--- title: "Update to `filter()` behaviour in bcdata v0.4.0" date: "2022-10-28" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Update to `filter()` behaviour in bcdata v0.4.0} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- This vignette describes a change in `{bcdata}` v0.4.0 related to using locally-executed functions in a `filter()` query with `bcdc_query_geodata()`: When using `bcdc_query_geodata()` with `filter()`, many functions are translated to a query plan that is passed to and executed on the server - this includes the CQL Geometry predicates such as `INTERESECTS()`, `CROSSES()`, `BBOX()` etc, as well as many base R functions. However you sometimes want to include a function call in your `filter()` statement which should be evaluated locally - i.e., it's an R function (often an `{sf}` function) with no equivalent function on the server. Prior to version 0.4.0, `{bcdata}` did a reasonable (though not perfect) job of detecting R functions inside a `filter()` statement that needed to be evaluated locally. In order to align with recommended best practices for `{dbplyr}` backends, as of v0.4.0, function calls that are to be evaluated locally now need to be wrapped in `local()`. For example, say we want to create a bounding box around two points and use that box to perform a spatial filter on the remote dataset, to give us just the set of local greenspaces that exist within that bounding box. ```r library(sf) library(bcdata) two_points <- st_sfc(st_point(c(1164434, 368738)), st_point(c(1203023, 412959)), crs = 3005) ``` Previously, we could just do this, with `sf::st_bbox()` embedded in the call: ```r bcdc_query_geodata("local-and-regional-greenspaces") %>% filter(BBOX(st_bbox(two_points, crs = st_crs(two_points)))) ``` ``` ## Error: Unable to process query. Did you use a function that should be evaluated locally? If so, try wrapping it in 'local()'. ``` However you must now use `local()` to force local evaluation of `st_bbox()` on your machine in R, before it is translated into a query plan to be executed on the server: ```r bcdc_query_geodata("local-and-regional-greenspaces") %>% filter(BBOX(local(st_bbox(two_points, crs = st_crs(two_points))))) ``` ``` ## Querying 'local-and-regional-greenspaces' record ## • Using collect() on this object will return 1154 features and 19 fields ## • At most six rows of the record are printed here ## ──────────────────────────────────────────────────────────────────────────────────────────────────── ## Simple feature collection with 6 features and 19 fields ## Geometry type: POLYGON ## Dimension: XY ## Bounding box: xmin: 1200113 ymin: 385903.5 xmax: 1202608 ymax: 386561.8 ## Projected CRS: NAD83 / BC Albers ## # A tibble: 6 × 20 ## id LOCAL…¹ PARK_…² PARK_…³ PARK_…⁴ REGIO…⁵ MUNIC…⁶ CIVIC…⁷ CIVIC…⁸ STREE…⁹ LATIT…˟ LONGI…˟ ## ## 1 WHSE_BASE… 3347 Konuks… Local Green … Capital Distri… 48.5 -123. ## 2 WHSE_BASE… 3304 Local Trail Capital Distri… 48.5 -123. ## 3 WHSE_BASE… 3380 Local Water … Capital Distri… 48.5 -123. ## 4 WHSE_BASE… 3369 Local Water … Capital Distri… 48.5 -123. ## 5 WHSE_BASE… 3453 Local Water … Capital Distri… 48.5 -123. ## 6 WHSE_BASE… 3361 Local Trail Capital Distri… 48.5 -123. ## # … with 8 more variables: WHEN_UPDATED , WEBSITE_URL , LICENCE_COMMENTS , ## # FEATURE_AREA_SQM , FEATURE_LENGTH_M , OBJECTID , SE_ANNO_CAD_DATA , ## # geometry , and abbreviated variable names ¹​LOCAL_REG_GREENSPACE_ID, ²​PARK_NAME, ## # ³​PARK_TYPE, ⁴​PARK_PRIMARY_USE, ⁵​REGIONAL_DISTRICT, ⁶​MUNICIPALITY, ⁷​CIVIC_NUMBER, ## # ⁸​CIVIC_NUMBER_SUFFIX, ⁹​STREET_NAME, ˟​LATITUDE, ˟​LONGITUDE ``` There is another illustration in the ["querying spatial data vignette"](https://bcgov.github.io/bcdata/articles/efficiently-query-spatial-data-in-the-bc-data-catalogue.html#a-note-about-using-local-r-functions-in-constructing-filter-queries).