Merge branch 'main' of https://github.com/UGA-IDD/SISMID-2024

SISMID-Module.bib

@@ -7,7 +7,8 @@ @book{Wickham2023
   month     =  jun,
   year      =  2023,
   address   = "Sebastopol, CA",
-  language  = "en"
+  language  = "en",
+  howpublished = "\url{https://r4ds.hadley.nz/}"
 }
 
 @BOOK{Matloff2011-gc,
@@ -20,3 +21,31 @@ @BOOK{Matloff2011-gc
   language  = "en"
 }
 
+@BOOK{Keyes2024-rg,
+  title     = "{R} for the Rest of Us: A statistics-free introduction",
+  author    = "Keyes, David",
+  publisher = "No Starch Press",
+  month     =  jun,
+  year      =  2024,
+  address   = "San Francisco, CA",
+  language  = "en"
+}
+
+@manual{Rintro,
+  title = "An introduction to {R}",
+  author = "{R Core team}",
+  year = 2024,
+  howpublished = "\url{https://cran.r-project.org/doc/manuals/r-release/R-intro.html}"
+}
+
+@misc{Carchedi_Kross_2024, title={Learn R, in R.}, url={https://swirlstats.com/}, journal={swirl}, author={Carchedi, Nick and Kross, Sean}, year={2024}} 
+
+@book{epir,
+  author = {Batra, Neale and Spina, Alex and Blomquist, Paula and Campbell, Finlay and Laurenson-Schafer, Henry and Florence, Isaac, and Fischer, Natalie and Ndiaye, Aminata and Coyer, Liza and Polonsky, Jonathan and Izawa, Yurie and Bailey, Chris and Molling, Daniel and Berry, Isha and Buajitti, Emma and Mousset, Mathilde and Hollis, Sara and Lin, Wen},
+  editor = {Batra, Neale},
+  title = {epiR Handbook},
+  publisher = {Applied Epi Incorporated},
+  year = {2021},
+  copyright = {Open Access},
+  howpublished = "\url{https://epirhandbook.com/}"
+}

_freeze/modules/Module00-Welcome/execute-results/html.json

@@ -1,7 +1,8 @@
 {
-  "hash": "8bfd8f2bc8586d363e99a6e7f763f712",
+  "hash": "ed52f4aca1e410664cf99c84a47733ab",
   "result": {
-    "markdown": "---\ntitle: \"Welcome to SISMID Workshop: Introduction to R\"\nformat: \n  revealjs:\n    scrollable: true\n    smaller: true\n    toc: false\n---\n\n\n## Welcome to SISMID Workshop: Introduction to R!\n\n**Amy Winter (she/her)** \n\nAssistant Professor, Department of Epidemiology and Biostatistics\n\nEmail: [email protected]\n\n</br>\n\n**Zane Billings (he/him)** \n\nPhD Candidate, Department of Epidemiology and Biostatistics\n\nEmail: [email protected]\n\n\n## Introductions\n\n* Name?\n* Current position / institution?\n* Past experience with other statistical programs, including R?\n* Why do you want to learn R?\n* Favorite useful app\n* Favorite guilty pleasure app\n\n\n## What is R?\n\n- R is a language and environment for statistical computing and graphics developed in 1991\n\n- R is the open source implementation of the [S language](https://en.wikipedia.org/wiki/S_(programming_language)), which was developed by [Bell laboratories](https://ca.slack-edge.com/T023TPZA8LF-U024EN26Q0L-113294823b2c-512) in the 70s.\n\n- The aim of the S language, as expressed by John Chambers, is \"to turn ideas into software, quickly and faithfully\"\n\n## What is R?\n\n- **R**oss Ihaka and **R**obert Gentleman at the University of Auckland, New Zealand developed R\n\n\n- R is both [open source](https://en.wikipedia.org/wiki/Open_source) and [open development](https://en.wikipedia.org/wiki/Open-source_software_development)\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](https://www.r-project.org/logo/Rlogo.png){fig-align='center' fig-alt='R logo' width=20%}\n:::\n:::\n\n\n## What is R?\n\n* R possesses an extensive catalog of statistical and graphical methods \n    * includes machine learning algorithm, linear regression, time series, statistical inference to name a few. \n\n* Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results\n\n\n## What is R?\n\n- Program: R is a clear and accessible programming tool\n- Transform: R is made up of a collection of packages/libraries designed specifically for statistical computing\n- Discover: Investigate the data, refine your hypothesis and analyze them\n- Model: R provides a wide array of tools to capture the right model for your data\n- Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world\n\n\n## Why R?\n\n* Free (open source)\n\n* High level language designed for statistical computing\n\n* Powerful and flexible - especially for data wrangling and visualization\n\n* Extensive add-on software (packages)\n\n* Strong community \n\n\n## Why not R?\n\n    \n* Little centralized support, relies on online community and package developers\n\n* Annoying to update\n\n* Slower, and more memory intensive, than the more traditional programming languages (C, Perl, Python)\n\n\n## Is R Difficult?\n\n* Short answer – It has a steep learning curve, like all programming languages\n* Years ago, R was a difficult language to master. \n* Hadley Wickham developed a collection of packages called tidyverse. Data manipulation became trivial and intuitive. Creating a graph was not so difficult anymore.\n\n\n## Overall Workshop Objectives\n\nBy the end of this workshop, you should be able to \n\n1. start a new project, read in data, and conduct basic data manipulation, analysis, and visualization\n2. know how to use and find packages/functions that we did not specifically learn in class\n3. troubleshoot errors\n\n\n## This workshop differs from \"Introduction to Tidyverse\"\n\nWe will focus this class on using **Base R** functions and packages, i.e., pre-installed into R and the basis for most other functions and packages! If you know Base R then are will be more equipped to use all the other useful/pretty packages that exit.\n\nThe Tidyverse is one set of useful/pretty sets of packages, designed to can make your code more **intuitive** as compared to the original older Base R. **Tidyverse advantages**:  \n\n-\t**consistent structure** - making it easier to learn how to use different packages\n-\tparticularly good for **wrangling** (manipulating, cleaning, joining) data  \n-\tmore flexible for **visualizing** data  \n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](https://tidyverse.tidyverse.org/logo.png){fig-align='center' fig-alt='Tidyverse hex sticker' width=10%}\n:::\n:::\n\n\n\n## Workshop Overview\n\n14 lecture blocks that will each:\n\n- Start with learning objectives\n- End with summary slides\n- Include mini-exercise(s) or a full exercise\n\nThemes that will show up throughout the workshop:\n\n- Reproducibility\n- Good coding techniques\n- Thinking algorithmically\n- [Basic terms / R jargon](https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf)\n\n\n## Reproducibility\n\nxxzane slides\n\n\n## Useful (+ Free) Resources\n\n**Want more?**  \n\n- R for Data Science: http://r4ds.had.co.nz/  \n(great general information)\n\n- Fundamentals of Data Visualization: https://clauswilke.com/dataviz/ \n\n- R for Epidemiology: https://www.r4epi.com/\n\n- The Epidemiologist R Handbook: https://epirhandbook.com/en/\n\n- R basics by Rafael A. Irizarry: https://rafalab.github.io/dsbook/r-basics.html\n(great general information)\n \n- Open Case Studies: https://www.opencasestudies.org/  \n(resource for specific public health cases with statistical implementation and interpretation)\n\n## Useful (+Free) Resources\n\n**Need help?** \n\n- Various \"Cheat Sheets\": https://github.com/rstudio/cheatsheets/\n\n- R reference card: http://cran.r-project.org/doc/contrib/Short-refcard.pdf  \n\n- R jargon: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf  \n\n- R vs Stata: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf  \n\n- R terminology: https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf\n\n\n## Installing R\n\n\nHopefully everyone has pre-installed R and RStudio.  We will take a moment to go around and make sure everyone is ready to go. Please open up your RStudio and leave it open as we check everyone's laptops.\n\n- Install the latest version from: [http://cran.r-project.org/](http://cran.r-project.org/ )\n- [Install RStudio](https://www.rstudio.com/products/rstudio/download/)\n\n\n",
+    "engine": "knitr",
+    "markdown": "---\ntitle: \"Welcome to SISMID Workshop: Introduction to R\"\nformat: \n  revealjs:\n    scrollable: true\n    smaller: true\n    toc: false\n---\n\n\n\n## Welcome to SISMID Workshop: Introduction to R!\n\n**Amy Winter (she/her)** \n\nAssistant Professor, Department of Epidemiology and Biostatistics\n\nEmail: [email protected]\n\n</br>\n\n**Zane Billings (he/him)** \n\nPhD Candidate, Department of Epidemiology and Biostatistics\n\nEmail: [email protected]\n\n\n## Introductions\n\n* Name?\n* Current position / institution?\n* Past experience with other statistical programs, including R?\n* Why do you want to learn R?\n* Favorite useful app\n* Favorite guilty pleasure app\n\n\n## What is R?\n\n- R is a language and environment for statistical computing and graphics developed in 1991\n\n- R is the open source implementation of the [S language](https://en.wikipedia.org/wiki/S_(programming_language)), which was developed by [Bell laboratories](https://ca.slack-edge.com/T023TPZA8LF-U024EN26Q0L-113294823b2c-512) in the 70s.\n\n- The aim of the S language, as expressed by John Chambers, is \"to turn ideas into software, quickly and faithfully\"\n\n## What is R?\n\n- **R**oss Ihaka and **R**obert Gentleman at the University of Auckland, New Zealand developed R\n\n\n- R is both [open source](https://en.wikipedia.org/wiki/Open_source) and [open development](https://en.wikipedia.org/wiki/Open-source_software_development)\n\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](https://www.r-project.org/logo/Rlogo.png){fig-align='center' fig-alt='R logo' width=20%}\n:::\n:::\n\n\n\n## What is R?\n\n* R possesses an extensive catalog of statistical and graphical methods \n    * includes machine learning algorithm, linear regression, time series, statistical inference to name a few. \n\n* Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results\n\n\n## What is R?\n\n- Program: R is a clear and accessible programming tool\n- Transform: R is made up of a collection of packages/libraries designed specifically for statistical computing\n- Discover: Investigate the data, refine your hypothesis and analyze them\n- Model: R provides a wide array of tools to capture the right model for your data\n- Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world\n\n\n## Why R?\n\n* Free (open source)\n\n* High level language designed for statistical computing\n\n* Powerful and flexible - especially for data wrangling and visualization\n\n* Extensive add-on software (packages)\n\n* Strong community \n\n\n## Why not R?\n\n    \n* Little centralized support, relies on online community and package developers\n\n* Annoying to update\n\n* Slower, and more memory intensive, than the more traditional programming languages (C, Perl, Python)\n\n\n## Is R Difficult?\n\n* Short answer – It has a steep learning curve, like all programming languages\n* Years ago, R was a difficult language to master. \n* Hadley Wickham developed a collection of packages called tidyverse. Data manipulation became trivial and intuitive. Creating a graph was not so difficult anymore.\n\n\n## Overall Workshop Objectives\n\nBy the end of this workshop, you should be able to \n\n1. start a new project, read in data, and conduct basic data manipulation, analysis, and visualization\n2. know how to use and find packages/functions that we did not specifically learn in class\n3. troubleshoot errors\n\n\n## This workshop differs from \"Introduction to Tidyverse\"\n\nWe will focus this class on using **Base R** functions and packages, i.e., pre-installed into R and the basis for most other functions and packages! If you know Base R then are will be more equipped to use all the other useful/pretty packages that exit.\n\nThe Tidyverse is one set of useful/pretty sets of packages, designed to can make your code more **intuitive** as compared to the original older Base R. **Tidyverse advantages**:  \n\n-\t**consistent structure** - making it easier to learn how to use different packages\n-\tparticularly good for **wrangling** (manipulating, cleaning, joining) data  \n-\tmore flexible for **visualizing** data  \n\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](https://tidyverse.tidyverse.org/logo.png){fig-align='center' fig-alt='Tidyverse hex sticker' width=10%}\n:::\n:::\n\n\n\n\n## Workshop Overview\n\n14 lecture blocks that will each:\n\n- Start with learning objectives\n- End with summary slides\n- Include mini-exercise(s) or a full exercise\n\nThemes that will show up throughout the workshop:\n\n- Reproducibility\n- Good coding techniques\n- Thinking algorithmically\n- [Basic terms / R jargon](https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf)\n\n\n## Reproducibility\n\n* **Reproducible research**: the idea that other people should be able to\nverify the claims you make -- usually by being able to see your data and run\nyour code.\n\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](../images/repspectrum.JPG){fig-align='center'}\n:::\n:::\n\n\n\n* **2023 was the US government's year of open science** -- specific\naspects of reproducibility will be mandated for federally funded research!\n* Sharing and documenting your code is a massive step towards making your\nwork reproducible, and the R ecosystem can play a big role in that!\n\n\n## Useful (+ Free) Resources\n\n**Want more?**  \n\n- R for Data Science: http://r4ds.had.co.nz/  \n(great general information)\n\n- Fundamentals of Data Visualization: https://clauswilke.com/dataviz/ \n\n- R for Epidemiology: https://www.r4epi.com/\n\n- The Epidemiologist R Handbook: https://epirhandbook.com/en/\n\n- R basics by Rafael A. Irizarry: https://rafalab.github.io/dsbook/r-basics.html\n(great general information)\n \n- Open Case Studies: https://www.opencasestudies.org/  \n(resource for specific public health cases with statistical implementation and interpretation)\n\n## Useful (+Free) Resources\n\n**Need help?** \n\n- Various \"Cheat Sheets\": https://github.com/rstudio/cheatsheets/\n\n- R reference card: http://cran.r-project.org/doc/contrib/Short-refcard.pdf  \n\n- R jargon: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf  \n\n- R vs Stata: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf  \n\n- R terminology: https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf\n\n\n## Installing R\n\n\nHopefully everyone has pre-installed R and RStudio.  We will take a moment to go around and make sure everyone is ready to go. Please open up your RStudio and leave it open as we check everyone's laptops.\n\n- Install the latest version from: [http://cran.r-project.org/](http://cran.r-project.org/ )\n- [Install RStudio](https://www.rstudio.com/products/rstudio/download/)\n\n\n",
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@@ -15,4 +16,4 @@
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