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Get Raw Benchmark Scores for Artist or Collection

Source: R/get-raw-scores.R
get_raw_scores.Rd

Retrieves percentile benchmark scores for an artist from the database. This function allows understanding of stylistic evolution and whether there is a trend over time with metrics associated with the artist's creations. It also helps identify phase changes or shifts in artistic approach along various benchmark dimensions.

Usage

get_raw_scores(artist, cn = NULL)

Arguments

artist

Character string. The artist UUID to query.

cn

Database connection object. Optional.

Value

A data.table in long format with the following columns:

  • artist_uuid: Artist UUID

  • art_uuid: Artwork UUID

  • collection_uuid: Collection UUID

  • start_date: Date the artwork was started

  • metric: Factor with 15 levels representing different benchmark metrics (e.g., "drawing_hours", "brush_strokes", "ave_bpm", etc.)

  • score: Numeric percentile score (0-100) for the metric

The returned data.table is keyed by artist_uuid, art_uuid, and collection_uuid, and sorted by start_date.

Details

The function queries 15 different percentile benchmark metrics including:

  • drawing_hours: Time spent on artwork

  • brush_strokes: Number of brush strokes

  • ave_bpm: Average brushstrokes per minute

  • color_generation_rate: Rate of new color introduction

  • early_late_color_ratio: Ratio of early to late color usage

  • ave_blend_rate: Average blending rate

  • n_unique_colors: Number of unique colors used

  • share_of_spectrum: Coverage of color spectrum

  • strokes_per_unique_color: Efficiency of color usage

  • frame_color_stability: Consistency of colors across frames

  • ave_colors_pstroke: Colors per stroke

  • brush_density: Spatial density of brush strokes

  • q75_color_freq: 75th percentile of color frequency

  • frame_color_variance: Variance in colors per frame

  • technique_phase_count: Number of technique phases

See also

Other utility: get_raw_styles()

Examples

if (FALSE) { # \dontrun{
# Get benchmark scores for an artist
artist <- "746b8207-72f5-4ab6-8d19-a91d03daec3d"
score_data <- get_raw_scores(artist = artist)

# Using an existing connection
cn <- artcore::dbc()
score_data <- get_raw_scores(artist = artist, cn = cn)
artcore::dbd(cn)

# View structure
str(score_data)

# Calculate average scores by date and metric
sumScores <- score_data[, list(avg = mean(score)), keyby = list(start_date, metric)]
} # }