Correlation Analyses in R


Previously, we described the essentials of R programming and provided quick start guides for importing data into R. Additionally, we described how to compute descriptive or summary statistics using R software.


This chapter contains articles for computing and visualizing correlation analyses in R. Recall that, correlation analysis is used to investigate the association between two or more variables. A simple example, is to evaluate whether there is a link between maternal age and child’s weight at birth.


Correlation Test Between Two Variables in R

Brief outline:

  • What is correlation test?
  • Methods for correlation analyses
  • Correlation formula
    • Pearson correlation formula
    • Spearman correlation formula
    • Kendall correlation formula
  • Compute correlation in R
    • R functions
    • Import your data into R
    • Visualize your data using scatter plots
    • Preliminary test to check the test assumptions
    • Pearson correlation test
    • Kendall rank correlation test
    • Spearman rank correlation coefficient
  • Interpret correlation coefficient

Read more: —> Correlation Test Between Two Variables in R.

Correlation Matrix: Analyze, Format and Visualize

Correlation matrix is used to analyze the correlation between multiple variables at the same time.

Brief outline:

  • What is correlation matrix?
  • Compute correlation matrix in R
    • R functions
    • Compute correlation matrix
    • Correlation matrix with significance levels (p-value)
    • A simple function to format the correlation matrix
    • Visualize correlation matrix
      • Use symnum() function: Symbolic number coding
      • Use corrplot() function: Draw a correlogram
      • Use chart.Correlation(): Draw scatter plots
      • Use heatmap()
scatter plot, chart

scatter plot, chart

Read more: —> Correlation Matrix: Analyze, Format and Visualize.

Visualize Correlation Matrix using Correlogram

Correlogram is a graph of correlation matrix. Useful to highlight the most correlated variables in a data table. In this plot, correlation coefficients are colored according to the value. Correlation matrix can be also reordered according to the degree of association between variables.

Brief outline:

  • Install R corrplot package
  • Data for correlation analysis
  • Computing correlation matrix
  • Correlogram : Visualizing the correlation matrix
    • Visualization methods
    • Types of correlogram layout
    • Reordering the correlation matrix
    • Changing the color of the correlogram
    • Changing the color and the rotation of text labels
    • Combining correlogram with the significance test
    • Customize the correlogram
library(corrplot)
library(RColorBrewer)
M <-cor(mtcars)
corrplot(M, type="upper", order="hclust",
         col=brewer.pal(n=8, name="RdYlBu"))

Read more: —> Visualize Correlation Matrix using Correlogram.

Elegant Correlation Table using xtable R Package

The aim of this article is to show you how to get the lower and the upper triangular part of a correlation matrix. We will use also xtable R package to display a nice correlation table.

Brief outline:

  • Correlation matrix analysis
  • Lower and upper triangular part of a correlation matrix
  • Use xtable R package to display nice correlation table in html format
  • Combine matrix of correlation coefficients and significance levels

Elegant correlation table using xtable R package

Read more: —> Elegant correlation table using xtable R package.

Correlation Matrix : An R Function to Do All You Need

The goal of this article is to provide you a custom R function, named rquery.cormat(), for calculating and visualizing easily a correlation matrix in a single line R code.

Brief outline:

  • Computing the correlation matrix using rquery.cormat()
    • Upper triangle of the correlation matrix
    • Full correlation matrix
    • Change the colors of the correlogram
    • Draw a heatmap
  • Format the correlation table
  • Description of rquery.cormat() function
source("http://www.sthda.com/upload/rquery_cormat.r")
mydata <- mtcars[, c(1,3,4,5,6,7)]
require("corrplot")
rquery.cormat(mydata)
$r
        hp  disp    wt  qsec  mpg drat
hp       1                            
disp  0.79     1                      
wt    0.66  0.89     1                
qsec -0.71 -0.43 -0.17     1          
mpg  -0.78 -0.85 -0.87  0.42    1     
drat -0.45 -0.71 -0.71 0.091 0.68    1
$p
          hp    disp      wt  qsec     mpg drat
hp         0                                   
disp 7.1e-08       0                           
wt   4.1e-05 1.2e-11       0                   
qsec 5.8e-06   0.013    0.34     0             
mpg  1.8e-07 9.4e-10 1.3e-10 0.017       0     
drat    0.01 5.3e-06 4.8e-06  0.62 1.8e-05    0
$sym
     hp disp wt qsec mpg drat
hp   1                       
disp ,  1                    
wt   ,  +    1               
qsec ,  .       1            
mpg  ,  +    +  .    1       
drat .  ,    ,       ,   1   
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1

Read more: —> Correlation Matrix : An R Function to Do All You Need.

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).


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