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Gather columns into key-value pairs

Source: R/gather.R
gather.Rd

[Superseded]

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").

Usage

gather(
  data,
  key = "key",
  value = "value",
  ...,
  na.rm = FALSE,
  convert = FALSE,
  factor_key = FALSE
)

Arguments

data

A data frame.

key, value

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.

na.rm

If TRUE, will remove rows from output where the value column is NA.

convert

If TRUE will automatically run type.convert() on the key column. This is useful if the column types are actually numeric, integer, or logical.

factor_key

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.

Rules for selection

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.

Examples

# 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     -1.82 
#>  2 2009-01-02 X     -0.247
#>  3 2009-01-03 X     -0.244
#>  4 2009-01-04 X     -0.283
#>  5 2009-01-05 X     -0.554
#>  6 2009-01-06 X      0.629
#>  7 2009-01-07 X      2.07 
#>  8 2009-01-08 X     -1.63 
#>  9 2009-01-09 X      0.512
#> 10 2009-01-10 X     -1.86 
#> # ℹ 20 more rows
stocks %>% gather("stock", "price", -time)
#> # A tibble: 30 × 3
#>    time       stock  price
#>    <date>     <chr>  <dbl>
#>  1 2009-01-01 X     -1.82 
#>  2 2009-01-02 X     -0.247
#>  3 2009-01-03 X     -0.244
#>  4 2009-01-04 X     -0.283
#>  5 2009-01-05 X     -0.554
#>  6 2009-01-06 X      0.629
#>  7 2009-01-07 X      2.07 
#>  8 2009-01-08 X     -1.63 
#>  9 2009-01-09 X      0.512
#> 10 2009-01-10 X     -1.86 
#> # ℹ 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