Standardize link references

01_install.Rmd

@@ -11,14 +11,14 @@ source("common.R")
 
 ## R and RStudio
 
-* Install [R, a free software environment for statistical computing and graphics][r-proj] from [CRAN][cran], the Comprehensive R Archive Network. I __highly recommend__ you install a precompiled binary distribution for your operating system -- use the links up at the top of the CRAN page linked above!
+* Install [R, a free software environment for statistical computing and graphics](https://www.r-project.org) from [CRAN](https://cloud.r-project.org), the Comprehensive R Archive Network. I __highly recommend__ you install a precompiled binary distribution for your operating system -- use the links up at the top of the CRAN page linked above!
 
 * Install RStudio's IDE (stands for _integrated development environment_), a powerful user interface for R. Get the Open Source Edition of RStudio Desktop.
 
- - I __highly recommend__ you run the [Preview version][rstudio-preview]. I find these quite stable and you'll get the cool new features! Update to new Preview versions often.
- - Of course, there are also official releases available [here][rstudio-official].
+ - I __highly recommend__ you run the [Preview version](https://www.rstudio.com/products/rstudio/download/preview/). I find these quite stable and you'll get the cool new features! Update to new Preview versions often.
+ - Of course, there are also official releases available [here](https://www.rstudio.com/products/rstudio/#Desktop).
  - RStudio comes with a __text editor__, so there is no immediate need to install a separate stand-alone editor.
- - RStudio can __interface with Git(Hub)__. However, you must do all the Git(Hub) set up [described elsewhere][happy-git] before you can take advantage of this.
+ - RStudio can __interface with Git(Hub)__. However, you must do all the Git(Hub) set up described elsewhere (see [Happy Git and GitHub for the useR]) before you can take advantage of this.
 
 If you have a pre-existing installation of R and/or RStudio, we __highly recommend__ that you reinstall both and get as current as possible. It can be considerably harder to run old software than new.
 
@@ -32,13 +32,13 @@ If you have a pre-existing installation of R and/or RStudio, we __highly recomme
 
 ## Testing testing
 
-* Do whatever is appropriate for your OS to launch RStudio. You should get a window similar to the screenshot you see [here][rstudio-workbench], but yours will be more boring because you haven't written any code or made any figures yet!
+* Do whatever is appropriate for your OS to launch RStudio. You should get a window similar to the screenshot you see [here](https://www.rstudio.com/wp-content/uploads/2014/04/rstudio-workbench.png), but yours will be more boring because you haven't written any code or made any figures yet!
 
 * Put your cursor in the pane labelled Console, which is where you interact with the live R process. Create a simple object with code like `x <- 2 * 4` (followed by enter or return). Then inspect the `x` object by typing `x` followed by enter or return. You should see the value 8 print to screen. If yes, you've succeeded in installing R and RStudio.
 
 ## Add-on packages
 
-R is an extensible system and many people share useful code they have developed as a _package_ via CRAN and GitHub. To install a package from CRAN, for example the [dplyr][dplyr-cran] package for data manipulation, here is one way to do it in the R console (there are others).
+R is an extensible system and many people share useful code they have developed as a _package_ via CRAN and GitHub. To install a package from CRAN, for example the [dplyr] package for data manipulation, here is one way to do it in the R console (there are others).
 
 ```r
 install.packages("dplyr", dependencies = TRUE)
@@ -48,19 +48,19 @@ By including `dependencies = TRUE`, we are being explicit and extra-careful to i
 
 You could use the above method to install the following packages, all of which we will use:
 
-* tidyr, [package webpage][tidyr-web]
-* ggplot2, [package webpage][ggplot2-web]
+* [tidyr]
+* [ggplot2]
 
 
 ## Further resources
 
 The above will get your basic setup ready but here are some links if you are interested in reading a bit further.
 
-* [How to Use RStudio][rstudio-support]
-* [RStudio's leads for learning R][rstudio-R-help]
-* [R FAQ][cran-faq]
-* [R Installation and Administration][cran-R-admin]
-* [More about add-on packages in the R Installation and Administration Manual][cran-add-ons]
+* [How to Use RStudio](https://support.rstudio.com/hc/en-us)
+* [RStudio's leads for learning R](https://support.rstudio.com/hc/en-us/articles/200552336-Getting-Help-with-R)
+* [R FAQ](https://cloud.r-project.org/faqs.html)
+* [R Installation and Administration](http://cloud.r-project.org/doc/manuals/R-admin.html)
+* [More about add-on packages in the R Installation and Administration Manual](https://cloud.r-project.org/doc/manuals/R-admin.html#Add_002don-packages)
 
 
 ```{r links, child="links.md"}

02_r-basics.Rmd

@@ -13,10 +13,10 @@ Launch RStudio/R.
 Notice the default panes:
 
 * Console (entire left)
-* Environment/History (tabbed in upper right)
-* Files/Plots/Packages/Help (tabbed in lower right)
+* Environment / History (tabbed in upper right)
+* Files / Plots / Packages / Help (tabbed in lower right)
 
-FYI: you can change the default location of the panes, among many other things: [Customizing RStudio][rstudio-customizing].
+__FYI:__ you can change the default location of the panes, among many other things: [Customizing RStudio](https://support.rstudio.com/hc/en-us/articles/200549016-Customizing-RStudio).
 
 Go into the Console, where we interact with the live R process.
 
@@ -37,7 +37,7 @@ You will make lots of assignments and the operator `<-` is a pain to type. Don't
 
 Notice that RStudio automagically surrounds `<-` with spaces, which demonstrates a useful code formatting practice. Code is miserable to read on a good day. Give your eyes a break and use spaces.
 
-RStudio offers many handy [keyboard shortcuts][rstudio-key-shortcuts]. Also, Alt+Shift+K brings up a keyboard shortcut reference card.
+RStudio offers many handy [keyboard shortcuts](https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts). Also, Alt+Shift+K brings up a keyboard shortcut reference card.
 
 Object names cannot start with a digit and cannot contain certain other characters such as a comma or a space. You will be wise to adopt a [convention for demarcating words][wiki-snake-case] in names.
 
@@ -147,7 +147,7 @@ To handle these real life situations, you need to make two decisions:
 As a beginning R user, it's OK to consider your workspace "real". _Very soon_, I urge you to evolve to the next level, where you consider your saved R scripts as "real". (In either case, of course the input data is very much real and requires preservation!) With the input data and the R code you used, you can reproduce
 _everything_. You can make your analysis fancier. You can get to the bottom of puzzling results and discover and fix bugs in your code. You can reuse the code to conduct similar analyses in new projects. You can remake a figure with different aspect ratio or save is as TIFF instead of PDF. You are ready to take questions. You are ready for the future.
 
-If you regard your workspace as "real" (saving and reloading all the time), if you need to redo analysis ... you're going to either redo a lot of typing (making mistakes all the way) or will have to mine your R history for the commands you used. Rather than [becoming an expert on managing the R history][rstudio-command-history], a better use of your time and psychic energy is to keep your "good" R code in a script for future reuse.
+If you regard your workspace as "real" (saving and reloading all the time), if you need to redo analysis ... you're going to either redo a lot of typing (making mistakes all the way) or will have to mine your R history for the commands you used. Rather than [becoming an expert on managing the R history](https://support.rstudio.com/hc/en-us/articles/200526217-Command-History), a better use of your time and psychic energy is to keep your "good" R code in a script for future reuse.
 
 Because it can be useful sometimes, note the commands you've recently run appear in the History pane.
 
@@ -197,7 +197,7 @@ But there's a better way. A way that also puts you on the path to managing your
 
 ## RStudio projects {#rprojs}
 
-Keeping all the files associated with a project organized together -- input data, R scripts, analytical results, figures -- is such a wise and common practice that RStudio has built-in support for this via its [_projects_][rstudio-using-projects].
+Keeping all the files associated with a project organized together -- input data, R scripts, analytical results, figures -- is such a wise and common practice that RStudio has built-in support for this via its [_projects_](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects).
 
 Let's make one to use for the rest of this workshop/class. Do this: *File > New Project...*. The directory name you choose here will be the project name. Call it whatever you want (or follow me for convenience).
 

05_data-care-feeding.Rmd

@@ -24,7 +24,7 @@ Now restart R. This will ensure you don't have any packages loaded from previous
 
 Why do we do this? So that the code you write is complete and re-runnable. If you return to a clean slate often, you will root out hidden dependencies where one snippet of code only works because it relies on objects created by code saved elsewhere or, much worse, never saved at all. Similarly, an aggressive clean slate approach will expose any usage of packages that have not been explicitly loaded. 
 
-Finally, open a new R script and develop and run your code from there. In RStudio, use *File > New File > R Script*. Save this script with a name ending in `.r` or `.R`, containing no spaces or other funny stuff, and that evokes whatever it is we're doing today. __Example:__ `cm004_data-care-feeding.r`.
+Finally, open a new R script and develop and run your code from there. In RStudio, use *File > New File > R Script*. Save this script with a name ending in `.r` or `.R`, containing no spaces or other funny stuff, and that evokes whatever it is we're doing today. Example: `cm004_data-care-feeding.r`.
 
 Another great idea is to do this in an R Markdown document. See [Test drive R Markdown](#r-markdown) for a refresher.
 
@@ -36,13 +36,13 @@ Whenever you have rectangular, spreadsheet-y data, your default data receptacle
  - keeping them in sync vis-a-vis row order
  - applying any filtering of observations uniformly
 * Most functions for inference, modelling, and graphing are happy to be passed a data frame via a `data =` argument. This has been true in base R for a long time.
-* The set of packages known as the [tidyverse][tidyverse-main-page] takes this one step further and explicitly prioritizes the processing of data frames. This includes popular packages like dplyr and ggplot2. In fact the tidyverse prioritizes a special flavor of data frame, called a "tibble".
+* The set of packages known as the [tidyverse] takes this one step further and explicitly prioritizes the processing of data frames. This includes popular packages like [dplyr] and [ggplot2]. In fact the tidyverse prioritizes a special flavor of data frame, called a "tibble".
 
 Data frames -- unlike general arrays or, specifically, matrices in R -- can hold variables of different flavors, such as character data (subject ID or name), quantitative data (white blood cell count), and categorical information (treated vs. untreated). If you use homogeneous structures, like matrices, for data analysis, you are likely to make the terrible mistake of spreading a dataset out over multiple, unlinked objects. Why? Because you can't put character data, such as subject name, into the numeric matrix that holds white blood cell count. This fragmentation is a Bad Idea.
 
 ## Get the Gapminder data
 
-We will work with some of the data from the [Gapminder project][gapminder-web]. I've released this as an [R package][gapminder-cran], so we can install it from CRAN like so:
+We will work with some of the data from the [Gapminder project](https://www.gapminder.org). I've released this as an R package called [gapminder], so we can install it from CRAN like so:
 
 ```{r eval = FALSE}
 install.packages("gapminder")
@@ -66,7 +66,7 @@ str(gapminder)
 
 We could print the `gapminder` object itself to screen. However, if you've used R before, you might be reluctant to do this, because large datasets just fill up your Console and provide very little insight.
 
-This is the first big win for **tibbles**. The [tidyverse][tidyverse-web] offers a special case of R's default data frame: the "tibble", which is a nod to the actual class of these objects, `tbl_df`.
+This is the first big win for **tibbles**. The tidyverse offers a special case of R's default data frame: the "tibble", which is a nod to the actual class of these objects, `tbl_df`.
 
 If you have not already done so, install the tidyverse meta-package now:
 
@@ -164,15 +164,15 @@ The __levels__ of the factor `continent` are "Africa", "Americas", etc. and this
 str(gapminder$continent)
 ```
 
-This [Janus][wiki-janus]-like nature of factors means they are rich with booby traps for the unsuspecting but they are a necessary evil. I recommend you resolve to learn how to [properly care and feed for factors](#factors-boss). The pros far outweigh the cons. Specifically in modelling and figure-making, factors are anticipated and accommodated by the functions and packages you will want to exploit.
+This [Janus]-like nature of factors means they are rich with booby traps for the unsuspecting but they are a necessary evil. I recommend you resolve to learn how to [properly care and feed for factors](#factors-boss). The pros far outweigh the cons. Specifically in modelling and figure-making, factors are anticipated and accommodated by the functions and packages you will want to exploit.
 
 Here we count how many observations are associated with each continent and, as usual, try to portray that info visually. This makes it much easier to quickly see that African countries are well represented in this dataset.
 ```{r tabulate-continent}
 table(gapminder$continent)
 barplot(table(gapminder$continent))
 ```
 
-In the figures below, we see how factors can be put to work in figures. The `continent` factor is easily mapped into "facets" or colors and a legend by the ggplot2 package. *Making figures with ggplot2 is covered in Chapter \@ref(ggplot2-tutorial) so feel free to just sit back and enjoy these plots or blindly copy/paste.*
+In the figures below, we see how factors can be put to work in figures. The `continent` factor is easily mapped into "facets" or colors and a legend by the [ggplot2] package. *Making figures with ggplot2 is covered in Chapter \@ref(ggplot2-tutorial) so feel free to just sit back and enjoy these plots or blindly copy/paste.*
 
 ```{r factors-nice-for-plots, fig.show = 'hold', out.width = '49%'}
 ## we exploit the fact that ggplot2 was installed and loaded via the tidyverse
@@ -221,7 +221,7 @@ plot(lifeExp ~ log(gdpPercap), gapminder, subset = year == 2007)
 
 * Use data frames!!!
 
-* Use the [tidyverse][tidyverse-web]!!! This will provide a special type of data frame called a "tibble" that has nice default printing behavior, among other benefits.
+* Use the [tidyverse]!!! This will provide a special type of data frame called a "tibble" that has nice default printing behavior, among other benefits.
 
 * When in doubt, `str()` something or print something.