1NT24IS227
Develop a script in r to calculate and visualize a correlatio matrix for a given dataset, wth color-coded cells indicating the strength and direction of correlation,using ggplot2’s geom_tile function
#load the required libraries
library(ggplot2)
library(tidyr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionWe use the built-in mtcars dataset
#preview the dataset
head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1#use the built-in mtcars dataset
data(mtcars)#compute correlation matrix
cor_matrix <- cor(mtcars)#convert matrix to a data frame for plotting
cor_df <- as.data.frame(as.table(cor_matrix))
head(cor_df) Var1 Var2 Freq
1 mpg mpg 1.0000000
2 cyl mpg -0.8521620
3 disp mpg -0.8475514
4 hp mpg -0.7761684
5 drat mpg 0.6811719
6 wt mpg -0.8676594cor(mtcars) computes pairwise correlation.as.table() flattens matrix into a long format table.Var1, Var2 and the correlation value Freq.p<-ggplot(cor_df,aes(x=Var1,y=Var2,fill = Freq))
p
p<-p+
geom_tile(color="white")
p
p<-p+ #draw title borders
scale_fill_gradient2(
low="blue",mid="white",high="red",
midpoint=0,limit = c(-1,1),
name="correlation"
)
p
p<-p +
geom_text(aes(label=round(Freq,2)),size=3)
p
p<-p+ #Show values
theme_minimal()
p
p<-p+
labs(
title = "Correlation Matrix(mtcars)",
x = "",y = ""
)
p
p<-p+
theme(axis.text.x=element_text(angle = 45,hjust = 1))
p