sp, PlotA family of related 1- or 2-dimensional scatterplots and related statistical analyses are presented as any combination of continuous or categorical variables: the traditional scatterplot of two continuous variables, a bubble (balloon) scatter plot from two categorical variables, a means plot from a categorical variable paired with a continuous variable, and a Cleveland dot plot as a scatterplot from a continuous variable paired with a categorical variable. Univariate distributions are summarized with either a 1-dimensional scatter plot of a continuous variable, or with a 1-dimensional bubble plot for a categorical variable as a more compact replacement of the traditional bar chart. The later is generalized to a matrix of 1-dimensional bubble plots here called the bubble plot frequency matrix.
The categorical variables have relatively few unique data values, formally defined as R factors, or as integer variables. Color and other properties of the display are provided according to the default color theme, which can be changed with the set function, or by changing the color of individual components of the graph such as grid lines, transparency, etc. The two variable scatter plots may include one or more data ellipses and a best fit line. Any provided variable labels, from Read or VariableLabels, serve as labels for the axis or axes of the graph.
ScatterPlot(x, y=NULL, by=NULL, data=mydata, type=NULL,
n.cat=getOption("n.cat"), digits.d=NULL,
stat=c("default", "count", "mean", "sd", "min", "max"), col.fill=getOption("col.fill.pt"),
col.stroke=getOption("col.stroke.pt"),
col.bg=getOption("col.bg"),
col.grid=getOption("col.grid"),
col.trans=NULL, col.area=NULL, col.box="black",
cex.axis=0.75, col.axis="gray30", xy.ticks=TRUE,
xlab=NULL, ylab=NULL, main=NULL, sub=NULL, cex=NULL,
value.labels=NULL, rotate.values=0, offset=0.5,
style=c("default", "regular", "bubble", "sunflower", "off"),
fit.line=NULL, col.fit.line="grey55",
shape.pts="circle", method="overplot",
means=TRUE, sort.y=FALSE,
segments.y=FALSE, segments.x=FALSE,
bubble.size=0.25, bubble.power=0.6, bubble.counts=TRUE,
col.low=NULL, col.hi=NULL,
ellipse=FALSE, col.ellipse="lightslategray",
col.fill.ellipse="transparent",
pt.reg="circle", pt.out="circle",
col.out30="firebrick2", col.out15="firebrick4", new=TRUE,
diag=FALSE, col.diag=par("fg"), lines.diag=FALSE,
quiet=getOption("quiet"),
pdf.file=NULL, pdf.width=NULL, pdf.height=NULL,
fun.call=NULL, ...)
sp(...)
Plot(...)
style="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 "x, then only "counts" applies. If x and
y, then, instead, "means", etc.col.stroke. Does not
apply if there is a by variable, which relies upon the default. If y-valby variable,
specified as a vector, one value for each level of by."grey90"."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.bubble.size.NULL), then the
value.labels are set to the factor leoffset."default", which becomes a "regular" scatterplot for
unless each variable has less than n.cat integer values, by default 10, when
a bubble plot is plotted with the corresponding joint freqFALSE, with options for
"loess" and for least squares, indicated by "ls". Or, if set to
TRUE, then a loess line.fit.line option is invoked.points.
The default value is 21, a circle with both a border and filled area, specified here
with col.pts and col.fill"overplot", but can also
provide "stack" to stack the points or
"jigger" to scramble the points.style="bubble".TRUE, then for a bubble plot, the count underlying a
bubble is displayed in the center of the bubble, unless the bubble is too small.
Setting this value sets default to style="bubble".TRUE, enclose a scatterplot with the .95 data ellipse from the
ellipse package. Or can specify a single numeric value greater than 0 and less than 1,
or a vector of levels to plot multiple ellipses.TRUE, fill the ellipse with col.ellipse. Usually
specify low opacity in the color specification, as shown in the examples.FALSE, then add the 1-D scatterplot to an existing graph.TRUE, then add
a diagonal line to a 2-dimensional scatter plot.diag=TRUE.diag=TRUE, then if lines.diag=TRUE, each point
in the scatter plot is connected to the diagonal line with a line segment, and
both axes are scaled in the same units.TRUE, no text output. Can change system default
with set function.knitr to pass the function call when
obtained from the abbreviated function call sp.plot, with an analysis of the correlation coefficient including hypothesis test and confidence interval. Two categorical variables, such as for Likert style analysis, produces a bubble plot, in which the size of each plotted point indicates the corresponding joint frequency, and a corresponding cross-tabulation analysis. This analysis is an alternative to the traditional BarChart. A categorical variable paired with a numeric variable yields a scatter plot with the means of each level of the categorical variable also plotted, and the summary statistics of the numeric variable for each level of the categorical variable. More information is obtained to list the categorical first in the function call. If the values of the first variable are numeric and sorted with equal intervals, then points are connected via line segments. If there is only one variable, a 1-dimensional scatter plot is produced for a numeric variable, based on the standard R function stripchart, and a 1-dimensional bubble plot is produced for a factor, with corresponding statistics.The value labels for each axis can be over-ridden from their values in the data to user supplied values with the value.labels option. This option is particularly useful for Likert style data coded as integers. Then, for example, a 0 in the data can be mapped into a "Strongly Disagree" on the plot. These value labels apply to integer categorical variables, and also to factor variables. To enhance the readability of the labels on the graph, any blanks in a value label translate into a new line in the resulting plot. Blanks are also transformed as such for the labels of factor variables.
DATA
The default input data frame is 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.
BUBBLE PLOT FREQUENCY MATRIX (BPFM)
A range of categorical variables for x may be specified, without specifying a y variable. A bubble plot results that illustrates the frequency of each response for each of the variables in a common figure. Each line of information, the bubbles and counts for a single variable, replaces the standard bar chart in a more compact display. Each variable in the matrix must have the same number of response categories, that is, levels. If not, then use the factor transformation with the levels option to ensure that the levels are the same for each variable. See the examples the end of the Transform function documentation. The BPFM is considerably condensed presentation of frequencies for a set of variables than are the corresponding bar charts.
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 ellipse function, written by Duncan Murdoch and E. D. Chow, from the ellipse package. The axes are automatically lengthened to provide space for the entire ellipse that extends beyond the maximum and minimum data values. Multiple numerical values of ellipse may also be specified, to obtain multiple ellipses.
ONE VARIABLE PLOT
The one variable plot is a 1-dimensional scatterplot, that is, a dot chart. For a numerical variable, 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. The plot can also be obtained as a bubble plot for a categorical variable.
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. The value of 10 is the default local value of n.cat, which can be set to any specified value. A sunflower plot can be requested in lieu of the bubble plot with the style option.
DIAGONAL
Useful particularly when comparing pre- and post- scores on some assessment, a diagonal line that runs from the lower-left corner of the graph to the upper-right corner represents the values of no change from a value on the x-axis that equals the corresponding value on the y-axis, where the pre and post scores are equal. Points on either side of that diagonal indicate + or - change. To provide this line, specify diag=TRUE, which will apply only to scatter plots with two numeric, non-categorical, variables. When so specified, for each data coordinate, a vertical line is drawn from the diagonal of no change to the point, unless lines.diag is set to FALSE. If diag=TRUE, then the axes limits are set so that each axis has the same beginning and ending point.
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.fill for the interior color of a plotted point. 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 dodgerblue. A gray scale is available with "gray", and other themes are available as explained in set, such as "sienna" and "orange.black". 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],ONLY VARIABLES ARE REFERENCED
A referenced variable in a lessR function can only be a variable name. This referenced variable must exist in either the referenced data frame, such as the default mydata, or in the user's workspace, more formally called the global environment. That is, expressions cannot be directly evaluated. For example:
> ScatterPlot(rnorm(50), rnorm(50)) # does NOT work}
Instead, do the following: > X <- rnorm(50) # create vector X in user workspace > Y <- rnorm(50) # create vector Y in user workspace > ScatterPlot(X,Y) # directly reference X and Y
ellipse function from the ellipse package package.
Gerbing, D. W. (2013). R Data Analysis without Programming, Chapter 8, NY: Routledge.
[object Object],[object Object]
plot, stripchart, title, par, Correlation, set.
# default scatterplot, x is not sorted so type is set to "p" # although data not attached, access each variable directly by its name ScatterPlot(Years, Salary)
# compare to standard R plot, which requires the mydata$ notation plot(mydata$Years, mydata$Salary)
# abbreviated function name # scatterplot, with loess line and filled ellipse with low opacity, .1 # save scatterplot to a pdf file sp(Years, Salary, fit.line=TRUE, ellipse=TRUE, col.fill.ellipse=rgb(.6,.3,.3,.1), pdf.file="MyScatterPlot.pdf")
# scatterplot with many ellipses sp(Years, Salary, ellipse=seq(.2,.9, .1))
# increase span (smoothing) from default of .75 # span is a loess parameter and generates a caution that can be # ignored that it is not a graphical parameter -- we know that #ScatterPlot(Years, Salary, fit.line="loess", span=1.25)
# custom scatterplot, with diagonal line, connecting line segments # also red axis labels ScatterPlot(Years, Salary, col.stroke="darkred", col.fill="plum", diag=TRUE, col.lab="red")
# scatterplot with a gray scale color theme # or, use theme(colors="gray") to invoke for all subsequent analyses # until reset back to default color of "dodgerblue" theme(colors="gray") ScatterPlot(Years, Salary) theme(colors="dodgerblue")
# by variable scatterplot with default point color, vary shapes ScatterPlot(Years, Salary, by=Gender) # by variable scatterplot with custom colors, keeps only 1 shape ScatterPlot(Years, Salary, by=Gender, col.stroke=c("steelblue", "hotpink")) # by variable with values of Gender for plotting symbols # reduce the size of the plotted symbols with cex<1 scatterplot(years,="" salary,="" by="Gender," shape.pts="c("F","M")," cex=".6)" #="" vary="" both="" shape="" and="" color="" col.stroke="c("steelblue"," "hotpink"),="">
# Default dot plot (1-variable scatter plot, continuous) ScatterPlot(Salary) # dot plot with custom colors for outliers ScatterPlot(Salary, pt.reg=23, col.out15="hotpink", col.out30="darkred") # one variable scatterplot with added jitter of points ScatterPlot(Salary, method="jitter") # by variable dot plot with custom colors, keeps only 1 shape ScatterPlot(Salary, by=Gender, col.stroke=c("steelblue", "hotpink"))
# Default 1-D bubble plot # frequency plot, replaces bar chart sp(Dept)
# scatterplot of continuous Y against categorical X # generates a means chart ScatterPlot(Dept, Salary) # rotated axis labels and then offset to fit sp(Dept, Salary, rotate.values=45, offset=1) # for this purpose, improved version of standard R stripchart stripchart(mydata$Salary ~ mydata$Dept, vertical=TRUE) # just plot means sp(Dept, Salary, stat="mean")
# scatter (bubble) plot of two categorical variables sp(Gender, Dept)
# Cleveland dot plot with row.names on the y-axis, sort by Salary sp(Salary, row.names, sort.y=TRUE) # with options sp(Salary, row.names, ylab="", sort.y=TRUE, segments.y=TRUE, col.bg="transparent", col.grid="transparent")
# Default dot plot (1-variable scatter plot, continuous) ScatterPlot(Salary) # dot plot with custom colors for outliers ScatterPlot(Salary, pt.reg=23, col.out15="hotpink", col.out30="darkred") # one variable scatterplot with added jitter of points ScatterPlot(Salary, method="jitter") # by variable dot plot with custom colors, keeps only 1 shape ScatterPlot(Salary, by=Gender, col.stroke=c("steelblue", "hotpink"))
# Default 1-D bubble plot sp(Dept) # frequency plot, replaces bar chart sp(Dept, stat="count")
# read Likert data, 0 to 5 scale mydata <- rd("Mach4", format="lessR", quiet=TRUE) # size of each plotted point (bubble) depends on its joint frequency # triggered by default when < n.cat=10 unique values for each variable ScatterPlot(m06, m07) # use value labels for the integer values LikertCats <- c("Strongly Disagree", "Disagree", "Slightly Disagree", "Slightly Agree", "Agree", "Strongly Agree") sp(m06, m07, value.labels=LikertCats) # get correlation analysis instead of cross-tab analysis ScatterPlot(m06, m07, n.cat=2) # plot Likert data and get sunflower plot with loess line ScatterPlot(m06, m07, style="sunflower", fit.line="loess") # compare to usual scatterplot of Likert data, transparency helps plot(mydata$m06, mydata$m07) ScatterPlot(m06, m07, style="regular", cex=3)
# generate a Bubble Plot Frequency Matrix (BPFM) # specify a range of x-variables, no y-variable # each line is a bubble plot of frequencies for a single variable sp(c(m06,m07,m09,m10), rotate=25, offset=1) # for each bubble, lighten fill color, make border black sp(m06:m12, col.fill=rgb(.094,.455,.804,alpha=.45), col.stroke="black") # color range sp(c(m06,m07,m09,m10), col.low="lemonchiffon2", col.hi="lightsteelblue2") # create BPFM for entire Mach IV scale with labels, store as a pdf file LikertCats <- c("Strongly Disagree", "Disagree", "Slightly Disagree", "Slightly Agree", "Agree", "Strongly Agree") sp(m01:m20, value.labels=LikertCats, pdf.file="MachFreqs.pdf")
# 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 the names Plot or ScatterPlot, here Plot is 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", style="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
ScatterPlot(eruptions, waiting, ellipse=TRUE, data=faithful)