spGenerates scatter plots for one or two variables. For two variables also produces an analysis of the correlation coefficient. If the values of the first specified value are sorted, then points are connected via line segments. The first variable can be numeric or a factor. The second variable must be numeric. For Likert style response data of two variables, so that each value has less than 10 unique integer values, the points in the plot are transformed into a bubble plot with the size of each bubble, i.e., point, determined by the corresponding joint frequency. An alternate name for ScatterPlot is just Plot.
One enhancement over the standard R plot function is the automatic inclusion of color. The color of the line segments and/or the points, background, area under the plotted line segments, grid lines, and border can each be explicitly specified, with default colors provided by one of the pre-defined color themes as defined by the set function.
If a scatterplot of two numeric variables is displayed, then the corresponding correlation coefficient as well as the hypothesis test of zero population correlation and the 95% confidence interval are also displayed. The same numeric values of the standard R function cor.test function are generated, though in a more readable format. Also, an option for the .95 data ellipse from John Fox's car package can enclose the points of the scatterplot.
For one variable, based on the standard R function stripchart, plots a one dimensional scatterplot, that is, a dot chart, also called a strip chart. Also identifies outliers according to the criteria specified by a box plot and displays the summary statistics for the variable. The dot plot is also invoked with the function names DotPlot or just dp, which are just alternate names for ScatterPlot when a single variable is referenced.
ScatterPlot(x, y=NULL, by=NULL, data=mydata, type=NULL, n.cat=getOption("n.cat"), col.fill=getOption("col.fill.pt"),
col.stroke=getOption("col.stroke.pt"),
col.bg=getOption("col.bg"),
col.grid=getOption("col.grid"),
col.area=NULL, col.box="black",
shape.pts="circle", cex.axis=.85, col.axis="gray30",
col.ticks="gray30", xy.ticks=TRUE,
xlab=NULL, ylab=NULL, main=NULL, cex=NULL,
x.start=NULL, x.end=NULL, y.start=NULL, y.end=NULL,
kind=c("default", "regular", "bubble", "sunflower"),
fit.line=c("none", "loess", "ls"), col.fit.line="grey55",
bubble.size=.25, plot.method="stack",
ellipse=FALSE, col.ellipse="lightslategray", fill.ellipse=TRUE,
pt.reg="circle", pt.out="circle",
col.out30="firebrick2", col.out15="firebrick4", new=TRUE,
quiet=getOption("quiet"),
pdf.file=NULL, pdf.width=5, pdf.height=5, ...)
sp(...)
Plot(...)
DotPlot(...)
dp(...)
kind="regular"
mydata."p" for
points, "l" for line, or "b" for both. If x and y are provided and
x is sorted so that a function is plotted, the default is "col.stroke. Does not
apply if there is a by variable, which relies upon the default.by variable,
specified as a vector, one value for each level of by."grey90".points.
The default value is 21, a circle with both a border and filled area, specified here
with col.pts and col.fill"black".xlab not
specified, then the label becomes the name of the corresponding variable. If
xy.ticks is FALSE, then no label is displayed. If no y vxy.ticks is FALSE, then no label displayed."default", which becomes a "regular" scatterplot for
most data. If Likert style response data is plotted, that is,
each variable has less than 10 integer values, then instead by default a bubble plot is "none", with options for
"loess" and "ls".fit.line option is invoked."stack", but can also provide
"jigger" to scramble the points.TRUE, enclose a scatterplot with the .95 data ellipse from the car package.TRUE, fill the ellipse with a translucent shade of col.ellipse.FALSE, then add the dot plot to an existing graph.TRUE, no text output. Can change system default
with set function.mydata. Specify another name with the data option. Regardless of its name, the data frame need not be attached to reference the variables directly by its name, that is, no need to invoke the mydata$name notation. The referenced variables can be in the data frame and/or the user's workspace, the global environment. ADAPTIVE GRAPHICS
Results for two variables are based on the standard plot and related graphic functions, with the additional provided color capabilities and other options including a center line. The plotting procedure utilizes ``adaptive graphics'', such that ScatterPlot chooses different default values for different characteristics of the specified plot and data values. The goal is to produce a desired graph from simply relying upon the default values, both of the ScatterPlot function itself, as well as the base R functions called by ScatterPlot, such as plot. Familiarity with the options permits complete control over the computed defaults, but this familiarity is intended to be optional for most situations.
TWO VARIABLE PLOT
When two variables are specified to plot, by default if the values of the first variable, x, are unsorted, or if there are unequal intervals between adjacent values, or if there is missing data for either variable, a scatterplot is produced, that is, a call to the standard R plot function with type="p" for points. By default, sorted values with equal intervals between adjacent values of the first of the two specified variables yields a function plot if there is no missing data for either variable, that is, a call to the standard R plot function with type="l", which connects each adjacent pair of points with a line segment.
BY VARIABLE
A variable specified with by= is a grouping variable that specifies that the plot is produced with the points for each group plotted with a different shape and/or color. By default, the shapes vary by group, and the color of the plot symbol remains the same for the groups. The default shapes, in this order, are "circle", "diamond", "square", "triup" for a triangle pointed up, and "tridown" for a triangle pointed down.
To explicitly vary the shapes, use shape.pts and a list of shape values in the standard R form with the c function to combine a list of values, one specified shape for each group, as shown in the examples. To explicitly vary the colors, use col.pts, such as with R standard color names. If col.pts is specified without shape.pts, then colors are varied, but not shapes. To vary both shapes and colors, specify values for both options, always with one shape or color specified for each level of the by variable.
Shapes beyond the standard list of named shapes, such as "circle", are also available as single characters. Any single letter, uppercase or lowercase, any single digit, and the characters "+", "*" and "#" are available, as illustrated in the examples. In the use of shape.pts, either use standard named shapes, or individual characters, but not both in a single specification.
SCATTERPLOT ELLIPSE
For a scatterplot of two numeric variables, the ellipse=TRUE option draws the .95 data ellipse as computed by the dataEllipse function, written by Georges Monette and John Fox, from the car package. Usually the minimum and maximum values of the axes should be manually extended beyond their default to accommodate the entire ellipse. To accomplish this extension, use the xlim and ylim options, such as xlim=c(30,350). Obtaining the desired axes limits may involve multiple runs of the ScatterPlot function. To provide more control over the display of the data ellipse beyond the provided col.ellipse and fill.ellipse options, run the dataEllipse function directly with the plot.points=FALSE option following ScatterPlot with ellipse=FALSE, the default.
ONE VARIABLE PLOT
The one variable plot is a one variable scatterplot, that is, a dot chart. Results are based on the standard stripchart function. Colors are provided by default and can also be specified. For gray scale output, potential outliers are plotted with squares and actual outliers are plotted with diamonds, otherwise shades of red are used to highlight outliers. The definition of outliers are from the R boxplot function.
LIKERT DATA A scatterplot of Likert type data is problematic because there are so few possibilities for points in the scatterplot. For example, for a scatterplot of two five-point Likert response data, there are only 25 possible paired values to plot, so most of the plotted points overlap with others. In this situation, that is, when there are less than 10 values for each of the two variables, a bubble plot is automatically provided, with the size of each point relative to the joint frequency of the paired data values. A sunflower plot can be requested in lieu of the bubble plot.
VARIABLE LABELS
Although standard R does not provide for variable labels, lessR can store the labels in the data frame with the data, obtained from the Read function. If this labels data frame exists, then the corresponding variable label is by default listed as the label for the corresponding axis and on the text output. For more information, see Read.
COLORS
Individual colors in the plot can be manipulated with options such as col.bars for the color of the histogram bars. A color theme for all the colors can be chosen for a specific plot with the colors option with the lessR function set. The default color theme is blue, but a gray scale is available with "gray", and other themes are available as explained in set, such as "red" and "green". Use the option ghost=TRUE for a black background, no grid lines and partial transparency of plotted colors.
Colors can also be changed for individual aspects of a scatterplot as well. To provide a warmer tone by slightly enhancing red, try col.bg=snow. Obtain a very light gray with col.bg=gray99. To darken the background gray, try col.bg=gray97 or lower numbers. See the lessR function showColors which provides an example of all available named colors.
PDF OUTPUT
Because of the customized graphic windowing system that maintains a unique graphic window for the Help function, the standard graphic output functions such as pdf do not work with the lessR graphics functions. Instead, to obtain pdf output, use the pdf.file option, perhaps with the optional pdf.width and pdf.height options. These files are written to the default working directory, which can be explicitly specified with the R setwd function.
ADDITIONAL OPTIONS
Commonly used graphical parameters that are available to the standard R function plot are also generally available to ScatterPlot, such as:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
dataEllipse function from the car package.plot, stripchart, title, par, Correlation, set.# scatterplot
# create simulated data, no population mean difference
# X has two values only, Y is numeric
# put into a data frame, required for formula version
n <- 12
Gender <- sample(c("Women", "Men"), size=n, replace=TRUE)
x <- round(rnorm(n=n, mean=50, sd=10), 2)
y <- round(rnorm(n=n, mean=50, sd=10), 2)
z <- round(rnorm(n=n, mean=50, sd=10), 2)
mydata <- data.frame(Gender,x,y,z)
rm(Gender); rm(x); rm(y); rm(z)
# default scatterplot, x is not sorted so type is set to "p"
# although data not attached, access each variable directly by its name
ScatterPlot(x, y)
# short name with Spearman correlation instead of Pearson
sp(x,y, method="spearman")
# compare to standard R plot, which requires the mydata$ notation
plot(mydata$x, mydata$y)
# save scatterplot to a pdf file
ScatterPlot(x, y, pdf.file="MyScatterScatterPlot.pdf")
# scatterplot, with ellipse and extended axes to accommodate the ellipse
ScatterPlot(x, y, ellipse=TRUE, xlim=c(20,80), ylim=c(20,80))
# scatterplot, with loess line
ScatterPlot(x, y, fit.line="loess")
# increase span (smoothing) from default of .75
ScatterPlot(x, y, fit.line="loess", span=1.25)
# custom scatterplot
ScatterPlot(x, y, col.stroke="darkred", col.fill="plum")
# scatterplot with a gray scale color theme
# or, use set(colors="gray") to invoke for all subsequent analyses
# until reset back to default color of "blue"
set(colors="gray")
ScatterPlot(x, y)
set(colors="blue")
# by variable scatterplot with default point color, vary shapes
ScatterPlot(x,y, by=Gender)
# by variable scatterplot with custom colors, keeps only 1 shape
ScatterPlot(x,y, by=Gender, col.stroke=c("steelblue", "hotpink"))
# by variable with values of Gender for plotting symbols
# reduce the size of Gender the plotted symbols with cex<1
ScatterPlot(x, y, by=Gender, shape.pts=c("M","F"), cex=.6)
# vary both shape and color
ScatterPlot(x, y, by=Gender, col.stroke=c("steelblue", "hotpink"),
shape.pts=c("M","F"))
# Default dot plot
ScatterPlot(y)
# Dot plot with custom colors for outliers
ScatterPlot(y, pt.reg=23, col.out15="hotpink", col.out30="darkred")
# one variable scatterplot with added jitter of points
ScatterPlot(x, plot.method="jitter", jitter=0.05)
# by variable dot plot with custom colors, keeps only 1 shape
ScatterPlot(x, by=Gender, col.stroke=c("steelblue", "hotpink"))
# bubble plot of simulated Likert data, 1 to 7 scale
# size of each plotted point (bubble) depends on its joint frequency
# triggered by default when < 10 unique values for each variable
x1 <- sample(1:7, size=100, replace=TRUE)
x2 <- sample(1:7, size=100, replace=TRUE)
ScatterPlot(x1,x2)
# compare to usual scatterplot of Likert data, transparency helps
plot(x1,x2)
ScatterPlot(x1,x2, kind="regular", cex=3)
# plot Likert data and get sunflower plot with loess line
ScatterPlot(x1,x2, kind="sunflower", fit.line="loess")
# scatterplot of continuous Y against categorical X, a factor
Pain <- sample(c("None", "Some", "Much", "Massive"), size=25, replace=TRUE)
Pain <- factor(Pain, levels=c("None", "Some", "Much", "Massive"), ordered=TRUE)
Cost <- round(rnorm(25,1000,100),2)
ScatterPlot(Pain, Cost)
# for this purpose, improved version of standard R stripchart
stripchart(Cost ~ Pain, vertical=TRUE)
# function curve
x <- seq(10,500,by=1)
y <- 18/sqrt(x)
# x is sorted with equal intervals so type set to "l" for line
# can use Plot or ScatterPlot, here Plot seems more appropriate
Plot(x, y)
# custom function plot
Plot(x, y, ylab="My Y", xlab="My X", col.stroke="blue",
col.bg="snow", col.area="lightsteelblue", col.grid="lightsalmon")
# modern art
n <- sample(2:30, size=1)
x <- rnorm(n)
y <- rnorm(n)
clr <- colors()
color1 <- clr[sample(1:length(clr), size=1)]
color2 <- clr[sample(1:length(clr), size=1)]
ScatterPlot(x, y, type="l", lty="dashed", lwd=3, col.area=color1,
col.stroke=color2, xy.ticks=FALSE, main="Modern Art",
cex.main=2, col.main="lightsteelblue", kind="regular",
n.cat=0)
# -----------------------------------------------
# variables in a different data frame than mydata
# -----------------------------------------------
# variables of interest are in a data frame which is not the default mydata
# although data not attached, access the variable directly by its name
data(dataEmployee)
ScatterPlot(Years, Salary, by=Gender, data=dataEmployee)Run the code above in your browser using DataLab