Title: Import and Export 'SPSS', 'Stata' and 'SAS' Files
Version: 2.5.4
Description: Import foreign statistical formats into R via the embedded 'ReadStat' C library, https://github.com/WizardMac/ReadStat.
License: MIT + file LICENSE
URL: https://haven.tidyverse.org, https://github.com/tidyverse/haven, https://github.com/WizardMac/ReadStat
BugReports: https://github.com/tidyverse/haven/issues
Depends: R (≥ 3.6)
Imports: cli (≥ 3.0.0), forcats (≥ 0.2.0), hms, lifecycle, methods, readr (≥ 0.1.0), rlang (≥ 0.4.0), tibble, tidyselect, vctrs (≥ 0.3.0)
Suggests: covr, crayon, fs, knitr, pillar (≥ 1.4.0), rmarkdown, testthat (≥ 3.0.0), utf8
LinkingTo: cpp11
VignetteBuilder: knitr
Config/Needs/website: tidyverse/tidytemplate
Config/testthat/edition: 3
Encoding: UTF-8
RoxygenNote: 7.2.3
SystemRequirements: GNU make, zlib: zlib1g-dev (deb), zlib-devel (rpm)
NeedsCompilation: yes
Packaged: 2023-11-30 13:28:43 UTC; hadleywickham
Author: Hadley Wickham [aut, cre], Evan Miller [aut, cph] (Author of included ReadStat code), Danny Smith [aut], Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>
Repository: CRAN
Date/Publication: 2023-11-30 15:10:02 UTC

haven: Import and Export 'SPSS', 'Stata' and 'SAS' Files

Description

logo

Import foreign statistical formats into R via the embedded 'ReadStat' C library, https://github.com/WizardMac/ReadStat.

Author(s)

Maintainer: Hadley Wickham hadley@posit.co

Authors:

Other contributors:

See Also

Useful links:

Convert labelled vectors to factors

Description

The base function as.factor() is not a generic, but forcats::as_factor() is. haven provides as_factor() methods for labelled() and labelled_spss() vectors, and data frames. By default, when applied to a data frame, it only affects labelled columns.

Usage

## S3 method for class 'data.frame'
as_factor(x, ..., only_labelled = TRUE)

## S3 method for class 'haven_labelled'
as_factor(
  x,
  levels = c("default", "labels", "values", "both"),
  ordered = FALSE,
  ...
)

## S3 method for class 'labelled'
as_factor(
  x,
  levels = c("default", "labels", "values", "both"),
  ordered = FALSE,
  ...
)

Arguments

x

Object to coerce to a factor.

...

Other arguments passed down to method.

only_labelled

Only apply to labelled columns?

levels

How to create the levels of the generated factor:

  • "default": uses labels where available, otherwise the values. Labels are sorted by value.

  • "both": like "default", but pastes together the level and value

  • "label": use only the labels; unlabelled values become NA

  • "values: use only the values

ordered

If TRUE create an ordered (ordinal) factor, if FALSE (the default) create a regular (nominal) factor.

Details

Includes methods for both class haven_labelled and labelled for backward compatibility.

Examples

x <- labelled(sample(5, 10, replace = TRUE), c(Bad = 1, Good = 5))

# Default method uses values where available
as_factor(x)
# You can also extract just the labels
as_factor(x, levels = "labels")
# Or just the values
as_factor(x, levels = "values")
# Or combine value and label
as_factor(x, levels = "both")

# as_factor() will preserve SPSS missing values from values and ranges
y <- labelled_spss(1:10, na_values = c(2, 4), na_range = c(8, 10))
as_factor(y)
# use zap_missing() first to convert to NAs
zap_missing(y)
as_factor(zap_missing(y))

Create a labelled vector.

Description

A labelled vector is a common data structure in other statistical environments, allowing you to assign text labels to specific values. This class makes it possible to import such labelled vectors in to R without loss of fidelity. This class provides few methods, as I expect you'll coerce to a standard R class (e.g. a factor()) soon after importing.

Usage

labelled(x = double(), labels = NULL, label = NULL)

is.labelled(x)

Arguments

x

A vector to label. Must be either numeric (integer or double) or character.

labels

A named vector or NULL. The vector should be the same type as x. Unlike factors, labels don't need to be exhaustive: only a fraction of the values might be labelled.

label

A short, human-readable description of the vector.

Examples

s1 <- labelled(c("M", "M", "F"), c(Male = "M", Female = "F"))
s2 <- labelled(c(1, 1, 2), c(Male = 1, Female = 2))
s3 <- labelled(
  c(1, 1, 2),
  c(Male = 1, Female = 2),
  label = "Assigned sex at birth"
)

# Unfortunately it's not possible to make as.factor work for labelled objects
# so instead use as_factor. This works for all types of labelled vectors.
as_factor(s1)
as_factor(s1, levels = "values")
as_factor(s2)

# Other statistical software supports multiple types of missing values
s3 <- labelled(
  c("M", "M", "F", "X", "N/A"),
  c(Male = "M", Female = "F", Refused = "X", "Not applicable" = "N/A")
)
s3
as_factor(s3)

# Often when you have a partially labelled numeric vector, labelled values
# are special types of missing. Use zap_labels to replace labels with missing
# values
x <- labelled(c(1, 2, 1, 2, 10, 9), c(Unknown = 9, Refused = 10))
zap_labels(x)

Labelled vectors for SPSS

Description

This class is only used when user_na = TRUE in read_sav(). It is similar to the labelled() class but it also models SPSS's user-defined missings, which can be up to three distinct values, or for numeric vectors a range.

Usage

labelled_spss(
  x = double(),
  labels = NULL,
  na_values = NULL,
  na_range = NULL,
  label = NULL
)

Arguments

x

A vector to label. Must be either numeric (integer or double) or character.

labels

A named vector or NULL. The vector should be the same type as x. Unlike factors, labels don't need to be exhaustive: only a fraction of the values might be labelled.

na_values

A vector of values that should also be considered as missing.

na_range

A numeric vector of length two giving the (inclusive) extents of the range. Use -Inf and Inf if you want the range to be open ended.

label

A short, human-readable description of the vector.

Examples

x1 <- labelled_spss(1:10, c(Good = 1, Bad = 8), na_values = c(9, 10))
is.na(x1)

x2 <- labelled_spss(
  1:10,
  c(Good = 1, Bad = 8),
  na_range = c(9, Inf),
  label = "Quality rating"
)
is.na(x2)

# Print data and metadata
x2

Description

This is a convenience function, useful to explore the variables of a newly imported dataset.

Usage

print_labels(x, name = NULL)

Arguments

x

A labelled vector

name

The name of the vector (optional)

Examples

s1 <- labelled(c("M", "M", "F"), c(Male = "M", Female = "F"))
s2 <- labelled(c(1, 1, 2), c(Male = 1, Female = 2))
labelled_df <- tibble::tibble(s1, s2)

for (var in names(labelled_df)) {
  print_labels(labelled_df[[var]], var)
}

Read and write Stata DTA files

Description

Currently haven can read and write logical, integer, numeric, character and factors. See labelled() for how labelled variables in Stata are handled in R.

Character vectors will be stored as strL if any components are strl_threshold bytes or longer (and version >= 13); otherwise they will be stored as the appropriate str#.

Usage

read_dta(
  file,
  encoding = NULL,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

read_stata(
  file,
  encoding = NULL,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

write_dta(
  data,
  path,
  version = 14,
  label = attr(data, "label"),
  strl_threshold = 2045,
  adjust_tz = TRUE
)

Arguments

file

Either a path to a file, a connection, or literal data (either a single string or a raw vector).

Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with ⁠http://⁠, ⁠https://⁠, ⁠ftp://⁠, or ⁠ftps://⁠ will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed.

Literal data is most useful for examples and tests. To be recognised as literal data, the input must be either wrapped with I(), be a string containing at least one new line, or be a vector containing at least one string with a new line.

Using a value of clipboard() will read from the system clipboard.

encoding

The character encoding used for the file. Generally, only needed for Stata 13 files and earlier. See Encoding section for details.

col_select

One or more selection expressions, like in dplyr::select(). Use c() or list() to use more than one expression. See ?dplyr::select for details on available selection options. Only the specified columns will be read from data_file.

skip

Number of lines to skip before reading data.

n_max

Maximum number of lines to read.

.name_repair

Treatment of problematic column names:

  • "minimal": No name repair or checks, beyond basic existence,

  • "unique": Make sure names are unique and not empty,

  • "check_unique": (default value), no name repair, but check they are unique,

  • "universal": Make the names unique and syntactic

  • a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R).

  • A purrr-style anonymous function, see rlang::as_function()

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

data

Data frame to write.

path

Path to a file where the data will be written.

version

File version to use. Supports versions 8-15.

label

Dataset label to use, or NULL. Defaults to the value stored in the "label" attribute of data. Must be <= 80 characters.

strl_threshold

Any character vectors with a maximum length greater than strl_threshold bytes will be stored as a long string (strL) instead of a standard string (str#) variable if version >= 13. This defaults to 2045, the maximum length of str# variables. See the Stata long string documentation for more details.

adjust_tz

Stata, SPSS and SAS do not have a concept of time zone, and all date-time variables are treated as UTC. adjust_tz controls how the timezone of date-time values is treated when writing.

  • If TRUE (the default) the timezone of date-time values is ignored, and they will display the same in R and Stata/SPSS/SAS, e.g. "2010-01-01 09:00:00 NZDT" will be written as "2010-01-01 09:00:00". Note that this changes the underlying numeric data, so use caution if preserving between-time-point differences is critical.

  • If FALSE, date-time values are written as the corresponding UTC value, e.g. "2010-01-01 09:00:00 NZDT" will be written as "2009-12-31 20:00:00".

Value

A tibble, data frame variant with nice defaults.

Variable labels are stored in the "label" attribute of each variable. It is not printed on the console, but the RStudio viewer will show it.

If a dataset label is defined in Stata, it will stored in the "label" attribute of the tibble.

write_dta() returns the input data invisibly.

Character encoding

Prior to Stata 14, files did not declare a text encoding, and the default encoding differed across platforms. If encoding = NULL, haven assumes the encoding is windows-1252, the text encoding used by Stata on Windows. Unfortunately Stata on Mac and Linux use a different default encoding, "latin1". If you encounter an error such as "Unable to convert string to the requested encoding", try encoding = "latin1"

For Stata 14 and later, you should not need to manually specify encoding value unless the value was incorrectly recorded in the source file.

Examples

path <- system.file("examples", "iris.dta", package = "haven")
read_dta(path)

tmp <- tempfile(fileext = ".dta")
write_dta(mtcars, tmp)
read_dta(tmp)
read_stata(tmp)

Read SAS files

Description

read_sas() supports both sas7bdat files and the accompanying sas7bcat files that SAS uses to record value labels.

Usage

read_sas(
  data_file,
  catalog_file = NULL,
  encoding = NULL,
  catalog_encoding = encoding,
  col_select = NULL,
  skip = 0L,
  n_max = Inf,
  cols_only = deprecated(),
  .name_repair = "unique"
)

Arguments

data_file, catalog_file

Path to data and catalog files. The files are processed with readr::datasource().

encoding, catalog_encoding

The character encoding used for the data_file and catalog_encoding respectively. A value of NULL uses the encoding specified in the file; use this argument to override it if it is incorrect.

col_select

One or more selection expressions, like in dplyr::select(). Use c() or list() to use more than one expression. See ?dplyr::select for details on available selection options. Only the specified columns will be read from data_file.

skip

Number of lines to skip before reading data.

n_max

Maximum number of lines to read.

cols_only

[Deprecated] cols_only is no longer supported; use col_select instead.

.name_repair

Treatment of problematic column names:

  • "minimal": No name repair or checks, beyond basic existence,

  • "unique": Make sure names are unique and not empty,

  • "check_unique": (default value), no name repair, but check they are unique,

  • "universal": Make the names unique and syntactic

  • a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R).

  • A purrr-style anonymous function, see rlang::as_function()

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

Value

A tibble, data frame variant with nice defaults.

Variable labels are stored in the "label" attribute of each variable. It is not printed on the console, but the RStudio viewer will show it.

write_sas() returns the input data invisibly.

Examples

path <- system.file("examples", "iris.sas7bdat", package = "haven")
read_sas(path)

Read and write SPSS files

Description

read_sav() reads both .sav and .zsav files; write_sav() creates .zsav files when compress = TRUE. read_por() reads .por files. read_spss() uses either read_por() or read_sav() based on the file extension.

Usage

read_sav(
  file,
  encoding = NULL,
  user_na = FALSE,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

read_por(
  file,
  user_na = FALSE,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

write_sav(data, path, compress = c("byte", "none", "zsav"), adjust_tz = TRUE)

read_spss(
  file,
  user_na = FALSE,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

Arguments

file

Either a path to a file, a connection, or literal data (either a single string or a raw vector).

Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with ⁠http://⁠, ⁠https://⁠, ⁠ftp://⁠, or ⁠ftps://⁠ will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed.

Literal data is most useful for examples and tests. To be recognised as literal data, the input must be either wrapped with I(), be a string containing at least one new line, or be a vector containing at least one string with a new line.

Using a value of clipboard() will read from the system clipboard.

encoding

The character encoding used for the file. The default, NULL, use the encoding specified in the file, but sometimes this value is incorrect and it is useful to be able to override it.

user_na

If TRUE variables with user defined missing will be read into labelled_spss() objects. If FALSE, the default, user-defined missings will be converted to NA.

col_select

One or more selection expressions, like in dplyr::select(). Use c() or list() to use more than one expression. See ?dplyr::select for details on available selection options. Only the specified columns will be read from data_file.

skip

Number of lines to skip before reading data.

n_max

Maximum number of lines to read.

.name_repair

Treatment of problematic column names:

  • "minimal": No name repair or checks, beyond basic existence,

  • "unique": Make sure names are unique and not empty,

  • "check_unique": (default value), no name repair, but check they are unique,

  • "universal": Make the names unique and syntactic

  • a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R).

  • A purrr-style anonymous function, see rlang::as_function()

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

data

Data frame to write.

path

Path to a file where the data will be written.

compress

Compression type to use:

  • "byte": the default, uses byte compression.

  • "none": no compression. This is useful for software that has issues with byte compressed .sav files (e.g. SAS).

  • "zsav": uses zlib compression and produces a .zsav file. zlib compression is supported by SPSS version 21.0 and above.

TRUE and FALSE can be used for backwards compatibility, and correspond to the "zsav" and "none" options respectively.

adjust_tz

Stata, SPSS and SAS do not have a concept of time zone, and all date-time variables are treated as UTC. adjust_tz controls how the timezone of date-time values is treated when writing.

  • If TRUE (the default) the timezone of date-time values is ignored, and they will display the same in R and Stata/SPSS/SAS, e.g. "2010-01-01 09:00:00 NZDT" will be written as "2010-01-01 09:00:00". Note that this changes the underlying numeric data, so use caution if preserving between-time-point differences is critical.

  • If FALSE, date-time values are written as the corresponding UTC value, e.g. "2010-01-01 09:00:00 NZDT" will be written as "2009-12-31 20:00:00".

Details

Currently haven can read and write logical, integer, numeric, character and factors. See labelled_spss() for how labelled variables in SPSS are handled in R.

Value

A tibble, data frame variant with nice defaults.

Variable labels are stored in the "label" attribute of each variable. It is not printed on the console, but the RStudio viewer will show it.

write_sav() returns the input data invisibly.

Examples

path <- system.file("examples", "iris.sav", package = "haven")
read_sav(path)

tmp <- tempfile(fileext = ".sav")
write_sav(mtcars, tmp)
read_sav(tmp)

Read and write SAS transport files

Description

The SAS transport format is a open format, as is required for submission of the data to the FDA.

Usage

read_xpt(
  file,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

write_xpt(
  data,
  path,
  version = 8,
  name = NULL,
  label = attr(data, "label"),
  adjust_tz = TRUE
)

Arguments

file

Either a path to a file, a connection, or literal data (either a single string or a raw vector).

Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with ⁠http://⁠, ⁠https://⁠, ⁠ftp://⁠, or ⁠ftps://⁠ will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed.

Literal data is most useful for examples and tests. To be recognised as literal data, the input must be either wrapped with I(), be a string containing at least one new line, or be a vector containing at least one string with a new line.

Using a value of clipboard() will read from the system clipboard.

col_select

One or more selection expressions, like in dplyr::select(). Use c() or list() to use more than one expression. See ?dplyr::select for details on available selection options. Only the specified columns will be read from data_file.

skip

Number of lines to skip before reading data.

n_max

Maximum number of lines to read.

.name_repair

Treatment of problematic column names:

  • "minimal": No name repair or checks, beyond basic existence,

  • "unique": Make sure names are unique and not empty,

  • "check_unique": (default value), no name repair, but check they are unique,

  • "universal": Make the names unique and syntactic

  • a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R).

  • A purrr-style anonymous function, see rlang::as_function()

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

data

Data frame to write.

path

Path to a file where the data will be written.

version

Version of transport file specification to use: either 5 or 8.

name

Member name to record in file. Defaults to file name sans extension. Must be <= 8 characters for version 5, and <= 32 characters for version 8.

label

Dataset label to use, or NULL. Defaults to the value stored in the "label" attribute of data.

Note that although SAS itself supports dataset labels up to 256 characters long, dataset labels in SAS transport files must be <= 40 characters.

adjust_tz

Stata, SPSS and SAS do not have a concept of time zone, and all date-time variables are treated as UTC. adjust_tz controls how the timezone of date-time values is treated when writing.

  • If TRUE (the default) the timezone of date-time values is ignored, and they will display the same in R and Stata/SPSS/SAS, e.g. "2010-01-01 09:00:00 NZDT" will be written as "2010-01-01 09:00:00". Note that this changes the underlying numeric data, so use caution if preserving between-time-point differences is critical.

  • If FALSE, date-time values are written as the corresponding UTC value, e.g. "2010-01-01 09:00:00 NZDT" will be written as "2009-12-31 20:00:00".

Value

A tibble, data frame variant with nice defaults.

Variable labels are stored in the "label" attribute of each variable. It is not printed on the console, but the RStudio viewer will show it.

If a dataset label is defined, it will be stored in the "label" attribute of the tibble.

write_xpt() returns the input data invisibly.

Examples

tmp <- tempfile(fileext = ".xpt")
write_xpt(mtcars, tmp)
read_xpt(tmp)

"Tagged" missing values

Description

"Tagged" missing values work exactly like regular R missing values except that they store one additional byte of information a tag, which is usually a letter ("a" to "z"). When by loading a SAS and Stata file, the tagged missing values always use lower case values.

Usage

tagged_na(...)

na_tag(x)

is_tagged_na(x, tag = NULL)

format_tagged_na(x, digits = getOption("digits"))

print_tagged_na(x, digits = getOption("digits"))

Arguments

...

Vectors containing single character. The letter will be used to "tag" the missing value.

x

A numeric vector

tag

If NULL, will only return true if the tag has this value.

digits

Number of digits to use in string representation

Details

format_tagged_na() and print_tagged_na() format tagged NA's as NA(a), NA(b), etc.

Examples

x <- c(1:5, tagged_na("a"), tagged_na("z"), NA)

# Tagged NA's work identically to regular NAs
x
is.na(x)

# To see that they're special, you need to use na_tag(),
# is_tagged_na(), or print_tagged_na():
is_tagged_na(x)
na_tag(x)
print_tagged_na(x)

# You can test for specific tagged NAs with the second argument
is_tagged_na(x, "a")

# Because the support for tagged's NAs is somewhat tagged on to R,
# the left-most NA will tend to be preserved in arithmetic operations.
na_tag(tagged_na("a") + tagged_na("z"))

Internal vctrs methods

Description

Internal vctrs methods

Usage

## S3 method for class 'haven_labelled'
vec_arith(op, x, y, ...)

Write SAS files

Description

[Deprecated]

write_sas() creates sas7bdat files. Unfortunately the SAS file format is complex and undocumented, so write_sas() is unreliable and in most cases SAS will not read files that it produces.

write_xpt() writes files in the open SAS transport format, which has limitations but will be reliably read by SAS.

Usage

write_sas(data, path)

Arguments

data

Data frame to write.

path

Path to file where the data will be written.

Convert empty strings into missing values

Description

Convert empty strings into missing values

Usage

zap_empty(x)

Arguments

x

A character vector

Value

A character vector with empty strings replaced by missing values.

See Also

Other zappers: zap_formats(), zap_labels(), zap_label(), zap_widths()

Examples

x <- c("a", "", "c")
zap_empty(x)

Remove format attributes

Description

To provide some mild support for round-tripping variables between Stata/SPSS and R, haven stores variable formats in an attribute: format.stata, format.spss, or format.sas. If this causes problems for your code, you can get rid of them with zap_formats.

Usage

zap_formats(x)

Arguments

x

A vector or data frame.

See Also

Other zappers: zap_empty(), zap_labels(), zap_label(), zap_widths()

Zap variable labels

Description

Removes variable label, leaving unlabelled vectors as is.

Usage

zap_label(x)

Arguments

x

A vector or data frame

See Also

zap_labels() to remove value labels.

Other zappers: zap_empty(), zap_formats(), zap_labels(), zap_widths()

Examples

x1 <- labelled(1:5, c(good = 1, bad = 5), label = "rating")
x1
zap_label(x1)

x2 <- labelled_spss(c(1:4, 9), label = "score", na_values = 9)
x2
zap_label(x2)

# zap_label also works with data frames
df <- tibble::tibble(x1, x2)
str(df)
str(zap_label(df))

Zap value labels

Description

Removes value labels, leaving unlabelled vectors as is. Use this if you want to simply drop all labels from a data frame.

Zapping labels from labelled_spss() also removes user-defined missing values by default, replacing with standard NAs. Use the user_na argument to override this behaviour.

Usage

zap_labels(x, ...)

## S3 method for class 'haven_labelled_spss'
zap_labels(x, ..., user_na = FALSE)

Arguments

x

A vector or data frame

...

Other arguments passed down to method.

user_na

If FALSE, the default, zap_labels() will convert labelled_spss() user-defined missing values to NA. If TRUE they will be treated like normal values.

See Also

zap_label() to remove variable labels.

Other zappers: zap_empty(), zap_formats(), zap_label(), zap_widths()

Examples

x1 <- labelled(1:5, c(good = 1, bad = 5))
x1
zap_labels(x1)

x2 <- labelled_spss(c(1:4, 9), c(good = 1, bad = 5), na_values = 9)
x2
zap_labels(x2)

# Keep the user defined missing values
zap_labels(x2, user_na = TRUE)

# zap_labels also works with data frames
df <- tibble::tibble(x1, x2)
df
zap_labels(df)

Zap special missings to regular R missings

Description

This is useful if you want to convert tagged missing values from SAS or Stata, or user-defined missings from SPSS, to regular R NA.

Usage

zap_missing(x)

Arguments

x

A vector or data frame

Examples

x1 <- labelled(
  c(1, 5, tagged_na("a", "b")),
  c(Unknown = tagged_na("a"), Refused = tagged_na("b"))
)
x1
zap_missing(x1)

x2 <- labelled_spss(
  c(1, 2, 1, 99),
  c(missing = 99),
  na_value = 99
)
x2
zap_missing(x2)

# You can also apply to data frames
df <- tibble::tibble(x1, x2, y = 4:1)
df
zap_missing(df)

Remove display width attributes

Description

To provide some mild support for round-tripping variables between SPSS and R, haven stores display widths in an attribute: display_width. If this causes problems for your code, you can get rid of them with zap_widths.

Usage

zap_widths(x)

Arguments

x

A vector or data frame.

See Also

Other zappers: zap_empty(), zap_formats(), zap_labels(), zap_label()