# 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.

# How this chapter is organized? # 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

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 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")
 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).