library(ggplot2)
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, union library(tidyr)PRG 15 DV
PROGRAM 15
Develop an R program to generate facet_wrap histograms,Density Plot Function,Boxplot with Notch and Outliers,Violin Plot,Correlation Matrix Heatmap,Dot Plot with Grouped Variables step 1: load required libraries
step 2:Multiple Histograms using facet_wrap
data(iris)
p1 <- ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram(binwidth = 0.3, fill = "skyblue", color = "black") +
facet_wrap(~ Species) +
labs(title = "Histogram: Sepal Length by Species", x = "Sepal Length", y = "Frequency") +
theme_minimal()
p1
step3:Density Plot Function
plot_density_by_group <- function(data, numeric_var, group_var, fill = TRUE) {
ggplot(data, aes_string(x = numeric_var, color = group_var, fill = group_var)) +
geom_density(alpha = if (fill) 0.4 else 0) +
labs(title = paste("Density Plot of", numeric_var, "by", group_var),
x = numeric_var, y = "Density") +
theme_minimal()
}
p2 <- plot_density_by_group(iris, "Sepal.Length", "Species")Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.p2
step 4:Boxplot with Notch and Outliers
p3 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_boxplot(notch = TRUE, notchwidth = 0.77, staplewidth = 0.3,
fill = "grey", color = "black", outlier.shape = 15, outlier.color = "yellow") +
labs(title = "Boxplot: Sepal Length by Species", x = "Species", y = "Sepal Length") +
theme_minimal()
p3
step 5:Violin Plot
mtcars$cyl <- as.factor(mtcars$cyl)
p4 <- ggplot(mtcars, aes(x = cyl, y = mpg, fill = cyl)) +
geom_violin(trim = FALSE) +
labs(title = "Violin Plot: MPG by Cylinder", x = "Cylinders", y = "Miles Per Gallon") +
theme_minimal()
p4
step 6:Dot Plot with Grouped Variables
data(ToothGrowth)
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
p5 <- ggplot(ToothGrowth, aes(x = dose, y = len, color = supp)) +
geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.8,
position = position_dodge(width = 0.8)) +
labs(title = "Dot Plot: Tooth Length by Dose", x = "Dose", y = "Tooth Length") +
theme_minimal()
p5Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.
step 7 correlation matrix heatmap
data(mtcars)
cor_matrix <-cor(mtcars)
head(cor_matrix) mpg cyl disp hp drat wt
mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.6811719 -0.8676594
cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.6999381 0.7824958
disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.7102139 0.8879799
hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.4487591 0.6587479
drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.0000000 -0.7124406
wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.7124406 1.0000000
qsec vs am gear carb
mpg 0.41868403 0.6640389 0.5998324 0.4802848 -0.5509251
cyl -0.59124207 -0.8108118 -0.5226070 -0.4926866 0.5269883
disp -0.43369788 -0.7104159 -0.5912270 -0.5555692 0.3949769
hp -0.70822339 -0.7230967 -0.2432043 -0.1257043 0.7498125
drat 0.09120476 0.4402785 0.7127111 0.6996101 -0.0907898
wt -0.17471588 -0.5549157 -0.6924953 -0.5832870 0.4276059 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 # Ensure Var1 and Var2 are treated as factors (important for axis ordering in geom_tile)
cor_df$Var1 <- as.factor(cor_df$Var1)
cor_df$Var2 <- as.factor(cor_df$Var2)
# Plot
p6 <- ggplot(cor_df, aes(x = Var1, y = Var2, fill = Freq)) +
geom_tile(color = "black") +
scale_fill_gradient2(
low = "violet", mid = "white", high = "red",
midpoint = 0, limit = c(-1, 1), name = "Correlation"
) +
geom_text(aes(label = round(Freq, 2)), size = 3) +
labs(
title = "Correlation Matrix Heatmap",
x = "Variable 1",
y = "Variable 2"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p6