Package 'haven' reference manual

Title:	Import and Export 'SPSS', 'Stata' and 'SAS' Files
Description:	Import foreign statistical formats into R via the embedded 'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
Authors:	Hadley Wickham [aut, cre], Evan Miller [aut, cph] (Author of included ReadStat code), Danny Smith [aut], Posit Software, PBC [cph, fnd]
Maintainer:	Hadley Wickham <[email protected]>
License:	MIT + file LICENSE
Version:	2.5.4
Built:	2024-11-27 06:56:16 UTC
Source:	CRAN

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,
  ...
)
## 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))
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)
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)
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
)
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
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

Print the labels of a labelled vector

Description

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

Usage

print_labels(x, name = NULL)
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)
}
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
)
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)
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"
)
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`	`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)
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"
)
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)
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
)
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)
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"))
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"))
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"))

Convert empty strings into missing values

Description

Convert empty strings into missing values

Usage

zap_empty(x)
zap_empty(x)

Arguments

`x`	A character vector

Value

A character vector with empty strings replaced by missing values.

Examples

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

Arguments

`x`	A vector or data frame.

Zap variable labels

Description

Removes variable label, leaving unlabelled vectors as is.

Usage

zap_label(x)
zap_label(x)

Arguments

`x`	A vector or data frame

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))
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)
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.

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)
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)
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)
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)
zap_widths(x)

Arguments

`x`	A vector or data frame.

Package 'haven'

Help Index

Convert labelled vectors to factors

Description

Usage

Arguments

Details

Examples

Create a labelled vector.

Description

Usage

Arguments

Examples

Labelled vectors for SPSS

Description

Usage

Arguments

Examples

Print the labels of a labelled vector

Description

Usage

Arguments

Examples

Read and write Stata DTA files

Description

Usage

Arguments

Value

Character encoding

Examples

Read SAS files

Description

Usage

Arguments

Value

Examples

Read and write SPSS files

Description

Usage

Arguments

Details

Value

Examples

Read and write SAS transport files

Description

Usage

Arguments

Value

Examples

"Tagged" missing values

Description

Usage

Arguments

Details

Examples

Convert empty strings into missing values

Description

Usage

Arguments

Value

See Also

Examples

Remove format attributes

Description

Usage

Arguments

See Also

Zap variable labels

Description

Usage

Arguments

See Also

Examples

Zap value labels

Description

Usage

Arguments

See Also

Examples

Zap special missings to regular R missings