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Generic Explorer

generic-explorer.Rmd

Generic explorer mode is for arbitrary tabular data that does not use the template dashboard schema. Drop a .csv or .rds data frame into data/ and vizRd creates an overview first.

Shipped generic data

The example project includes generic_sales.csv:

example_path <- system.file("extdata", "example_project", package = "vizRd")
generic_path <- file.path(example_path, "data", "generic_sales.csv")
read.csv(generic_path, nrows = 3, check.names = FALSE)
#>         date region product units revenue discounted           notes
#> 1 2025-01-01  North Starter    12   240.0      FALSE     Launch week
#> 2 2025-01-02  South Starter     8   160.0       TRUE  Intro discount
#> 3 2025-01-03   East     Pro     5   375.5      FALSE Channel partner

This file has dates, regions, products, numeric measures, logical-like values, and missing notes. It is intentionally not template-shaped.

Role inference

Role inference classifies columns as numeric, categorical, date/time, logical, text, or unknown. The explorer preserves raw column names and records labels, confidence, reasons, missingness, distinct counts, and deterministic preview rows.

This role inference step is deterministic so the same generic_sales.csv columns receive the same roles across sessions.

ds <- vizRd::load_dataset("generic_sales", example_path)
ds$schema$type
#> [1] "explorer"
ds$explorer$columns[, c("name", "role", "confidence", "reason")]
#>         name        role confidence                                 reason
#> 1       date    datetime       high         Column uses a date/time class.
#> 2     region categorical     medium Text has low cardinality for grouping.
#> 3    product categorical     medium Text has low cardinality for grouping.
#> 4      units     numeric       high            Column uses a numeric type.
#> 5    revenue     numeric       high            Column uses a numeric type.
#> 6 discounted     logical       high Column uses logical TRUE/FALSE values.
#> 7      notes categorical     medium Text has low cardinality for grouping.

Overview first

Explorer datasets open on an overview so users can inspect row counts, column roles, warnings, and a preview before choosing a chart. Empty, zero-column, large, or high-cardinality data is reported through structured warnings rather than blank output.

Generic chart families

Chart eligibility is strict and column-driven. The app enables charts when the selected columns satisfy each contract and gives compact invalid reasons when they do not.

Common labels include:

  • Histogram for one numeric column
  • Count for categorical frequency
  • Scatter for two numeric columns
  • Aggregate for numeric-by-category summaries
  • Time series for date/time trends
  • Boxplot for numeric-by-category comparison
  • Missingness for missing-value overview

For documentation tests and users scanning quickly, the main pairwise and date families are scatter and timeseries-style trends.

Scatter and time series charts use deterministic limits for large data so CRAN examples and tests stay reproducible; the same eligibility path covers timeseries-style date trends.