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Polyglots for Data Science #python #sas #r #stats #spss #matlab #julia #octave

In the future I think analysts need to be polyglots- you will need to know more than one language for crunching data.

SAS, Python, R, Julia,SPSS,Matlab- Pick Any Two ;) or Any Three.

No, you can’t count C or Java as a statistical  language :) :)

Efforts to promote Polyglots in Statistical Software are-

1) R for SAS and SPSS Users (free or book)

2) R for Stata Users (book)

3) SAS and R (blog and book)

4) Using Python and R together

Probably we need a Python and R for Data Analysis book- just like we have for SAS and R books.

5) Matlab   and R

Reference (http://mathesaurus.sourceforge.net/matlab-python-xref.pdf ) includes Python

5) Octave and R

package http://cran.r-project.org/web/packages/RcppOctave/vignettes/RcppOctave.pdf includes Matlab

reference http://cran.r-project.org/doc/contrib/R-and-octave.txt

6) Julia and python

  • PyPlot uses the Julia PyCall package to call Python’s matplotlib directly from Julia

7) SPSS and Python is here

8) SPSS and R is as below

  • The Essentials for R for Statistics versions 22, 21, 20, and 19 are available here.
  • This link will take you to the SourceForge site where the Version 18 Essentials and Plugins are hosted.

     

9) Using R from Clojure – Incanter

Use embedded R from Clojure and Incanter http://github.com/jolby/rincanter

Using R for random number creation from time stamps #rstats

Suppose – let us just suppose- you want to create random numbers that are reproducible , and derived from time stamps

Here is the code in R

> a=as.numeric(Sys.time())
> set.seed(a)
> rnorm(log(a))

Note- you can create a custom function  ( I used  the log) for generating random numbers of the system time too. This creates a random numbered list of pseudo random numbers (since nothing machine driven is purely random in the strict philosophy of the word)

a=as.numeric(Sys.time())
set.seed(a)
abs(100000000*rnorm(abs(log(a))))

[1]  39621645  99451316 109889294 110275233 278994547   6554596  38654159  68748122   8920823  13293010
[11]  57664241  24533980 174529340 105304151 168006526  39173857  12810354 145341412 241341095  86568818
[21] 105672257

Possible applications- things that need both random numbers (like encryption keys) and time stamps (like events , web or industrial logs or as pseudo random pass codes in Google 2 factor authentication )

Note I used the rnorm function but you could possibly draw the functions also as a random input (rnorm or rcauchy)

Again I would trust my own random ness than one generated by an arm of US Govt (see http://www.nist.gov/itl/csd/ct/nist_beacon.cfm )

Update- Random numbers in R

http://stat.ethz.ch/R-manual/R-patched/library/base/html/Random.html

Details

The currently available RNG kinds are given below. kind is partially matched to this list. The default is "Mersenne-Twister".

"Wichmann-Hill"
The seed, .Random.seed[-1] == r[1:3] is an integer vector of length 3, where each r[i] is in 1:(p[i] - 1), where p is the length 3 vector of primes, p = (30269, 30307, 30323). The Wichmann–Hill generator has a cycle length of 6.9536e12 (= prod(p-1)/4, see Applied Statistics (1984) 33, 123 which corrects the original article).

"Marsaglia-Multicarry":
A multiply-with-carry RNG is used, as recommended by George Marsaglia in his post to the mailing list ‘sci.stat.math’. It has a period of more than 2^60 and has passed all tests (according to Marsaglia). The seed is two integers (all values allowed).

"Super-Duper":
Marsaglia’s famous Super-Duper from the 70’s. This is the original version which does not pass the MTUPLE test of the Diehard battery. It has a period of about 4.6*10^18 for most initial seeds. The seed is two integers (all values allowed for the first seed: the second must be odd).

We use the implementation by Reeds et al. (1982–84).

The two seeds are the Tausworthe and congruence long integers, respectively. A one-to-one mapping to S’s .Random.seed[1:12] is possible but we will not publish one, not least as this generator is not exactly the same as that in recent versions of S-PLUS.

"Mersenne-Twister":
From Matsumoto and Nishimura (1998). A twisted GFSR with period 2^19937 – 1 and equidistribution in 623 consecutive dimensions (over the whole period). The ‘seed’ is a 624-dimensional set of 32-bit integers plus a current position in that set.

"Knuth-TAOCP-2002":
A 32-bit integer GFSR using lagged Fibonacci sequences with subtraction. That is, the recurrence used is

X[j] = (X[j-100] – X[j-37]) mod 2^30

and the ‘seed’ is the set of the 100 last numbers (actually recorded as 101 numbers, the last being a cyclic shift of the buffer). The period is around 2^129.

"Knuth-TAOCP":
An earlier version from Knuth (1997).

The 2002 version was not backwards compatible with the earlier version: the initialization of the GFSR from the seed was altered. R did not allow you to choose consecutive seeds, the reported ‘weakness’, and already scrambled the seeds.

Initialization of this generator is done in interpreted R code and so takes a short but noticeable time.

"L'Ecuyer-CMRG":
A ‘combined multiple-recursive generator’ from L’Ecuyer (1999), each element of which is a feedback multiplicative generator with three integer elements: thus the seed is a (signed) integer vector of length 6. The period is around 2^191.

The 6 elements of the seed are internally regarded as 32-bit unsigned integers. Neither the first three nor the last three should be all zero, and they are limited to less than 4294967087 and 4294944443 respectively.

This is not particularly interesting of itself, but provides the basis for the multiple streams used in package parallel.

"user-supplied":
Use a user-supplied generator.

 

Function RNGkind allows user-coded uniform and normal random number generators to be supplied.

Iris for Big Data #rstats #bigdata

Quote of the Day-

it is impossible to be a data scientist without knowing iris 

#Anonymous #Quotes

 

Revolution Analytics has been nice enough to provide both datasets and code for analyzing Big Data in R.

http://www.revolutionanalytics.com/subscriptions/datasets/

http://packages.revolutionanalytics.com/datasets/

Site was updated so here are the new links

 

while the Datasets collection is still elementary, as a R Instructor I find this list extremely useful. However I wish they look at some other repositories and make .xdf and “tidy” csv versions. A little bit of RODBC usage should help, and so will some descriptions. Maybe they should partner with Quandl, DataMarket, or Infochimps on this initiative than do it alone.

 

Overall there can be a R package (like a Big Data version of the famous datasets package in R)

But a nice and very useful effort

Revolution R Datasets

More code-

http://blog.revolutionanalytics.com/2013/08/big-data-sets-for-r.html

Also a recent project made by a student of mine on Revolution Datasets and using their blog posts.

Note how much more better the above project is than use the mini and super clean datasets within R (like Boston)

 

Hat TIP- R’s very own Mr Smith
Unrelated-
For more on IRIS

 

Using ifelse in R for creating new variables #rstats #data #manipulation

The ifelse function is simple and powerful and can help in data manipulation within R. Here I create a categoric variable from specific values in a numeric variable

> data(iris)

> iris$Type=ifelse(iris$Sepal.Length<5.8,”Small Flower”,”Big Flower”)
> table(iris$Type)
Big Flower Small Flower
77           73

The parameters  of ifelse is quite simple

Usage

ifelse(test, yes, no)
Arguments

test
an object which can be coerced to logical mode.

yes
return values for true elements of test.

no
return values for false elements of tes

 

Basics of Data Handling for R beginners #rstats

  • Assigning Objects

We can create new data objects and variables quite easily within R. We use the = or the → operator to denote assigning an object to it’s name. For the purpose of this article we will use = to assign objectnames and objects. This is very useful when we are doing data manipulation as we can reuse the manipulated data as inputs for other steps in our analysis.

 

Types of Data Objects in R

  • Lists

A list is simply a collection of data. We create a list using the c operator.

The following code creates a list named numlist from 6 input numeric data

numlist=c(1,2,3,4,5,78)

 

The following code creates a list named charlist from 6 input character data

charlist=c(“John”,”Peter”,”Simon”,”Paul”,”Francis”)

 

The following code creates a list named mixlistfrom both numeric and character data.

mixlist=c(1,2,3,4,”R language”,”Ajay”)

 

  • Matrices

Matrix is a two dimensional collection of data in rows and columns, unlike a list which is basically one dimensional. We can create a matrix using the matrix command while specifying the number of rows by nrow and number of columns by ncol paramter.

In the following code , we create an matrix named ajay and the data is input in 3 rows as specified, but it is entered into first column, then second column , so on.

ajay=matrix(c(1,2,3,4,5,6,12,18,24),nrow=3)

ajay

[,1] [,2] [,3]

[1,] 1 4 12

[2,] 2 5 18

[3,] 3 6 24

 

However please note the effect of using the byrow=T (TRUE) option. In the following code we create an matrix named ajay and the data is input in 3 rows as specified, but it is entered into first row, then second row , so on.

 

>ajay=matrix(c(1,2,3,4,5,6,12,18,24),nrow=3,byrow=T)

>ajay

[,1] [,2] [,3]

[1,] 1 2 3

[2,] 4 5 6

[3,] 12 18 24

  • Data Frames

A data frame is a list of variables of the same number of rows with unique row names. The column names are the names of the variables.

 

6 weeks Data Scientist Online Courses #rstats

Hosting a 6 weekend live online certification course on Business Analytics with R starting June 1 at Edureka.Check www.edureka.in/r-for-analytics for more details. Course has been decided to ensure more open data science than current expensive offerings that are tech rather than business oriented but more support and customization than a MOOC This is because many business customers don’t care if it is lapply or ddapply, or command line or GUI, as long  as they get good ROI on time and money spent in shifting to R from other analytics software.

Screenshot from 2013-05-28 07:16:41

 

 

Using a Linux only package in Windows #rstats

Here is some R code for using a R package that has only a tar.gz file available (used to load R packages in Linux) and no Zip file available (used to load R packages in Windows).

Step 1- Download the tar.gz file.

Step 2 Unzip it (twice) using 7zip

Step 3 Change the path variable below to your unzipped, downloaded location for the R sub folder within the package folder .

Step 4 Copy and Paste this in R

Step 5 Start using the R package in Windows (where 75% of the money and clients and businesses still are)

Caveat Emptor- No X Dependencies (ok!)

path="C:\\Users\\KUs\\Desktop\\segue\\R"
b=dir(path)
c=length(b)
for (i in 1:c){source(gsub(" ","",paste(path,"\\",b[i])))}
ls()

 

R2D2

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