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### This file is part of the 'foreign' package for R.
###
### Read SPSS system data files
###
### Copyright 2000-2002 Saikat DebRoy <saikat$stat.wisc.edu>
### Douglas M. Bates <bates$stat.wisc.edu>,
### Thomas Lumley
### Copyright 2007-9 R Core Development Team
### This file is part of the `foreign' package for R and related languages.
### It is made available under the terms of the GNU General Public
### License, version 2, or at your option, any later version,
### incorporated herein by reference.
###
### This program is distributed in the hope that it will be
### useful, but WITHOUT ANY WARRANTY; without even the implied
### warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
### PURPOSE. See the GNU General Public License for more
### details.
###
### You should have received a copy of the GNU General Public
### License along with this program; if not, a copy is available at
### http://www.r-project.org/Licenses/
read.spss <- function(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)
{
trim <- function(strings, trim=TRUE)
if (trim) sub(" +$","",strings) else strings
## mappings taken from win-iconv
knownCP <- c("UCS-2LE" = 1200, "UCS-2BE" = 1201,
"macroman" = 10000, " UCS-4LE" = 12000, "UCS-4BE" = 12001,
"koi8-r" = 20866, "koi8-u" = 21866,
"latin1" = 28591, "latin2" = 28592, "latin3" = 28593,
## latin-9 seems most portable, but only on Windows
## R >= 2.10.0. libiconv doesn't know latin9.
"latin4" = 28594, "latin-9" = 28605,
"ISO-2022-JP" = 50221, "euc-jp" = 51932,
"UTF-8" = 65001,
"ASCII" = 20127,
## pages known to glibc and libiconv
"CP1250" = 1250,
"CP1251" = 1251,
"CP1252" = 1252,
"CP1253" = 1253,
"CP1254" = 1254,
"CP1255" = 1255,
"CP1256" = 1256,
"CP1257" = 1257,
"CP1258" = 1258,
"CP874" = 874,
"CP936" = 936)
if(length(grep("^(http|ftp|https)://", file))) {
tmp <- tempfile()
download.file(file, tmp, quiet = TRUE, mode = "wb")
file <- tmp
on.exit(unlink(file))
}
rval <- .Call(do_read_SPSS, file)
codepage <- attr(rval, "codepage")
if(is.null(codepage)) codepage <- 2 # .por files
if(!capabilities("iconv")) reencode <- FALSE
if(!identical(reencode, FALSE)) {
cp <- "unknown"
if(is.character(reencode)) {
cp <- reencode
reencode <- TRUE
} else if(codepage == 20127) {
reencode <- FALSE # ASCII
} else if(m <- match(codepage, knownCP, 0L)) {
cp <-names(knownCP)[m]
} else if (codepage < 200) {
## small numbers are not codepages, and real codepages are large
attr(rval, "codepage") <- NULL
reencode <- FALSE
} else cp <- paste("CP", codepage, sep="")
if(is.na(reencode)) reencode <- l10n_info()[["UTF-8"]]
if(reencode) {
message("re-encoding from ", cp)
names(rval) <- iconv(names(rval), cp, "")
vl <- attr(rval, "variable.labels")
nm <- names(vl)
vl <- iconv(vl, cp, "")
names(vl) <- iconv(nm, cp, "")
attr(rval, "variable.labels") <- vl
for(i in seq_along(rval)) {
xi <- rval[[i]]
if(is.character(xi)) rval[[i]] <- iconv(xi, cp, "")
}
}
}
miss <- attr(rval, "missings")
if(!is.null(miss)) {
if(reencode) {
nm <- names(miss)
nm <- iconv(nm, cp, "")
names(miss) <- iconv(nm, cp, "")
for(i in seq_along(miss))
if(is.character(miss[[i]]$value))
miss[[i]]$value <- iconv(miss[[i]]$value, cp, "")
attr(rval, "missings") <- miss
}
if(use.missings)
for(v in names(rval)) {
tp <- miss[[v]]$type
if(tp %in% "none") next
if(tp %in% c("one", "two", "three")) {
xi <- rval[[v]]
other <- miss[[v]]$value
## FIXME: do we need to worry about padding for string vals?
xi[xi %in% other] <- NA
rval[[v]] <- xi
## NB: not much tested from here down
} else if(tp == "low" || tp == "low+1") {
xi <- rval[[v]]
z <- miss[[v]]$value
if(tp == "low+1") xi[ xi <= z[1L] | xi == z[2L] ] <- NA
else xi[xi <= z[1L]] <- NA
rval[[v]] <- xi
} else if(tp == "high" || tp == "high+1") {
xi <- rval[[v]]
z <- miss[[v]]$value
if(tp == "high+1") xi[ xi >= z[1L] | xi == z[2L] ] <- NA
else xi[ xi >= z[1L] ] <- NA
rval[[v]] <- xi
} else if(tp == "range" || tp == "range+1") {
xi <- rval[[v]]
z <- miss[[v]]$value
if(tp == "range+1")
xi[ xi >= z[1L] | xi <= z[2L] | xi[xi == z[3L]] ] <- NA
else
xi[ xi >= z[1L] | xi <= z[2L] ] <- NA
rval[[v]] <- xi
} else
warning(gettextf("missingness type %s is not handled", tp),
domain = NA)
}
} else use.missings <- FALSE
vl <- attr(rval,"label.table")
if(reencode) names(vl) <- iconv(names(vl), cp, "")
has.vl <- which(!sapply(vl, is.null))
for(v in has.vl) {
nm <- names(vl)[[v]]
nvalues <- length(na.omit(unique(rval[[nm]])))
nlabels <- length(vl[[v]])
if(reencode && nlabels) {
nm2 <- names(vl[[v]])
vl[[v]] <- iconv(vl[[v]], cp, "")
names(vl[[v]]) <- iconv(nm2, cp, "")
}
if(use.missings && !is.null(mv <- miss[[v]]$value))
vl[[v]] <- vl[[v]][! vl[[v]] %in% mv]
if (use.value.labels &&
(!is.finite(max.value.labels) || nvalues <= max.value.labels) &&
nlabels >= nvalues) {
rval[[nm]] <- factor(trim(rval[[nm]], trim_values),
levels = rev(trim(vl[[v]], trim_values)),
labels = rev(trim(names(vl[[v]]), trim.factor.names)))
} else
attr(rval[[nm]], "value.labels") <- vl[[v]]
}
if(reencode) attr(rval, "label.table") <- vl
if (to.data.frame) {
varlab <- attr(rval, "variable.labels")
rval <- as.data.frame(rval)
attr(rval, "variable.labels") <- varlab
if(codepage > 500) attr(rval, "codepage") <- codepage
}
rval
}
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