How to share your iPython (or iJulia code)

Basically

 

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

https://gist.github.com/ (assumes you have a github account)

Screenshot 2014-05-06 22.06.43

 

4) Paste the url of the Gist into http://nbviewer.ipython.org/ 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

 

6)

(example here- http://nbviewer.ipython.org/gist/decisionstats/62c5387624a9ba9015a4)

 

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

a=readtable(“1.csv”)

or df=readtable(“adult.data.txt”,header=false)

or

df= collect(readdlm(“adult.csv”))

or from package

Using RDatasets

iris=dataset(“datasets”,”iris”)

  • 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)

Plots

using Gadfly

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

plot(iris,x=”SepalLength”,y=”SepalWidth”,color=”Species”)

using PyPlot

boxplot(df[:x15])

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

describe(df[:x1])
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%

 

NOTES-

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

http://nbviewer.ipython.org/gist/decisionstats/62c5387624a9ba9015a4

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

http://www.swirlstats.com/

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]

From- http://simplystatistics.org/2013/09/27/announcing-statistics-with-interactive-r-learning-software-environment/

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”

http://cran.r-project.org/web/packages/swirl/index.html

Screenshot 2014-05-06 15.20.33

 

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

From

https://github.com/swirldev/swirl_courses#swirl-courses

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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