**[ [[R:introduction|Collection: Introduction to R]] ]**

====== The Chi-Square test ======

===== Prerequisites =====

  * Your data must have the form of a contingency table (see [[R:contingency-tables|Contingency Tables]]). 

===== How to calculate the chi-square test =====

Assuming that you have a contingency table that is stored in a variable called ''mytable'', you calculate a chi-square test using the ''chisq.test'' command as follows:

	chisq.test(mytable, correct = FALSE)

The option ''correct = FALSE'' ensures that the standard chi-square test is calculated, i.e., that no corrections are applied. If more than a quarter of the cells in your table have an expected value smaller than 5, you should use Yates' continuity correction; you can do this by not adding the ''correct'' option at all (i.e., R applies the correction by default), or by using the option ''correct = TRUE''.

By default, R does not show the expected frequencies or the residuals, but they are created as internal variables by the ''chi.square()'' function internally, and you can force R to output them by attaching the name of the internal variable to the function, separated by a ''$'' sign:

To show the expected frequencies, attach the option expected to the end of the command, separated by a $ sign:

	chisq.test(mytable, correct = FALSE)$expected

or

	chisq.test(mytable, correct = FALSE)$residuals


===== Additional information =====

  * When you report the result of a chi-square test, you should include a) the chi-square value, b) the degrees of freedom, and c) the p-value
  * If you want to present your data visually, a bar plot is usually the right way of doing so (see [[R:bar-plots|Bar Plots]]).