Resources I have found useful

On this page, you will find links to online resources that I have found extremely useful. I'm putting this up here in the hope that it is of help to others too!

Since I taught myself R mainly from the internet, I have a few websites I frequently accessed to learn things:

For a basic introduction to R, this website covers a lot: Introduction to R

For plotting in R, I only use the ggplot2 package. It is very powerful, and quite frankly, I’m still learning what can be done using it as it can do a LOT!
Check out it's documentation on github: ggplot2
Also check out this useful list of many of the different visualisations that you can do: Top 50 ggplot2 Visualisations

For producing publication ready plots combining multiple plots and images into one figure, this is my go to package: cowplot

Recently, I have been working on some data which is circular (directional data), and I found this website quite useful in visualising circular data using ggplot2: Circle Plots with ggplot2

My go to statistical tool nowadays is mixed effects modelling with an appropriate error structure. After years of trial and error, I now have a good workflow in my approach to these models.

After the initial steps of looking at the data and some summary statistics, I use this package to compare how the response variable in the model fits various distributions: fitdistrplus

I then use one of many excellent packages in R to fit mixed-effects models. For simple linear mixed effects models and for non-linear models I use the lmer or the nlme package.
Here is an excellent website that covers the different packages by Ben Bolker and others: GLMM FAQ
For more complex generalised linear models, I now use these packages: gamlss and glmmTMB

After model fitting, I then test the model assumptions. Although some of the packages have their own in-built functions, I always use the following package in addition: DHARMa

Apart from using summary statistics, I also use the excellent emmeans package for post-hoc comparisons: emmeans

In addition to using custom code in ggplot2 (check the R tab) to visualise these model predictions, I have also started using this package: ggeffects

I recently learned python from an excellent online course in Coursera: Python for Everybody
The set of 5 courses in the python specialisation helped me find my way around the basics quite well.
Most of the material in the course can also be found in this website: PY4E

For a course oriented more towards using python in research, I recommend this one in EdX: Using Python for Research
It’s case studies are pretty great when it comes to introducing numpy, matplotlib and pandas packages.

The pandas package for python makes handling data quite similar to how it’s done in R, and I can not recommend it enough.
Here are a few sites that I’m still frequenting to get a hold on all that pandas can do: Using Pandas and Pandas Cheat Sheet

I built this website from scratch using things I had learned mainly from an excellent course in Coursera: Web Design Specialisation

Another great resource (that get’s mentioned in the course a lot) is this website: Introduction to HTML and CSS

Bootstrap 4 is the framework I used for the website layout, and its flexbox grid layout system is a great tool to get a responsive website up and running: Bootstrap 4

Google and Stack Overflow are my go to sources for any problems I ran into. A few tweaks to an existing answer, and I mostly had what I needed.