Apache Arrow Documentation Analysis

Kapa.ai Question Bot Analysis - June 23 to September 23, 2025

Summary

Key Findings

User improvement opportunities: Analysis of 2,666 user questions reveals significant opportunities to reduce support efforts and improve user experience through documentation improvements.

Areas to focus on: High-uncertainty topics with frequent questions represent the highest-value documentation updates.

Analysis Period: June 23 to September 23, 2025

Total Questions: 2,666 across 1,076 conversations

Daily Average: 29 questions per day

Metrics
Metric	Value	Impact
Uncertainty Rate	13.4%	Questions where bot was uncertain about answer quality
Question Volume	2,666	Total questions analyzed over 3-month period
Top Gap Area	Other	229 uncertain questions in this area

Documentation Gaps Analysis

High-Priority Topics by Uncertainty

Code

# Enhanced topic classification
topic_analysis <- data |>
  mutate(
    question_clean = str_to_lower(question),
    primary_topic = case_when(
      str_detect(question_clean, "parquet") ~ "Parquet Files",
      str_detect(question_clean, "pandas|dataframe") ~ "Pandas Integration",
      str_detect(question_clean, "schema|type|column") ~ "Schema & Data Types",
      str_detect(question_clean, "memory|performance|speed|slow") ~ "Performance & Memory",
      str_detect(question_clean, "install|error|import|setup") ~ "Installation & Setup",
      str_detect(question_clean, "filter|select|query") ~ "Data Operations",
      str_detect(question_clean, "write|save|export") ~ "Data Export",
      str_detect(question_clean, "read|load|open") ~ "Data Loading",
      str_detect(question_clean, "convert|cast|transform") ~ "Data Conversion",
      str_detect(question_clean, "spark|dask|ray") ~ "Big Data Integration",
      TRUE ~ "General/Other"
    )
  ) |>
  group_by(primary_topic) |>
  summarise(
    total_questions = n(),
    uncertain_questions = sum(is_uncertain, na.rm = TRUE),
    uncertainty_rate = mean(is_uncertain, na.rm = TRUE),
    avg_question_length = mean(question_length, na.rm = TRUE),
    .groups = "drop"
  ) |>
  filter(total_questions >= 20) |> # Focus on topics with sufficient data
  arrange(desc(uncertainty_rate))

# Create priority matrix visualization
priority_plot <- topic_analysis |>
  ggplot(aes(x = total_questions, y = uncertainty_rate, size = uncertain_questions)) +
  geom_point(alpha = 0.7, color = "steelblue") +
  geom_text(aes(label = primary_topic), vjust = -0.5, hjust = 0.5, size = 3) +
  scale_size_continuous(name = "Uncertain\nQuestions", range = c(3, 12)) +
  scale_x_continuous(labels = scales::comma) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Documentation Gap Priority Matrix",
    subtitle = "Topics in upper-right quadrant need immediate attention",
    x = "Total Questions (Volume)",
    y = "Uncertainty Rate (Quality Gap)",
    caption = "Size indicates number of uncertain questions"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold")
  )

ggplotly(priority_plot, tooltip = c("x", "y", "size", "label"))

Temporal Patterns & Support Load

Code

# Daily question volume and uncertainty trends
daily_trends <- data |>
  group_by(date) |>
  summarise(
    questions = n(),
    uncertainty_rate = mean(is_uncertain, na.rm = TRUE),
    avg_question_length = mean(question_length),
    .groups = "drop"
  )

# Weekly aggregation for cleaner visualization
weekly_trends <- data |>
  group_by(week) |>
  summarise(
    questions = n(),
    uncertainty_rate = mean(is_uncertain, na.rm = TRUE),
    threads = n_distinct(thread_id),
    .groups = "drop"
  )

# Hourly patterns
hourly_patterns <- data |>
  group_by(hour) |>
  summarise(
    questions = n(),
    uncertainty_rate = mean(is_uncertain, na.rm = TRUE),
    .groups = "drop"
  )

# Create simpler temporal visualizations without secondary axes
p1 <- weekly_trends |>
  ggplot(aes(x = week, y = questions)) +
  geom_col(alpha = 0.7, fill = "steelblue") +
  labs(
    title = "Weekly Question Volume",
    x = "Week",
    y = "Questions per Week"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

p2 <- hourly_patterns |>
  ggplot(aes(x = hour, y = questions)) +
  geom_col(alpha = 0.7, fill = "darkgreen") +
  scale_x_continuous(breaks = seq(0, 23, 4)) +
  labs(
    title = "Daily Usage Patterns (UTC Time)",
    x = "Hour of Day (UTC)",
    y = "Questions per Hour"
  ) +
  theme_minimal()

# Uncertainty patterns
p3 <- weekly_trends |>
  ggplot(aes(x = week, y = uncertainty_rate)) +
  geom_line(color = "red", linewidth = 1.2, group = 1) +
  geom_point(color = "red", size = 2) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Weekly Answer Uncertainty Rate",
    x = "Week",
    y = "Uncertainty Rate"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Combine plots
gridExtra::grid.arrange(p1, p3, p2, ncol = 1)

Question Origin Analysis

Code

# Analyze which documentation pages generate most questions
origin_analysis <- data |>
  filter(!is.na(question_origin_url), question_origin_url != "") |>
  # Extract meaningful page names
  mutate(
    page_path = str_extract(question_origin_url, "(?<=docs/).*$"),
    page_category = case_when(
      str_detect(page_path, "python/generated") ~ "Python API Reference",
      str_detect(page_path, "python/") ~ "Python User Guide",
      str_detect(page_path, "cpp/") ~ "C++ Documentation",
      str_detect(page_path, "r/") ~ "R Documentation",
      str_detect(page_path, "format/") ~ "Format Specification",
      TRUE ~ "General/Other"
    ),
    specific_page = str_extract(page_path, "[^/]+\\.html$") |> str_remove("\\.html$")
  ) |>
  count(page_category, specific_page, sort = TRUE) |>
  group_by(page_category) |>
  slice_head(n = 10) |> # Top 10 pages per category
  ungroup()

# Visualize top question sources
origin_plot <- origin_analysis |>
  group_by(page_category) |>
  summarise(total_questions = sum(n), .groups = "drop") |>
  ggplot(aes(x = reorder(page_category, total_questions), y = total_questions)) +
  geom_col(fill = "steelblue", alpha = 0.8) +
  geom_text(aes(label = scales::comma(total_questions)), hjust = -0.1) +
  coord_flip() +
  labs(
    title = "Question Sources by Documentation Section",
    x = "Documentation Section",
    y = "Number of Questions"
  ) +
  theme_minimal()

ggplotly(origin_plot)

Recommendations

Priority Action Items

Based on the analysis, here are the highest-impact documentation improvements:

Prioritized Documentation Improvements
Priority	Action	Expected Impact	Effort Level
🔴 Critical	Create comprehensive Parquet troubleshooting guide or cheatsheet	Address uncertain questions	High
🔴 Critical	Improve Pandas integration examples and cookbook	Address uncertain questions	High
🟡 High	Add performance optimization best practices page	Reduce performance-related support tickets	Medium
🟡 High	Enhance installation documentation with common error solutions	Improve new user onboarding success rate	Low
🟢 Medium	Create cross-reference links between related API pages	Reduce navigation-related questions	Low

Success Metrics to Track

After implementing these improvements, monitor:

Uncertainty Rate: Target reduction from 13.4%
Question Volume: Monitor reduction in high-frequency question topics
Repeat Questions: Monitor reduction in duplicate/similar questions

Report generated on September 30, 2025 • Analysis covers Jun 23 - Sep 23, 2025