PRG 14 DV

Author

ANUSHA

PROGRAM 14

Develop a R program to calculate and visualize a corelation matrics for a given data set with colour coded cells indicating the strength and direction of corelations using ggplot2’s geom_tile function

step1 load the required libraries

library(ggplot2)
library(tidyr)
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

step 2 load 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

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

cor_matrix<- cor(mtcars)
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 -0.8676594

# Use a sample dataset (replace mtcars with your own dataset if needed)
data <- mtcars

# Step 1: Calculate the correlation matrix
cor_matrix <- cor(data)

# Step 2: Convert the matrix to a long format data frame
cor_df <- melt(cor_matrix)

ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white") +
  geom_text(aes(label = round(value, 2)), color = "black", size = 3.5) +
  
  scale_fill_gradient2(
    low = "red",
    high = "green",
    mid = "white",
    midpoint = 0,
    limit = c(-1, 1),
    name = "Correlation"
  ) +
  theme_minimal() +
  coord_fixed() +
  labs(
    title = "Correlation Matrix Heatmap with Values",
    x = "",
    y = ""
  ) +
  theme(
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  )