Correlation with R - Not for beginners One should have the basic concept in statistics to understand this and the different terms associated with this work sheet. #Correlation #R #Data & Analytics

1. Analytics With RIn R-Studio
CORRELATION
#One should have the basic concept in statistics and R to understand this and the different terms associated with this work
sheet.
# Correlation gives us the strength of association between the variables.
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# Example 1
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data(trees) # We will be using the inbuilt R dataset “trees”.
View(trees)
names(trees)
attach(trees)
?cor.test # Test for association between paired samples, using one of Pearson’s product moment correlation coefficient, Ken-
dall’s tau or Spearman’s rho.
plot(Girth, Height, main = “Scatterplot”, las = 1) # Plotting the variable girth & height.
cor(Girth, Height, method = “pearson”) # We are calculating the correlation between Girth and Height based on the method
of Pearson correlation. The value comes to 0.52 which is the R value which means there is a A moderate uphill (positive)
relationship between variables.
cor(Girth, Height, method = “spearman”) # We are calculating searmans Rank Correlation coefficient.
cor(Girth, Height, method = “kendall”) # We are calculating searmans Rank Kendalls coefficient.
# Cor.test - here we will use this function to test that null hypothesis(H0) = 0 or correlation = 0.
cor.test(Girth, Height, method = “pearson”) # By default it is a two sided test
# We get the correlation - Sample estimate: cor = 0.519
# We get the 95% confidence interval for the correlation
# We get the Test statistics - 3.2722 and P value - 0.0027 that the correlation = 0
cor.test(Girth, Height, method = “pearson”, alt = “greater”, conf.level = 0.99) # We change it to a one tailed test with greater
than value and also change the confidence level.
cov(Girth, Height) # Co-variance
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# Example 2
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# Calculating correlation matrix and confidence intervals, i.e. correlation between many variables in a matrix view.
# We will use the data set “swiss”.
data(swiss)
?swiss
cor(swiss) # Correlation matrix
round(cor(swiss), 2) # Rounding of to two decimal places
cor.test(swiss$Fertility, swiss$Education)
# To get probability value for an entire matrix we need to install a package to do it
install.packages(“Hmisc”)
require(Hmisc)
# We need to take the data frame and convert into a matrix to get the correlation matrix and p values.
as.matrix(swiss) # converting data frame into matrix.
rcorr(as.matrix(swiss)) # gives correlation between each variable and the probability.
For queries and more information
feel free to contact me :
jeromegomes89@hotmail.com
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By:
Jerome
Gomes
Packages needed:
1. ggplot2
2. Hmisc