Print the result of cacs_run() — print.cacs_run

Prints a result of cacs_run() as a header about the run, two tables of the five rates, and a preview of the rows.

Usage

# S3 method for class 'cacs_run_result'
print(x, ..., n_head = 5, n_tail = 3)

Arguments

x: A cacs_run_result object, as returned by cacs_run().
...: Passed to the print method for tibbles (tibble::print.tbl()) for the preview, such as n, the number of rows to show.
n_head, n_tail: Single numbers that have no effect (n in ... sets the number of rows).

Value

x, invisibly.

Details

Each part of the output starts with a title line:

catchmentACS run result: The time the result was created and the version of catchmentACS that created it, the routing service and the drive times given to cacs_run(), and the routing profile of the drive-time areas. The drive times are the ones given in the call, so when the areas come from precomputed_isochrones the line can name a drive time that no row of the result has. The line "Sites" gives the number of sites with at least one drive-time area (success), the number of sites given to cacs_run() in sites (total), and total minus success, but not below 0 (failed). If precomputed_isochrones has areas for sites that are not in sites, success counts them too, so it can exceed total and failed can miss sites without a drive-time area. When tracts were skipped because they have no area, one more line gives the length of the skipped_geoids attribute, which lists each such tract once for each of its variables.
Top 5 rates (cross-site mean +/- sd): One row for each of the five rates, highest mean first. The columns are the unweighted mean (mean_estimate) and the standard deviation (sd_estimate) of the rate over all site and drive-time pairs, leaving out missing values, and n, the number of pairs, including those where the rate is missing.
Rates per site: The estimates of the five rates, with one row for each site and drive time. The table is shown when the result has at most getOption("catchmentACS.summary_per_site_max") sites (12 by default). With 0 it is never shown, and with Inf always.
Tibble preview: The rows, printed as a tibble. Before this title, the line "Wall clock" gives the time that cacs_run() took, in seconds. The last line names as_tibble(x), which returns a plain tibble (see as_tibble.cacs_run_result()), and x[], which is the same object and prints in the same way.

The rate tables need the columns variable, estimate, and moe, so they are not shown for the list-column form of the result. Subsets made with [ or with dplyr verbs such as dplyr::filter(), and tables made with dplyr::count() or dplyr::distinct(), keep the class and are printed in the same way, with the header of the whole result. Printing one that has those three columns but no site_id column gives an error. Without the cacs_run_result_metadata attribute, the result is printed as a plain tibble.

Capturing the output

The tables are ordinary printed output, and every other line is an R message written with the cli package. These calls keep different parts:

capture.output(print(x))                    # the tables
capture.output(print(x), type = "message")  # the cli output
testthat::capture_messages(print(x))        # the cli output, in tests
suppressMessages(print(x))                  # prints only the tables

Examples

# A bundled cacs_run() result for a 10-minute area in Birmingham, Alabama
out <- readRDS(system.file("extdata", "visual_walkthrough_fixture.rds",
                           package = "catchmentACS"))$run_result

# One site and one drive time, so the standard deviations are NA
print(out, n = 5)
#> 
#> ── catchmentACS run result ─────────────────────────────────────────────────────
#> Generated at: 2026-05-29 17:15:15
#> Package version: 0.5.0
#> Provider: osrm / car
#> Drive times: 10 min
#> Sites: 1 success / 0 failed / 1 total
#> 
#> ── Top 5 rates (cross-site mean +/- sd) ──
#> 
#> # A tibble: 5 × 4
#>   variable                  mean_estimate sd_estimate     n
#>   <chr>                             <dbl>       <dbl> <int>
#> 1 labor_force_participation        0.649           NA     1
#> 2 poverty_rate                     0.193           NA     1
#> 3 snap_rate                        0.112           NA     1
#> 4 ssi_rate                         0.0504          NA     1
#> 5 unemp_rate                       0.0424          NA     1
#> ── Rates per site ──
#> 
#> # A tibble: 1 × 7
#>   site_id   drive_time_min poverty_rate snap_rate ssi_rate unemp_rate
#>   <chr>              <int>        <dbl>     <dbl>    <dbl>      <dbl>
#> 1 AL_BHM_01             10        0.193     0.112   0.0504     0.0424
#> # ℹ 1 more variable: labor_force_participation <dbl>
#> Wall clock: 0.2s
#> 
#> ── Tibble preview ──
#> 
#> # A tibble: 19 × 27
#>   site_id   drive_time_min ring_topology variable   estimate   moe weight_sum
#>   <chr>              <int> <chr>         <chr>         <dbl> <dbl>      <dbl>
#> 1 AL_BHM_01             10 cumulative    B01003_001   64227. 2133.       23.3
#> 2 AL_BHM_01             10 cumulative    B11001_001   31139. 1067.       23.3
#> 3 AL_BHM_01             10 cumulative    B17001_001   58947. 2112.       23.3
#> 4 AL_BHM_01             10 cumulative    B17001_002   11403. 1373.       23.3
#> 5 AL_BHM_01             10 cumulative    B19013_001   68803. 5658.       23.3
#> # ℹ 14 more rows
#> # ℹ 20 more variables: n_tracts <int>, n_tracts_num <int>, n_tracts_den <int>,
#> #   provider <chr>, profile <chr>, osm_snapshot_date <chr>, acs_year <int>,
#> #   weight_method <chr>, estimand_family <chr>, weight_basis <chr>,
#> #   moe_formula_requested <chr>, moe_formula_effective <chr>,
#> #   moe_fallback <lgl>, moe_fallback_reason <chr>, failure_origin <chr>,
#> #   weight_uncertainty_propagated <lgl>, est_total <dbl>, …
#> ℹ For full output: `as_tibble(x)` or `x[]`

Print the result of `cacs_run()`