SmartEDA

As explained in Statistics Slam Dunk

SmartEDA is an R package that provides automated exploratory data analysis through descriptive summaries, diagnostics, and visualizations.

SmartEDA automates exploratory data analysis in R by combining descriptive summaries, diagnostics, and visualizations. It is especially useful for quickly inspecting realistic data before deeper analysis. section 17.0 section 17.4.3

A practical SmartEDA workflow moves from prepared data to a broad diagnostic report:

• Prepare a focused data frame with dplyr, derive analysis variables such as scoring margin and spread movement, and convert categorical results to factors. section 17.4.3
• Generate an interactive HTML report with ExpReport() for a high-level overview. section 17.4.3
• Inspect data structure and completeness with ExpData(), including row counts, variable types, missing values, and zero-variance variables. section 17.4.3
• Review numeric distributions and relationships with ExpNumStat(), ExpOutQQ(), ExpNumViz(), and scatterplots. section 17.4.3
• Summarize and plot categorical variables with ExpCTable() and ExpCatViz(). section 17.4.3

Comparing NBA spreads with final margins

For 1,230 NBA games, create margin = ptsR - ptsH, compare that margin with opening and closing home spreads, and classify spreadmove as up, down, or same. Run ExpReport(oddsdf4, op_file = "oddsdf4.html") and inspect ExpData(), ExpNumStat(oddsdf4, Outlier = TRUE, round = 2), ExpOutQQ(), ExpNumViz(), ExpCTable(), and ExpCatViz(). The categorical summary finds 639 games with spreads moving up, 397 moving down, and 194 unchanged. section 17.4.3

Figure 17.12 The top of an interactive HTML EDA report from the SmartEDA package

Browse the interactive HTML report preview to see how SmartEDA gathers exploratory results into one navigable artifact. section 17.4.3

How Statistics Slam Dunk covers it

How deep it goes

Demonstrates preparation, report generation, numeric and categorical diagnostics, and visualization, but stops short of predictive modeling.

Examples

Uses NBA scores, opening and closing spreads, spread movement, HTML reports, QQ plots, density plots, scatterplots, tables, and bar charts.

What it assumes

Assumes basic R data wrangling and factor handling while demonstrating exploratory diagnostics through concrete NBA data.

What you can do after

Apply it via the SmartEDA package to produce interactive reports and inspect data quality, distributions, outliers, relationships, and category frequencies.

How it connects

Connects exploratory data analysis with dplyr, tidyr, ggplot2, tableone, and DataExplorer.

liveBookmanning.com

Read Statistics Slam Dunk on liveBook Get the book at manning.com

Related concepts

• data explorationuses · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.
• ExpReport() functionused by · The ExpReport() function from SmartEDA generates a standalone HTML exploratory data analysis report from a dataset.
• DataExploreralternative to · DataExplorer is an R package that automates exploratory data analysis by generating data profiling summaries and visualizations.
• ExpData() functionused by · The ExpData() function from SmartEDA returns tabular summaries describing a dataset's dimensions, variable classes, structure, and missing data.

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.