Emory IBS 519 Guest Lectures: Fall 2024

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by Melinda Higgins, PhD; https://melindahiggins.netlify.app/

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• Week 2 Lecture: Intro to R, RStudio and Rmarkdown
  • This class will cover:
  • Materials
• Week 3 Lecture: Data Exploration and Wrangling (with dplyr)
  • This class will cover:
  • Materials
• Week 4 Lecture: Reproducible Research and RMarkdown
  • Today’s class will cover:
  • Materials for wk4
    • Main Examples Today:
    • IF TIME - optional demos:
    • Follow-up from wk3
    • More resources:
• R/RStudio Helpful Resources

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Week 2 Lecture: Intro to R, RStudio and Rmarkdown

This class will cover:

• Explore RStudio environment
  • command line
  • R scripts
  • packages
  • getting help
• RStudio Education
• R markdown intro (brief)
• Importing & viewing data
  • abalone dataset
  • str(), dim(), head(), tail() functions
• Data types - classes of variables
  • strings
  • integers
  • numeric
  • logical
  • factors (brief intro)

Materials

• RStudio Cloud Project “IBS_519_wk1_lecture02_mhiggins”
  • three R scripts
  • and simple Rmarkdown report to get started
  • stored on Github
• Optional: Intro to R and RStudio
• Getting Started with Packages
• Optional: Introduction to R Objects
• Optional: R Objects - Brief Overview of Factors
• Optional: Intro to ggplot2 (i.e. week 2)
• Optional:
  • Intro to Data Wrangling with dplyr (i.e. week 3)

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Week 3 Lecture: Data Exploration and Wrangling (with dplyr)

This class will cover:

• Data exploration
  • numerical exploration
    • summary() function
    • describe() function from Hmisc package
    • describe() function from pysch package
  • graphical exploration
    • histograms
    • scatterplots
    • learn more at:
      • R Graphics Cookbook
      • Cookbook for R, graphics codes for book
      • R Graph Gallery
• more with the abalone dataset
• Tidyverse - dyplr package
  • glimpse
  • select
  • filter (explore and clean)
  • arrange
  • mutate (making new variables)
  • rename
  • summarise
  • group_by

Materials

• RStudio Cloud Project - in class exercises
  • see “IBS_519_wk3_lecture06_mhiggins” on RStudio.cloud
  • also see Github repository
• RStudio Cloud Project - Homework 3
  • see “Assignment3_HW3_DataWrangling_Cleaning” on RStudio.cloud
  • also see Github repository

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Week 4 Lecture: Reproducible Research and RMarkdown

Today’s class will cover:

• Make your life easier with Rmarkdown
  • Rmarkdown Output Options
  • RMarkdown Formats
• Writing a step-by-step analysis report
  • make DOCX and HTML reports (PDF optional)
• Using parameters - automate your work - learn more at:
  • RMarkdown: Definitive Guide, Ch 15
  • RMarkdown Cookbook: Ch 17.4
• Making other “Rmarkdown” formats
  • see Rmarkdown Galley
  • Documents (HTML, DOC and PDF - see notes below)
  • Slides (HTML slidy and ioslides; PDF Beamer; and Microsoft Powerpoint PPT)
  • and other templates - see RMarkdown Gallery to learn more
• more on making PDF documents:
  • PDF requires some version of LaTeX - easiest is installing the tinytex package
    • see https://yihui.org/tinytex/
    • after installing the tinytex package, you still have to run tinytex::install_tinytex() to actually install the tinytex program with all of the LaTeX packages.
    • Learn more about LaTeX at:
      • https://www.latex-project.org/
      • https://www.overleaf.com/

Materials for wk4

RStudio Cloud Project “IBS_519_wk4_lecture08_mhiggins” - files explained:

This project is also posted in Github at https://github.com/melindahiggins2000/IBS_519_wk4_lecture08_mhiggins

Main Examples Today:

Files:

• 01_Basic_RmarkdownReport.Rmd
• 02_Default_RmarkdownTemplate.Rmd
  • make reports (HTML, DOCX and PDF)
  • make slides (slidy, ioslides, beamer and PPT)
• 03_AbaloneReport_simple.Rmd
• 04_AbaloneReport_paramTemplate.Rmd
• 05_AbaloneReport_paramInYAML.Rmd
• 06_AbaloneReport_param_UserInput.Rmd

Packages used in main examples today:

• tidyverse (includes readr, dplyr, ggplot2)
• knitr
• printr
• rmarkdown
• purrr
• gtsummary

IF TIME - optional demos:

Files:

• rmdformats_readthedown.Rmd
• flexdashboard.Rmd
• EDA_tools.Rmd

Packages needed:

• For EDA_tools.Rmd
  • skimr
  • summarytools
• For rmdformats_readthedown.Rmd
  • rmdformats
• For flexdashboard.Rmd
  • flexdashboard

Follow-up from wk3

• review the order_sort_arrange.R scipt

More resources:

• knitr package
• printr package

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R/RStudio Helpful Resources

• Download: R from CRAN
  • This is where you can download the R language software for FREE for your own computer.
  • Choose your operating system (Mac OS or Windows or Linux/Unix)
  • NOTE: For Windows, you should also download and install Rtools - this is technically optional, but is useful to have. Make sure to download the one for your R version.
• Download: RStudio IDE Desktop
  • Note: Windows is listed at the top - just scroll down to see the installer for the Mac OS as well. There are also installers for the versions of Linux/Unix.
• RStudio Education
• RStudio Cloud Tutorials
• ** Quick-R **
• Rmarkdown Tutorial

• tidyverse

• Datacamp
• R for SAS Users - My Datacamp Course
• Coursera
• Reproducible Templates for Analysis and Dissemination - My Coursera Course
• Emory N741
• Emory N736
• Book: Statistical Inference via Data Science
• Book: The Epidemiologist R Handbook
• Book/Course: Stat 545