R Workshop

Module 6: “To Infinity and Beyond” (2)

2018-04-25
Bobae Kang
(Bobae.Kang@illinois.gov)

• We cannot know everything.
• In fact, no one knows everything!
• “Someone has already done it.”

  Source: AZ Quotes

Source: Wikimedia Commons

• First, try yourself. If you cannot solve it, then go online.

15 min rule: when stuck, you HAVE to try on your own for 15 min; after 15 min, you HAVE to ask for help.- Brain AMA pic.twitter.com/MS7FnjXoGH

— Rachel Thomas (@math_rachel) August 14, 2016

• We all make typos. Check for typos!
  • RStudio does not check for typos automatically
  • But we can refer to an error caused by the typos
• Check if a package is loaded before using its functions
  • Error in some_function() : could not find function "some_function"

# these are equivalent
?some_function
help(some_function)

• Looking ino the documentation is often the best way to understand what a function is and how to use it.
• Using the ? followed by the function name or help() brings out the documentation if available

• When an error is thrown, it comes with an error message
• Error messages often have rich information about what went wrong and where it went wrong
• If we are working with custom functions we defined, RStudio's debugging tools can help us to spot the source of an error in the script and debug it
  • See this article on debugging with RStudio
  • See this video by RStudio on introduction to debugging

Source: Google.com

• Be succinct and specific
  • The search term should be a set of keywords
  • Package names and/or function names are good keywords
  • Using the relevant error message as a serach term can help
• Look at questions/answers on platforms like:
  • Stack Overflow
  • Quora
• Refer to “official” resources if available

Source: R Project

• The Comprehensive R Archive Network (CRAN) has many resources for R and R packages, including the following
  • Manuals
  • Task Views
  • Package pages

• CRAN offers the following manuals:
  • “An Introduction to R”
  • “R Data Import/Export”
  • “R Installation and Administration”
  • “Writing R Extensions”
  • “The R language definition” (draft)
  • “R Internals”

• A Task View offers a brief introduction to a particular topic and an annotated list of relevant R packages
• CRAN has tasks views on a selection of topics, including:
  • Machine learning & Statistical Learning
  • Multivariate Statistics
  • Official Statistics & Survey Methodology
  • Statistics for the Social Sciences
  • Survival Analysis
  • Time Series Analysis

• Each contributed package that is listed on CRAN has a page
• A reference manual and vignettes can be found on the CRAN package page
• To directly get to the package page, try on your broswer:
  • “https://cran.project.org/package=[package-name]”
  • Replace [package-name] with any existing package name

• Packages have reference manuals that contain documentation for all its contents (i.e. functions and datasets)
  • Basically, it is a collection of help() documentations in a pdf format
• Reference manual can also be found by googling
  • Try “package-name pdf” as your search term

• Packages often have vignettes to introduce its contents
  • Some vignettes can be accessed via vignette("package") on console
  • Other vignettes are found on the pacakge page on CRAN
• Unfortunately, not all packages have vignettes.

• RStudio's website offer many useful resources under “Resources” menu, including the following:
  • Cheet sheets page has download links to over 30 “cheat sheets” on R and R packages in the PDF format.
  • Webinar & videos page

• Currently, 13 RStudio cheat sheets are available, including:
  • “Data Transformation with dplyr”
  • “Data Import”
  • “Data Visualization with ggplot2”
  • “Date and times with lubridate”
  • “Work with strings with stringr”
• Currently, there are 15 user-made cheat sheats as well
• Some cheat sheats can also be found in RStudio IDE menu
  • “Help > Cheatsheets”

• RStudio's webinars and videos offer materials covering a variety of subjects.
• Some materials are organized by topics, including:
  • “RStudio Essentials”
  • “Shiny Essentials” and “Advanced Shiny”
    • Some videos here are also available via Shiny website
  • “The Essentials of Data Science”
  • “Advanced Data Science”
• Materials from RStudio's annual conference, rstudio::conf, are also made available.

• Tidyverse has its own website to
  • introduce tidyverse packages
  • share updates and news on tidyverse, and
  • offer guides to training matarials
• There are also child websites for many of tidyverse packages
  • URL: “[package-name].tidyverse.org”

• RStudio has a separate website focused on all things R Markdown.
  • Articles page offers a number of tutorials on creating various sorts of R Markdown documents
  • Formats page provides links to reference matarials on various R Markdown formats and templates

• RStudio also has a separate website on everything Shiny
  • Video & wrttien tutorial page has links to tutorial videos and articles on Shiny as well as recorded conference presentations and webinars
    
  • Articles page offers a list of web articles on building Shiny applications
  • Reference page contains links to upgrade notes and function references for lastest as well as previous versions of the Shiny package

• htmlwidgets for R website presents brief descriptions and examples for various packages for incorporating interactive widgets into R ecosystem
  • Currently ~100 widgets are registered
    • See its “Gallery” page
  • Popular htmlwidgets packages include:
    • plotly and highcharter for interactive visualizations
    • leaflet for interactive maps
    • DT for interactive data tables

Source: “Community (TV series)”, Wikipedia

• R-bloggers is a blog that collects and features articles and blog posts on R and programming in R from a variety of sources.
• The blog offers an excellent way to stay up-to-date on new packages and developments in the R community.
• Its posts cover new updates in R and major R packages, tutorials, information on upcoming events and conferences, and much more.

• Healy, K. (2018). Data Visualization: A practical introduction.
• Grolemund, G. and Wickham, H. (2017). R for Data Science.
• Lovelace, R. et al. (2018). Geocomputation in R.
• Wickham, H. (2017). Advanced R.
• Wilke, C. (n.d.). Fundamentals of Data Visualization.

Visit the bookdown package website to find many more free online books on R!

• Kabacoff, R. Quick-R.
• Prahbhakaran, S. r-statistics.co.
• U of Cincinnati. UC Business Analytics R Programming Guide.
• Wollschlaeger, D. R Examples Repository.
• Yau, C. R Tutorial.

And, of course, this workshop's website :)

Source: GitHub

“GitHub is a development platform inspired by the way you work. From open source to business, you can host and review code, manage projects, and build software alongside millions of other developers.” - GitHub.com

• Most R packages are available as GitHub repositories, which can be “cloned” and downloaded if wanted.
• Many R package authors offer brief explanations and even quick tutorials for their packages on the GitHub repositories.

Source: worldview.stanford.edu

• DataCamp
• Coursera
• edX

• Requires registration and log-in
• Some free courses are available, but most are paid courses with one free chapter
• Cost is $25/month with the annual plan or $29/month
  • Onces subscribed, all courses become available
• Offers 70+ courses on R
  • Each course is short (~4 hours) and focused on a specific topic
  • Ranging from basic to intermediate level

• Requires registration and log-in
• Offers courses, specializations and online degrees
  • Find out more about different options here
• You can “audit” a course for free
  • Course Certificate and online support are available for a fee ($29-$99)
• Notable contents
  • Data Science Specialization (10 courses)
  • Statistics with R Specialization (5 courses)

• Requires registration and log-in
• Courses are free and mostly self-paced
  • edX offer verified certificate for individual courses and XSeries certificate for XSeries programs for a small fee
• Courses are usually organized in a college-course like format
• Perhaps better for learning basics on topics like:
  • Computer science and programming
  • Probability and statistics

Source: Giphy

• Start using R for your work
  • Use the available workshop materials as a guide
• Start reading R for Data Science book
• Learn HTML/CSS for fully customizing your R Markdown documents and more
• Come talk to me if you need:
  • Help on your R code (Remember “the 15 minutes rule”!)
  • Recommendations for study materials

• Video lectures for workshop modules
• A “Resources” page on the website
• Update the icjiar package
• Short articles on specific topics, such as:
  • Connecting to SQL server on RStudio
  • Git and GitHub for version control and collaboration
  • Using “Chrome DevTools” to inspect HTML documents
  • … any suggestions?
• Regular ICJIA R user meeting?

Source: Giphy