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")
),for some functions, like
data_select()
ordata_rename()
,select
can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.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
orc(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:
, orregex()
.starts_with()
,ends_with()
, andcontains()
accept several patterns, e.gstarts_with("Sep", "Petal")
.regex()
can be used to define regular expression patterns.a function testing for logical conditions, e.g.
is.numeric()
(oris.numeric
), or any user-defined function that selects the variables for which the function returnsTRUE
(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, theexclude
argument can be used alternatively. For instance,select=-ends_with("Length")
(with-
) is equivalent toexclude=ends_with("Length")
(no-
). In case negation should not work as expected, use theexclude
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 fromexclude
will be excluded instead of selected. IfNULL
(the default), excludes no columns.- remove_na
Logical. If
TRUE
, missing values in the column provided inexpand
are removed from the data frame. IfFALSE
andexpand
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 inselect
, ignores lower/upper case in the search pattern when matching against variable names.- verbose
Toggle warnings.
- regex
Logical, if
TRUE
, the search pattern fromselect
will be treated as regular expression. Whenregex = 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()
orselect = 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.
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