Fix broken links

_episodes/02-unix-shell.md

@@ -78,7 +78,7 @@ Since then, I’ve learned that it’s just another way to navigate your compute
 For instance, you can use it to combine existing tools into a pipeline to automate analyses, you can write a script to do things for you and improve reproducibility, you can interact with remote machines and supercomputers that are far away from you, and sometimes it’s the only option for the program you want to run.
 
 We’re going to use it to:
-1. Organize our R code and plots from the [R plotting lesson](https://umswc.github.io/curriculum/01-r-plotting/index.html).
+1. Organize our R code and plots from the [R plotting lesson]({{ page.root }}/01-r-plotting/).
 1. Perform version control using git during the rest of the workshop.
 
 ## What the Shell looks like
@@ -401,7 +401,7 @@ pwd
 ## Working with files and directories
 _[Back to top](#contents)_
 
-Now that we know how to move around your computer using the command line, our next step is to organize the project that we started in the [R plotting lesson](https://umswc.github.io/curriculum/01-r-plotting/index.html)
+Now that we know how to move around your computer using the command line, our next step is to organize the project that we started in the [R plotting lesson]({{ page.root }}/01-r-plotting/)
 You might ask: why would I use the command line when I could just use the GUI?
 My best response is that if you ever need to use a high-performance computing cluster (such as Great Lakes at the University of Michigan), you’ll have no other option.
 You might also come to like it more than clicking around to get places once you get comfortable, because it’s a lot faster!

_episodes/03-intro-git-github.md

@@ -1426,9 +1426,8 @@ $ git init
 ```
 {: .language-bash}
 
-If you remember back to the earlier [lesson]({{ page.root }}/04-changes/) where we added and
-committed our earlier work on `notes.txt`, we had a diagram of the local repository
-which looked like this:
+If you remember back to when we added and committed our earlier work on
+`notes.txt`, we had a diagram of the local repository which looked like this:
 
 ![The Local Repository with Git Staging Area]({{ page.root }}/fig/git/git-staging-area.svg)
 

_episodes/06-conclusion.md

@@ -40,7 +40,7 @@ There are many local opportunities at the University of Michigan or around the A
 
 * [Software Carpentry Workshops](https://umswc.github.io/workshops/)
     - We're always looking for more helpers & instructors!
-* [CoderSpaces](https://datascience.isr.umich.edu/), [CoderSpaces Slack Space](https://join.slack.com/t/umcoderspaces/signup)
+* [CoderSpaces](https://datascience.isr.umich.edu/events/coderspaces/)
 * [Data Analysis Networking Group (_DANG!_)](https://um-dang.github.io/)
 * [CSCAR consulting](https://cscar.research.umich.edu/) and [workshops](https://cscar.research.umich.edu/events/category/workshops/)
 * [Local meetup groups](https://www.meetup.com/topics/computer-programming/us/mi/ann_arbor/)
@@ -102,7 +102,7 @@ In the following, we list some strategies and resources we find useful. As you m
 ### R Markdown
 
 * [Getting started with R Markdown Online Tutorial](https://rmarkdown.rstudio.com/lesson-1.html)
-* [R Markdown Cheat Sheet](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf)
+* [R Markdown Cheat Sheet](https://github.com/rstudio/cheatsheets/blob/main/rmarkdown-2.0.pdf)
 * [R Markdown Reference Guide](https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf)
 
 ### Free learning platforms available at U-M

_episodes_rmd/01-r-plotting.Rmd

@@ -23,6 +23,10 @@ keypoints:
 
 ---
 
+<!-- Begin Link shortcuts -->
+[setup_instructions]: {{ page.root }}/setup/
+<!-- End Link shortuts -->
+
 ```{r, include=FALSE}
 source("../bin/chunk-options.R")
 knitr_fig_path("01-")
@@ -33,6 +37,7 @@ knitr_fig_path("01-")
 set.seed(20210109)
 ```
 
+
 ### Contents
 1. [Introduction to R and RStudio](#introduction-to-r-and-rstudio)
 1. [Loading and reviewing data](#loading-and-reviewing-data)
@@ -169,7 +174,7 @@ library(tidyverse)
 
 > ## What's with all those messages???
 >
-> When you loaded the `tidyverse` package, you probably got a message like the one we got above. Don't panic! These messages are just giving you more information about what happened when you loaded `tidyverse`. `Tidyverse` is actually a collection of several different packages, so the first section of the message tells us what packages were installed when we loaded `tidyverse` (these include `ggplot2`, which we'll be using a lot in this lesson, and `dyplr`, which you'll be introduced to tomorrow in the [R for Data Analysis lesson](05-r-markdown.Rmd)).
+> When you loaded the `tidyverse` package, you probably got a message like the one we got above. Don't panic! These messages are just giving you more information about what happened when you loaded `tidyverse`. `Tidyverse` is actually a collection of several different packages, so the first section of the message tells us what packages were installed when we loaded `tidyverse` (these include `ggplot2`, which we'll be using a lot in this lesson, and `dyplr`, which you'll be introduced to tomorrow in the [R for Data Analysis lesson]({{ page.root }}/04-r-data-analysis/)).
 
 > The second section of messages gives a list of "conflicts." Sometimes, the same function name will be used in two different packages, and R has to decide which function to use. For example, our message says that:
 >
@@ -189,7 +194,7 @@ library(tidyverse)
 >
 > You can also find them in RStudio by going to the "Help" menu and selecting "Cheat Sheets". The two that will be most helpful in this workshop are "Data Visualization with ggplot2", "Data Transformation with dplyr", "R Markdown Cheat Sheet", and "R Markdown Reference Guide".
 >
-> For things that aren't on the cheat sheets, [Google is your best friend](../_episodes/06-conclusion.md). Even expert coders use Google when they're stuck or trying something new!
+> For things that aren't on the cheat sheets, [Google is your best friend]({{ page.root }}/06-conclusion/). Even expert coders use Google when they're stuck or trying something new!
 >
 {: .testimonial}
 
@@ -1174,3 +1179,4 @@ _[Back to top](#contents)_
 - Factor: a way of storing data to let R know the values are discrete so they get special treatment
 
 
+

_episodes_rmd/05-r-markdown.Rmd

@@ -217,7 +217,7 @@ first, we will check the status of our repository with the `git status`. This wi
 Next, we will add the new files that we want tracked by git up with `git add <filename(s)>`.  This adds any file(s) we list to version control.  
 Then we commit the file or changes with `git commit -m "<your commit message>"` and we add a message with the commit to describe the changes you are committing and it adds them to the git log.  
 Now, we can push those changes from our local computer to our github repo with `git push`. This will upload the changes to the git directory on the remote server.  
-If you don't remember how to do this, you can review the [git lesson](_episodes/03-intro-git-github.md).
+If you don't remember how to do this, you can review the [git lesson]({{ page.root }}/03-intro-git-github/).
 
 It's looking pretty good, but there seem to be a few extra bits that we don't need in the report. For example, the report shows that we load the tidyverse package and the accompanying messages.
 
@@ -542,7 +542,7 @@ This dataset has information on the gross domestic expenditure on research and d
 
 _Data:_ Gross domestic expenditure on research and development (R & D)
 
-_Data source:_ [UN data](data.un.org), under "Science and technology"
+_Data source:_ [UN data](http://data.un.org), under "Science and technology"
 
 _Data path:_ `data/SYB63_286_202009_GDP_on_RnD.csv`
 
@@ -644,7 +644,7 @@ Now we're going to work with the CO2 and R&D datasets together.
 
 Unfortunately, we don't have the exact same dates for all of them.
 
-1. First, read in the CO2 dataset. You can use the code from the [R for data analysis](_episodes/04-r-data-analysis.md) lesson to clean the CO2 data.
+1. First, read in the CO2 dataset. You can use the code from the [R for data analysis]({{ page.root }}/04-r-data-analysis/) lesson to clean the CO2 data.
 
 > ## Solution
 > ```{r}

bin/dependencies.R

@@ -9,9 +9,9 @@ install_required_packages <- function(lib = NULL, repos = getOption("repos", def
     c("rprojroot", "desc", "remotes", "renv"),
     rownames(installed.packages(lib.loc = lib))
   )
-
-  install.packages(missing_pkgs, lib = lib, repos = repos)
-
+  if (length(missing_pkgs) > 0) {
+    install.packages(missing_pkgs, lib = lib, repos = "https://cran.rstudio.com/")
+  }
 }
 
 find_root <- function() {