This function divides a data.frame into smaller chunks based on the specified
number of rows per chunk (chunk_size) or the specified number of chunks
(n_chunks). If neither is provided, it defaults to splitting the data.frame
into a minimum of 5 chunks or less if the data.frame has fewer than 5 rows.
The function ensures that the data is evenly distributed among the chunks as
much as possible.
Arguments
- data
data.frame. The data.frame to be split into chunks.- chunk_size
Integer. Number of rows each chunk should contain. It must be a positive integer and less than the number of rows in
data.- n_chunks
Integer. Number of chunks to split the data.frame into. It must be a positive integer.
- prefix
Character. Prefix for the names of the chunks. Default is "Chunk".
Value
A list of data.frames, where each data.frame represents a chunk of
the original data.frame. The names of the list elements are constructed
using the prefix parameter followed by an underscore and the chunk number
(e.g., "Chunk_1", "Chunk_2", ...).
Examples
split_df_to_chunks(mtcars, chunk_size = 16)
#> $Chunk_1
#> # A tibble: 16 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
#> 12 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3
#> 13 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3
#> 14 15.2 8 276. 180 3.07 3.78 18 0 0 3 3
#> 15 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
#> 16 10.4 8 460 215 3 5.42 17.8 0 0 3 4
#>
#> $Chunk_2
#> # A tibble: 16 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
#> 2 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 3 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 4 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 5 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
#> 6 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2
#> 7 15.2 8 304 150 3.15 3.44 17.3 0 0 3 2
#> 8 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
#> 9 19.2 8 400 175 3.08 3.84 17.0 0 0 3 2
#> 10 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
#> 11 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 12 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 13 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 14 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 15 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 16 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#>
# -------------------------------------------
split_df_to_chunks(mtcars, n_chunks = 3)
#> $Chunk_1
#> # A tibble: 11 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
#>
#> $Chunk_2
#> # A tibble: 11 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3
#> 2 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3
#> 3 15.2 8 276. 180 3.07 3.78 18 0 0 3 3
#> 4 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
#> 5 10.4 8 460 215 3 5.42 17.8 0 0 3 4
#> 6 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
#> 7 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 8 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 9 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 10 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
#> 11 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2
#>
#> $Chunk_3
#> # A tibble: 10 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 15.2 8 304 150 3.15 3.44 17.3 0 0 3 2
#> 2 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
#> 3 19.2 8 400 175 3.08 3.84 17.0 0 0 3 2
#> 4 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
#> 5 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 6 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 7 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 8 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 9 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 10 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#>
# -------------------------------------------
split_df_to_chunks(mtcars, n_chunks = 3, prefix = "T")
#> $T_1
#> # A tibble: 11 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
#>
#> $T_2
#> # A tibble: 11 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3
#> 2 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3
#> 3 15.2 8 276. 180 3.07 3.78 18 0 0 3 3
#> 4 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
#> 5 10.4 8 460 215 3 5.42 17.8 0 0 3 4
#> 6 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
#> 7 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 8 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 9 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 10 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
#> 11 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2
#>
#> $T_3
#> # A tibble: 10 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 15.2 8 304 150 3.15 3.44 17.3 0 0 3 2
#> 2 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
#> 3 19.2 8 400 175 3.08 3.84 17.0 0 0 3 2
#> 4 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
#> 5 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 6 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 7 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 8 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 9 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 10 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#>