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

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,\dots.

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)   # list structure with attributes

dat <- read.spss(file=sav, to.data.frame=TRUE) 
str(dat)   # now a data.frame


### Now we use an example file that is not very well structured and 
### hence may need some special treatment with appropriate argument settings.
### Expect lots of warnings as value labels (corresponding to R factor labels) are uncomplete, 
### and an unsupported long string variable is present in the data
(sav <- system.file("files", "testdata.sav", package = "foreign"))

### Examples for add.undeclared.levels:
## add.undeclared.levels = "sort" (default):
x.sort <- read.spss(file=sav, to.data.frame = TRUE)
## add.undeclared.levels = "append":
x.append <- read.spss(file=sav, to.data.frame = TRUE, 
    add.undeclared.levels = "append")
## add.undeclared.levels = "no":
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)


### Examples for duplicated.value.labels:
## duplicated.value.labels = "append" (default)
x.append <- read.spss(file=sav, to.data.frame=TRUE)
## duplicated.value.labels = "condense"
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)

    
## Long Strings (>255 chars) are imported in consecutive separate variables 
## (see warning about subtype 14):
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"))

## first part: x$string_500:
cat.long.string(x$string_500)
## second part: x$STRIN0:
cat.long.string(x$STRIN0)
## complete long string:
long.string <- apply(x[,c("string_500", "STRIN0")], 1, paste, collapse="")
cat.long.string(long.string)