{"id":7232,"date":"2018-10-01T20:51:04","date_gmt":"2018-10-01T20:51:04","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&#038;p=7232"},"modified":"2018-10-19T07:18:33","modified_gmt":"2018-10-19T05:18:33","slug":"select-data-frame-columns-in-r","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/lessons\/select-data-frame-columns-in-r\/","title":{"rendered":"Select Data Frame Columns in R"},"content":{"rendered":"<p>&nbsp;<\/p>\n<div id=\"rdoc\">\n<p>In this tutorial, you will learn how to <strong>select<\/strong> or <strong>subset<\/strong> data frame <strong>columns<\/strong> by names and position using the R function <code>select()<\/code> and <code>pull()<\/code> [in <em>dplyr<\/em> package]. We\u2019ll also show how to remove columns from a data frame.<\/p>\n<p>You will learn how to use the following functions:<\/p>\n<ul>\n<li><strong>pull<\/strong>(): Extract column values as a vector. The column of interest can be specified either by name or by index.<\/li>\n<li><strong>select<\/strong>(): Extract one or multiple columns as a data table. It can be also used to remove columns from the data frame.<\/li>\n<li><strong>select_if<\/strong>(): Select columns based on a particular condition. One can use this function to, for example, select columns if they are numeric.<\/li>\n<li><strong>Helper functions<\/strong> - <em>starts_with<\/em>(), <em>ends_with<\/em>(), <em>contains<\/em>(), <em>matches<\/em>(), <em>one_of<\/em>(): Select columns\/variables based on their names<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/data-manipulation-in-r\/images\/select-or-subset-columns-in-r.png\" alt=\"Select Columns of a Data Frame 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=\"#extract-column-values-as-a-vector\">Extract column values as a vector<\/a><\/li>\n<li><a href=\"#extract-columns-as-a-data-table\">Extract columns as a data table<\/a>\n<ul>\n<li><a href=\"#select-column-by-position\">Select column by position<\/a><\/li>\n<li><a href=\"#select-columns-by-names\">Select columns by names<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#select-column-based-on-a-condtion\">Select column based on a condtion<\/a><\/li>\n<li><a href=\"#remove-columns\">Remove columns<\/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=\"extract-column-values-as-a-vector\" class=\"section level2\">\n<h2>Extract column values as a vector<\/h2>\n<pre class=\"r\"><code>my_data %&gt;% pull(Species)<\/code><\/pre>\n<pre><code>##   [1] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##   [7] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [13] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [19] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [25] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [31] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [37] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [43] setosa     setosa     setosa     setosa     setosa     setosa    \r\n##  [49] setosa     setosa     versicolor versicolor versicolor versicolor\r\n##  [55] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [61] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [67] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [73] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [79] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [85] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [91] versicolor versicolor versicolor versicolor versicolor versicolor\r\n##  [97] versicolor versicolor versicolor versicolor virginica  virginica \r\n## [103] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [109] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [115] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [121] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [127] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [133] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [139] virginica  virginica  virginica  virginica  virginica  virginica \r\n## [145] virginica  virginica  virginica  virginica  virginica  virginica \r\n## Levels: setosa versicolor virginica<\/code><\/pre>\n<\/div>\n<div id=\"extract-columns-as-a-data-table\" class=\"section level2\">\n<h2>Extract columns as a data table<\/h2>\n<div id=\"select-column-by-position\" class=\"section level3\">\n<h3>Select column by position<\/h3>\n<ul>\n<li>Select columns 1 to 3:<\/li>\n<\/ul>\n<pre class=\"r\"><code>my_data %&gt;% select(1:3)<\/code><\/pre>\n<ul>\n<li>Select column 1 and 3 but not 2:<\/li>\n<\/ul>\n<pre class=\"r\"><code>my_data %&gt;% select(1, 3)<\/code><\/pre>\n<\/div>\n<div id=\"select-columns-by-names\" class=\"section level3\">\n<h3>Select columns by names<\/h3>\n<p>Select columns by names: Sepal.Length and Petal.Length<\/p>\n<pre class=\"r\"><code>my_data %&gt;% select(Sepal.Length, Petal.Length)<\/code><\/pre>\n<pre><code>## # A tibble: 150 x 2\r\n##   Sepal.Length Petal.Length\r\n##          &lt;dbl&gt;        &lt;dbl&gt;\r\n## 1          5.1          1.4\r\n## 2          4.9          1.4\r\n## 3          4.7          1.3\r\n## 4          4.6          1.5\r\n## 5          5            1.4\r\n## 6          5.4          1.7\r\n## # ... with 144 more rows<\/code><\/pre>\n<p>Select all columns from Sepal.Length to Petal.Length<\/p>\n<pre class=\"r\"><code>my_data %&gt;% select(Sepal.Length:Petal.Length)<\/code><\/pre>\n<pre><code>## # A tibble: 150 x 3\r\n##   Sepal.Length Sepal.Width Petal.Length\r\n##          &lt;dbl&gt;       &lt;dbl&gt;        &lt;dbl&gt;\r\n## 1          5.1         3.5          1.4\r\n## 2          4.9         3            1.4\r\n## 3          4.7         3.2          1.3\r\n## 4          4.6         3.1          1.5\r\n## 5          5           3.6          1.4\r\n## 6          5.4         3.9          1.7\r\n## # ... with 144 more rows<\/code><\/pre>\n<div class=\"success\">\n<p>There are several special functions that can be used inside select(): <strong>starts_with<\/strong>(), <strong>ends_with<\/strong>(), <strong>contains<\/strong>(), <strong>matches<\/strong>(), <strong>one_of<\/strong>(), etc.<\/p>\n<\/div>\n<pre class=\"r\"><code># Select column whose name starts with \"Petal\"\r\nmy_data %&gt;% select(starts_with(\"Petal\"))\r\n\r\n# Select column whose name ends with \"Width\"\r\nmy_data %&gt;% select(ends_with(\"Width\"))\r\n\r\n# Select columns whose names contains \"etal\"\r\nmy_data %&gt;% select(contains(\"etal\"))\r\n  \r\n# Select columns whose name maches a regular expression\r\nmy_data %&gt;% select(matches(\".t.\"))\r\n\r\n# selects variables provided in a character vector.\r\nmy_data %&gt;% select(one_of(c(\"Sepal.Length\", \"Petal.Length\")))<\/code><\/pre>\n<\/div>\n<\/div>\n<div id=\"select-column-based-on-a-condtion\" class=\"section level2\">\n<h2>Select column based on a condtion<\/h2>\n<p>It\u2019s possible to apply a function to the columns. The columns for which the function returns TRUE are selected.<\/p>\n<p>Select only numeric columns:<\/p>\n<pre class=\"r\"><code>my_data %&gt;% select_if(is.numeric)<\/code><\/pre>\n<pre><code>## # A tibble: 150 x 4\r\n##   Sepal.Length Sepal.Width Petal.Length Petal.Width\r\n##          &lt;dbl&gt;       &lt;dbl&gt;        &lt;dbl&gt;       &lt;dbl&gt;\r\n## 1          5.1         3.5          1.4         0.2\r\n## 2          4.9         3            1.4         0.2\r\n## 3          4.7         3.2          1.3         0.2\r\n## 4          4.6         3.1          1.5         0.2\r\n## 5          5           3.6          1.4         0.2\r\n## 6          5.4         3.9          1.7         0.4\r\n## # ... with 144 more rows<\/code><\/pre>\n<\/div>\n<div id=\"remove-columns\" class=\"section level2\">\n<h2>Remove columns<\/h2>\n<div class=\"warning\">\n<p>Note that, to remove a column from a data frame, prepend its name by minus <strong>-<\/strong>.<\/p>\n<\/div>\n<p>Removing Sepal.Length and Petal.Length columns:<\/p>\n<pre class=\"r\"><code>my_data %&gt;% select(-Sepal.Length, -Petal.Length)<\/code><\/pre>\n<p>Removing all columns from Sepal.Length to Petal.Length:<\/p>\n<pre class=\"r\"><code>my_data %&gt;% select(-(Sepal.Length:Petal.Length))<\/code><\/pre>\n<pre><code>## # A tibble: 150 x 2\r\n##   Petal.Width Species\r\n##         &lt;dbl&gt; &lt;fct&gt;  \r\n## 1         0.2 setosa \r\n## 2         0.2 setosa \r\n## 3         0.2 setosa \r\n## 4         0.2 setosa \r\n## 5         0.2 setosa \r\n## 6         0.4 setosa \r\n## # ... with 144 more rows<\/code><\/pre>\n<p>Removing all columns whose name starts with \u201cPetal\u201d:<\/p>\n<pre class=\"r\"><code>my_data %&gt;% select(-starts_with(\"Petal\"))<\/code><\/pre>\n<pre><code>## # A tibble: 150 x 3\r\n##   Sepal.Length Sepal.Width Species\r\n##          &lt;dbl&gt;       &lt;dbl&gt; &lt;fct&gt;  \r\n## 1          5.1         3.5 setosa \r\n## 2          4.9         3   setosa \r\n## 3          4.7         3.2 setosa \r\n## 4          4.6         3.1 setosa \r\n## 5          5           3.6 setosa \r\n## 6          5.4         3.9 setosa \r\n## # ... with 144 more rows<\/code><\/pre>\n<div class=\"warning\">\n<p>Note that, if you want to drop columns by position, the syntax is as follow.<\/p>\n<\/div>\n<pre class=\"r\"><code># Drop column 1\r\nmy_data %&gt;% select(-1)\r\n\r\n# Drop columns 1 to 3\r\nmy_data %&gt;% select(-(1:3))\r\n\r\n# Drop columns 1 and 3 but not 2\r\nmy_data %&gt;% select(-1, -3)<\/code><\/pre>\n<\/div>\n<div id=\"summary\" class=\"section level2\">\n<h2>Summary<\/h2>\n<p>In this tutorial, we describe how to select columns by positions and by names. Additionally, we present how to remove columns from a data frame.<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>You will learn how to select data frame columns by names and position. We\u2019ll also show how to remove columns from a data frame.<\/p>\n","protected":false},"author":1,"featured_media":7733,"parent":0,"menu_order":1,"comment_status":"open","ping_status":"closed","template":"","class_list":["post-7232","dt_lessons","type-dt_lessons","status-publish","has-post-thumbnail","hentry","lesson_complexity-easy"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Select Data Frame Columns in R - Datanovia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.datanovia.com\/en\/lessons\/select-data-frame-columns-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Select Data Frame Columns in R - Datanovia\" \/>\n<meta property=\"og:description\" content=\"You will learn how to select data frame columns by names and position. 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