{"id":8440,"date":"2019-01-16T06:30:47","date_gmt":"2019-01-16T04:30:47","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=8440"},"modified":"2019-12-25T10:52:44","modified_gmt":"2019-12-25T08:52:44","slug":"easily-create-a-correlation-network-in-r-using-the-corrr-package","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/blog\/easily-create-a-correlation-network-in-r-using-the-corrr-package\/","title":{"rendered":"Easily Create a Correlation Network in R using the Corrr Package"},"content":{"rendered":"<div id=\"rdoc\">\n<p>This article describes how to plot a <strong>correlation network in R<\/strong> using the corrr package.<\/p>\n<p>Related article: <a href=\"https:\/\/www.datanovia.com\/en\/blog\/easy-correlation-matrix-analysis-in-r-using-corrr-package\/\">Easy Correlation Matrix Analysis in R Using Corrr Package<\/a><\/p>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#load-required-r-packages\">Load required R packages<\/a><\/li>\n<li><a href=\"#data\">Data<\/a><\/li>\n<li><a href=\"#compute-correlation-matrix\">Compute correlation matrix<\/a><\/li>\n<li><a href=\"#create-a-correlation-network\">Create a correlation network<\/a><\/li>\n<li><a href=\"#cleaning-up-the-correlation-network\">Cleaning up the correlation network<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"load-required-r-packages\" class=\"section level2\">\n<h2>Load required R packages<\/h2>\n<ul>\n<li><code>tidyverse<\/code>: easy data manipulation and visualization<\/li>\n<li><code>corrr<\/code>: correlation matrix analysis<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(tidyverse)  \r\nlibrary(corrr)<\/code><\/pre>\n<\/div>\n<div id=\"data\" class=\"section level2\">\n<h2>Data<\/h2>\n<pre class=\"r\"><code>data(\"airquality\") \r\nhead(airquality)<\/code><\/pre>\n<pre><code>##   Ozone Solar.R Wind Temp Month Day\r\n## 1    41     190  7.4   67     5   1\r\n## 2    36     118  8.0   72     5   2\r\n## 3    12     149 12.6   74     5   3\r\n## 4    18     313 11.5   62     5   4\r\n## 5    NA      NA 14.3   56     5   5\r\n## 6    28      NA 14.9   66     5   6<\/code><\/pre>\n<\/div>\n<div id=\"compute-correlation-matrix\" class=\"section level2\">\n<h2>Compute correlation matrix<\/h2>\n<pre class=\"r\"><code>res.cor &lt;- correlate(airquality)\r\nres.cor<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 7\r\n##   rowname    Ozone  Solar.R     Wind    Temp     Month       Day\r\n##   &lt;chr&gt;      &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;\r\n## 1 Ozone    NA        0.348   -0.602    0.698   0.165    -0.0132 \r\n## 2 Solar.R   0.348   NA       -0.0568   0.276  -0.0753   -0.150  \r\n## 3 Wind     -0.602   -0.0568  NA       -0.458  -0.178     0.0272 \r\n## 4 Temp      0.698    0.276   -0.458   NA       0.421    -0.131  \r\n## 5 Month     0.165   -0.0753  -0.178    0.421  NA        -0.00796\r\n## 6 Day      -0.0132  -0.150    0.0272  -0.131  -0.00796  NA<\/code><\/pre>\n<p><code>fashion()<\/code> the correlations for pleasant viewing:<\/p>\n<pre class=\"r\"><code>res.cor %&gt;% fashion()<\/code><\/pre>\n<pre><code>##   rowname Ozone Solar.R Wind Temp Month  Day\r\n## 1   Ozone           .35 -.60  .70   .16 -.01\r\n## 2 Solar.R   .35         -.06  .28  -.08 -.15\r\n## 3    Wind  -.60    -.06      -.46  -.18  .03\r\n## 4    Temp   .70     .28 -.46        .42 -.13\r\n## 5   Month   .16    -.08 -.18  .42       -.01\r\n## 6     Day  -.01    -.15  .03 -.13  -.01<\/code><\/pre>\n<\/div>\n<div id=\"create-a-correlation-network\" class=\"section level2\">\n<h2>Create a correlation network<\/h2>\n<p>The R function <code>network_plot()<\/code> can be used to visualize and explore correlations.<\/p>\n<pre class=\"r\"><code>airquality %&gt;% correlate() %&gt;% \r\n  network_plot(min_cor = 0.3)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-network-using-corrr-visualize-correlations-1.png\" width=\"384\" \/><\/p>\n<p>The option <code>min_cor<\/code> indicates the required minimum correlation value for a correlation to be plotted.<\/p>\n<p><strong>Each point reprents a variable<\/strong>. Variable that are highly correlated are clustered together. The positioning of variables is handled by multidimensional scaling of the absolute values of the correlations.<\/p>\n<p>For example, it can be seen from the above plot that the variables <code>Ozone<\/code>, <code>Wind<\/code> and <code>Temp<\/code> are clustering together (which makes sense).<\/p>\n<p><strong>Each path represents a correlation<\/strong> between the two variables that it joins. Blue color represents a positive correlation, and a red color corresponds to a negative correlation.<\/p>\n<p>The width and transparency of the path represent the strength of the correlation (wider and less transparent = stronger correlation).<\/p>\n<p>For example, it can be seen that the positive correlation between <code>Ozone<\/code> and <code>Temp<\/code> is stronger than the positive correlation between <code>Ozone<\/code> and <code>Solar.R<\/code>.<\/p>\n<\/div>\n<div id=\"cleaning-up-the-correlation-network\" class=\"section level2\">\n<h2>Cleaning up the correlation network<\/h2>\n<p>We can clean this up by increasing the <code>min_cor<\/code>, thus plotting fewer correlation paths:<\/p>\n<pre class=\"r\"><code>mtcars %&gt;% correlate() %&gt;% \r\n  network_plot(min_cor = .7)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-network-using-corrr-cleaning-correlation-network-1.png\" width=\"384\" \/><\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article describes how to plot a correlation network in R using the corrr package. Related article: Easy Correlation Matrix Analysis in R Using Corrr Package Contents: Load required R [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7865,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[132],"tags":[141],"class_list":["post-8440","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-basic-statistics","tag-correlation-analysis"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Easily Create a Correlation Network in R using the Corrr Package - Datanovia<\/title>\n<meta name=\"description\" content=\"This article describes how to plot a correlation network in R using the corrr package.\" \/>\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\/blog\/easily-create-a-correlation-network-in-r-using-the-corrr-package\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Easily Create a Correlation Network in R using the Corrr Package - 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