- A laptop is required for this bootcamp. Please do not forget the chargers.
- A ZOOM link and password will be emailed to your registration email.
- Cloud Studio: https://rstudio.cloud/project/2549590
Pre-bootcamp: Install R and Rstudio (YouTube video)
- Install R on your laptops. (R must be installed before RStudio). Please install a recent version of R software from https://cran.r-project.org/ .
- Install RStudio on your laptops. (RStudio must be installed after R). RStudio is a popular user-friendly editor and environment to run R. Please download a “RStudio Desktop” Open Source Edition from http://www.rstudio.com/products/rstudio/download/, and follow the instruction for installation.
Please complete this voluntary and anonymous pre-survey
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9:45am-10:00 Presurvey. R and Rstudio. Download the GitHub repo in Zip File.
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Ask TAs to add TA in their zoom alias. Open up breakout rooms for TAs to help participant trouble-shoot.
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10am-11am Basic R Coding in RStudio ; Video
- Basic R code, Markdown file, code block, RStudio interface, Data frame, and basic data visualization and analysis.
- Introduction to R and Rstudio with simple exercises. Video
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11am-11:10 Break (Group Virtual Photo)
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11:10- 12:10pm Explore a dataframe of GISAID submission records. Video
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12:10-1pm Lunch break
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1-2pm Learn R though COVID19 daily cases by counties in USA ; Video
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2pm-2:10 break
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2:10-3pm Integrate heterogenous data sets: Analyze Google Mobility and COVID19 cases; Video
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10am-11am Coding in R; Video
- Molar solution exercise. Video
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11:am-11:10am, break
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11:10-12:10 input, output; Video
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lunch break
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1pm - Simple statistical analysis with 2020 USA Election results; Video
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Participants are encourage to share their theiry own computational experiences and projects.
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Anonymous survey
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Please join the linkedIn group for future collaborations, https://www.linkedin.com/groups/12279083/
Please take this anononymous voluntary Post-survey
If people want to learn R before and after the bootcamps, there are many free online books at
- https://bookdown.org/
- http://ucanalytics.com/blogs/learn-r-12-books-and-online-resources/
- https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/linear-regression.html