https://github.com/melindahiggins2000/ibs_519_fall2024_mhiggins
IBS_519_Fall2024_mhiggins
https://github.com/melindahiggins2000/ibs_519_fall2024_mhiggins
Last synced: 3 months ago
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IBS_519_Fall2024_mhiggins
- Host: GitHub
- URL: https://github.com/melindahiggins2000/ibs_519_fall2024_mhiggins
- Owner: melindahiggins2000
- Created: 2024-08-26T20:10:07.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-26T20:20:25.000Z (10 months ago)
- Last Synced: 2025-01-23T14:39:00.935Z (5 months ago)
- Homepage: https://melindahiggins2000.github.io/IBS_519_Fall2024_mhiggins/
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Emory IBS 519 Guest Lectures: Fall 2024
{:.no_toc}
-----
by Melinda Higgins, PhD; [https://melindahiggins.netlify.app/](https://melindahiggins.netlify.app/)
-----
* TOC 1
{:toc}
-----
# 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](https://education.rstudio.com/)
* [R markdown intro (brief)](https://rmarkdown.rstudio.com/)
* Importing & viewing data
- [abalone dataset](https://archive.ics.uci.edu/ml/datasets/abalone)
- `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](https://github.com/melindahiggins2000/IBS_519_wk1_lecture02_mhiggins)
* Optional: [Intro to R and RStudio](https://melindahiggins2000.github.io/CDC_Rworkshop_April2022/CDCRworkshop_April2022_Module01.html#1)
* [Getting Started with Packages](https://melindahiggins2000.github.io/CDC_Rworkshop_April2022/CDCRworkshop_April2022_Module02.html#1)
* Optional: [Introduction to R Objects](https://melindahiggins2000.github.io/N741_Spring2021_lesson04_dataWranglingDplyr/N741_IntroductionToRObjects_mkh.html#1)
* Optional: [R Objects - Brief Overview of Factors](https://melindahiggins2000.github.io/N741_Spring2021_lesson04_dataWranglingDplyr/N741_RObjectsFactors_mkh.html#1)
* Optional: [Intro to ggplot2 (i.e. week 2)](https://melindahiggins2000.github.io/CDC_Rworkshop_April2022/CDCRworkshop_April2022_Module03.html#1)
* Optional:
- [Intro to Data Wrangling with dplyr (i.e. week 3)](https://melindahiggins2000.github.io/CDC_Rworkshop_April2022/CDCRworkshop_April2022_Module04.html#1)
-----
# Week 3 Lecture: Data Exploration and Wrangling (with dplyr)
## This class will cover:
* Data exploration
- numerical exploration
- `summary()` function
- `describe()` function from [`Hmisc` package](https://hbiostat.org/R/Hmisc/)
- `describe()` function from [`pysch` package](https://cran.r-project.org/web/packages/psych/index.html)
- graphical exploration
- histograms
- scatterplots
- learn more at:
- [R Graphics Cookbook](https://r-graphics.org/)
- [Cookbook for R, graphics codes for book](http://www.cookbook-r.com/Graphs/)
- [R Graph Gallery](https://r-graph-gallery.com/)
* more with the `abalone` dataset
* Tidyverse - [`dyplr` package](https://dplyr.tidyverse.org/index.html)
- 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](https://github.com/melindahiggins2000/IBS_519_wk3_lecture06_mhiggins)
* RStudio Cloud Project - Homework 3
- see "Assignment3_HW3_DataWrangling_Cleaning" on RStudio.cloud
- also see [Github repository](https://github.com/melindahiggins2000/IBS_519_Fall2023_HW3)
-----
# Week 4 Lecture: Reproducible Research and RMarkdown
## Today's class will cover:
* Make your life easier with [Rmarkdown](https://rmarkdown.rstudio.com/)
- [Rmarkdown Output Options](https://rmarkdown.rstudio.com/gallery.html)
- [RMarkdown Formats](https://rmarkdown.rstudio.com/formats.html)
* 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](https://bookdown.org/yihui/rmarkdown/parameterized-reports.html)
- [RMarkdown Cookbook: Ch 17.4](https://bookdown.org/yihui/rmarkdown-cookbook/parameterized-reports.html)
* Making other "Rmarkdown" formats
- see [Rmarkdown Galley](https://rmarkdown.rstudio.com/gallery.html)
- 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/](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.latex-project.org/)
* [https://www.overleaf.com/](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](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](https://cran.r-project.org/web/packages/knitr/index.html)
* [`printr` package](https://cran.r-project.org/web/packages/printr/)
-----
# R/RStudio Helpful Resources
* [Download: R from CRAN](https://cran.r-project.org/)
- 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](https://cran.r-project.org/bin/windows/) - this is technically optional, but is useful to have. Make sure to download the one for your R version.
* [Download: RStudio IDE Desktop](https://www.rstudio.com/products/rstudio/download/#download)
- 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](https://education.rstudio.com/)
* [RStudio Cloud Tutorials](https://rstudio.cloud/learn/primers)
* [** Quick-R **](https://www.statmethods.net/)
* [Rmarkdown Tutorial](https://rmarkdown.rstudio.com/lesson-1.html)
* [tidyverse](https://www.tidyverse.org/)
* [Datacamp](https://www.datacamp.com/)
* [R for SAS Users - My Datacamp Course](https://www.datacamp.com/courses/r-for-sas-users)
* [Coursera](https://www.coursera.org/)
* [Reproducible Templates for Analysis and Dissemination - My Coursera Course](https://www.coursera.org/learn/reproducible-templates-analysis)
* [Emory N741](https://melindahiggins2000.github.io/N741bigdata/)
* [Emory N736](https://melindahiggins2000.github.io/N736/)
* [Book: Statistical Inference via Data Science](https://moderndive.com/)
* [Book: The Epidemiologist R Handbook](https://epirhandbook.com/en/index.html)
* [Book/Course: Stat 545](https://stat545.com/)