panelbuild

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

R-CMD-check

Installation

You can install the development version of panelbuild from GitHub:

# install.packages("remotes")
remotes::install_github("desirajulavanya/panelbuild")

Quick start

library(panelbuild)

audit <- audit_panel(example_panel, id = id, time = year)

audit
## 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
audit_report(audit)
## panelbuild Panel Audit Report
## ==========================
## 
## Dataset
## -------
## Data: example_panel
## Unit variable: id
## Time variable: year
## 
## Panel structure
## ---------------
## Units: 3
## Time periods: 4
## Observed rows: 9
## Observed unit-time cells: 8
## Expected unit-time cells: 12
## Missing unit-time cells: 4
## Duplicate unit-time cells: 1
## Balanced panel: No
## 
## Recommended next steps
## ----------------------
## * Resolve duplicate unit-time observations before completing the panel.
## * Use `duplicate_cells(audit)` or `duplicate_summary()` to inspect duplicates.
## * Inspect missing unit-time cells before estimation.
## * Use `missing_cells(audit)` or `gap_summary()` to review panel gaps.
## * Use `complete_panel()` only after duplicate unit-time cells are resolved.

Why panelbuild?

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

panelbuild helps researchers identify and document these problems before estimation.

Basic example

library(panelbuild)

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

Audit a panel dataset

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

Find duplicate unit-time observations

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 panel gaps

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 panel issues

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 the panel grid

complete_panel() creates a full unit-time grid while preserving observed values. It does not impute missing outcomes.

Because complete_panel() requires unique id-time cells, we first create a version of the example data without duplicates.

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: ¹​panelbuild_completed_cell
## # ℹ 1 more variable: panelbuild_audit_action <chr>

Main functions

Package goal

The goal of panelbuild is to provide a transparent and reproducible workflow for panel-data quality assurance before statistical modeling.

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