A spine plot is similar to a mosaic plot and stacked bar chart. Use spineplot()  function to draw spine plots using R. There are quite a number of potential arguments you can use:

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, ...)

The major parameters are:

Your data might be in one of two forms, which affects the kind of plot you get:

• category ~ category results in a spine plot (like a 100% stacked bar chart).
• factor ~ numeric results in a spinogram (like a histogram).

Spine plots

If your data are category ~ category your spineplot results in a kind of stacked bar chart.

Look at the VADeaths dataset (a matrix) as an example:

VADeaths

      Rural Male Rural Female Urban Male Urban Female
50-54       11.7          8.7       15.4          8.4
55-59       18.1         11.7       24.3         13.6
60-64       26.9         20.3       37.0         19.3
65-69       41.0         30.9       54.6         35.1
70-74       66.0         54.3       71.1         50.0

spineplot(VADeaths)

A simple spine plot from a categorical matrix

You can tinker with the graphical parameters to make the chart look “nicer”:

# Custom colours, bar space, and axis labels
spineplot(VADeaths, col = terrain.colors(4),
          off = 5,
          xlab = "Age Class",
          ylab = "Category")

Graphical parameters used to prettify a spine plot

It is hard to resize name labels, as cex, las and so on do not work! The solution is to set these parameters globally using par() and reset them after drawing your plot.

In the following example custom names are also used to help “fit” labels in the plot:

opar <- par(cex.axis = 0.6, las = 2)
spineplot(USPersonalExpenditure,
          xlab = "", ylab = "",
          xaxlabels = c("FT", "HO", "MH", "PC", "PE"))
par(opar)

Axis labels are set using par() before drawing a spineplot

Multi-dimensional tables

The spineplot() function can only deal with 2-dimensional objects. If you have a multi-dimensional table you need to collapse the table to 2D.

spineplot(HairEyeColor)

Error in spineplot.default(HairEyeColor) :
  a 2-way table has to be specified

x <- margin.table(HairEyeColor, margin = c(1,2))
x

       Eye
Hair    Brown Blue Hazel Green
  Black    68   20    15     5
  Brown   119   84    54    29
  Red      26   17    14    14
  Blond     7   94    10    16

spineplot(x, col = c("brown", "blue", "tan", "green"))

A multi-dimensional table needs to be collapsed to 2D for plotting

Spinograms

A spinogram is a spineplot where the data is in the form factor ~ numeric. A spinogram is analogous to a histogram.

spineplot(tension ~ breaks, data = warpbreaks)

A spinogram is a form of histogram

If you have numeric data you can use factor() to convert the data:

# Use factor(x) to "convert" numeric
spineplot(factor(Month) ~ Ozone,
          data = airquality,
          col = heat.colors(5))

A spinogram where numeric data are converted to a factor before plotting

caption: : A spinogram where numeric data are converted to a factor before plotting

Use the breaks argument as you would for hist() to change the breakpoints (you can enter a single integer or a numeric vector).

spineplot(feed ~ weight, data = chickwts, breaks = 4)

Using the breaks argument to alter the breakpoints in a spinogram

It can be tricky to read a spinogram and it is not trivial to add a legend for the colors. See Tips and Tricks article about legends here.

• This article is partly in support of my book An Introduction to R see the publications page for more information.
• An Introduction to R will be published by Pelagic Publishing. See all my books at Pelagic Publishing.
• For more articles visit the Tips and Tricks page and look for the various categories or use the search box.
• Visit our other site at GardenersOwn for a more ecological matters.

This article is partly in support of my book An Introduction to R see the publications page for more information.

The post Spine plots using R appeared first on Data Analytics.

The main functions are curve() and plot.function() but you can simply use plot().

curve(expr, from = NULL, to = NULL, n = 101, add = FALSE,
     type = "l", xname = "x", xlab = xname, ylab = NULL,
     log = NULL, xlim = NULL, ...)

plot(x, y = 0, to = 1, from = y, xlim = NULL, ylab = NULL, ...)

Essentially you use (or make) a function that takes values of x and returns a single value. The arguments are largely self-explanatory but:

• expr, x — an expression or function that returns a single result.
• from, to — the limits of the input (default 0–1).
• n — the number of “points” to draw (these will be evenly spaced between from and to).
• ... — regular graphical arguments can be used.

Simple Math and Trigonometry

You can visualize built-in functions. Note that you can use regular graphics arguments to augment the basic plot.

Here is a plot of the sqrt function:

curve(sqrt, from = 0, to = 100, ylab = "Square Root", las = 1)

Plot of the square root function sqrt()

Here is a simple log plot:

curve(log, from = 0, to = 100, las = 1, lwd = 2, col = "blue")

Plot of log function using curve()

Adding to plots

Use the add = TRUE argument to add a curve() to an existing plot.

curve(sin, -pi*2, pi*2, lty = 2, lwd = 1.5, col = "blue",
      ylab = "Function", ylim = c(-1,1.5))

curve(cos, -pi*2, pi*2, lty = 3, col = "red", lwd = 2, add = TRUE)

# Add legend and title
legend(x = "topright", legend = c("Sine", "Cosine"),
      lty = c(2, 3), lwd = c(1.5, 2),
      col = c("blue", "red"), bty = "n")

title(main = "Sine and Cosine functions")

Plot of functions sin and cos using curve()

Custom functions

You can define your own function to plot. Remember that the result should be a single value. In this example we define two functions to convert between Celsius and Fahrenheit:

# Conversion of temperature
cels <- function(x) (x-32) * 5/9
fahr <- function(x) x*9/5 + 32

Now you can use from and to arguments to set the limits for the input (the default is 0–1).

curve(cels, from = 32, to = 100, xname = "Farenheit",
      ylab = "Celsius", las = 1)

curve(fahr, from = 0, to = 50, xname = "Celsius",
      ylab = "Fahrenheit", las = 1)

Plots using custom function, temperature conversion

Function arguments

If your function requires additional arguments you need to do something different. In this example you can see the Manning equation, which is used to estimate speed of fluids in pipes/tubes:

manning <- function(r, g, c = 0.1) (r^(2/3) * g^0.5/c)

curve(manning) # fails

Error in manning(x) : argument "g" is missing, with no default

The plotting fails. You need to pre-define all arguments as you cannot “pass-through” additional arguments to your function:

manning <- function(r, g = 0.01, c = 0.1) (r^(2/3) * g^0.5/c)

curve(manning) # works

Plot of a custom function with parameters

In the following example you see a built-in function pt() used to visualize the Student’s t distribution.

# pt needs df and lower.tail arguments
PT <- function(x) pt(q = x, df = 100, lower.tail = FALSE)

curve(PT, from = -3, to = 3, las = 1, xname = "t-value",
     n = 20, type = "o", pch = 16, ylab = "probability")

Plot of Student’s t distribution using a function “wrapper”

The workaround is to create a “wrapper” function that calls the actual function you want with the appropriate arguments. Note that in this example the n argument was used to plot 20 points, along with type and pch to create a line with over-plotted points.

This post is part of the support for the new book An Introduction to R. See Publications home page for more details.

• An Introduction to R will be published by Pelagic Publishing. See all my books at Pelagic Publishing.
• For more articles visit the Tips and Tricks page and look for the various categories or use the search box.
• See also the Knowledge Base where there are other topics related to R and data science.
• Look at our other books on the Publications Page.
• Visit our other site at GardenersOwn for a more ecological matters.

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