Generic by-id loader for any ArcGIS Online Feature Service item
Source:R/datasets_tps_arcgis_hub.R
morie_datasets_arcgis_item_by_id.RdPortal-agnostic sibling to
morie_datasets_tps_arcgis_hub_by_id(). Works for ANY ArcGIS
Online item GUID (not just TPS Hub catalog entries). Same five
format paths (json / geojson / csv / shapefile / fgdb).
Usage
morie_datasets_arcgis_item_by_id(
item_id,
format = "json",
where = "1=1",
max_features = NULL,
layer_idx = 0L,
dest = NULL
)Arguments
- item_id
32-char hex GUID.
- format
One of
"json"(default),"geojson","csv","shapefile","fgdb".- where
Optional SoQL-style WHERE for the FeatureServer query. Default
"1=1".- max_features
Optional row cap.
- layer_idx
Integer layer index (default
0L).- dest
Optional destination path for binary downloads.
Details
The hub_id is ALWAYS resolved live (via the items API) because
there's no included catalog for non-TPS items. If you find
yourself calling this against the same item repeatedly, consider
adding a named wrapper (e.g. the shipped
morie_datasets_toronto_zoning_per_neighbourhood
wraps EsriCanadaEducation's af06159170914808983959df6163fc86
with included fixtures for offline use).
Examples
# \donttest{
df <- try(morie_datasets_arcgis_item_by_id(
"af06159170914808983959df6163fc86",
format = "json"
))
if (!inherits(df, "try-error")) head(df)
#> OBJECTID Neighbourhood Total_Area Total_Population Pop_Males
#> 1 1 Yonge-St.Clair 1.2 11655 5235
#> 2 2 York University Heights 13.2 27715 13580
#> 3 3 Lansing-Westgate 5.4 14640 6950
#> 4 4 Yorkdale-Glen Park 5.9 14685 6750
#> 5 5 Stonegate-Queensway 7.9 24690 11935
#> 6 6 Tam O'Shanter-Sullivan 5.5 27390 12840
#> Pop_Females Pop0_4years Pop5_9years Pop10_14years Pop15_19years Pop20_24years
#> 1 6420 395 345 235 285 645
#> 2 14125 1645 1385 1380 1750 3280
#> 3 7695 845 725 745 795 965
#> 4 7940 640 690 815 835 845
#> 5 12745 1395 1235 1265 1295 1190
#> 6 14555 1460 1380 1370 1560 1765
#> Pop25_29years Pop30_34years Pop35_39years Pop40_44years Pop45_49years
#> 1 1355 1125 900 860 755
#> 2 2815 2120 1800 1820 1940
#> 3 1185 1290 1170 1175 1150
#> 4 890 895 860 1050 1085
#> 5 1270 1575 1865 1990 2190
#> 6 1665 1605 1850 1965 2230
#> Pop50_54years Pop55_59years Pop60_64years Pop65_69years Pop70_74years
#> 1 715 760 820 755 525
#> 2 1750 1335 1150 870 890
#> 3 1035 855 750 515 405
#> 4 1030 850 765 610 690
#> 5 2120 1790 1515 940 830
#> 6 1925 1655 1435 1235 1220
#> Pop75_79years Pop80_84years Pop85yearsandover Seniors55andover
#> 1 445 365 395 4075
#> 2 825 545 395 6010
#> 3 360 330 360 3565
#> 4 755 690 700 5070
#> 5 755 635 825 7295
#> 6 1190 945 970 8595
#> Seniors65andover Child0_14 Youth15_24 HomeLanguageCategory Language_Chinese
#> 1 2505 985 925 11445 250
#> 2 3545 4405 5040 26340 1750
#> 3 1960 2300 1755 14285 995
#> 4 3450 2140 1690 13685 640
#> 5 3985 3895 2465 23980 330
#> 6 5520 4170 3305 26195 8220
#> Language_Italian Language_Korean Language_Persian Language_Portuguese
#> 1 120 55 120 55
#> 2 2280 265 370 285
#> 3 165 580 630 90
#> 4 2950 50 70 810
#> 5 755 135 65 555
#> 6 340 170 420 70
#> Language_Russian Language_Spanish Language_Tagalog Language_Tamil
#> 1 180 230 95 10
#> 2 430 1475 930 1175
#> 3 770 320 460 15
#> 4 85 625 605 120
#> 5 635 455 260 25
#> 6 65 315 820 1150
#> Language_Urdu
#> 1 10
#> 2 580
#> 3 45
#> 4 60
#> 5 35
#> 6 780
# }