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Correlation Matrix Visualization using ggplot2

Develop a R program to calculate and visualize co-relation matrix for a given dataset, with color coded cells indicating the strength and direction of co-relation using ggplot2 and geom_tile function.

library(ggplot2)
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(tidyr)

Dataset

We use the built-in mtcars 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

Cundefined

data(mtcars)
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

Explanation:

• cor(mtcars) computes pairwise correlation.

• as.table() flattens the matrix into a long-format table.

• The result has 3 columns: Var1, Var2, and the correlation value (Freq).

Step 2: Visualize Using ggplot2::geom_tile

ggplot(cor_df, aes(x=Var1, y=Var2, fill = Freq)) +
  geom_tile(color = "white") + 
  scale_fill_gradient2(
    low= "blue", mid= "white", high= "red",
    midpoint = 0, limit = c(-1,1),
    name = "Correlation"
  ) + 
  geom_text(aes(label = round(Freq, 2)), size = 3) + 
  theme_minimal() +
  labs(
     title = "Correlation Matrix (mtcars)",
    x = "", y = ""
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Step	Description
cor()	Computes correlation values between numeric variables.
as.table() + as.data.frame()	Converts matrix into a long format suitable for plotting.
ggplot()	Initializes the plot using long-form data.
geom_tile()	Creates color-coded tiles based on correlation values.
scale_fill_gradient2()	Applies a diverging color scale: red (strong +ve), blue (strong -ve), white (neutral).
geom_text()	Adds correlation values as text in each cell.
theme_minimal()	Cleans up the plot visually.
axis.text.x rotation	Tilts x-axis labels for better readability.