Introduction to Base Graphics in R

visualization

r-programming

Learn how to create plots using R’s built-in graphics functions

Published

February 15, 2023

R comes with a built-in graphics system known as “base graphics.” These functions provide a straightforward way to create plots without requiring additional packages.

Basic Plot Types

Scatter Plots

# Basic scatter plot
plot(mtcars$wt, mtcars$mpg, 
     main = "Car Weight vs. Fuel Efficiency",
     xlab = "Weight (1000 lbs)",
     ylab = "Miles Per Gallon",
     pch = 19,  # Solid circle point type
     col = "blue")

# Add a grid
grid()

# Add a trend line
abline(lm(mpg ~ wt, data = mtcars), col = "red", lwd = 2)

# Add a legend
legend("topright", 
       legend = "Trend Line", 
       col = "red", 
       lwd = 2)

Line Plots

# Create some data
x <- 1:10
y <- x^2

# Basic line plot
plot(x, y, 
     type = "l",  # 'l' for line
     main = "Line Plot Example",
     xlab = "X Values",
     ylab = "Y Values",
     col = "darkgreen",
     lwd = 2)

# Add points to the line
points(x, y, pch = 19, col = "darkgreen")

# Add another line
lines(x, 2*x, col = "blue", lwd = 2)

# Add a legend
legend("topleft", 
       legend = c("y = x^2", "y = 2x"), 
       col = c("darkgreen", "blue"), 
       lwd = 2)

Bar Plots

# Create a simple frequency table
cylinders <- table(mtcars$cyl)

# Basic bar plot
barplot(cylinders,
        main = "Car Cylinders Distribution",
        xlab = "Number of Cylinders",
        ylab = "Frequency",
        col = c("lightblue", "skyblue", "steelblue"),
        border = "white")

# Add text labels on top of bars
text(x = barplot(cylinders), 
     y = cylinders + 1, 
     labels = cylinders, 
     col = "black")

Histograms

# Basic histogram
hist(mtcars$mpg,
     main = "Distribution of Fuel Efficiency",
     xlab = "Miles Per Gallon",
     col = "lightgreen",
     border = "white",
     breaks = 10)  # Number of bins

# Add a density curve
hist(mtcars$mpg,
     main = "Distribution with Density Curve",
     xlab = "Miles Per Gallon",
     col = "lightgreen",
     border = "white",
     freq = FALSE)  # Show density instead of frequency

# Add a normal density curve
curve(dnorm(x, mean = mean(mtcars$mpg), sd = sd(mtcars$mpg)),
      add = TRUE, col = "darkred", lwd = 2)

# Add a legend
legend("topright", 
       legend = "Normal Distribution", 
       col = "darkred", 
       lwd = 2)

Box Plots

# Basic box plot
boxplot(mpg ~ cyl, data = mtcars,
        main = "Fuel Efficiency by Cylinder Count",
        xlab = "Number of Cylinders",
        ylab = "Miles Per Gallon",
        col = c("lightpink", "lightblue", "lightgreen"))

# Add a title to the plot
title("Comparison of MPG Distribution")

Customizing Plots

Plot Parameters

# Create a customized scatter plot
plot(mtcars$wt, mtcars$mpg,
     main = "Customized Scatter Plot",
     xlab = "Weight (1000 lbs)",
     ylab = "Miles Per Gallon",
     pch = 16,       # Point type
     col = "purple", # Point color
     cex = 1.5,      # Point size
     type = "p",     # Plot type ('p' for points)
     lwd = 2,        # Line width
     bty = "l",      # Box type ('l' for L-shaped)
     xlim = c(1, 6), # X-axis limits
     ylim = c(10, 35) # Y-axis limits
)

Colors and Point Types

# Create a plot showing different point types and colors
plot(1:20, 1:20, 
     type = "n",  # 'n' for no plotting
     main = "Point Types (pch) in R",
     xlab = "Point Type (pch value)",
     ylab = "")

# Add points with different pch values
for (i in 1:20) {
  points(i, 10, pch = i, cex = 2)
  text(i, 12, labels = i)
}

# Show different colors
plot(1:8, rep(1, 8), 
     type = "n",
     main = "Basic Colors in R",
     xlab = "", ylab = "",
     xlim = c(0.5, 8.5), ylim = c(0, 2),
     axes = FALSE)

colors <- c("black", "red", "green", "blue", "cyan", "magenta", "yellow", "gray")
for (i in 1:8) {
  points(i, 1, pch = 19, col = colors[i], cex = 3)
  text(i, 0.7, labels = colors[i])
}

Multiple Plots in One Figure

# Set up a 2x2 plotting area
par(mfrow = c(2, 2))

# Plot 1: Scatter plot
plot(mtcars$wt, mtcars$mpg, main = "Weight vs MPG", pch = 19)

# Plot 2: Histogram
hist(mtcars$mpg, main = "MPG Distribution", col = "lightblue")

# Plot 3: Box plot
boxplot(mpg ~ cyl, data = mtcars, main = "MPG by Cylinders", col = "lightgreen")

# Plot 4: Bar plot
barplot(table(mtcars$gear), main = "Gear Count", col = "salmon")

# Reset to 1x1 plotting area
par(mfrow = c(1, 1))

Adding Elements to Plots

# Create a basic plot
plot(mtcars$wt, mtcars$mpg, 
     type = "n",  # Start with an empty plot
     main = "Car Weight vs. Fuel Efficiency",
     xlab = "Weight (1000 lbs)",
     ylab = "Miles Per Gallon")

# Add points with different colors by cylinder
cyl_colors <- c("red", "green", "blue")
for (i in unique(mtcars$cyl)) {
  subset_idx <- mtcars$cyl == i
  points(mtcars$wt[subset_idx], mtcars$mpg[subset_idx], 
         col = cyl_colors[i/4],  # 4,6,8 cylinders mapped to colors
         pch = 19)
}

# Add a legend
legend("topright", 
       legend = c("4 cylinders", "6 cylinders", "8 cylinders"), 
       col = cyl_colors, 
       pch = 19)

# Add text annotations
text(mtcars$wt[mtcars$mpg > 30], mtcars$mpg[mtcars$mpg > 30], 
     labels = rownames(mtcars)[mtcars$mpg > 30],
     pos = 4)  # Position 4 is to the right

# Add a horizontal line at mean MPG
abline(h = mean(mtcars$mpg), lty = 2, col = "darkgray")
text(5, mean(mtcars$mpg) + 1, "Mean MPG", col = "darkgray")

Saving Plots

# Example of how to save a plot (not run)
# png("my_plot.png", width = 800, height = 600)
plot(mtcars$wt, mtcars$mpg, main = "Plot to Save", pch = 19, col = "blue")

# dev.off()  # Close the device to save the file

# Other formats
# pdf("my_plot.pdf", width = 8, height = 6)
# jpeg("my_plot.jpg", width = 800, height = 600, quality = 100)
# svg("my_plot.svg", width = 8, height = 6)

Base graphics in R provide a foundation for creating plots. While newer packages like ggplot2 offer more sophisticated options, base graphics remain valuable for quick visualizations and understanding plotting fundamentals in R.