If a variable contains observations with multiple delimited values, this separates the values and places each one in its own row.
separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE)A data frame.
A selection of columns. If empty, all variables are
selected. You can supply bare variable names, select all
variables between x and z with x:z, exclude y with -y. For
more options, see the dplyr::select() documentation. See also
the section on selection rules below.
Separator delimiting collapsed values.
If TRUE will automatically run
type.convert() on the key column. This is useful if the column
types are actually numeric, integer, or logical.
Arguments for selecting columns are passed to
tidyselect::vars_select() and are treated specially. Unlike other
verbs, selecting functions make a strict distinction between data
expressions and context expressions.
A data expression is either a bare name like x or an expression
like x:y or c(x, y). In a data expression, you can only refer
to columns from the data frame.
Everything else is a context expression in which you can only
refer to objects that you have defined with <-.
For instance, col1:col3 is a data expression that refers to data
columns, while seq(start, end) is a context expression that
refers to objects from the contexts.
If you really need to refer to contextual objects from a data
expression, you can unquote them with the tidy eval operator
!!. This operator evaluates its argument in the context and
inlines the result in the surrounding function call. For instance,
c(x, !! x) selects the x column within the data frame and the
column referred to by the object x defined in the context (which
can contain either a column name as string or a column position).
# NOT RUN {
df <- data.frame(
x = 1:3,
y = c("a", "d,e,f", "g,h"),
z = c("1", "2,3,4", "5,6"),
stringsAsFactors = FALSE
)
separate_rows(df, y, z, convert = TRUE)
# }
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