Development on gather()
is complete, and for new code we recommend
switching to pivot_longer()
, which is easier to use, more featureful, and
still under active development.
df %>% gather("key", "value", x, y, z)
is equivalent to
df %>% pivot_longer(c(x, y, z), names_to = "key", values_to = "value")
See more details in vignette("pivot")
.
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 is 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 need to refer to contextual objects from a data expression, you can
use all_of()
or any_of()
. These functions are used to select
data-variables whose names are stored in a env-variable. For instance,
all_of(a)
selects the variables listed in the character vector a
.
For more details, see the tidyselect::select_helpers()
documentation.
library(dplyr)
# From https://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)
#> # A tibble: 30 × 3
#> time stock price
#> <date> <chr> <dbl>
#> 1 2009-01-01 X -2.78
#> 2 2009-01-02 X 0.900
#> 3 2009-01-03 X 0.0940
#> 4 2009-01-04 X 0.0834
#> 5 2009-01-05 X -0.260
#> 6 2009-01-06 X -0.474
#> 7 2009-01-07 X 0.572
#> 8 2009-01-08 X 0.673
#> 9 2009-01-09 X 1.19
#> 10 2009-01-10 X 0.0128
#> # … with 20 more rows
stocks %>% gather("stock", "price", -time)
#> # A tibble: 30 × 3
#> time stock price
#> <date> <chr> <dbl>
#> 1 2009-01-01 X -2.78
#> 2 2009-01-02 X 0.900
#> 3 2009-01-03 X 0.0940
#> 4 2009-01-04 X 0.0834
#> 5 2009-01-05 X -0.260
#> 6 2009-01-06 X -0.474
#> 7 2009-01-07 X 0.572
#> 8 2009-01-08 X 0.673
#> 9 2009-01-09 X 1.19
#> 10 2009-01-10 X 0.0128
#> # … with 20 more rows
# 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)
#> Species flower_att measurement
#> 1 setosa Sepal.Length 5.1
#> 2 versicolor Sepal.Length 7.0
#> 3 virginica Sepal.Length 6.3
#> 4 setosa Sepal.Width 3.5
#> 5 versicolor Sepal.Width 3.2
#> 6 virginica Sepal.Width 3.3
#> 7 setosa Petal.Length 1.4
#> 8 versicolor Petal.Length 4.7
#> 9 virginica Petal.Length 6.0
#> 10 setosa Petal.Width 0.2
#> 11 versicolor Petal.Width 1.4
#> 12 virginica Petal.Width 2.5
# same result but less verbose
gather(mini_iris, key = "flower_att", value = "measurement", -Species)
#> Species flower_att measurement
#> 1 setosa Sepal.Length 5.1
#> 2 versicolor Sepal.Length 7.0
#> 3 virginica Sepal.Length 6.3
#> 4 setosa Sepal.Width 3.5
#> 5 versicolor Sepal.Width 3.2
#> 6 virginica Sepal.Width 3.3
#> 7 setosa Petal.Length 1.4
#> 8 versicolor Petal.Length 4.7
#> 9 virginica Petal.Length 6.0
#> 10 setosa Petal.Width 0.2
#> 11 versicolor Petal.Width 1.4
#> 12 virginica Petal.Width 2.5
# 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)
#> # A tibble: 12 × 3
#> # Groups: Species [3]
#> Species flower_att measurement
#> <fct> <chr> <dbl>
#> 1 setosa Sepal.Length 5.1
#> 2 versicolor Sepal.Length 7
#> 3 virginica Sepal.Length 6.3
#> 4 setosa Sepal.Width 3.5
#> 5 versicolor Sepal.Width 3.2
#> 6 virginica Sepal.Width 3.3
#> 7 setosa Petal.Length 1.4
#> 8 versicolor Petal.Length 4.7
#> 9 virginica Petal.Length 6
#> 10 setosa Petal.Width 0.2
#> 11 versicolor Petal.Width 1.4
#> 12 virginica Petal.Width 2.5