dataProfilerR
Automated Exploratory Data Analysis and Dataset Profiling
Profiles a data frame with minimal input: column type inference, missing-value analysis, distributional summary statistics (including skewness and kurtosis), normality tests, outlier detection, correlation and categorical-association analysis, date-column profiling, grouped comparisons and an overall data-quality score, alongside a set of 'ggplot2' visualisations. A single entry point, profile_data(), returns a structured S3 object holding metadata, statistics, diagnostics and plots, with print(), summary() and plot() methods, and report() renders the whole profile to a self-contained HTML file. Statistical methods include the Shapiro-Wilk normality test as implemented by Royston (1995) <doi:10.2307/2986146> and the Anderson-Darling test following Stephens (1974) <doi:10.1080/01621459.1974.10480196>, with power comparisons of these tests in Yap and Sim (2011) <doi:10.1080/00949655.2010.520163>, and the categorical association measure of Cramer (1946, ISBN:9780691080048).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1 |
rolling linux/jammy R-4.5 | dataProfilerR_0.2.1.tar.gz |
157.1 KiB |
0.2.1 |
rolling linux/noble R-4.5 | dataProfilerR_0.2.1.tar.gz |
156.8 KiB |
0.2.1 |
rolling source/ R- | dataProfilerR_0.2.1.tar.gz |
66.8 KiB |
0.2.1 |
latest linux/jammy R-4.5 | dataProfilerR_0.2.1.tar.gz |
157.1 KiB |
0.2.1 |
latest linux/noble R-4.5 | dataProfilerR_0.2.1.tar.gz |
156.8 KiB |
0.2.1 |
latest source/ R- | dataProfilerR_0.2.1.tar.gz |
66.8 KiB |
0.2.1 |
2026-04-23 source/ R- | dataProfilerR_0.2.1.tar.gz |
0 B |