import pandas as pd

# Load, clean, and analyze campaign finance data
df = pd.read_csv("contributions.csv")
df['date'] = pd.to_datetime(df['date'])
monthly = df.groupby(df['date'].dt.month)['amount'].sum()
print(monthly)

// Interactive bar chart with D3
d3.csv("data.csv").then(data => {
  const svg = d3.select("#chart")
    .append("svg")
    .attr("width", 600)
    .attr("height", 400);

  // ... bindings and rendering
});

library(tidyverse)

# Analyze election results with statistical summary
results <- read_csv("election_data.csv")
results %>%
  group_by(county) %>%
  summarize(
    avg_turnout = mean(turnout),
    margin = mean(dem_votes - rep_votes)
  ) %>%
  ggplot(aes(x = avg_turnout, y = margin)) +
  geom_point() +
  geom_smooth(method = "lm")

-- Find top donors by total contributions
SELECT
    donor_name,
    COUNT(*) as num_contributions,
    SUM(amount) as total_amount
FROM contributions
WHERE date >= '2024-01-01'
GROUP BY donor_name
ORDER BY total_amount DESC
LIMIT 20;

# Rename all PDFs to include date prefix
for f in *.pdf; do
  mv "$f" "2024-12-$(basename "$f")"
done

# Count lines in all CSV files
wc -l *.csv

# Find all files modified today
find . -mtime 0 -type f