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itslikethisnow/state_education_data

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App README

State Education Data Dashboard

An interactive R Shiny dashboard for analyzing state-level education data, creating composite scores, and identifying market opportunities for professional development services.

Features

Core Functionality

  • —Data Explorer: Interactive data tables with search, filter, and sort capabilities
  • —Visualizations: Bar charts, scatter plots, box plots, and US choropleth maps
  • —Composite Score Builder: Create weighted composite scores with custom normalization
  • —State Comparison: Side-by-side analysis of multiple states across key metrics
  • —Export Capabilities: Download filtered data and visualizations

Key Data Categories

  • —Title II-A funding awards (2021-2024)
  • —Demographics and poverty statistics
  • —NAEP science scores
  • —Education expenditures
  • —Teacher metrics and salaries
  • —Professional development usage patterns
  • —NGSS adoption status

Installation

Required R Packages

r
install.packages(c(
  "shiny",
  "shinydashboard", 
  "DT",
  "plotly",
  "dplyr",
  "readr",
  "shinyWidgets",
  "shinycssloaders",
  "tidyr",
  "shinyjs",
  "RColorBrewer"
))

Running the Dashboard

  1. 1.Place the data files (merged_data.csv and variables.csv) in the dashboard directory
  2. 2.Open R/RStudio and set working directory to the dashboard folder
  3. 3.Run the application:
r
shiny::runApp()

Usage Guide

Data Explorer Tab

  1. 1.Use the sidebar to select variables for analysis
  2. 2.Filter the interactive data table using column filters
  3. 3.View summary statistics for numeric variables
  4. 4.Download filtered data as CSV

Visualizations Tab

  1. 1.Choose chart type: bar chart, scatter plot, box plot, or US map
  2. 2.For scatter plots: select X and Y variables
  3. 3.For maps: choose a numeric variable to display geographically
  4. 4.Use "Highlight Specific States" to emphasize states of interest
  5. 5.Interactive charts support zooming, panning, and hover details

Composite Scores Tab

  1. 1.Preset Configurations: Choose from predefined scoring models:
  2. 2.PD Investment Focus: Emphasizes professional development spending
  3. 3.Science Alignment Focus: Prioritizes science standards and performance
  4. 4.Market Opportunity: Highlights states with large student populations and funding
  1. 1.Custom Scoring:
  2. 2.Select variables to include in composite score
  3. 3.Adjust weights using sliders (must sum to 100%)
  4. 4.Choose normalization for each variable
  5. 5.Calculate scores and view rankings
  1. 1.Normalization Options:
  2. 2.Variables are scaled 0-100 for comparison
  3. 3.Poverty variables are reverse-scored (lower poverty = higher score)
  4. 4.Categorical variables (NGSS adoption) receive fixed scores

State Comparison Tab

  1. 1.Select 2-5 states for comparison
  2. 2.Choose variables to analyze
  3. 3.View comparative radar chart (normalized 0-100 scale)
  4. 4.Download comparison table as CSV

Data Processing Features

Composite Scoring

  • —Weighted Averages: Combine multiple metrics with custom weights
  • —Normalization: Min-max scaling (0-100) or z-score standardization
  • —Missing Data Handling: States with missing data are flagged
  • —Reverse Scoring: Automatically applied to poverty and student-teacher ratio

Visualization Types

  • —Bar Charts: Top 20 states for selected variable
  • —Scatter Plots: Correlation analysis with trend lines
  • —Box Plots: Distribution comparison across variables
  • —Choropleth Maps: Geographic patterns using state boundaries

File Structure

dashboard/
├── app.R                   # Main Shiny application
├── data_processing.R       # Helper functions for data manipulation
├── plot_functions.R        # Visualization functions
├── merged_data.csv         # State education dataset
├── variables.csv           # Variable definitions and sources
└── README.md              # This documentation

Data Sources

  • —U.S. Department of Education (Title II-A funding)
  • —The Nation's Report Card (NAEP scores, demographics)
  • —NCES Digest of Education Statistics (expenditures)
  • —NEA Rankings of the States (teacher data)
  • —OpenSciEd (NGSS adoption status)

Troubleshooting

Common Issues

  1. 1.Package Installation: Ensure all required packages are installed
  2. 2.Data Files: Verify CSV files are in correct directory
  3. 3.Memory: Large datasets may require increased R memory allocation
  4. 4.Browser: Use modern browsers (Chrome, Firefox, Edge) for optimal performance

Performance Tips

  • —Limit variable selections for faster processing
  • —Use preset configurations for quick analysis
  • —Filter data before creating visualizations
  • —Close unused browser tabs to free memory

Support

For questions or issues with the dashboard, please refer to the variable definitions in the Help tab or check the R console for error messages.

Version Information

  • —Built with R 4.0+ and Shiny 1.7+
  • —Tested on Windows, macOS, and Linux
  • —Responsive design supports desktop and tablet viewing --- title: State Education Data emoji: 🔥 colorFrom: red colorTo: purple sdk: docker pinned: false ---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference