How to Create a Plot in R

R offers powerful tools for data visualization. Learn how to create various types of plots using base R and ggplot2.

Base R Plotting

Simple Scatter Plot

# Create sample data
x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 5, 4, 6)

# Basic scatter plot
plot(x, y)

# With customization
plot(x, y,
     main = "My Scatter Plot",
     xlab = "X Axis",
     ylab = "Y Axis",
     col = "blue",
     pch = 19)  # Solid circles

Line Plot

# Line plot
plot(x, y, type = "l", col = "red")

# Points and lines
plot(x, y, type = "b", col = "blue")

# Multiple lines
plot(x, y, type = "l", col = "blue")
lines(x, y * 1.5, col = "red")
legend("topleft", legend = c("Series 1", "Series 2"),
       col = c("blue", "red"), lty = 1)

Bar Plot

# Simple bar plot
values <- c(3, 5, 2, 8, 4)
names <- c("A", "B", "C", "D", "E")

barplot(values,
        names.arg = names,
        main = "Bar Chart",
        col = "steelblue")

# Horizontal bars
barplot(values, names.arg = names, horiz = TRUE)

Histogram

# Generate random data
data <- rnorm(1000, mean = 50, sd = 10)

# Histogram
hist(data,
     main = "Distribution",
     xlab = "Values",
     col = "lightblue",
     breaks = 30)

ggplot2 Basics

ggplot2 uses a grammar of graphics approach:

library(ggplot2)

# Create a data frame
df <- data.frame(
    x = c(1, 2, 3, 4, 5),
    y = c(2, 4, 5, 4, 6),
    group = c("A", "A", "B", "B", "B")
)

# Basic scatter plot
ggplot(df, aes(x = x, y = y)) +
    geom_point()

# With customization
ggplot(df, aes(x = x, y = y, color = group)) +
    geom_point(size = 3) +
    labs(title = "Scatter Plot",
         x = "X Axis",
         y = "Y Axis") +
    theme_minimal()

Common ggplot2 Visualizations

Scatter Plot with Trend Line

ggplot(df, aes(x = x, y = y)) +
    geom_point() +
    geom_smooth(method = "lm", se = FALSE, color = "red")

Bar Chart

# Create data
sales <- data.frame(
    product = c("A", "B", "C", "D"),
    revenue = c(100, 150, 80, 200)
)

ggplot(sales, aes(x = product, y = revenue)) +
    geom_bar(stat = "identity", fill = "steelblue") +
    labs(title = "Sales by Product")

Line Chart

# Time series data
time_data <- data.frame(
    month = 1:12,
    value = c(10, 12, 15, 18, 22, 25, 28, 26, 23, 18, 14, 11)
)

ggplot(time_data, aes(x = month, y = value)) +
    geom_line(color = "blue", size = 1) +
    geom_point(color = "blue", size = 2) +
    scale_x_continuous(breaks = 1:12) +
    labs(title = "Monthly Trends")

Histogram

# Generate data
df <- data.frame(values = rnorm(1000, mean = 50, sd = 10))

ggplot(df, aes(x = values)) +
    geom_histogram(bins = 30, fill = "lightblue", color = "black") +
    labs(title = "Distribution of Values")

Box Plot

# Create grouped data
df <- data.frame(
    group = rep(c("A", "B", "C"), each = 100),
    value = c(rnorm(100, 10), rnorm(100, 15), rnorm(100, 12))
)

ggplot(df, aes(x = group, y = value, fill = group)) +
    geom_boxplot() +
    labs(title = "Distribution by Group")

Customizing ggplot2

Themes

# Built-in themes
ggplot(df, aes(x = x, y = y)) +
    geom_point() +
    theme_minimal()  # or theme_bw(), theme_classic(), theme_dark()

# Custom theme elements
ggplot(df, aes(x = x, y = y)) +
    geom_point() +
    theme(
        plot.title = element_text(size = 16, face = "bold"),
        axis.text = element_text(size = 12),
        panel.background = element_rect(fill = "white")
    )

Colors and Scales

# Custom colors
ggplot(df, aes(x = x, y = y, color = group)) +
    geom_point(size = 3) +
    scale_color_manual(values = c("A" = "red", "B" = "blue"))

# Color gradients
ggplot(df, aes(x = x, y = y, color = y)) +
    geom_point(size = 3) +
    scale_color_gradient(low = "blue", high = "red")

Faceting

Create multiple plots:

ggplot(df, aes(x = x, y = y)) +
    geom_point() +
    facet_wrap(~ group)  # Separate plot for each group

Saving Plots

# Save ggplot
p <- ggplot(df, aes(x = x, y = y)) + geom_point()
ggsave("my_plot.png", p, width = 8, height = 6, dpi = 300)

# Save base R plot
png("my_plot.png", width = 800, height = 600)
plot(x, y)
dev.off()

Summary

• Use base R plot() for quick visualizations
• Use ggplot2 for publication-quality graphics
• ggplot2 syntax: ggplot(data, aes()) + geom_*()
• Customize with labs(), theme(), and scale_*() functions
• Save plots with ggsave() or png()/pdf() + dev.off()