Logistics of Teaching with R

Logistical Needs

Here are some basic areas needed for just about every R-based class.

1. Providing R
2. Distributing data files
3. Distributing class notes
4. Distributing R commands

These same areas apply to other software: JMP, Python, Matlab, Mathematica, etc.

Providing R

Some Options:

• Install R/RStudio on lab machines
• Install R/RStudio on student machines
• Use an R/RStudio server

Distributing Data Files

• Students download files and use basic R functions to read them in, e.g. read.csv()

• Give students links to data files on web server. Again, use the basic R functions, e.g.

  read.csv("http://dl.dropboxusercontent.com/u/5098197/StatisticalModeling/mydata1.csv")

• Functions for reading in files with short names, e.g. mosaic::fetchData()
• Existing packages, e.g. data(Galton, package='mosaic')

• Shared, editable files (e.g. for group projects)

and even …

• Your own package for your own course.

Links to Data Files on Web Server

Of course, that’s how you would distribute files for download.

Rather than posting a link to the file, post the command for reading in the file to the current data session.

todaysData <- read.csv(file="http://dl.dropboxusercontent.com/u/5098197/StatisticalModeling/mydata1.csv")

Students cut-and-paste the command from course web site to their R session.

TIPS

Make sure the protocol is http://, not https://

Use tinyurl.com` to translate your long URL to a short one.

fetchData("http://tinyurl.com/q9pbn7t")

## Complete file name given.  No searching necessary.

##       Who Age
## 1    Bill   3
## 2 Charley   4
## 3   Debby   5

Advantages

• Easy to find, use, and to update data.
• Posting command minimizes possibilities for typing mistakes.
• Many datasets already on book sites, e.g. Lock^5

Functions for reading in files with short names

Set up a web repository for files. Write a function that looks up short names on that repository.

Example:

G <- mosaic::fetchData("Dome.csv")

## Retrieving from http://www.mosaic-web.org/go/datasets/Dome.csv

Advantages

• Easy to use new data sets in class: one step process

Disadvantages

• Writing such a function is not trivial for newbies
• mosaic::fetchData() doesn’t automatically connect to your repository, just the mosaic one.

Existing Packages

Packages are the standard way for distributing R software. They also provide facilities for data, for notes, and for Rmd templates.

The data set you want to use may already exist in a CRAN or other package.

Command to access the data:

• Loaded packages : e.g. data(Galton) or sometimes just Galton
• Installed but unloaded packages: data(BOD, package='datasets')

Finding the data you want:

• Look at packages on CRAN etc. Get list by naming the package, e.g.

  data(package='datasets')

  There are thousands of datasets available.

Shared, Simultaneously Editable Data

For student project collaborations, or …

When collecting data from the class, let the students enter it for you.

• A nice way to teach about the organization of data. You can see and show the class as it’s being mis-entered.

require(mosaic)
ECOTS <- fetchGoogle("https://docs.google.com/spreadsheet/pub?key=0Am13enSalO74dFBPVVZyTndFWlBndVVVSmpENGpPRFE&single=true&gid=0&output=csv")

Your own R Package

It’s not hard to make a simple R package. Include your data in the data directory of the package.

Example: * The sources https://github.com/dtkaplan/ECOTS-ICOTS-2014/tree/master/ECOTS/CourseR * Installation via

```r
require(devtools)
install_github("dtkaplan/ECOTS-ICOTS-2014/ECOTS/CourseR")
help(dataDocExample)
```

Distributing Class Notes

Use R/Markdown to write notes.

Options:

• Publish the notes to Rpubs.com and post link on your website
  • Example: http://rpubs.com/dtkaplan/ecots-modeling
  • Example: http://rpubs.com/dtkaplan/ECOTS-logistics
• Keep the html files on your web server.
  Even better: Keep the sources there so that the notes will be updated automatically when you change them. Even better: Keep your RStudio Project on the web server.
• Use an RStudio project linked to GitHub. This is very powerful, but has a steep learning curve.

Distributing R Commands

Suggestion: Use R Markdown and distribute the commands just like your class notes. Give a link to the .Rmd file.

Have students copy and paste from their browser into the RStudio editor, and use “Chunks/Run All”. That will source the code.

Time for a Teach-with-R package

Make it easy for instructors to set up the logistics.

Interested in helping?