Chapter 15 Miscellaneous Resources
15.2 Misc Cool Packages to check out
taskscheduleR allows you to automate R processes (Windows only).
List of reproducible packages in R here
beepr and BRRR You can also use built-in text if you are on a MAC (it won’t work on PC/Windows computers).
system("say And if you don\\'t know, now you know!")
See a recent blog post here and here on making sounds in RStudio
```r, echo=TRUE, eval=FALSE say_something <- function(message) {
message <- paste0("$speak.Speak('", message, "');\"")
system2(command = "PowerShell",
args = c("-Command",
"\"Add-Type -AssemblyName System.Speech;",
"$speak = New-Object System.Speech.Synthesis.SpeechSynthesizer;",
message
))
}
x <- “Job X” say_something(message = paste(x, “is done”) ```
15.3 Lesson Plan
It might be a decent idea to add “difficulty levels” for each of these. So lessons would be structured as
- Required Learning
- Test your skills
- Noob
- Not Bad
- Hardcore
Nirosha sent a nice example if ever I grow this into a course here it includes marking
15.3.1 Lesson 1
Show animation I made with Jeff
Show a video of someone trying to make those changes in Excel vs in RStudio. The timesaving you have.
It might be best to create a YouTube video where I go through each of these steps. Add a slider for time like GamersNexus
Installation
- R
- RStudio
- Andrew must create a script to install a bunch of packages here. Take inspiration from GitHub
- Installing
tiny_tex
on Mac (see Joel’s screenshots)
- Zotero
- Adding BibTex plug-in
- Setting up BibTex in Zotero
- Export a .bib file from Zotero
- GitHub
- Create a GitHub account and key
Project Directories
- Download my project directory example
- Explain what the .RProj file is and what it does
- It sets your directories. Unlike MATLAB.
15.3.2 Lesson 2
Explain RMarkdown Chunks and get them to knit the document
- YAML options
- Figure captions
- Table captions
- Adding a reference
- In text summary statistics
- Inserting a model using apastats
- Maybe have students create their own docx template for extra credit but this might be too much to ask for
15.3.3 Lesson 3: Data visualisation with ggplot2
- Load data
- wide vs long format (ggplot2 likes long format)
- ggplot elements (break this down step by step. Check saved youtube video for inspiration)
- saving our plot using
ggsave
to ourimages
directory - Making a loop for figures (this might take some time)