spineplot

From graphics v3.6.2 by R-core R-core@R-project.org

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, …)
# S3 method for default
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, …)
# S3 method for formula
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, drop.unused.levels = FALSE)

Arguments

x: an object, the default method expects either a single variable (interpreted to be the explanatory variable) or a 2-way table. See details.
y: a "factor" interpreted to be the dependent variable
formula: a "formula" of type y ~ 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 to hist and can be a list of arguments.
tol.ylab: convenience tolerance parameter for y-axis 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 to 2 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 call gray.colors.
main, xlab, ylab: character strings for annotation
xaxlabels, yaxlabels: character vectors for annotation of x and y axis. Default to levels(y) and levels(x), respectively for the spine plot. For xaxlabels 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.
drop.unused.levels: should factors have unused levels dropped? Defaults to FALSE.

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 2-way 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 multi-way contingency tables. Journal of the American Statistical Association, 89, 190--200. 10.2307/2291215.

Hartigan, J.A., and Kleiner, B. (1984). A mosaic of television ratings. The American Statistician, 38, 32--35. 10.2307/2683556.

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, 23--33.

Examples

library(graphics)

# NOT RUN {
## 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
## (two-way 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 o-ring 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)
# }

Documentation reproduced from package graphics, version 3.6.2, License: Part of R 3.6.2

Community examples

mark@niemannross.com at Nov 29, 2018 graphics v3.5.1

[Example files for LinkedIn Learning course:](https://linkedin-learning.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) ) ```