The examples below use a different data set: Click minarets or within R
load(url("http://www.unige.ch/ses/sococ/cl/edat/minarets.Rdata"))

• names(minarets) Shows the names of the variables in the data set
• str(minarets) Note that there are several factor variables (categorical variables), one of them is ordered (PolInter).
• attach(minarets) to make variables names accessible directly

• table(PolInter) Frequency table showing counts
• xtabs(~gender) Same, but uses formula interface
• freq1<-table(PolInter) Create a table for further use
• prop.table(freq1) Table with proportions
• barplot(freq1,main="Count of Political Interest") Barchart of the table
• mosaicplot(freq1)Mosaic plot of the table
• mosaic(freq1) Mosaic plot of the table (requires packace {vcd}

Several functions produce simple crosstabulations (contingency tables); table, ftable, xtabs.

Producing tables containing only frequencies (counts):

• table(PolInter,gender) Bivariate table
• ftable(language~PolInter) Same using formula interface
• xtabs(~gender+language) Same using formula interface
• xtabs(~gender+language+vote) Three variables
• ftable(xtabs(~gender+language+vote)) Same, but alternative presentation
• tab1<- table(PolInter,gender) store table as object for further use
• addmargins(tab1) add margins (row and column sums to table)
• margin.table(tab1,1) display row margins
• margin.table(tab1,2) display column margins

Proportions computed from a table of counts:

• tab1<- table(PolInter,gender) store frequency count table as object for further use
• prop.table(tab1) total proportions
• prop.table(tab1,2) proportions columnwise
• prop.table(tab1,1) proportions rowwise
• ftable(prop.table(tab1)) alternative presentation

CrossTable produces crosstabs similar to the ones produced in the past by SPSS or SAS. By default the table cells show counts, chi-square contributions, row, column and total proportions (default, SAS) or percentages (SPSS format).

• library(gmodels)
• CrossTable(gender,language) crosstabulations with total, row and column proportions
• CrossTable(gender,language, format="SPSS") crosstabulations with total, row and column percentages
• CrossTable(PolInter,language,digits=1,prop.r=F,prop.t=F,prop.chisq=F,format="SPSS") Only column percentages with a single decimal digit.
• CrossTable(vote1,language,missing.include=TRUE) Missing values are included in the table.
• CrossTable(PolInter,language,chisq=TRUE) add chi-square test
• CrossTable(PolInter,language,fisher=TRUE, mcnemar=TRUE) add fisher and McNemar tests

The following values can be displays in the cells (shown with default values)

• summary(tab1) Chi square test
• assocstats(tab1) Association coefficients; package {vcd}
• chisq.test(tab1) Chisquare test
• fisher.test(tab1, alternative="greater") Fisher's exact test

For 2x2 tables:

• polinter1 <-recode(PolInter,"c('--','-')='low';c('+','++')='high';else=NA") recode PolInter into to categories; requires package {car}
• tab2<-table(polinter1,gender) produce a 2x2 table
• oddsratio(tab2, log=FALSE) {vcd} odds ratios
• summary(oddsratio(tab2)) more odds ratios

More coefficients:

• Packages polycor, epitools, and {rms have functions to produce other association coefficients, namely Polychoric any polyserial correlations, Kendall's tau, γ Somer's D and others.
• See also the vcd package

The descr package provides similar functions but with some additional options.

• CrossTable(language, gender) nearly identical to the CrossTable function described above.
• crosstab(language,gender) wrapper function, produces by default a mosaic plot; has a weighting option
• freq(language) Frequency table with a barchart

Note for SPSS users: descr has several functions that help you to read/write SPSS label and missing value commands.

• tab1<-table(PolInter,gender) Create a table of counts (object of class table)
• barplot(tab1) Stacked barchart
• barplot(tab1,beside=T) Barchart (not stacked)
• barplot(tab1,horiz=T) Bars are shown horizontally
• dotchart(tab1) Cleveland's dot chart
• mosaicplot(tab1) from the {graphics} package
• mosaic(tab1)in the {vcd} package
• Association plots: assoc()in the {vcd}

See on-line documentation for titles and legends.

You will find most of these graphics in packagesPackages lattice and ggplot with many more options and control on how charts are produced and displayed. See the documentation for details.

• library(lattice)
• barchart(vote~gender+PolInter, data=minarets)

• library(ggplot2)
• ggplot(minarets,aes(x=PolInter)) + geom_bar()
• ggplot(minarets,aes(x=PolInter,fill=gender)) + geom_bar()
• ggplot(na.omit(minarets),aes(x=PolInter,fill=gender)) + geom_bar(position="dodge")

• xtable package: prepare tables for Latex or HTML

• Working with factor variables