Appendix B — An Overview of Plotting Data in R

Author

Adam Spiegler, University of Colorado Denver

Open In Colab

Introduction

Plots can provide a useful visual summary of the data. Sometimes, a nice plot or two is all that is need for statistical analysis. In this document, we cover a basic overview of creating some plots in R.

Here’s a link to a more thorough coverage of plotting in R: https://r-graph-gallery.com/index.html.

Help Documentation

The plotting functions introduced in this document have robust help documentation with lots of options to customize your plots. If you want to view help documentation for any of the functions used in this document, run commands such?hist, ?plot, ?table, and so on.

# access help documentation for hist
?hist  #Side panel should open with help doc

What Are Packages in R?

R packages are a collection functions, sample data, and/or other code scripts. R installs a set of default packages during installation.

Run the code cell below to get a list of all default R packages that are already installed.

# See a list of installed default packages
allpack <- installed.packages()
rownames(allpack)

Loading Packages with the library() Command

Each time we start or restart a new session and want to access the library of functions and data in the package, we need to load the library of files in the package with the library() command.

To demonstrate how to create common statistical plots in R, we will use the storms data set which is located in the package dplyr.

  • The dplyr package is already installed in Google Colaboratory
  • We still need to use a library command to load the package.
  • Run the code cell below to load the dplyr package.
# load the library of functions and data in dplyr
library(dplyr)

Reloading Packages When Restarting a Session

If we take a break in our work, it is possible our R session will time out and close. Each time we restart an R session, we will need to rerun library() commands in order reload any packages we plan to use.

The same caution applies to any objects, vectors, or data frames we create or edit in an R session. If a session times out, and we want to use an object x that we previously created, we will need to run the code cell(s) where object x is created again before we can refer back to x in the current session.

BE SURE YOU RUN THE COMMAND library(dplyr) BEFORE ATTEMPTING TO RUN ANY OF THE CODE CELLS BELOW!

Summarizing storms Data

The package dplyr contains a data set called storms. Let’s find some useful information about this data.

  • The first code cell below will open the help manual for storms in a side bar.
    • Feel free to close the help side bar.
  • The second code cell below will provide a numeric summary of all variables in the storms data.
  • Recall we need to first run the command library(dplyr) in the code cell above to be able to access storms.
# be sure to run the code cell above first
# so you have loaded the dplyr package
?storms  
# See a summary of all variables
summary(storms)
     name                year          month             day       
 Length:19066       Min.   :1975   Min.   : 1.000   Min.   : 1.00  
 Class :character   1st Qu.:1993   1st Qu.: 8.000   1st Qu.: 8.00  
 Mode  :character   Median :2004   Median : 9.000   Median :16.00  
                    Mean   :2002   Mean   : 8.699   Mean   :15.78  
                    3rd Qu.:2012   3rd Qu.: 9.000   3rd Qu.:24.00  
                    Max.   :2021   Max.   :12.000   Max.   :31.00  
                                                                   
      hour             lat             long                         status    
 Min.   : 0.000   Min.   : 7.00   Min.   :-109.30   tropical storm     :6684  
 1st Qu.: 5.000   1st Qu.:18.40   1st Qu.: -78.70   hurricane          :4684  
 Median :12.000   Median :26.60   Median : -62.25   tropical depression:3525  
 Mean   : 9.094   Mean   :26.99   Mean   : -61.52   extratropical      :2068  
 3rd Qu.:18.000   3rd Qu.:33.70   3rd Qu.: -45.60   other low          :1405  
 Max.   :23.000   Max.   :70.70   Max.   :  13.50   subtropical storm  : 292  
                                                    (Other)            : 408  
    category          wind           pressure      tropicalstorm_force_diameter
 Min.   :1.000   Min.   : 10.00   Min.   : 882.0   Min.   :   0.0              
 1st Qu.:1.000   1st Qu.: 30.00   1st Qu.: 987.0   1st Qu.:   0.0              
 Median :1.000   Median : 45.00   Median :1000.0   Median : 110.0              
 Mean   :1.898   Mean   : 50.02   Mean   : 993.6   Mean   : 146.3              
 3rd Qu.:3.000   3rd Qu.: 65.00   3rd Qu.:1007.0   3rd Qu.: 220.0              
 Max.   :5.000   Max.   :165.00   Max.   :1024.0   Max.   :1440.0              
 NA's   :14382                                     NA's   :9512                
 hurricane_force_diameter
 Min.   :  0.00          
 1st Qu.:  0.00          
 Median :  0.00          
 Mean   : 14.81          
 3rd Qu.:  0.00          
 Max.   :300.00          
 NA's   :9512

One Quantitative Variable

Often a graph or plot is a more preferred format to summarize a variable than a summary statistics. The documentation below explains we could graphically summarize the quantitative variable pressure.

Histograms

The hist function can be used create a histogram of a numerical vector.

hist(storms$pressure,  # plot pressure variable in storms data
     xlab = "storm pressure (in millibars)",  # x-axis label
     main = "Distribution of Storm Pressure",  # main title
     breaks = 10,  # number of breaks or bins
     col = "aquamarine4")  # color of bars

Density plots

A histogram is more sensitive to its options. For example, a histogram with 3 breaks may tell a different story than plotting the same data with 20 breaks.

Thus, we may prefer to use a density plot.

  1. First compute density of pressure.
  1. The plot() function will then create a density plot.
  • For more advanced density plots see https://r-graph-gallery.com/density-plot.html.
  • If a variable is categorical, plot() will create a different plot, namely a bar chart.
  • plot() can also be used to generate a plot to compare two different variables.
  • The output of plot() depends on the type and number of variables that we input in the function.
# approximate densities and then plot
plot(density(storms$pressure),
     xlab = "storm pressure (in millibars)",  # horizontal axis label
     main = "Distribution of Storm Pressure")  # main title

Boxplots

Boxplots are another useful plot for presenting the distribution of a quantitative variable using quartiles and the five number summary.

# create boxplot of quantitative variable
boxplot(storms$pressure,
        ylab = "storm pressure (in millibars)",  # horizontal axis label
        col = "gold",  # color of box
        main = "Distribution of Storm Pressure")  # main title

Changing the Layout of Boxplots

# horizontally aligned boxplot
boxplot(storms$pressure,
        horizontal = TRUE,  # display horizontally
        xlab = "storm pressure (in millibars)",  # horizontal axis label
        main = "Distribution of Storm Pressure",  # main title
        col = "azure3")  # color

One Qualitative Variable

Qualitative (also called categorical) variables required other types of plots. For example, we cannot create a density or boxplot for a qualitative variable. Qualitative variables may be stored as characters (such as the status variable) or values (such as the category variable). This brings up a good question:

How can we tell whether a variable is stored as a numerical variable, a categorical variable, or perhaps as a string of characters?

Checking the Data Type

The typeof() command can help identify what is the type of a variable.

typeof(storms$status)
[1] "integer"
typeof(storms$category)
[1] "double"

Data Types

From the output above, we see:

  • The variable status is initially read as an integer.
  • The individual values are strings of characters such as “tropical storm” or “hurricane”.
  • The summary statistics of status are counts that are stored as integers.
  • The variable category is initially read as double or decimal values.
  • The individual values are ordinal integers “1”, “2”, “3”, “4”, and “5” for category of hurricane.
  • There are 14,2328 NA (or missing) values corresponding to the observations that are not hurricanes.
  • The summary statistics of category (such as the mean) are stored decimals.
  • However, we would like to treat category as a qualitative variable and plot how many storms fall into each category.

Caution with Data Types and Using plot()

If we try to use the general plot() function, R will give its best guess at which plot makes the most sense to display the data. If the data is stored as the wrong data type, plot() will not work as we might expect.

  • Run the two code cells below, and notice the following:
    • The output of the plot(storms$status) looks like a reasonable bar chart.
    • The output of plot(storms$category) does not nicely summarize the counts of how many storms are in each category.
plot(storms$status)  # plot of status

plot(storms$category)  # plot of category

Creating Bar Charts From Tables

The table() function will count the number of times a value (or string of characters) occurs in a vector or variable.

One way to improve the initial plot of categories above is as follows:

  1. First use the table() command to count how many storms are in each category.
  2. Then create a bar chart using the barplot() function.
cat.table <- table(storms$category)  # create table of counts
cat.table  # print table to screen

   1    2    3    4    5 
2478  973  579  539  115 
# create bar chart from table counts
barplot(cat.table,  # input table from previous code cell
        main = "Distribution of Hurricane Categories",  # main title
        xlab = "Hurricane Category",  # horizontal axis label
        ylab = "Frequency",  # vertical axis label
        col = "steelblue")  # fill color of bars

Relative Frequency Tables and Bar Charts

If instead of plotting the number of hurricanes in each category we wish to plot the proportion of all hurricanes in each category, we can use the prop.table() function to convert the table counts to proportions relative to the grand total.

Run the two code cells below to create a relative frequency bar chart.

  1. We input our previous table of counts, cat.table, into the function prop.table() to convert counts to proportions.
  2. Then we create a bar chart of the resulting proportions.
cat.prop <- prop.table(cat.table)  # create table of proportions
barplot(cat.prop,  # input table of proportions
        main = "Relative Frequency of Hurricane Categories",  # main title
        xlab = "Hurricane Category",  # horizontal axis label
        ylab = "Proportion",  # vertical axis label
        col = "steelblue")  # fill color of bars

Caution with prop.table()
  • The input into prop.table() must be a table rather than a vector or data frame column.
  • The code cell below does return a relative frequency table as we would expect since we did not first create a table of counts from storms$category.
temp <- prop.table(storms$category)  # do not input a vector
head(temp)  #  print first several entries of result
[1] NA NA NA NA NA NA

Pie Charts with pie()

Pie charts can also be used to illustrate the distribution of one qualitative variable.

?pie
# create pie chart of one qualitative variable
pie(cat.table,  # input table
    main = "Distribution of Hurricane Categories")  # main title

Converting to a factor() and Then plot()

One common issue with a qualitative variable is that it is often stored as the wrong datatype.

  • Qualitative data should typically be stored as a factor.

Another way we can create a bar chart of the counts in each category is to:

  1. First convert the qualitative variable to a factor.
  2. Then use plot() to create an appropriate plot.

Run the code cell below to first see the summary output of the category variable after converting it to a factor.

# creates a copy of storms data set
# so we don't overwrite original storms
storms2 <- storms  

storms2$category <- factor(storms$category)  # convert category to factor
summary(storms2$category)  # get new summary of categories
    1     2     3     4     5  NA's 
 2478   973   579   539   115 14382

Notice the summary is a table of counts in each hurricane category.

  • Once the variable status is converted to a factor, the plot() function will know to use a bar chart to give a summary display.
# create bar chart from counts of a factor
plot(storms2$category,  # input a factor
     main = "Distribution of Hurricane Category",  # main title
     xlab = "Hurrican Category",  # horizontal axis label
     ylab = "Frequency",  # vertical axis label
     col = "steelblue")  # color of fill of ba

  • Recall without first changing category to a factor, plot() will create a different graph.
# default plot of category when not first converted to factor
plot(storms$category)

Plotting One Quantitative and One Qualitative Variable<

Imagine we would like to compare the wind speeds of storms by status. In this case, we would like to compare a quantitative variable (wind) for different classes of a qualitative variable (status).

Side by Side Boxplots

There are many classes of storms status in storms.

In the storms data:

  • wind is a quantitative variable.
  • status is a qualitative variable.
  • We can use the default plot() function to create a side by side boxplots.
# create a vector of fill colors
# one color for each status type.
my.colors <- c("green", "purple", "grey", "red", 
               "blue", "gold", "cyan", "pink", "orange")

plot(wind ~ status,  # quantitative first ~ categorical second
     data = storms,  # name of data frame
     col = my.colors,  # fill colors
     main = "Wind Speeds of Storms by Status")  # main title

Adding a Legend to Plots

  • There are a lot of different status of storms.
  • It is not easy (or possible) to tell which boxplot corresponds to which storm status.
  • Adding a legend to the plot will help!
# create a table of status counts
# we will pull of the row names of the table
# as the labels in the legend
status.table <- table(storms$status)

plot(wind ~ status,  # quantitative first ~ categorical second
     data = storms,  # name of data frame
     col = my.colors,  # fill colors colors
     ylab = "Wind speed in knots",  # vertical axis label
     main = "Wind Speeds of Storms by Status")  # main title

# we can add a legend to identify which plot is which storm status
legend(x = "topright",  # place legend in top right corner
       legend=rownames(status.table),  # each row of table is label in legend
       fill = my.colors)  # fill colors

Subsetting Data by Category

There are many classes of storms status in storms. Often, we want to only focus on a smaller subset of classes. We can restrict our attention to compare the wind speeds of three of the classes: “tropical storm”, “tropical depression”, and “hurricane”.

  • We can subset storms data frame into three separate data frames, one for each status of storm, using the subset() function.
  • Curious to learn more about subset? Run ?subset in a code cell to access help documentation.
  • Then we can create three separate boxplots of the wind speeds for each status.
# split data by storm status
hur <- subset(storms,  # data frame name
              status == "hurricane",  # logical test to select observations
              select = wind)  # which quantitative variable(s) to select

trop.storm <- subset(storms, 
                     status == "tropical storm",  # tropical storms
                     select = wind)
trop.dep <- subset(storms, 
                   status == "tropical depression",   # tropical depressions
                   select = wind)

# create side by side boxplot
# for each of the three subsets
boxplot(hur$wind, trop.storm$wind, trop.dep$wind, 
        main = "Windspeed of Storms", 
        names = c("Hurricanes", "Tropical Storms", "Tropical Depressions"), 
        col = c("red", "blue", "green"), 
        xlab = "Wind speed in knots", 
        horizontal = TRUE)

Relationship Between Two Qualitative Variables

Imagine we would like to compare the number of different category hurricanes that occurred in each month. In this case, we would like to compare two qualitative variables, namely category and month.

Creating Contingency or Two-Way Table

The command table(x) will count the number of times a value (or string of characters) occurs in a vector x.

The command table(x, y) will similarly create a contingency (or two-way) table to jointly compare counts of x and y.

# create a contingency table for status and month
con.table <- table(storms$category, storms$month) 
con.table  # print output to screen
   
       1    4    5    6    7    8    9   10   11   12
  1    5    0    0   18  140  581 1099  462  140   33
  2    0    0    0    0   25  198  571  150   29    0
  3    0    0    0    0   18  113  346   86   16    0
  4    0    0    0    0   18  114  295   88   24    0
  5    0    0    0    0    1   32   69   13    0    0

Creating Grouped Frequency Bar Charts

After creating a two-way table, we can present the results visually in a grouped bar chart.

# create a vector of colors
my.colors2 <- c("green", "purple", "grey", "red", "blue") 

# create side by side bar chart
barplot(con.table,  # use counts from contingency table
        beside = TRUE,  # groups side-by-side
        main = "Category Hurricanes By Month",  # main title
        xlab = "Month",  # horizontal axis label
        col = my.colors2,  # fill color of bars
        ylab = "Frequency")  # vertical axis label

# add a legend to plot
legend(x="topleft",  # place legend in top left
       legend=rownames(con.table),  # get labels from row name in contingency table
       fill = my.colors2)  # use same fill colors

Grouped Frequency Bar Charts

  • Note beside = FALSE is the default.
  • If we do not specify a beside option, a stacked bar chart is created instead.
  • In the second code cell, we also add a legend to the plot.
########################################################
# Note this has already been run in a previous section
# You do not need to run again if already created
#######################################################

# create a contingency table for status and month
con.table <- table(storms$category, storms$month) 
con.table  # print output to screen
# create a vector of colors
my.colors2 <- c("green", "purple", "grey", "red", "blue") 

# create stacked bar chart
barplot(con.table,  # use counts from contingency table
        main = "Category Hurricanes By Month",  # main title
        xlab = "Month",  # horizontal axis label
        col = my.colors2,  # color of bars
        ylab = "Frequency")  # vertical axis label

# add legend to plot
legend(x="topleft",  # place legend in top left
       legend=rownames(con.table),  # get labels
       fill = my.colors2)  # use same colors

Stacked Bar Charts Relative to Grand Total

  1. First we create a contingency table using table(x, y).
  2. Then we use prop.table([table_name]) to convert to frequencies to proportions out of the grand total.
  3. Finally we can create a group bar chart of relative frequencies.
# create two-table of counts
con.table <- table(storms$category, storms$month)

# convert counts to proportions
con.prop <- prop.table(con.table) 

# create a vector of colors
my.colors2 <- c("green", "purple", "grey", "red", "blue") 

# create stacked bar chart
barplot(con.table,  # use counts from contingency table
        main = "Category Hurricanes By Month",  # main title
        xlab = "Month",  # horizontal axis label
        col = my.colors2,  # color of bars
        ylab = "Relative Frequency (of grand total)")  # vertical axis label

legend(x="topleft",  # place legend in top left
       legend=rownames(con.table),  # get labels
       fill = my.colors)  # use same fill colors

Stacked Bar Chart Relative to Column Totals

Often, we would like the proportions in the table to be computed out of the total in each column (instead of the grand total).

  • We add the option 2 inside prop.table().
  • In this example, each column is a different month.
# create two-table of counts
con.table <- table(storms$category, storms$month)

# convert counts to proportions
# note the option 2 added to command below
con.prop.column <- prop.table(con.table, 2)  

# create a vector of colors
my.colors2 <- c("green", "purple", "grey", "red", "blue") 

# create stacked bar chart
barplot(con.prop.column,  # use counts from contingency table
        main = "Category Hurricanes By Month",  # main title
        xlab = "Month",  # horizontal axis label
        col = my.colors2,  # color of bars
        ylab = "Relative Frequency (to month totals")  # vertical axis label

legend(x="topleft",  # place legend in top left
       legend=rownames(con.table),  # get labels
       fill = my.colors)  # use same fill colors

Relationship Between Two Quantitative Variables

Imagine we would like to compare the wind speeds (wind) to the pressure (pressure). In this case, we would like to compare two quantitative variables.

A scatter plot can be used to identify the relationship between two quantitative variables.

  • If both variables are quantitative, the plot() function by default will create a scatter plot to compare the two variables.

  • For other types of scatter plots, see documentation: https://r-graph-gallery.com/scatterplot.html.

# create a scatter plot
# first variable wind is response (y-axis)
# second variable pressure is predictor (x-axis)

plot(wind ~ pressure,  # response ~ predictor(s)
     data = storms,  # data frame name
     main = "Relation of Pressure and Wind Speed of Storms",  # main title
     xlab = "Pressure (in millibars)",  # horizontal axis label
     ylab = "Wind Speed (in knots)")  # vertical axis label

Arranging Multiple Plots in an Array

par(mfrow = c(2, 2))  # create a 2 x 2 array of plots

# the next 5 plots created will be arranged in the array
boxplot(storms$wind)  # create boxplot of wind speed

# code below creates a histogram of wind speed
# we can add many options to customize
hist(storms$wind, xlab = "wind speed (in knots)",   # x-axis label
     ylab = "Frequency",  # y-axis label
     main = "Distribution of Storm Wind Speed 1975-2020",  # main label
     col = "steelblue")  # change color of bars

plot(storms$status, col = "gold")  # plots status, which is categorical

plot(wind ~ pressure, data = storms)  # plots two numerical variables

# create a table of status counts
# we will pull of the row names of the table
# as the labels in the legend
status.table <- table(storms$status)

plot(wind ~ status,  # quantitative first ~ categorical second
     data = storms,  # name of data frame
     col = my.colors,  # fill colors colors
     ylab = "Wind speed in knots",  # vertical axis label
     main = "Wind Speeds of Storms by Status")  # main title

# we can add a legend to identify which plot is which storm status
legend(x = "topright",  # place legend in top right corner
       legend=rownames(status.table),  # each row of table is label in legend
       fill = my.colors)  # fill colors

More Advanced Plots with ggplot2

The previous plots were created using R’s base graphics system.

  • base graphics are fast and simple to produce while looking professional.

A fancier alternative is to construct plots using the ggplot2 package.

  • The gg stands for Grammar of Graphics.

In its simplest form, to construct a (useful) plot in ggplot2, you need to provide:

  • A ggplot object.
    • This is usually the object that holds your data frame.
    • The data frame is passed to ggplot via the first data argument.
  • A geometry object.
    • Roughly speaking, this is the kind of plot you want.
    • e.g., geom_histogram for a histogram, geom_point for a scatter plot, geom_density for a density plot.
  • An aesthetic mapping.
    • Aesthetic mappings describe how variables in the data are mapped to visual properties of a geometry.
    • This is where you specify which variable with be the x variable, the y variable, which variable will control color in the plots, etc.
  • See https://ggplot2.tidyverse.org for documentation.
  • Download a ggplot2 cheatsheet.

Loading ggplot2

  • The ggplot2 package is already installed as a default package in Google Colaboratory.

  • However, recall each time we start or restart a new session and want to access the library of functions and data in the package, we need to load the library of files in the package with the library command.

  • Run the first code cell below to load the ggplot2 package.

  • If restarting a new session, you also need to reload the dplyr package to access storms data.

library(ggplot2)  # make sure you have installed ggplot2 package
# may need to reload
library(dplyr)

Plotting One Numerical Variable with ggplot2

To create various types of plots for one quantitative variable, such as wind:

  • The ggplot object is the data frame storms.
  • The aesthetic is the variable wind that we will plot on the x-axis.
  • Geometric objects histogram, density, and boxplot are specified in each of the three code cells below.
  • There a numerous options we can include as well.
# create a histogram
ggplot(storms, aes(x = wind)) + 
  geom_histogram(fill = "steelblue", color="black")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# create a density plot
ggplot(storms, aes(x = wind)) + 
  geom_density(color="red") + 
  theme_bw() # adding theme_bw()  makes white background

# create a boxplot
ggplot(storms, aes(x = wind)) + 
  geom_boxplot(color="black", fill="blueviolet")

Scatter Plots with ggplot2

To create a scatter plot to compare two quantitative variables such as wind speed and pressure of storms:

  • The ggplot object is the data frame storms.
  • The aesthetic are the variables
    • pressure is the predictor plotted on the x-axis.
    • wind is the response plotted on the y-axis.t
  • Geometric object is scatter.
# create a scatter plot
ggplot(storms) + 
  geom_point(aes(x = pressure, y = wind))

Scaling ggplot2 plots

In general, scaling is the process by which ggplot2 maps variables to unique values. When this is done for discrete numeric or qualitative variables, ggplot2 will often scale the variable to distinct colors, symbols, or sizes, depending on the aesthetic mapped.

In the example below, we map the status variable to the color aesthetic, which is then scaled to different colors for the different status levels.

# scatter plot with scaling
ggplot(storms) + 
  geom_point(aes(x = pressure, y = wind, color = status))

Scaling by Shape

Alternatively, we can map the status variable to the shape aesthetic, which creates a plot with different shapes for each observation based on the status level.

  • By default, 6 shapes can be used.
  • There are 9 different status of storms.
  • The last option manually sets the shapes for each status to avoid an error.
# scaling by shape
ggplot(storms) + 
  geom_point(aes(x = pressure, y = wind, shape = status)) + 
  scale_shape_manual(values=0:8)  # manually setting shapes

Applying Multiple Scales

We can even combine these two aesthetic mappings in a single plot to get different colors and symbols for each level of month and status, respectively.

  • By default, 6 shapes can be used.
  • There are 9 different status of storms.
  • The last option manually sets the shapes for each status to avoid an error.
# scaling by month and status
ggplot(storms) + 
  geom_point(aes(x = pressure, y = wind, color = month, shape = status)) + 
  scale_shape_manual(values=0:8)  # manually setting shapes for status

Facetting in ggplot2

Faceting creates separate panels (facets) of a data frame based on one or more faceting variables.

To create various scatter plots (one for each category) to compare two quantitative variables such as wind speed and pressure of storms, we can add a facet_grid.

  • Note the NA plot corresponds to the storms that are not hurricanes.
# faceting by category
ggplot(storms) + 
  geom_point(aes(x = pressure, y = wind)) + 
  facet_grid(~ category)

Bar Charts with ggplot2

Imagine we would like to compare the number of different types of storms (status) that occurred in each month.

Stacked Bar Charts of Counts with ggplot2

To create a stacked bar chart of counts for one or more qualitative variable:

  • The ggplot object is the data frame storms.
  • Geometric object is geom_bar.
  • The aesthetic is specified as:
    • Fill color, (fill) is status.
    • The height of each bar is summarizing the statistic (stat) is "count".
    • The position="stack" creates a stacked bar chart of counts.
# stacks bars on top of each other 
ggplot(storms, aes(x=month)) + 
    geom_bar(aes(fill=status), stat = "count", position="stack") + 
    ggtitle("Occurrence of Storms by Month")

Stacked Relative Frequency Bar Charts with ggplot2

To create a stacked bar chart of relative frequencies for two qualitative variables:

  • The ggplot object is the data frame storms.
  • Geometric object is geom_bar.
  • The aesthetic is specified as:
    • Fill color, (fill) is status.
    • The height of each bar is summarizing the statistic (stat) is "count".
    • The position="fill" creates a stacked bar chart of relative frequencies.
# stacks bars and standardizing each stack
ggplot(storms, aes(x=month)) + 
    geom_bar(aes(fill=status), stat = "count", position="fill") +  
    ggtitle("Occurrence of Storms by Month")

Grouped Bar Charts of Counts with ggplot2

To create various types of bar plots for one or more qualitative variables:

  • The ggplot object is the data frame storms.
  • Geometric object is geom_bar.
  • The aesthetic is specified as:
    • Fill color, (fill) is status.
    • The height of each bar is summarizing the statistic (stat) is "count".
    • The position="dodge" creates a stacked bar chart.
# creates grouped bar chart
ggplot(storms, aes(x=month)) + 
    geom_bar(aes(fill=status), stat = "count", position="dodge") +  
    ggtitle("Occurrence of Storms by Month")

Spatial Plots with mapview

Load Library

library(mapview)  # load spatial mapping package

Mapping All Storms by Status

mapview(storms, xcol = "long", ycol = "lat", 
        zcol = "status", 
        crs = 4269, grid = FALSE)

Mapping Category 5 Hurricanes

First we filter out observations with category equal to 5.

cat5 <- subset(storms , category == "5")  # keep only category 5
mapview(cat5, xcol = "long", ycol = "lat", cex = "wind", crs = 4269, grid = FALSE)
mapview(cat5, xcol = "long", ycol = "lat", zcol = "name", cex = "wind", crs = 4269, grid = FALSE)

Creative Commons License Information

Creative Commons License

Statistical Methods: Exploring the Uncertain by Adam Spiegler is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.