data_to_wide: Reshape (pivot) data from long to wide

In datawizard: Easy Data Wrangling and Statistical Transformations

Reshape (pivot) data from long to wide

Description

This function "widens" data, increasing the number of columns and decreasing the number of rows. This is a dependency-free base-R equivalent of tidyr::pivot_wider().

Usage

data_to_wide(
  data,
  id_cols = NULL,
  values_from = "Value",
  names_from = "Name",
  names_sep = "_",
  names_prefix = "",
  names_glue = NULL,
  values_fill = NULL,
  verbose = TRUE,
  ...,
  colnames_from,
  rows_from,
  sep
)

reshape_wider(
  data,
  id_cols = NULL,
  values_from = "Value",
  names_from = "Name",
  names_sep = "_",
  names_prefix = "",
  names_glue = NULL,
  values_fill = NULL,
  verbose = TRUE,
  ...,
  colnames_from,
  rows_from,
  sep
)

Arguments

data	A data frame to pivot.
id_cols	The name of the column that identifies the rows. If NULL, it will use all the unique rows.
values_from	The name of the column that contains the values to be used as future variable values.
names_from	The name of the column that contains the levels to be used as future column names.
names_sep	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	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	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	Optionally, a (scalar) value that will be used to replace missing values in the new columns created.
verbose	Toggle warnings.
...	Not used for now.
colnames_from	Deprecated. Use names_from instead.
rows_from	Deprecated. Use id_cols instead.
sep	Deprecated. Use names_sep instead.

Value

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

See Also

• Functions to rename stuff: data_rename(), data_rename_rows(), data_addprefix(), data_addsuffix()

• Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()

• Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()

• Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()

• Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()

• Split and merge data frames: data_partition(), data_merge()

• Functions to find or select columns: data_select(), data_find()

• Functions to filter rows: data_match(), data_filter()

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")


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

data_to_wide(
  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
)
production <- data_filter(production, (product == "A" & country == "AI") | product == "B")

production$production <- rnorm(nrow(production))

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

datawizard documentation built on Sept. 15, 2023, 9:06 a.m.