).
an integer or string specifying what to return. 1 - "as.first" preserves the attributes of the first element of the list, 2/3/4 - "data.frame"/"data.table"/"tibble" coerces to specific objects, and 5 - "list" returns a (named) list.
a long list or data frame-like object formed by combining the rows / elements of the input objects. The return argument controls the exact format of the output.
# These are the same
rowbind(mtcars, mtcars)
#> 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 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 34 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 35 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 36 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 37 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 38 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 39 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 40 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 41 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 42 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 43 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 44 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 45 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 46 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 47 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 48 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 49 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 50 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 51 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 52 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 53 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 54 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 55 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 56 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 57 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 58 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 59 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 60 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 61 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 62 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 63 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 64 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
rowbind(list(mtcars, mtcars))
#> 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 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 34 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 35 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 36 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 37 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 38 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 39 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 40 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 41 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 42 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 43 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 44 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 45 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 46 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 47 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 48 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 49 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 50 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 51 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 52 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 53 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 54 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 55 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 56 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 57 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 58 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 59 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 60 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 61 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 62 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 63 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 64 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# With id column
rowbind(mtcars, mtcars, idcol = "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 1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 1 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 1 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 1 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 1 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 1 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 1 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 1 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 1 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 1 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 1 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 1 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 1 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 1 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 1 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 1 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 1 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 1 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 1 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 1 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 1 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 1 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 1 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 1 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 1 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 2 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 34 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 35 2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 36 2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 37 2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 38 2 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 39 2 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 40 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 41 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 42 2 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 43 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 44 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 45 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 46 2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 47 2 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 48 2 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 49 2 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 50 2 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 51 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 52 2 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 53 2 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 54 2 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 55 2 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 56 2 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 57 2 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 58 2 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 59 2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 60 2 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 61 2 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 62 2 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 63 2 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 64 2 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
rowbind(a = mtcars, b = mtcars, idcol = "id")
#> 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 a 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 a 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 a 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 a 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 a 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 a 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 a 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 a 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 a 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 a 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 a 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 a 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 a 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 a 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 a 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 a 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 a 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 a 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 a 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 a 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 a 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 a 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 a 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 a 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 a 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 a 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 a 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 a 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 b 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 34 b 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 35 b 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 36 b 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 37 b 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 38 b 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 39 b 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 40 b 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 41 b 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 42 b 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 43 b 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 44 b 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 45 b 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 46 b 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 47 b 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 48 b 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 49 b 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 50 b 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 51 b 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 52 b 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 53 b 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 54 b 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 55 b 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 56 b 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 57 b 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 58 b 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 59 b 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 60 b 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 61 b 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 62 b 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 63 b 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 64 b 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# With saving row-names
rowbind(mtcars, mtcars, row.names = "cars")
#> cars mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 34 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 35 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 36 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 37 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 38 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 39 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 40 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 41 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 42 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 43 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 44 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 45 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 46 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 47 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 48 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 49 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 50 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 51 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 52 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 53 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 54 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 55 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 56 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 57 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 58 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 59 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 60 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 61 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 62 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 63 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 64 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
rowbind(a = mtcars, b = mtcars, idcol = "id", row.names = "cars")
#> id cars mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 a Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 a Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 a Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 4 a Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 5 a Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 a Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 a Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 a Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 a Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 a Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 a Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 a Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 a Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 a Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 a Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 a Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 a Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 a Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 a Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 a Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 a Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 a Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 a AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 a Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 a Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 a Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 a Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 a Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 a Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 a Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 a Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 a Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 b Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 34 b Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 35 b Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 36 b Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 37 b Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 38 b Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 39 b Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 40 b Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 41 b Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 42 b Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 43 b Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 44 b Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 45 b Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 46 b Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 47 b Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 48 b Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 49 b Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 50 b Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 51 b Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 52 b Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 53 b Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 54 b Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 55 b AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 56 b Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 57 b Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 58 b Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 59 b Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 60 b Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 61 b Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 62 b Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 63 b Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 64 b Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Filling up columns
rowbind(mtcars, mtcars[2:8], fill = TRUE)
#> 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 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 33 NA 6 160.0 110 3.90 2.620 16.46 0 NA NA NA
#> 34 NA 6 160.0 110 3.90 2.875 17.02 0 NA NA NA
#> 35 NA 4 108.0 93 3.85 2.320 18.61 1 NA NA NA
#> 36 NA 6 258.0 110 3.08 3.215 19.44 1 NA NA NA
#> 37 NA 8 360.0 175 3.15 3.440 17.02 0 NA NA NA
#> 38 NA 6 225.0 105 2.76 3.460 20.22 1 NA NA NA
#> 39 NA 8 360.0 245 3.21 3.570 15.84 0 NA NA NA
#> 40 NA 4 146.7 62 3.69 3.190 20.00 1 NA NA NA
#> 41 NA 4 140.8 95 3.92 3.150 22.90 1 NA NA NA
#> 42 NA 6 167.6 123 3.92 3.440 18.30 1 NA NA NA
#> 43 NA 6 167.6 123 3.92 3.440 18.90 1 NA NA NA
#> 44 NA 8 275.8 180 3.07 4.070 17.40 0 NA NA NA
#> 45 NA 8 275.8 180 3.07 3.730 17.60 0 NA NA NA
#> 46 NA 8 275.8 180 3.07 3.780 18.00 0 NA NA NA
#> 47 NA 8 472.0 205 2.93 5.250 17.98 0 NA NA NA
#> 48 NA 8 460.0 215 3.00 5.424 17.82 0 NA NA NA
#> 49 NA 8 440.0 230 3.23 5.345 17.42 0 NA NA NA
#> 50 NA 4 78.7 66 4.08 2.200 19.47 1 NA NA NA
#> 51 NA 4 75.7 52 4.93 1.615 18.52 1 NA NA NA
#> 52 NA 4 71.1 65 4.22 1.835 19.90 1 NA NA NA
#> 53 NA 4 120.1 97 3.70 2.465 20.01 1 NA NA NA
#> 54 NA 8 318.0 150 2.76 3.520 16.87 0 NA NA NA
#> 55 NA 8 304.0 150 3.15 3.435 17.30 0 NA NA NA
#> 56 NA 8 350.0 245 3.73 3.840 15.41 0 NA NA NA
#> 57 NA 8 400.0 175 3.08 3.845 17.05 0 NA NA NA
#> 58 NA 4 79.0 66 4.08 1.935 18.90 1 NA NA NA
#> 59 NA 4 120.3 91 4.43 2.140 16.70 0 NA NA NA
#> 60 NA 4 95.1 113 3.77 1.513 16.90 1 NA NA NA
#> 61 NA 8 351.0 264 4.22 3.170 14.50 0 NA NA NA
#> 62 NA 6 145.0 175 3.62 2.770 15.50 0 NA NA NA
#> 63 NA 8 301.0 335 3.54 3.570 14.60 0 NA NA NA
#> 64 NA 4 121.0 109 4.11 2.780 18.60 1 NA NA NA