Using R for Basic Cross Tabulation Analysis: Part Three, Using the xtabs Function

Using R to Work with GSS Survey Data Part Three: Using xtabs to Create and Analyze Tables

A tutorial by D. M. Wiig
In Part Two of this series of tutorials I discussed how to find and import a data set from the NORC GSS survey. The focus of that tutorial was on the GSS2010 data set that was imported into the R workspace in SPSS format and then loaded into an R data frame for analysis.

Use the following code to load the data set into an R workspace:

>install.packages(“Hmisc”) #need for file import
>install.packages(“foreign”) #need for file import
>#get spss gss file and put into data frame
>gssdataframe <- spss.get(“/path-to-your-file/GSS2010.sav”, use.value.labels=TRUE)

The xtabs function provides a quick way to generate and view a cross tabulation of two variables and allows the user to specify one or more control variables in the cross tabulation. Using the variables “ partyid” and “polviews” the cross tablulation is generated with:

>#use xtabs to produce a table
>gsstab <- xtabs(~ partyid + polviews, data=gssdataframe)

To view the resulting table use:

>gsstab #show table

To view summary statistics generated use:


This summary shows the number of cases in the table, the number of factors and the Chi-square value for the table.

Variables used in social science research are often interrelated so it is desirable to control for one or more variables in order to further examine the variables of interest. The table created in the gsstab data frame shows the relationship between political ideology and political party affiliation. To look at the relationship by gender use the following:

>#use xtabs to produce a table with a control variable
>gsstab2 <- xtabs(~ partyid + polviews+ sex, data=gssdataframe)

To view the new table use:


To view summary statistics for the table enter:


As noted above xtabs is a quick and powerful function to create N x N tables with or without control variables. In the next tutorial I explore the use of the ca function to produce a basic Correspondence analysis of underlying dimensions in an N x N table.


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