pivot_wider: Pivot data from long to wide

In poorman: A Poor Man's Dependency Free Recreation of 'dplyr'

Pivot data from long to wide

Description

pivot_wider() "widens" data, increasing the number of columns and decreasing the number of rows. The inverse transformation is pivot_longer().

Usage

pivot_wider(
  data,
  id_cols = NULL,
  values_from = "Value",
  names_from = "Name",
  names_sep = "_",
  names_prefix = "",
  names_glue = NULL,
  values_fill = NULL,
  ...
)

Arguments

data	data.frame. The data to pivot.
id_cols	character(1). The name of the column that identifies the rows. If NULL, it will use all the unique rows.
values_from	character(n). The name of the column that contains the values to be used as future variable values.
names_from	character(n). The name of the column(s) that contains the levels to be used as future column names.
names_sep	character(1). If names_from or values_from contains multiple variables, this will be used to join their values together into a single string to use as a column name.
names_prefix	character(1). String added to the start of every variable name. This is particularly useful if names_from is a numeric vector and you want to create syntactic variable names.
names_glue	character(1). Instead of names_sep and names_prefix, you can supply a glue specification that uses the names_from columns to create custom column names. Note that the only delimiters supported by names_glue are curly brackets, ⁠{⁠ and ⁠}⁠.
values_fill	numeric(n). Optionally, a (scalar) value that will be used to replace missing values in the new columns created.
...	Not used for now.

Value

If a tibble was provided as input, pivot_wider() also returns a tibble. Otherwise, it returns a data frame.

Examples

data_long <- read.table(header = TRUE, text = "
 subject sex condition measurement
       1   M   control         7.9
       1   M     cond1        12.3
       1   M     cond2        10.7
       2   F   control         6.3
       2   F     cond1        10.6
       2   F     cond2        11.1
       3   F   control         9.5
       3   F     cond1        13.1
       3   F     cond2        13.8
       4   M   control        11.5
       4   M     cond1        13.4
       4   M     cond2        12.9")


pivot_wider(
  data_long,
  id_cols = "subject",
  names_from = "condition",
  values_from = "measurement"
)

pivot_wider(
  data_long,
  id_cols = "subject",
  names_from = "condition",
  values_from = "measurement",
  names_prefix = "Var.",
  names_sep = "."
)

production <- expand.grid(
  product = c("A", "B"),
  country = c("AI", "EI"),
  year = 2000:2014
) %>%
  filter((product == "A" & country == "AI") | product == "B") %>%
  mutate(production = rnorm(nrow(.)))

pivot_wider(
  production,
  names_from = c("product", "country"),
  values_from = "production",
  names_glue = "prod_{product}_{country}"
)

poorman documentation built on Nov. 2, 2023, 5:27 p.m.