How kamecon/tfm_kschool works
This repository automates survey processing: optical-reader data is cleaned, then used for descriptive and empirical analysis, reports, and an interactive dashboard. The README documents the principal run order and product capabilities, while the sampled Python source confirms the cleaning input and CSV output. Analysis and reporting internals were not sampled, so their connections are shown only where the README supports them; the dashboard’s runtime wiring is not verified.
Main entry points: encuesta.R R processing · limpieza.py Survey cleaner · encuesta.csv Clean survey CSV · Reporte Descriptive analysis
Components 12
Data cleaning
- Raw survey data
Replicacion/Data/encuesta.txt - Survey cleaner
Replicacion/limpieza.py - Clean survey CSV
encuesta.csv
Analysis and reporting
- R processing
Replicacion/encuesta.R - Descriptive analysis
Replicacion/Reporte - Empirical analysis
Replicacion/analisis_empirico.html
Results delivery
- Aggregate reports
Replicacion/Reporte/Prueba_reporte.Rmd - Professor reports
Replicacion/Reporte - Interactive dashboard
Replicacion/Reporte - Analysis write-up
Replicacion/analisis_empirico.html
Other components
- Project operator
- Dashboard users
Connections 11
- Project operator → Survey cleaner: runs first
readme.md - Raw survey data → Survey cleaner: reads
Replicacion/limpieza.py - Survey cleaner → Clean survey CSV: writes CSV
Replicacion/limpieza.py - Project operator → R processing: runs second
readme.md - Clean survey CSV → R processing: processes next
readme.md - R processing → Descriptive analysis: produces analysis
readme.md - R processing → Empirical analysis: produces results
readme.md - R processing → Professor reports: generates reports
readme.md - Descriptive analysis → Aggregate reports: presents results
readme.md - Dashboard users → Interactive dashboard: views reports
readme.md - Empirical analysis → Analysis write-up: documented in
readme.md