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ExpData() function

As explained in Statistics Slam Dunk

The ExpData() function from SmartEDA returns tabular summaries describing a dataset's dimensions, variable classes, structure, and missing data.

ExpData() provides a quick structural audit of a dataset, reporting its dimensions, variable classes, organization, and missing cases before deeper exploratory analysis. section 17.4.3

Choose the output type according to the question you need to answer:

  • Use type = 1 for a high-level table containing row and column counts, variable-class counts, zero-variance variables, and completeness information. section 17.4.3
  • Use type = 2 for variable-level structure, including names, types, row counts, and missing-value counts. section 17.4.3

Audit a basketball odds dataset

Run ExpData(oddsdf4, type = 1) to see that the dataset has 1,230 rows, 15 variables, nine numeric or integer variables, six factors, and complete cases for all variables. Then run ExpData(oddsdf4, type = 2) to inspect each variable's type, sample size, and missing-count column. section 17.4.3

How Statistics Slam Dunk covers it

How deep it goes
Returns high-level counts or per-variable structure, including missingness; stops short of numerical, categorical, and graphical summaries.
Examples
Uses the oddsdf4 basketball dataset to inspect dimensions, variable classes, complete cases, and per-variable missing counts.
What it assumes
Assumes a prepared data frame and basic familiarity with variables, rows, columns, and missing cases.
What you can do after
Diagnose/debug with it by identifying dataset shape, variable types, structural details, and missingness before analysis.
How it connects
Connects exploratory data analysis with missing data, variable types, data structure, and statistical summaries.
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