How to share your iPython (or iJulia code)



1) Download as Ipython file from the File Option

Screenshot 2014-05-06 22.02.54


2) Use notepad to open the file downloaded. Copy the text contents

Screenshot 2014-05-06 22.06.03

3) Create a new gist at by pasting the text from step 2 here (assumes you have a github account)

Screenshot 2014-05-06 22.06.43


4) Paste the url of the Gist into to get your iNotebook url for sharing

5) To update your notebook, simply copy and paste the new IPython code by editing the gist again



(example here-


Screenshot 2014-05-06 22.08.22

Beginner’s Notes in JULIA Language

  • Packages
  1. Pkg.add(“RDatasets ”)  installs package RDatasets
  2. using  RDatasets –loads package RDatasets
  3. Pkg.update() Updates all packages


some packages to install IJulia, RDatasets, PyCall,PyPlot,Gadfly,Rif

  • Data Input -pwd() – Gets you the current working directory
  1. cd(“C:/Path”) -Sets the working directory to the new path , here C:/Path
  2. readdir() – Lists all the files present in the current working directory
  3. using DataFrames


or df=readtable(“”,header=false)


df= collect(readdlm(“adult.csv”))

or from package

Using RDatasets


  • Object Inspection
  1. summary(a) Gives the structure of object named  including class, dimensions,
  2. colnames(a) Gives the names of variables of the object
  3. typeof(a) Gives the class of a object like data.frame, list,matrix, vector etc

size(a) Givesthe dimension of object (rows column)


using Gadfly

plot(df,x=”x1″ ,color=”x15″,Geom.histogram)


using PyPlot


Note- we can use df[:x15] notation to refer to x15 variable in Data Frame df

For missing values we use Data Arrays and @data to convert object to Data Array

Then use removeNA ( or dropna in Julia 0.3) to remove missing values so as to run functions like mean etc

The describe function gives the numerical summary

Min      17.0
1st Qu.  28.0
Median   37.0
Mean     38.58164675532078
3rd Qu.  48.0
Max      90.0
NAs      0
NA%      0.0%



1) Doesnt work very well on Win 32

2) Two interfaces – command line or IJulia Notebook

3) If you type an object name , gives you the first twenty and last twenty rows- which is quite intuitive designed.

4) PyCall is an interface to Python and Rif is an interface to R- but I had issues trying to work with Rif

5) Basically even simple things( functions!) are renamed in Julia- the effort seems to keep it distinct with R

6) PyPlot for basic plots and Gadfly for ggplot2 plots


Note- some of it was shown here-Updated

Use swirl to learn and teach R very very easily and interactively #rstats

I really love this new package for making R easy to learn ( and ergo to teach) . See swirl

Screenshot 2014-05-06 15.21.51

a clever and painstaking way to teach R – this one deserves kudos to the package creators

Author: Nick Carchedi [aut, cre],

Bill Bauer [aut],

Gina Grdina [aut],

Sean Kross [aut]


A typical swirl session has a user load the package from the R console, choose from a menu of options the course he or she would like to take, then work through 10-15 minute interactive modules, each covering a particular topic.

A module generally alternates between instructional text output to the user and prompts for the user to answer questions.

One question may ask for the result of a simple numerical calculation, while another requires the user to enter an actual R command (which is parsed and executed, if correct) to perform a requested task.

Multiple choice, text-based and approximate numerical answers are also fair game.

Whenever the user answers a question incorrectly, immediate feedback is given in the form of a hint before prompting her to try again.

Finally, plots, figures, and even videos may be incorporated into a module for the sake of reinforcing the methods or concepts being taught.

Note I really hope people who have been passionate about creating the wonderful tutorials and slides for R take a second or two to demo the CRAN package “swirl”

Screenshot 2014-05-06 15.20.33


Hopefully we can see Big Data or even R Hadoop Tutorials on swirl soon


The following are some of our more popular courses:

  • R Programming
  • Regression Models (in progress)
  • Data Analysis
  • Mathematical Biostatistics Boot Camp
  • Open Intro




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