Before data analysis, formulate questions to guide your exploration. Good questions lead to meaningful insights.

Consider:

• Distribution: Typical values, spread, unusual values?
• Relationship: How do variables interact? Correlation?
• Comparison: How do groups compare on a metric?

# Example: What is the distribution of ages? Is there a relationship between income and education?

ggplot2 is an R package for powerful data visualization, based on the “Grammar of Graphics”. It builds plots layer by layer.

Core Components:

• Data: The dataset.
• Aesthetics (aes()): How variables map to visual properties (x, y, color, size).
• Geoms (geom_ functions): Geometric objects (points, lines, bars).
• Stats: Statistical transformations (e.g., binning, smoothing).
• Facets: Subplots for comparisons.
• Scales: Control mapping from data to aesthetics.
• Themes: Overall visual style.

Display the distribution of a single continuous variable by showing the frequency of observations in defined bins.

# Histogram of horsepower
ggplot(data = mtcars, aes(x = hp)) +
  geom_histogram(binwidth = 20, fill = "steelblue", color = "white", alpha = 0.8) +
  labs(title = "Distribution of Horsepower", x = "Horsepower (HP)", y = "Frequency") 

Used to display counts or proportions of categorical variables, great for comparing values across different categories.

# Bar chart of cylinder counts
ggplot(data = mtcars, aes(x = factor(cyl))) +
  geom_bar(fill = "darkgreen", alpha = 0.8) +
  labs(title = "Count of Cars by Number of Cylinders", x = "Number of Cylinders", y = "Count") 

The most common way to start visualizing is by adding geom_point() to create a scatter plot, showing the relationship between two continuous variables.

# Scatter plot: MPG vs. HP
ggplot(data = mtcars, aes(x = hp, y = mpg)) + # Define data and basic aesthetics
  geom_point(size = 3, alpha = 0.7) + # Add points
  labs(title = "Horsepower vs. Miles Per Gallon", x = "Horsepower", y = "Miles Per Gallon") 

Besides x and y, aes() allows mapping data variables to other visual properties:

• color: Outline color for points/bars, line color.
• fill: Fill color for bars/polygons.
• size: Size of points or lines.
• shape: Shape of points.
• alpha: Transparency.
• linetype: Style of lines (solid, dashed, dotted).

Map a continuous variable to color or size to show its variation across data points.

ggplot(data = mtcars, aes(x = hp, y = mpg, size = wt, color = qsec)) + geom_point(alpha = 0.7) +
  scale_size_continuous(range = c(2, 8)) + # Control min/max size
  scale_color_viridis_c(option = "magma") + # Use a colorblind-friendly palette
  labs(title = "MPG vs. HP (Size by Weight, Color by Q-Mile Time)",x = "Horsepower", y = "Miles Per Gallon") 

Map a categorical variable to color to distinguish groups.

ggplot(data = mtcars, aes(x = factor(cyl), fill = factor(am))) +
  geom_bar(position = "dodge", alpha = 0.8) + # 'dodge' separates bars by fill group
  scale_fill_manual(values = c("0" = "darkorange", "1" = "darkblue"), labels = c("Automatic", "Manual"), name = "Transmission") + labs(title = "Cars by Cylinders and Transmission Type", x = "Number of Cylinders", y = "Count") 

Summarize the distribution of a continuous variable for different categories, showing median, quartiles, and potential outliers.

# Box plot of MPG by number of cylinders
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_boxplot(fill = "lightblue", color = "darkblue", alpha = 0.8) +
  labs(title = "MPG Distribution by Number of Cylinders", x = "Number of Cylinders", y = "Miles Per Gallon (MPG)") 

Ideal for visualizing trends of one or more continuous variables over an ordered sequence, most commonly time.

# Example using a time series-like dataset (e.g., economics from ggplot2)
ggplot(data = economics, aes(x = date, y = unemploy)) +
  geom_line(color = "purple", linewidth = 1.2) +
  labs(title = "Unemployment Rate Over Time", x = "Date", y = "Unemployment (Thousands)") 

Similar to line plots, but fill the area between the line and the x-axis. Useful for showing magnitude of change and total contribution over time.

# Example using economics data
ggplot(data = economics, aes(x = date, y = pop)) +
  geom_area(fill = "lightcoral", alpha = 0.7, color = "darkred") +
  labs(title = "Population Growth Over Time", x = "Date", y = "Population (Thousands)") 

geom_smooth() adds a smoothed mean to your plots, often used to visualize trends or relationships in scatter plots.

# Scatter plot with a smoothed linear trend line
ggplot(data = mtcars, aes(x = disp, y = mpg)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE, color = "red") + labs(title = "Displacement vs. MPG with Linear Trend")

Faceting allows you to create multiple subplots based on the levels of one or more categorical variables. Use facet_wrap() or facet_grid().

ggplot(data = mtcars, aes(x = disp, y = mpg)) +
  geom_point(alpha = 0.7, color = "darkblue") +
  facet_wrap(~ cyl) + # Facet by 'cyl' variable
  labs(title = "Displacement vs. MPG by Cylinder Count",
       subtitle = "Comparing car performance across different engine sizes")

You have fine-grained control over axis limits, breaks, and labels using scale_x_continuous(), scale_y_continuous(), and labs().

ggplot(data = mtcars, aes(x = wt, y = qsec)) + geom_point(color = "purple", size = 3, alpha = 0.8) +
  labs(x = "Vehicle Weight (in 1000 lbs)", y = "Quarter-Mile Time (in seconds)") +
  scale_x_continuous(limits = c(1, 6), breaks = seq(1, 6, by = 1)) +
  scale_y_continuous(limits = c(14, 24), breaks = seq(14, 24, by = 2)) 

Clear titles and subtitles are essential for making your plots informative and easy to understand. Use labs() to add these elements.

ggplot(data = mtcars, aes(x = hp, y = mpg)) +
  geom_point(color = "darkgreen", size = 3, alpha = 0.7) +
  labs(title = "Relationship Between Horsepower and Miles Per Gallon", subtitle = "Insights from the mtcars dataset",caption = "Data source: 1974 Motor Trend US magazine") 

ggplot2 provides powerful scale_color_ and scale_fill_ functions to control the colors used in your plots.

# Example using manual colors for a categorical variable
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
  geom_boxplot(alpha = 0.7) +
  scale_fill_discrete() + 
  labs(title = "MPG Distribution by Cylinders (Custom Colors)", x = "Number of Cylinders", y = "Miles Per Gallon") 

We’ll use rnaturalearth for built-in world map data and then create a basic static map.

# Load world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Basic world map
ggplot(data = world) +
  geom_sf(fill = "lightgray", color = "white", size = 0.2) + theme_void() + labs(title = "Basic World Map")

Choropleth maps color geographic areas based on a variable’s value, showing spatial distribution. We map a data variable to the fill aesthetic of geom_sf().

ggplot(data = world) +
  geom_sf(aes(fill = pop_est), color = "white", size = 0.2) +
  scale_fill_viridis_c(option = "magma", name = "Estimated Population (log10 Scale)",
                       labels = scales::comma, trans = "log10") + # Apply log10 transformation
  theme_void() + labs(title = "World Population Estimate (Log10 Scale, 2020 Est.)")

Overlay additional spatial data (points, lines, or other polygons) on your base map by adding more geom_sf() layers.

# Plot world map with cities
ggplot(data = world) +
  geom_sf(fill = "lightgray", color = "white", size = 0.2) +
  geom_sf(data = cities, color = "red", size = 4, shape = 17, alpha = 0.8) + # Add city points
  theme_void() + labs(title = "World Map with Major Cities")