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Gather takes multiple columns and collapses into key-value pairs,
duplicating all other columns as needed. You use gather()
when
you notice that you have columns that are not variables.
gather(data, key = "key", value = "value", ..., na.rm = FALSE,
convert = FALSE, factor_key = FALSE)
A data frame.
Names of new key and value columns, as strings or symbols.
This argument is passed by expression and supports
quasiquotation (you can unquote strings
and symbols). The name is captured from the expression with
rlang::ensym()
(note that this kind of interface where
symbols do not represent actual objects is now discouraged in the
tidyverse; we support it here for backward compatibility).
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.
If TRUE
, will remove rows from output where the
value column in NA
.
If TRUE
will automatically run
type.convert()
on the key column. This is useful if the column
types are actually numeric, integer, or logical.
If FALSE
, the default, the key values will be
stored as a character vector. If TRUE
, will be stored as a factor,
which preserves the original ordering of the columns.
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 {
library(dplyr)
# From http://stackoverflow.com/questions/1181060
stocks <- tibble(
time = as.Date('2009-01-01') + 0:9,
X = rnorm(10, 0, 1),
Y = rnorm(10, 0, 2),
Z = rnorm(10, 0, 4)
)
gather(stocks, stock, price, -time)
stocks %>% gather(stock, price, -time)
# get first observation for each Species in iris data -- base R
mini_iris <- iris[c(1, 51, 101), ]
# gather Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
gather(mini_iris, key = flower_att, value = measurement,
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)
# same result but less verbose
gather(mini_iris, key = flower_att, value = measurement, -Species)
# repeat iris example using dplyr and the pipe operator
library(dplyr)
mini_iris <-
iris %>%
group_by(Species) %>%
slice(1)
mini_iris %>% gather(key = flower_att, value = measurement, -Species)
# }
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