2. Visualize: Scatter, Box, and Violin Plots

Two Variables

A typical scatterplot visualizes the relationship of two continuous variables, here Years worked at a company, and annual Salary. Following is the function call to XY() for the default visualization.

Because d is the default name of the data frame that contains the variables for analysis, the data parameter that names the input data frame need not be specified. That is, no need to specify data=d, though this parameter can be explicitly included in the function call if desired.

XY(Years, Salary)

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Enhance the default scatterplot with parameter enhance. The visualization includes the mean of each variable indicated by the respective line through the scatterplot, the 95% confidence ellipse, labeled outliers, least-squares regression line with 95% confidence interval, and the corresponding regression line with the outliers removed.

XY(Years, Salary, enhance=TRUE)

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The default for formatting both axis labels is to round numeric values of thousands, such as 100000 to 100K. With parameter axis_fmt, this default of to {"K"} can be changed. Also can specify {","} to insert commas in large numbers with a decimal point or {"."} to insert periods, or {""} to turn off formatting. The value of {"K"} can also be combined with {","} or {"."} by forming a vector of values, such as c("K", ",").

Axis labels can also be formatted by adding a prefix to a numeric value with the parameters axis_x_prefix and axis_y_prefix, such as $ or €. The specified value can be multiple characters, such as for the Brazilian currency, R$.

XY(Years, Salary, axis_fmt=",", axis_y_prefix="$")

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A variety of fit lines can be plotted. The available values: "loess" for general non-linear fit, "lm" for linear least squares, "null" for the null (flat line) model, "exp" for the exponential growth and decay, "quad" for the quadratic model, and power for the general power beyond 2. Setting fit to TRUE plots the "loess" line. With the value of power, specify the value of the root with parameter fit_power.

Here, plot the general non-linear fit. For emphasis set plot_errors to TRUE to plot the residuals from the line. The sum of the squared errors is displayed to facilitate the comparison of different models.

XY(Years, Salary, fit="loess", plot_errors=TRUE)

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Next, plot the exponential fit and show the residuals from the exponential curve. These data are approximately linear so the exponential curve does not vary far from a straight line. The function displays the corresponding sum of squared errors to assist in comparing various models to each other.

XY(Years, Salary, fit="exp", plot_errors=TRUE)

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The parameter transforms the y variable to the specified power from the default of 1 before doing the regression analysis. The availability of this parameter provides for a wide range of modifications to the underlying functional form of the fit curve.

Three Variables

Map a continuous variable, such as Pre, to the plotted points with the size parameter, a bubble plot.

XY(Years, Salary, size=Pre)

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Indicate multiple variables to plot along either axis with a vector defined according to the base R function c(). Plot the linear model for each variable according to the fit parameter set to "lm". By default, when multiple lines are plotted on the same panel, the confidence interval is turned off by internally setting the parameter fit_se set to 0. Explicitly override this parameter value as needed.

XY(c(Pre, Post), Salary, fit="lm", fit_se=0)

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VBS Plot of 5 Variables

Read the data and convert the values of numerically valued categorical variables to meaningful labels.

d <- Read("Cars93")

d$Airbags <- factor(d$Airbags, levels=0:2, labels=c("none", "driver", "drv+pas"))
d$DriveTrain <- factor(d$DriveTrain, levels=0:2, labels=c("rear", "front", "all"))
d$Manual <- factor(d$Manual, levels=0:1, labels=c("Not_Avail", "Available"))

Visualize the scatterplot of MPGhiway and HP, stratified against three categorical variables: Airbags plotted in different colors for each scatterplot, and separate scatterplots for all six combinations of the levels of DriveTrain and Manual.

XY(x=MPGhiway, y=HP, by=Airbags, facet=c(DriveTrain, Manual))

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## 
## ---------- Summary Statistics for MPGhiway

Scatterplot Matrix

To plot a scatterplot matrix, specify multiple variables for the first parameter value, x, repeated for the second parameter, y. Define these multiple variables as a vector, such as defined by c(). Request the non-linear fit line and corresponding confidence interval by specifying TRUE or loess for the fit parameter. Request a linear fit line with the value of "lm".

d <- Read("Employee")

XY(c(Salary, Years, Pre, Post), c(Salary, Years, Pre, Post), fit="lm")

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Smoothed, Contoured, and Binned Scatterplots

Smoothing and binning are two procedures for visualizing a relationship with many data values.

To obtain a larger data set, in this example generate random data with base R rnorm(), then plot. XY() first checks the presence of the specified variables in the global environment (workspace). If not there, then from a data frame, of which the default value is d. Here, randomly generate values from normal populations for x and y in the workspace.

set.seed(13)
x=rnorm(4000)
y= 8*x + rnorm(4000,1, 30)
XY(x, y)

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With large data sets, even for continuous variables there can be much over-plotting of points. One strategy to address this issue smooths the scatterplot by setting the type parameter to smooth. The individual points superimposed on the smoothed plot are potential outliers. The default number of plotted outliers is 100. Turn off the plotting of outliers completely by setting parameter smooth_points to 0. Show the linear trend with fit set to "lm".

XY(x, y, type="smooth", fit="lm")

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Another strategy for alleviating over-plotting makes the fill color mostly transparent with the transparency parameter, or turn off completely by setting fill to "off". The closer the value of trans is to 1, the more transparent is the fill.

XY(x, y, transparency=0.95)

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Contour plots are another effective way to visualize scatter plots with much data. By default, the parameter contours_n is set at 10. XY() provides a threshold for deleting points for consideration of plotting the contour curves. Otherwise, if there are extreme outliers, the axes extend to their maximum and minimum values, typically resulting in much white space that surrounds the visible contour plot. The extreme values of outlier points with low density round down to zero on the color scale. The parameter contours_pad, with a default value of 0, can adjust the white space to pad the resulting contour curve. Increase the parameter value to add more padding to the plot.

XY(x, y, type="contour")

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Another way to visualize a relationship when there are many data points is to bin the x-axis. Specify the number of bins with parameter n_bins. XY() then computes the mean of y for each bin and connects the means by line segments. This procedure plots the conditional means by default without any assumption of form such as linearity. Specify the stat parameter for median to compute the median of y for each bin. The standard XY() parameters fill, color, size and segments also apply.

XY(x, y, n_bins=5)

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Cleveland Dot Plot

Create a Cleveland dot plot when one of the variables has unique (ID) values. In this example, for a single variable, row names are on the y-axis. The default plots sorts by the value plotted with the default value of parameter sort of "+" for an ascending plot. Set to "-" for a descending plot and "0" for no sorting.

XY(Salary, row_names)

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The standard scatterplot version of a Cleveland dot plot follows, with no sorting and no line segments.

XY(Salary, row_names, sort="0", segments_y=FALSE)

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This Cleveland dot plot has two x-variables, indicated as a standard R vector with the c() function. In this situation, the two points on each row are connected with a line segment. By default the rows are sorted by distance between the successive points.

XY(c(Pre, Post), row_names)

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Two Continuous, One Categorical

Plot a scatterplot of two continuous variables for each level of a categorical variable on the same panel with the by parameter. Here, plot Years and Salary each for the two levels of Gender in the data. Colors and geometric plot shapes can distinguish between the plots. For all variables except an ordered factor, the default plots according to the default qualitative color palette, "hues", with the geometric shape of a point.

XY(Years, Salary, by=Gender)

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Change the plot colors with the fill (interior) and color (exterior or edge) parameters. Because there are two levels of the by variable, specify two fill colors and two edge colors each with an R vector defined by the c() function. Also, include the regression line for each group with the fit parameter and increase the size of the plotted points with the size parameter.

XY(Years, Salary, by=Gender, size=2, fit="lm",
     fill=c(M="olivedrab3", W="gold1"), 
     color=c(M="darkgreen", W="gold4")
)

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Change the plotted shapes with the shape parameter. The default value is "circle" with both an exterior color and filled interior, specified with "color" and "fill". Other possible values, with fillable interiors, are "circle", "square", "diamond", "triup" (triangle up), and "tridown" (triangle down). Other possible values include all uppercase and lowercase letters, all digits, and most punctuation characters. The numbers 0 through 25 defined by the R points() function also apply. If plotting levels according to by, then list one shape for each level to be plotted.

Or, request default shapes across the different by groups by setting parameter shapes to "vary".

XY(Years, Salary, by=Gender, shape="vary")

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A Trellis (facet) plot creates a separate panel for the plot of each level of the categorical variable. Generate Trellis plots with the facet parameter. In this example, plot the best-fit linear model for the data in each panel according to the fit parameter. By default, the 95% confidence interval for each line is also displayed.

XY(Years, Salary, facet=Gender, fit="lm")

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## 
## ---------- Summary Statistics for Years

Turn off the confidence interval by setting the parameter fit_se to 0 for the value of the confidence level.

One Continuous, One Categorical

A categorical variable plotted with a continuous variable results in a traditional scatterplot though, of course, the scatter is confined to the straight lines that represent the levels of the categorical variable, its values.

The first two parameters of XY() are x and y. In this example, the categorical variable, Dept, listed second, specifies the y variable, as in y=Dept. There is no distinction in this function call for two continues variables or one continuous and one categorical. The XY() function evaluates each variable for continuity and responds appropriately.

XY(Salary, Dept)

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To avoid point overlap, if there is at least one duplicated value of continuous y for any level of categorical x, by default some horizontal jitter for each plotted point is added, which was not needed in this example. Manually adjust the jitter with either parameter jitter_x or, if x is continuous and y categorical, the jitter_y parameter. In addition, if the categorical variable is an R factor or a variable of type character, by default the mean of the continuous variable is displayed at each level of the categorical variable, as well in the text output. If the categorical variable is numeric, better to convert the variable to a factor to have just the categories on the axis and not a continuous scale. For example, d$Gender <- factor(d$Gender).

Another helpful technique for large data sets is to add some fill transparency with the transparency parameter, with values such as 0.8 and 0.9. The combination of jitter and transparency allows for plotting many thousands of points.

Show the different distributions of the continuous variable across the levels of the categorical variable with a scatterplot. Here, show the distribution of Salary for Males and Females across the various departments.

XY(Salary, Dept, by=Gender)

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Two Continuous, Three Categorical

To illustrate, first, the data. Use the Cars93 data set that is installed with lessR, which describes characteristics of 1993 cars.

d <- Read("Cars93")

Two of the categorical variables are integer coded 0 and 1, so recode to R factors to obtain more descriptive labels. For clarity, convert the relevant categorical variables to factors, including Cylinders the number of cylinders for a car, for consistency.

d$Trans <- factor(d$Manual, levels=0:1, labels=c("Auto", "Manual"))
d$Source <- factor(d$Source, levels=0:1, labels=c("Foreign", "Domestic"))
d$Cylinders <- factor(d$Cylinders, levels=c(4,6,8))

Two Continuous, Three Categorical

XY() can display the relationships for up to five variables. The two primary variables, x and y, that form the basis of the scatter plot, are continuous. Usually these two variables are listed first in the function call and so do not need their parameter names specified. Indicate two categorical variables that form the Trellis panels with parameter facet. Call these two variables the Trellis variables, which define a Trellis panel for each combination of their values. Finally, there can be a categorical grouping variable, the by variable, which plots different groups within each Trellis panel.

Plot MPGcity according to Weight. Specify the number of Cylinders and Manual transmission or not as Trellis conditioning variables to form the Trellis plot. Specify the Source of the vehicle, Foreign or Domestic as a grouping variable to plot with separate colors on each panel. Use the parameter value n_axis_x_skip=2 to include only every third axis tick label due to the lack of room to avoid overlapping labels.

XY(Weight, MPGcity, by=Source, facet=c(Cylinders,Trans), n_axis_x_skip=2, n_row=2)

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## 
## ---------- Summary Statistics for Weight

From the visualization the patterns emerge. As Weight increases city MPG decreases. Domestic cars tend to weigh more. Foreign cars tend to have fewer cylinders, which also leads to better fuel mileage.