[custom] fix lesson contents

datacarpentry · Feb 7, 2023 · e5e2191 · e5e2191
1 parent a4bdf28
commit e5e2191
Show file tree

Hide file tree

Showing 19 changed files with 2,172 additions and 230 deletions.
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -1,2 +1,4 @@
+^renv$
+^renv\.lock$
 ^\.travis\.yml$
 ^tic\.R$
diff --git a/config.yaml b/config.yaml
@@ -70,6 +70,11 @@ episodes:
 
 # Information for Learners
 learners: 
+- reference.md
+- intro-R-handout.Rmd
+- starting-with-data-handout.Rmd
+- data-wrangling-handout.Rmd
+- data-visualisation-handout.Rmd
 
 # Information for Instructors
 instructors: 

diff --git a/episodes/.here b/episodes/.here
diff --git a/episodes/.ignore-05-databases.Rmd b/episodes/.ignore-05-databases.Rmd
@@ -18,21 +18,19 @@ keypoints:
 - "First key point."
 ---
 
-
 ```{r, include=FALSE}
 source("../bin/chunk-options.R")
 knitr_fig_path("06-")
 source("../bin/download_data.R")
 ```
 
-
 ## Introduction
 
-A common problem with R in that all operations are conducted in-memory. Here, "memory" means a computer 
-drive called Random Access Memory (RAM for short) which is phsically installed on your computer. RAM 
+A common problem with R in that all operations are conducted in-memory. Here, "memory" means a computer
+drive called Random Access Memory (RAM for short) which is phsically installed on your computer. RAM
 allows for data to be read in nearly the same amount of time regardless of its physical location
 inside the memory. This is in stark contrast to the speed data could be read from CDs, DVDs, or other
-storage media, where the speed of transfer depended on how quickly the drive could rotate or the 
+storage media, where the speed of transfer depended on how quickly the drive could rotate or the
 arm could move. Unfortunately, your computer has a limited amount of RAM, so the amount of data you
 can work with is limited by the available memory. So far, we have used small datasets that can
 easily fit into your computer's memory. But what about datasets that are too large for your
@@ -47,12 +45,10 @@ Once we have made the connection to the database, much of what we do will look f
 
 In this lesson, we will be connecting to an SQLite database, which allows us to send strings containing SQL statements directly from R to the database and recieve the results. In addition, we will be connecting to the database in such a way that we can use 'dplyr' functions to operate directly on the database tables.
 
-
 ## Prelminaries
 
 First, install and load the neccessary packages. You can install the `RSQLite` package with
 
-
 ```{r, eval=FALSE}
 install.packages("RSQLite")
 ```
@@ -141,18 +137,22 @@ data
 dbClearResult(results)
 ```
 
-> ## Exercise
->
-> What happens if you send invalid SQL syntax?
->
-> > ## Solution
-> >
-> > An error message is returned from SQLite.
-> > Notice that R is just the conduit; it cannot check the SQL syntax.
-> >
-> >
-> {: .solution}
-{: .challenge}
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+## Exercise
+
+What happens if you send invalid SQL syntax?
+
+:::::::::::::::  solution
+
+## Solution
+
+An error message is returned from SQLite.
+Notice that R is just the conduit; it cannot check the SQL syntax.
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
 
 We can also create a new database and add tables to it. Let's base this new dataframe on the Question1 table that can be found in our existing database.
 
@@ -213,7 +213,7 @@ SN7577_d %>%
   summarize(avg_age = mean(numage))
 ```
 
-Notice that on the `nrow` command we get NA rather than a count of rows. Thisis because `dplyr` doesn't hold the full table even after the 'Select * ...'
+Notice that on the `nrow` command we get NA rather than a count of rows. Thisis because `dplyr` doesn't hold the full table even after the 'Select \* ...'
 
 If you need the row count you can use
 
@@ -222,25 +222,32 @@ SN7577_d %>%
   tally()
 ```
 
-> ## Exercise
->
-> Store the SN7577 table as an object for `dplyr` use.
->
-> Write a query using `dplyr` functions that will return the average age (`numage`) by sex for all records where
-> the response for Q2 is missing (missing values are indicated by a value of -1).
->
-> > ## Solution
-> >
-> > ```{r}
-> > SN7577_d <- tbl(mydb_dplyr, sql("SELECT * FROM SN7577"))
-> >
-> > SN7577_d %>%
-> >   filter(Q2 == -1)   %>%
-> >   group_by(sex)   %>%
-> >   summarize(avg_age = mean(numage))
-> > ```
-> >
-> {: .solution}
-{: .challenge}
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+## Exercise
+
+Store the SN7577 table as an object for `dplyr` use.
+
+Write a query using `dplyr` functions that will return the average age (`numage`) by sex for all records where
+the response for Q2 is missing (missing values are indicated by a value of -1).
+
+:::::::::::::::  solution
+
+## Solution
+
+```{r}
+SN7577_d <- tbl(mydb_dplyr, sql("SELECT * FROM SN7577"))
+
+SN7577_d %>%
+  filter(Q2 == -1)   %>%
+  group_by(sex)   %>%
+  summarize(avg_age = mean(numage))
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
 
 {% include links.md %}
+
+
diff --git a/episodes/00-intro.Rmd b/episodes/00-intro.Rmd
@@ -5,8 +5,8 @@ exercises: 15
 source: Rmd
 ---
 
-```{r, include=FALSE}
-source("../bin/download_data.R")
+```{r setup, include=FALSE}
+source("data/download_data.R")
 ```
 
 ::::::::::::::::::::::::::::::::::::::: objectives
@@ -122,15 +122,15 @@ freely available to extend R's native capabilities.
 
 <div class="col-md-6">
 
-```{r rstudio-analogy, echo = FALSE, fig.show = "hold", out.width = "100%", fig.alt = "RStudio extends what R can do, and makes it easier to write R code and interact with R."}
+```{r rstudio-analogy, echo=FALSE, fig.show="hold", out.width="100%", fig.alt="RStudio extends what R can do, and makes it easier to write R code and interact with R."}
 knitr::include_graphics("fig/r-manual.jpeg")
 ```
 
 </div>
 
 <div class="col-md-6">
 
-```{r rstudio-analogy-2, echo = FALSE, fig.show = "hold", fig.alt = "automatic car gear shift representing the ease of RStudio", out.width="100%"}
+```{r rstudio-analogy-2, echo=FALSE, fig.show="hold", fig.alt="automatic car gear shift representing the ease of RStudio", out.width="100%"}
 knitr::include_graphics("fig/r-automatic.jpeg")
 ```
 
@@ -407,8 +407,6 @@ proceeds, messages relating to its progress will be written to the console.
 You will be able to see all of the packages which are actually being
 installed.
 
-
-
 :::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
@@ -432,8 +430,6 @@ was written to the console before the start of the installation messages.
 
 You could also have installed the **`tidyverse`** packages by running this command directly at the R terminal.
 
-
-
 :::::::::::::::::::::::::::::::::::::::: keypoints
 
 - Use RStudio to write and run R programs.

diff --git a/episodes/01-intro-to-r.Rmd b/episodes/01-intro-to-r.Rmd
@@ -5,8 +5,8 @@ exercises: 30
 source: Rmd
 ---
 
-```{r, include=FALSE}
-source("../bin/download_data.R")
+```{r setup, include=FALSE}
+source("data/download_data.R")
 ```
 
 ::::::::::::::::::::::::::::::::::::::: objectives
@@ -104,7 +104,6 @@ have drastically different meanings. However, in this lesson, the two words
 are used synonymously. For more information see:
 [https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects](https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects)
 
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 When assigning a value to an object, R does not print anything. You
@@ -160,8 +159,6 @@ The value of `area_acres` is still 6.175 because you have not
 re-run the line `area_acres <- 2.47 * area_hectares` since
 changing the value of `area_hectares`.
 
-
-
 :::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
@@ -306,7 +303,6 @@ Type in `?round` at the console and then look at the output in the Help pane.
 What other functions exist that are similar to `round`?
 How do you use the `digits` parameter in the round function?
 
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Vectors and data types
@@ -414,7 +410,6 @@ single vector?
 
 R implicitly converts them to all be the same type.
 
-
 :::::::::::::::::::::::::
 
 What will happen in each of these examples? (hint: use `class()`
@@ -437,7 +432,6 @@ Vectors can be of only one data type. R tries to
 convert (coerce) the content of this vector to find a "common
 denominator" that doesn't lose any information.
 
-
 :::::::::::::::::::::::::
 
 How many values in `combined_logical` are `"TRUE"` (as a character) in the
@@ -460,7 +454,6 @@ first time the vector is evaluated. Therefore, the `TRUE` in
 gets converted into a `1` before it gets converted into `"1"` in
 `combined_logical`.
 
-
 :::::::::::::::::::::::::
 
 You've probably noticed that objects of different types get
@@ -604,10 +597,10 @@ Recall that you can use the `typeof()` function to find the type of your atomic
 ## Exercise
 
 1. Using this vector of rooms, create a new vector with the NAs removed.
-  
-  ```r
-  rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)
-  ```
+
+```r
+rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)
+```
 
 2. Use the function `median()` to calculate the median of the `rooms` vector.
 
@@ -637,8 +630,6 @@ Now that we have learned how to write scripts, and the basics of R's data
 structures, we are ready to start working with the SAFI dataset we have been
 using in the other lessons, and learn about data frames.
 
-
-
 :::::::::::::::::::::::::::::::::::::::: keypoints
 
 - Access individual values by location using `[]`.

diff --git a/episodes/02-starting-with-data.Rmd b/episodes/02-starting-with-data.Rmd
@@ -5,8 +5,8 @@ exercises: 30
 source: Rmd
 ---
 
-```{r, include=FALSE}
-source("../bin/download_data.R")
+```{r setup, include=FALSE}
+source("data/download_data.R")
 ```
 
 ::::::::::::::::::::::::::::::::::::::: objectives
@@ -181,7 +181,6 @@ the help for `read_csv()` by typing `?read_csv` to learn more. There is also
 the `read_tsv()` for tab-separated data files, and `read_delim()` allows you
 to specify more details about the structure of your file.
 
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 Note that `read_csv()` actually loads the data as a tibble.
@@ -259,7 +258,6 @@ Different ways of specifying these coordinates can lead to results with
 different classes. This is covered in the Software Carpentry lesson
 [R for Reproducible Scientific Analysis](https://swcarpentry.github.io/r-novice-gapminder/).
 
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ```{r, purl=FALSE}
@@ -320,12 +318,12 @@ names of the columns.
   row 100 of the `interviews` dataset.
 
 2. Notice how `nrow()` gave you the number of rows in the tibble?
-  
-  - Use that number to pull out just that last row in the tibble.
-  - Compare that with what you see as the last row using `tail()` to make
-    sure it's meeting expectations.
-  - Pull out that last row using `nrow()` instead of the row number.
-  - Create a new tibble (`interviews_last`) from that last row.
+
+- Use that number to pull out just that last row in the tibble.
+- Compare that with what you see as the last row using `tail()` to make
+  sure it's meeting expectations.
+- Pull out that last row using `nrow()` instead of the row number.
+- Create a new tibble (`interviews_last`) from that last row.
 
 3. Using the number of rows in the interviews dataset that you found in
   question 2, extract the row that is in the middle of the dataset. Store
@@ -655,8 +653,6 @@ variables to date.
 mdy(char_dates)
 ```
 
-
-
 :::::::::::::::::::::::::::::::::::::::: keypoints
 
 - Use read\_csv to read tabular data in R.

diff --git a/episodes/03-dplyr.Rmd b/episodes/03-dplyr.Rmd
@@ -5,8 +5,8 @@ exercises: 15
 source: Rmd
 ---
 
-```{r, include=FALSE}
-source("../bin/download_data.R")
+```{r setup, include=FALSE}
+source("data/download_data.R")
 ```
 
 ::::::::::::::::::::::::::::::::::::::: objectives
@@ -46,7 +46,6 @@ variants of different function and option names. For this lesson, we utilize
 the American spellings of different functions; however, feel free to use
 the regional variant for where you are teaching.
 
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## What is an R package?
@@ -79,7 +78,6 @@ the package [`data.table`](https://rdatatable.gitlab.io/data.table/). See this
 for example to get a sense of the differences between using `base`, `tidyverse`, and
 `data.table`.
 
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Learning **`dplyr`**
@@ -504,8 +502,6 @@ interviews %>%
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-
-
 :::::::::::::::::::::::::::::::::::::::: keypoints
 
 - Use the `dplyr` package to manipulate dataframes.

diff --git a/episodes/04-tidyr.Rmd b/episodes/04-tidyr.Rmd
@@ -5,8 +5,8 @@ exercises: 15
 source: Rmd
 ---
 
-```{r, include=FALSE}
-source("../bin/download_data.R")
+```{r setup, include=FALSE}
+source("data/download_data.R")
 ```
 
 ::::::::::::::::::::::::::::::::::::::: objectives
@@ -485,8 +485,6 @@ if (!dir.exists("data_output")) dir.create("data_output")
 write_csv(interviews_plotting, "data_output/interviews_plotting.csv")
 ```
 
-
-
 :::::::::::::::::::::::::::::::::::::::: keypoints
 
 - Use the `tidyr` package to change the layout of dataframes.