# scat1d

0th

Percentile

##### One-Dimensional Scatter Diagram, Spike Histogram, or Density

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 $\code{eps}*\var{w}$ 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 $[-\code{jitfrac}*\var{w}, \code{jitfrac}*\var{w}]$, 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 (seetfrac).

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. 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 distortthe histogram.

histSpike is another method for showing a high-resolution data distribution that is particularly good for very large datasets (say $\code{n} > 1000$). By default, histSpike bins the continuous x variable into 100 equal-width bins and then computes the frequency counts within bins (if n does not exceed 10, no binning is done). 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.

histSpikeg is similar to histSpike but is for adding layers to a ggplot2 graphics object or traces to a plotly object. histSpikeg can also add lowess curves to the plot.

Keywords
hplot, distribution, dplot, aplot
##### Usage
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=FALSE,
preserve=FALSE, fill=1/3, limit=TRUE, nhistSpike=2000, nint=100,
type=c('proportion','count','density'), grid=FALSE, …)jitter2(x, …)# S3 method for default
jitter2(x, fill=1/3, limit=TRUE, eps=0,
presorted=FALSE, …)# S3 method for data.frame
jitter2(x, …)datadensity(object, …)# S3 method for 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=NULL, tck=sc(-.009,-.002),
ranges=NULL, labels=NULL, …)
# sc(a,b) means default to a if number of axes <= 3,="" b="" if="">=50, use
# linear interpolation within 3-50histSpike(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, …)histSpikeg(formula=NULL, predictions=NULL, data, plotly=NULL,
lowess=FALSE, xlim=NULL, ylim=NULL,
side=1, nint=100,
frac=function(f) 0.01 + 0.02*sqrt(f-1)/sqrt(max(f,2)-1),
span=3/4, histcol='black', showlegend=TRUE)
##### Arguments
x

a vector of numeric data, or a data frame (for jitter2)

object

a data frame or list (even with unequal number of observations per variable, as long as group is notspecified)

side

axis side to use (1=bottom (default for histSpike), 2=left, 3=top (default for scat1d), 4=right)

frac

fraction of smaller of vertical and horizontal axes for tick mark lengths. Can be negative to move tick marks outside of plot. For histSpike, this is the relative y-direction length to be used for the largest frequency. When scat1d calls histSpike, it multiplies its frac argument by 2.5. For histSpikeg, frac is a function of f, the vector of all frequencies. The default function scales tick marks so that they are between 0.01 and 0.03 of the y range, linearly scaled in the square root of the frequency less one.

jitfrac

fraction of axis for jittering. If $\code{jitfrac} \le 0$, no jittering is done. If preserve=TRUE, the amount of jittering is independent of jitfrac.

tfrac

Fraction of tick mark to actually draw. If $\code{tfrac}<1$, 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 number of non-missing observations n is less than 125, and $\max{(.1, 125/\var{n})}$ otherwise.

eps

fraction of axis for determining overlapping points in x. 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 still preserved.

lwd

line width for tick marks, passed to segments

col

color for tick marks, passed to segments

y

specify a vector the same length as x 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. If the curve is already represented as a table look-up, you may specify it using the curve argument instead. y may be a scalar to use a constant verticalplacement.

curve

a list containing elements 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 histSpike, interpolated y values are derived for binmidpoints.

bottom.align

set to TRUE 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.align defaults to TRUE if nint>1. In other words, if you are only labeling the first and last axis tick mark, the scat1d tick marks are centered on the variable's axis.

preserve

set to TRUE to invoke jitter2

fill

maximum fraction of the axis filled by jittered values. If d are duplicated values between a lower value l and upper value u, then d will be spread within $\pm \code{fill}*\min{(\var{u}-\var{d},\var{d}-\var{l})}/2$.

limit

specifies a limit for maximum shift in jittered values. Duplicate values will be spread within $\pm\code{fill}*\min{(\var{u}-\var{d},\var{d}-\var{l})}/2$. The default TRUE restricts jittering to the smallest $\min{(\var{u}-\var{d},\var{d}-\var{l})}/2$ observed and results in equal amount of jittering for all d. Setting to FALSE allows for locally different amount of jittering, using maximum space available.

nhistSpike

If the number of observations exceeds or equals nhistSpike, scat1d will automatically call histSpike to draw the data density, to prevent the graphics file from being too large.

type

used by or passed to 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.

grid

set to TRUE if the R grid package is in effect for the current plot

nint

number of intervals to divide each continuous variable's axis for datadensity. For histSpike, is the number of equal-width intervals for which to bin x, and if instead nint is a character string (e.g.,nint="all"), the frequency tabulation is done with no binning. In other words, frequencies for all unique values of x are derived and plotted. For histSpikeg, if x has no more than nint unique values, all observed values are used, otherwise the data are rounded before tabulation so that there are no more than nint intervals.

optional arguments passed to scat1d from datadensity or to histSpike from scat1d. For histSpikep are passed to the lines list to add_trace.

presorted

set to TRUE to prevent from sorting for determining the order $\var{l}<\var{d}<\var{u}$. This is usefull if an existing meaningfull local order would be destroyed by sorting, as in $\sin{(\pi*\code{sort}(\code{round}(\code{runif}(1000,0,10),1)))}$.

group

an optional stratification variable, which is converted to a factor vector if it is not one already

which

set which="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

set 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.

col.group

colors representing the group strata. The vector of colors is recycled to be the same length as the levels of group.

n.unique

number of unique values a numeric variable must have before it is considered to be a continuous variable

show.na

set to FALSE to suppress drawing the number of NAs to the right of each axis

naxes

number of axes to draw on each page before starting a new plot. You can set naxes larger than the number of variables in the data frame if you want to compress the plot vertically.

q

a vector of quantiles to display. By default, quantiles are not shown.

cex.axis

character size for draw labels for axis tick marks

cex.var

character size for variable names and frequence of NAs

lmgp

spacing between numeric axis labels and axis (see par for mgp)

tck

see tck under par

ranges

a list containing ranges for some or all of the numeric variables. If ranges 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(10,100), pressure=c(50,150)).

labels

a vector of labels to use in labeling the axes for datadensity.data.frame. Default is to use the names of the variable in the input data frame. Note: margin widths computed for setting aside names of variables use the names, and not these labels.

minf

For 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. datadensity.data.frame passes minf=0.075 to scat1d to pass to histSpike. Note that specifying minf will cause the shape of the histogram to be distorted somewhat.

mult.width

multiplier for the smoothing window width computed by histSpike when type="density"

xlim

a 2-vector specifying the outer limits of x for binning (and plotting, if add=FALSE and nint is a number). For histSpikeg, observations outside the xlim range are ignored.

ylim

y-axis range for plotting (if add=FALSE). Often needed for histSpikeg to help scale the tick mark line segments.

xlab

x-axis label (add=FALSE); default is name of input argument x

ylab

y-axis label (add=FALSE)

add

set to TRUE to add the spike-histogram to an existing plot, to show marginal data densities

formula

a formula of the form y ~ x1 or y ~ x1 + … where y is the name of the y-axis variable being plotted with ggplot, x1 is the name of the x-axis variable, and optional … are variables used by ggplot to produce multiple curves on a panel and/or facets.

predictions

the data frame being plotted by ggplot, containing x and y coordinates of curves. If omitted, spike histograms are drawn at the bottom (default) or top of the plot according to side.

data

for histSpikeg is a mandatory data frame containing raw data whose frequency distribution is to be summarized, using variables in formula.

plotly

an existing plotly object. If not NULL, histSpikeg uses plotly instead of ggplot.

lowess

set to TRUE to have histSpikeg add a geom_line layer to the ggplot2 graphic, containing lowess() nonparametric smoothers. This causes the returned value of histSpikeg to be a list with two components: "hist" and "lowess" each containing a layer. Fortunately, ggplot2 plots both layers automatically. If the dependent variable is binary, iter=0 is passed to lowess so that outlier detection is turned off; otherwise iter=3 is passed.

span

passed to lowess as the f argument

histcol

color of line segments (tick marks) for histSpikeg. Default is black. Set to any color or to "default" to use the prevailing colors for the graphic.

showlegend

set to FALSE too have the added plotly traces not have entries in the plot legend

##### Details

For 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.

##### Value

histSpike returns the actual range of x used in its binning

##### Side Effects

scat1d adds line segments to plot. datadensity.data.frame draws a complete plot. histSpike draws a complete plot or adds to an existing plot.

##### See Also

segments, jitter, rug, plsmo, lowess, stripplot, hist.data.frame,Ecdf, hist, histogram, table, density, stat_plsmo, histboxp

##### Aliases
• scat1d
• jitter2
• jitter2.default
• jitter2.data.frame
• datadensity
• datadensity.data.frame
• histSpike
• histSpikeg
##### Examples
# NOT RUN {
plot(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 list

# }
# NOT RUN {
require(rms)
f <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)),
data=d)
p <- Predict(f, cholesterol, sex)
g <- ggplot(p, aes(x=cholesterol, y=yhat, color=sex)) + geom_line() +
xlab(xl2) + ylim(-1, 1)
g <- g + geom_ribbon(data=p, aes(ymin=lower, ymax=upper), alpha=0.2,
linetype=0, show_guide=FALSE)
g + histSpikeg(yhat ~ cholesterol + sex, p, d)

# colors <- c('red', 'blue')
# p <- plot_ly(x=x, y=y, color=g, colors=colors, mode='markers')
# histSpikep(p, x, y, z, color=g, colors=colors)
# }

Documentation reproduced from package Hmisc, version 4.3-0, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.