Data visualization with R and ggplot2
The ggplot2 ( Grammar of Graphics ) is a free, open-source visualization package widely used in R Programming Language. It includes several layers on which it is governed. The layers are as follows:
Layers with the grammar of graphics
- Data: The element is the data set itself.
- Aesthetics: The data is to map onto the Aesthetics attributes such as x-axis, y-axis, color, fill, size, labels, alpha, shape, line width, line type.
- Geometrics: How our data being displayed using point, line, histogram, bar, boxplot.
- Facets: It displays the subset of the data using Columns and rows.
- Statistics: Binning, smoothing, descriptive, intermediate.
- Coordinates: the space between data and display using Cartesian, fixed, polar, limits.
- Themes: Non-data link.
Importing the Dataset
We will use the mtcars(motor trend car road test) dataset which is a built in dataset in R. It comprise fuel consumption and 10 aspects of automobile design and performance for 32 automobiles.
install.packages("dplyr")
library(dplyr)
head(mtcars)
Output:
Now we print the summary of mtcars dataset using summary function.
summary(mtcars)
Output:
We devise visualizations on mtcars dataset which includes 32 car brands and 11 attributes using ggplot2 layers.
1. Data Layer
The data layer we define the source of the information to be visualize, let’s use the mtcars dataset in the ggplot2 package.
library(ggplot2)
library(dplyr)
ggplot(data = mtcars) +
labs(title = "MTCars Data Plot")
Output:
2. Aesthetic Layer
Here we will display and map dataset into certain aesthetics.
ggplot(data = mtcars, aes(x = hp, y = mpg, col = disp))+
labs(title = "MTCars Data Plot")
Output:
3. Geometric layer
The geometric layer control the essential elements, see how our data being displayed using point, line, histogram, bar, boxplot.
ggplot(data = mtcars, aes(x = hp, y = mpg, col = disp)) +
geom_point() +
labs(title = "Miles per Gallon vs Horsepower",
x = "Horsepower",
y = "Miles per Gallon")
Output:
# Adding size
ggplot(data = mtcars, aes(x = hp, y = mpg, size = disp)) +
geom_point() +
labs(title = "Miles per Gallon vs Horsepower",
x = "Horsepower",
y = "Miles per Gallon")
# Adding shape and color
ggplot(data = mtcars, aes(x = hp, y = mpg, col = factor(cyl),
shape = factor(am))) +geom_point() +
labs(title = "Miles per Gallon vs Horsepower",
x = "Horsepower",
y = "Miles per Gallon")
# Histogram plot
ggplot(data = mtcars, aes(x = hp)) +
geom_histogram(binwidth = 5) +
labs(title = "Histogram of Horsepower",
x = "Horsepower",
y = "Count")
Output:
4. Facet Layer
The facet layer is used to split the data up into subsets of the entire dataset and it allows the subsets to be visualized on the same plot. Here we separate rows according to transmission type and Separate columns according to cylinders.
p <- ggplot(data = mtcars, aes(x = hp, y = mpg, shape = factor(cyl))) + geom_point()
p + facet_grid(am ~ .) +
labs(title = "Miles per Gallon vs Horsepower",
x = "Horsepower",
y = "Miles per Gallon")
p <- ggplot(data = mtcars, aes(x = hp, y = mpg, shape = factor(cyl))) + geom_point()
p + facet_grid(. ~ cyl) +
labs(title = "Miles per Gallon vs Horsepower",
x = "Horsepower",
y = "Miles per Gallon")
Output:
5. Statistics layer
This layer transforms our data using binning, smoothing, descriptive, intermediate summaries.
ggplot(data = mtcars, aes(x = hp, y = mpg)) +
geom_point() +
stat_smooth(method = lm, col = "red") +
labs(title = "Miles per Gallon vs Horsepower")
Output:
6. Coordinates layer
In these layers, data coordinates are mapped together to the mentioned plane of the graphic and we adjust the axis and changes the spacing of displayed data with Control plot dimensions.
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point() +
stat_smooth(method = lm, col = "red") +
scale_y_continuous("Miles per Gallon", limits = c(2, 35), expand = c(0, 0)) +
scale_x_continuous("Weight", limits = c(0, 25), expand = c(0, 0)) +
coord_equal() +
labs(title = "Miles per Gallon vs Weight",
x = "Weight",
y = "Miles per Gallon")
Output:
- Coord_cartesian() to proper zoom in
ggplot(data = mtcars, aes(x = wt, y = hp, col = am)) +
geom_point() + geom_smooth() +
coord_cartesian(xlim = c(3, 6))
Output:
7. Theme Layer
This layer controls the finer points of display like the font size and background color properties.
Example 1: element_rect() function
ggplot(data = mtcars, aes(x = hp, y = mpg)) +
geom_point() +
facet_grid(. ~ cyl) +
theme(plot.background = element_rect(fill = "blue", colour = "gray")) +
labs(title = "Miles per Gallon vs Horsepower")
Output:
Example 2:
ggplot(data = mtcars, aes(x = hp, y = mpg)) +
geom_point() + facet_grid(am ~ cyl) +
theme_gray()+
labs(title = "Miles per Gallon vs Horsepower")
Output:
Contour plot for the mtcars dataset
The ggplot2 in R provides various types of visualizations where more parameters can be used included, like a contour plot. stat_density_2d to generate the 2D density contour plot. The aesthetics x and y specify the variables on the x-axis and y-axis, respectively. The fill aesthetic is set to ..level.. to map fill color to density levels.
install.packages("ggplot2")
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", color = "white") +
scale_fill_viridis_c() +
labs(title = "2D Density Contour Plot of mtcars Dataset",
x = "Weight (wt)",
y = "Miles per Gallon (mpg)",
fill = "Density") +
theme_minimal()
Output:
Creating a panel of different plots
The gridExtra packages to create histograms for four different variables ("Miles per Gallon," "Displacement," "Horsepower," and "Drat") from the mtcars dataset.
Each histogram is visually represented in a distinctive color (blue, red, green, and orange) with white borders. The resulting grid of histograms provides a quick visual overview of the distribution of these car-related variables.
library(ggplot2)
library(gridExtra)
selected_cols <- c("mpg", "disp", "hp", "drat")
selected_data <- mtcars[, selected_cols]
hist_plot_mpg <- ggplot(selected_data, aes(x = mpg)) +
geom_histogram(binwidth = 2, fill = "blue", color = "white") +
labs(title = "Histogram: Miles per Gallon", x = "Miles per Gallon", y = "Frequency")
hist_plot_disp <- ggplot(selected_data, aes(x = disp)) +
geom_histogram(binwidth = 50, fill = "red", color = "white") +
labs(title = "Histogram: Displacement", x = "Displacement", y = "Frequency")
hist_plot_hp <- ggplot(selected_data, aes(x = hp)) +
geom_histogram(binwidth = 20, fill = "green", color = "white") +
labs(title = "Histogram: Horsepower", x = "Horsepower", y = "Frequency")
hist_plot_drat <- ggplot(selected_data, aes(x = drat)) +
geom_histogram(binwidth = 0.5, fill = "orange", color = "white") +
labs(title = "Histogram: Drat", x = "Drat", y = "Frequency")
grid.arrange(hist_plot_mpg, hist_plot_disp, hist_plot_hp, hist_plot_drat,
ncol = 2)
Output:
Save and extract R plots:
To save and extract plots in R, you can use the ggsave function from the ggplot2 package. In this example , We used ggplot to construct a plot and the ggsave function to save it as a PDF file (plot.pdf) and a PNG image file (plot.png). By including the correct file extension, you can indicate the intended file format.
plot <- ggplot(data = mtcars, aes(x = hp, y = mpg)) +
geom_point() +
labs(title = "Miles per Gallon vs Horsepower")
# Save the plot as an image file (e.g., PNG or PDF)
ggsave("plot.png", plot)
ggsave("plot.pdf", plot)
extracted_plot <- plot
plot
Output:
In this article we explored data visualization in R using the ggplot2 package. We explored it layers , types of plots and functions and also how to save the plots for later use.
