spineplot
Spine Plots and Spinograms
Spine plots are a special cases of mosaic plots, and can be seen as a generalization of stacked (or highlighted) bar plots. Analogously, spinograms are an extension of histograms.
 Keywords
 hplot
Usage
spineplot(x, ...)
"spineplot"(x, y = NULL, breaks = NULL, tol.ylab = 0.05, off = NULL, ylevels = NULL, col = NULL, main = "", xlab = NULL, ylab = NULL, xaxlabels = NULL, yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), axes = TRUE, ...)
"spineplot"(formula, data = NULL, breaks = NULL, tol.ylab = 0.05, off = NULL, ylevels = NULL, col = NULL, main = "", xlab = NULL, ylab = NULL, xaxlabels = NULL, yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), axes = TRUE, ..., subset = NULL)
Arguments
 x
 an object, the default method expects either a single variable (interpreted to be the explanatory variable) or a 2way table. See details.
 y
 a
"factor"
interpreted to be the dependent variable  formula
 a
"formula"
of typey ~ x
with a single dependent"factor"
and a single explanatory variable.  data
 an optional data frame.
 breaks
 if the explanatory variable is numeric, this controls how
it is discretized.
breaks
is passed tohist
and can be a list of arguments.  tol.ylab
 convenience tolerance parameter for yaxis annotation. If the distance between two labels drops under this threshold, they are plotted equidistantly.
 off
 vertical offset between the bars (in per cent). It is fixed to
0
for spinograms and defaults to2
for spine plots.  ylevels
 a character or numeric vector specifying in which order the levels of the dependent variable should be plotted.
 col
 a vector of fill colors of the same length as
levels(y)
. The default is to callgray.colors
.  main, xlab, ylab
 character strings for annotation
 xaxlabels, yaxlabels
 character vectors for annotation of x and y axis.
Default to
levels(y)
andlevels(x)
, respectively for the spine plot. Forxaxlabels
in the spinogram, the breaks are used.  xlim, ylim
 the range of x and y values with sensible defaults.
 axes
 logical. If
FALSE
all axes (including those giving level names) are suppressed.  ...
 additional arguments passed to
rect
.  subset
 an optional vector specifying a subset of observations to be used for plotting.
Details
spineplot
creates either a spinogram or a spine plot. It can
be called via spineplot(x, y)
or spineplot(y ~ x)
where
y
is interpreted to be the dependent variable (and has to be
categorical) and x
the explanatory variable. x
can be
either categorical (then a spine plot is created) or numerical (then a
spinogram is plotted). Additionally, spineplot
can also be
called with only a single argument which then has to be a 2way table,
interpreted to correspond to table(x, y)
.
Both, spine plots and spinograms, are essentially mosaic plots with
special formatting of spacing and shading. Conceptually, they plot
$P(y  x)$ against $P(x)$. For the spine plot (where both
$x$ and $y$ are categorical), both quantities are approximated
by the corresponding empirical relative frequencies. For the
spinogram (where $x$ is numerical), $x$ is first discretized
(by calling hist
with breaks
argument) and then
empirical relative frequencies are taken.
Thus, spine plots can also be seen as a generalization of stacked bar
plots where not the heights but the widths of the bars corresponds to
the relative frequencies of x
. The heights of the bars then
correspond to the conditional relative frequencies of y
in
every x
group. Analogously, spinograms extend stacked
histograms.
Value

The table visualized is returned invisibly.
References
Friendly, M. (1994), Mosaic displays for multiway contingency tables. Journal of the American Statistical Association, 89, 190200.
Hartigan, J.A., and Kleiner, B. (1984), A mosaic of television ratings. The American Statistician, 38, 3235.
Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.
Hummel, J. (1996), Linked bar charts: Analysing categorical data graphically. Computational Statistics, 11, 2333.
See Also
Examples
library(graphics)
## treatment and improvement of patients with rheumatoid arthritis
treatment < factor(rep(c(1, 2), c(43, 41)), levels = c(1, 2),
labels = c("placebo", "treated"))
improved < factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),
levels = c(1, 2, 3),
labels = c("none", "some", "marked"))
## (dependence on a categorical variable)
(spineplot(improved ~ treatment))
## applications and admissions by department at UC Berkeley
## (twoway tables)
(spineplot(margin.table(UCBAdmissions, c(3, 2)),
main = "Applications at UCB"))
(spineplot(margin.table(UCBAdmissions, c(3, 1)),
main = "Admissions at UCB"))
## NASA space shuttle oring failures
fail < factor(c(2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1,
1, 1, 1, 2, 1, 1, 1, 1, 1),
levels = c(1, 2), labels = c("no", "yes"))
temperature < c(53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70,
70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81)
## (dependence on a numerical variable)
(spineplot(fail ~ temperature))
(spineplot(fail ~ temperature, breaks = 3))
(spineplot(fail ~ temperature, breaks = quantile(temperature)))
## highlighting for failures
spineplot(fail ~ temperature, ylevels = 2:1)
Community examples
[Example files for LinkedIn Learning course:](https://linkedinlearning.pxf.io/rweekly_spineplot) ```r # Description: Example file for spineplot # main idea: creating spineplots # width of bars = frequency of X # height of bars = frequency of y # Y must be a factor and is the dependent variable spineplot(ChickWeight$weight, ChickWeight$Diet) # spineplot(x,y) # interesting observations # Height of bars indicates obs per diet. Diet 1 has more obs # Width of bars indicates obs per weight. More chicks are weighed between 50 and 100 # or... spineplot(Diet ~ weight, data = ChickWeight) # spineplot(y ~ x) # bells and whistles spineplot(Diet ~ weight, data = ChickWeight, breaks = fivenum(ChickWeight$weight), col = c(5:8), xlab = "Chicken Weight", ylab = "Chicken Diet") # The above is actually a spinogram  like a histogram # example of a true spine plot. Both x and y must be factors spineplot(Diet ~ factor(weight), data = ChickWeight, col = c(5:8)) spineplot(factor(weight) ~ Diet, data = ChickWeight, col = c(1:nlevels(factor(ChickWeight$weight)))) # subset of data # use the 1st half of data. Would make sense to have a more sophisticated selection spineplot(Diet ~ factor(weight), data = ChickWeight, subset = c(1:(578/2)), drop.unused.levels = TRUE, col = c(4:7) ) ```