datawizard (version 0.13.0)

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

Value

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

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.

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.

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.

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 that contains 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'.

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(), extract_column_names()

• 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")

# 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}"
)

# 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_"
)

# filling missing values with 0
data_to_wide(
  d,
  id_cols = "Subject",
  names_from = "Days",
  values_from = "Reaction",
  names_prefix = "Reaction_Day_",
  values_fill = 0
)