Contact code club hosts: Martin Schobben and Ilja Kocken.
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At our department of Earth Sciences, we have introduced monthly code club meetings for those interested, mainly focusing on using R for data analysis and plotting.
The notes to our meetings are available in this repository, sorted by date.
We’ve recently shifted the aims of our code club to be more beginner-friendly, so please don’t hesitate to show up!
PPGU has organised a meeting for data visualization for the upcoming NAC conference.
| date | possible topic | instructor |
|---|---|---|
| <2021-03-17 Wed> | PPGU | Ilja/Martin/Peter |
We highly recommend attending the Introduction to R and data courses, in which the ITS staff teaches you the basics of R in one full day event. The courses so far have filled up very rapidly!
| date | topic and packages used | instructor |
|---|---|---|
| <2019-01-23 Wed> | RMarkdown | Martin |
| <2019-02-27 Wed> | Plotting devices, ggplot fancy labels, faceting | Martin/Ilja |
| <2019-03-27 Wed> | for loops, general recommendations, monte-carlo smoothing? | Ilja/Martin |
| <2019-05-22 Wed> | time series analysis, moving averages, loess, pangaear, | Martin/Ilja |
| <2019-06-26 Wed> | agemodels, linear interpolation, plotting, clam, rbacon | Ilja |
| <2019-06-26 Wed> | agemodels with uncertainty using clam and rbacon | Thomas |
| <2019-11-27 Wed> | more on ggplot2—no notes? | Ilja |
| <2020-01-29 Wed> | general working meeting | Ilja/Martin/Peter |
| <2020-02-26 Wed> | general working meeting | Ilja/Martin/Peter |
| <2021-03-17 Wed> | PPGU workshop | Ilja/Martin/Peter |
| <2021-03-24 Wed> | general working meeting (PPGU) | Ilja/Martin/Peter |
Download and install the latest version of R from https://cran.r-project.org/. Note that on MacOS you need to install some extra stuff, so follow the install guide for MacOS (previous link) and don’t forget to install XQuartz.
RStudio is probably the easiest and most convenient integrated desktop environment (IDE) for R, so next, download and install RStudio.
If you’re a beginner, it’s probably fastest to learn the basics of R by yourself! We recommend using interactive courses or packages over books, because the exercises make you obtain the new skills much faster!
Once you have installed R and RStudio, don’t get tempted into clicking
everywhere with the mouse! R is a programming language, speak to it with the
keyboard! Go to the command prompt, bottom left thingie that starts with >
(shortcut key ctrl + 1), and type! Now of course you don’t want to type
everything over every time, so you write a script in the topleft part (ctrl +
2) and send bits of it to the command prompt.
To get started, you can try to use this nice interactive package. Go to the
command prompt and type the following, each line followed by return. Then
type in the answers to the questions it asks and go through a few of the
exercises if you want.
install.packages("swirl")
library("swirl")
swirl()Here you’ve installed your first package. Packages are the bread and butter of
R, why write code if somebody else has already done the hard work for you? We
strongly recommend using all of the R-packages in the tidyverse, such as readr
and readxl for reading in data, dplyr for data manipulation and ggplot2 for
plotting. Check out the PDF cheatsheets from RStudio for quick visual reminders
of what everything does.
Then take some of the nice online courses, for which you don’t need R on your computer. You can do everything in the browser! This datacamp introduction course is a great start. This datacamp visualisation with ggplot2 is a great place to learn about the grammar of graphics.
If you prefer books, try out the nice free introductory book R for data science. When you’ve progressed a bit, the book Advanced R is a great resource for learning more. If you want to take a 1-day interactive course, the UU library provides one. It’s pretty much always full so sign up early. All of their resources are available online.