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Expand (i.e. replicate rows) a data frame

Source: R/data_replicate.R
data_replicate.Rd

Expand a data frame by replicating rows based on another variable that contains the counts of replications per row.

Usage

data_replicate(
  data,
  expand = NULL,
  select = NULL,
  exclude = NULL,
  remove_na = FALSE,
  ignore_case = FALSE,
  verbose = TRUE,
  regex = FALSE,
  ...
)

Arguments

data

A data frame.

expand

The name of the column that contains the counts of replications for each row. Can also be a numeric value, indicating the position of that column. Note that the variable indicated by expand must be an integer vector.

select

Variables that will be included when performing the required tasks. Can be either

  • a variable specified as a literal variable name (e.g., column_name),

  • a string with the variable name (e.g., "column_name"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),

  • a formula with variable names (e.g., ~column_1 + column_2),

  • a vector of positive integers, giving the positions counting from the left (e.g. 1 or c(1, 3, 5)),

  • a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),

  • one of the following select-helpers: starts_with(), ends_with(), contains(), a range using : or regex(""). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal").

  • or a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),

  • ranges specified via literal variable names, select-helpers (except regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a -, e.g. -ends_with(""), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

remove_na

Logical. If TRUE, missing values in the column provided in expand are removed from the data frame. If FALSE and expand contains missing values, the function will throw an error.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

verbose

Toggle warnings.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. regex = TRUE is comparable to using one of the two select-helpers, select = contains("") or select = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

...

Currently not used.

Value

A dataframe with each row replicated as many times as defined in expand.

Examples

data(mtcars)
data_replicate(head(mtcars), "carb")
#>     mpg cyl disp  hp drat    wt  qsec vs am gear
#> 1  21.0   6  160 110 3.90 2.620 16.46  0  1    4
#> 2  21.0   6  160 110 3.90 2.620 16.46  0  1    4
#> 3  21.0   6  160 110 3.90 2.620 16.46  0  1    4
#> 4  21.0   6  160 110 3.90 2.620 16.46  0  1    4
#> 5  21.0   6  160 110 3.90 2.875 17.02  0  1    4
#> 6  21.0   6  160 110 3.90 2.875 17.02  0  1    4
#> 7  21.0   6  160 110 3.90 2.875 17.02  0  1    4
#> 8  21.0   6  160 110 3.90 2.875 17.02  0  1    4
#> 9  22.8   4  108  93 3.85 2.320 18.61  1  1    4
#> 10 21.4   6  258 110 3.08 3.215 19.44  1  0    3
#> 11 18.7   8  360 175 3.15 3.440 17.02  0  0    3
#> 12 18.7   8  360 175 3.15 3.440 17.02  0  0    3
#> 13 18.1   6  225 105 2.76 3.460 20.22  1  0    3