![]() > with(infert, table(education, induced))Īlternatively, you can get the same result using the “xtabs” function. First off, we’ll generate aīasic contingency table that shows a frequency count. ![]() Once loaded, you can start working on it. ![]() ![]() Generating a Contingency Table in RĪs said earlier, I’ll be working on “infert” in this tutorial, so we’llįirst start with loading the dataset in R. Proceed to show how you can generate a contingency table in R. Now that you know what the information in a contingency table means, I’ll The information you need to determine a relationship between these two variables can be found in the cells of the table. The rows represent the years of education the women have received whereas the columns show the number of induced abortions they had. Understanding what the values in this table function mean requires me to take you into the details of how a contingency table is actually generated. This flat matrix form data is made up of a character string of different values, with marginal distribution that allows us to create a 2×2 contingency table or larger to calculate all sorts of different factor statistics, including the chi square statistic, the phi coefficient, conditional independence, column percentage, and others that we can put in a flat table or bar chart. It shows data of infertility among women with the education they received (in years) against their number of induced abortions. In this tutorial, I’ll be using a built-in data set of R, “infert” for its structural simplicity. I’ll begin by showing you a contingency table. This is easier than trying to perform those calculations with a different R object such as a data frame that has a different column variable set up. You can find conditional probability, relative frequency, expected value, the chi squared statistic, and other similar statistic measures from the character vector contingency table object. ![]() Tests with a null hypothesis such as the Chi Square test and Fisher’s Exact test can be conducted with a breeze if you have a flat contingency table for the data set instead of a data frame. When working with big data, as statisticians normally do, a contingency table condenses a large number of observations and neatly displays them in a table that makes readability and further calculations particularly easier. You may be able to crunch the numbers for a small data set on paper, but when working with larger data, you need more sophisticated tools and a contingency table is one of them. As a first step you would want to see the frequency count of each variable against a condition. Imagine yourself in a position where you want to determine a relationship between two variables. The Purpose and Uses of a Contingency Table ![]()
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