importers memisc 0.99.25.4

Object Oriented Interface to Foreign Files

Description

Importer 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 ``description``s, 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"))

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"))

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"))

Stata.file(file,
           iconv=TRUE,
           encoded=if(new_format)
                        getOption("Stata.new.encoding","utf-8")
                   else getOption("Stata.old.encoding","cp1252"))

## The most important methods for "importer" objects are:
## S4 method for signature 'importer'
subset(x, subset, select, drop = FALSE, ...)

## S4 method for signature 'importer'
as.data.set(x,row.names=NULL,optional=NULL,
                    compress.storage.modes=FALSE,...)

## S4 method for signature 'importer'
head(x,n=20,...)
## S4 method for signature 'importer'
tail(x,n=20,...)

Arguments

file

character string; the path to the file containing the data

...

Other arguments. spss.file() passes them on to spss.portable.file() of spss.system.file(). Other function ignore further arguments.

columns.file

character string; the path to an SPSS/PSPP syntax file with a DATA LIST FIXED statement

varlab.file

character string; the path to an SPSS/PSPP syntax file with a VARIABLE LABELS statement

codes.file

character string; the path to an SPSS/PSPP syntax file with a VALUE LABELS statement

missval.file

character string; the path to an SPSS/PSPP syntax file with a MISSING VALUES statement

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 for spss.system.file this is only a fallback, as the function uses the encoding information present in the file if it is present.

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, subset returns only a single item object and not a data.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 head or tail

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 the as.data.set and subset methods for objects of class "importer"

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.

See also

codebook, description, read.spss

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/Rtmp7bTT29/file7ee2558b667a/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 VOTED - FOR WALLACE
PROFESSIONAL, SEMI-PROFESSIONAL 22.727273 50.00000 0.0000000
SELF-EMPLOYED, MANAGERIAL, SUPERVISORY 9.589041 61.64384 0.0000000
OTHER WHITE-COLLAR (CLERICAL, SALES, ET 37.974684 39.24051 0.0000000
SKILLED AND SEMI-SKILLED 51.829268 14.63415 0.6097561
PROTECTIVE SERVICE 16.666667 33.33333 0.0000000
UNSKILLED, INCLUDING FARM AND SERVICE W 32.941176 11.76471 0.0000000
FARM OPERATORS AND MANAGERS 24.761905 13.33333 0.0000000
STUDENT 14.285714 28.57143 0.0000000
UNEMPLOYED 0.000000 0.00000 0.0000000
RETIRED, TOO OLD OR UNABLE TO WORK 27.027027 43.24324 0.0000000
HOUSEWIFE 17.857143 28.57143 0.0000000
<NA> 33.333333 14.81481 0.0000000

occupation.hh VOTED - FOR OTHER VOTED - NA FOR WHOM DID NOT VOTE N
PROFESSIONAL, SEMI-PROFESSIONAL 2.272727 2.272727 22.72727 44
SELF-EMPLOYED, MANAGERIAL, SUPERVISORY 1.369863 1.369863 26.02740 73
OTHER WHITE-COLLAR (CLERICAL, SALES, ET 0.000000 5.063291 17.72152 79
SKILLED AND SEMI-SKILLED 1.219512 2.439024 29.26829 164
PROTECTIVE SERVICE 16.666667 0.000000 33.33333 6
UNSKILLED, INCLUDING FARM AND SERVICE W 0.000000 4.705882 50.58824 85
FARM OPERATORS AND MANAGERS 2.857143 1.904762 57.14286 105
STUDENT 0.000000 0.000000 57.14286 7
UNEMPLOYED 0.000000 20.000000 80.00000 5
RETIRED, TOO OLD OR UNABLE TO WORK 2.702703 2.702703 24.32432 37
HOUSEWIFE 0.000000 0.000000 53.57143 28
<NA> 7.407407 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)

Deviance Residuals:
     Min        1Q    Median        3Q       Max
-1.46927  -1.12217   0.00036   1.23367   1.32323

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