Data

For the exercises and projects you need to work with a dataset. The dataset can be from your own research (and that you have permission to openly share) or a dataset that you can find in open repositories. The following repositories can be helpful to find data:

• UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php or https://archive-beta.ics.uci.edu/
• KAGGLE (most datasets come along with analyses (mostly in Python) from others): https://www.kaggle.com/datasets
• “awesomedata” GitHub Repository (datasets are classified by topic, but files may come in different formats): https://github.com/awesomedata/awesome-public-datasets
• rspatialdata: https://rspatialdata.github.io/
• Datasets available in R can be seen with data() or data(package = "namepackage")

Week 1

Install R and RStudio R and RStudio

Introduction to R (TODO exercises) PDF https://www-binf.bio.uu.nl/rdb/te/Introduction_to_R.pdf

Carry out an exploratory analysis of a dataset

Weeks 2-3

Visualize data using different packages such as ggplot2, plotly, gganimate and HTML widgets

Install Git and create a GitHub account Git and GitHub

Week 4

Create an R Markdown document

Create a GitHub repository and upload R Markdown document

Weeks 5-8

Work on the development of a flexdashboard or a Shiny app

Weeks 9-11

Work on the development of a Shiny app or an R package

Week 12-15

Work on the development of an R package

Create a website with several R Markdown documents

Create a presentation

Create a flexdashboard using data retrieved from spatial packages

Create a webpage with blogdown

Create a book with bookdown