R started in 1993:

`r 2019 - 1993 =` 26 years ago

A lot has happened !

• base R vs “tidyverse”
• R GUI vs RStudio

Data Science is an emerging field at the crossing of Statistics, Computer science & Data analysis

source: R for Data Science

• be able to successfully import and transform data in R (%>% & dplyr)
• be able to choose and implement suitable and beautiful data visualizations (ggplot2)
• be able to have a reproducible workflow through dynamic reporting
• understand the difference and commonalities between:
  • software development
  • data analysis

1. (brief) recap on R basics
2. Dynamic reproducible reporting with Rmarkdown
3. Data manipulation with dplyr
4. Data visualizationwith ggplot2

Each time:

• some key theoretical concepts
• practicals exercise to develop your abilities and your autonomy

• Google (or any other web search engine) is your friend !

• DRY vs WET:

  • Don’t Repeat Yourself !
  • or Write Everything Twice (you have time to spare)

=> use function()

• Rstudio: use up-to-date modern tools

• use Rstudio projects

=> live demo

• loop and functions (DRY)

oOen BADAS1_practical.html and follow along…

Tidy + Universe

Hadley Whickam <3

1. each column represent a different variable
2. each row represent one observation
3. different observation types are stored in different tables (i.e. data.frame)

=> tidyverse: a collection of packages for working with/analyzing tidy data

• RStudio cheat sheets and webinars
  (https://www.rstudio.com/resources/cheatsheets/)
  (https://resources.rstudio.com/the-essentials-of-data-science)

• R for data science (R4DS), G Grolemund & H Whickam, (https://r4ds.had.co.nz/)

• What they forgot to teach you about R, J Brian & J Hester (https://whattheyforgot.org/)

• Twitter #rstats (welcoming community)

• many more …