There is no such thing as an R system file similar to a Stata .dta or an SPSS .sav file. Instead, R reads data from a variety of formats “including files created in other statistical packages” directly into working memory. R generally lacks intuitive commands for data management, so users typically prefer to clean and prepare data with SAS, Stata, or SPSS. Once the data are ready, several functions are available for getting the data into R.
Reading Data Files in SPSS, Stata, and SAS formats
The foreign package can be used to read data stored as SPSS .sav files, Stata .dta files, or SAS XPORT libraries. If foreign is not already installed on your local computer, go to the Packages menu and choose Install package(s).
If prompted, choose the closest CRAN mirror. When the Packages dialog box appears, scroll down to choose foreign and then click OK.
To use the commands in foreign one must first attach the library using the library function. At the prompt, type
> library(foreign)
As an example of reading data from other formats, assume that there is an SPSS file called survey.sav saved in the directory C:\mydata. The read.spss function from the foreign library will read the file into R.
> dataSPSS<-read.spss("C:/mydata/survey.sav", to.data.frame=TRUE)
This creates a data object called dataSPSS that is ready for analysis. The to.data.frame argument, whose default value is FALSE, tells R to treat the object as a data frame. Note that when specifying the pathname, R understands forward slashes whereas Windows reads backward slashes. If it is necessary to read in several data files from the same directory, the amount of typing can be reduced by first setting the working directory and then using the relative pathname. For example,
> setwd("C:/mydata")
> dataSPSS<-read.spss("survey.sav", to.data.frame=TRUE)
Alternatively, if one prefers to search for the location of a data file, one can type
> dataSPSS<-(file.choose(), to.data.frame=TRUE)
This will open a dialog box that can be used to navigate to the appropriate folder.
R will assume that any value labels recorded in the SPSS file refer to factors (categorical variables) and will store the labels rather than the original number. For example, a variable named gender may be coded 0=male and 1=female, and the labels are saved in the .sav file. When R reads in the data from SPSS, the values of the variable will be "male" and "female" rather than "0" and "1". This is the default behavior, but it can be changed in the call to the read.spss function:
> dataSPSS<-read.spss(file.choose(), use.value.labels=FALSE)
Reading Stata files is equally straightforward using the read.dta function. Assuming there is a Stata data file survey.dta in the C:\mydata folder, the appropriate syntax is
> dataStata<-read.dta("C:/mydata/survey.dta")
or
> dataStata<-read.dta(file.choose())
The created object is automatically a data frame. The default is to convert value labels into factor levels ("male" and "female" rather than "0" and "1"), but this can be turned off.
> dataStata<read.dta(file.choose(), convert.factors=FALSE)
Note that Stata sometimes changes how it stores data files from one version to the next, and the foreign package may lag a little behind. If the read.dta command returns an error, try saving the data in Stata using the .saveold command. This will create a .dta file saved in a previous version of Stata that read.dta may be more likely to recognize.
R can also read SAS XPORT libraries. The function takes only a single argument, the pathname:
> dataXPORT<-read.xport("C:/mydata/survey")
The function returns a data frame if there is a single dataset in the library or a list of data frames if there are multiple datasets.
Reading in ASCII files
R can also easily read in space-, tab-, and comma-delimited text files. The read.table function handles the first two cases; read.csv handles the other. Say there is an ASCII data file survey.dat in which white space separates the values for each variable. The following syntax reads in this data.
> dataTEXT<-read.table("C:/mydata/survey.dat", header=TRUE, sep= " ")
The header argument tells R that the first row includes variable names. Its default is FALSE. The sep argument specifies that values are separated by any white space, which is the default. If the values are separated by tabs, the value of the sep argument is changed to
> dataTAB<-read.table("C:/mydata/survey.dat", header=TRUE, sep= "\t")
The read.csv command is available for reading data files with comma-separated values.
> dataCOM<-read.csv("C:/mydata/survey.csv", header=TRUE)
The following are also equivalent:
> setwd("C:/mydata") > dataCOM<-read.csv("survey.csv", header=TRUE)
and
> dataCOM<-read.csv(file.choose(), header=TRUE)
It is also possible to read fixed format ASCII files (those with pre-specified columns and no delimiters) using the read.fwf function. However, this task is tedious (as it is in any package). For ICPSR data, it is recommended to use the available setup files to read fixed format data into another package and then use the commands in R's foreign library.
Data in Excel Format
The easiest way to get Excel data into R is to save the spreadsheet as a comma-separated file and use R's read.csv function. The file type can be altered in Excel by changing the Save as type option to CSV (Comma Delimited).
