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20-resources.Rmd
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# Resources {#resources}
## Intermediate R
The presentations at a [local SIAM
workshop in Spring 2018](https://siam.math.uconn.edu/events/) given by
Wenjie Wang, a former PhD student of mine, now at Eli Lilly, can be
enlighting:
- <https://github.com/wenjie2wang/2018-01-19-siam>
- <https://github.com/wenjie2wang/2018-04-06-siam>
The __Advanced R Programming__ book [@wickham2019advanced] by Hadley
Wickham is available at <https://adv-r.hadley.nz/>. The source that
generated the book is kindly made available at GitHub:
<https://github.com/hadley/adv-r>. It is a great learning experience
to complile the book from the source, during which you may pick up
many necessary skills.
To make R work efficiently, a lot of skills are needed. Even an
experienced user would find unified resources of efficient R
programming [@gillespie2016efficient] useful:
<https://csgillespie.github.io/efficientR/>.
## RMarkdown/Bookdown/Blogdown
Following the step by step the instructions in Yihui Xie's online book
on __bookdown__ [@xie2016bookdown] at
<https://bookdown.org/yihui/bookdown/>, you will be amazed how quickly
you can learn to produce cool-looking documents and even book
manuscripts. If you are a keener, you may as well follow Yihui's
__blogdown__ [@xie2017blogdown], see the online book
<https://bookdown.org/yihui/blogdown/>, to build your own website
using R Markdown.
Build your own website with [blogdown](https://bookdown.org/yihui/blogdown/).
UConn Data Science Lab has a template for cool, reproducible
statistical [report](https://statds.org/template/).
## Git and GitHub
RStudio has made using Git quite straightforward. The online tutorial
by Jenny Bryan, Happy Git and GitHub for the useR at
<https://happygitwithr.com/>, is a very useful tool to get started.
Hadley Wickham's guide to Git and GitHub:
\url{http://r-pkgs.had.co.nz/git.html}.
A good tutorial in Chinese by Xuefeng Liao is
[here](https://www.liaoxuefeng.com/wiki/896043488029600).
## Styles
If you have used R, but never paid attention to R programming styles,
a style clinic would be a necessary step. A good place to start Hadley
Wickham's tidyverse style guide at <http://style.tidyverse.org/>. From
my experience, the two most commonly overlooked styles for beginners
are spacing and indentation. Appropriate spacing and indentation would
immediately make crowdly piled code much more eye-friendly. Such
styles can be automatically enforced by R packages such as **formatr**
or **lintr**. Two important styles that cannot be automatically
corrected are naming and documentation. As in any programming
languate, naming of R objects (variables, functions, files, etc.)
shoud be informative, concise, and consistent with certain naming
convention. Documentation needs to be sufficient, concise, and kept
close to the code; tools like R package **roxygen2** can be very
helpful.
## Writing
It takes a lot of effort to become better at writing. Many small
things can give you a jump start. My PhD student Jieying Jiao, who
just defended her thesis on March 12, 2020, compiled a list of writing
[tips](https://github.com/JieyingJiao/Writing-Style-Tips), which is a
must-read for all my own students.
## Data
Clubear has [manually cleaned
data](https://mp.weixin.qq.com/s?__biz=MzA5MjEyMTYwMg==&mid=2650247264&idx=5&sn=4d689da89074323f587fcb181a9cb2b5&exportkey=AnXq3yYNpseb6OhcgYOzIYc%3D&pass_ticket=zGnr6IL5%2Fi7Q2VyfMz0rNZgfr%2FkjSi8ISggrhBrAd7c7cEPVCByBslrOaZCDuWw3).
Data Science Acadamy of Guizhou Province has a team who developed an R
package [GzbdiDataSet on GitHub](https://github.com/skyyangxin/GzbdiDataSet).
Kaggle's [COVID-19 effort])https://www.kaggle.com/covid19?utm_medium=email&utm_source=intercom&utm_campaign=covid19-landing-email) aims to find factors that impact the
transmission of COVID-19 (particularly those that map to the NASEM/WHO
[open scientific
questions](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks?utm_medium=email&utm_source=intercom&utm_campaign=ai-for-ai-cord19-email)).