data_to_wide: Reshape (pivot) data from long to wide
In datawizard: Easy Data Wrangling and Statistical Transformations

data_to_wide

R Documentation

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,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

reshape_wider(
  data,
  id_cols = NULL,
  values_from = "Value",
  names_from = "Name",
  names_sep = "_",
  names_prefix = "",
  names_glue = NULL,
  values_fill = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

Arguments

`data`	A data frame to convert to wide format, so that it has more columns and fewer rows post-widening than pre-widening.
`id_cols`	The name of the column that identifies the rows in the data by which observations are grouped and the gathered data is spread into new columns. Usually, this is a variable containing an ID for observations that have been repeatedly measured. If `NULL`, it will use all remaining columns that are not in `names_from` or `values_from` as ID columns. `id_cols` can also be a character vector with more than one name of identifier columns. See also 'Details' and 'Examples'.
`values_from`	The name of the columns in the original data that contains the values used to fill the new columns created in the widened data. Can also be one of the selection helpers (see argument `select` in `data_select()`).
`names_from`	The name of the column in the original data whose values will be used for naming the new columns created in the widened data. Each unique value in this column will become the name of one of these new columns. In case `names_prefix` is provided, column names will be concatenated with the string given in `names_prefix`. If `values_from` specifies more than one variable that should be widened, the new column names are a combination of the old column names in `values_from` and the values from `names_from`, to avoid duplicate 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`	Defunct argument, which has no function anymore. Will be removed in future versions.
`ignore_case`	Logical, if `TRUE` and when one of the select-helpers or a regular expression is used in `select`, ignores lower/upper case in the search pattern when matching against variable names.
`regex`	Logical, if `TRUE`, the search pattern from `select` will be treated as regular expression. When `regex = TRUE`, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. `regex = TRUE` is comparable to using one of the two select-helpers, `select = contains()` or `select = regex()`, however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.
`verbose`	Toggle warnings.
`...`	Not used for now.

Details

Reshaping data into wide format usually means that the input data frame is in long format, where multiple measurements taken on the same subject are stored in multiple rows. The wide format stores the same information in a single row, with each measurement stored in a separate column. Thus, the necessary information for data_to_wide() is:

The name of the column(s) that identify the groups or repeated measurements (id_cols).
The name of the column whose values will become the new column names (names_from). Since these values may not necessarily reflect appropriate column names, you can use names_prefix to add a prefix to each newly created column name.
The name of the column(s) that contain the values (values_from) for the new columns that are created by names_from.

In other words: repeated measurements, as indicated by id_cols, that are saved into the column values_from will be spread into new columns, which will be named after the values in names_from. See also 'Examples'.

Value

If a tibble was provided as input, data_to_wide() 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")

# converting long data into wide format
data_to_wide(
  data_long,
  id_cols = "subject",
  names_from = "condition",
  values_from = "measurement"
)

# converting long data into wide format with custom column names
data_to_wide(
  data_long,
  id_cols = "subject",
  names_from = "condition",
  values_from = "measurement",
  names_prefix = "Var.",
  names_sep = "."
)

# converting long data into wide format, combining multiple columns
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}"
)

# reshaping multiple long columns into wide format. to avoid duplicate
# column names, new names are a combination of the old column names in
# `values_from` and the values from `names_from`
data_long <- read.table(header = TRUE, text = "
subject_id time score anxiety test
         1    1    10       5   NA
         1    2    NA       7   NA
         2    1    15       6   NA
         2    2    12      NA   NA
         3    1    18       8   NA
         5    2    11       4   NA
         4    1    NA       5   NA
         4    2    14      NA   NA")

data_to_wide(
  data_long,
  id_cols = "subject_id",
  names_from = "time",
  values_from = c("score", "anxiety", "test")
)

# using the "sleepstudy" dataset
data(sleepstudy, package = "lme4")

# the sleepstudy data contains repeated measurements of average reaction
# times for each subjects over multiple days, in a sleep deprivation study.
# It is in long-format, i.e. each row corresponds to a single measurement.
# The variable "Days" contains the timepoint of the measurement, and
# "Reaction" contains the measurement itself. Converting this data to wide
# format will create a new column for each day, with the reaction time as the
# value.
head(sleepstudy)

data_to_wide(
  sleepstudy,
  id_cols = "Subject",
  names_from = "Days",
  values_from = "Reaction"
)

# clearer column names
data_to_wide(
  sleepstudy,
  id_cols = "Subject",
  names_from = "Days",
  values_from = "Reaction",
  names_prefix = "Reaction_Day_"
)

# For unequal group sizes, missing information is filled with NA
d <- subset(sleepstudy, Days %in% c(0, 1, 2, 3, 4))[c(1:9, 11:13, 16:17, 21), ]

# long format, different number of "Subjects"
d

data_to_wide(
  d,
  id_cols = "Subject",
  names_from = "Days",
  values_from = "Reaction",
  names_prefix = "Reaction_Day_"
)

datawizard documentation built on April 26, 2026, 5:06 p.m.