Data Analysis with R and Python
On Track
Large volumes of data are most useful if you can study them with intensive data analysis. The open source language, R [1], is a powerful tool for evaluating an existing database. The R language offers a variety of statistical functions, but R can do more.
This article shows how to use R for a sample application that evaluates comet data. An Apache web server visualizes the results of statistical reports – with the help of web technologies such as HTML, JavaScript, jQuery [3], and CSS3, which Python creates in combination with R and the MongoDB [4] database.
Comet Rising
Figure 1 shows how the report generator of the sample application displays the comet data in Firefox. The selection list at the top lets the user select a report variant. If you click on Send
, JavaScript sends an HTTP request to the Apache web server, which then generates the report.
The Python scripts first save the comet data in a MongoDB database. Scripts then parse the data and draw on R to create the report in the form of a graphic, which ends up as a PNG file in a public directory on the web server. The server sends the URL back to the browser as a response to the HTTP request.
Web Server
Using the instructions in Listing 1, the developer first prepares the Apache web server on Ubuntu 12.04 for running the sample application. The web server copies the script from the listing (with root privileges) to the /etc/apache2/site-available/rconf
path. The command sudo a2ensite
binds it into the web server's configuration; sudo service apache restart
enables the extended configuration by restarting the web server.
Listing 1
apache.conf
01 Listen 8080 02 <VirtualHost *:8080> 03 DocumentRoot /home/pa/www 04 <Directory /home/pa/www> 05 Options +ExecCGI 06 AddHandler cgi-script .py 07 </Directory> 08 </VirtualHost>
Users can then access the sample application via the URL http://localhost:8080
. Line 1 tells Apache to listen on port 8080; lines 2 to 8 handle the incoming HTTP requests. Lines 5 and 6 allow Python scripts to execute via the web server's CGI interface, assuming they reside in the /home/pa/www
root directory.
Data Store
The sample application uses the free NoSQL, MongoDB [4] database system as its data repository. The commands from Listing 2 install the current version 2.6.2 on Ubuntu 12.04. Line 1 retrieves the keys for the external repository, and line 2 integrates the key. Line 3 updates the package list. The last two lines install mongodb-org
and the current version of the Python Pymongo interface.
Listing 2
Installing Mongo DB and Py-Mongo
01 sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 7F0CEB10 02 echo 'deb http://downloads-distro.mongodb.org/repo/ubuntu-upstart dist \ 10gen' | sudo tee /etc/apt/sources.list.d/mongodb.list 03 sudo apt-get update 04 sudo apt-get install -y mongodb-org 05 sudo easy_install pymongo
To import the sample data into MongoDB, you need the Python import_comets.py
script from Listing 3.
Listing 3
bin/import_comets.py
01 from pymongo import MongoClient 02 import csv 03 import sys 04 05 def pfl(val): 06 try: 07 return float(val) 08 except: 09 return None 10 11 with open(sys.argv[1]) as csvfile: 12 collec = MongoClient()["galaxy"]["comets"] 13 for row in csv.reader(csvfile, delimiter="\t"): 14 try: 15 collec.insert({"name":row[0],"observer":row[1],"type":row[2],"period":\ pfl(row[3]), "ecc":pfl(row[4]),"semaj_axs":pfl(row[5]), \ "perih_dist":pfl(row[6]), "incl":pfl(row[7]), "abs_mag":pfl(row[8])}) 16 except: 17 print "Error: could not import: ", row
The python import_comets.py data/comets.csv
command starts the import at the command line. The script then parses the sample data from the CSV file, data/comets.csv
.
Line 1 integrates MongoClient
from the Python pymongo
package; the next two lines import the csv
and sys
modules. Line 11 reads the CSV file path from the field in the sys.argv
command-line parameter, opens the file, and stores the resulting descriptor in the csvfile
variable.
If they do not already exist, the Python script then creates the MongoDB database galaxy
and the comets
data collection in line 12. The reader()
method then parses the CSV file and splits it into columns based on the tab character.
The for
loop fetches the next line from the reader
object and stores it in the row
field. Line 15 then finally stores the record from the row
in the form of a Python dictionary with key/value pairs in the MongoDB database. The pfl()
function converts numeric values to floating points. If the conversion fails, the script returns a value of None
.
The keys match the attributes in Table 1. The sample data provides characteristic parameters for known comets. Comets primarily differ in terms of their trajectory shapes. Just like planets, comets move in repetitive elliptical orbits (Figure 2).
Table 1
Overview of Comet Data
Attribute | Meaning |
---|---|
name
|
Comet name |
observer
|
First observed by |
type
|
Comet type: RP recurring periodically; NP not period
|
period
|
Orbit time in years |
ecc
|
Numerical eccentrictity ? of the orbit |
semaj_axs
|
Semi-major axis in astronomic units; 1AU = 1.4960 x 1011m |
perih_dist
|
Next perihedron distance in AU |
incl
|
Incidence angle of the orbit in degrees |
abs_mag
|
Relative brightness |
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