The previous post described how I went about cleaning up some yfd data using Python and numpy. I have no doubt it can be done in fewer lines of code, but I think the post described how useful it can be to manipulate arrays rather than looping through everything. With the data cleaned up, I hoped to visualize my newborn son’s sleep schedule. I recently received an example that does the same thing as my python code, but in 3 lines! It uses R, ggplot2, and plyr. A few more lines can generate pretty plots like this (box plot of sleep length in hrs vs. start time):
As the plot above shows, my son doesn’t sleep a helluva lot during the day. The boxplot also illustrates how volatile his night sleeping has been. This tells me I need to do a better job of getting the boy to nap during the day in hopes of producing longer and more restful sleep periods at night.
While Python has been my gateway drug into the world of programming, I’ve been itching to try out a plotting package based on R, ggplot2. R is a popular language in the statistics community that has enjoyed some good press recently. Anyway, my little sleep duration project seemed perfect for some R exploration.
After searching around on the Interweb, I managed to write some broken R code that didn’t really do what I wanted. Luckily, Hadley Wickham (the author of plyr and ggplot2) took pity on me and offered up some example code to point me in the right direction. I was shocked at the efficiency of the example, particularly given all the wrangling I had to do in python. Now, just for the record, I’m not making any statements about R vs. Python. Hadley obviously created plyr and ggplot2 to make R easier to use, and I imagine the same could be (or already has been) done for python. I just lack the experience and education to know!
Anyway, plyr and ggplot2 are very nice libraries that offer yet more reasons to learn R. Thank you Professor Wickham! Between python and R, I’ve got to believe one can slice and dice almost anything. If I could only get rpy2 working…
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