Univariate Continuous data (Part 2 of 2)

Chapter 11.

Exploring Univariate Continuous Data using `ggplot2` and `ggpubr`

This chapter demonstrates the use of the popular ggplot2 and ggpubr packages to further explore univariate, continuous data.

ggplot2: In the ggplot2 package for instance, the function geom_boxplot() produces box plots, geom_violin() creates violin plots, and geom_histogram() and geom_density() generate histograms and density plots, respectively. The related ggbeeswarm package can be used for creating bee swarm plots. [1]
ggpubr: The ggpubr package in R augments ggplot2 by offering tools for creating publication-ready plots. It enables simplified plotting with easy-to-use functions like gghistogram(), ggdensity(), ggboxplot(), ggviolin, and makes it easy to merge multiple plots with ggarrange(), and provides specialized themes for a polished look. Essentially, ggpubr merges ggplot2’s extensive customization with the ease of creating visually appealing and informative plots. [2]
We load the necessary packages, including ggplot2, dplyr and ggthemes packages. The package ggthemes allows us to use a variety of themes. [3]

# Load the required libraries, suppressing annoying startup messages
library(dplyr, quietly = TRUE, warn.conflicts = FALSE)
library(tibble, quietly = TRUE, warn.conflicts = FALSE)
library(ggplot2, quietly = TRUE, warn.conflicts = FALSE) 
library(ggpubr, quietly = TRUE, warn.conflicts = FALSE) 

library(rmarkdown, quietly = TRUE, warn.conflicts = FALSE) 
library(knitr, quietly = TRUE, warn.conflicts = FALSE) 
library(kableExtra, quietly = TRUE, warn.conflicts = FALSE) 

library(ggthemes)

Data: Suppose we run the following code to prepare the mtcars data for subsequent analysis and save it in a tibble called tb.

# Read the mtcars dataset into a tibble called tb
data(mtcars)
tb <- as_tibble(mtcars)

# Convert relevant columns into factor variables
tb$cyl <- as.factor(tb$cyl) # cyl = {4,6,8}, number of cylinders
tb$am <- as.factor(tb$am) # am = {0,1}, 0:automatic, 1: manual transmission
tb$vs <- as.factor(tb$vs) # vs = {0,1}, v-shaped engine, 0:no, 1:yes
tb$gear <- as.factor(tb$gear) # gear = {3,4,5}, number of gears

Let’s take a closer look at some of the most effective ways of Visualizing Univariate Continuous Data using ggplot2 and related packages, including

Bee Swarm plots using ggbeeswarm
Histograms using ggplot2 and ggpubr
PDF and CDF Density plots using ggplot2 and ggpubr
Bar plots using ggplot2
Box plots using ggplot2 and ggpubr
Violin plots using ggplot2 and ggpubr
Quantile-Quantile (Q-Q) Plots using ggplot2

Note that it is inconvenient to create Stem-and-Leaf plots using ggplot2.

Histogram using `ggplot2`

Histogram with binwidth

The following code creates a histogram using the ggplot2 package. Here, we pre-specify the bin width and the resulting number of bins in the histogram depend on the range of the data. [1]

# histogram using ggplot2 to visualize the distribution of mpg 
ggplot(tb, 
       aes(x = mpg)) +  # Set mpg as the x-axis variable
  geom_histogram(binwidth = 4,  # Define the binwidth for the histogram
                 fill = "gold",  # Set the fill color of the bins to gold
                 color = "black") +  # Set the border color of the bin
  theme_minimal() +  # Apply a minimal theme for a cleaner look
  labs(title = "Histogram of Mileage, (binwidth = 4)", 
       x = "Miles Per Gallon (mpg)",  # Label for the x-axis
       y = "Frequency")  # Label for the y-axis

Discussion:

The code ggplot(tb, aes(x = mpg)) initializes a plot using the tb data frame, mapping the mpg column to the x-axis.
The histogram is created with geom_histogram(), using an adjustable binwidth = 4. Given this bin width, the resulting number of bins in the histogram depend on the range of mpg.
The binwidth argument specifies the width of the bins in the histogram, and we have chosen 4 as an arbitrary width.
We use fill and color to set the bar colors to be gold with a black border.
A clean appearance is achieved with theme_minimal(), and titles and labels are added using labs(). [1]

Histogram with bins

We could alternately set the number of bins in the histogram, instead of specifying the bin width. In this case, the bin-width gets calculated depending on the range of the data and the specified number of bins.

# Create a histogram of mpg from the tb dataset
ggplot(tb, aes(x = mpg)) +
  geom_histogram(bins = 8, fill = "gold", color = "black") + 
  # Set histogram bins to 8 with gold fill and black border
  theme_minimal() + # Use a minimalistic theme
  labs(title = "Histogram of Mileage, (bins = 8)", 
       x = "Miles Per Gallon (mpg)", 
       y = "Frequency") # Add title and axis labels

Discussion:

We instruct R to create a histogram having 8 bins of equal width, by setting bins = 8 in geom_histogram()
The width of each bin is adjusted by dividing the range of mpg by the number of specified bins. [1]

Histogram with bin range

Alternately, we can specify custom bin ranges in a histogram. In this this approach, we supply a vector of breakpoints which defines the range of each bin. For example, the following code defines histogram bins with ranges of 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, for the mpg variable

# Plot a histogram of mpg values from the tb dataset using ggplot2
ggplot(tb, aes(x = mpg)) +
  geom_histogram(breaks = seq(5, 40, by = 5), 
                 fill = "gold", 
                 color = "black") + 
  # Define histogram breaks at intervals of 5
  theme_minimal() + # Apply a minimal theme for clarity
  labs(title = "Histogram of Mileage, (breaks of 5)", 
       x = "Miles Per Gallon (mpg)", 
       y = "Frequency") # Add plot title and axis labels

Discussion:

ggplot(tb, aes(x = mpg)) initializes a ggplot object with the tb data frame and sets the mpg column as the x-axis variable.
geom_histogram() adds a histogram layer, in which breaks = seq(5, 40, by = 5) specifies bin edges using a sequence that starts at 5, ends at 40, and increases by 5 units. This results in bins like [5,10), [10,15), and so on. [1]

Histogram using `ggpubr`

Recreating a histogram with binwidth of 4, using ggpubr:

# Load the ggpubr package for enhanced ggplot2-based plotting
library(ggpubr)

# Create a histogram using gghistogram from ggpubr
gghistogram(tb, 
            x = "mpg",  # Set 'mpg' as the variable for histogram
            binwidth = 4,  # Set the width of bins in the histogram
            add = "mean",  # Add a line indicating the mean of 'mpg'
            rug = TRUE,  # Add a rug plot to show individual data points
            color = "black",  # Set the color of bins to black
            fill = "lightpink",  # Set the fill color of bins to light pink
            title = "Histogram of Mileage, (binwidth = 4)"
)

Discussion:

Here, we’re invoking the gghistogram() function from ggpubr to craft a histogram. The data source is specified as tb, and the variable of interest is mpg.
x = "mpg": This denotes the variable from tb we’re visualizing.
binwidth = 4: Each bin in the histogram will span a range of 4 units of mpg.
add = "mean": Superimposes the mean of mpg on the histogram.
rug = TRUE: Includes a rug plot at the base, which displays individual data points.
color = "black": The outline of the bars will be in black.
fill = "lightpink": Bars in the histogram will be filled with a light pink shade.
title =: Gives a descriptive title to the histogram.
In sum, we’re visualizing the distribution of the mpg variable from the tb dataset as a light pink histogram, emphasized with black borders, with a bin width of 4 units. We’ve also marked the mean value and showcased individual data points as a rug plot beneath the histogram. [2]

Recreating a histogram with 8 bins, using package ggpubr:

# Load the ggpubr package
library(ggpubr)

# Create a histogram of the 'mpg' variable from the tb dataset
gghistogram(tb, 
            x = "mpg",  # Specify 'mpg' for the x-axis
            bins = 8,  # Set the number of bins to 8
            add = "median",  # Add a line indicating the median
            rug = TRUE,  # rug plot to show individual data points
            color = "black",  # color the histogram bins black
            fill = "lightpink",  # fill color of the bins light pink
            title = "Histogram of Mileage, showing median (bins = 8)" 
)

Discussion:

In essence, the difference is that this code visualizes the mpg data from the tb dataset as a histogram with 8 bins, set using bins = 8. [2]

Probability Density Function (PDF) plot using `ggplot2`

Recall that this type of plot shows the distribution of a single variable, and the area under the curve represents the probability of an observation falling within a particular range of values. The following code generates it using ggplot2:

# Use ggplot2 to create a density plot of mpg values from the tb dataset
ggplot(tb, aes(x = mpg)) +
  geom_density(fill = "gold") +  # density plot with gold fill color
  theme_minimal() +  # Apply a minimal theme for a clean  appearance
  labs(title = "Probability Density Function of Miles Per Gallon (mpg)",  
       x = "Miles Per Gallon (mpg)",  # Label for the x-axis
       y = "density")  # Label for the y-axis

Discussion:

We designate our data source and the aesthetic mappings using the ggplot() function. The aesthetic mapping for x is mpg.
Subsequently, we append a density plot to our plot by using the geom_density() function. We fill the area under the curve by setting fill to “gold”. [1]

PDF using `ggpubr`

The following R code creates a PDF of the mpg variable in the tb dataset, using the ggdensity() function from the ggpubr package.

# Load the ggpubr package for enhanced data visualization
library(ggpubr)

# Create a probability density function plot for 'mpg' using ggdensity
ggdensity(tb, 
          x = "mpg",  # Specify 'mpg' as the variable for the plot
          add = "mean",  # Add a line indicating the mean of 'mpg'
          rug = TRUE,  # rug plot to show individual data points
          color = "black",  # color of the plot  black
          fill = "lightpink",  # Set the fill color to light pink
          title = "PDF of Miles Per Gallon (mpg), using ggpubr",  
          xlab = "Mileage (mpg)", 
          ylab = "Probability Density Function (PDF)" 
)

Discussion:

The ggdensity() function from ggpubr package, is utilized here to visualize a probability density function (PDF) of the mpg variable from the tb dataset.
x = "mpg": This specifies the column mpg from the tb dataset as the variable we aim to visualize.
add = "mean": This argument ensures that a line or marker is added to the plot, indicating the mean value of the mpg data.
rug = TRUE: By setting this to TRUE, a rug plot is added at the bottom, showcasing individual data points.
color = "black": This defines the border color of the density plot as black.
fill = "lightpink": This fills the interior of the density plot with a light pink color.
title = : This provides a descriptive title to our plot, aiding in clarity and understanding. [3]

Cumulative Distribution Function (CDF) Plot using `ggplot2`

The following code generates a CDF using ggplot2:

# Load required library
library(ggplot2)

# Create a CDF (Cumulative Distribution Function) plot for mpg
ggplot(tb, aes(x = mpg)) +
  stat_ecdf(geom = "line", color = "blue") +  # plot the CDF
  labs(x = "Miles Per Gallon (mpg)", y = "CDF",  
       title = "Cumulative Distribution Function (CDF) of mpg") +  
  theme_minimal()  # Apply a minimalistic theme for clarity

Discussion:

Here, we’re initiating a plot using the ggplot() function, specifying tb as our data source and the mpg column as the variable of interest. `
We employ the stat_ecdf() function to represent the empirical cumulative distribution function (CDF) of the mpg data. The geom = "line" argument means the CDF will be displayed as a continuous line, and color = "blue" ensures this line is blue.
The labs() function is used to define axis labels and a plot title, enhancing readability.
By invoking theme_minimal(), we apply a clean and straightforward theme to our plot, which removes extraneous details and emphasizes content.
To sum up, this code creates a plot depicting the cumulative distribution function (CDF) of the mpg data from the tb dataset. [1]

Boxplots using `ggplot2`

The following code generates a boxplot using ggplot2:

# Use ggplot2 to create a boxplot for the mpg variable from the tb dataset
ggplot(tb, aes(y = mpg)) +
  geom_boxplot(fill = "gold") +  # Create a boxplot with gold color
  theme_minimal() +  # Apply a minimal theme for a clean appearance
  coord_flip() +  # Flip coordinates to horizontal boxplot
  labs(title = "Boxplot of Mileage (mpg)",  # Set the title
       y = "Miles Per Gallon (mpg)")  # Label for the y-axis

Discussion:

ggplot(tb, aes(y = mpg)): Initializes a ggplot2 plot using tb with mpg as the y-axis.
geom_boxplot(fill = "gold"): Adds a gold-filled boxplot layer.
theme_minimal(): Applies a clean, minimalistic theme to the plot.
coord_flip(): Flips the plot to display a horizontal boxplot.
labs(): Sets the plot title to “Boxplot of Mileage (mpg)” and labels the x-axis as “Miles Per Gallon (mpg)”.
In summary, this code creates a horizontal boxplot of the mpg values from the tb data frame, with the boxes filled in gold color, presented with a minimalistic theme, and labeled appropriately. [1]

Boxplot using `ggpubr`

The following code recreates the boxplot using the ggboxplot() function from the ggpubr package.

# Load the ggpubr package for enhanced boxplot functionalities
library(ggpubr)

# Create a boxplot for the 'mpg' using ggboxplot from ggpubr
ggboxplot(tb, 
          y = "mpg",  # Specify 'mpg' as the variable for the boxplot
          orientation = "horizontal",  # Set the orientation to horizontal
          rug = TRUE,  # Add a rug plot to show individual data points
          color = "black",  # Set the color to black
          fill = "lightpink",  # Set the fill color  to light pink
          add = "jitter",  # Add jitter to display individual data points
          title = "Boxplot of Mileage (mpg), using ggpubr::ggboxplot()"  
)

Discussion:

ggboxplot(tb, y = "mpg"): Creates a boxplot of mpg values from the tb data frame.
orientation = "horizontal": Sets the boxplot to display horizontally.
rug = TRUE: Shows a rug plot indicating the density of data points.
color = "black": Sets the boxplot’s border color to black.
fill = "lightpink": Colors the inside of the boxes light pink.
add = "jitter": Adds jittered points to the boxplot for clearer data visualization.
title = ..": Sets the plot’s title. [2]

Violin plot using `ggplot2`

The following code generates a violin plot using ggplot2, adding a boxplot to the violin plot:

# Use ggplot2 to create a combined violin and boxplot for mpg values 
ggplot(tb, aes(x = "", y = mpg)) +
  geom_violin(fill = "gold") +  # Create a violin plot with gold fill color
  geom_boxplot(fill = "skyblue", width = 0.2) +  
  # Overlay a boxplot with sky blue fill and reduced width
  labs(x = "", # Set labels for x and y axes (x-label is empty)
       y = "Miles Per Gallon (mpg)",  
       title = "Violin Plot with Boxplot of Miles Per Gallon (mpg)") +  
  coord_flip() +  # Flip coordinates to make the plot horizontal
  theme_minimal()  # Apply a minimal theme for a simple appearance

Discussion:

geom_violin() generates the violin plot
geom_boxplot() embeds a box plot within it. [1]

Violin plot using `ggpubr`

The following code recreates the violin plot using the ggviolin() function from the ggpubr package.

# Load the ggpubr package for enhanced data visualization
library(ggpubr)

# Create a violin plot for the 'mpg' variable using ggviolin
ggviolin(tb, 
         y = "mpg",  # Specify 'mpg' as the variable for the violin plot
         orientation = "horizontal",  # Set the orientation to horizontal
         rug = TRUE,  # Include a rug plot to show individual data points
         color = "black",  # Outline color of the violin  black
         fill = "lightpink",  # Fill color of the violin light pink
         add = "boxplot", add.params = list(fill = "lightblue"),  
         # Add a boxplot inside the violin plot with light blue fill
         title = "Violin plot of mpg, using ggpubr::ggviolin()"
)

Discussion:

tb: This refers to the dataset we’re using. The dataset should have a variable named “mpg” as we’ve specified it in the next parameter.
y = "mpg": This indicates that we want the “mpg” variable (Miles Per Gallon) from the tb dataset to be plotted on the y-axis.
orientation = "horizontal": This parameter sets the orientation of the violin plot to be horizontal.
rug = TRUE: This adds a “rug” to the plot, which essentially places small vertical bars (or ticks) at the actual data points along the axis.
color = "black": The edge color of the violin plot is set to black.
fill = "lightpink": This sets the main color inside the violin plot to light pink.
add = "boxplot": This specifies that a boxplot should be added inside the violin plot. A boxplot provides a summary of the distribution, showing the median, quartiles, and potential outliers.
add.params = list(fill = "lightblue"): This further customizes the added boxplot by setting its fill color to light blue.
title = "Violin plot of Miles Per Gallon (mpg), using ggpubr::ggviolin()": This sets the title of the plot.
In summary, the code generates a horizontal violin plot for the “mpg” variable from the tb dataset. The violin plot showcases the distribution of the “mpg” variable, colored in light pink with a light blue boxplot added inside. The rug adds an additional layer of visualization, showing the actual data points along the axis. [2]

Bee Swarm plot using `ggbeeswarm`

The bee swarm plot is an alternative to the box plot, where each point is plotted in a manner that avoids overlap.
We use the ggbeeswarm package on the mpg column of the tb tibble.

# Load necessary libraries
library(ggplot2)
library(ggbeeswarm) # Provides the geom_beeswarm() function

# Create a bee swarm plot for visualizing the distribution of mpg
ggplot(tb, 
       aes(x = 1,  # Dummy x-axis value
           y = mpg)) +  # mpg values on the y-axis
  geom_beeswarm() +  # Use beeswarm plot  points
  labs(title = "Bee Swarm plot of Miles Per Gallon (mpg)",  
       y = "Miles Per Gallon (mpg)") +  # Label for the y-axis
  theme_minimal()  # theme for a clean and clear presentation

Discussion:

Initially, we declare our dataset and the aesthetic mappings, defining how variables in the data are visually represented. For the bee swarm plot, we only need a y aesthetic, which is mpg. We set the x aesthetic to 1 as a placeholder, because bee swarm plots require an x aesthetic, but we only have one variable.
Following that, we append a bee swarm plot using the geom_beeswarm() function.
We use the labs() function to label the plot.
We then adopt a minimalist theme by using theme_minimal() to give our plot a sleek and simple look. [4]

Quantile-Quantile (Q-Q) Plots using `ggplot2`

Recall that a Q-Q plot is a graphical method for comparing two probability distributions by plotting their quantiles against each other. The following code generates a Quantile-Quantile (Q-Q) plot using ggplot2. [1]

# Use ggplot2 to create a Q-Q plot for the mpg variable 
ggplot(tb, aes(sample = mpg)) +
  stat_qq() +  # Create a Q-Q plot
  stat_qq_line() +  # Add a line to the Q-Q plot for reference
  labs(x = "Theoretical Quantiles",  # Label for the x-axis
       y = "Sample Quantiles",  # Label for the y-axis
       title = "Q-Q Plot of Miles Per Gallon (mpg)") +  
  theme_minimal()  # Apply a minimal theme for a clean appearance

Discussion:

ggplot(tb, aes(sample = mpg)) : Here, we initiate a ggplot graphic using the tb dataset and set the aesthetic (aes) to the “mpg” variable. In the context of the stat_qq() function (which we’ll come to shortly), the sample aesthetic specifies the data variable for which we want to create the Q-Q plot.
stat_qq() : This function adds the Q-Q plot points to the graphic. The x-axis of this plot represents the quantiles from a theoretical distribution (often the standard normal distribution), and the y-axis represents the quantiles from our sample data (“mpg”).
stat_qq_line() : This function adds a reference line to the Q-Q plot, which represents the expected line if the sample comes from the specified distribution (again, often the standard normal distribution). If our data points lie roughly on this line, it suggests that the data follows the theoretical distribution.
In summary, this code produces a Q-Q plot for the “mpg” variable from the tb dataset. The plot compares the quantiles of “mpg” against the quantiles of a theoretical distribution, often the standard normal distribution. [1]

Visualizing Summary Statistics

Bar Plot visualizing the Mean and SD using `ggplot2`

We can create a Bar Plot to visualize the mean of a continuous variable. [1]

# Create a summary data frame
mpgSummary <- tb %>%
  summarise(
    MeanMpg = mean(mpg, na.rm = TRUE),  # Calculate the mean of mpg
    SDMpg = sd(mpg, na.rm = TRUE)  # Calculate the standard deviation 
  )

# Create a bar plot visualizing the mean
ggplot(mpgSummary, 
       aes(y = "", 
           x = MeanMpg)) +
  geom_bar(stat = "identity",  # Use bars to represent values
           fill = "lightblue", 
           color = "blue", 
           width = 0.2  # Set bar color and width
           ) +
  labs(x = "mpg", 
       y = "",  # Set x-axis label to 'mpg' with y-axis label empty
       title = "Bar Plot showing Mean of Mileage (mpg)") +  
  theme_minimal()  # a minimal theme for a cleaner look

We can extend this to create a Bar Plot with Error Bars to visualization the Mean and the Standard Deviation of a continuous variable. [1]

# Create a summary data frame
mpgSummary <- tb %>%
  summarise(
    MeanMpg = mean(mpg, na.rm = TRUE),  # Calculate the mean of mpg
    SDMpg = sd(mpg, na.rm = TRUE)  # Calculate the standard deviation 
  )

# Create a bar plot visualizing the mean and error bars visualizing the SD
ggplot(mpgSummary, 
       aes(y = "", 
           x = MeanMpg)) +
  geom_bar(stat = "identity", 
           fill = "lightblue", 
           color = "blue", 
           width = 0.2  # Create a bar to represent the mean mpg
           ) +
  geom_errorbar(aes(xmin = MeanMpg - SDMpg,  
                    xmax = MeanMpg + SDMpg),  
                # Set the lower, upper limit of the error bar
                color = "red",  # Color the error bars red
                width = 0.1)  # Set the width of the error bars

Discussion

Summary Data Frame (mpgSummary) Creation:

mpgSummary <- tb %>% summarise(): This line is initializing the creation of mpgSummary data frame using the data in tb (which is assumed to contain the mtcars dataset) and the summarise function from the dplyr package.
MeanMpg = mean(mpg, na.rm = TRUE): Calculates the mean of the mpg column, ignoring any NA values, and creates a new column MeanMpg in mpgSummary to store this value.
SDMpg = sd(mpg, na.rm = TRUE): Calculates the standard deviation of the mpg column, ignoring any NA values, and creates a new column SDMpg in mpgSummary to store this value.

Bar Plot with Error Bars Creation:

ggplot(mpgSummary, aes(y = "", x = MeanMpg)) +: Initializes the creation of a ggplot object using the mpgSummary data frame and sets the aesthetic mappings where x is mapped to MeanMpg and y is set to an empty string.
geom_bar(stat = "identity", fill = "lightblue", color = "blue", width = 0.3): Adds a bar geometry to represent the mean mpg (MeanMpg). The bar is colored light blue with a blue border and has a width of 0.3.
geom_errorbar(aes(xmin = MeanMpg - SDMpg, xmax = MeanMpg + SDMpg), color = "red", width = 0.3): Adds error bars to represent the variability of mpg. The error bars extend from MeanMpg - SDMpg to MeanMpg + SDMpg and are colored red with a width of 0.3.
labs(x = "mpg", y = "", title = "Bar Plot showing (Mean +/- SD) of Mileage (mpg)"): Adds labels to the plot, with “mpg” as the x-axis label, no y-axis label, and a title indicating that the plot shows the mean and standard deviation of mileage.
theme_minimal(): Applies a minimal theme to the plot for a clean and uncluttered appearance.
The end result of executing this code is a bar plot visualizing the mean of the mpg column and error bars representing one standard deviation above and below the mean, giving a sense of the variability of the miles per gallon in the mtcars dataset.

Combining Plots using `ggarrange()`

The following code showcases two plots side-by-side. [2]

# Load necessary libraries
library(ggplot2)
library(ggpubr)

# Create a histogram of the 'mpg' variable using gghistogram
PlotHist <- gghistogram(tb, 
            x = "mpg",  # Set 'mpg' as the variable for the histogram
            binwidth = 4,  # Define the width of the bins
            add = "mean",  # Add a line indicating the mean of 'mpg'
            rug = TRUE,  # Include a rug plot to show data points
            color = "black",  # Set the color of the histogram to black
            fill = "lightpink",  # Set the fill color of the histogram 
            title = "Histogram (binwidth = 4)"  # Title of the histogram
)

# Create a box plot of the 'mpg' variable using ggboxplot
PlotBox <- ggboxplot(tb, 
            y = "mpg",  # Set 'mpg' as the variable for the box plot
            rug = TRUE,  # Include a rug plot to show individual data points
            color = "black",  # Set the color of the box plot to black
            fill = "lightpink",  # Set the fill color of the box plot 
            add = "jitter",  # Add jitter to show individual data points
            title = "Boxplot of Mileage"  
)

# Combine the plots using ggarrange()
combined_plot <- ggarrange(PlotHist, PlotBox, ncol = 2)  
# Arrange the histogram and box plot side by side

# Display the combined plot
print(combined_plot)

Discussion:

We create a histogram and a boxplot using the same code discussed above.
ggarrange(PlotHist, PlotBox, ncol = 2) : This code helps us combine multiple plots into one. Here, PlotHist (the histogram) and PlotBox (the boxplot) are arranged side by side, as indicated by ncol = 2.
In essence, this code facilitates a side-by-side comparison of the distribution of the mpg variable from the tb dataset using two different types of visualizations: a histogram and a boxplot. [1]

Summary of Chapter 11 – Univariate Continuous data (Part 2 of 2)

In this chapter, we explore how to visualize univariate continuous data using the ggplot2 package in R. We use the mtcars data set, converting it to a tibble called tb for easier manipulation.

The visualization methods we cover include histograms, density plots (Probability Density Function and Cumulative Density Function), box plots, bee swarm plots, violin plots, and Q-Q plots. These are created using functions like geom_histogram(), geom_density(), geom_boxplot(), geom_beeswarm(), geom_violin(), stat_qq(), and stat_qq_line().

For the histogram, we can adjust bin width, color, and number of bins, or define custom bin ranges. The density plots provide a visual representation of the distribution of a variable, and we can color the area under the curve. To create the CDF plot, we first arrange our data and calculate the cumulative distribution, which is plotted as a line graph. For the violin plot, we show how to add a box plot within the violin for additional information. Finally, we explore Q-Q plots, which compare the quantiles of our data to a theoretical distribution, useful for assessing if the data follows a certain theoretical distribution.

References

ggplot2:

[1] Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. Retrieved from https://ggplot2.tidyverse.org

Wickham, H., & Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.

Wickham, H. (2020). ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics (Version 3.3.2) [Computer Software]. Retrieved from https://CRAN.R-project.org/package=ggplot2

Wickham, H., et al. (2020). dplyr: A Grammar of Data Manipulation (Version 1.0.2) [Computer Software]. Retrieved from https://CRAN.R-project.org/package=dplyr

Wilkinson, L. (2005). The Grammar of Graphics (2nd ed.). Springer-Verlag.

Wickham, H., et al. (2020). tibble: Simple Data Frames (Version 3.0.3) [Computer Software]. Retrieved from https://CRAN.R-project.org/package=tibble

ggpubr:

[2] Kassambara, A. (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R Package Version 0.6.0. Retrieved from https://rpkgs.datanovia.com/ggpubr/.

ggthemes:

[3] Arnold, J.B. (2020). ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’ (Version 4.2.0) [Computer Software]. Retrieved from https://CRAN.R-project.org/package=ggthemes

ggbeeswarm:

[4] Eklund, A. (2020). ggbeeswarm: Categorical Scatter (Violin Point) Plots. R Package Version 0.6.0. Retrieved from https://CRAN.R-project.org/package=ggbeeswarm

Univariate Continuous data (Part 2 of 2)

Exploring Univariate Continuous Data using ggplot2 and ggpubr

Histogram using ggplot2

Histogram with binwidth

Histogram with bins

Histogram with bin range

Histogram using ggpubr

Probability Density Function (PDF) plot using ggplot2

PDF using ggpubr

Cumulative Distribution Function (CDF) Plot using ggplot2

Boxplots using ggplot2

Boxplot using ggpubr

Violin plot using ggplot2

Violin plot using ggpubr

Bee Swarm plot using ggbeeswarm

Quantile-Quantile (Q-Q) Plots using ggplot2