This type of graph is more complex than the ones presented above, so it is detailed in a separate article. perfectly normal, such as skewness and variance. The overall look of the table is very simple. The mean can be computed with the mean() function: The median can be computed thanks to the median() function: since the quantile of order 0.5 ($$q_{0.5}$$) corresponds to the median. Applying the logarithm transformation can be done with the log() function. The IQR criterion means that all observations above $$q_{0.75} + 1.5 \cdot IQR$$ and below $$q_{0.25} - 1.5 \cdot IQR$$ (where $$q_{0.25}$$ and $$q_{0.75}$$ correspond to first and third quartile respectively) are considered as potential outliers by R. The minimum and maximum in the boxplot are represented without these suspected outliers. This tutorial covers the key features we are initially interested in understanding for categorical data, to include: To illustrate ways to compute these summary statistics and to visualize categorical data, I’ll demonstrate using this data which contains artificial supermarket transaction data. Tip: to compute the standard deviation (or variance) of multiple variables at the same time, use lapply() with the appropriate statistics as second argument: The command dat[, 1:4] selects the variables 1 to 4 as the fifth variable is a qualitative variable and the standard deviation cannot be computed on such type of variable. Along the same lines, if your dependent variable is continuous, you can also look at using boxplot categorical data views (example of how to do side by side boxplots here). As you have guessed, any quantile can also be computed with the quantile() function. If you need to publish or share your graphs, I suggest using {ggplot2} if you can, otherwise the default graphics will do the job. Note that the output of the range() function is actually an object containing the minimum and maximum (in that order). The bigger the deviation between the points and the reference line and the more they lie outside the confidence bands, the less likely that the normality condition is met. Density plot is a smoothed version of the histogram and is used in the same concept, that is, to represent the distribution of a numeric variable. I can also do the same plot by Gender and by Marital status. For a large dataset, it gives you a bite-sized summary that can help Now that you have As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion. Beginner to advanced resources for the R programming language. As has been mentioned, means, SDs and hinge points are not meaningful for categorical data. See the vignette of the package for more information on this matter as these ratios are beyond the scope of this article.↩︎, Newsletter This article explains how to compute the main descriptive statistics in R and how to present them graphically. In this case we assess the count of customers by marital status, gender, and location: We can also produce contingency tables that present the proportions (percentages) of each category or combination of categories. Resources to help you simplify data collection and analysis using R. Automate all the things! Fortunately, R allows you to do that in a number of ways as well. I illustrate each of the 4 functions in the following sections. You can calculate median(), Normality tests such as Shapiro-Wilk or Kolmogorov-Smirnov tests can also be used to test whether the data follow a normal distribution or not. variability you need to install additional packages. fair estimate of what your data means. However, in practice, normality tests are often considered as too conservative in the sense that sometimes a very limited number of observations may cause the normality condition to be violated. Instead of having the frequencies (i.e.. the number of cases) you can also have the relative frequencies in each subgroup by adding the table() function inside the prop.table() function: Note that you can also compute the percentages by row or by column by adding a second argument to the prop.table() function: 1 for row, or 2 for column: Barplots can only be done on qualitative variables (see the difference with a quantative variable here). The mode of the variable Sepal.Length is thus 5. Minimum and maximum can be found thanks to the min() and max() functions: gives you the minimum and maximum directly. Change the order if you want to switch the two variables. To briefly recap what have been said in that article, descriptive statistics (in the broad sense of the term) is a branch of statistics aiming at summarizing, describing and presenting a series of values or a dataset. Bar plots, in the purest form, are useful to represent the relationships between more than two variables in the form of vertical bars. For instance, if we want to compute the mean for the variables Sepal.Length and Sepal.Width by Species and Size: Thanks for reading. The one liner below does a couple of things. Histograms have been presented earlier, so here is how to draw a QQ-plot: Or a QQ-plot with confidence bands with the qqPlot() function from the {car} package: If points are close to the reference line (sometimes referred as Henry’s line) and within the confidence bands, the normality assumption can be considered as met. In order to check whether size is significantly associated with species, we could perform a Chi-square test of independence since both variables are categorical variables. See a recap of the different data types in R if needed. There are, however, many more functions and packages to perform more advanced descriptive statistics in R. In this section, I present some of them with applications to our dataset. The variable Sepal.Length does not seem to follow a normal distribution because several points lie outside the confidence bands. Statisticians, especially financial statisticians, are often interested in knowing whether their data fits a normal distribution. In our context, this indicates that species and size are dependent and that there is a significant relationship between the two variables. , you can create your own function to compute the range: which is equivalent than $$max - min$$ presented above. A nominal variable is said to be binary or dichotomous if it is limited to two categories. Statisticians often need That concludes our introduction to how To Plot Categorical Data in R. As you can see, there are number of tools here which can help you explore your data…, Interested in Learning More About Categorical Data Analysis in R? For this reason, scatterplots are often used to visualize a potential correlation between two variables. In this article, we focus only on the implementation in R of the most common descriptive statistics and their visualizations (when deemed appropriate). See how you can easily draw graphs from the {ggplot2} package without having to code it yourself. It is standard to characterize categorical data by counts and percentages. All plots displayed in this article can be customized. I’ll be using an in-built dataset of R called “warpbreaks”. scatter or the spread of values in the set. To draw a histogram in R, use hist(): Add the arguments breaks = inside the hist() function if you want to change the number of bins. As you have guessed, any quantile can also be computed with the quantile() function. Syed Abdul Hadi is an aspiring undergrad with a keen interest in data analytics using mathematical models and data processing software. Graphs from the {ggplot2} package usually have a better look but it requires more advanced coding skills (see the article “Graphics in R with ggplot2” to learn more). Task 6: Calculate Descriptive Statistics on all Columns There are functions in R that can be applied to each column for performing certain calculations on them. This tells you Frequencies:The number of observations for a particular category 2. Our next package will be the furniture package. such questions. A boxplot graphically represents the distribution of a quantitative variable by visually displaying five common location summary (minimum, median, first/third quartiles and maximum) and any observation that was classified as a suspected outlier using the interquartile range (IQR) criterion. A mosaic plot allows to visualize a contingency table of two qualitative variables: The mosaic plot shows that, for our sample, the proportion of big and small flowers is clearly different between the three species. It is also possible to create a contingency table for each level of a third categorical variable thanks to the combination of the stby() and ctable() functions. Boxplots are even more informative when presented side-by-side for comparing and contrasting distributions from two or more groups.

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