Splits a dataframe row-wise or col-wise into any arbitrary number of dataframes

This function splits a dataframe into any number of dataframes such that they can be rejoined by using rbind()/dplyr::bind_rows() for unrbind() or cbind()/dplyr::bind_cols() for uncbind(). The user may find it appropriate to go on and apply messy() to each new dataframe independently to impede rejoining.

Usage

unrbind(data, sizes = NULL, probs = NULL, names = NULL, shuffle = TRUE)

uncbind(data, sizes = NULL, probs = NULL, names = NULL, shuffle = TRUE)

Arguments

data: input dataframe
sizes: A vector of numeric inputs summing to nrow(data) for unrbind() or ncol(data) for uncbind(); the number of rows of each resulting dataframe. See probs for an alternative approach. If neither are provided, the dataframe will be split roughly in half.
probs: A vector of numeric inputs summing to 1; the proportion of rows/columns in each resulting dataframe. An alternative to sizes.
names: The names of the output list. If NULL the list will be unnamed.
shuffle: Shuffle rows in unrbind() or columns in uncbind()? Defaults to TRUE.

Value

A list of dataframes

Details

Real data can often be found in disparate files. For example, data reports may come in monthly and require row-binding together to obtain a complete annual time series. Scientific results may arrive from different laboratories and require binding together for further analysis and comparisons. This function may simulate a single dataframe having come from different sources and requiring binding back together. Base R's split() offers an alternative to unrbind(), but requires a pre-existing factor column to split by and cannot as easily create random splits in the data.

Author

Jack Davison

Examples

unrbind(dplyr::tibble(mtcars), probs = c(0.5, 0.3, 0.2))
#> [[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  21.4     6 258     110  3.08  3.22  19.4     1     0     3     1
#>  2  17.8     6 168.    123  3.92  3.44  18.9     1     0     4     4
#>  3  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2
#>  4  26       4 120.     91  4.43  2.14  16.7     0     1     5     2
#>  5  21       6 160     110  3.9   2.62  16.5     0     1     4     4
#>  6  13.3     8 350     245  3.73  3.84  15.4     0     0     3     4
#>  7  19.7     6 145     175  3.62  2.77  15.5     0     1     5     6
#>  8  19.2     8 400     175  3.08  3.84  17.0     0     0     3     2
#>  9  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
#> 10  15.2     8 276.    180  3.07  3.78  18       0     0     3     3
#> 11  15.2     8 304     150  3.15  3.44  17.3     0     0     3     2
#> 12  21       6 160     110  3.9   2.88  17.0     0     1     4     4
#> 13  10.4     8 460     215  3     5.42  17.8     0     0     3     4
#> 14  22.8     4 141.     95  3.92  3.15  22.9     1     0     4     2
#> 15  14.7     8 440     230  3.23  5.34  17.4     0     0     3     4
#> 16  18.1     6 225     105  2.76  3.46  20.2     1     0     3     1
#> 
#> [[2]]
#> # 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  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
#>  2  16.4     8 276.    180  3.07  4.07  17.4     0     0     3     3
#>  3  21.5     4 120.     97  3.7   2.46  20.0     1     0     3     1
#>  4  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
#>  5  19.2     6 168.    123  3.92  3.44  18.3     1     0     4     4
#>  6  22.8     4 108      93  3.85  2.32  18.6     1     1     4     1
#>  7  10.4     8 472     205  2.93  5.25  18.0     0     0     3     4
#>  8  24.4     4 147.     62  3.69  3.19  20       1     0     4     2
#>  9  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
#> 10  21.4     4 121     109  4.11  2.78  18.6     1     1     4     2
#> 
#> [[3]]
#> # A tibble: 6 × 11
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  15.5     8  318    150  2.76  3.52  16.9     0     0     3     2
#> 2  15.8     8  351    264  4.22  3.17  14.5     0     1     5     4
#> 3  17.3     8  276.   180  3.07  3.73  17.6     0     0     3     3
#> 4  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#> 5  15       8  301    335  3.54  3.57  14.6     0     1     5     8
#> 6  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#> 

uncbind(dplyr::tibble(mtcars), probs = c(0.5, 0.3, 0.2))
#> [[1]]
#> # A tibble: 32 × 6
#>       hp    wt  gear  disp    am    vs
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1   110  2.62     4  160      1     0
#>  2   110  2.88     4  160      1     0
#>  3    93  2.32     4  108      1     1
#>  4   110  3.22     3  258      0     1
#>  5   175  3.44     3  360      0     0
#>  6   105  3.46     3  225      0     1
#>  7   245  3.57     3  360      0     0
#>  8    62  3.19     4  147.     0     1
#>  9    95  3.15     4  141.     0     1
#> 10   123  3.44     4  168.     0     1
#> # ℹ 22 more rows
#> 
#> [[2]]
#> # A tibble: 32 × 3
#>     qsec   cyl  carb
#>    <dbl> <dbl> <dbl>
#>  1  16.5     6     4
#>  2  17.0     6     4
#>  3  18.6     4     1
#>  4  19.4     6     1
#>  5  17.0     8     2
#>  6  20.2     6     1
#>  7  15.8     8     4
#>  8  20       4     2
#>  9  22.9     4     2
#> 10  18.3     6     4
#> # ℹ 22 more rows
#> 
#> [[3]]
#> # A tibble: 32 × 2
#>      mpg  drat
#>    <dbl> <dbl>
#>  1  21    3.9 
#>  2  21    3.9 
#>  3  22.8  3.85
#>  4  21.4  3.08
#>  5  18.7  3.15
#>  6  18.1  2.76
#>  7  14.3  3.21
#>  8  24.4  3.69
#>  9  22.8  3.92
#> 10  19.2  3.92
#> # ℹ 22 more rows
#>