cm006 2015-09-24 Thursday overview
- Peer review closes today for Homework 01. TAs will then come through through your repos and comment. We send marks via email using the email you provided in the class survey.
- Homework 02 due sometime next Tuesday 2015-09-29.
- Explore the
gapminderdata.frame - Make some figures with
ggplot2. - Practice writing R Markdown.
- Explore the
- Today we’ll handle the last mechanical bit: R Markdown.
- By end of class, you’ll have seen all the course mechanics: Git(Hub), (R) Markdown, HW submission, peer review, Discussion, etc.
- Questions? Comments?
- Test drive R Markdown
- In class exercise:
- Make a plot of our enrollment data in and R Markdown document.
How to bring that into R?
read.csv("https://gist.githubusercontent.com/jennybc/924fe242a31e0239762f/raw/ea615f4a811a6e9e8a1fe95020a4407785181a21/2015_STAT545_enrollment.csv")
- What’s next?
- Next week we’ll move on to
dplyrand understanding the different flavours of R objects
- Next week we’ll move on to
Slides and links
I’ve given two talks this year that discuss the importance of R Markdown in an analytical workflow and how nicely it plays with GitHub. We looked at some of these slides.
- New tools and workflows for data analysis, Workshop on Visualization for Big Data: Strategies and Principles, Fields Institute, February 2015
- GitHub repo with links and slides: jennybc/2015-02-23_bryan-fields-talk
- Slides on speakerdeck
- How R Markdown + Git + GitHub changed my (teaching?) life, R Summit, June 2015
- GitHub repo with links and slides: jennybc/2015-06-28_r-summit-talk
- Slides on speakerdeck
Q and A from 2014
R Markdown is not the only way to get a pretty report. You can also get one from an R script.
- When editing a
.Rfile, RStudio offers a button for this is the spiral bound notebook icon, near where the “Knit HTML” button appears for.Rmd. Click and you get “Compile Notebook”. - Under the hood, you are using a function called
spin()from theknitrpackage. - Jenny’s
ggplot2tutorial includes alot of Markdown that was made this way, i.e. from.Rnot.Rmd- look at source vs. rendered to get a feel for this
- download the source and render yourself!
- Basically, the special comment prefix
#'becomes very important - R Markdown: prose is queen, R code is sequestered in special chunks
- R script: code is queen, prose is sequestered in special comments
- You can get virtually same HTML report either way. Which to choose? Depends whether the report is primary goal (choose
.Rmd) or a by-product (choose.R).
I passed around hard copies of these 5 books (note: some links to eBooks and repositories are tuned to UBC users):
R Graphics Cookbook by Winston Chang, O’Reilly (2013). The graphs section of his Cookbook for R website will give you a good sense of the book, which contains more material in greater detail.
ggplot2: Elegant Graphics for Data Analysis available via SpringerLink by Hadley Wickham, Springer (2009) | online docs (nice!) | author’s website for the book, including all the code | author’s landing page for the package
R Graphics, 2nd edition available via StatsNetbase by Paul Murrell, Chapman & Hall/CRC Press (2011) | author’s webpage for the book | GoogleBooks search | companion R package, RGraphics, on CRAN
Dynamic documents with R and
knitrby Yihui Xie, part of the CRC Press / Chapman & Hall R Series (2013). ISBN: 9781482203530. No online access (yet?).Reproducible Research with R & RStudio by Christopher Gandrud, part of the CRC Press / Chapman & Hall R Series (2013). ISBN: 978-1466572843. Book website | Examples and code | Book source. No online access (yet?).