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, unionlibrary(tidyr)
library(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithsdata(iris)
head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosastr(iris)'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...str(ToothGrowth)'data.frame': 60 obs. of 3 variables:
$ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
$ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
$ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram(binwidth = 0.3, color = "red", fill = "green", alpha = 0.7) +
facet_wrap(~ Species) +
labs(title = "Distribution of Sepal Length by Species",
x = "Sepal Length",
y = "Frequency") +
theme_minimal()
df <- mtcars
df$cyl <- as.factor(df$cyl)
ggplot(df, aes(x = mpg, fill = cyl)) +
geom_density(alpha = 0.3) +
labs(title = "Density Plot of MPG by Number of Cylinders",
x = "Miles per Gallon (MPG)",
y = "Density") +
scale_fill_brewer(palette = "Set1") +
theme_minimal()
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_boxplot(
notch = TRUE,
notchwidth = 0.5,
outlier.colour = "red", # Correct spelling
fill = "lightblue"
) +
labs(
title = "Basic Box Plot",
x = "Species",
y = "Sepal Length"
) +
theme_minimal()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin(trim = FALSE) + # Show full distribution
geom_boxplot(width = 0.1, fill = "white") + # Optional: add boxplot inside violin
labs(title = "Distribution of Sepal Length by Species",
x = "Species",
y = "Sepal Length") +
theme_minimal()
ToothGrowth$dose <- as.factor(ToothGrowth$dose)ggplot(ToothGrowth, aes(x = dose, y = len, color = supp)) +
geom_dotplot(
binaxis = "y",
stackdir = "center",
dotsize = 0.6,
position = position_dodge(0.8),
binwidth = 1.5
) +
labs(
title = "Tooth Growth by Supplement Type and Dose",
x = "Dose (mg)",
y = "Tooth Length"
) +
theme_minimal()
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 1cor_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 = "green",
high = "pink",
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)
)