{"id":7239,"date":"2018-10-01T21:47:16","date_gmt":"2018-10-01T21:47:16","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&#038;p=7239"},"modified":"2019-12-03T12:54:36","modified_gmt":"2019-12-03T10:54:36","slug":"compute-summary-statistics-in-r","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/lessons\/compute-summary-statistics-in-r\/","title":{"rendered":"Compute Summary Statistics in R"},"content":{"rendered":"<p>&nbsp;<\/p>\n<div id=\"rdoc\">\n<p>This tutorial introduces how to easily compute <strong>statistcal summaries<\/strong> in R using the <strong>dplyr<\/strong> package.<\/p>\n<p>You will learn, how to:<\/p>\n<ul>\n<li>Compute summary statistics for ungrouped data, as well as, for data that are grouped by one or multiple variables. R functions: <strong>summarise<\/strong>() and <strong>group_by<\/strong>().<\/li>\n<li>Summarise multiple variable columns. R functions:\n<ul>\n<li><strong>summarise_all<\/strong>(): apply summary functions to every columns in the data frame.<\/li>\n<li><strong>summarise_at<\/strong>(): apply summary functions to specific columns selected with a character vector<\/li>\n<li><strong>summarise_if<\/strong>(): apply summary functions to columns selected with a predicate function that returns TRUE.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/data-manipulation-in-r\/images\/compute-summary-statistics-in-r.png\" alt=\"Compute Summary Statistics in R\" \/><\/p>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#required-packages\">Required packages<\/a><\/li>\n<li><a href=\"#demo-dataset\">Demo dataset<\/a><\/li>\n<li><a href=\"#summary-statistics-of-ungrouped-data\">Summary statistics of ungrouped data<\/a><\/li>\n<li><a href=\"#summary-statistics-of-grouped-data\">Summary statistics of grouped data<\/a>\n<ul>\n<li><a href=\"#group-by-one-variable\">Group by one variable<\/a><\/li>\n<li><a href=\"#group-by-multiple-variables\">Group by multiple variables<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#summarise-multiple-variables\">Summarise multiple variables<\/a>\n<ul>\n<li><a href=\"#key-r-functions\">Key R functions<\/a><\/li>\n<li><a href=\"#summarise-variables\">Summarise variables<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#useful-statistical-summary-functions\">Useful statistical summary functions<\/a><\/li>\n<li><a href=\"#summary\">Summary<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"required-packages\" class=\"section level2\">\n<h2>Required packages<\/h2>\n<p>Load the <code>tidyverse<\/code> packages, which include <code>dplyr<\/code>:<\/p>\n<pre class=\"r\"><code>library(tidyverse)<\/code><\/pre>\n<\/div>\n<div id=\"demo-dataset\" class=\"section level2\">\n<h2>Demo dataset<\/h2>\n<p>We\u2019ll use the R built-in iris data set, which we start by converting into a tibble data frame (tbl_df) for easier data analysis.<\/p>\n<pre class=\"r\"><code>my_data &lt;- as_tibble(iris)\r\nmy_data<\/code><\/pre>\n<pre><code>## # A tibble: 150 x 5\r\n##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species\r\n##          &lt;dbl&gt;       &lt;dbl&gt;        &lt;dbl&gt;       &lt;dbl&gt; &lt;fct&gt;  \r\n## 1          5.1         3.5          1.4         0.2 setosa \r\n## 2          4.9         3            1.4         0.2 setosa \r\n## 3          4.7         3.2          1.3         0.2 setosa \r\n## 4          4.6         3.1          1.5         0.2 setosa \r\n## 5          5           3.6          1.4         0.2 setosa \r\n## 6          5.4         3.9          1.7         0.4 setosa \r\n## # ... with 144 more rows<\/code><\/pre>\n<\/div>\n<div id=\"summary-statistics-of-ungrouped-data\" class=\"section level2\">\n<h2>Summary statistics of ungrouped data<\/h2>\n<p>Compute the mean of Sepal.Length and Petal.Length as well as the number of observations using the function <em>n<\/em>():<\/p>\n<pre class=\"r\"><code>my_data %&gt;%\r\n  summarise(\r\n          count = n(),\r\n          mean_sep = mean(Sepal.Length, na.rm = TRUE),\r\n          mean_pet = mean(Petal.Length, na.rm = TRUE)\r\n          )<\/code><\/pre>\n<pre><code>## # A tibble: 1 x 3\r\n##   count mean_sep mean_pet\r\n##   &lt;int&gt;    &lt;dbl&gt;    &lt;dbl&gt;\r\n## 1   150     5.84     3.76<\/code><\/pre>\n<div class=\"notice\">\n<p>Note that, we used the additional argument <em>na.rm<\/em> to remove NAs, before computing means.<\/p>\n<\/div>\n<\/div>\n<div id=\"summary-statistics-of-grouped-data\" class=\"section level2\">\n<h2>Summary statistics of grouped data<\/h2>\n<p>Key R functions: <code>group_by()<\/code> and <code>summarise()<\/code><\/p>\n<div id=\"group-by-one-variable\" class=\"section level3\">\n<h3>Group by one variable<\/h3>\n<pre class=\"r\"><code>my_data %&gt;%\r\n  group_by(Species) %&gt;%\r\n  summarise(\r\n          count = n(),\r\n          mean_sep = mean(Sepal.Length),\r\n          mean_pet = mean(Petal.Length)\r\n            )<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 4\r\n##   Species    count mean_sep mean_pet\r\n##   &lt;fct&gt;      &lt;int&gt;    &lt;dbl&gt;    &lt;dbl&gt;\r\n## 1 setosa        50     5.01     1.46\r\n## 2 versicolor    50     5.94     4.26\r\n## 3 virginica     50     6.59     5.55<\/code><\/pre>\n<div class=\"success\">\n<p>Note that, it\u2019s possible to combine multiple operations using the <em>maggrittr<\/em> forward-pipe operator : <em>%&gt;%<\/em>. For example, <em>x %&gt;% f<\/em> is equivalent to <em>f(x)<\/em>.<\/p>\n<p>In the R code above:<\/p>\n<ul>\n<li>first, my_data is passed to group_by() function<\/li>\n<li>next, the output of group_by() is passed to summarise() function<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div id=\"group-by-multiple-variables\" class=\"section level3\">\n<h3>Group by multiple variables<\/h3>\n<pre class=\"r\"><code># ToothGrowth demo data sets\r\nhead(ToothGrowth)<\/code><\/pre>\n<pre><code>##    len supp dose\r\n## 1  4.2   VC  0.5\r\n## 2 11.5   VC  0.5\r\n## 3  7.3   VC  0.5\r\n## 4  5.8   VC  0.5\r\n## 5  6.4   VC  0.5\r\n## 6 10.0   VC  0.5<\/code><\/pre>\n<pre class=\"r\"><code># Summarize\r\nToothGrowth %&gt;%\r\ngroup_by(supp, dose) %&gt;%\r\n  summarise(\r\n    n = n(),\r\n    mean = mean(len),\r\n    sd = sd(len)\r\n  )<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 5\r\n## # Groups:   supp [?]\r\n##   supp   dose     n  mean    sd\r\n##   &lt;fct&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt;\r\n## 1 OJ      0.5    10 13.2   4.46\r\n## 2 OJ      1      10 22.7   3.91\r\n## 3 OJ      2      10 26.1   2.66\r\n## 4 VC      0.5    10  7.98  2.75\r\n## 5 VC      1      10 16.8   2.52\r\n## 6 VC      2      10 26.1   4.80<\/code><\/pre>\n<\/div>\n<\/div>\n<div id=\"summarise-multiple-variables\" class=\"section level2\">\n<h2>Summarise multiple variables<\/h2>\n<div id=\"key-r-functions\" class=\"section level3\">\n<h3>Key R functions<\/h3>\n<p>The functions <code>summarise_all()<\/code>, <code>summarise_at()<\/code> and <code>summarise_if()<\/code> can be used to summarise multiple columns at once.<\/p>\n<p>The simplified formats are as follow:<\/p>\n<pre class=\"r\"><code>summarise_all(.tbl, .funs, ...)\r\nsummarise_if(.tbl, .predicate, .funs, ...)\r\nsummarise_at(.tbl, .vars, .funs, ...)<\/code><\/pre>\n<ul>\n<li>.tbl: a tbl data frame<\/li>\n<li>.funs: List of function calls generated by <code>funs()<\/code>, or a character vector of function names, or simply a function.<\/li>\n<li>\u2026: Additional arguments for the function calls in .funs.<\/li>\n<li>.predicate: A predicate function to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected.<\/li>\n<\/ul>\n<\/div>\n<div id=\"summarise-variables\" class=\"section level3\">\n<h3>Summarise variables<\/h3>\n<ul>\n<li>Summarise all variables - compute the mean of all variables:<\/li>\n<\/ul>\n<pre class=\"r\"><code>my_data %&gt;%\r\n  group_by(Species) %&gt;%\r\n  summarise_all(mean)<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 5\r\n##   Species    Sepal.Length Sepal.Width Petal.Length Petal.Width\r\n##   &lt;fct&gt;             &lt;dbl&gt;       &lt;dbl&gt;        &lt;dbl&gt;       &lt;dbl&gt;\r\n## 1 setosa             5.01        3.43         1.46       0.246\r\n## 2 versicolor         5.94        2.77         4.26       1.33 \r\n## 3 virginica          6.59        2.97         5.55       2.03<\/code><\/pre>\n<ul>\n<li>Summarise specific variables selected with a character vector:<\/li>\n<\/ul>\n<pre class=\"r\"><code>my_data %&gt;%\r\n  group_by(Species) %&gt;%\r\n  summarise_at(c(\"Sepal.Length\", \"Sepal.Width\"), mean, na.rm = TRUE)<\/code><\/pre>\n<ul>\n<li>Summarise specific variables selected with a predicate function:<\/li>\n<\/ul>\n<pre class=\"r\"><code>my_data %&gt;%\r\n  group_by(Species) %&gt;%\r\n  summarise_if(is.numeric, mean, na.rm = TRUE)<\/code><\/pre>\n<\/div>\n<\/div>\n<div id=\"useful-statistical-summary-functions\" class=\"section level2\">\n<h2>Useful statistical summary functions<\/h2>\n<p>This section presents some R functions for computing statistical summaries.<\/p>\n<p>Measure of location:<\/p>\n<ul>\n<li><em>mean<\/em>(x): sum of x divided by the length<\/li>\n<li><em>median<\/em>(x): 50% of x is above and 50% is below<\/li>\n<\/ul>\n<p>Measure of variation:<\/p>\n<ul>\n<li><em>sd<\/em>(x): standard deviation<\/li>\n<li><em>IQR<\/em>(x): interquartile range (robust equivalent of sd when outliers are present in the data)<\/li>\n<li><em>mad(x)<\/em>: median absolute deviation (robust equivalent of sd when outliers are present in the data)<\/li>\n<\/ul>\n<p>Measure of rank:<\/p>\n<ul>\n<li><em>min<\/em>(x): minimum value of x<\/li>\n<li><em>max<\/em>(x): maximum value of x<\/li>\n<li><em>quantile<\/em>(x, 0.25): 25% of x is below this value<\/li>\n<\/ul>\n<p>Measure of position:<\/p>\n<ul>\n<li><em>first<\/em>(x): equivalent to x[1]<\/li>\n<li><em>nth<\/em>(x, 2): equivalent to n&lt;-2; x[n]<\/li>\n<li><em>last<\/em>(x): equivalent to x[length(x)]<\/li>\n<\/ul>\n<p>Counts:<\/p>\n<ul>\n<li><em>n<\/em>(x): the number of element in x<\/li>\n<li><em>sum<\/em>(!is.na(x)): count non-missing values<\/li>\n<li><em>n_distinct<\/em>(x): count the number of unique value<\/li>\n<\/ul>\n<p>Counts and proportions of logical values:<\/p>\n<ul>\n<li><em>sum<\/em>(x &gt; 10): count the number of elements where x &gt; 10<\/li>\n<li><em>mean<\/em>(y == 0): proportion of elements where y = 0<\/li>\n<\/ul>\n<\/div>\n<div id=\"summary\" class=\"section level2\">\n<h2>Summary<\/h2>\n<p>In this tutorial, we describe how to easily compute statistical summaries using the R functions <code>summarise()<\/code> and <code>group_by()<\/code> [in <strong>dplyr<\/strong> package].<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This tutorial introduces how to easily compute statistcal summaries in R using the dplyr package. 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