Introduction

panelbuild provides tools for auditing, validating, and preparing panel datasets before statistical analysis.

Panel datasets often contain duplicate unit-time observations, missing time periods, irregular gaps, and imbalance. These issues can affect fixed effects models, difference-in-differences designs, event studies, and other panel-data methods.

The goal of panelbuild is to help users identify these issues before estimation.

Load the package

library(panelbuild)

Example panel dataset

panelbuild includes a small example dataset called example_panel.

data(example_panel)

example_panel
#>   id year outcome treatment
#> 1  1 2020      10         0
#> 2  1 2021      12         1
#> 3  1 2021      13         1
#> 4  2 2020      20         0
#> 5  2 2022      25         1
#> 6  3 2020      30         0
#> 7  3 2021      31         0
#> 8  3 2022      32         1
#> 9  3 2023      33         1

The dataset intentionally includes:

a duplicate unit-time observation
missing unit-time cells
an unbalanced panel structure

This makes it useful for demonstrating panel-data diagnostics.

Audit the panel

The main function is audit_panel().

audit_panel(example_panel, id = id, time = year)
#> Panel audit
#> 
#> Data: example_panel
#> Unit variable: id
#> Time variable: year
#> 
#> Units: 3
#> Time periods: 4
#> Observed rows: 9
#> Observed id-time cells: 8
#> Expected id-time cells: 12
#> Missing id-time cells: 4
#> Duplicate id-time cells: 1
#> Balanced panel: No

This gives a quick overview of the panel structure, including whether the panel is balanced and whether there are missing or duplicate unit-time cells.

Find duplicate observations

Duplicate unit-time observations are a common problem in panel datasets.

duplicate_summary(example_panel, id = id, time = year)
#> # A tibble: 1 × 3
#>      id panelbuild_duplicate_cells panelbuild_duplicate_extra_rows
#>   <dbl>                      <int>                           <int>
#> 1     1                          1                               1

Summarize gaps

gap_summary() identifies missing time periods by panel unit.

gap_summary(example_panel, id = id, time = year)
#> # A tibble: 2 × 2
#>      id panelbuild_missing_periods
#>   <dbl>                      <int>
#> 1     1                          2
#> 2     2                          2

Flag row-level issues

flag_panel_issues() adds diagnostic flags to the data.

flag_panel_issues(example_panel, id = id, time = year)
#> # A tibble: 9 × 7
#>      id  year outcome treatment panelbuild_row_id panelbuild_id_time_n
#>   <dbl> <dbl>   <dbl>     <dbl>             <int>                <int>
#> 1     1  2020      10         0                 1                    1
#> 2     1  2021      12         1                 2                    2
#> 3     1  2021      13         1                 3                    2
#> 4     2  2020      20         0                 4                    1
#> 5     2  2022      25         1                 5                    1
#> 6     3  2020      30         0                 6                    1
#> 7     3  2021      31         0                 7                    1
#> 8     3  2022      32         1                 8                    1
#> 9     3  2023      33         1                 9                    1
#> # ℹ 1 more variable: panelbuild_duplicate_cell <lgl>

Complete a panel grid

complete_panel() creates a complete unit-time grid. It does not impute missing outcome values.

Because complete_panel() requires unique unit-time cells, we first remove duplicate id-time observations from the example dataset.

example_panel_unique <- example_panel |>
  dplyr::distinct(id, year, .keep_all = TRUE)

complete_panel(example_panel_unique, id = id, time = year)
#> # A tibble: 12 × 7
#>       id  year outcome treatment panelbuild_original_row panelbuild_completed_…¹
#>    <dbl> <dbl>   <dbl>     <dbl> <lgl>                   <lgl>                  
#>  1     1  2020      10         0 TRUE                    FALSE                  
#>  2     1  2021      12         1 TRUE                    FALSE                  
#>  3     1  2022      NA        NA FALSE                   TRUE                   
#>  4     1  2023      NA        NA FALSE                   TRUE                   
#>  5     2  2020      20         0 TRUE                    FALSE                  
#>  6     2  2021      NA        NA FALSE                   TRUE                   
#>  7     2  2022      25         1 TRUE                    FALSE                  
#>  8     2  2023      NA        NA FALSE                   TRUE                   
#>  9     3  2020      30         0 TRUE                    FALSE                  
#> 10     3  2021      31         0 TRUE                    FALSE                  
#> 11     3  2022      32         1 TRUE                    FALSE                  
#> 12     3  2023      33         1 TRUE                    FALSE                  
#> # ℹ abbreviated name: ¹panelbuild_completed_cell
#> # ℹ 1 more variable: panelbuild_audit_action <chr>

Typical workflow

A typical panelbuild workflow is:

library(panelbuild)

audit_panel(my_data, id = unit_id, time = year)

duplicate_summary(my_data, id = unit_id, time = year)

gap_summary(my_data, id = unit_id, time = year)

clean_data <- my_data |>
  dplyr::distinct(unit_id, year, .keep_all = TRUE)

complete_panel(clean_data, id = unit_id, time = year)

Summary

panelbuild is designed to provide a transparent and reproducible workflow for panel-data quality assurance.

Use it before fitting panel models, difference-in-differences designs, event studies, or other longitudinal-data analyses.

Getting Started with panelbuild