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


ifelse(test, yes, no)

an object which can be coerced to logical mode.

return values for true elements of test.

return values for false elements of tes


So many R Packages Everywhere, which one do I use? #rstats

Some thoughts on R Packages

  • CRAN is no longer the sole repository for many useful R packages. This includes R Forge, Google Code and increasingly Github
  • CRAN lacks the flexibility and social aspect of Github.
  • CRAN Views is the only thing that lists subject wide listing of R packages. The categorization is however done more on methods than on use cases or business domains.
  • Multiple R packages for the same thing. Which one do I use? Only Stack Overflow helps with that. No rating , no recommendation system
  • The packages suggested by R package feature needs better and automatic association analysis . Right now it is manual and dependent on package author and maintainer.
  • Quis custodiet ipsos custodes? Who guards the guardians of R packages. In an era of cyber security, we need better transparency on security measures within R packages especially given the international nature of the project.  I am very sure I ( or anyone) can create R code to communicate discretely especially on Windows

  • I would rather not install anything on my local machine, and read the package directly from the CRAN . CRAN was designed in an era of low bandwidth- this needs to be upgraded.
  • Note I am refraining respectfully from the atrocious nature of aesthetics in the home website. Many statisticians feel no use of making R user friendly. My professors at U tenn (from which I dropped out in 2 sems) were horrified when I took courses in graphic design as I wanted to know more on the A and B, which make the A/B testing of statistical design. Now that I am getting older, I get horrified by the lack of HTML, CSS and JQuery by some of the brightest programmers in this project.
  • Please comment below.


Using R for Cricket Analysis #rstats #IPL

#Downloading the Data for batting across all formats of cricket
tables=readHTMLTable(url,stringsAsFactors = F)
#Note we wrote stringsAsFactors=F in this to avoid getting factor variables, 
#since we will need to convert these variables to numeric variables
table2=tables$"Overall figures"
#Creating new variables from Span
#Creating New Variables. In cricket a not out score is denoted by * which can cause data quality error. 
#This is treated by grepl for finding and gsub for removing the *. 
#Note the double \ to escape regex charachter
#Creating a FOR Loop (!) to convert variables to numeric variables
for (i in 3:17) {
+     table2[, i] <- as.numeric(table2[, i])
+ }

and we see why Sachin Tendulkar is the best (by using ggplot via Deducer)


Also see 

  • Freaknomics Challenge-
    1. Prove match fixing does not and cannot exist in IPL
    2. Create an ideal fantasy team