![]() ![]() # how to make frequency table in r (nicer version) Here my dataset does not have missing labels therefore, it doesn’t change much but it is an important feature of the function. The “epiDisplay” package, however, tells you exactly how many data points are missing values and even gives you the remaining stats of your data with and without including the missing data points. One more thing you may have noticed in the earlier method was that if your data misses some labels for some of the observations, it doesn’t tell you there’s missing values. If, for instance, you wish to know what percentage of cars have 8 cylinders or what fraction of cars have 4 gears, this method allows you to get your answer using the same report. It gives you a highly featured report of your dataset that includes descriptive statistics functions like absolute frequency, cumulative frequencies and proportions. Similarly, a two way frequency table can be generated using numerous functions, however, one other function that I’ll be going over in this tutorial comes in the “epiDisplay” package. The number of ways through which you can perform a simple task in R is exhaustive and each method has its own pros and cons. Generating a More Refined Frequency Table in R This table includes distinct values, making creating a frequency count or relative frequency table fairly easy, but this can also work with a categorical variable instead of a numeric variable- think pie chart or histogram. This table is a little more explanatory with the columns and rows labeled. I’ll start by checking the range of the number of cylinders present in the cars. In this tutorial, I will be categorizing cars in my data set according to their number of cylinders. The most common and straight forward method of generating a frequency table in R is through the use of the table function. > data(mtcars) Generating a Frequency Table in R This isn’t always needed but is good practice to keep things organized in your code. You can load the data set into your environment using the data() function. It contains information about the mileage, number of forward gears, number of carburetors and cylinders for various cars. I’ll be using a built-in data set of R called “mtcars”. In this tutorial, I will be going over some techniques of generating frequency tables using R. Good packages for creating frequency tables in R include ggmodels, dplyr, and epiDisplay. Each different R function for creating a good data table output has its own benefits, from creating a column header and row names to column index, table command, character vector support, being able to import a data file, or multiple columns, but many need a specific R package to properly show you how to make a table in R code. There are several easy ways to create an R frequency table, ranging from using the factor () and R table () functions in Base R to specific packages. By the end of this guide, you’ll have a solid understanding of how to create and interpret frequency tables in R, and how to apply them to your own data analysis projects. We’ll cover the basic concepts of frequency tables, such as counts, percentages, and cumulative frequencies, as well as some advanced techniques for visualizing and analyzing categorical data. In this article, we’ll explore how to create frequency tables in R using both base R functions and the tidyverse packages. Whether you’re working with survey data, market research, or social sciences, frequency tables can help you understand the distribution and patterns of categorical variables. ![]() ![]() These are a common way to summarize categorical data in statistics, and R provides a powerful set of tools to create and analyze them. Frequency tables are used by statisticians to study categorical data, counting how often a variable appears in their data set. ![]()
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