Student Dashboard

Integrated Python + R Analytics (Supabase)

Portfolio Note: This report demonstrates a polyglot data workflow. I utilized Python for its robust API ecosystem to interface with Supabase and perform complex data merging. I then leveraged R and the reticulate library to generate publication-quality tables and statistical visualizations, showcasing the ability to choose the best tool for each stage of the data lifecycle.

• Overview
• Student Distribution
• Attendance Analysis
• Assessment Analysis
• School Comparison
• Interactive Tools
• Correlation Analysis

Overview

Key Metrics
Metric	Value
Total Students	50
Average Score	76.7
% EL	36%
% SPED	22%

Student Distribution

Attendance Analysis

Assessment Analysis

EL vs Non-EL

SPED vs Non-SPED

School Comparison

Correlation Analysis

# Demonstrating Python's strength in matrix manipulation
# Encode categorical → numeric
corr_df = assessment_merged.copy()

corr_df['EL_num'] = corr_df['EL_flag'].map({'EL':1,'Non-EL':0})
corr_df['SPED_num'] = corr_df['SPED_flag'].map({'SPED':1,'Non-SPED':0})

pivot_scores = corr_df.pivot_table(index='student_id', columns='subject', values='score')

final_corr = pivot_scores.copy()
final_corr['EL'] = corr_df.groupby('student_id')['EL_num'].first()
final_corr['SPED'] = corr_df.groupby('student_id')['SPED_num'].first()
final_corr['Grade'] = corr_df.groupby('student_id')['grade_level'].first()

corr_matrix = final_corr.corr()