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/

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🛠️ Installation

To install the package in your R console, run:

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

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⚙️ Workflow: AmericasBarometer Variable & Value Labels

For the full online guide, see:

📖 LAPOP Data Guide for R Users (pkgdown article)

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.

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🎨 Workflow: AmericasBarometer Data Visualization

1. Load the package in R:

   library(lapop)

2. Load LAPOP Lab fonts:

   lapop_fonts()

3. Apply the AmericasBarometer sampling design 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/ts_DVcode(s)_IVcode(s)_graphtype.filetype.

   • 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()

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🤝 Workflow: Contributing to the lapop R Package

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

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