We covered the main functions to compute the most common and basic descriptive statistics. See the different variables types in R if you need a refresh. The range can then be easily computed, as you have guessed, by subtracting the minimum from the maximum: To my knowledge, there is no default function to compute the range. If you do not need information about missing values, add the report.nas = FALSE argument: And for a minimalist output with only counts and proportions: The ctable() function produces cross-tabulations (also known as contingency tables) for pairs of categorical variables. Median – the value between the higher half and lower half of a set of numbers. Boxplots are even more informative when presented side-by-side for comparing and contrasting distributions from two or more groups. Descriptive Statistics is the foundation block of summarizing data. Contribute When it comes to descriptive statistics examples, problems and solutions, we can give numerous of them to explain and support the general definition and types. The statistical software are paid as well as free. Note that the variable Species is not numeric, so descriptive statistics cannot be computed for this variable and NA are displayed. It’s to help you get a feel for the data, to tell us what happened in the past and to highlight potential relationships between variables. A data set is a collection of responses or observations from a sample or entire population.. The method that uses the shortest piece of code is usually preferred as a shorter piece of code is less prone to coding errors and more readable. The variable Sepal.Length does not seem to follow a normal distribution because several points lie outside the confidence bands. # Descriptive statistics summarize and organize characteristics of a data set. Source: LFSAB1105. # nbr.val, nbr.null, nbr.na, min max, range, sum, 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. R in Action (2nd ed) significantly expands upon this material. The sleep data set—provided by the datasets package—shows the effects of two different drugs on ten patients. Week 1: Calculations with R Software. If a data frame is provided, all non-numerical columns are ignored so you do not have to remove them yourself before running the function. Computing correlation in R requires a detailed explanation so I wrote an article covering correlation and correlation test. summaryBy(mpg + wt ~ cyl + vs, data = mtcars, Tip: if you have a large number of variables, add the transpose = TRUE argument for a better display. The package is centered around 4 functions: A combination of these 4 functions is usually more than enough for most descriptive analyses. A simple way of generating summary statistics by grouping variable is available in the psych package. Summary statistics tables or an exploratory data analysis are the most common ways in order to familiarize oneself with a data set. The functions plot() and density() are used together to draw a density plot: The last type of descriptive plot is a correlation plot, also called a correlogram. It defines the desired table using a model formula and a function. Descriptive statistics . # 5 lowest and 5 highest scores, library(pastecs) There exists many measures to summarize a dataset. c(m = mean(x), s = sd(x)) All plots displayed in this article can be customized. See online or in the above mentioned article for more information about the purpose and usage of each measure. See the vignette of the package for more information on this matter as these ratios are beyond the scope of this article.↩︎, Newsletter # combination of the levels of cyl and vs, Want to practice interactively? If you need more descriptive statistics, use stat.desc() from the package {pastecs}: You can have even more statistics (i.e., skewness, kurtosis and normality test) by adding the argument norm = TRUE in the previous function. We covered the main functions to compute the most common and basic descriptive statistics. This article explains how to compute the main descriptive statistics in R and how to present them graphically. Descriptive statistics is a set of brief descriptive coefficients that summarize a given data set representative of an entire or sample population. Change the order if you want to switch the two variables. Visualization: We should understand these features of the data through statistics andvisualization R Tutorial •Calculating descriptive statistics in R •Creating graphs for different types of data (histograms, boxplots, scatterplots) •Useful R commands for working with multivariate data (apply and its derivatives) •Basic clustering and PCA analysis stat.desc(mydata) This means you can actually access the minimum with: This reminds us that, in R, there are often several ways to arrive at the same result. # Tukey min,lower-hinge, median,upper-hinge,max Extra is the increase in hours of sleep; group is the drug given, 1 or 2; and ID is the patient ID, 1 to 10.. I’ll be using this data set to show how to perform descriptive statistics of groups within a data set, when the data set is long (as opposed to wide). However, the methods presented here and in the article “descriptive statistics by hand” are the easiest and most “standard” ones. One package for descriptive statistics I often use for my projects in R is the {summarytools} package. In this blog post, I am going to show you how to create descriptive summary statistics tables in R. We use the dataset iris throughout the article. Moreover, the package has been built with R Markdown in mind, meaning that outputs render well in HTML reports. For instance, when drawing a scatterplot of the length of the sepal and the length of the petal: There seems to be a positive association between the two variables. This article explains how to compute the main descriptive statistics in R and how to present them graphically. I illustrate each of the 4 functions in the following sections. To display column or total proportions, add the prop = "c" or prop = "t" arguments, respectively: To remove proportions altogether, add the argument prop = "n". One method of obtaining descriptive statistics is to use the sapply( ) function with a specified summary statistic. 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. R function sd() Boxplots are really useful in descriptive statistics and are often underused (mostly because it is not well understood by the public). Descriptive statistics in R do not concern with the impact of the data. Descriptive Statistics; Data Visualization; The first and best place to start is to calculate basic summary descriptive statistics on your data. However, we can easily find it thanks to the functions table() and sort(): table() gives the number of occurrences for each unique value, then sort() with the argument decreasing = TRUE displays the number of occurrences from highest to lowest. As the median, the first and third quartiles can be computed thanks to the quantile() function and by setting the second argument to 0.25 or 0.75: You may have seen that the results above are slightly different than the results you would have found if you compute the first and third quartiles by hand. In particular, the virginica species is the biggest, and the setosa species is the smallest of the three species (in terms of sepal length since the variable size is based on the variable Sepal.Length). The dataset includes 150 observations so in this case the number of bins can be set to 12. 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. A rule of thumb (known as Sturges’ law) is that the number of bins should be the rounded value of the square root of the number of observations. Descriptive Statistics . Like boxplots, scatterplots are even more informative when differentiating the points according to a factor, in this case the species: Line plots, particularly useful in time series or finance, can be created by adding the type = "l" argument in the plot() function: In order to check the normality assumption of a variable (normality means that the data follow a normal distribution, also known as a Gaussian distribution), we usually use histograms and/or QQ-plots.1 See an article discussing about the normal distribution and how to evaluate the normality assumption in R if you need a refresh on that subject. Instead of having the frequencies (i.e.. the number of cases) you can also have the relative frequencies (i.e., proportions) 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: See the section on advanced descriptive statistics for more advanced contingency tables. Marginals:The totals in a cross tabulation by row or column 4. To learn more about the reasoning behind each descriptive statistics, how to compute them by hand and how to interpret them, read the article “Descriptive statistics by hand”. The freq() function produces frequency tables with frequencies, proportions, as well as missing data information. In our context, this indicates that species and size are dependent and that there is a significant relationship between the two variables. This type of graph is more complex than the ones presented above, so it is detailed in a separate article. It describes the data and gives more detailed knowledge about the data. summary(mydata) More precisely, I’m using the tapply function: There are only 2 categorical variables in our dataset, so let’s use the tabacco dataset which has 4 categorical variables (i.e., gender, age group, smoker, diseased). We draw a barplot of the qualitative variable size: You can also draw a barplot of the relative frequencies instead of the frequencies by adding prop.table() as we did earlier: A histogram gives an idea about the distribution of a quantitative variable. # Descriptive statistics by groups. To go further, we can see from the table that setosa flowers seem to be larger in size than virginica flowers. Let’s first clarify the main purpose of descriptive data analysis. This tutorial covers the key features we are initially interested in understanding for categorical data, to include: 1. Published on July 9, 2020 by Pritha Bhandari. # item name ,item number, nvalid, Outputs that follow display much better in R Markdown reports, but in this article I limit myself to the raw outputs as the goal is to show how the functions work, not how to make them render well. Here is a simple example. 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. A barplot is a tool to visualize the distribution of a qualitative variable. See how to do this test by hand and in R. Note that Species are in rows and size in column because we specified Species and then size in table(). R provides a wide range of functions for obtaining summary statistics. In addition to that, summary statistics tables are very easy and fast to create and therefore so common. Introduction. The central tendency is something we calculate because we often want to know about the “average” or “middle” of our data.The two most commonly used measures of central tendency can easily be obtained using R; the mean and the median. In this example, I’ll show how to use the basic installation of the R programming language to return descriptive summary statistics by group. For instance, there is only one big setosa flower, while there are 49 small setosa flowers in the dataset. It is normal, there are many methods to compute them (R actually has 7 methods to compute the quantiles!). We’ll first start with loading the dataset into R. Steps to Get the Descriptive Statistics for … At least this was true in the past. library(psych) The standard deviation and the variance is computed with the sd() and var() functions: Remember from the article descriptive statistics by hand that the standard deviation and the variance are different whether we compute it for a sample or a population (see the difference between sample and population). For this reason, scatterplots are often used to visualize a potential correlation between two variables. An introduction to descriptive statistics. describe(mydata) Before drawing a boxplot of our data, see below a graph explaining the information present on a boxplot: How to interpret a boxplot? Applying the logarithm transformation can be done with the log() function. The IQR criterion means that all observations above \(q_{0.75} + 1.5 \cdot IQR\) or 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. I'm looking for a way to produce descriptive statistics by group number in R. There is another answer on here I found, which uses dplyr, but I'm having too many problems with it and would like to see what alternatives others might recommend.. I hope this article helped you to do descriptive statistics in R. If you would like to do the same by hand or understand what these statistics represent, I invite you to read the article “Descriptive statistics by hand”. Try this free course on statistics and R, Copyright © 2017 Robert I. Kabacoff, Ph.D. | Sitemap. If you need to publish or share your graphs, I suggest using {ggplot2} if you can, otherwise the default graphics will do the job. 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. # get means for variables in data frame mydata We create the variable size which corresponds to small if the length of the petal is smaller than the median of all flowers, big otherwise: Here is a recap of the occurrences by size: We now create a contingency table of the two variables Species and size with the table() function: The contingency table gives the number of cases in each subgroup. A major advantage of this function is that it accepts single vectors as well as data frames. FUN = function(x) { However, if you are familiar with writing functions in R Use promo code ria38 for a 38% discount. For example, # mean,median,25th and 75th quartiles,min,max And for non-English speakers, built-in translations exist for French, Portuguese, Spanish, Russian and Turkish. In order to compute these descriptive statistics by group (e.g., Species in our dataset), use the descr() function in combination with the stby() function: The dfSummary() function generates a summary table with statistics, frequencies and graphs for all variables in a dataset. Histograms are a bit similar to barplots, but histograms are used for quantitative variables whereas barplots are used for qualitative variables. Descriptive statistics In the course of learning a bit about how to generate data summaries in R, one will inevitably learn some useful R syntax and commands. 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. For your information, a mosaic plot can also be done via the mosaic() function from the {vcd} package: Barplots can only be done on qualitative variables (see the difference with a quantitative variable here). The packages used in this chapter include: • psych • FSA • lattice • ggplot2 • plyr • boot • rcompanion The following commands will install these packages if they are not already installed: if(!require(psych)){install.packages("psych")} if(!require(FSA)){install.packages("FSA")} if(!require(lattice)){install.packages("lattice")} if(!require(ggplot2)){install.packages("ggplot2")} if(!require(plyr)){install.packages("plyr")} if(!require(boot)){install.packages("boot")} if(!require(rcompani… This code to find the mode can also be applied to qualitative variables such as Species: Another descriptive statistics is the correlation coefficient. 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. Follow this order, or specify the name of the arguments if you do not follow this order. The mode of the variable Sepal.Length is thus 5. For instance, if we want to compute the mean for the variables Sepal.Length and Sepal.Width by Species and Size: Thanks for reading. Nowadays, thanks to the packages from the tidyverse, it is very easy and fast to compute descriptive statistics by any stratifying variable(s). Learn Descriptive Statistics online with courses like RStudio for Six Sigma - Basic Descriptive Statistics and Calculating Descriptive Statistics in R. However, customizing plots is beyond the scope of this article so all plots are presented without any customization. See how to draw a correlogram to highlight the most correlated variables in a dataset. This dataset is imported by default in R, you only need to load it by running iris: Below a preview of this dataset and its structure: The dataset contains 150 observations and 5 variables, representing the length and width of the sepal and petal and the species of 150 flowers. 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. R provides a wide range of functions for obtaining summary statistics. fivenum(x), library(Hmisc) Cumulative commands should be used with other commands to produce additional useful results; for example, the running mean. The p-value is close to 0 so we reject the null hypothesis of independence between the two variables. Plots can be created that show the data and indicating summary statistics. Edit the Targetfield on the Shortcuttab to read "C:\Program Files\R\R‐2.5.1\bin\Rgui.exe" ‐‐sdi(including the quotes exactly as shown, and assuming that you've installed R to the default location). Frequencies:The number of observations for a particular category 2. Lecture 01 : Introduction to R Software ; Lecture 02 : Basics and R as a Calculator ; Lecture 03 : Calculations with Data Vectors ; Lecture 04 : Built-in Commands and Missing Data Handling ; Lecture 05 : Operations with Matrices ; Week 2: Introduction to Descriptive statistics, frequency distribution In R, the standard deviation and the variance are computed as if the data represent a sample (so the denominator is \(n - 1\), where \(n\) is the number of observations). We want to group the data by Species and then: compute the number of element in each group. library(doBy) FAQ Descriptive Statistics courses from top universities and industry leaders. 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