Run EJAM analysis for several radii and numbers of sitepoints, recording how long each step takes

Usage

speedtest(
  n = 10,
  sitepoints = NULL,
  fips = NULL,
  shapefile = NULL,
  analysis_type = NULL,
  analysis_subtype = NULL,
  weighting = "frs",
  radii = c(1, 3.106856, 5, 10, 31.06856)[1:3],
  avoidorphans = FALSE,
  test_ejamit = FALSE,
  test_getblocksnearby = TRUE,
  test_doaggregate = TRUE,
  test_batch.summarize = FALSE,
  logging = FALSE,
  logfolder = ".",
  logfilename = "log_n_datetime.txt",
  honk_when_ready = TRUE,
  saveoutput = FALSE,
  collect_detailed = FALSE,
  detail_point_counts = c(1L, 2L, 10L),
  detailed_csv = NULL,
  plot = TRUE,
  getblocks_diagnostics_shown = FALSE,
  ...
)

Arguments

n: optional, vector of 1 or more counts of how many random points to test, or set to 0 to interactively pick file of points in RStudio (n is ignored if sitepoints provided)
sitepoints: optional, (use if you do not want random points) data.frame of points or path/file with points, where columns are lat and lon in decimal degrees
fips: optional vector of FIPS codes to time FIPS-based analysis instead of point-buffer analysis. If provided, speedtest() times whole ejamit() runs rather than the point-specific substeps.
shapefile: optional shapefile path or object to time polygon-based analysis instead of point-buffer analysis. If provided, speedtest() times whole ejamit() runs rather than the point-specific substeps.
analysis_type: optional label for the kind of analysis being timed. Usually inferred as "points", "fips", or "shapefile".
analysis_subtype: optional label for the subtype being timed. Usually inferred as "point_buffer", "polygon", or the FIPS type such as "city" or "county".
weighting: optional, if using random points, how to weight them, such as facilities, people, or blockgroups. see testpoints_n()
radii: optional, one or more radius values in miles to use in creating circular buffers when findings residents nearby each of sitepoints. The default list includes one that is 5km (approx 3.1 miles)
avoidorphans: see getblocksnearby() or ejamit() regarding this param
test_ejamit: whether to test only ejamit() instead of its subcomponents like getblocksnearby(), doaggregate(), etc
test_getblocksnearby: whether to include this function in timing - not used because always done
test_doaggregate: whether to include this function in timing
test_batch.summarize: whether to include this function in timing
logging: logical optional, whether to save log file with timings of steps. NOTE this slows it down though.
logfolder: optional, name of folder for log file
logfilename: optional, name of log file to go in folder
honk_when_ready: optional, self-explanatory
saveoutput: but this slows it down if set to TRUE to save each run as .rda file
collect_detailed: if TRUE, also collect a per-run timing table in the schema used by legacy Analysis_timing_results.csv files
detail_point_counts: when collect_detailed = TRUE, ensure runs for these counts are also included when possible. For random-point runs, these values are added to n if they are less than or equal to max(n). For explicit sitepoints, fips, or shapefile inputs, the first k rows or codes are used for each requested k that is less than or equal to the input size.
detailed_csv: optional path to a .csv file where that detailed timing table should be written. If provided, collect_detailed is forced to TRUE.
plot: whether to create plot of results
getblocks_diagnostics_shown: set TRUE to see more details on block counts etc.
...: passed to plotting function

Value

A summary timing table with one row per (points, radius) run. If collect_detailed = TRUE, the returned table also has an attribute called "detailed_results" containing a per-run timing table in the legacy Analysis_timing_results.csv schema.

Details

This is essentially a test script that times each step of EJAM for a large dataset

pick a sample size (n) (or enter sitepoints, or set n=0 to interactively pick file of points in RStudio)
pick n random points
pick a few different radii for circular buffering
analyze indicators in circular buffers and overall (find blocks nearby and then calc indicators)
get stats that summarize those indicators
compare times between steps and radii and other approaches or tools

Examples

if (FALSE) { # \dontrun{
  speedseen_few <- EJAM:::speedtest(c(50, 500), radii = c(1, 3.106856),
    logging = FALSE, honk = FALSE)

  speedseen_nearer_to1k <- EJAM:::speedtest(n = c(1e2, 1e3, 1e4),
    radii = c(1, 3.106856, 5),
    logging=TRUE, honk=FALSE)
  save( speedseen_nearer_to1k, file = "~/../Downloads/speedseen_nearer_to1k.rda")
  rstudioapi::savePlotAsImage(        "~/../Downloads/speedseen_nearer_to1k.png")

  speedseen_all <- EJAM:::speedtest(
    n = c(1e2,1e3,1e4),
    radii=c(1, 3.106856, 5, 10, 31.06856),
    logging=TRUE, honk=TRUE
  )

  EJAM:::speedtest(
    n = c(100, 1000),
    radii = c(1, 3.106856),
    collect_detailed = TRUE,
    detail_point_counts = c(1, 2, 10),
    detailed_csv = "data-raw/Analysis_timing_results_new.csv",
    logging = FALSE,
    honk_when_ready = FALSE,
    plot = FALSE
  )

  EJAM:::speedtest(
    fips = EJAM::fips_counties_from_state_abbrev("DE"),
    collect_detailed = TRUE,
    detail_point_counts = c(1, 3),
    detailed_csv = "data-raw/Analysis_timing_results_fips.csv",
    plot = FALSE,
    honk_when_ready = FALSE
  )

  EJAM:::speedtest(
    shapefile = system.file(
      "testdata/shapes/portland_folder_shp/Neighborhoods_regions.shp",
      package = "EJAM"
    ),
    collect_detailed = TRUE,
    detail_point_counts = c(1, 3, 25),
    detailed_csv = "data-raw/Analysis_timing_results_shapefile.csv",
    plot = FALSE,
    honk_when_ready = FALSE
  )
 } # }

Run EJAM analysis for several radii and numbers of sitepoints, recording how long each step takes

Usage

Arguments

Value

Details

See also

Examples