A 3h introductory workshop: learning R by doing, oriented to actuaries and risk managers!
Course materials for the online edition of the course in September 2021.
The R programming language is an invaluable tool for data science. This course equips you with the fundamental tools for data analysis in R and RStudio.
📆 November 25, 2021
🕗 From 1.30pm to 4.30pm
📍 online,
organized by AG-AI
Course materials will be posted in the days before the workshop.
You’ll come out of this course with basic understanding of R as an environment for data handling, data exploration and data visualization. Via a set of hands-on demonstrations and exercises you first study the basics of the R syntax, you will explore RStudio as an integrated development environment for R programming and learn about R packages. You explore the different types of objects, data structures and data types used in R. Then, you focus on working with data sets in R; import, export, data manipulations, data cleaning and wrangling. You will meet powerful packages such as data.table and the packages from the tidyverse. Finally, you will use R to create various types of insightful graphics and discover the basics of writing and using functions in R.
The course is designed for new R users, no prior knowledge is required.
The detailed schedule is subject to small changes. Lecture material will be added in the days before the workshop.
A pdf of the lecture sheets can be downloaded here.
| Session | Duration | Description | Lecture material | Script |
|---|---|---|---|---|
| Day 0 | your own pace | Prework | create your account on RStudio Cloud OR install R and RStudio on your local machine | script in R |
| Day 1 | 13.30 - 13.45 | Prologue | sheets in html or pdf | script in R |
| 13.45 - 14.20 | R syntax, object and data types | sheets in html | script in R | |
| 14.30 - 15.20 | Working with data in R | sheets in html | script in R | |
| 15.30 - 15.45 | More on data visualization in R | sheets in html | script in R | |
| 15.45 - 16.05 | Conditionals and control flow | sheets in html | script in R | |
| 16.05 - 16.30 | Writing functions | sheets in html | script in R |
You have two options to join the coding exercises covered during the workshop. Either you join the RStudio cloud workspace dedicated to the workshop, and then you’ll run R in the cloud, from your browser. Or you use your local installation of R and RStudio.
We kindly ask participants to join the RStudio Cloud as default!
You will join our workspace on R Studio Cloud. This enables a very accessible set-up for working with R in the cloud for the less experienced user!
https://rstudio.cloud/spaces/143507/join?access_code=j%2FGjLwr6Pwix9FucSdDT3Q3XIbZARdRt8aii7ttZ
Here are the steps you should take (before the workshop):
- visit the above link
- log in by creating an account for RStudio Cloud or by using your Google or GitHub login credentials
- join the space
- at the top of your screen you see ‘Projects’, click ‘Projects’
- with the ‘copy’ button (on the right) you can make your own version of the ‘learn R by doing’ project; in this copy you can work on the exercises, add comments etc.
- you should now be able to visit the ‘learn R by doing’ project and see the ‘scripts’ and ‘data’ folders on the right. Open and run the ‘installation-instructions.R’ script from the scripts folder, to see if everything works fine.
We will have everything set up for you in the correct way. You only have to login!
Alternatively, you can bring a laptop with a recent version of R and RStudio installed. Make sure you can connect your laptop to the internet (or download the course material one day before the start of the workshop). You will need:
- R (at least 3.5.2 https://cloud.r-project.org/bin/windows/base/)
- RStudio (https://www.rstudio.com/products/rstudio/download/#download)
In the prework folder you will find a step-by-step guide to installing
R and RStudio (though a bit outdated).
Please run the following script in your R session to install the required packages
packages <- c("tidyverse", "here", "rstudioapi", "MASS", "actuar", "lubridate", "readr", "readxl", "haven")
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
if(sum(!(packages %in% installed.packages()[, "Package"]))) {
stop(paste('The following required packages are not installed:\n',
paste(packages[which(!(packages %in% installed.packages()[, "Package"]))], collapse = ', ')));
} else {
message("Everything is set up correctly. You are ready to go.")
}These instructions can also be downloaded as .R script from the
scripts folder. Open this script in RStudio (via File > Open file),
select the code and press the Run button top right in the script window.
Katrien Antonio is professor in insurance data science at KU Leuven and associate professor at University of Amsterdam. She teaches courses on data science for insurance, life and non-life insurance mathematics and loss models. Research-wise Katrien puts focus on pricing, reserving and fraud analytics, as well as mortality dynamics.
Jonas Crevecoeur is a post-doctoral researcher in biostatistics at KU Leuven. He recently obtained his PhD within the insurance research group at KU Leuven and holds the degrees of MSc in Mathematics, MSc in Insurance Studies and MSc in Financial and Actuarial Engineering (KU Leuven). Before starting the PhD program he worked as an intern with QBE Re (Belgium office) where he studied multiline products and copulas. Jonas was a PhD fellow of the Research Foundation - Flanders (FWO, PhD fellowship fundamental research).
Roel Henckaerts PhD recently obtained his PhD at KU Leuven with a thesis on machine learning methods for insurance pricing. Roel holds the degrees of MSc in Mathematical Engineering, MSc in Insurance Studies and Financial and Actuarial Engineering (KU Leuven). Before starting the PhD program he worked as an intern with AIG (London office) and KBC. Roel is PhD fellow of the Research Foundation - Flanders (FWO, PhD fellowship strategic basic research). Roel is now with Prophecy Labs: an AI/ML startup with experience in building end-to-end data solutions that provide concrete business value.
Happy learning!