Object Oriented Interface to Foreign Files
importers.RdImporter objects are objects that refer to an external data file. Currently only Stata files, SPSS system, portable, and fixed-column files are supported.
Data are actually imported by `translating' an
importer file into a data.set using
as.data.set or subset.
The importer mechanism is more flexible and extensible
than read.spss and read.dta
of package "foreign", as most of the parsing of the file headers is done in R.
It is also adapted to efficiently load large data sets.
Most importantly, importer objects support the
labels, missing.values,
and descriptions, provided by this package.
Usage
spss.file(file,...)
spss.fixed.file(file,
columns.file,
varlab.file=NULL,
codes.file=NULL,
missval.file=NULL,
count.cases=TRUE,
to.lower=getOption("spss.fixed.to.lower",FALSE),
iconv=TRUE,
encoded=getOption("spss.fixed.encoding","cp1252"),
negative2missing = FALSE)
spss.portable.file(file,
varlab.file=NULL,
codes.file=NULL,
missval.file=NULL,
count.cases=TRUE,
to.lower=getOption("spss.por.to.lower",FALSE),
iconv=TRUE,
encoded=getOption("spss.por.encoding","cp1252"),
negative2missing = FALSE)
spss.system.file(file,
varlab.file=NULL,
codes.file=NULL,
missval.file=NULL,
count.cases=TRUE,
to.lower=getOption("spss.sav.to.lower",FALSE),
iconv=TRUE,
encoded=getOption("spss.sav.encoding","cp1252"),
ignore.scale.info = FALSE,
negative2missing = FALSE)
Stata.file(file,
iconv=TRUE,
encoded=if(new_format)
getOption("Stata.new.encoding","utf-8")
else getOption("Stata.old.encoding","cp1252"),
negative2missing = FALSE)
## The most important methods for "importer" objects are:
# S3 method for spss.system.importer
subset(x, subset, select, drop = FALSE, ...)
# S3 method for spss.portable.importer
subset(x, subset, select, drop = FALSE, ...)
# S3 method for spss.fixed.importer
subset(x, subset, select, drop = FALSE, ...)
# S3 method for Stata.importer
subset(x, subset, select, drop = FALSE, ...)
# S3 method for Stata_new.importer
subset(x, subset, select, drop = FALSE, ...)
# S4 method for importer
as.data.set(x,row.names=NULL,optional=NULL,
compress.storage.modes=FALSE,...)
# S4 method for importer
head(x,n=20,...)
# S4 method for importer
tail(x,n=20,...)Arguments
- file
character string; the path to the file containing the data
- ...
Other arguments.
spss.file()passes them on tospss.portable.file()ofspss.system.file(). Other function ignore further arguments.- columns.file
character string; the path to an SPSS/PSPP syntax file with a
DATA LIST FIXEDstatement- varlab.file
character string; the path to an SPSS/PSPP syntax file with a
VARIABLE LABELSstatement- codes.file
character string; the path to an SPSS/PSPP syntax file with a
VALUE LABELSstatement- missval.file
character string; the path to an SPSS/PSPP syntax file with a
MISSING VALUESstatement- count.cases
logical; should cases in file be counted? This takes effect only if the data file does not already contain information about the number of cases.
- to.lower
logical; should variable names changed to lower case?
- iconv
logical; should strings (in labels and variables) changed into encoding of the platform?
- encoded
a cacharacter string; the way characters are encoded in the improrted file. For the available encoding options see
?iconvlist. Using this argument forspss.system.filethis is only a fallback, as the function uses the encoding information present in the file if it is present.- negative2missing
logical; should negative values be marked as missing values? This is the convention of some newer data sets that are available e.g. from the GESIS data archive.
- ignore.scale.info
logical; should information about measuremnt scale levels provided in the file be ignored?
- x
an object that inherits from class
"importer".- subset
a logical vector or an expression containing variables from the external data file that evaluates to logical.
- select
a vector of variable names from the external data file. This may also be a named vector, where the names give the names into which the variables from the external data file are renamed.
- drop
a logical value, that determines what happens if only one column is selected. If TRUE and only one column is selected,
subsetreturns only a singleitemobject and not adata.set.- row.names
ignored, present only for compatibility.
- optional
ignored, present only for compatibility.
- compress.storage.modes
logical value; if TRUE floating point values are converted to integers if possible without loss of information.
- n
integer; the number of rows to be shown by
headortail
Value
spss.fixed.file, spss.portable.file,
spss.system.file, and Stata.file
return, respectively, objects of class
"spss.fixed.importer", "spss.portable.importer",
"spss.system.importer", "Stata.importer", or "Stata_new.importer",
which, by inheritance, are also objects of class "importer".
"Stata.importer" is for files in the format of Stata versions up
to 12, while "Stata_new.importer" is for files in the newer
format of Stata versions from 13.
Objects of class "importer" have at least the following two slots:
- ptr
an external pointer
- variables
a list of objects of class
"item.vector"which provides a `prototype' for the"data.set"set objects returned by theas.data.setandsubsetmethods for objects of class"importer"
The as.data.frame for importer objects does
the actual data import and returns a data frame. Note that in contrast
to read.spss, the variable names of the
resulting data frame will be lower case, unless the importer function
is called with to.lower=FALSE. If long variable names
are defined (in case of a PSPP/SPSS system file), they take
precedence and are not coerced to lower case.
Details
A call to a `constructor' for an importer object, that is,
spss.fixed.file, spss.portable.file, spss.sysntax.file,
or Stata.file,
causes R to read in the header of the data file and/or
the syntax files that contain information about
the variables, such as the columns that they occupy
(in case of spss.fixed.file), variable labels,
value labels and missing values.
The information in the file header and/or the accompagnying
files is then processed to prepare the file for importing.
Thus the inner structure of an importer object may
well vary according to what type of file is to imported and
what additional information is given.
The as.data.set and subset methods
for "importer" objects internally use the
generic functions seekData, readData, readSlice,
and readChunk, which have methods for the
subclasses of "importer".
These functions are not callable
from outside the package, however.
The subset method for "importer" objects reads in
the data `chunk-wise' to create the subset of observations if
the option "subset.chunk.size" is set to a non-NULL
value, e.g. by options(subset.chunk.size=1000). This may be
useful in case of very large data sets from which only a tiny subset
of observations is needed for analysis.
Since the functions described here are more or less complete rewrite
based on the description of the file structure provided
by the documenation for PSPP, they are perhaps not as thorougly tested as the
functions in the foreign package, apart from the frequent use
by the author of this package.
Examples
# Extract American National Election Study of 1948
nes1948.por <- unzip(system.file("anes/NES1948.ZIP",package="memisc"),
"NES1948.POR",exdir=tempfile())
# Get information about the variables contained.
nes1948 <- spss.portable.file(nes1948.por)
#> Warning: 9 variables have duplicated labels:
#> V480004, V480012, V480020, V480021A, V480021B, V480033A, V480033B,
#> V480034A, V480034B
# The data are not yet loaded:
show(nes1948)
#>
#> SPSS portable file '/tmp/RtmpMrtkuU/file18411172187a/NES1948.POR'
#> with 67 variables and 662 observations
# ... but one can see what variables are present:
description(nes1948)
#>
#> VVERSION 'NES VERSION NUMBER'
#> VDSETNO 'NES DATASET NUMBER'
#> V480001 'ICPSR ARCHIVE NUMBER'
#> V480002 'INTERVIEW NUMBER'
#> V480003 'POP CLASSIFICATION'
#> V480004 'CODER'
#> V480005 'NUMBER OF CALLS TO R'
#> V480006 'R REMEMBER PREVIOUS INT'
#> V480007 'INTR INTERVIEW THIS R'
#> V480008 'PRVS PRE-ELCTN R REINT'
#> V480009 'R INT IN PRE/POSTELCTN'
#> V480010 'RENT CNTRL KEPT/DROPPED'
#> V480011 'GOVT CONTROL PRICES'
#> V480012 'WHAT TO DO W TFT-HT ACT'
#> V480013 'PRESLELCTN OTCM SURPRISE'
#> V480014A 'WHY PPL VTD FOR TRUMAN 1'
#> V480014B 'WHY PPL VTD FOR TRUMAN 2'
#> V480015A 'WHY PPL VTD AGNST TRUMAN 1'
#> V480015B 'WHY PPL VTD AGNST TRUMAN 2'
#> V480016A 'WHY PPL VTD FOR DEWEY 1'
#> V480016B 'WHY PPL VTD FOR DEWEY 2'
#> V480017A 'WHY PPL VTD AGNST DEWEY 1'
#> V480017B 'WHY PPL VTD AGNST DEWEY 2'
#> V480018 'DID R VOTE/FOR WHOM'
#> V480019 'WN DECIDE FOR WHOM TO VT'
#> V480020 'CNSD VT FOR SOMEONE ELSE'
#> V480021A 'XWHY DID NOT VT FOR HIM 1'
#> V480021B 'XWHY DID NOT VT FOR HIM 2'
#> V480022A 'WHY VT THE WAY YOU DID 1'
#> V480022B 'WHY VT THE WAY YOU DID 2'
#> V480023 'VOTED STRAIGHT TICKET'
#> V480024 'R NOT VT-IF VT,FOR WHOM'
#> V480025A 'R NOT VT-WHY DID NOT VT 1'
#> V480025B 'R NOT VT-WHY DID NOT VT 2'
#> V480026 'R NOT VT-WAS R REG TO VT'
#> V480027 'VTD IN PRVS PRESL ELCTN'
#> V480028 'VTD FOR WHOM IN 1944'
#> V480029 'OCCUPATION OF HEAD'
#> V480030 'HEAD BELONG TO LBR UN'
#> V480031A 'GRPS IDENTIFIED W TRUMAN 1'
#> V480031B 'GRPS IDENTIFIED W TRUMAN 2'
#> V480031C 'GRPS IDENTIFIED W TRUMAN 3'
#> V480032A 'GRPS IDENTIFIED W DEWEY 1'
#> V480032B 'GRPS IDENTIFIED W DEWEY 2'
#> V480032C 'GRPS IDENTIFIED W DEWEY 3'
#> V480033A 'ISSUES CONNECTED W TRMN 1'
#> V480033B 'ISSUES CONNECTED W TRMN 2'
#> V480034A 'ISSUES CONNECTED W DEWEY 1'
#> V480034B 'ISSUES CONNECTED W DEWEY 2'
#> V480035A 'PERSONAL ATTRIBUTE TRMN 1'
#> V480035B 'PERSONAL ATTRIBUTE TRMN 2'
#> V480036A 'PERSONAL ATTRIBUTE DEWEY 1'
#> V480036B 'PERSONAL ATTRIBUTE DEWEY 2'
#> V480037 'CMPN INCIDENTS MENTIONED'
#> V480038 '41-PRESLELCTN PLAN TO VT'
#> V480039 '41-PLAN TO VT REP/DEM'
#> V480040 '41-USA'S CNCRN W OTHERS'
#> V480041 '41-SATISD USA TWRD RUSS'
#> V480042 '41-INFORMATION LEVEL'
#> V480043 '41-USA GV IN,AGRT RUSS'
#> V480044 '41-USA-RUSS AGRT VIA U.N'
#> V480045 'SEX OF RESPONDENT'
#> V480046 'RACE OF RESPONDENT'
#> V480047 'AGE OF RESPONDENT'
#> V480048 'EDUCATION OF RESPONDENT'
#> V480049 'TOTAL 1948 INCOME'
#> V480050 'RELIGIOUS PREFERENCE'
#>
# Now a subset of the data is loaded:
vote.socdem.48 <- subset(nes1948,
select=c(
V480018,
V480029,
V480030,
V480045,
V480046,
V480047,
V480048,
V480049,
V480050
))
# Let's make the names more descriptive:
vote.socdem.48 <- rename(vote.socdem.48,
V480018 = "vote",
V480029 = "occupation.hh",
V480030 = "unionized.hh",
V480045 = "gender",
V480046 = "race",
V480047 = "age",
V480048 = "education",
V480049 = "total.income",
V480050 = "religious.pref"
)
# It is also possible to do both
# in one step:
# vote.socdem.48 <- subset(nes1948,
# select=c(
# vote = V480018,
# occupation.hh = V480029,
# unionized.hh = V480030,
# gender = V480045,
# race = V480046,
# age = V480047,
# education = V480048,
# total.income = V480049,
# religious.pref = V480050
# ))
# We examine the data more closely:
codebook(vote.socdem.48)
#> ================================================================================
#>
#> vote 'DID R VOTE/FOR WHOM'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 'VOTED - FOR TRUMAN' 212 32.1 32.0
#> 2 'VOTED - FOR DEWEY' 178 27.0 26.9
#> 3 'VOTED - FOR WALLACE' 1 0.2 0.2
#> 4 'VOTED - FOR OTHER' 11 1.7 1.7
#> 5 'VOTED - NA FOR WHOM' 20 3.0 3.0
#> 6 'DID NOT VOTE' 238 36.1 36.0
#> 9 M 'NA WHETHER VOTED' 2 0.3
#>
#> ================================================================================
#>
#> occupation.hh 'OCCUPATION OF HEAD'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 99
#>
#> Values and labels N Valid Total
#>
#> 10 'PROFESSIONAL, SEMI-PROFESSIONAL' 44 6.9 6.6
#> 20 'SELF-EMPLOYED, MANAGERIAL, SUPERVISORY' 73 11.5 11.0
#> 30 'OTHER WHITE-COLLAR (CLERICAL, SALES, ET' 79 12.5 11.9
#> 40 'SKILLED AND SEMI-SKILLED' 164 25.9 24.8
#> 60 'PROTECTIVE SERVICE' 6 0.9 0.9
#> 70 'UNSKILLED, INCLUDING FARM AND SERVICE W' 85 13.4 12.8
#> 80 'FARM OPERATORS AND MANAGERS' 105 16.6 15.9
#> 92 'STUDENT' 7 1.1 1.1
#> 94 'UNEMPLOYED' 5 0.8 0.8
#> 95 'RETIRED, TOO OLD OR UNABLE TO WORK' 38 6.0 5.7
#> 96 'HOUSEWIFE' 28 4.4 4.2
#> 99 M 'NA' 28 4.2
#>
#> ================================================================================
#>
#> unionized.hh 'HEAD BELONG TO LBR UN'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 8 - Inf
#>
#> Values and labels N Valid Total
#>
#> 1 'YES' 150 23.3 22.7
#> 2 'NO' 493 76.7 74.5
#> 8 M 'DK' 5 0.8
#> 9 M 'NA' 14 2.1
#>
#> ================================================================================
#>
#> gender 'SEX OF RESPONDENT'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 'MALE' 302 45.8 45.6
#> 2 'FEMALE' 357 54.2 53.9
#> 9 M 'NA' 3 0.5
#>
#> ================================================================================
#>
#> race 'RACE OF RESPONDENT'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 'WHITE' 585 90.7 88.4
#> 2 'NEGRO' 60 9.3 9.1
#> 3 'OTHER' 0 0.0 0.0
#> 9 M 'NA' 17 2.6
#>
#> ================================================================================
#>
#> age 'AGE OF RESPONDENT'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 '18-24' 57 8.7 8.6
#> 2 '25-34' 142 21.7 21.5
#> 3 '35-44' 174 26.6 26.3
#> 4 '45-54' 125 19.1 18.9
#> 5 '55-64' 86 13.1 13.0
#> 6 '65 AND OVER' 70 10.7 10.6
#> 9 M 'NA' 8 1.2
#>
#> ================================================================================
#>
#> education 'EDUCATION OF RESPONDENT'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 'GRADE SCHOOL' 292 44.4 44.1
#> 2 'HIGH SCHOOL' 266 40.4 40.2
#> 3 'COLLEGE' 100 15.2 15.1
#> 9 M 'NA' 4 0.6
#>
#> ================================================================================
#>
#> total.income 'TOTAL 1948 INCOME'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 'UNDER $500' 25 3.8 3.8
#> 2 '$500-$999' 43 6.6 6.5
#> 3 '$1000-1999' 110 16.8 16.6
#> 4 '$2000-2999' 185 28.2 27.9
#> 5 '$3000-3999' 142 21.7 21.5
#> 6 '$4000-4999' 66 10.1 10.0
#> 7 '$5000 AND OVER' 84 12.8 12.7
#> 9 M 'NA' 7 1.1
#>
#> ================================================================================
#>
#> religious.pref 'RELIGIOUS PREFERENCE'
#>
#> --------------------------------------------------------------------------------
#>
#> Storage mode: double
#> Measurement: nominal
#> Missing values: 9
#>
#> Values and labels N Valid Total
#>
#> 1 'PROTESTANT' 460 70.0 69.5
#> 2 'CATHOLIC' 140 21.3 21.1
#> 3 'JEWISH' 25 3.8 3.8
#> 4 'OTHER' 14 2.1 2.1
#> 5 'NONE' 18 2.7 2.7
#> 9 M 'NA' 5 0.8
#>
# ... and conduct some analyses.
#
t(genTable(percent(vote)~occupation.hh,data=vote.socdem.48))
#>
#> occupation.hh VOTED - FOR TRUMAN VOTED - FOR DEWEY
#> PROFESSIONAL, SEMI-PROFESSIONAL 22.727273 50.00000
#> SELF-EMPLOYED, MANAGERIAL, SUPERVISORY 9.589041 61.64384
#> OTHER WHITE-COLLAR (CLERICAL, SALES, ET 37.974684 39.24051
#> SKILLED AND SEMI-SKILLED 51.829268 14.63415
#> PROTECTIVE SERVICE 16.666667 33.33333
#> UNSKILLED, INCLUDING FARM AND SERVICE W 32.941176 11.76471
#> FARM OPERATORS AND MANAGERS 24.761905 13.33333
#> STUDENT 14.285714 28.57143
#> UNEMPLOYED 0.000000 0.00000
#> RETIRED, TOO OLD OR UNABLE TO WORK 27.027027 43.24324
#> HOUSEWIFE 17.857143 28.57143
#> <NA> 33.333333 14.81481
#>
#> occupation.hh VOTED - FOR WALLACE VOTED - FOR OTHER
#> PROFESSIONAL, SEMI-PROFESSIONAL 0.0000000 2.272727
#> SELF-EMPLOYED, MANAGERIAL, SUPERVISORY 0.0000000 1.369863
#> OTHER WHITE-COLLAR (CLERICAL, SALES, ET 0.0000000 0.000000
#> SKILLED AND SEMI-SKILLED 0.6097561 1.219512
#> PROTECTIVE SERVICE 0.0000000 16.666667
#> UNSKILLED, INCLUDING FARM AND SERVICE W 0.0000000 0.000000
#> FARM OPERATORS AND MANAGERS 0.0000000 2.857143
#> STUDENT 0.0000000 0.000000
#> UNEMPLOYED 0.0000000 0.000000
#> RETIRED, TOO OLD OR UNABLE TO WORK 0.0000000 2.702703
#> HOUSEWIFE 0.0000000 0.000000
#> <NA> 0.0000000 7.407407
#>
#> occupation.hh VOTED - NA FOR WHOM DID NOT VOTE N
#> PROFESSIONAL, SEMI-PROFESSIONAL 2.272727 22.72727 44
#> SELF-EMPLOYED, MANAGERIAL, SUPERVISORY 1.369863 26.02740 73
#> OTHER WHITE-COLLAR (CLERICAL, SALES, ET 5.063291 17.72152 79
#> SKILLED AND SEMI-SKILLED 2.439024 29.26829 164
#> PROTECTIVE SERVICE 0.000000 33.33333 6
#> UNSKILLED, INCLUDING FARM AND SERVICE W 4.705882 50.58824 85
#> FARM OPERATORS AND MANAGERS 1.904762 57.14286 105
#> STUDENT 0.000000 57.14286 7
#> UNEMPLOYED 20.000000 80.00000 5
#> RETIRED, TOO OLD OR UNABLE TO WORK 2.702703 24.32432 37
#> HOUSEWIFE 0.000000 53.57143 28
#> <NA> 7.407407 37.03704 27
# We consider only the two main candidates.
vote.socdem.48 <- within(vote.socdem.48,{
truman.dewey <- vote
valid.values(truman.dewey) <- 1:2
truman.dewey <- relabel(truman.dewey,
"VOTED - FOR TRUMAN" = "Truman",
"VOTED - FOR DEWEY" = "Dewey")
})
summary(truman.relig.glm <- glm((truman.dewey=="Truman")~religious.pref,
data=vote.socdem.48,
family="binomial",
))
#>
#> Call:
#> glm(formula = (truman.dewey == "Truman") ~ religious.pref, family = "binomial",
#> data = vote.socdem.48)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.13134 0.12831 -1.024 0.30604
#> religious.prefCATHOLIC 0.79550 0.24442 3.255 0.00114 **
#> religious.prefJEWISH 16.69740 536.55453 0.031 0.97517
#> religious.prefOTHER -0.05099 0.61898 -0.082 0.93435
#> religious.prefNONE -0.20514 0.59943 -0.342 0.73219
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 537.69 on 389 degrees of freedom
#> Residual deviance: 500.69 on 385 degrees of freedom
#> (272 observations deleted due to missingness)
#> AIC: 510.69
#>
#> Number of Fisher Scoring iterations: 15
#>