Bar Chart of One Variable
One of the most frequently encountered visualizations is the bar chart, created for the values of a categorical variable that are each associated with a corresponding value of a numerical variable.
Bar chart: Plot a bar for each level of a categorical variable with its height scaled according to the value of an associated numerical variable.
A call to a bar chart function contains, at a minimum, the name of
the categorical variable with the categories to be plotted. With the
BarChart() function, that variable name is the first
argument passed to the function. In this example, the only
argument passed to the function is the variable name as the data frame
is named d, the lessR default value. Or,
specify the data frame that contains the variable(s) of interest with
the data parameter.
The following illustrates the call to BarChart() with a
categorical variable named \(x\).
If only a single categorical variable is passed to
BarChart(), the numerical value associated with each bar is
the corresponding count of the number of occurrences, automatically
computed.
Default Bar Chart
Consider the categorical variable Dept in the Employee data table.
Use BarChart() to tabulate and display the visualization of
the number of employees in each department, here relying upon the
default data frame (table) named d. Otherwise add the
data= option for a data frame with another name.
Bar chart of tablulated counts of employees in each department.
## >>> Suggestions
## BarChart(Dept, horiz=TRUE) # horizontal bar chart
## BarChart(Dept, fill="reds") # red bars of varying lightness
## PieChart(Dept) # doughnut (ring) chart
## Plot(Dept) # bubble plot
## Plot(Dept, stat="count") # lollipop plot
##
## --- Dept ---
##
## Missing Values: 1
##
## ACCT ADMN FINC MKTG SALE Total
## Frequencies: 5 6 4 6 15 36
## Proportions: 0.139 0.167 0.111 0.167 0.417 1.000
##
## Chi-squared test of null hypothesis of equal probabilities
## Chisq = 10.944, df = 4, p-value = 0.027The default color theme, "colors", fills the bars in the
bar chart with with different hues (according to the default qualitative
palette). See more explanation of this and related color palettes in the
vignette Customize.
BarChart() also labels each bar with the associated
numerical value. The function provides the corresponding frequency
distribution, the table that lists the count of each category, from
which the bar chart is constructed.
We do not need to see this output to the R console repeated again for
different bar charts of the same data, so turn off for now with the
parameter quiet set to TRUE. Can set this
option for each call to BarChart(), or can set as the
default for subsequent analyses with the style()
function.
Custom Colors
Specify a single fill color with the fill parameter, the
edge color of the bars with color. Set the transparency
level with transparency. Against a lighter background,
display the value for each bar with a darker color using the
values_color parameter. To specify a color, use color
names, specify a color with either its rgb() or
hcl() color space coordinates, or use the
lessR custom color palette function
getColors().
Use the theme parameter to change the entire color
theme: “colors”, “lightbronze”, “dodgerblue”, “slatered”, “darkred”,
“gray”, “gold”, “darkgreen”, “blue”, “red”, “rose”, “green”, “purple”,
“sienna”, “brown”, “orange”, “white”, and “light”. In this example,
changing the full theme accomplishes the same as changing the fill
color. Turn off the displayed value on each bar with the parameter
values set to off. Specify a horizontal bar
chart with base R parameter horiz.
Or, can use style() to change the theme for subsequent
visualizations as well. See the Customize vignette.
Dept is not an ordinal variable (i.e., with ordered values set by the
base R factor() function). Ordinal variables plot by
default with a range of the same hue from light to dark. To illustrate,
can choose many different sequential palettes from
getColors(): “reds”, “rusts”, “browns”, “olives”, “greens”,
“emeralds”, “turquoises”, “aquas”, “blues”, “purples”, “violets”,
“magentas”, and “grays”.
The color-blind family of viridis palettes are also available: “viridis”, “cividis”, “magma”, “inferno”, “plasma”. The bar graph below indicates the primary viridis palette.
For something different, many Wes Anderson movie themes are available: “BottleRocket1”, “BottleRocket2”, “Rushmore1”, “Rushmore”, “Royal1”, “Royal2”, “Zissou1”, “Darjeeling1”, “Darjeeling2”, “Chevalier1”, “FantasticFox1”, “Moonrise1”, “Moonrise2”, “Moonrise3”, “Cavalcanti1”, “GrandBudapest1”, “GrandBudapest2”, “IsleofDogs1”, “IsleofDogs2”.
Rotate Labels
Rotate and offset the axis labels with rotate_x and
offset parameters. Do a descending sort of the categories
by frequencies with the sort parameter.
Instead of arbitrarily setting the value of the interior color of the
bars with the fill parameter, map the value of the
tabulated count to the bar fill. With mapping, the color of
the bars depends upon the bar height. The higher the bar, the darker the
color. Specify (count) as the fill color to map the values
of the numerical variable to the fill color.
Specify the Numerical Variable
One possibility begins with the values of the \(x\) and \(y\) variables, such as in a table, and then
create the bar chart directly from this summary table. To do so, enter
the paired data values into a data file such as with Excel, and then
read into R with Read(). When calling
BarChart(), specify the categorical \(x\) variable and then the numerical \(y\) variable.
When the numeric variable is specified, the data are a summary
(pivot) table, with one row for each level of the categorical variable
plotted. For example, suppose a summary table contains the departments
and the mean salary for each department. Obtain the summary table with
the lessR pivot() function (which has its
own vignette). For the data frame d, calculate the mean of
numerical variable Salary across levels of the categorical
variable Dept.
## Dept Salary_n na mean
## 1 ACCT 5 0 61792.78
## 2 ADMN 6 0 81277.12
## 3 FINC 4 0 69010.68
## 4 MKTG 6 0 70257.13
## 5 SALE 15 0 78830.07
## 6 <NA> 1 0 53772.58The general syntax follows for processing this form of the data follows.
The bar chart follows, with the aggregated data stored in the data
frame named a, so explicitly identify with the
data parameter. For only one variable analyzed, the
computed mean of the Salary variable in the a data
frame from the previous call to pivot() is named
mean by default.
Statistical Transformation of \(y\)
As seen, by default in the absence of other information,
BarChart() defines the numerical variable plotted as the
count the occurrence of each level. Can define other statistical
transformations of the numerical value of \(y\) with the stat parameter.
Possible values of stat: "sum",
"mean", "sd", "dev",
"min", "median", and "max". The
"dev" value displays the mean deviations to further
facilitate a comparison among levels.
Here the \(x\)-variable is Dept, and
\(y\)-variable is Salary.
Display bars for values of dev <= 0 in a different color
than values above with the fill_split parameter set at
0. Do an ascending sort with the sort
parameter set at "+".
Compare this visualization of the mean deviations with the previous visualization of the means for each Dept.
Annotation
Annotate a plot with the add parameter. To add a
rectangle use the "rect" value of add. Here
set the rectangle around the message centered at <3,10>. To
specify a rectangle requires two corners of the rectangle,
<x1,y1> and <x2,y2>. To specify
text requires just a single coordinate, <x1,y1>. With
the add parameter, the message follows the specification of
"rect", so the coordinates of the text message follow the
coordinates for the rectangle.
First lighten the fill color of the annotation with the
add_fill parameter for the style()
function.
style(add_fill="aliceblue")
BarChart(Dept, add=c("rect", "Employees by\nDepartment"),
x1=c(1.75,3), y1=c(11, 10), x2=4.25, y2=9)
