Read an SPSS Data File
Description
read.spss reads a file stored by the SPSS save or
export commands.
This was orignally written in 2000 and has limited support for changes
in SPSS formats since (which have not been many).
Usage
read.spss(file, use.value.labels = TRUE, to.data.frame = FALSE,
max.value.labels = Inf, trim.factor.names = FALSE,
trim_values = TRUE, reencode = NA, use.missings = to.data.frame,
sub = ".", add.undeclared.levels = c("sort", "append", "no"),
duplicated.value.labels = c("append", "condense"),
duplicated.value.labels.infix = "_duplicated_", ...)
Arguments
file |
character string: the name of the file or URL to read.
|
use.value.labels |
logical: convert variables with value labels
into R factors with those levels? This is only done if there are
at least as many labels as values of the variable (when values
without a matching label are returned as NA).
|
to.data.frame |
logical: return a data frame?
|
max.value.labels |
logical: only variables with value labels and
at most this many unique values will be converted to factors if
TRUE.
|
trim.factor.names |
logical: trim trailing spaces from factor levels?
|
trim_values |
logical: should values and value labels have
trailing spaces ignored when matching for use.value.labels = TRUE?
|
reencode |
logical: should character strings be re-encoded to the
current locale. The default, NA, means to do so in UTF-8 or latin-1
locales, only. Alternatively a character string specifying an encoding to
assume for the file.
|
use.missings |
logical: should information on user-defined
missing values be used to set the corresponding values to NA?
|
sub |
character string: If not NA it is used by iconv
to replace any non-convertible bytes in character/factor input.
Default is ".". For back compatibility with foreign
versions <= 0.8-68 use sub=NA.
|
add.undeclared.levels |
character:
specify how to handle variables with at least one value label and further
non-missing values that have no value label (like a factor levels in R).
For "sort" (the default) it adds undeclared factor levels to the
already declared levels (and labels) and sort them according to level,
for "append" it appends undeclared factor levels to declared levels
(and labels) without sorting, and
for "no" this does not convert to factor in case of numeric SPSS levels
(not labels), and still converts to factor if the SPSS levels are characters
and to.data.frame=TRUE.
For back compatibility with foreign versions <= 0.8-68 use
add.undeclared.levels="no" (not recommended as this may convert some
values with missing corresponding value labels to NA).
|
duplicated.value.labels |
character: what to do with duplicated value
labels for different levels.
For "append" (the default), the first original value label is kept
while further duplicated labels are renamed to
paste0(label, duplicated.value.labels.infix, level),
for "condense", all levels with identical labels are condensed into
exactly the first of these levels in R.
Back compatibility with foreign versions <= 0.8-68 is not given as
R versions >= 3.4.0 no longer support duplicated factor labels.
|
duplicated.value.labels.infix |
character: the infix used for labels of
factor levels with duplicated value labels in SPSS (default "_duplicated_")
if duplicated.value.labels="append".
|
... |
passed to as.data.frame if to.data.frame = TRUE.
|
Details
This uses modified code from the PSPP project
(http://www.gnu.org/software/pspp/ for reading the SPSS formats.
If the filename appears to be a URL (of schemes ‘http:’,
‘ftp:’ or ‘https:’) the URL is first downloaded to a
temporary file and then read. (‘https:’ is supported where
supported by download.file with its current default
method.)
Occasionally in SPSS, value labels will be added to some values of a
continuous variable (e.g. to distinguish different types of missing
data), and you will not want these variables converted to factors. By
setting max.value.labels you can specify that variables with a
large number of distinct values are not converted to factors even if
they have value labels.
If SPSS variable labels are present, they are returned as the
"variable.labels" attribute of the answer.
Fixed length strings (including value labels) are padded on the right
with spaces by SPSS, and so are read that way by R. The default
argument trim_values=TRUE causes trailing spaces to be ignored
when matching to value labels, as examples have been seen where the
strings and the value labels had different amounts of padding. See
the examples for sub for ways to remove trailing spaces
in character data.
URL https://learn.microsoft.com/en-us/windows/win32/intl/code-page-identifiers
provides a list of translations from Windows codepage numbers to
encoding names that iconv is likely to know about and so
suitable values for reencode. Automatic re-encoding is
attempted for apparent codepages of 200 or more in a UTF-8 or latin-1 locale:
some other high-numbered codepages can be re-encoded on most systems,
but the encoding names are platform-dependent (see
iconvlist).
Value
A list (or optionally a data frame) with one component for each
variable in the saved data set.
If what looks like a Windows codepage was recorded in the SPSS file,
it is attached (as a number) as attribute "codepage" to the
result.
There may be attributes "label.table" and
"variable.labels". Attribute "label.table" is a named
list of value labels with one element per variable, either NULL
or a named character vector. Attribute "variable.labels" is a
named character vector with names the short variable names and
elements the long names.
If there are user-defined missing values, there will be a attribute
"Missings". This is a named list with one list element per
variable. Each element has an element type, a length-one
character vector giving the type of missingness, and may also have an
element value with the values corresponding to missingness.
This is a complex subject (where the R and C source code for
read.spss is the main documentation), but the simplest cases
are types "one", "two" and "three" with a
corresponding number of (real or string) values whose labels can be
found from the "label.table" attribute. Other possibilities are
a finite or semi-infinite range, possibly plus a single value.
See also http://www.gnu.org/software/pspp/manual/html_node/Missing-Observations.html#Missing-Observations.
Note
If SPSS value labels are converted to factors the underlying numerical
codes will not in general be the same as the SPSS numerical
values, since the numerical codes in R are always 1,2,3,….
You may see warnings about the file encoding for SPSS save
files: it is possible such files contain non-ASCII character data
which need re-encoding. The most common occurrence is Windows codepage
1252, a superset of Latin-1. The encoding is recorded (as an integer)
in attribute "codepage" of the result if it looks like a
Windows codepage. Automatic re-encoding is done only in UTF-8 and latin-1
locales: see argument reencode.
Author(s)
Saikat DebRoy and the R-core team
See Also
A different interface also based on the PSPP codebase is available in
package memisc: see its help for spss.system.file.
Examples
(sav <- system.file("files", "electric.sav", package = "foreign"))
dat <- read.spss(file=sav)
str(dat)
dat <- read.spss(file=sav, to.data.frame=TRUE)
str(dat)
(sav <- system.file("files", "testdata.sav", package = "foreign"))
x.sort <- read.spss(file=sav, to.data.frame = TRUE)
x.append <- read.spss(file=sav, to.data.frame = TRUE,
add.undeclared.levels = "append")
x.no <- read.spss(file=sav, to.data.frame = TRUE,
add.undeclared.levels = "no")
levels(x.sort$factor_n_undeclared)
levels(x.append$factor_n_undeclared)
str(x.no$factor_n_undeclared)
x.append <- read.spss(file=sav, to.data.frame=TRUE)
x.condense <- read.spss(file=sav, to.data.frame=TRUE,
duplicated.value.labels = "condense")
levels(x.append$factor_n_duplicated)
levels(x.condense$factor_n_duplicated)
as.numeric(x.append$factor_n_duplicated)
as.numeric(x.condense$factor_n_duplicated)
x <- read.spss(file=sav, to.data.frame=TRUE, stringsAsFactors=FALSE)
cat.long.string <- function(x, w=70) cat(paste(strwrap(x, width=w), "\n"))
cat.long.string(x$string_500)
cat.long.string(x$STRIN0)
long.string <- apply(x[,c("string_500", "STRIN0")], 1, paste, collapse="")
cat.long.string(long.string)