Day 1

1.1. R-basics

• Course presentation;
• How to prepare a working directory (rules of good data science);
• File names and type of data files;
• The R studio IDE;
• Reading and Writing Data;
• Scripting;
• Packages (and GitHub);
• Ask for help;

1.2. Workflow basics

• Using the console
• Practice
• Using the scripts
• Practice

Day 2

2. Data wrangling

• Welcome to the Tidyverse;
• filter() #Pick observations by their values
• arrange() #Reorder the rows
• select() #Pick variables by their names
• mutate() #Create new variables with functions of existing variables
• summarise() #Collapse many values down to a single summary
• group_by() #which changes the scope of each function from the entire dataset to a group
• saving data

2.1. Fishing effort exercise 1

3. Data visualization

• Welcome to ggplot2
• Exploratory data analysis
• Data visualization

3.1. Fishing effort exercise 2

Day 3

4. R-functions and iterations

• R-functions and shortcuts;
• The purrr package;

5. Knitting

• the rmarkdown package;

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Weblinks

Basics

• R for data science: https://r4ds.had.co.nz
• Modern R dive: https://moderndive.com/index.html
• Data wrangling with R: https://cengel.github.io/R-data-wrangling/
• The Tidyverse style guide: https://style.tidyverse.org/index.html

Advanced

• Advanced R: https://adv-r.hadley.nz/
• Efficient R programming: https://bookdown.org/csgillespie/efficientR/
• R programming for Data Science: https://bookdown.org/rdpeng/rprogdatascience/
• Hands on programming with R: https://rstudio-education.github.io/hopr/

Visualization

• Fundamentals of data visualization: https://serialmentor.com/dataviz/
• Interactive animations: https://plotly-r.com/
• Top 50 visualization with R: http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
• The R graph gallery: https://www.r-graph-gallery.com/
• Graphs cookbook: http://www.cookbook-r.com/Graphs/

Documents creation

• Rmarkdown definitive guide: https://bookdown.org/yihui/rmarkdown/
• Bookdown & blogdown: https://bookdown.org

Miscellaneous tutorials

• Getting started with R and R-studio: https://ourcodingclub.github.io/2016/11/13/intro-to-r.html
• Coding Club Tutorials: https://ourcodingclub.github.io/tutorials/
• Cleaning and visualizing genomic data: http://varianceexplained.org/r/tidy-genomics/
• R for Reproducible Scientific Analysis: https://swcarpentry.github.io/r-novice-gapminder/
• R-Studio Primers: https://rstudio.cloud/learn/primers