lapop R package

These are helper functions to wrangle labels and produce visualizations of 'AmericasBarometer' data following LAPOP Lab's editorial guidelines.

🔗 Package website: https://lapop-central.github.io/lapop/

---

🛠️ Installation

To install the package from CRAN in your console, run:

install.packages(lapop)

For the most recent version, please use:

# pak::pak("lapop-central/lapop")
install.packages("lapop", repos = c('https://lapop-central.r-universe.dev', 'https://cloud.r-project.org'))

### OR

devtools::install_github("lapop-central/lapop", force = TRUE)

---

⚙️ Workflow: 'AmericasBarometer' Variable & Value Labels

For the full online guide, see:

📖 LAPOP Data Guide for R Users

1. Data Structure

'AmericasBarometer' datasets are distributed in Stata .dta format with multilingual metadata (question wording and response options) embedded as attributes. These support cross-national and longitudinal comparability.

2. Preferred Loading Method

Use:

readstata13::read.dta13()

to preserve the full metadata structure.

Other methods such as haven::read_dta() or rio::import() may fail to import the STATA attributes.

3. Variable Labels (Question Wording)

• Stored in the expansion.fields attribute.
• Use lpr_extract_notes() to convert into a tidy data frame.
• Assign preferred language labels with lpr_set_attr() using the appropriate noteid.

4. Value Labels (Response Options)

• Stored in the label.table attribute.
• Use lpr_set_ros() to assign these response labels in English, Spanish, or Portuguese.

---

🎨 Workflow: 'AmericasBarometer' Data Visualization

📖 LAPOP Visualization Guide

1. Load the package in R:

   library(lapop)
   

2. LAPOP Lab fonts is automatically loaded, yet you can also manually if needed:

   lapop_fonts()
   

3. Apply the 'AmericasBarometer' design effects with:

   lpr_data()
   

4. Choose the appropriate lpr graph type:

   • Histograms: lpr_hist()
   • Cross-country comparison: lpr_cc()
   • Time series: lpr_ts()
   • Breakdown by covariates: lpr_mover()

5. Store the output in an R object.

• File names: .csv and graphics files should have the same name. Their names should be in the following standard format: CountryYear_DVcode(s)_IVcode(s)_graphtype.extension

• Examples:

  • mex21_countfair1_hist.csv
  • hnd_b4_ts.svg
  • ab23_vic1ext_pais_cc.svg

• There will be some cases that do not easily fit this standard. Use your best judgment.

6. Use the corresponding lapop plotting function to produce the visualization:

   • Examples: lapop_hist(), lapop_cc(), lapop_ts(), etc.

7. Export the figure to your machine with:

lapop_save()

---

🤝 Workflow: Contributing to the lapop R Package

Create a new branch for your feature or bug fix.

Document exported functions with roxygen2 comments. Add or update tests in tests/testthat/.

Commit your changes and push the branch to your fork. Submit a pull request with a clear description of the change.

1. Fork the repository and clone it to your local machine.
2. Create a new branch for your feature or fix.
3. Add or modify code in R/ folder.
4. Document the function with roxygen2 comments.
5. Run devtools::document() to update NAMESPACE and man/.
6. Run devtools::test() and devtools::check()
7. Commit your changes and push the branch to your fork.
8. Submit a pull request with a clear description of the change.

If you find a bug, please consider contributing to the lapop package — we spent all our money on coffee, empanadas, and data cleaning.

---