Use to split dataframes for parallel processing
chunk_df(.gdf, ..., .nchunks = parallel::detectCores(), .as_list = TRUE)# Chunking will split the rows of the dataframe as evenly
# as possible into the number chunks specified.
library(dplyr)
Theoph %>% chunk_df(.nchunks = 6)
#> $`1`
#> Subject Wt Dose Time conc chunk__
#> 1 1 79.6 4.02 0.00 0.74 1
#> 2 1 79.6 4.02 0.25 2.84 1
#> 3 1 79.6 4.02 0.57 6.57 1
#> 4 1 79.6 4.02 1.12 10.50 1
#> 5 1 79.6 4.02 2.02 9.66 1
#> 6 1 79.6 4.02 3.82 8.58 1
#> 7 1 79.6 4.02 5.10 8.36 1
#> 8 1 79.6 4.02 7.03 7.47 1
#> 9 1 79.6 4.02 9.05 6.89 1
#> 10 1 79.6 4.02 12.12 5.94 1
#> 11 1 79.6 4.02 24.37 3.28 1
#> 12 2 72.4 4.40 0.00 0.00 1
#> 13 2 72.4 4.40 0.27 1.72 1
#> 14 2 72.4 4.40 0.52 7.91 1
#> 15 2 72.4 4.40 1.00 8.31 1
#> 16 2 72.4 4.40 1.92 8.33 1
#> 17 2 72.4 4.40 3.50 6.85 1
#> 18 2 72.4 4.40 5.02 6.08 1
#> 19 2 72.4 4.40 7.03 5.40 1
#> 20 2 72.4 4.40 9.00 4.55 1
#> 21 2 72.4 4.40 12.00 3.01 1
#> 22 2 72.4 4.40 24.30 0.90 1
#>
#> $`2`
#> Subject Wt Dose Time conc chunk__
#> 23 3 70.5 4.53 0.00 0.00 2
#> 24 3 70.5 4.53 0.27 4.40 2
#> 25 3 70.5 4.53 0.58 6.90 2
#> 26 3 70.5 4.53 1.02 8.20 2
#> 27 3 70.5 4.53 2.02 7.80 2
#> 28 3 70.5 4.53 3.62 7.50 2
#> 29 3 70.5 4.53 5.08 6.20 2
#> 30 3 70.5 4.53 7.07 5.30 2
#> 31 3 70.5 4.53 9.00 4.90 2
#> 32 3 70.5 4.53 12.15 3.70 2
#> 33 3 70.5 4.53 24.17 1.05 2
#> 34 4 72.7 4.40 0.00 0.00 2
#> 35 4 72.7 4.40 0.35 1.89 2
#> 36 4 72.7 4.40 0.60 4.60 2
#> 37 4 72.7 4.40 1.07 8.60 2
#> 38 4 72.7 4.40 2.13 8.38 2
#> 39 4 72.7 4.40 3.50 7.54 2
#> 40 4 72.7 4.40 5.02 6.88 2
#> 41 4 72.7 4.40 7.02 5.78 2
#> 42 4 72.7 4.40 9.02 5.33 2
#> 43 4 72.7 4.40 11.98 4.19 2
#> 44 4 72.7 4.40 24.65 1.15 2
#>
#> $`3`
#> Subject Wt Dose Time conc chunk__
#> 45 5 54.6 5.86 0.00 0.00 3
#> 46 5 54.6 5.86 0.30 2.02 3
#> 47 5 54.6 5.86 0.52 5.63 3
#> 48 5 54.6 5.86 1.00 11.40 3
#> 49 5 54.6 5.86 2.02 9.33 3
#> 50 5 54.6 5.86 3.50 8.74 3
#> 51 5 54.6 5.86 5.02 7.56 3
#> 52 5 54.6 5.86 7.02 7.09 3
#> 53 5 54.6 5.86 9.10 5.90 3
#> 54 5 54.6 5.86 12.00 4.37 3
#> 55 5 54.6 5.86 24.35 1.57 3
#> 56 6 80.0 4.00 0.00 0.00 3
#> 57 6 80.0 4.00 0.27 1.29 3
#> 58 6 80.0 4.00 0.58 3.08 3
#> 59 6 80.0 4.00 1.15 6.44 3
#> 60 6 80.0 4.00 2.03 6.32 3
#> 61 6 80.0 4.00 3.57 5.53 3
#> 62 6 80.0 4.00 5.00 4.94 3
#> 63 6 80.0 4.00 7.00 4.02 3
#> 64 6 80.0 4.00 9.22 3.46 3
#> 65 6 80.0 4.00 12.10 2.78 3
#> 66 6 80.0 4.00 23.85 0.92 3
#>
#> $`4`
#> Subject Wt Dose Time conc chunk__
#> 67 7 64.6 4.95 0.00 0.15 4
#> 68 7 64.6 4.95 0.25 0.85 4
#> 69 7 64.6 4.95 0.50 2.35 4
#> 70 7 64.6 4.95 1.02 5.02 4
#> 71 7 64.6 4.95 2.02 6.58 4
#> 72 7 64.6 4.95 3.48 7.09 4
#> 73 7 64.6 4.95 5.00 6.66 4
#> 74 7 64.6 4.95 6.98 5.25 4
#> 75 7 64.6 4.95 9.00 4.39 4
#> 76 7 64.6 4.95 12.05 3.53 4
#> 77 7 64.6 4.95 24.22 1.15 4
#> 78 8 70.5 4.53 0.00 0.00 4
#> 79 8 70.5 4.53 0.25 3.05 4
#> 80 8 70.5 4.53 0.52 3.05 4
#> 81 8 70.5 4.53 0.98 7.31 4
#> 82 8 70.5 4.53 2.02 7.56 4
#> 83 8 70.5 4.53 3.53 6.59 4
#> 84 8 70.5 4.53 5.05 5.88 4
#> 85 8 70.5 4.53 7.15 4.73 4
#> 86 8 70.5 4.53 9.07 4.57 4
#> 87 8 70.5 4.53 12.10 3.00 4
#> 88 8 70.5 4.53 24.12 1.25 4
#>
#> $`5`
#> Subject Wt Dose Time conc chunk__
#> 89 9 86.4 3.1 0.00 0.00 5
#> 90 9 86.4 3.1 0.30 7.37 5
#> 91 9 86.4 3.1 0.63 9.03 5
#> 92 9 86.4 3.1 1.05 7.14 5
#> 93 9 86.4 3.1 2.02 6.33 5
#> 94 9 86.4 3.1 3.53 5.66 5
#> 95 9 86.4 3.1 5.02 5.67 5
#> 96 9 86.4 3.1 7.17 4.24 5
#> 97 9 86.4 3.1 8.80 4.11 5
#> 98 9 86.4 3.1 11.60 3.16 5
#> 99 9 86.4 3.1 24.43 1.12 5
#> 100 10 58.2 5.5 0.00 0.24 5
#> 101 10 58.2 5.5 0.37 2.89 5
#> 102 10 58.2 5.5 0.77 5.22 5
#> 103 10 58.2 5.5 1.02 6.41 5
#> 104 10 58.2 5.5 2.05 7.83 5
#> 105 10 58.2 5.5 3.55 10.21 5
#> 106 10 58.2 5.5 5.05 9.18 5
#> 107 10 58.2 5.5 7.08 8.02 5
#> 108 10 58.2 5.5 9.38 7.14 5
#> 109 10 58.2 5.5 12.10 5.68 5
#> 110 10 58.2 5.5 23.70 2.42 5
#>
#> $`6`
#> Subject Wt Dose Time conc chunk__
#> 111 11 65.0 4.92 0.00 0.00 6
#> 112 11 65.0 4.92 0.25 4.86 6
#> 113 11 65.0 4.92 0.50 7.24 6
#> 114 11 65.0 4.92 0.98 8.00 6
#> 115 11 65.0 4.92 1.98 6.81 6
#> 116 11 65.0 4.92 3.60 5.87 6
#> 117 11 65.0 4.92 5.02 5.22 6
#> 118 11 65.0 4.92 7.03 4.45 6
#> 119 11 65.0 4.92 9.03 3.62 6
#> 120 11 65.0 4.92 12.12 2.69 6
#> 121 11 65.0 4.92 24.08 0.86 6
#> 122 12 60.5 5.30 0.00 0.00 6
#> 123 12 60.5 5.30 0.25 1.25 6
#> 124 12 60.5 5.30 0.50 3.96 6
#> 125 12 60.5 5.30 1.00 7.82 6
#> 126 12 60.5 5.30 2.00 9.72 6
#> 127 12 60.5 5.30 3.52 9.75 6
#> 128 12 60.5 5.30 5.07 8.57 6
#> 129 12 60.5 5.30 7.07 6.59 6
#> 130 12 60.5 5.30 9.03 6.11 6
#> 131 12 60.5 5.30 12.05 4.57 6
#> 132 12 60.5 5.30 24.15 1.17 6
#>
# Chunking with grouped data will evenly distribute the
# number of unique group elements across the chunks.
Theoph %>% group_by(Subject) %>% chunk_df(.nchunks = 5)
#> $`1`
#> # A tibble: 33 × 6
#> # Groups: Subject [3]
#> Subject Wt Dose Time conc chunk__
#> <ord> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 79.6 4.02 0 0.74 1
#> 2 1 79.6 4.02 0.25 2.84 1
#> 3 1 79.6 4.02 0.57 6.57 1
#> 4 1 79.6 4.02 1.12 10.5 1
#> 5 1 79.6 4.02 2.02 9.66 1
#> 6 1 79.6 4.02 3.82 8.58 1
#> 7 1 79.6 4.02 5.1 8.36 1
#> 8 1 79.6 4.02 7.03 7.47 1
#> 9 1 79.6 4.02 9.05 6.89 1
#> 10 1 79.6 4.02 12.1 5.94 1
#> # ℹ 23 more rows
#>
#> $`2`
#> # A tibble: 33 × 6
#> # Groups: Subject [3]
#> Subject Wt Dose Time conc chunk__
#> <ord> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 4 72.7 4.4 0 0 2
#> 2 4 72.7 4.4 0.35 1.89 2
#> 3 4 72.7 4.4 0.6 4.6 2
#> 4 4 72.7 4.4 1.07 8.6 2
#> 5 4 72.7 4.4 2.13 8.38 2
#> 6 4 72.7 4.4 3.5 7.54 2
#> 7 4 72.7 4.4 5.02 6.88 2
#> 8 4 72.7 4.4 7.02 5.78 2
#> 9 4 72.7 4.4 9.02 5.33 2
#> 10 4 72.7 4.4 12.0 4.19 2
#> # ℹ 23 more rows
#>
#> $`3`
#> # A tibble: 22 × 6
#> # Groups: Subject [2]
#> Subject Wt Dose Time conc chunk__
#> <ord> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 7 64.6 4.95 0 0.15 3
#> 2 7 64.6 4.95 0.25 0.85 3
#> 3 7 64.6 4.95 0.5 2.35 3
#> 4 7 64.6 4.95 1.02 5.02 3
#> 5 7 64.6 4.95 2.02 6.58 3
#> 6 7 64.6 4.95 3.48 7.09 3
#> 7 7 64.6 4.95 5 6.66 3
#> 8 7 64.6 4.95 6.98 5.25 3
#> 9 7 64.6 4.95 9 4.39 3
#> 10 7 64.6 4.95 12.0 3.53 3
#> # ℹ 12 more rows
#>
#> $`4`
#> # A tibble: 22 × 6
#> # Groups: Subject [2]
#> Subject Wt Dose Time conc chunk__
#> <ord> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 9 86.4 3.1 0 0 4
#> 2 9 86.4 3.1 0.3 7.37 4
#> 3 9 86.4 3.1 0.63 9.03 4
#> 4 9 86.4 3.1 1.05 7.14 4
#> 5 9 86.4 3.1 2.02 6.33 4
#> 6 9 86.4 3.1 3.53 5.66 4
#> 7 9 86.4 3.1 5.02 5.67 4
#> 8 9 86.4 3.1 7.17 4.24 4
#> 9 9 86.4 3.1 8.8 4.11 4
#> 10 9 86.4 3.1 11.6 3.16 4
#> # ℹ 12 more rows
#>
#> $`5`
#> # A tibble: 22 × 6
#> # Groups: Subject [2]
#> Subject Wt Dose Time conc chunk__
#> <ord> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 11 65 4.92 0 0 5
#> 2 11 65 4.92 0.25 4.86 5
#> 3 11 65 4.92 0.5 7.24 5
#> 4 11 65 4.92 0.98 8 5
#> 5 11 65 4.92 1.98 6.81 5
#> 6 11 65 4.92 3.6 5.87 5
#> 7 11 65 4.92 5.02 5.22 5
#> 8 11 65 4.92 7.03 4.45 5
#> 9 11 65 4.92 9.03 3.62 5
#> 10 11 65 4.92 12.1 2.69 5
#> # ℹ 12 more rows
#>
# The dataframe can be retained by preventing the output
# from being a list.
Theoph %>% chunk_df(Subject, .nchunks = 3, .as_list = FALSE)
#> # A tibble: 132 × 6
#> # Groups: Subject [12]
#> Subject Wt Dose Time conc chunk__
#> <ord> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 79.6 4.02 0 0.74 1
#> 2 1 79.6 4.02 0.25 2.84 1
#> 3 1 79.6 4.02 0.57 6.57 1
#> 4 1 79.6 4.02 1.12 10.5 1
#> 5 1 79.6 4.02 2.02 9.66 1
#> 6 1 79.6 4.02 3.82 8.58 1
#> 7 1 79.6 4.02 5.1 8.36 1
#> 8 1 79.6 4.02 7.03 7.47 1
#> 9 1 79.6 4.02 9.05 6.89 1
#> 10 1 79.6 4.02 12.1 5.94 1
#> # ℹ 122 more rows