Graphics¶

Introduction¶

This section shows how to make R graphics from rpy2, using some of the different graphics systems available to R users.

The purpose of this section is to get users going, and be able to figure out by reading the R documentation how to perform the same plot in rpy2.

Graphical devices¶

With R, all graphics are plotted into a so-called graphical device. Graphical devices can be interactive, like for example X11, or non-interactive, like png or pdf. Non-interactive devices appear to be files. It is possible to create custom graphical devices from Python/rpy2, but this an advanced topic (see Custom graphical devices).

By default an interactive R session will open an interactive device when needing one. If a non-interactive graphical device is needed, one will have to specify it.

Note

Do not forget to close a non-interactive device when done. This can be required to flush pending data from the buffer.

The module grdevices aims at representing the R package grDevices*. Example with the R functions png and dev.off:

from rpy2.robjects.packages import importr
grdevices = importr('grDevices')

grdevices.png(file="path/to/file.png", width=512, height=512)
# plotting code here
grdevices.dev_off()

The package contains an Environment grdevices_env that can be used to access an object known to belong to that R packages, e.g.:

>>> palette = grdevices.palette()
>>> print(palette)
[1] "black"   "red"     "green3"  "blue"    "cyan"    "magenta" "yellow"
[8] "gray"

Getting ready¶

To run examples in this section we first import rpy2.robjects and define few helper functions.

from rpy2 import robjects
from rpy2.robjects import Formula, Environment
from rpy2.robjects.vectors import IntVector, FloatVector
from rpy2.robjects.lib import grid
from rpy2.robjects.packages import importr, data
from rpy2.rinterface_lib.embedded import RRuntimeError
import warnings

# The R 'print' function
rprint = robjects.globalenv.find("print")
stats = importr('stats')
grdevices = importr('grDevices')
base = importr('base')
datasets = importr('datasets')

grid.activate()

Package lattice¶

Introduction¶

Importing the package lattice is done the same as it is done for other R packages.

lattice = importr('lattice')

Scatter plot¶

We use the dataset mtcars, and will use the lattice function xyplot to make scatter plots.

xyplot = lattice.xyplot

Lattice is working with formulae (see Formulae), therefore we build one and store values in its environment. Making a plot is then a matter of calling the function xyplot with the formula as as an argument.

datasets = importr('datasets')
mtcars = data(datasets).fetch('mtcars')['mtcars']
formula = Formula('mpg ~ wt')
formula.getenvironment()['mpg'] = mtcars.rx2('mpg')
formula.getenvironment()['wt'] = mtcars.rx2('wt')

p = lattice.xyplot(formula)
rprint(p)

The display of group information can be done simply by using the named parameter groups. This will indicate the different groups by color-coding.

p = lattice.xyplot(formula, groups = mtcars.rx2('cyl'))
rprint(p)

An alternative to color-coding is to have points is different panels. In lattice, this done by specifying it in the formula.

formula = Formula('mpg ~ wt | cyl')
formula.getenvironment()['mpg'] = mtcars.rx2('mpg')
formula.getenvironment()['wt'] = mtcars.rx2('wt')
formula.getenvironment()['cyl'] = mtcars.rx2('cyl')

p = lattice.xyplot(formula, layout = IntVector((3, 1)))
rprint(p)

Box plot¶

p = lattice.bwplot(Formula('mpg ~ factor(cyl) | gear'),
                   data = mtcars, fill = 'grey')
rprint(p, nrow=1)

Other plots¶

The R package lattice contains a number of other plots, which unfortunately cannot all be detailled here.

tmpenv = data(datasets).fetch("volcano")
volcano = tmpenv["volcano"]

p = lattice.wireframe(volcano, shade = True,
                      zlab = "",
                      aspect = FloatVector((61.0/87, 0.4)),
                      light_source = IntVector((10,0,10)))
rprint(p)

_images/graphics_lattice_wireframe_1.png

Splitting the information into different panels can also be specified in the formula. Here we show an artifial example where the split is made according to the values plotted on the Z axis.

reshape2 = importr('reshape2')
dataf = reshape2.melt(volcano)
dataf = dataf.cbind(ct = lattice.equal_count(dataf.rx2("value"), number=3, overlap=1/4))
p = lattice.wireframe(Formula('value ~ Var1 * Var2 | ct'), 
                      data = dataf, shade = True,
                      aspect = FloatVector((61.0/87, 0.4)),
                      light_source = IntVector((10,0,10)))
rprint(p, nrow = 1)

_images/graphics_lattice_wireframe_2.png

Package ggplot2¶

Introduction¶

The R package ggplot2 implements the Grammar of Graphics. While more documentation on the package and its usage with R can be found on the ggplot2 website, this section will introduce the basic concepts required to build plots. Obviously, the R package ggplot2 is expected to be installed in the R used from rpy2.

The package is using the grid lower-level plotting infrastructure, that can be accessed through the module rpy2.robjects.lib.grid. Whenever separate plots on the same device, or arbitrary graphical elements overlaid, or significant plot customization, or editing, are needed some knowledge of grid will be required.

Here again, having data in a DataFrame is expected (see DataFrame for more information on such objects).

import math, datetime
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
base = importr('base')

mtcars = data(datasets).fetch('mtcars')['mtcars']

rnorm = stats.rnorm
dataf_rnorm = robjects.DataFrame({'value': rnorm(300, mean=0) + rnorm(100, mean=3),
                                  'other_value': rnorm(300, mean=0) + rnorm(100, mean=3),
                                  'mean': IntVector([0, ]*300 + [3, ] * 100)})

Plot¶

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='wt', y='mpg') +
      ggplot2.geom_point())

pp.plot()

Aesthethics mapping¶

An important concept for the grammar of graphics is the mapping of variables, or columns in a data frame, to graphical representations.

Like it was shown for lattice, a third variable can be represented on the same plot using color encoding, and this is now done by specifying that as a mapping (the parameter col when calling the constructor for the AesString).

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') +
      ggplot2.geom_point())

pp.plot()

_images/graphics_ggplot2mtcarscolcyl.png

The size of the graphical symbols plotted (here circular dots) can also be mapped to a variable:

pp = (gp +
      ggplot2.aes_string(x='wt', y='mpg', size='factor(carb)',
                         col='factor(cyl)', shape='factor(gear)') +
      ggplot2.geom_point())

pp.plot()

Geometry¶

The geometry is how the data are represented. So far we used a scatter plot of points, but there are other ways to represent our data.

Looking at the distribution of univariate data can be achieved with an histogram:

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='wt') +
      ggplot2.geom_histogram(bins = 30))

#pp.plot()

_images/graphics_ggplot2geomhistogram.png

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='wt', fill='factor(cyl)') +
      ggplot2.geom_histogram(bins=30))

pp.plot()

_images/graphics_ggplot2geomhistogramfillcyl.png

Barplot-based representations of several densities on the same figure can often be lacking clarity and line-based representation, either geom_freqpoly() (representation of the frequency as broken lines) or geom_density() (plot a density estimate), can be in better.

pp = (gp +
      ggplot2.aes_string(x='value', fill='factor(mean)') +
      ggplot2.geom_density(alpha = 0.5))

_images/graphics_ggplot2geomfreqpolyfillcyl.png

Whenever a large number of points are present, it can become interesting to represent the density of “dots” on the scatterplot.

With 2D bins:

gp = ggplot2.ggplot(dataf_rnorm)

pp = (gp +
      ggplot2.aes_string(x='value', y='other_value') +
      ggplot2.geom_bin2d() +
      ggplot2.ggtitle('geom_bin2d'))
pp.plot(vp = vp)

With a kernel density estimate:

gp = ggplot2.ggplot(dataf_rnorm)

pp = (gp +
      ggplot2.aes_string(x='value', y='other_value') +
      ggplot2.geom_density2d() +
      ggplot2.ggtitle('geom_density2d'))
pp.plot(vp = vp)

With hexagonal bins:

gp = ggplot2.ggplot(dataf_rnorm)

pp = (gp +
      ggplot2.aes_string(x='value', y='other_value') +
      ggplot2.geom_hex() +
      ggplot2.ggtitle('geom_hex'))
pp.plot(vp = vp)

Box plot:

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='factor(cyl)', y='mpg') +
      ggplot2.geom_boxplot())

pp.plot()

Boxplots can be used to represent a summary of the data with an emphasis on location and spread.

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='factor(cyl)', y='mpg', fill='factor(cyl)') +
      ggplot2.geom_boxplot())

pp.plot()

_images/graphics_ggplot2aescolboxplot.png

Models fitted to the data are also easy to add to a plot:

pp = (gp +
      ggplot2.aes_string(x='wt', y='mpg') +
      ggplot2.geom_point() +
      ggplot2.stat_smooth(method = 'lm'))
pp.plot()

The method can be one of {glm, gam, loess, rlm}, and formula can be specified to declared the fitting (see example below).

_images/graphics_ggplot2addsmoothmethods.png

The constructor for GeomSmooth also accepts a parameter groupr that indicates if the fit should be done according to groups.

pp = (gp +
      ggplot2.aes_string(x='wt', y='mpg') +
      ggplot2.geom_point() +
      ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
                          method = 'lm'))
pp.plot()

Encoding the information in the column cyl is again only a matter of specifying it in the AesString mapping.

pp = (ggplot2.ggplot(mtcars) +
      ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') +
      ggplot2.geom_point() +
      ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
                          method = 'lm'))
pp.plot()

_images/graphics_ggplot2_smoothbycylwithcolours.png

As can already be observed in the examples with GeomSmooth, several geometry objects can be added on the top of each other in order to create the final plot. For example, a marginal rug can be added to the axis of a regular scatterplot:

gp = ggplot2.ggplot(mtcars)

pp = (gp +
      ggplot2.aes_string(x='wt', y='mpg') +
      ggplot2.geom_point() +
      ggplot2.geom_rug())

pp.plot()

_images/graphics_ggplot2geompointandrug.png

gp = ggplot2.ggplot(dataf_rnorm)

pp = (gp +
      ggplot2.aes_string(x='value', y='other_value') +
      ggplot2.geom_point(alpha = 0.3) +
      ggplot2.geom_density2d(ggplot2.aes_string(col = '..level..')) +
      ggplot2.ggtitle('point + density'))
pp.plot()

_images/graphics_ggplot2geompointdensity2d.png

Polygons can be used for maps, as shown in the relatively artificial example below:

map = importr('maps')
fr = ggplot2.map_data('france')

# add a column indicating which region names have an "o".
fr = fr.cbind(has_o = base.grepl('o', fr.rx2("region"),
                                 ignore_case = True))
p = (ggplot2.ggplot(fr) +
     ggplot2.geom_polygon(
        ggplot2.aes_string(x = 'long', y = 'lat',
                           group = 'group', fill = 'has_o'),
        col="black"))
p.plot()

Axes¶

Axes can be transformed and configured in various ways.

A common transformation is the log-transform of the coordinates.

from rpy2.robjects.lib import grid
grid.newpage()
grid.viewport(layout=grid.layout(2, 3)).push()

ggplot2_rpack = importr('ggplot2')
diamonds = ggplot2_rpack.__rdata__.fetch('diamonds')['diamonds']
gp = ggplot2.ggplot(diamonds)

for col_i, trans in enumerate(("identity", "log2", "sqrt")):
   # fetch viewport at position col_i+1 on the first row
   vp = grid.viewport(**{'layout.pos.col':col_i+1, 'layout.pos.row': 1})
   pp = (gp +
         ggplot2.aes_string(x='carat', y='price') +
         ggplot2.geom_point(alpha = 0.1, size = 1) +
         ggplot2.coord_trans(x = trans, y = trans) +
         ggplot2.ggtitle("%s on axis" % trans))
   # plot into the viewport
   pp.plot(vp = vp)

   # fetch viewport at position col_i+1 on the second row
   vp = grid.viewport(**{'layout.pos.col':col_i+1, 'layout.pos.row': 2})
   pp = (gp +
         ggplot2.aes_string(x='%s(carat)' % trans, y='%s(price)' % trans) +
         ggplot2.geom_point(alpha = 0.1, size = 1) +
         ggplot2.ggtitle("%s(<variable>)" % trans))
   pp.plot(vp = vp)

_images/graphics_ggplot2mtcars_coordtrans.png

Note

The red square is an example of adding graphical elements to a ggplot2 figure.

vp = grid.viewport(**{'layout.pos.col':2, 'layout.pos.row': 1})
g = grid.rect(x = grid.unit(0.7, "npc"),
              y = grid.unit(0.2, "npc"),
              width = grid.unit(0.1, "npc"),
              height = grid.unit(0.1, "npc"),
              gp = grid.gpar(fill = "red"),
              vp = vp)
g.draw()

Extensions and new features¶

The R package ggplot2 is under active development, and new methods (geometry, summary statistics, theme customizations) are added regularly. In addition to this there exists a dynamic ecosystem of R packages proposing extensions, and a user may need R code for ggplot2 not included in our module. The following steps should make writing the Python wrapper code a very minimal effort in many cases:

Identify the matching type of extension in our class diagram for rpy2.robjects.lib.ggplot2 (for example, is this an new “geometry”, statistics, coordinates, theme ?). For example the ggplot2 function stat_quantile is a statistics and the best matching class is rpy2.robjects.lib.ggplot2.Stat. The more general ancestor class rpy2.robjects.lib.ggplot2.GBaseObject could also be used.
Implement a child class that assign to the class attribute _constructor the R constructor function. For example stat_quantile. The Python callable mapping the R constructor is then the class method new(). The complete implementation for stat_quantile is then:
```
from rpy2.robjects.packages import importr
ggplot2_rpack = importr('ggplot2')
class StatQuantile(Stat):
    """ Continuous quantiles """
    _constructor = ggplot2_rpack.stat_quantile

stat_quantile = StatQuantile.new
```
The callable stat_quantile() can now be used like any other ggplot2 statistics in rpy2.robjects.lib.ggplot2 (and the object it returns can be added to compose a final ggplot figure).

Class diagram¶

Inheritance diagram of rpy2.robjects.lib.ggplot2.GBaseObject, rpy2.robjects.lib.ggplot2.Coord, rpy2.robjects.lib.ggplot2.Element, rpy2.robjects.lib.ggplot2.Facet, rpy2.robjects.lib.ggplot2.Geom, rpy2.robjects.lib.ggplot2.GGPlot, rpy2.robjects.lib.ggplot2.Scale, rpy2.robjects.lib.ggplot2.Stat, rpy2.robjects.lib.ggplot2.Theme

Package grid¶

The grid package is the underlying plotting environment for lattice and ggplot2 figures. In few words, it consists in pushing and poping systems of coordinates (viewports) into a stack, and plotting graphical elements into them. The system can be thought of as a scene graph, with each viewport a node in the graph.

>>> from rpy2.robjects.lib import grid

Getting a new page is achieved by calling the function grid.newpage().

Calling layout() will create a layout, e.g. create a layout with one row and 3 columns:

>>> lt = grid.layout(1, 3)

That layout can be used to construct a viewport:

>>> vp = grid.viewport(layout = lt)

The created viewport corresponds to a graphical entity. Pushing into the current viewport, can be done by using the class method grid.Viewport.push():

>>> vp.push()

Example:

grid.newpage()
# create a rows/columns layout
lt = grid.layout(2, 3)
vp = grid.viewport(layout = lt)
# push it the plotting stack
vp.push()

# create a viewport located at (1,1) in the layout
vp = grid.viewport(**{'layout.pos.col':1, 'layout.pos.row': 1})
# create a (unit) rectangle in that viewport
grid.rect(vp = vp).draw()

vp = grid.viewport(**{'layout.pos.col':2, 'layout.pos.row': 2})
# create text in the viewport at (1,2)
grid.text("foo", vp = vp).draw()

vp = grid.viewport(**{'layout.pos.col':3, 'layout.pos.row': 1})
# create a (unit) circle in the viewport (1,3)
grid.circle(vp = vp).draw()

Custom ggplot2 layout with grid¶

grid.newpage()

# create a viewport as the main plot
vp = grid.viewport(width = 1, height = 1) 
vp.push()

tmpenv = data(datasets).fetch("rock")
rock = tmpenv["rock"]

p = (ggplot2.ggplot(rock) +
     ggplot2.geom_point(ggplot2.aes_string(x = 'area', y = 'peri')))
p += ggplot2.theme_bw()
p.plot(vp = vp)

vp = grid.viewport(width = 0.6, height = 0.6, x = 0.37, y=0.69)
vp.push()
p = (ggplot2.ggplot(rock) +
     ggplot2.geom_point(ggplot2.aes_string(x = 'area', y = 'shape')) +
     ggplot2.theme(**{'axis.text.x': ggplot2.element_text(angle = 45)}))

p.plot(vp = vp)

Classes¶

class rpy2.robjects.lib.grid.Viewport(o)[source]¶

Bases: rpy2.robjects.robject.RObject

Drawing context. Viewports can be thought of as nodes in a scene graph.

classmethod current()[source]¶: Return the current viewport in the stack.

classmethod default(**kwargs)[source]¶

classmethod down(name, strict=False, recording=True)[source]¶: Return the number of Viewports it went down

classmethod pop(n)[source]¶: Pop n viewports from the stack.

push(recording=True)[source]¶

classmethod seek(name, recording=True)[source]¶: Seek and return a Viewport given its name

classmethod up(n, recording=True)[source]¶: Go up n viewports

classmethod viewport(**kwargs)[source]¶: Constructor: create a Viewport

class rpy2.robjects.lib.grid.Grob(o)[source]¶

Bases: rpy2.robjects.robject.RObject

Graphical object

draw(recording=True)[source]¶: Draw a graphical object (calling the R function grid::grid.raw())

classmethod grob(**kwargs)[source]¶: Constructor (uses the R function grid::grob())

class rpy2.robjects.lib.grid.GTree(o)[source]¶

Bases: rpy2.robjects.lib.grid.Grob

gTree

classmethod grobtree(**kwargs)[source]¶: Constructor (uses the R function grid::grobTree())

classmethod gtree(**kwargs)[source]¶: Constructor (uses the R function grid::gTree())

Class diagram¶

Inheritance diagram of rpy2.robjects.lib.grid

Table of Contents

Previous topic

Next topic

This Page

Graphics¶

Introduction¶

Graphical devices¶

Getting ready¶

Package lattice¶

Introduction¶

Scatter plot¶

Box plot¶

Other plots¶

Package ggplot2¶

Introduction¶

Plot¶

Aesthethics mapping¶

Geometry¶

Axes¶

Facets¶

Extensions and new features¶

Class diagram¶

Package grid¶

Custom ggplot2 layout with grid¶

Classes¶

Class diagram¶