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5. Data Visualization with ggplot2

ggplot2 is the gold-standard visualization package in R — and one of the best statistical visualization systems in any language. It is based on the Grammar of Graphics by Leland Wilkinson, which breaks plots into logical layers.

Install & Load (if not already in tidyverse)

R

# install.packages("ggplot2") library(ggplot2)

5.1 ggplot2 Grammar of Graphics – Core Logic

Every ggplot follows this structure:

R

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + geom_<TYPE>() + labs(...) + theme(...)

Key components:

• data → the dataset (usually a data frame)

• aes() → aesthetics: map variables to visual properties (x, y, color, size, fill, shape…)

• geom_ → geometric objects: points, lines, bars, histograms, etc.

• labs() → titles, axis labels, caption

• theme() → appearance (fonts, colors, grid, background)

Basic template

R

ggplot(data = mtcars, mapping = aes(x = wt, y = mpg)) + geom_point() + labs(title = "Car Weight vs MPG", x = "Weight (1000 lbs)", y = "Miles per Gallon") + theme_minimal()

5.2 Scatter Plots, Line Charts, Bar Plots & Histograms

Scatter Plot

R

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) + geom_point(size = 4, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE) + labs(title = "Weight vs MPG by Cylinders", color = "Number of Cylinders") + theme_bw()

Line Chart (time series or trend)

R

library(gapminder) gapminder %>% filter(country %in% c("India", "China", "United States")) %>% ggplot(aes(x = year, y = lifeExp, color = country)) + geom_line(size = 1.2) + geom_point(size = 3) + labs(title = "Life Expectancy Over Time", x = "Year", y = "Life Expectancy (years)") + theme_minimal()

Bar Plot

R

ggplot(diamonds, aes(x = cut, fill = cut)) + geom_bar() + labs(title = "Diamond Cuts Distribution", x = "Cut Quality", y = "Count") + scale_fill_brewer(palette = "Set2") + theme_light()

Histogram

R

ggplot(diamonds, aes(x = price)) + geom_histogram(bins = 50, fill = "steelblue", color = "black") + labs(title = "Price Distribution of Diamonds", x = "Price (USD)", y = "Frequency") + theme_classic()

5.3 Boxplots, Violin Plots & Density Plots

Boxplot

R

ggplot(tips, aes(x = day, y = total_bill, fill = day)) + geom_boxplot(outlier.shape = 21, outlier.size = 3) + labs(title = "Total Bill by Day", x = "Day", y = "Total Bill (USD)") + theme_minimal()

Violin Plot (shows density + boxplot)

R

ggplot(tips, aes(x = day, y = tip, fill = sex)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.1, fill = "white") + labs(title = "Tip Distribution by Day and Gender") + theme_light()

Density Plot

R

ggplot(diamonds, aes(x = price, fill = cut)) + geom_density(alpha = 0.6) + labs(title = "Price Density by Cut Quality") + theme_minimal()

5.4 Faceting, Themes & Publication-Ready Plots

Faceting – Split plots by category

R

ggplot(mpg, aes(x = displ, y = hwy)) + geom_point(aes(color = class), size = 3, alpha = 0.7) + geom_smooth(method = "loess", color = "red") + facet_wrap(~ class, scales = "free_y") + labs(title = "Engine Displacement vs Highway MPG by Vehicle Class") + theme_minimal(base_size = 14)

Popular themes

R

theme_minimal() # clean & modern theme_bw() # black & white theme_classic() # minimal lines theme_light() # light background theme_dark() # dark mode

Publication-ready plot template

R

p <- ggplot(diamonds, aes(x = carat, y = price, color = clarity)) + geom_point(alpha = 0.5, size = 2) + geom_smooth(method = "lm", se = FALSE) + labs(title = "Diamond Price vs Carat by Clarity", x = "Carat", y = "Price (USD)") + scale_color_brewer(palette = "Set1") + theme_minimal(base_size = 16) + theme( plot.title = element_text(face = "bold", hjust = 0.5), axis.title = element_text(face = "bold"), legend.position = "top" ) # Save high-resolution ggsave("diamond_plot.png", plot = p, width = 10, height = 7, dpi = 300)

5.5 Advanced Visuals – Heatmaps, Correlation Plots, Marginal Plots

Heatmap (Correlation)

R

corr <- cor(mtcars) ggplot(melt(corr), aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = "white") + geom_text(aes(label = round(value, 2)), color = "black") + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0) + labs(title = "Correlation Heatmap – mtcars Dataset") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))

Marginal Plots (joint distribution)

R

library(ggExtra) p <- ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point(aes(color = factor(cyl)), size = 3) + theme_minimal() ggMarginal(p, type = "histogram", fill = "skyblue", color = "black")

Advanced Correlation Plot

R

library(GGally) ggpairs(mtcars[, c("mpg", "hp", "wt", "qsec")], aes(color = factor(cyl)), upper = list(continuous = "cor"))

This completes the full Data Visualization with ggplot2 section — now you can create beautiful, insightful, and publication-ready visualizations in R

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