On the course home page, under that week. They are called “notes” and there are typically several different links to notes on different topics.
These are short questions that you should be able to answer based on your reading of the notes. They involve little or no computing.
The point of the drill is to help you identify the concepts that you need to learn, and to test whether you have succeeded.
Each week in class, you’ll be given a short assignment that involves creation of an Rmd/HTML document and typically some computing about data.
The assignments are posted only one week at a time, so they can be updated based on the class’s situation.
A weekly assignment given at one Wednesday class is due the by the next class.
You should complete the readings and drill exercises before the class meets. Based on the readings, you should make two postings on different subjects in the Weekly Discussion forum. You are welcome to go back and update things after class, but the initial post should be before the class meeting.
For the drill problems
Write brief answers on paper and hand them in at the class meeting.
For the reading discussion postings
Just post them!
For the weekly assignments
You will be writing an Rmd document and compiling it to HTML. It is the HTML file that you will hand in, typically uploading it to Moodle.
When you compile your Rmd to HTML, you will give instructions to embed other relevant documents within the HTML. In particular, you’ll embed the Rmd sources themselves.
The R community distributes software in the form of “packages.” The DCF package contains data, a bit of course-specific software, and an assignment template in Rmd.
The DCF package is on a widely used open-source software sharing site called Github. You can install it from within the R session.
devtools::install_github( "dtkaplan/DCFdevel")Notice that the name in the quoted string is “DCFdevel”, but the package is DCF.
Just install it again.
It’s still under development. This includes both getting things to work right and incorporating new ideas.
Desktop is generally faster and more reliable. (The Server won’t work for you if the network is down.) It also connects well with services such as Dropbox or Google Drive.
The disadvantage: you can’t access your desktop from another computer. This is useful, for instance, when you are working with another person, as in the nightly R help sessions.
This list will evolve over time, but at present, the following commands will do the job:
install.packages( "devtools" )
install.packages( "mosaic" )
install.packages( "yaml" )
devtools::install_github( "rstudio/rmarkdown" )
devtools::install_github( "dtkaplan/DCFdevel" )Just FYI: GitHub is a collaborative document server widely used for open-source software. Packages that are undergoing rapid development are often served via GitHub.
It is, of course, easy to install RStudio desktop on another computer or to re-install it on a newly fixed (and wiped) computer. The issue is retaining the files you created in RStudio.
Backing up files is a generic problem, not something related to RStudio in particular. Services like Dropbox or Google Drive (and others) provide automatic backup.
If you are using RStudio desktop, consider doing your work in a directory that’s automatically backed up. There’s no conflict between Dropbox or Google Drive and RStudio: they compliment one another.
Absolutely. RStudio Server has an “export” facility that will download a file or directory structure to any computer running the browser. It also has an import utility for uploading a zip file containing any content you want to put on RStudio Server.
On a human level, it can be confusing to work on the same project both in RStudio Server and in the Desktop. Consider doing your work on just one of the Server or the Desktop. If you decide to change at a later date, no problem.
To change your password, from within the RStudio app in your browser, choose TOOLS/SHELL. A small window will open up with a prompt. Give this command:
passwdYou will be prompted for a new password. Don’t forget it!
If you want to create a spreadsheet that can be exported as CSV, use this link: https://g.co/oldsheets
At some point we’ll have a good, easy-to-use connection between R and Google Spreadsheets, but for now, reading the CSV directly from Google is the best we have.
A free service that allows you to post HTML files to the web directly from within RStudio. This is convenient for a couple of reasons:
Set up an account. See http://rpubs.com/users/new.
When you want to publish an HTML file from within RStudio, press the little “publish” button within the HTML viewer. You’ll be prompted to login to RPubs as necessarily.
Go to the address for your account name. It will look like http://rpubs.com/[your user name].
Please use the comment system to make suggestions, point out errors, or to discuss the topic.
Comments
What are the Disqus Comments for?
At the end of each reading, etc., is a space for posting comments, replies, etc. Use these to post corrections, comments, etc. on that particular reading. You can also post questions about the reading there. (You can use those same questions or comments for your two weekly postings.)