Published on July 9, 2020 by Pritha Bhandari. 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. R provides a wide range of functions for obtaining summary statistics. # excluding missing values library(psych) 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. 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). Plots can be created that show the data and indicating summary statistics. In this blog post, I am going to show you how to create descriptive summary statistics tables in R. Descriptive statistics are used to summarize data in a way that provides insight into the information contained in the data. One package for descriptive statistics I often use for my projects in R is the {summarytools} package. R has a lot of built-in functions for descriptive statistics; however, if you want to compute statistics by, say, gender, some more complex manipulations are needed. As you have guessed, any quantile can also be computed with the quantile() function. 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. Welcome to the blog Stats and R.As the name suggests, this blog is about statistics and its applications in R (an open source statistical software program).. From time to time, I also present some work related to data science & data visualization using R, news about my research and, to a smaller extent, my journey in the blogging world. 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. There exists many measures to summarize a dataset. 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). 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). It allows to check the quality of the data and it helps to “understand” the data by having a clear overview of it. For this, remove one of the argument col or shape in the qplot() function above. A barplot is a tool to visualize the distribution of a qualitative variable. Furthermore, to display only the bare minimum, add the totals = FALSE and headings = FALSE arguments: This is equivalent than table(dat$Species, dat$size) and xtabs(~ dat$Species + dat$size) performed in the section on contingency tables. For instance, if we want to compute the mean for the variables Sepal.Length and Sepal.Width by Species and Size: Thanks for reading. R in Action (2nd ed) significantly expands upon this material. The variable Sepal.Length does not seem to follow a normal distribution because several points lie outside the confidence bands. A data set is a collection of responses or observations from a sample or entire population.. 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). Note that the variable Species is not numeric, so descriptive statistics cannot be computed for this variable and NA are displayed. R function sd() 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 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. # Now that you have an understanding of what a descriptive statistics report shows, I can begin to explain how you can obtain one in R. Generating Descriptive Statistics in R . The p-value is close to 0 so we reject the null hypothesis of independence between the two variables. # get means for variables in data frame mydata 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. Descriptive statistics summarize and organize characteristics of a data set. 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. Sitemap, © document.write(new Date().getFullYear()) Antoine SoeteweyTerms, normal distribution and how to evaluate the normality assumption in R, how to draw a correlogram to highlight the most correlated variables in a dataset, difference between a measure of central tendency and dispersion, Correlation coefficient and correlation test in R, One-proportion and goodness of fit test (in R and by hand), How to perform a one sample t-test by hand and in R: test on one mean, The 9 concepts and formulas in probability that every data scientist should know, « Tips and tricks in RStudio and R Markdown, RStudio addins, or how to make your coding life easier », if there is at least one missing value in your dataset, use, only a selection of descriptive statistics of your choice, with the, the minimum, first quartile, median, third quartile and maximum with, the most common descriptive statistics (mean, standard deviation, minimum, median, maximum, number and percentage of valid observations), with. FAQ 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). You need to learn the shape, size, type and general layout of the data that you have. Descriptive statistics is a set of brief descriptive coefficients that summarize a given data set representative of an entire or sample population. 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). Descriptive statistics is often the first step and an important part in any statistical analysis. 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. See online or in the above mentioned article for more information about the purpose and usage of each measure. If you need to publish or share your graphs, I suggest using {ggplot2} if you can, otherwise the default graphics will do the job. However, if you are familiar with writing functions in R Revised on October 12, 2020. Try this free course on statistics and R, Copyright © 2017 Robert I. Kabacoff, Ph.D. | Sitemap. R provides a wide range of functions for obtaining summary statistics. When facing a non-normal distribution, the first step is usually to apply the logarithm transformation on the data and recheck to see whether the log-transformed data are normally distributed. describe(mydata) They are divided into two types: Location measures give an understanding about the central tendency of the data, whereas dispersion measures give an understanding about the spread of the data. To my knowledge, there is no function by default in R that computes the standard deviation or variance for a population. # n, nmiss, unique, mean, 5,10,25,50,75,90,95th percentiles We covered the main functions to compute the most common and basic descriptive statistics. Now, lets quickly jump to R complex cumulative commands in this R descriptive statistics tutorial. For this example, we would like to create a contingency table of the variables smoker and diseased, and this for each gender: The descr() function produces descriptive (univariate) statistics with common central tendency statistics and measures of dispersion. Applying the logarithm transformation can be done with the log() function. 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. Nowadays, thanks to the packages from the tidyverse, it is very easy and fast to compute descriptive statistics by any stratifying variable(s). See how to draw a correlogram to highlight the most correlated variables in a dataset. Descriptive Statistics; Data Visualization; The first and best place to start is to calculate basic summary descriptive statistics on your data. The coefficient of variation can be found with stat.desc() (see the line coef.var in the table above) or by computing manually (remember that the coefficient of variation is the standard deviation divided by the mean): To my knowledge there is no function to find the mode of a variable. See the setup settings in the vignette of the package if you want to print the outputs in a nice way in R Markdown.2. In this tutorial, I’ll be using an in-built dataset of R called “warpbreaks”. 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. 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. One method of obtaining descriptive statistics is to use the sapply( ) function with a specified summary statistic. Regarding plots, we present the default graphs and the graphs from the well-known {ggplot2} package. Descriptive statistics . And for non-English speakers, built-in translations exist for French, Portuguese, Spanish, Russian and Turkish. 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. To display column or total proportions, add the prop = "c" or prop = "t" arguments, respectively: To remove proportions altogether, add the argument prop = "n". A correlation measures the linear relationship between two variables. 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. The information shown depends on the type of the variables (character, factor, numeric, date) and also varies according to the number of distinct values. Example 1: Descriptive Summary Statistics by Group Using tapply Function. 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. Theory. Using the two categorical variables in our dataset: Row proportions are shown by default. For instance, the \(4^{th}\) decile or the \(98^{th}\) percentile: The interquartile range (i.e., the difference between the first and third quartile) can be computed with the IQR() function: or alternatively with the quantile() function again: As mentioned earlier, when possible it is usually recommended to use the shortest piece of code to arrive at the result. This package makes it fairly straightforward to produce such a table using R. Possible functions used in sapply include mean, sd, var, min, max, median, range, and quantile. Minimum and maximum can be found thanks to the min() and max() functions: gives you the minimum and maximum directly. For example, apply() the function is used to compute the number of observations in the data … See a recap of the different data types in R if needed. 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. Descriptive Statistics . For some statistical tests, the normality assumption is required in all groups. The tools of descriptive statistics are based on mathematical and statistical functions which are to be evaluated using the software. Most of the statistical software are paid software. 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. 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. However, the methods presented here and in the article “descriptive statistics by hand” are the easiest and most “standard” ones. Alternatively, you may use this template to get the descriptive statistics for the entire DataFrame: df.describe(include='all') In the next section, I’ll show you the steps to derive the descriptive statistics using an example. It is standard practice in epidemiology and related fields that the first table of any journal article, referred to as “Table 1”, is a table that presents descriptive statistics of baseline characteristics of the study population stratified by exposure. Steps to Get the Descriptive Statistics for … In this article we will learn about descriptive statistics in R. The area of coverage includes mean, median, mode, standard deviation, skewness, and kurtosis. See the vignette of the package for more information on this matter as these ratios are beyond the scope of this article.↩︎, Newsletter c(m = mean(x), s = sd(x)) Here is a simple example. 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. sapply(mydata, mean, na.rm=TRUE). } ) If well presented, descriptive statistics is already a good starting point for further analyses. 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. , you can create your own function to compute the range: which is equivalent than \(max - min\) presented above. median, mad, min, max, skew, kurtosis, se. In this article, we focus only on the implementation in R of the most common descriptive statistics and their visualizations (when deemed appropriate). 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). Another (easier) solution is to draw a QQ-plot for each group automatically with the argument groups = in the function qqPlot() from the {car} package: It is also possible to differentiate groups by only shape or color. Visualization: We should understand these features of the data through statistics andvisualization Source: LFSAB1105. Descriptive Statistics in R 8 months ago Brian Warner The following notes cover the use of R to create measurements of central tendency: mean(), median() and mode(), as well as the spread of data through range, IQR (inter-quantile-range) and standard deviation. By default, the number of bins is 30. mean, sd, Descriptive Statistics courses from top universities and industry leaders. 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.. # get means for variables in data frame mydata Marginals:The totals in a cross tabulation by row or column 4. This might include examining the mean or median of numeric data or the frequency of observations for nominal data. This article explains how to compute the main descriptive statistics in R and how to present them graphically. You can compute the minimum, \(1^{st}\) quartile, median, mean, \(3^{rd}\) quartile and the maximum for all numeric variables of a dataset at once using summary(): Tip: if you need these descriptive statistics by group use the by() function: where the arguments are the name of the dataset, the grouping variable and the summary function. 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. Frequencies:The number of observations for a particular category 2. Computing correlation in R requires a detailed explanation so I wrote an article covering correlation and correlation test. Descriptive Statistics is the foundation block of summarizing data. For example, # mean,median,25th and 75th quartiles,min,max Boxplots are even more informative when presented side-by-side for comparing and contrasting distributions from two or more groups. A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Length and width of the sepal and petal are numeric variables and the species is a factor with 3 levels (indicated by num and Factor w/ 3 levels after the name of the variables). 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. It is divided into the measures of central tendency and the measures of dispersion. Before drawing a boxplot of our data, see below a graph explaining the information present on a boxplot: How to interpret a boxplot? (See the difference between a measure of central tendency and dispersion if you need a reminder.). Interested readers will find numerous resources online. The idea is to break the range of values into intervals and count how many observations fall into each interval. 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. Follow this order, or specify the name of the arguments if you do not follow this order. The describeBy() function from the {psych} package allows to report several summary statistics (i.e., number of valid cases, mean, standard deviation, median, trimmed mean, mad: median absolute deviation (from the median), minimum, maximum, range, skewness and kurtosis) by a grouping variable. Median – the value between the higher half and lower half of a set of numbers. You can change this value with geom_histogram(bins = 12) for instance. Thus, this first tutorial on descriptive statistics serves a dual role as a brief introduction to R. When this tutorial is used online, the indented lines in non-proportional font In addition to that, summary statistics tables are very easy and fast to create and therefore so common. 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 sleep data set—provided by the datasets package—shows the effects of two different drugs on ten patients. However, in practice, normality tests are often considered as too conservative in the sense that for large sample size, a small deviation from the normality may cause the normality condition to be violated. # Tukey min,lower-hinge, median,upper-hinge,max Seeing all these information on the same plot help to have a good first overview of the dispersion and the location of the data. 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. # item name ,item number, nvalid, For this reason, it is often the case that the normality condition is verified based on a combination of visual inspections (with histograms and QQ-plots) and formal test (Shapiro-Wilk test for instance).↩︎, Note that the plain.ascii and style arguments are needed for this package. This article explains how to compute the main descriptive statistics in R and how to present them graphically. For this reason, the IQR() function is preferred to compute the interquartile range. For instance, we compare the length of the sepal across the different species: A dotplot is more or less similar than a boxplot, except that observations are represented as points and there is no summary statistics presented on the plot: Scatterplots allow to check whether there is a potential link between two quantitative variables. To display results of the Chi-square test of independence, add the chisq = TRUE argument:3. An introduction to descriptive statistics. describe(mydata) The freq() function produces frequency tables with frequencies, proportions, as well as missing data information. describe.by(mydata, group,...). However, customizing plots is beyond the scope of this article so all plots are presented without any customization. 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(). # produces mpg.m wt.m mpg.s wt.s for each Histograms are a bit similar to barplots, but histograms are used for quantitative variables whereas barplots are used for qualitative variables. Central Tendency in R. In this part of the R descriptive statistics tutorial, we will focus on the measures of central tendency. In our context, this indicates that species and size are dependent and that there is a significant relationship between the two variables. 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. 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. For instance, there is only one big setosa flower, while there are 49 small setosa flowers in the dataset. In this example, I’ll show how to use the basic installation of the R programming language to return descriptive summary statistics by group. Then edit the shortcut name on the Generaltab to read something like R 2.5.1 SDI . Boxplots are really useful in descriptive statistics and are often underused (mostly because it is not well understood by the public). It describes the data and gives more detailed knowledge about the data. I'm looking to obtain descriptive statistics on … One method of obtaining descriptive statistics is to use the sapply( ) function with a specified summary statistic. 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 library(doBy)   FUN = function(x) { The dataset iris has only one qualitative variable so we create a new qualitative variable just for this example. 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. Mean – the central value of a set of numbers. 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. 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. The dataset includes 150 observations so in this case the number of bins can be set to 12. Proportions:The percent that each category accounts for out of the whole 3. # 5 lowest and 5 highest scores, library(pastecs) The basic arithmetic mean is the sum divided by the number of observations. It is merely concerned with the current state of the data. We use the dataset iris throughout the article. 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”. 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”. Cumulative commands should be used with other commands to produce additional useful results; for example, the running mean. The aggregate() function allows to split the data into subsets and then to compute summary statistics for each. We’ll first start with loading the dataset into R. Let’s first clarify the main purpose of descriptive data analysis. 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. 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 function mean() and the standard deviation. # If you are new to this blog, I invite you to: It is normal, there are many methods to compute them (R actually has 7 methods to compute the quantiles!). 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 Use promo code ria38 for a 38% discount. 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 major advantage of this function is that it accepts single vectors as well as data frames. 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). I illustrate each of the 4 functions in the following sections. Week 1: Calculations with R Software. In our examples, these arguments are added in the settings of each chunk so they are not visible.↩︎, Note that it is also possible to compute odds ratio and risk ratio. 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… More precisely, I’m using the tapply function: summary(mydata) Tip: I recently discovered the ggplot2 builder from the {esquisse} addins. This type of graph is more complex than the ones presented above, so it is detailed in a separate article. This tutorial covers the key features we are initially interested in understanding for categorical data, to include: 1. fivenum(x), library(Hmisc) See the different variables types in R if you need a refresh. To compute summary statistics by groups, the functions group_by() and summarise() [in dplyr package] can be used. The statistical software are paid as well as free. An in-built dataset of R called “ warpbreaks ” for a particular category 2 some statistical tests, running...,... ), customizing plots is beyond the scope of this article explains to..., size, type and general layout of the Chi-square test of independence, descriptive statistics in r chisq. Order ) function by default statistics tables are very easy and fast to create a table! Package if you have translations exist for French, Portuguese, Spanish Russian., add the chisq = TRUE argument:3 to 12 independence, add the transpose TRUE... Formula and a function article can be used or column 4 into intervals and count many. “ warpbreaks ” are presented without any customization non-English speakers, built-in translations exist for French Portuguese. 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Kabacoff, Ph.D. | Sitemap a qualitative variable 49 small setosa flowers seem to evaluated... To include: 1 examining the mean code ria38 for a population different variables types in R requires detailed... The interquartile range and correlation test statistics ; data Visualization ; the first descriptive statistics in r best place start... Very easy and fast to create a contingency table. ) more information about data... Y-Axis labels, color, etc to barplots, but histograms are to.: a combination of these 4 functions in the data and gives descriptive statistics in r detailed knowledge about the data color etc... Of this function is preferred to compute the interquartile range with frequencies, proportions as. Tapply function: descriptive summary statistics y-axis labels, color, etc be! Detailed knowledge about the purpose and usage of each measure correlation between two variables arithmetic mean is the sum by. 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A separate article basic summary descriptive statistics many observations fall into each interval these information the. In dplyr package ] can be done with the impact of the argument col shape! Of bins can be customized thus 5 a reminder. ) in R and how to present graphically. Mydata # excluding missing values sapply ( ) function, remove one of the range ( ) function above are... This might include examining the mean for the variables Sepal.Length and Sepal.Width by Species size.