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Distinct authors per repo-month, by source and popularity stratum

Source: R/table-activity.R
author_density_tbl.Rd

For one source, count distinct issue authors active per calendar month.

Usage

author_density_tbl(
  issue_authors_tbl,
  repo_tbl,
  source_name,
  n_strata = 4L,
  contrib_threshold = 0.01,
  contrib_min = -Inf,
  window = 12L,
  date_start = as.Date("2015-01-01"),
  date_end = NULL
)

Arguments

issue_authors_tbl

As returned by fetch_issue_authors().

repo_tbl

As returned by build_repo_tbl().

source_name

One of repo_tbl$source ("pypi", "npm", "joss", "ropensci").

n_strata

Number of popularity strata.

contrib_threshold

Issues whose author's contribution is at or below this value count as "non-contributor" issues. Default 0.01 (allow a small nonzero commit share and still call it "non-contributor").

contrib_min

Lower bound (exclusive) on contribution; authors are counted when contrib_min < contribution <= contrib_threshold. Default -Inf (no lower bound, i.e. the non-core-vs-core split is governed by contrib_threshold alone, as in the original non-core headcount).

window

Trailing aggregation window, in months. Default 12: each reported month's n_metric/n_repo_months sum that month and the preceding 11.

date_start, date_end

Date bounds on the analysis window; date_end defaults to the start of the current month.

Value

A tibble with one row per (popularity stratum, month): popularity_stratum, month, n_metric (trailing sum of distinct authors active that month whose contribution fell in (contrib_min, contrib_threshold]), n_repo_months, rate. Carries metric = "issues", window, contrib_threshold, and source_name as attributes - metric is deliberately set to "issues" rather than something like "authors" so the result can be passed straight into plot_activity(), whose y-axis label should then be overridden (e.g. via + ggplot2::labs(y = ...)) since the value isn't actually an issue rate.

Details

contrib_min/contrib_threshold together select which authors count, via contrib_min < contribution <= contrib_threshold: the defaults (-Inf, 0.01) counts only non-core contributors. In contrast, contrib_min = 0.01, contrib_threshold = Inf counts core authors only.

Examples

if (FALSE) { # \dontrun{
ad <- author_density_tbl (issue_authors_tbl, repo_tbl, "pypi")
plot_activity (ad) + ggplot2::labs (y = "Distinct non-core authors")

# All contributors, core and non-core alike:
ad_all <- author_density_tbl (
    issue_authors_tbl, repo_tbl, "pypi",
    contrib_min = -Inf, contrib_threshold = Inf
)
} # }