Add 'Reproducible Reports with Quarto' Episode

config.yaml

@@ -64,6 +64,7 @@ episodes:
 - 02-starting-with-data.Rmd
 - 03-data-cleaning-and-transformation.Rmd
 - 04-data-viz-ggplot.Rmd
+- 05-reproducible-reports.Rmd
 
 # Information for Learners
 learners: 

episodes/01-intro-to-r.Rmd

@@ -99,9 +99,8 @@ the Environment pane in the upper right, you will notice a new object,
 Here are some tips for naming objects in R:
 
 - **Do not use names of functions that already exist in R:** There are some
-  names that cannot be used because they are the names of fundamental functions in
-  R (e.g., `if`, `else`, `for`, see
-  [here](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Reserved.html)
+  fundamental functions in R (e.g., `if`, `else`, `for`, see the
+  [list of reserved words](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Reserved.html)
   for a complete list. In general, even if it's allowed, it's best to not use
   other function names (e.g., `c`, `T`, `mean`, `data`, `df`, `weights`). If in
   doubt, check the help to see if the name is already in use.

episodes/04-data-viz-ggplot.Rmd

@@ -60,7 +60,7 @@ library(lubridate)  # load lubridate
 The`lubridate` package is installed with the tidyverse, but is not one of the core tidyverse packages loaded with `library(tidyverse)`, so it needs to be explicitly called. `lubridate` makes working with dates and times easier in R.
 
 We also load the `books_reformatted` data we saved in the previous
-lesson. Here, we'll assign it to `books2`. You can create the reformatted data by running the codes from [here](https://librarycarpentry.github.io/lc-r/03-data-cleaning-and-transformation.html#exporting-data) or by loading the saved CSV file from previous episode.
+lesson. Here, we'll assign it to `books2`. You can create the reformatted data by running the [codes for exporting data](https://librarycarpentry.github.io/lc-r/03-data-cleaning-and-transformation.html#exporting-data) or by loading the saved CSV file from previous episode.
 
 ```{r, purl=FALSE, eval=FALSE}
 books2 <- read_csv("data_output/books_reformatted.csv")  # load the data and assign it to books
@@ -109,7 +109,7 @@ ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()
 - The `mapping` argument defines the variables mapped to various aesthetics of the plot, e.g. the x and y axis.
 - The `geom_function` argument defines the type of plot, e.g. barplot, scatter plot, boxplot.
 
-::::::::::::::::::::::::::::::::::::::::::::; callout
+::::::::::::::::::::::::::::::::::::::::::::  callout
 
 ## `ggplot2` versus `ggplot`
 

episodes/05-reproducible-reports.Rmd

@@ -0,0 +1,286 @@
+---
+title: Reproducible Reports with Quarto
+teaching: 45
+exercises: 15
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Describe the value of reproducible reporting.
+- Create a new Quarto document (`.qmd`) in RStudio.
+- Use Markdown syntax to format text.
+- Create and run code chunks within a Quarto document.
+- Render a Quarto document to an HTML report.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I combine my code, results, and narrative into a single document?
+- How can I automatically update my reports when my data changes?
+- What is Quarto and how does it differ from a standard R script?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+```{r, include=FALSE}
+source("files/download_data.R")
+library(tidyverse)
+# Read raw data and apply cleaning steps from previous episodes
+books2 <- read_csv("data/books.csv") %>%
+  rename(
+    title = X245.ab,
+    author = X245.c,
+    callnumber = CALL...BIBLIO.,
+    isbn = ISN,
+    pubyear = X008.Date.One,
+    subCollection = BCODE1,
+    format = BCODE2,
+    location = LOCATION,
+    tot_chkout = TOT.CHKOUT,
+    loutdate = LOUTDATE,
+    subject = SUBJECT,
+    callnumber2 = CALL...ITEM.
+  ) %>%
+  mutate(
+    pubyear = as.integer(pubyear),
+    subCollection = recode(subCollection,
+      "-" = "general collection",
+      u = "government documents",
+      r = "reference",
+      b = "k-12 materials",
+      j = "juvenile",
+      s = "special collections",
+      c = "computer files",
+      t = "theses",
+      a = "archives",
+      z = "reserves"
+    ),
+    format = recode(format,
+      a = "book",
+      e = "serial",
+      w = "microform",
+      s = "e-gov doc",
+      o = "map",
+      n = "database",
+      k = "cd-rom",
+      m = "image",
+      "5" = "kit/object",
+      "4" = "online video"
+    )
+  )
+```
+
+## Introduction to Reproducible Reporting
+
+So far, we have been writing code in `.R` scripts. This is excellent for data analysis, but what happens when you need to share your findings with a colleague or a library director? You might copy a plot into a Word document or an email, then type out your interpretation. 
+
+But what if the data changes next month? You would have to re-run your script, re-save the plot, copy it back into Word, and update your text. This manual process is prone to errors and tedious.
+
+**Quarto** allows you to combine your code, its output (plots, tables), and your narrative text into a single document. When you "render" the document, R runs the code and produces a polished report (HTML, PDF, or Word) automatically.
+
+## Creating a Quarto Document
+
+To create a new Quarto document in RStudio:
+
+1. Click the **File** menu.
+2. Select **New File** > **Quarto Document...**
+3. In the dialog box, give your document a **Title** (e.g., "Library Usage Report") and enter your name as **Author**.
+4. Ensure **HTML** is selected as the output format.
+5. Click **Create**.
+
+RStudio will open a new file with some example content. Notice the file extension is `.qmd`.
+
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+## Quarto vs. RMarkdown
+
+If you have used R before, you might be familiar with RMarkdown (`.Rmd`). Quarto (`.qmd`) is the next-generation version of RMarkdown. It works very similarly but supports more languages (like Python and Julia) and has better features for scientific publishing.
+
+::::::::::::::::::::::::::::::::::::::::: 
+
+## Anatomy of a Quarto Document
+
+A Quarto document has three main parts:
+
+### 1. The YAML Header
+
+At the very top, enclosed between two lines of `---`, is the **YAML Header**. This contains metadata about the document.
+
+```yaml
+---
+title: "Library Usage Report"
+author: "Your Name"
+format: html
+---
+```
+
+### 2. Markdown Text
+
+The white space is where you write your narrative. You use **Markdown** syntax to format text. 
+
+- `**Bold**` for **bold text**
+- `*Italics*` for *italics*
+- `# Heading 1` for a main title
+- `## Heading 2` for a section title
+- `- List item` for bullet points
+
+### 3. Code Chunks
+
+Code chunks are where your R code lives. They start with ` ```{r} ` and end with ` ``` `.
+
+````
+```{r}
+# This is a code chunk
+summary(cars)
+```
+````
+
+You can insert a new chunk by clicking the **+C** button in the editor toolbar, or by pressing <kbd>Ctrl</kbd>+<kbd>Alt</kbd>+<kbd>I</kbd> (Windows/Linux) or <kbd>Cmd</kbd>+<kbd>Option</kbd>+<kbd>I</kbd> (Mac).
+
+## Your First Report
+
+Let's clean up the example file and create a report using our `books` data.
+
+1.  Delete everything in the file *below* the YAML header.
+2.  Add a new **setup** code chunk to load our libraries and prepare the data.
+
+```{{r}}
+#| label: setup
+#| include: false
+
+library(tidyverse)
+
+# Load data and rename columns for clarity
+books2 <- read_csv("data/books.csv") %>%
+  rename(
+    subCollection = BCODE1,
+    tot_chkout = TOT.CHKOUT,
+    format = BCODE2
+  ) %>%
+  mutate(
+    subCollection = recode(subCollection, 
+      "-" = "general collection", 
+      j = "juvenile", 
+      b = "k-12 materials"
+    )
+  )
+```
+
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+## Chunk Options
+
+Notice the lines starting with `#|`. These are **chunk options**. 
+- `#| label: setup` gives the chunk a name.
+- `#| include: false` runs the code but hides the code and output from the final report. This is great for loading data silently.
+
+::::::::::::::::::::::::::::::::::::::::: 
+
+### Adding Analysis
+
+Now, let's add a section header and some text.
+
+```markdown
+## High Usage Items
+
+We are analyzing items with more than 10 checkouts to understand circulation patterns across sub-collections.
+```
+
+Next, insert a new code chunk and paste the plotting code we developed in the previous episode (ggplot2).
+
+````
+```{r}
+#| label: plot-high-usage
+#| echo: false
+
+# Filter for high usage
+booksHighUsage <- books2 %>%
+  filter(!is.na(tot_chkout),
+         tot_chkout > 10)
+
+# Create the plot
+ggplot(data = booksHighUsage, 
+       aes(x = subCollection, y = tot_chkout)) +
+  geom_boxplot(alpha = 0) +
+  geom_jitter(alpha = 0.5, color = "tomato") +
+  scale_y_log10() +
+  labs(title = "Distribution of Checkouts by Sub-Collection",
+       x = "Sub-Collection",
+       y = "Total Checkouts (Log Scale)") +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+```
+````
+
+Setting `#| echo: false` will display the *plot* in the report, but hide the R *code* that generated it. This is often preferred for reports intended for non-coders.
+
+## Rendering the Document
+
+Now comes the magic. Click the **Render** button (blue arrow icon) at the top of the editor pane.
+
+RStudio will:
+1.  Run all your code chunks from scratch.
+2.  Generate the plots and results.
+3.  Combine them with your text.
+4.  Create a new file named `library_usage_report.html` in your project folder.
+5.  Open a preview of the report.
+
+:::::::::::::::::::::::::::::::::::::::  challenge
+
+## Challenge: Add a Summary Table
+
+1.  Add a new header `## Summary Statistics` to your Quarto document.
+2.  Insert a new code chunk.
+3.  Write code to calculate the mean checkouts per format (Hint: use `group_by(format)` and `summarize()`).
+4.  Render the document again to see your new table included in the report.
+
+:::::::::::::::  solution
+
+## Solution
+
+Add this to your document:
+
+```markdown
+## Summary Statistics
+
+The table below shows the average checkouts for each item format.
+```
+
+````
+```{r}
+#| label: summary-table
+
+books2 %>%
+  group_by(format) %>%
+  summarize(mean_checkouts = mean(tot_chkout, na.rm = TRUE)) %>%
+  arrange(desc(mean_checkouts))
+```
+````
+
+Render the document to see the updated report.
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Why This Matters
+
+By using Quarto, your report is now **reproducible**. 
+
+If you download a new version of `books.csv` next month:
+1.  Save it to your `data/` folder.
+2.  Open your Quarto document.
+3.  Click **Render**.
+
+Your report will automatically update with the new data, creating a fresh plot and table without you having to copy-paste a single thing.
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- **Quarto** allows you to mix code and text to create reproducible reports.
+- Use the **YAML header** to configure document metadata like title and output format.
+- **Code chunks** run R code and can display or hide input/output using options like `#| echo: false`.
+- **Rendering** the document executes the code and produces the final output (HTML, PDF, etc.).
+- This workflow saves time and reduces errors when reporting on data that changes over time.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::