Frequencies and Crosstabs
This section describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results.
Generating Frequency Tables
R provides many methods for creating frequency and contingency tables. Three are described below. In the following examples, assume that A, B, and C represent categorical variables.
You can generate frequency tables using the table( ) function, tables of proportions using the prop.table( ) function, and marginal frequencies using margin.table( ).
# 2-Way Frequency Table attach(mydata) mytable <- table(A,B) # A will be rows, B will be columns mytable # print table margin.table(mytable, 1) # A frequencies (summed over B) margin.table(mytable, 2) # B frequencies (summed over A) prop.table(mytable) # cell percentages prop.table(mytable, 1) # row percentages prop.table(mytable, 2) # column percentages
table( ) can also generate multidimensional tables based on 3 or more categorical variables. In this case, use the ftable( ) function to print the results more attractively.
# 3-Way Frequency Table mytable <- table(A, B, C) ftable(mytable)
Table ignores missing values. To include NA as a category in counts, include the table option exclude=NULL if the variable is a vector. If the variable is a factor you have to create a new factor using newfactor <- factor(oldfactor, exclude=NULL).
The xtabs( ) function allows you to create crosstabulations using formula style input.
# 3-Way Frequency Table mytable <- xtabs(~A+B+c, data=mydata) ftable(mytable) # print table summary(mytable) # chi-square test of indepedence
If a variable is included on the left side of the formula, it is assumed to be a vector of frequencies (useful if the data have already been tabulated).
The CrossTable( ) function in the gmodels package produces crosstabulations modeled after PROC FREQ in SAS or CROSSTABS in SPSS. It has a wealth of options.
# 2-Way Cross Tabulation library(gmodels) CrossTable(mydata$myrowvar, mydata$mycolvar)
There are options to report percentages (row, column, cell), specify decimal places, produce Chi-square, Fisher, and McNemar tests of independence, report expected and residual values (pearson, standardized, adjusted standardized), include missing values as valid, annotate with row and column titles, and format as SAS or SPSS style output!
See help(CrossTable) for details.
Tests of Independence
For 2-way tables you can use chisq.test(mytable) to test independence of the row and column variable. By default, the p-value is calculated from the asymptotic chi-squared distribution of the test statistic. Optionally, the p-value can be derived via Monte Carlo simultation.
Fisher Exact Test
fisher.test(x) provides an exact test of independence. x is a two dimensional contingency table in matrix form.
Mantel - Haenszel test
Use the mantelhaen.test(x) function to perform a Cochran-Mantel-Haenszel chi-squared test of the null hypothesis that two nominal variables are conditionally independent in each stratum, assuming that there is no three-way interaction. x is a 3 dimensional contingency table, where the last dimension refers to the strata.
You can use the loglm( ) function in the MASS package to produce log-linear models. For example, let's assume we have a 3-way contingency table based on variables A, B, and C.
library(MASS) mytable <- xtabs(~A+B+C, data=mydata)
We can perform the following tests:
Mutual Independence : A, B, and C are pairwise independent. ```R loglm(~A+B+C, mytable)
**Partial Independence** : A is partially independent of B and C (i.e., A is independent of the composite variable BC). ```R loglin(~A+B+C+B*C, mytable)
Conditional Independence: A is independent of B, given C. ```R loglm(~A+B+C+AC+BC, mytable)
**No Three-Way Interaction** ```R loglm(~A+B+C+A*B+A*C+B*C, mytable)
Martin Theus and Stephan Lauer have written an excellent article on Visualizing Loglinear Models, using mosaic plots.
Measures of Association
The assocstats(mytable) function in the vcd package calculates the phi coefficient, contingency coefficient, and Cramer's V for an rxc table. The kappa(mytable) function in the vcd package calculates Cohen's kappa and weighted kappa for a confusion matrix. See Richard Darlington's article on Measures of Association in Crosstab Tables for an excellent review of these statistics.
Use bar and pie charts for visualizing frequencies in one dimension.
Use the vcd package for visualizing relationships among categorical data (e.g. mosaic and association plots).
Use the ca package for correspondence analysis (visually exploring relationships between rows and columns in contingency tables).
To practice making these charts, try the data visualization course at DataCamp.
Converting Frequency Tables to an "Original" Flat file
Finally, there may be times that you wil need the original "flat file" data frame rather than the frequency table. Marc Schwartz has provided code on the Rhelp mailing list for converting a table back into a data frame.