Histograms
David Gerbing
Data
Most of the examples are an analysis of data in the Employee data set, included with lessR. First read the Employee data into the data frame d. See the Read and Write vignette for more details.
##
## >>> Suggestions
## Details about your data, Enter: details() for d, or details(name)
##
## Data Types
## ------------------------------------------------------------
## character: Non-numeric data values
## integer: Numeric data values, integers only
## double: Numeric data values with decimal digits
## ------------------------------------------------------------
##
## Variable Missing Unique
## Name Type Values Values Values First and last values
## ------------------------------------------------------------------------------------------
## 1 Years integer 36 1 16 7 NA 15 ... 1 2 10
## 2 Gender character 37 0 2 M M M ... F F M
## 3 Dept character 36 1 5 ADMN SALE SALE ... MKTG SALE FINC
## 4 Salary double 37 0 37 53788.26 94494.58 ... 56508.32 57562.36
## 5 JobSat character 35 2 3 med low low ... high low high
## 6 Plan integer 37 0 3 1 1 3 ... 2 2 1
## 7 Pre integer 37 0 27 82 62 96 ... 83 59 80
## 8 Post integer 37 0 22 92 74 97 ... 90 71 87
## ------------------------------------------------------------------------------------------
Histogram
One of the most frequently encountered visualizations for continuous variables is the histogram, which outlines the general shape the underlying distribution.
Histogram: Bin similar values into a group, then plot the frequency of occurrence of the data values in each bin as the height of the corresponding bar.
A call to a function to create a histogram contains the name of the continuous variable that contains the values to be plotted. With the Histogram() 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 default value. The following illustrates the call to Histogram() with a continuous variable named \(x\).
To illustrate, consider the continuous variable Salary in the Employee data table. Use Histogram() to tabulate and display the number of employees in each department, here relying upon the default data frame (table) named d so the data= parameter is not needed.
## >>> Suggestions
## bin_width: set the width of each bin
## bin_start: set the start of the first bin
## bin_end: set the end of the last bin
## Density(Salary) # smoothed density curves plus histogram
## Plot(Salary) # Violin/Box/Scatterplot (VBS) plot
##
##
## --- Salary ---
##
## n miss mean sd min mdn max
## 37 0 73795.557 21799.533 46124.970 69547.600 134419.230
##
##
## (Box plot) Outliers: 1
##
## Small Large
## ----- -----
## 134419.2
##
##
## Bin Width: 10000
## Number of Bins: 10
##
## Bin Midpnt Count Prop Cumul.c Cumul.p
## ---------------------------------------------------------
## 40000 > 50000 45000 4 0.11 4 0.11
## 50000 > 60000 55000 8 0.22 12 0.32
## 60000 > 70000 65000 8 0.22 20 0.54
## 70000 > 80000 75000 5 0.14 25 0.68
## 80000 > 90000 85000 3 0.08 28 0.76
## 90000 > 100000 95000 5 0.14 33 0.89
## 100000 > 110000 105000 1 0.03 34 0.92
## 110000 > 120000 115000 1 0.03 35 0.95
## 120000 > 130000 125000 1 0.03 36 0.97
## 130000 > 140000 135000 1 0.03 37 1.00
The Histogram() function provides a default color theme. The function also provides the corresponding frequency distribution, summary statistics, the table that lists the count of each category, from which the histogram is constructed, as well as an outlier analysis based on Tukey’s rules for box plots.
Customize the Histogram
The parameters bin_start, bin_width, and bin_end are available to customize the histogram.
Histogram(Salary, bin_start=35000, bin_width=14000)
## >>> Suggestions
## bin_end: set the end of the last bin
## Density(Salary) # smoothed density curves plus histogram
## Plot(Salary) # Violin/Box/Scatterplot (VBS) plot
##
##
## --- Salary ---
##
## n miss mean sd min mdn max
## 37 0 73795.557 21799.533 46124.970 69547.600 134419.230
##
##
## (Box plot) Outliers: 1
##
## Small Large
## ----- -----
## 134419.2
##
##
## Bin Width: 14000
## Number of Bins: 8
##
## Bin Midpnt Count Prop Cumul.c Cumul.p
## ---------------------------------------------------------
## 35000 > 49000 42000 1 0.03 1 0.03
## 49000 > 63000 56000 14 0.38 15 0.41
## 63000 > 77000 70000 9 0.24 24 0.65
## 77000 > 91000 84000 4 0.11 28 0.76
## 91000 > 105000 98000 5 0.14 33 0.89
## 105000 > 119000 112000 2 0.05 35 0.95
## 119000 > 133000 126000 1 0.03 36 0.97
## 133000 > 147000 140000 1 0.03 37 1.00
Easy to change the color, either by changing the color theme with style(), or just change the fill color with fill. Can refer to standard R colors, as shown with lessR function showColors(), or implicitly invoke the lessR color palette generating function getColors(). Each 30 degrees of the color wheel is named, such as "greens", "rusts", etc, and implements a sequential color palette.
Use the color parameter to set the border color, here turned off.
Histogram(Salary, fill="reds", color="transparent")
## >>> Suggestions
## bin_width: set the width of each bin
## bin_start: set the start of the first bin
## bin_end: set the end of the last bin
## Density(Salary) # smoothed density curves plus histogram
## Plot(Salary) # Violin/Box/Scatterplot (VBS) plot
##
##
## --- Salary ---
##
## n miss mean sd min mdn max
## 37 0 73795.557 21799.533 46124.970 69547.600 134419.230
##
##
## (Box plot) Outliers: 1
##
## Small Large
## ----- -----
## 134419.2
##
##
## Bin Width: 10000
## Number of Bins: 10
##
## Bin Midpnt Count Prop Cumul.c Cumul.p
## ---------------------------------------------------------
## 40000 > 50000 45000 4 0.11 4 0.11
## 50000 > 60000 55000 8 0.22 12 0.32
## 60000 > 70000 65000 8 0.22 20 0.54
## 70000 > 80000 75000 5 0.14 25 0.68
## 80000 > 90000 85000 3 0.08 28 0.76
## 90000 > 100000 95000 5 0.14 33 0.89
## 100000 > 110000 105000 1 0.03 34 0.92
## 110000 > 120000 115000 1 0.03 35 0.95
## 120000 > 130000 125000 1 0.03 36 0.97
## 130000 > 140000 135000 1 0.03 37 1.00
Density Plot
The histogram portrays a continuous distribution with discrete bins.
Density plot: A smooth curve that estimates the underlying continuous distribution.
To invoke, add the density parameter. The result is the filled density curve superimposed on the histogram.
Histogram(Salary, density=TRUE)
##
##
## --- Salary ---
##
## n miss mean sd min mdn max
## 37 0 73795.557 21799.533 46124.970 69547.600 134419.230
VBS Plot
A more modern version of the density plot combines the violin plot, box plot, and scatter plot into a single visualization, called here the VBS plot.
## [Violin/Box/Scatterplot graphics from Deepayan Sarkar's lattice package]
## >>> Suggestions
## Plot(Salary, out_cut=2, fences=TRUE, vbs_mean=TRUE) # Label two outliers ...
## Plot(Salary, box_adj=TRUE) # Adjust boxplot whiskers for asymmetry
##
## --- Salary ---
## Present: 37
## Missing: 0
## Total : 37
##
## Mean : 73795.557
## Stnd Dev : 21799.533
## IQR : 31012.560
## Skew : 0.190 [medcouple, -1 to 1]
##
## Minimum : 46124.970
## Lower Whisker: 46124.970
## 1st Quartile : 56772.950
## Median : 69547.600
## 3rd Quartile : 87785.510
## Upper Whisker: 122563.380
## Maximum : 134419.230
##
##
## (Box plot) Outliers: 1
##
## Small Large
## ----- -----
## Correll, Trevon 134419.23
##
##
## Number of duplicated values: 0
##
##
## Parameter values (can be manually set)
## -------------------------------------------------------
## size: 0.61 size of plotted points
## jitter_y: 0.45 random vertical movement of points
## jitter_x: 0.00 random horizontal movement of points
## bw: 9529.04 set bandwidth higher for smoother edges
Full Manual
Use the base R help() function to view the full manual for Histogram(). Simply enter a question mark followed by the name of the function.
?Histogram
More
More on Histograms and other visualizations from lessR and other packages such as ggplot2 at:
Gerbing, D., R Visualizations: Derive Meaning from Data, CRC Press, May, 2020, ISBN 978-1138599635.
