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-    "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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+    "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\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## Course website\n\n* All of the materials for this course can be found online here: [here](https://uga-idd.github.io/SISMID-2024/).\n* This contains the schedule, course resources, and online versions of all of\nour slide decks.\n* The **Course Resources** page contains download links for all of the data,\nexercises, and slides for this class.\n* Please feel free to download these resources and share them -- all of the\ncourse content is under the [Creative Commons BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/).\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\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\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\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\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\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](../images/repspectrum.JPG){fig-align='center'}\n:::\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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