In celebration of my achieving 10,000 “reputation” on Stack Overflow, I’m re-posting one of my questions from there that was (as I had expected) deleted after being live for about 5 hours. In that time, I never really got a satisfactory answer, so if anyone wants to offer one in the comments, that would be great!
… here is a goddess that I am happy to worship…
Anyone who has spent some time in India is sure to have noticed the slogans painted on the back of trucks, autos, and other vehicles advising “we two, ours one”. This is part of India’s “family planning” efforts–efforts which have had a pretty bumpy history that included a forced sterilization program.
Originally, the slogans were “we two, ours two”, or at least that was the catchy English version–regional languages usually had a slogan more along the lines of “one family, two children”. And, the change to the new slogan led to at least one humorous math discussion with an auto driver who commented that, “Earlier, it was ‘we two, ours two’; now, it is ‘we two, ours one’. What’s next? ‘We two, ours half?’”
Anyway, keen observers might have noticed the following new addition to selected trucks:
After a little bit more work, there’s a new stratified random sampling function, this one letting you sample from a data frame, returning all the variables for each of your samples as a nice data frame that you can continue working on as usual.
Get the function at http://news.mrdwab.com/stratified. Usage notes in the head of the function.
IMPORTANT: This is here mostly to remind me of how I solved my problem. You should read Stratified random sampling in R from a data frame if you really want to use this function.
I know that sampling is quite complex, and I will admit that I know very little about its complexities. Fortunately, software like R lets you draw simple random samples pretty easily, either either with or without replacement. Unfortunately, I could not find any feature to allow me to do simple stratified random sampling, at least not with the features I was looking for. Fortunately again, with a little bit of experimenting, it can be pretty easy to learn how to write functions in R when a direct solution does not present itself.
This post shares my initial “work-in-progress” on writing an R function for stratified sampling.
A year ago, I wrote a post about reshaping data from a wide format to a long format. I thought that considering how much time had passed, it would be good to revisit R’s in-built reshape functions. For these examples, I’ve copied the Stata examples from the UCLA Academic Technology Services’s “Reshape data wide to long” page. Since the data is provided in Stata dta files, you need to first load the “foreign” package to be able to read the data in R.
I was flipping through some of the old books that I used to scribble in way back when, and I came across a page that had the following:
This was a little idea that I had long before So, sue me already, back when I loved making mix-tapes for my friends. This mix-tape never materialized, and since then, my music exposure has increased quite a bit, leading to what I think is a pretty damn impressive compilation….
Read on… download… enjoy!