One-Month-Old Blog

UPDATE: While I’m already half way through the much recommended book by Zed A. Shaw – Learn Python The Hard Way, I’m still doing my research on other great resources to help me get started with Python. This page listing 10 Python blogs worth following, in particular emphasizes Mouse vs python to be the most useful. Starting the 10th of June, I’ll be engaged on a 9-week-long MOOC on Computer Science using Python, offered by MIT.

It’s been 2 months since I got started with R, and although my progress seems fast to me, it appears so mainly because R comes with insanely helpful packages that reduce large chunks of code into simple functions. Not only that, data visualization and graphics generated in R are beautiful and elegant. For example, the following code generates a Mandelbrot set created through the first 50 iterations of equation z = z2 + c plotted for different complex constants c

library(caTools)         # external package providing write.gif function
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F",
                                 "yellow", "#FF7F00", "red", "#7F0000"))
m <- 1000                # define size
C <- complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ),
              imag=rep(seq(-1.2,1.2, length.out=m), m ) )
C <- matrix(C,m,m)       # reshape as square matrix of complex numbers
Z <- 0                   # initialize Z to zero
X <- array(0, c(m,m,50)) # initialize output 3D array
for (k in 1:50) {        # loop with 50 iterations
  Z <- Z^2+C             # the central difference equation
  X[,,k] <- exp(-abs(Z)) # capture results
write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=800)


This is just an illustration of the power of a dozen or so lines of R code. Just as there are a ridiculous many packages in R, there are countless modules packed into many thousands of packages in Python to make life simpler, so I wasn’t surprised to find a module called antigravity, that can be imported in Python like this:

 import antigravity  

and voila, you are redirected to this telling XKCD tale of Cueball performing gravity-defying stunts with Python.



Upgrading R / Installing R-3.2.0 on Ubuntu

Till recently, I was using R-3.1.1 on Windows OS. Then on April 16, 2015 (10 days ago), they released R-3.2.0. Upgrading it on Windows was easy peasy, not like the headache Ubuntu gave me.

I recently got a Dell Vostro 14 3000 series laptop with Ubuntu 12.04 installed. I haven’t yet upgraded to Ubuntu 14.04 because the graphics drivers for this computer aren’t available for that version. Besides, I’m not much of a gamer. If I were, I wouldn’t care for Ubuntu!

Anyway, I tried installing by typing the following on Terminal:

 sudo apt-get update  
 sudo apt-get install r-base r-base-dev  

R did get installed, but not the latest version. A much older version R-2.14.1. I later found out after quite a lot of time spent on StackExchange, that I had to choose a CRAN mirror that was geographically close to my computer, which would then act as a “software source” for the latest version of R. Now that explained why the above sudo commands weren’t getting me the desired version of software. It was because the the Ubuntu / Canonical software repositories only had an older R version. Also, the distribution line had to match the codename of my Ubuntu version (12.04 LTS).

 codename=$(lsb_release -c -s)  
 echo "deb $codename/" | sudo tee -a /etc/apt/sources.list > /dev/null  

Note that instead of one must replace it with the geographically closest CRAN mirror. Also, the Ubuntu archives on CRAN are signed with the key of Michael Rutter <marutter@gmail> with key ID E084DAB9. So we type in the following:

 sudo apt-key adv --keyserver --recv-keys E084DAB9  
 sudo add-apt-repository ppa:marutter/rdev

Followed by what we would normally have done:

 sudo apt-get update  
 sudo apt-get upgrade  
 sudo apt-get install r-base r-base-dev  

This did the job for me, and I had R-3.2.0 installed successfully on my Ubuntu system. Compare this to Windows, where all you have to do is type in 3 lines (in R, and not Shell):


And to think I left Windows for Linux! I am a Linux newb, and God only knows why I wanted to try out Linux, but on giving it some thought, I think I know why

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