{"id":8438,"date":"2019-01-16T05:50:25","date_gmt":"2019-01-16T03:50:25","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=8438"},"modified":"2019-12-25T10:53:22","modified_gmt":"2019-12-25T08:53:22","slug":"easy-correlation-matrix-analysis-in-r-using-corrr-package","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/blog\/easy-correlation-matrix-analysis-in-r-using-corrr-package\/","title":{"rendered":"Easy Correlation Matrix Analysis in R Using Corrr Package"},"content":{"rendered":"<div id=\"rdoc\">\n<p>This article describes how to easily compute and explore <strong>correlation matrix in R<\/strong> using the <strong>corrr<\/strong> package.<\/p>\n<p>The corrr package makes it easy to ignore the diagonal, focusing on the correlations of certain variables against others, or reordering and visualizing the correlation matrix. It can also compute correlation matrix from data frames in databases.<\/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-preparation\">Data preparation<\/a><\/li>\n<li><a href=\"#compute-correlation-matrix\">Compute correlation matrix<\/a><\/li>\n<li><a href=\"#key-corrr-functions-for-exploring-correlation-matrix\">Key corrr functions for exploring correlation matrix<\/a><\/li>\n<li><a href=\"#focus-on-specific-columnsrows\">Focus on specific columns\/rows<\/a><\/li>\n<li><a href=\"#reorder-the-correlation-matrix\">Reorder the correlation matrix<\/a><\/li>\n<li><a href=\"#shave-off-upperlower-triangle\">Shave off upper\/lower triangle<\/a><\/li>\n<li><a href=\"#stretch-correlation-data-frame-into-long-format\">Stretch correlation data frame into long format<\/a><\/li>\n<li><a href=\"#manipulate-the-correlations-using-both-tidyverse-and-corrr-packages\">Manipulate the correlations using both tidyverse and corrr packages<\/a><\/li>\n<li><a href=\"#viualize-and-interpret-the-correlations\">Viualize and interpret the correlations<\/a><\/li>\n<li><a href=\"#correlate-data-in-databases\">Correlate data in databases<\/a><\/li>\n<li><a href=\"#reference\">Reference<\/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-preparation\" class=\"section level2\">\n<h2>Data preparation<\/h2>\n<pre class=\"r\"><code># Select columns of interest\r\nmydata &lt;- mtcars %&gt;% \r\n  select(mpg, disp, hp, drat, wt, qsec)\r\n# Add some missing values\r\nmydata$hp[3] &lt;- NA\r\n# Inspect the data\r\nhead(mydata, 3)<\/code><\/pre>\n<pre><code>##                mpg disp  hp drat   wt qsec\r\n## Mazda RX4     21.0  160 110 3.90 2.62 16.5\r\n## Mazda RX4 Wag 21.0  160 110 3.90 2.88 17.0\r\n## Datsun 710    22.8  108  NA 3.85 2.32 18.6<\/code><\/pre>\n<\/div>\n<div id=\"compute-correlation-matrix\" class=\"section level2\">\n<h2>Compute correlation matrix<\/h2>\n<p>Key R function: <code>correlate()<\/code>, which is a wrapper around the <code>cor()<\/code> R base function but with the following advantages:<\/p>\n<ul>\n<li>Handles missing values by default with the option<code>use = \"pairwise.complete.obs\"<\/code><\/li>\n<li>Diagonal values is set to <code>NA<\/code>, so that it can be easily removed<\/li>\n<li>Returns a data frame, which can be easily manipulated using the tidyverse package.<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(corrr)\r\nres.cor &lt;- correlate(mydata)\r\nres.cor<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 7\r\n##   rowname     mpg    disp      hp     drat      wt     qsec\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 mpg      NA      -0.848  -0.775   0.681   -0.868   0.419 \r\n## 2 disp     -0.848  NA       0.786  -0.710    0.888  -0.434 \r\n## 3 hp       -0.775   0.786  NA      -0.443    0.651  -0.706 \r\n## 4 drat      0.681  -0.710  -0.443  NA       -0.712   0.0912\r\n## 5 wt       -0.868   0.888   0.651  -0.712   NA      -0.175 \r\n## 6 qsec      0.419  -0.434  -0.706   0.0912  -0.175  NA<\/code><\/pre>\n<p>Additional arguments for the function <code>correlate()<\/code>, include:<\/p>\n<ul>\n<li><code>method<\/code>: a character string indicating which correlation coefficient (or covariance) is to be computed. One of \u201cpearson\u201d (default), \u201ckendall\u201d, or \u201cspearman\u201d: can be abbreviated.<\/li>\n<li><code>diagonal<\/code>: Value (typically numeric or NA) to set the diagonal to.<\/li>\n<\/ul>\n<p>For example, type this:<\/p>\n<pre class=\"r\"><code>correlate(mydata, method = \"spearman\", diagonal = 1)<\/code><\/pre>\n<\/div>\n<div id=\"key-corrr-functions-for-exploring-correlation-matrix\" class=\"section level2\">\n<h2>Key corrr functions for exploring correlation matrix<\/h2>\n<p>The <code>corrr<\/code> R package comes also with some key functions facilitating the exploration of the correlation matrix. Here\u2019s a diagram showing the primary corrr functions:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/images\/corrr-r-package.png\" alt=\"Corrr R package\" \/><\/p>\n<p>The corrr API is designed with data pipelines in mind (e.g., to use <code>%&gt;%<\/code> from the magrittr package). After <code>correlate()<\/code>, the primary corrr functions take a <code>cor_df<\/code> as their first argument, and return a <code>cor_df<\/code> or <code>tbl<\/code> (or output like a plot). These functions serve one of three purposes:<\/p>\n<p>Internal changes (<code>cor_df<\/code> out):<\/p>\n<ul>\n<li><code>shave()<\/code> the upper or lower triangle (set to NA).<\/li>\n<li><code>rearrange()<\/code> the columns and rows based on correlation strengths.<\/li>\n<\/ul>\n<p>Reshape structure (<code>tbl<\/code> or <code>cor_df<\/code> out):<\/p>\n<ul>\n<li><code>focus()<\/code> on select columns and rows.<\/li>\n<li><code>stretch()<\/code> into a long format.<\/li>\n<\/ul>\n<p>Output\/visualisations (console\/plot out):<\/p>\n<ul>\n<li><code>fashion()<\/code> the correlations for pretty printing.<\/li>\n<li><code>rplot()<\/code> the correlations with shapes in place of the values.<\/li>\n<li><code>network_plot()<\/code> the correlations in a network.<\/li>\n<\/ul>\n<p>You can also easily manipulate the correlation results using the <code>tidyverse<\/code> verbs. For example, filter correlations above 0.8:<\/p>\n<pre class=\"r\"><code>res.cor %&gt;%  \r\n  gather(-rowname, key = \"colname\", value = \"cor\") %&gt;% \r\n  filter(abs(cor) &gt; 0.8)<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 3\r\n##   rowname colname    cor\r\n##   &lt;chr&gt;   &lt;chr&gt;    &lt;dbl&gt;\r\n## 1 disp    mpg     -0.848\r\n## 2 wt      mpg     -0.868\r\n## 3 mpg     disp    -0.848\r\n## 4 wt      disp     0.888\r\n## 5 mpg     wt      -0.868\r\n## 6 disp    wt       0.888<\/code><\/pre>\n<\/div>\n<div id=\"focus-on-specific-columnsrows\" class=\"section level2\">\n<h2>Focus on specific columns\/rows<\/h2>\n<p>The function <code>focus()<\/code> makes it possible to <code>focus()<\/code> on columns and rows. This function acts just like dplyr\u2019s <code>select()<\/code>, but also excludes the selected columns from the rows (or everything else with the <code>mirror<\/code> argument).<\/p>\n<ul>\n<li>Select correlation results with columns of interests. The selected columns are excluded from the rows:<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% \r\n  focus(mpg, disp, hp)<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 4\r\n##   rowname    mpg   disp     hp\r\n##   &lt;chr&gt;    &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;\r\n## 1 drat     0.681 -0.710 -0.443\r\n## 2 wt      -0.868  0.888  0.651\r\n## 3 qsec     0.419 -0.434 -0.706<\/code><\/pre>\n<ul>\n<li>Mirror the selected columns:<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% \r\n  focus(mpg, disp, hp, mirror = TRUE)<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 4\r\n##   rowname     mpg    disp      hp\r\n##   &lt;chr&gt;     &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;\r\n## 1 mpg      NA      -0.848  -0.775\r\n## 2 disp     -0.848  NA       0.786\r\n## 3 hp       -0.775   0.786  NA<\/code><\/pre>\n<ul>\n<li>Remove unwanted columns:<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% \r\n  focus(-mpg, -disp, -hp)<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 4\r\n##   rowname   drat     wt   qsec\r\n##   &lt;chr&gt;    &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;\r\n## 1 mpg      0.681 -0.868  0.419\r\n## 2 disp    -0.710  0.888 -0.434\r\n## 3 hp      -0.443  0.651 -0.706<\/code><\/pre>\n<ul>\n<li>Select columns by regular expression<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% \r\n  focus(matches(\"^d\"))<\/code><\/pre>\n<pre><code>## # A tibble: 4 x 3\r\n##   rowname   disp    drat\r\n##   &lt;chr&gt;    &lt;dbl&gt;   &lt;dbl&gt;\r\n## 1 mpg     -0.848  0.681 \r\n## 2 hp       0.786 -0.443 \r\n## 3 wt       0.888 -0.712 \r\n## 4 qsec    -0.434  0.0912<\/code><\/pre>\n<ul>\n<li>Select correlation above 0.8:<\/li>\n<\/ul>\n<pre class=\"r\"><code>any_over_90 &lt;- function(x) any(x &gt; .8, na.rm = TRUE)\r\nres.cor %&gt;% \r\n  focus_if(any_over_90, mirror = TRUE)<\/code><\/pre>\n<pre><code>## # A tibble: 2 x 3\r\n##   rowname   disp     wt\r\n##   &lt;chr&gt;    &lt;dbl&gt;  &lt;dbl&gt;\r\n## 1 disp    NA      0.888\r\n## 2 wt       0.888 NA<\/code><\/pre>\n<ul>\n<li>Focus on correlations of one variable with all others:<\/li>\n<\/ul>\n<pre class=\"r\"><code># Extract the correlation\r\nres.cor %&gt;% \r\n  focus(mpg)<\/code><\/pre>\n<pre><code>## # A tibble: 5 x 2\r\n##   rowname    mpg\r\n##   &lt;chr&gt;    &lt;dbl&gt;\r\n## 1 disp    -0.848\r\n## 2 hp      -0.775\r\n## 3 drat     0.681\r\n## 4 wt      -0.868\r\n## 5 qsec     0.419<\/code><\/pre>\n<pre class=\"r\"><code># Plot the correlation between mpg and all others\r\nres.cor %&gt;%\r\n  focus(mpg) %&gt;%\r\n  mutate(rowname = reorder(rowname, mpg)) %&gt;%\r\n  ggplot(aes(rowname, mpg)) +\r\n    geom_col() + coord_flip() +\r\n  theme_bw()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-matrix-analysis-in-r-using-corrr-correlation-1.png\" width=\"288\" \/><\/p>\n<\/div>\n<div id=\"reorder-the-correlation-matrix\" class=\"section level2\">\n<h2>Reorder the correlation matrix<\/h2>\n<p>You can also <code>rearrange()<\/code> the entire data frame based on clustering algorithms:<\/p>\n<pre class=\"r\"><code>res.cor %&gt;% rearrange()<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 7\r\n##   rowname      wt     drat    disp     mpg      hp     qsec\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 wt       NA      -0.712    0.888  -0.868   0.651  -0.175 \r\n## 2 drat     -0.712  NA       -0.710   0.681  -0.443   0.0912\r\n## 3 disp      0.888  -0.710   NA      -0.848   0.786  -0.434 \r\n## 4 mpg      -0.868   0.681   -0.848  NA      -0.775   0.419 \r\n## 5 hp        0.651  -0.443    0.786  -0.775  NA      -0.706 \r\n## 6 qsec     -0.175   0.0912  -0.434   0.419  -0.706  NA<\/code><\/pre>\n<\/div>\n<div id=\"shave-off-upperlower-triangle\" class=\"section level2\">\n<h2>Shave off upper\/lower triangle<\/h2>\n<p><code>shave()<\/code> the upper\/lower triangle to missing values<\/p>\n<pre class=\"r\"><code>res.cor %&gt;% shave()<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 7\r\n##   rowname     mpg    disp      hp     drat      wt  qsec\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 mpg      NA      NA      NA      NA       NA        NA\r\n## 2 disp     -0.848  NA      NA      NA       NA        NA\r\n## 3 hp       -0.775   0.786  NA      NA       NA        NA\r\n## 4 drat      0.681  -0.710  -0.443  NA       NA        NA\r\n## 5 wt       -0.868   0.888   0.651  -0.712   NA        NA\r\n## 6 qsec      0.419  -0.434  -0.706   0.0912  -0.175    NA<\/code><\/pre>\n<\/div>\n<div id=\"stretch-correlation-data-frame-into-long-format\" class=\"section level2\">\n<h2>Stretch correlation data frame into long format<\/h2>\n<pre class=\"r\"><code>res.cor %&gt;% stretch()<\/code><\/pre>\n<pre><code>## # A tibble: 36 x 3\r\n##   x     y           r\r\n##   &lt;chr&gt; &lt;chr&gt;   &lt;dbl&gt;\r\n## 1 mpg   mpg    NA    \r\n## 2 mpg   disp   -0.848\r\n## 3 mpg   hp     -0.775\r\n## 4 mpg   drat    0.681\r\n## 5 mpg   wt     -0.868\r\n## 6 mpg   qsec    0.419\r\n## # \u2026 with 30 more rows<\/code><\/pre>\n<\/div>\n<div id=\"manipulate-the-correlations-using-both-tidyverse-and-corrr-packages\" class=\"section level2\">\n<h2>Manipulate the correlations using both tidyverse and corrr packages<\/h2>\n<p>Visualize the distribution of the correlation coefficients:<\/p>\n<pre class=\"r\"><code>res.cor %&gt;%\r\n  shave() %&gt;% \r\n  stretch(na.rm = TRUE) %&gt;% \r\n  ggplot(aes(r)) +\r\n    geom_histogram(bins = 10)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-matrix-analysis-in-r-using-corrr-unnamed-chunk-16-1.png\" width=\"518.4\" \/><\/p>\n<p>Rearrange and filter the correlation matrix:<\/p>\n<pre class=\"r\"><code>res.cor %&gt;%\r\n  focus(mpg:drat, mirror = TRUE) %&gt;% \r\n  rearrange() %&gt;% \r\n  shave(upper = FALSE) %&gt;% \r\n  select(-hp) %&gt;% \r\n  filter(rowname != \"drat\")<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 4\r\n##   rowname     mpg    disp   drat\r\n##   &lt;chr&gt;     &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;\r\n## 1 hp       -0.775   0.786 -0.443\r\n## 2 mpg      NA      -0.848  0.681\r\n## 3 disp     NA      NA     -0.710<\/code><\/pre>\n<\/div>\n<div id=\"viualize-and-interpret-the-correlations\" class=\"section level2\">\n<h2>Viualize and interpret the correlations<\/h2>\n<ul>\n<li><code>fashion<\/code>able correlations:<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% fashion()<\/code><\/pre>\n<pre><code>##   rowname  mpg disp   hp drat   wt qsec\r\n## 1     mpg      -.85 -.77  .68 -.87  .42\r\n## 2    disp -.85       .79 -.71  .89 -.43\r\n## 3      hp -.77  .79      -.44  .65 -.71\r\n## 4    drat  .68 -.71 -.44      -.71  .09\r\n## 5      wt -.87  .89  .65 -.71      -.17\r\n## 6    qsec  .42 -.43 -.71  .09 -.17<\/code><\/pre>\n<pre class=\"r\"><code>res.cor %&gt;%\r\n  focus(mpg:drat, mirror = TRUE) %&gt;% \r\n  rearrange() %&gt;% \r\n  shave(upper = FALSE) %&gt;% \r\n  select(-hp) %&gt;% \r\n  filter(rowname != \"drat\") %&gt;% \r\n  fashion()<\/code><\/pre>\n<pre><code>##   rowname  mpg disp drat\r\n## 1      hp -.77  .79 -.44\r\n## 2     mpg      -.85  .68\r\n## 3    disp           -.71<\/code><\/pre>\n<ul>\n<li>Make a correlogram using <code>rplot()<\/code>:<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% rplot()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-matrix-analysis-in-r-using-corrr-correlogram-1.png\" width=\"288\" \/><\/p>\n<ul>\n<li>Rearrange and then plot the lower triangle:<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;%\r\n  rearrange(method = \"MDS\", absolute = FALSE) %&gt;%\r\n  shave() %&gt;% \r\n  rplot(shape = 15, colours = c(\"red\", \"green\"))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-matrix-analysis-in-r-using-corrr-lower-triangle-1.png\" width=\"288\" \/><\/p>\n<ul>\n<li>Make a network plot<\/li>\n<\/ul>\n<pre class=\"r\"><code>res.cor %&gt;% network_plot(min_cor = .6)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/correlation-matrix-analysis-in-r-using-corrr-network-plot-1.png\" width=\"384\" \/><\/p>\n<\/div>\n<div id=\"correlate-data-in-databases\" class=\"section level2\">\n<h2>Correlate data in databases<\/h2>\n<ul>\n<li>Using SQLite database:<\/li>\n<\/ul>\n<pre class=\"r\"><code>con &lt;- DBI::dbConnect(RSQLite::SQLite(), path = \":dbname:\")\r\ndb_mtcars &lt;- copy_to(con, mtcars)\r\nclass(db_mtcars)<\/code><\/pre>\n<p><code>correlate()<\/code> detects DB backend, uses <code>tidyeval<\/code> to calculate correlations in the database, and returns correlation data frame.<\/p>\n<pre class=\"r\"><code>db_mtcars %&gt;% correlate(use = \"complete.obs\")<\/code><\/pre>\n<ul>\n<li>Using spark:<\/li>\n<\/ul>\n<pre class=\"r\"><code>sc &lt;- sparklyr::spark_connect(master = \"local\")\r\nmtcars_tbl &lt;- copy_to(sc, mtcars)\r\ncorrelate(mtcars_tbl, use = \"complete.obs\")<\/code><\/pre>\n<\/div>\n<div id=\"reference\" class=\"section level2\">\n<h2>Reference<\/h2>\n<ul>\n<li><a href=\"https:\/\/drsimonj.svbtle.com\/exploring-correlations-in-r-with-corrr\">Exploring correlations in R with corrr<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article describes how to easily compute and explore correlation matrix in R using the corrr package. 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