datawizard (version 0.6.0)

data_to_wide: 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
)

Value

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

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.

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")


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

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

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

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