Data visualization is a crucial aspect of data analysis, allowing you to understand patterns, trends, and insights effectively. R, with its rich ecosystem of packages, offers powerful tools for creating a wide variety of visualizations. In this tutorial, we’ll explore how to create basic graphs using both base R and the ggplot2 package, one of the most popular visualization libraries in R.
Before diving into data visualization, ensure you have R and a code editor or IDE like RStudio or Visual Studio Code installed on your computer.
For demonstration purposes, we’ll use the built-in mtcars dataset, which contains data about various car models.
# Load the dataset
data(mtcars)
# View the first few rows
head(mtcars)| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 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 | 1 |
While base R offers basic plotting capabilities, ggplot2 provides a more flexible and powerful system for creating complex plots.
# Install ggplot2 if you haven't already
install.packages("ggplot2")
# Load ggplot2
library(ggplot2)Installing package into ‘/home/jumashafara/R/x86_64-pc-linux-gnu-library/4.3’
(as ‘lib’ is unspecified)
Purpose: To visualize the relationship between two continuous variables.
# Scatter plot of mpg vs. wt
plot(mtcars$wt, mtcars$mpg,
main = "Scatter Plot of MPG vs Weight",
xlab = "Weight (1000 lbs)",
ylab = "Miles Per Gallon (MPG)",
pch = 19, # Solid circles
col = "#008374")
Explanation: - plot(x, y): Creates a scatter plot with x on the horizontal axis and y on the vertical axis. - main: Title of the plot. - xlab and ylab: Labels for the x and y axes. - pch: Plotting symbol (19 is a solid circle). - col: Color of the points.
Purpose: To compare categorical data.
# Bar chart of the number of cars by number of cylinders
cyl_counts <- table(mtcars$cyl)
barplot(cyl_counts,
main = "Number of Cars by Cylinders",
xlab = "Number of Cylinders",
ylab = "Frequency",
col = "#008374")
Explanation: - table(): Creates a frequency table of the categorical variable. - barplot(): Generates a bar chart from the frequency table.
Purpose: To display the distribution of a continuous variable.
# Histogram of MPG
hist(mtcars$mpg,
main = "Histogram of MPG",
xlab = "Miles Per Gallon (MPG)",
col = "#66fdee",
border = "#008374")
Explanation: - hist(): Creates a histogram. - col: Fill color of the bars. - border: Color of the bar borders.
Purpose: To show the distribution of a continuous variable and identify outliers.
# Box plot of MPG by number of cylinders
boxplot(mpg ~ cyl, data = mtcars,
main = "Box Plot of MPG by Cylinders",
xlab = "Number of Cylinders",
ylab = "Miles Per Gallon (MPG)",
col = "#66fdee")
Explanation: - boxplot(y ~ x, data): Creates a box plot of y grouped by x.
Purpose: To display trends over time or ordered categories.
# Line graph of MPG for each car (ordered by weight)
mtcars_ordered <- mtcars[order(mtcars$wt), ]
plot(mtcars_ordered$wt, mtcars_ordered$mpg,
type = "o", # Both lines and points
main = "Line Graph of MPG vs Weight",
xlab = "Weight (1000 lbs)",
ylab = "Miles Per Gallon (MPG)",
col = "purple")
Explanation: - type = "o": Overplotted points and lines. - Ordering the data can make the line graph more meaningful.
ggplot2 follows the Grammar of Graphics, allowing for a more structured and layered approach to building plots.
# Scatter plot of mpg vs wt using ggplot2
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "#008374", size = 3) +
ggtitle("Scatter Plot of MPG vs Weight") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal()
Explanation: - ggplot(data, aes()): Initializes the plot with data and aesthetic mappings. - geom_point(): Adds points to the plot. - ggtitle(), xlab(), ylab(): Add title and axis labels. - theme_minimal(): Applies a minimal theme to the plot.
# Bar chart of the number of cars by cylinders using ggplot2
ggplot(mtcars, aes(x = factor(cyl))) +
geom_bar(fill = "#00fdee") +
ggtitle("Number of Cars by Cylinders") +
xlab("Number of Cylinders") +
ylab("Frequency") +
theme_minimal()
Explanation: - aes(x = factor(cyl)): Treats cyl as a categorical variable. - geom_bar(): Automatically counts the number of occurrences for each category.
# Histogram of MPG using ggplot2
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(binwidth = 2, fill = "#66fdee", color = "#008374") +
ggtitle("Histogram of MPG") +
xlab("Miles Per Gallon (MPG)") +
ylab("Frequency") +
theme_minimal()
Explanation: - geom_histogram(): Creates a histogram. - binwidth: Sets the width of each bin.
# Box plot of MPG by cylinders using ggplot2
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot(fill = "#008374") +
ggtitle("Box Plot of MPG by Cylinders") +
xlab("Number of Cylinders") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal()
Explanation: - geom_boxplot(): Creates a box plot.
# Line graph of MPG vs Weight using ggplot2
ggplot(mtcars_ordered, aes(x = wt, y = mpg)) +
geom_line(color = "#008374") +
geom_point(color = "#66fdee", size = 2) +
ggtitle("Line Graph of MPG vs Weight") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal()
Explanation: - geom_line(): Adds lines. - geom_point(): Adds points.
Customization enhances the readability and aesthetics of your plots. Here are some common customizations:
ggplot2 offers several themes to change the overall look of the plot.
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "#008374") +
ggtitle("Scatter Plot of MPG vs Weight") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_bw() # Changes to a black and white theme
Other themes: - theme_minimal() - theme_classic() - theme_dark()
You can map variables to colors for more informative plots.
# Scatter plot with color based on number of cylinders
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size=3) +
ggtitle("Scatter Plot of MPG vs Weight by Cylinders") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal() +
scale_color_manual(values = c("#909090", "#66fdee", "#008374"),
name = "Cylinders")
Explanation: - color = factor(cyl): Colors points based on the number of cylinders. - scale_color_manual(): Manually sets the colors and legend title.
Faceting allows you to create multiple plots based on a categorical variable.
# Scatter plot faceted by number of cylinders
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "#008374", size=3) +
ggtitle("Scatter Plot of MPG vs Weight by Cylinders") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal() +
facet_wrap(~ cyl)
Explanation: - facet_wrap(~ cyl): Creates a separate plot for each level of cyl.
You can add text labels or annotations to highlight specific points.
# Scatter plot with labels for cars with highest MPG
top_mpg <- mtcars[mtcars$mpg > 30, ]
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "#008374") +
geom_text(data = top_mpg, aes(label = rownames(top_mpg)),
vjust = -1, size = 3) +
ggtitle("Scatter Plot of MPG vs Weight with Labels") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal()
Explanation: - geom_text(): Adds text labels to specific points. - vjust: Vertical adjustment of text position.
After creating your plot, you might want to save it to a file.
# Save scatter plot as PNG
png("scatter_mpg_wt.png", width = 800, height = 600)
plot(mtcars$wt, mtcars$mpg,
main = "Scatter Plot of MPG vs Weight",
xlab = "Weight (1000 lbs)",
ylab = "Miles Per Gallon (MPG)",
pch = 19,
col = "blue")
dev.off()# Create the plot
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "blue", size = 3) +
ggtitle("Scatter Plot of MPG vs Weight") +
xlab("Weight (1000 lbs)") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal()
# Save the plot
ggsave("ggplot_scatter_mpg_wt.png", plot = p, width = 8, height = 6, dpi = 300)Explanation: - ggsave(): Saves the last plot or a specified plot to a file. - width, height: Size of the saved plot in inches. - dpi: Resolution of the saved plot.
In this tutorial, we’ve covered the basics of data visualization in R using both base R and ggplot2. Here’s a quick recap:
plotly or shiny for interactive plots.dplyr and tidyr to prepare your data for visualization.Happy plotting!