class: center, middle, inverse, title-slide # Welcome to the Intermediate R
course! --- # Please do this as we get ready: <!-- Introduce instructors and helpers. --> - ✅ Re-install r3 `remotes::install_gitlab("rostools/r3", upgrade = TRUE, force = TRUE)` - Open your `LearnR3.Rproj` and hit `Ctrl-Shift-L` to make sure that works (if not, let us know) --- class: center, middle # Motivation for this course ## How do you *exactly* do data analysis? What's the workflow? ??? When I started out doing research during my Masters, I always wondered *how* do researchers go about doing data analysis... what was their workflow *exactly* like. No one ever really taught that. Even on online tutorials, mostly pieces of code or how to use code are taught... but never teaching the bigger picture... How do researchers in their daily work write R code and do their data analysis? This question is why the overall workflow is the primary focus in this course and partly with the beginner one. I try to focus on the bigger picture and the overall workflow you would do for doing data analysis. So, why is it that there isn't much information on how researchers do data analysis? --- # ...Because code sharing is almost non-existent in science -- Very few papers provide code <a name=cite-Leek2017a></a><a name=cite-Considine2017a></a>[(https://doi.org/10.1146/annurev-statistics-060116-054104); (https://doi.org/10.1007/s11306-017-1299-3)] -- Why? Likely due to: - Lack of awareness and training - Difficulty of adoption - No incentive or reward - Little to no culture to do it ??? It's because code sharing basically doesn't exist in the vast majority of scientific fields - Intro to how limited code sharing is, research on it, etc. How many have read a method in a paper and wondered how they <u>actually</u> did it? ... you've probably realize by now, way more is done than shown in the "Methods" I definitely have in my research career. We want to change the culture around code by encouraging and teaching how to share code and to write better code in general. --- ## Code sharing: From scientific principle of "reproducibility" -- .pull-left[ ### Replicability - ...often confused with "replicability" <a name=cite-Plesser2018a></a>[(https://doi.org/10.3389/fninf.2017.00076)]<sup>1</sup> - Repeating a study by *independently* performing another identical study - Difficult, usually needs funding - Linked to the "irreproducibility crisis"<sup>2</sup> ] -- .pull-right[ ### Reproducibility - Generating the exact same results when using the same data and code - Should be easy right? Wrong, often just as hard - *Question*: If we can't even *reproduce* a studies results, how can we expect to replicate it? ] .footnote[ 1. Also from an American Statistical Association [statement](https://www.amstat.org/asa/files/pdfs/POL-ReproducibleResearchRecommendations.pdf). 2. Or rather "irreplicability crisis". ] --- class: middle, center # These issues can be fixed by creating and nurturing a culture of openness --- class: middle, center # Goal of this course? Start changing the culture by providing the training --- class: center, middle # Question: ## For those that have done or are doing data processing, does it take up a lot of time? How much? ??? Question, mostly for my own information... --- ## Course setup and layout - Course is mix of: - "Code-alongs" (we type and explain, you type along) - Hands-on exercises - Use r3 package to help with learning - True to our mission, material publicly accessible and [openly licensed](https://r-cubed-intermediate.rostools.org/license.html) - <https://r-cubed-intermediate.rostools.org/> --- ## Getting or asking for help 🙋 .pull-left[ - Put the sticky on your laptop to get help - There are lots of helpers - Team members, try to help out too ] .pull-right[ - We're all learning here! - This is a supportive and safe environment - Remember our [Code of Conduct](conduct.html) ] --- class: middle, center ## Practice using stickies: Have you re-installed r3? --- class: middle, center ## Activity: Stand and arrange based on question ??? We're going to do a "stand and re-arrange yourself" activity based on some questions I ask. --- class: middle, center ## Who has not yet used R? ??? Go into different corners for "yes" and "no". --- class: middle, center ## How do you perceive your skill in R? ??? Along the wall, arrange to one side is "novice/basic" and other side is "advanced". --- class: middle, center ## Who has had formal training in specifically *coding* in R or in general? ??? Staying where you are, raise your hand if you would answer yes to this question. If it was part of a statistics course, it doesn't really count. --- class: middle, center ## Who has struggled with using R? ??? Again, staying where you are, raise your hand if you've struggled or still struggle with R? Ok, you can get back to your seats. --- class: middle, center # First time running this intermediate course... ## So you're also co-creators! Your feedback is necessary to improve it 😁 ??? So you are also the co-creators here, your feedback will improve this and we're collaborating together. You're learning, but also helping to improve the material and in how we teach you. What makes sense, what is confusing, and so on. --- # References <a name=bib-Leek2017a></a>[](#cite-Leek2017a) J. T. Leek and L. R. Jager. "Is Most Published Research Really False?" In: _Annual Review of Statistics and Its Application_ 4.1 (Mar. 2017), pp. 109-122. DOI: [10.1146/annurev-statistics-060116-054104](https://doi.org/10.1146%2Fannurev-statistics-060116-054104). <a name=bib-Considine2017a></a>[](#cite-Considine2017a) E. C. Considine, G. Thomas, et al. "Critical Review of Reporting of the Data Analysis Step in Metabolomics". In: _Metabolomics_ 14.1 (Dec. 2017). DOI: [10.1007/s11306-017-1299-3](https://doi.org/10.1007%2Fs11306-017-1299-3). <a name=bib-Plesser2018a></a>[](#cite-Plesser2018a) H. E. Plesser. "Reproducibility Vs. Replicability: A Brief History of a Confused Terminology". In: _Frontiers in Neuroinformatics_ 11 (Jan. 2018). DOI: [10.3389/fninf.2017.00076](https://doi.org/10.3389%2Ffninf.2017.00076).