For quite a while I’ve been wanting to explore the mapping and spatial analysis capabilities of R. Maps are cool and R is cool, so maps + R should be awesome. I’ve dabbled here and there but thought I’d try to do a few simple projects to start to build some familiarity with this topic.
In our ongoing series on using Python for occupancy analysis, here's a tutorial on doing facet plots with ggplot2 (yes, the R package) from an IPython notebook via rmagic.
For many of us in the business analytics world, working with dates and times is bread and butter stuff. We can work all kinds of magic with Excel's "serial" datetimes and worksheet functions like DATEVALUE(), WEEKDAY(), MONTH(), DATE() and many more. We can wield VBA functions like DateDiff with the best of them.
In my spreadsheet modeling class this semester, I gave an assignment that involved doing some basic pivot tables and histograms for a dataset containing (fake) patient records from a post-anethesia care unit (PACU). It's the place you go after having surgery until you recover sufficiently to either go home (for outpatient surgery) and head back to your hospital room.
pacu_analysis.zip contains the data, the assignment, and the R markdown file for this tutorial.
You'll see that one of the questions involves having students reflect on why certain kinds of analytical tasks are difficult to do in Excel. I have them read one of my previous posts on using R for a similar analysis task.
So, I thought it would be fun to do some of the things asked for in this Excel assignment but to use R instead. It is a very useful exercise and I think those somewhat new to R (especially coming from an Excel-centric world like a business school) will pick up some good tips and continue to add to their R knowledge base.
As I've been learning and using Python more and more for business analytics'ey things that I previously would have done in Excel or Access with some VBA sprinkled in, I thought it would be a good idea to put together a list of some of the resources I've found to be useful. As you start to read this, you'll notice that there are quite a few references to "scientific computing" and "scientists".
One of the examples I use in my business analytics class to illustrate the power of optimization based decision support tools involves a simple shift scheduling problem. I've developed two different spreadsheet models for a one day, 24 hour, shift scheduling problem. Here's a screenshot from the second model:
In one of the classes I teach on business analytics, I was putting together a quick review of descriptive statistics using Excel's Data Analysis ToolPak and various statistical functions. I was doing a little bit on the Empirical Rule and wanted to show how we see if one of our data columns conformed to predictions made by the rule.
I teach a lot of business statistics courses. The textbook I use, Applied Statistics in Business and Economics (Doane and Seward), has a nice test bank and I use it to create online quizzes in Moodle, the open source learning management system. It can be a little tricky to get test banks from various textbooks imported into Moodle. So, I created a short video to show one way of doing it.
As part of a Stream Ecology course I took in Winter 2013 (BIO 571 at Oakland University, taught by Prof. Scott Tiegs), I wrote a little paper on data processing and analysis issues that arise with the use of stream temperature digital data loggers. As part of this project, I started messing around with using Python to process the log files created by these devices.