scat1d adds tick marks (bar codes. rug plot) on any of the four
sides of an existing plot, corresponding with non-missing values of a
vector x. This is used to show the data density. Can also place
the tick marks along a curve by specifying y-coordinates to go along
with the x values.
If any two values of x are within eps*w of each other, where eps
defaults to .001 and w is the span of the intended axis, values of
x are jittered by adding a value uniformly distributed in
[-jitfrac*w, jitfrac*w], where jitfrac defaults to .008.
Specifying preserve=TRUE invokes jitter2 with a different logic of
jittering. Allows plotting random sub-segments to handle very large
x vectors (see tfrac).
jitter2 is a generic method for jittering, which does not add
random noise. It retains unique values and ranks, and randomly
spreads duplicate values at equidistant positions within limits of
enclosing values. jitter2 is especially useful for numeric
variables with discrete values, like rating scales. Missing values
are allowed and are returned. Currently implemented methods are
jitter2.default for vectors and jitter2.data.frame which returns
a data.frame with each numeric column jittered.
datadensity is a generic method used to show data densities in more
complex situations. In the Design library there is a datadensity
method for use with plot.Design. Here, another datadensity method
is defined for data frames. Depending on the which argument, some
or all of the variables in a data frame will be displayed, with
scat1d used to display continuous variables and, by default, bars
used to display frequencies of categorical, character, or discrete
numeric variables. For such variables, when the total length of value
labels exceeds 200, only the first few characters from each level are used.
By default, datadensity.data.frame will construct
one axis (i.e., one strip) per variable in the data frame. Variable
names appear to the left of the axes, and the number of missing values
(if greater than zero) appear to the right of the axes. An optional
group variable can be used for stratification, where the different
strata are depicted using different colors. If the q vector is
specified, the desired quantiles (over all groups) are displayed
with solid triangles below each axis.
When the sample size exceeds 2000 (this value may be modified using
the nhistSpike argument, datadensity calls histSpike instead of
scat1d to show the data density for numeric variables. This results
in a histogram-like display that makes the resulting graphics file
much smaller. In this case, datadensity uses the minf argument
(see below) so that very infrequent data values will not be lost on
the variable's axis, although this will slightly distort the histogram.
histSpike is another method for showing a high-resolution data
distribution that is particularly good for very large datasets (say
n > 1000). By
default, histSpike bins the continuous x variable into 100
equal-width bins and then computes the frequency counts within bins.
If add=FALSE (the default), the function displays either proportions or
frequencies as in a vertical histogram. Instead of bars, spikes are
used to depict the frequencies. If add=FALSE, the function assumes you
are adding small density displays that are intended to take up a small
amount of space in the margins of the overall plot. The frac
argument is used as with scat1d to determine the relative length of
the whole plot that is used to represent the maximum frequency. No
jittering is done by histSpike.
histSpike can also graph a kernel density estimate for x, or add a
small density curve to any of 4 sides of an existing plot. When y
or curve is specified, the density or spikes are drawn with respect
to the curve rather than the x-axis.
scat1d(x, side=3, frac=0.02, jitfrac=0.008, tfrac,
eps=ifelse(preserve,0,.001),
lwd=0.1, col=par("col"),
y=NULL, curve=NULL,
bottom.align=type=='density',
preserve=FALSE, fill=1/3, limit=TRUE, nhistSpike=2000, nint=100,
type=c('proportion','count','density'), grid=FALSE, ...)jitter2(x, ...)
## S3 method for class 'default':
jitter2(x, fill=1/3, limit=TRUE, eps=0, presorted=FALSE, ...)
## S3 method for class 'data.frame':
jitter2(x, ...)
datadensity(object, ...)
## S3 method for class 'data.frame':
datadensity(object, group,
which=c("all","continuous","categorical"),
method.cat=c("bar","freq"),
col.group=1:10,
n.unique=10, show.na=TRUE, nint=1, naxes,
q, bottom.align=nint>1,
cex.axis=sc(.5,.3), cex.var=sc(.8,.3),
lmgp=sc(-.2,-.625), tck=sc(-.009,-.002), ranges, labels, ...)
# sc(a,b) means default to a if number of axes <= 3,="" b="" if="">=50, use
# linear interpolation within 3-50
histSpike(x, side=1, nint=100, frac=.05, minf=NULL, mult.width=1,
type=c('proportion','count','density'),
xlim=range(x), ylim=c(0,max(f)), xlab=deparse(substitute(x)),
ylab=switch(type,proportion='Proportion',
count ='Frequency',
density ='Density'),
y=NULL, curve=NULL, add=FALSE,
bottom.align=type=='density', col=par('col'), lwd=par('lwd'),
grid=FALSE, ...)
jitter2)group is not specified)histSpike), 2=left,
3=top (default for scat1d), 4=right)histSpike,
this is the relative length to be used for the largest frequency.
When scat1d calls tfrac<1< code="">,
will draw a random fraction tfrac of the line segment at each point.
This is useful for very large samples or ones with some very dense points.
The default value is 1 if the nux. For
preserve=TRUE the default is 0 and original unique values are
retained, bigger values of eps tends to bias observations from dense
to sparse regions, but ranks are stisegmentssegmentsx to draw tick marks along
a curve instead of by one of the axes. The y values are often
predicted values from a model. The side argument is ignored
when y is given.x and y for which linear interpolation
is used to derive y values corresponding to values of x. This
results in tick marks being drawn along the curve. For histSpikeTRUE to have the bottoms of tick marks (for side=1 or
side=3) aligned at the y-coordinate. The default behavior is to
center the tick marks. For datadensity.data.frame, bottom.alignTRUE to invoke jitter2d are
duplicated values between a lower value l and upper value u, then
d will be spread within +/- fill*min(u-d,d-l)/2.+/- fill*min(limit,min(u-d,d-l)/2). The
default TRUE restricts jittering to the smallest min(u-d,d-l)/2 observed and
results in equal nhistSpike, scat1d
will automatically call histSpike to draw the data density, to
prevent the graphics file from being too large.histSpike. Set to "count" to display
frequency counts rather than relative frequencies, or "density" to
display a kernel density estimate computed using the density function.TRUE if the Rgrid package is in effect for the
current plotdatadensity.
For histSpike, is the number of equal-width intervals for which to
bin x, and if instead nint is a character string (e.g.,
scat1d from datadensity or to
histSpike from scat1dTRUE to prevent from sorting for determining the order lfactor
vector if it is not one alreadywhich="continuous" to only plot continuous variables, or
which="categorical" to only plot categorical, character, or discrete
numeric ones. By default, all types of variables are depicted.method.cat="freq" to depict frequencies of categorical variables
with digits representing the cell frequencies, with size proportional
to the square root of the frequency. By default, vertical bars are used.group strata. The vector of colors is
recycled to be the same length as the levels of group.FALSE to suppress drawing the number of NAs to the right of
each axisnaxes larger than the number of variables in the data frame
if you want to compress the plot vertically.NAspar for mgp)tck under parranges is not given or if a certain variable is not found in the
list, the empirical range, modified by pretty, is used. Example:
ranges=list(age=c(10,datadensity.data.frame. Default is to use the names of the
variables in the input data frame. Note: margin widths computed for
setting aside names of variables use the names, and not these histSpike, if minf is specified low bin frequencies are set to
a minimum value of minf times the maximum bin frequency, so that
rare data points will remain visible. A good choice of minf is
0.075. histSpike when
type="density"x for binning (and
plotting, if add=FALSE and nint is a number)y-axis range for plotting (if add=FALSE)x-axis label (add=FALSE); default is name of input argument xy-axis label (add=FALSE)TRUE to add the spike-histogram to an existing plot, to show
marginal data densitieshistSpike returns the actual range of x used in its binningscat1d adds line segments to plot. datadensity.data.frame draws a
complete plot. histSpike draws a complete plot or adds to an
existing plot.scat1d the length of line segments used is frac*min(par()$pin)
/ par()$uin[opp] data units, where opp is the index of the opposite
axis and frac defaults to .02. Assumes that plot has already been
called. Current par("usr") is used to determine the range of data
for the axis of the current plot. This range is used in jittering and
in constructing line segments.segments, jitter, rug, plsmo, stripplot,
hist.data.frame,ecdf,
hist, histogram, table, densityplot(x <- rnorm(50), y <- 3*x + rnorm(50)/2 )
scat1d(x) # density bars on top of graph
scat1d(y, 4) # density bars at right
histSpike(x, add=TRUE) # histogram instead, 100 bins
histSpike(y, 4, add=TRUE)
histSpike(x, type='density', add=TRUE) # smooth density at bottom
histSpike(y, 4, type='density', add=TRUE)
smooth <- lowess(x, y) # add nonparametric regression curve
lines(smooth) # Note: plsmo() does this
scat1d(x, y=approx(smooth, xout=x)$y) # data density on curve
scat1d(x, curve=smooth) # same effect as previous command
histSpike(x, curve=smooth, add=TRUE) # same as previous but with histogram
histSpike(x, curve=smooth, type='density', add=TRUE)
# same but smooth density over curve
plot(x <- rnorm(250), y <- 3*x + rnorm(250)/2)
scat1d(x, tfrac=0) # dots randomly spaced from axis
scat1d(y, 4, frac=-.03) # bars outside axis
scat1d(y, 2, tfrac=.2) # same bars with smaller random fraction
x <- c(0:3,rep(4,3),5,rep(7,10),9)
plot(x, jitter2(x)) # original versus jittered values
abline(0,1) # unique values unjittered on abline
points(x+0.1, jitter2(x, limit=FALSE), col=2)
# allow locally maximum jittering
points(x+0.2, jitter2(x, fill=1), col=3); abline(h=seq(0.5,9,1), lty=2)
# fill 3/3 instead of 1/3
x <- rnorm(200,0,2)+1; y <- x^2
x2 <- round((x+rnorm(200))/2)*2
x3 <- round((x+rnorm(200))/4)*4
dfram <- data.frame(y,x,x2,x3)
plot(dfram$x2, dfram$y) # jitter2 via scat1d
scat1d(dfram$x2, y=dfram$y, preserve=TRUE, col=2)
scat1d(dfram$x2, preserve=TRUE, frac=-0.02, col=2)
scat1d(dfram$y, 4, preserve=TRUE, frac=-0.02, col=2)
pairs(jitter2(dfram)) # pairs for jittered data.frame
# This gets reasonable pairwise scatter plots for all combinations of
# variables where
#
# - continuous variables (with unique values) are not jittered at all, thus
# all relations between continuous variables are shown as they are,
# extreme values have exact positions.
#
# - discrete variables get a reasonable amount of jittering, whether they
# have 2, 3, 5, 10, 20 \dots levels
#
# - different from adding noise, jitter2() will use the available space
# optimally and no value will randomly mask another
#
# If you want a scatterplot with lowess smooths on the *exact* values and
# the point clouds shown jittered, you just need
#
pairs( dfram ,panel=function(x,y) { points(jitter2(x),jitter2(y))
lines(lowess(x,y)) } )
datadensity(dfram) # graphical snapshot of entire data frame
datadensity(dfram, group=cut2(dfram$x2,g=3))
# stratify points and frequencies by
# x2 tertiles and use 3 colors
# datadensity.data.frame(split(x, grouping.variable))
# need to explicitly invoke datadensity.data.frame when the
# first argument is a listRun the code above in your browser using DataLab