The Complex Human Data Summer School I teach into has a variety of introductory content on R programming, data analysis, github, open science, programming online experiments, among other topics.
Tom Griffiths’ Bayesian reading list provides extensive background on Bayesian inference. If you're interested in nonparametric Bayesian methods, you might also want to look at Sharon Goldwater's reading list.
While at Adelaide I taught a computational cognitive science course with my colleague Dani Navarro, now at Sydney. The course is now defunct but the content is (we think) still broadly useful for people with some (not extensive) programming background who want an introduction to some of the topics that we study. It is archived here.
Learning Statistics with R is a great (free) online textbook that was designed to introduce beginning psychology students to statistics using R. As such it is an excellent resource for both statistics (seriously it is one of the clearest intro stats books I know of) and R. Since it was written a while ago it doesn't have much on ggplot or tidyverse, although Dani is creating newer materials more focused on using R, which do incorporate ggplot and tidyverse, here in R for Psychological Science
R for Data Science is a great (free) online textbook that walks you through using R for data science. The focus isn't on psychological tests but on data wrangling, visualisation, and so forth, with chapters on tidyverse and ggplot2.
RYouWithMe is a collection of introductory learning resources designed specifically for newbies from the fabulous folks at RLadiesSydney, which include ggplot2 and tidyverse. Not finished yet.
Data Skills for Reproducible Science contains the materials from a course which aims to teach students the basic principles of reproducible research and to provide practical training in data processing and analysis in the statistical programming language R. With weekly assignments if that kind of thing helps!
RStudioPrimers looks like a great set of primers on all sorts of important topics, including ggplot2 and tidyverse amongst other things.
Data Visualisation: A practical introduction walks you through how to make nice graphs with R, but also talks about general principles of good visualisation more theoretically.
The R cheat sheets are exactly what they sound like – great cheat sheets for a lot of useful packages. Not great for teaching yourself but great for reminding yourself. I use them a lot.
Most my papers are written in LaTeX rather than a word processor. Nowadays I use overleaf to prepare all my documents (which is nice because it makes it easy for multiple people to edit at once) but you can also install LaTeX on your own computer. I believe Windows users will need to install ghostscript first, then install a LaTeX distribution like MiKTeX. On a Mac, MacTeX does all the hard work, and TeXShop provides a nice user interface. If you're using LaTeX for document preparation, then you'll need a BiBTeX-friendly reference manager. You can either maintain your own .bib file, or you could look into something like Zotero, a free reference manager that is nicely integrated with most browsers (it began as a Firefox extension).
Here is some good advice about How to structure a paper
And some thoughts about Why academics stink at writing