swcarpentry · Bisaloo · Mar 22, 2024 · Mar 21, 2024 · Mar 22, 2024 · Mar 22, 2024
diff --git a/episodes/01-starting-with-data.Rmd b/episodes/01-starting-with-data.Rmd
@@ -27,9 +27,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 We are studying inflammation in patients who have been given a new treatment for
 arthritis, and need to analyze the first dozen data sets (here is a link to [download the data for this lesson](data/r-novice-inflammation-data.zip)).
 The data sets are stored in [comma-separated values](../learners/reference.md#comma-separated-values-csv)
@@ -511,7 +508,7 @@ To support this, we can use the `apply` function.
 ## Getting Help
 
 To learn about a function in R, e.g. `apply`, we can read its help
-documention by running `help(apply)` or `?apply`.
+documentation by running `help(apply)` or `?apply`.
 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::

diff --git a/episodes/02-func-R.Rmd b/episodes/02-func-R.Rmd
@@ -23,9 +23,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 If we only had one data set to analyze, it would probably be faster to load the file into a spreadsheet and use that to plot some simple statistics.
 But we have twelve files to check, and may have more in the future.
 In this lesson, we'll learn how to write a function so that we can repeat several operations with a single command.
@@ -250,7 +247,7 @@ mySum <- function(input_1, input_2 = 10) {
 
 2. Read the error message: `argument "input_1" is missing, with no default`
   means that no value for `input_1` is provided in the function call,
-  and neither in the function's defintion. Thus, the addition in the
+  and neither in the function's definition. Thus, the addition in the
   function body can not be completed.
 
 :::::::::::::::::::::::::

diff --git a/episodes/03-loops-R.Rmd b/episodes/03-loops-R.Rmd
@@ -23,9 +23,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 We have created a function called `analyze` that creates graphs of the minimum, average, and maximum daily inflammation rates for a single data set:
 
 ```{r inflammation-01, fig.alt=c("Scatterplot of average daily inflammation over 40 days, increasing in a mostly straight line until day 20, and then decreasing in a mostly straight line to visualize inflammation levels in a single dataset.", "A scatterplot of maximum daily inflammation over 40 days, increasing in a straight line until day 20, and then decreasing in a straight line to visualize inflammation levels in a single dataset.", "A scatterplot of minimum daily inflammation over 40 days, increasing in a step-wise pattern until day 20, and then decreasing in a step-wise pattern to visualize inflammation levels in a single dataset.")}

diff --git a/episodes/04-cond.Rmd b/episodes/04-cond.Rmd
@@ -21,9 +21,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 Our previous lessons have shown us how to manipulate data, define our own functions, and repeat things.
 However, the programs we have written so far always do the same things, regardless of what data they're given.
 We want programs to make choices based on the values they are manipulating.

diff --git a/episodes/05-cmdline.Rmd b/episodes/05-cmdline.Rmd
@@ -20,9 +20,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 The R Console and other interactive tools like RStudio are great for prototyping code and exploring data, but sooner or later we will want to use our program in a pipeline or run it in a shell script to process thousands of data files.
 In order to do that, we need to make our programs work like other Unix command-line tools.
 For example, we may want a program that reads a data set and prints the average inflammation per patient:

diff --git a/episodes/06-best-practices-R.Rmd b/episodes/06-best-practices-R.Rmd
@@ -21,9 +21,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r source, include=FALSE}
-```
-
 ### Keep track of who wrote your code and its intended purpose
 
 Starting your code with an annotated description of what the code does when it is run will help you when you have to look at or change it in the future. Just one or two lines at the beginning of the file can save you or someone else a lot of time and effort when trying to understand what a particular script does.

diff --git a/episodes/07-knitr-R.Rmd b/episodes/07-knitr-R.Rmd
@@ -21,9 +21,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 `knitr` is an R package that allows you to organize your notes, code, and results in a single document. It's a great tool for "literate programming" -- the idea that your code should be readable by humans as well as computers! It also keeps your writing and results together, so if you collect some new data or change how you clean the data, you just have to re-compile the document and you're up to date!
 
 You write `knitr` documents in a simple plain text-like format called markdown, which allows you to format text using intuitive notation, so that you can focus on the content you're writing and generating a well-formatted document when needed. In fact, you can turn your plain text (and R code and results) into an HTML file or, if you have an installation of LaTeX and [Pandoc][pandoc] on your machine, a PDF, or even a Word document (if you must!).

diff --git a/episodes/08-making-packages-R.Rmd b/episodes/08-making-packages-R.Rmd
@@ -20,9 +20,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 Why should you make your own R packages?
 
 **Reproducible research!**
@@ -233,7 +230,7 @@ kelvin_to_fahrenheit <- function(temp_K) {
 
 
 [r-open-sci]: https://devguide.ropensci.org/building.html
-[r-pkgs-desc]: https://r-pkgs.had.co.nz/description.html
+[r-pkgs-desc]: https://r-pkgs.org/description.html
 [devtools]: https://cran.r-project.org/package=devtools
 [roxygen2]: https://cran.r-project.org/package=roxygen2
 

diff --git a/episodes/09-supp-intro-rstudio.Rmd b/episodes/09-supp-intro-rstudio.Rmd
@@ -19,9 +19,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 Let's start by learning about our tool.
 
 - Point to the different panels: Console, Scripts, Environments, Plots.

diff --git a/episodes/10-supp-addressing-data.Rmd b/episodes/10-supp-addressing-data.Rmd
@@ -18,9 +18,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 R is a powerful language for data manipulation.
 There are three main ways for addressing data inside R objects.
 

diff --git a/episodes/11-supp-read-write-csv.Rmd b/episodes/11-supp-read-write-csv.Rmd
@@ -19,9 +19,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 The most common way that scientists store data is in Excel spreadsheets.
 While there are R packages designed to access data from Excel spreadsheets (e.g., gdata, RODBC, XLConnect, xlsx, RExcel),
 users often find it easier to save their spreadsheets in [comma-separated values](reference.html#comma-separated-values-csv\) files \(CSV)
@@ -216,7 +213,7 @@ That's better!
 It is common for data sets to have missing values, or mistakes.
 The convention for recording missing values often depends on the individual who collected the data and can be recorded as `n.a.`, `--`, or empty cells " ".
 R recognises the reserved character string `NA` as a missing value, but not some of the examples above.
-Let's say the inflamation scale in the data set we used earlier `inflammation-01.csv` actually starts at `1` for no inflamation and the zero values (`0`) were a missed observation.
+Let's say the inflammation scale in the data set we used earlier `inflammation-01.csv` actually starts at `1` for no inflammation and the zero values (`0`) were a missed observation.
 Looking at the `?read.csv` help page is there an argument we could use to ensure all zeros (`0`) are read in as `NA`?
 Perhaps, in the `car-speeds.csv` data contains mistakes and the person measuring the car speeds could not accurately distinguish between "Black or "Blue" cars.
 Is there a way to specify more than one 'string', such as "Black" and "Blue", to be replaced by `NA`

diff --git a/episodes/12-supp-factors.Rmd b/episodes/12-supp-factors.Rmd
@@ -20,9 +20,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 Factors are used to represent categorical data. Factors can be ordered or
 unordered and are an important class for statistical analysis and for plotting.
 

diff --git a/episodes/13-supp-data-structures.Rmd b/episodes/13-supp-data-structures.Rmd
@@ -23,9 +23,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 ### Understanding Basic Data Types and Data Structures in R
 
 To make the best of the R language, you'll need a strong understanding of the

diff --git a/episodes/14-supp-call-stack.Rmd b/episodes/14-supp-call-stack.Rmd
@@ -20,9 +20,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 ### The Call Stack
 
 Let's take a closer look at what happens when we call `fahrenheit_to_kelvin(32)`.
@@ -140,8 +137,8 @@ result <- edges(highlight(inner_vec, outer_vec))
 
 
 [man]: https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Environment-objects
-[chapter]: https://adv-r.had.co.nz/Environments.html
-[adv-r]: https://adv-r.had.co.nz/
+[chapter]: https://adv-r.hadley.nz/environments.html
+[adv-r]: https://adv-r.hadley.nz/index.html
 
 
 :::::::::::::::::::::::::::::::::::::::: keypoints

diff --git a/episodes/15-supp-loops-in-depth.Rmd b/episodes/15-supp-loops-in-depth.Rmd
@@ -20,9 +20,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE}
-```
-
 In R you have multiple options when repeating calculations: vectorized operations, `for` loops, and `apply` functions.
 
 This lesson is an extension of [Analyzing Multiple Data Sets](03-loops-R.Rmd).