Visualizing data with ggplot from Python

Using my rudimentary knowledge of Python, I was interested in exploring the use of rpy2 to eventually be able to bring together spatial data analysis done in Python, with some higher level tools in R - in this case the powerful graphics library ggplot2 to visualize the results.

My setup is Mac OS 10.7.3, Python 2.7, R 2.14. (R needs to be compiled with '--enable-R-shlib', which the official CRAN binary for Mac is). Also required are Xcode and NumPy.

There is no binary for rpy2 for my configuration available, so I downloaded the source (2.2.3). Extract somewhere, change into the rpy2-2.2.3 directory and install with:

sudo python setup.py build install

The Python code below takes a csv file (output from a some prior geoprocessing done with ArcPy) and produces a graphic with a map and a scatterplot - see the comments for further details.

Data can be downloaded here.

import pandas import rpy2.robjects as robjects from rpy2.robjects.packages import importr from rpy2.robjects.lib import grid from rpy2.robjects.lib import ggplot2 ## read in the distances to railroad (we calculated) neardist = pandas.read_csv('data/NearDistance.csv') ## convert to R dataframe, via Python Dictionary data type neardist_dataf = {'OBAMA_SHAR': robjects.FloatVector(neardist['OBAMA_SHAR']), 'NEAR_DIST': robjects.FloatVector(neardist['NEAR_DIST'])} RR_distance = robjects.DataFrame(neardist_dataf) print RR_distance.colnames ## we use R instance of robjects to issue R commands ## load pre-prepared IL map data sets and print contents: robjects.r('print(load("data/IL.Rdata"))') ## loaded data sets can now be accessed through R handle ## note that different from R dot . is not valid for Python variable names! IL_railroads = robjects.r('IL.railroads') IL_final = robjects.r('IL.final') ## import device driver from R with importr to plot to PNG ## we can then call any function in the grdevices package grdevices = importr('grDevices') grdevices.png(file='mapplot.png', width=1300, height=1000) ## plot the map ## note that the order matters when we add another layer in ggplot (here IL_railroads): first aes, then data, that's different from R ## (see http://permalink.gmane.org/gmane.comp.python.rpy/2349) ## note that we use dictionary to set the opts to be able to set options as keywords, for example legend.key.size p_map = ggplot2.ggplot(IL_final) + \ ggplot2.geom_polygon(ggplot2.aes(x = 'long', y = 'lat', group = 'group', color = 'ObamaShare', fill = 'ObamaShare')) + \ ggplot2.scale_fill_gradient(high = 'blue', low = 'red') + \ ggplot2.scale_fill_continuous(name = "Obama Vote Share") + \ ggplot2.scale_colour_continuous(name = "Obama Vote Share") + \ ggplot2.opts(**{'legend.position': 'left', 'legend.key.size': robjects.r.unit(2, 'lines'), 'legend.title' : ggplot2.theme_text(size = 14, hjust=0), \ 'legend.text': ggplot2.theme_text(size = 12), 'title' : "Obama Vote Share and Distance to Railroads in IL", \ 'plot.title': ggplot2.theme_text(size = 24), 'plot.margin': robjects.r.unit(robjects.r.rep(0,4),'lines'), \ 'panel.background': ggplot2.theme_blank(), 'panel.grid.minor': ggplot2.theme_blank(), 'panel.grid.major': ggplot2.theme_blank(), \ 'axis.ticks': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.title.y': ggplot2.theme_blank(), \ 'axis.title.x': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.text.x': ggplot2.theme_blank(), \ 'axis.text.y': ggplot2.theme_blank()} ) + \ ggplot2.geom_line(ggplot2.aes(x='long', y='lat', group='group'), data=IL_railroads, color='grey', size=0.2) + \ ggplot2.coord_equal() p_map.plot() ## add the scatterplot ## define layout of subplot with viewports vp_sub = grid.viewport(x = 0.19, y = 0.2, width = 0.32, height = 0.4) p_sub = ggplot2.ggplot(RR_distance) + \ ggplot2.aes_string(x = 'OBAMA_SHAR', y= 'NEAR_DIST') + \ ggplot2.geom_point(ggplot2.aes(color='OBAMA_SHAR')) + \ ggplot2.stat_smooth(color="black") + \ ggplot2.opts(**{'legend.position': 'none'}) + \ ggplot2.scale_x_continuous("Obama Vote Share") + \ ggplot2.scale_y_continuous("Distance to nearest Railroad") p_sub.plot(vp=vp_sub) grdevices.dev_off()

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5 thoughts on “Visualizing data with ggplot from Python”

Curious why you didn't use R's read.csv function?

Claudia Engel says:

July 17, 2012 at 4:43 pm

No particular reason other than that I was interested in showing how the conversion into an R data frame would look like.

More to the point, why not use pandas?

Is it now possible to use ggplot w/ Pandas or simple Python objects/dataframes and skip the R stuff altogether, or do you have to be working with R objects to plot to ggplot.

(I'd be interested to use ggplot instead of the matplotlib alternatives, but only if it was frictionless.

cengel says:

June 1, 2014 at 5:58 pm

ggplot is now also available as Python library. See here for examples.

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Visualizing data with ggplot from Python

5 thoughts on “Visualizing data with ggplot from Python”