Azad Rasul


9- Correlations in R programming

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Azad Rasul

Published on Jul 19, 2021

3 min read

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Correlation measures the intensity of the relationship between the two variables.

Tip: Correlation implies association, not causation.

Download data used in this tutorial.

Load csv data:

df<- read.csv("D:\\R4Researchers\\LAI_factors.csv")

Using cor() function:

cor(df$LAI_India,df$LST_India, method="pearson") 
# -0.5035808 # inversely correlated

Using cov() function:

cov(df$LAI_India,df$LST_India) #Covariance
# -0.0080975

Pearson correlation coefficient (r)

cor.test(df$LAI_India,df$LST_India, method="pearson")

#Pearson's product-moment correlation

#data:  df$LAI_India and df$LST_India
#t = -2.1809, df = 14, p-value = 0.04674
#alternative hypothesis: true correlation is not equal to 0
#95 percent confidence interval:
# -0.79966707 -0.01049536
#sample estimates:
#       cor 

In the above output:

  • t is the t-test statistic value (t = -2.1809),
  • df is the degrees of freedom (df= 14),
  • p-value is the significance level of the t-test (p-value =0.04674).
  • is the confidence interval of the correlation coefficient at 95% ( = [-0.79966707, -0.01049536]);
  • sample estimates is the correlation coefficient (cor = -0.5035808)

Spearman's rank correlation coefficient (rho)

cor.test(df$LAI_India,df$LST_India, method="spearman")  

#data:  df$LAI_India and df$LST_India
#S = 1074.7, p-value = 0.01842
#alternative hypothesis: true rho is not equal to 0
#sample estimates:
#       rho 

#Warning message:
#In cor.test.default(df$LAI_India, df$LST_India, method = "spearman") :
#  Cannot compute exact p-value with ties

Kendall rank correlation coefficient (tau).

Kendall's tau is the same as for Pearson's r and Spearman's rho

cor.test(df$LAI_India,df$LST_India, method="kendall") 
#Kendall's rank correlation tau

#data:  df$LAI_India and df$LST_India
#z = -2.1475, p-value = 0.03175
#alternative hypothesis: true tau is not equal to 0
#sample estimates:
#       tau 

#Warning message:
#In cor.test.default(df$LAI_India, df$LST_India, method = "kendall") :
#  Cannot compute exact p-value with ties

Extract correlation coefficient and p.value from correlation tests:

pe<- cor.test(df$LAI_India,df$LST_India, method="pearson")
# Extract the p.value
# 0.04673796

# Extract (r) correlation coefficient

spe<- cor.test(df$LAI_India,df$LST_India, method="spearman")
# Extract the p.value
# 0.01841523

# Extract (rho) correlation coefficient
# rho 
# -0.5803983

ken<- cor.test(df$LAI_India,df$LST_India, method="kendal")
# Extract the p.value
# 0.0317547

# Extract (tau) correlation coefficient
# tau 
# -0.4109547

Interpret correlation coefficient

  • -1 indicates a strong negative correlation.
  • 0 means that there is no association between the two variables.
  • 1 indicates a strong positive correlation

Based on previous tests, a significant relationship was found between land surface temperature and lai vegetation index in India.

Create simple data and perform correlations between them:

cor.test(c(1,3,5,7), c(1,3,5,7), method="pearson")
cor.test(c(1,3,5,7), c(1,3,5,7), method='spearman')
cor.test(c(1,3,5,7), c(1,3,5,7), method='kendal')
cor.test(c(1,3,5,7), c(1,3,5,1), method="pearson")
cor.test(c(1,3,5,7), c(1,3,5,1), method='spearman')
cor.test(c(1,3,5,7), c(1,3,5,1), method='kendal')

Matrix of correlations with significance levels

Tip: usually, correlations to be considered should be statistically significant.

rcorr(as.matrix(df), type="pearson") # type can be pearson or spearman
rcorr(as.matrix(df), type='spearman') # data should be matrix

IQ score and hours weekly watching tv

Create data:

iq<- c(86, 97, 99, 100, 101, 103, 106, 110, 112, 113)
tv<- c(2, 20, 28, 27, 50, 29, 7, 17, 6, 12)

Correlation test:

cor.test(iq, tv, method="pearson")
cor.test(iq, tv, method='spearman')
cor.test(iq, tv, method='kendal')

Based on all three tests no significant correlation was found between IQ score and TV watching.

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