Merge pull request #105 from tidy-survey-r/add-styler

Add styler chunks to the beginning of each chapter.

05-descriptive-analysis.Rmd

@@ -1,5 +1,11 @@
 # Descriptive Analyses in {srvyr} {#c05-descriptive-analysis}
 
+```{r}
+#| label: desc-styler
+#| include: false
+knitr::opts_chunk$set(tidy = 'styler')
+```
+
 ::: {.prereqbox-header}
 `r if (knitr:::is_html_output()) '### Prerequisites {- #prereq5}'`
 :::

07-modeling.Rmd

@@ -1,5 +1,11 @@
 # Modeling {#c07-modeling}
 
+```{r}
+#| label: model-styler
+#| include: false
+knitr::opts_chunk$set(tidy = 'styler')
+```
+
 ::: {.prereqbox-header}
 `r if (knitr:::is_html_output()) '### Prerequisites {- #prereq7}'`
 :::

08-communicating-results.Rmd

@@ -3,6 +3,7 @@
 # Communicating results {#c08-communicating-results}
 
 ```{r}
+#| label: results-styler
 #| include: false
 knitr::opts_chunk$set(tidy = 'styler')
 ```

09-reproducible-data.Rmd

@@ -5,7 +5,9 @@
 #| include: false
 knitr::opts_chunk$set(tidy = 'styler')
 ```
+
 ## Introduction
+
 Reproducing a data analysis's results is a crucial aspect of any research. First, reproducibility serves as a form of quality assurance. If we pass an analysis project to another person, they should be able to run the entire project from start to finish and obtain the same results. They can critically assess the methodology and code while detecting potential errors. Another goal of reproducibility is enabling the verification of our analysis. When someone else is able to check our results, it ensures the integrity of the analyses by determining that the conclusions are not dependent on a particular person running the code or workflow on a particular day or in a particular environment.
 
 Not only is reproducibility a key component in ethical and accurate research, but it is also a requirement for many scientific journals. For example, the Journal of Survey Statistics and Methodology (JSSAM) and Public Opinion Quarterly (POQ) require authors to make code, data, and methodology transparent and accessible to other researchers who wish to verify or build on existing work.
@@ -52,7 +54,7 @@ In a project-based workflow, all paths are relative and, by default, relative to
 
 ```{r}
 #| eval: false
-#| label: project-file-example
+#| label: reprex-project-file-example
 anes <- 
   read_csv(here::here("data", "anes2020_clean.csv"))
 ```

92-AppendixC.Rmd

@@ -1,5 +1,11 @@
 # Importing survey data into R
 
+```{r}
+#| label: readr-styler
+#| include: false
+knitr::opts_chunk$set(tidy = 'styler')
+```
+
 To analyze a survey, we need to import the survey data into R. This process is often referred to as importing, loading, or reading in data. Survey files come in different formats depending on the software used to create them. One of the many advantages of R is the flexibility in handling various data formats, regardless of their file extensions. Here are examples of common public-use survey file formats we may encounter:
 
 * Delimiter-separated text files