Fix urls, precompile vignettes

vignettes/bcdata.Rmd.orig

@@ -48,7 +48,7 @@ library(ggplot2)
 
 The `bcdata` [R](https://www.r-project.org/) package contains functions for searching & retrieving data from the [B.C. Data Catalogue]( https://catalogue.data.gov.bc.ca).
 
-The [B.C. Data Catalogue](https://www2.gov.bc.ca/gov/content?id=79B5224167334667A44C9E8B5143D0C5) is the place to find British Columbia Government data, applications and web services. Much of the data are released under the [Open Government Licence --- British Columbia](https://www2.gov.bc.ca/gov/content/data/open-data/open-government-licence-bc), as well as numerous other [licences](https://catalogue.data.gov.bc.ca/dataset?download_audience=Public).
+The [B.C. Data Catalogue](https://www2.gov.bc.ca/gov/content?id=79B5224167334667A44C9E8B5143D0C5) is the place to find British Columbia Government data, applications and web services. Much of the data are released under the [Open Government Licence --- British Columbia](https://www2.gov.bc.ca/gov/content/data/policy-standards/open-data/open-government-licence-bc), as well as numerous other [licences](https://catalogue.data.gov.bc.ca/dataset?download_audience=Public).
 
 
 

vignettes/efficiently-query-spatial-data-in-the-bc-data-catalogue.Rmd.orig

@@ -220,7 +220,7 @@ This output is what being created by the dplyr code outlined above.
 
 ## Using B.C. Geographic Warehouse (BCGW) layer names
 
-If you are familiar with the [B.C. Geographic Warehouse (BCGW)](https://www2.gov.bc.ca/gov/content/data/geographic-data-services/bc-spatial-data-infrastructure/bc-geographic-warehouse),
+If you are familiar with the [B.C. Geographic Warehouse (BCGW)](https://www2.gov.bc.ca/gov/content/data/finding-and-sharing/bc-geographic-warehouse),
 you may already know the name of a layer that you want from the BCGW.
 `bcdc_query_geodata()` (as well as all other related functions)
 supports supplying that name directly. For example, the

vignettes/explore-silviculture-data-using-bcdata.Rmd

@@ -1,6 +1,6 @@
 ---
 title: "Exploring Silviculture Data with bcdata"
-date: "2022-10-28"
+date: "2024-12-11"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Exploring Silviculture Data with bcdata}
@@ -47,7 +47,7 @@ Let's use the `bcdata` package to search, retrieve and explore the RESULTS silvi
 To start, let's load the `bcdata` package. We will also load the `dplyr` and `ggplot2` packages to help us work with the data. You can learn more about the `dplyr` package [here](https://dplyr.tidyverse.org/) and the `ggplot2` package [here](https://ggplot2.tidyverse.org/):
 
 
-```r
+``` r
 library(bcdata)
 library(dplyr)
 library(ggplot2)
@@ -59,7 +59,7 @@ library(ggplot2)
 We can gather the data we need to answer our question using the [RESULTS - silviculture forest cover dataset](https://catalogue.data.gov.bc.ca/dataset/258bb088-4113-47b1-b568-ce20bd64e3e3). First, let's take a look at the metadata record using `bcdc_get_record()`:
 
 
-```r
+``` r
 # Get the metadata using the human-readable record name
 bcdc_get_record("results-forest-cover-silviculture")
 #> B.C. Data Catalogue Record: RESULTS - Forest Cover Silviculture
@@ -84,12 +84,12 @@ bcdc_get_record("results-forest-cover-silviculture")
 We see that this is a [Web Feature Service-enabled](https://en.wikipedia.org/wiki/Web_Feature_Service) geospatial data set--the list of data resources includes `WMS getCapabilities request`--so we can query and retrieve this geospatial data set using `bcdc_query_geodata()`:
 
 
-```r
+``` r
 # Query the data using the permanent ID of the record to guard against name changes
 bcdc_query_geodata("258bb088-4113-47b1-b568-ce20bd64e3e3")
 #> Querying 'results-forest-cover-silviculture' record
-#> • Using collect() on this object will return 920718 features and 159 fields
-#> • Accessing this record requires pagination and will make 921 separate requests to the WFS.
+#> • Using collect() on this object will return 957189 features and 159 fields
+#> • Accessing this record requires pagination and will make 96 separate requests to the WFS.
 #> • See ?bcdc_options
 #> • At most six rows of the record are printed here
 #> ────────────────────────────────────────────────────────────────────────────────────────────────────
@@ -99,21 +99,21 @@ bcdc_query_geodata("258bb088-4113-47b1-b568-ce20bd64e3e3")
 #> Bounding box:  xmin: 1184167 ymin: 526455.5 xmax: 1801754 ymax: 1083545
 #> Projected CRS: NAD83 / BC Albers
 #> # A tibble: 6 × 160
-#>   id         FORES…¹ STOCK…² OPENI…³ STAND…⁴ SILV_…⁵ SILV_…⁶ SILV_…⁷ SILV_…⁸ STOCK…⁹ STOCK…˟ STOCK…˟
-#>   <chr>        <int>   <int>   <int> <chr>   <chr>     <dbl>   <dbl>   <int> <chr>   <chr>   <chr>  
-#> 1 WHSE_FORE… 4177991 2243439 1724262 1       C           1.6     1.6       0 IMM     ART     <NA>   
-#> 2 WHSE_FORE… 3994007      NA 1248495 <NA>    91          1.2     1.2       0 MAT     NAT     <NA>   
-#> 3 WHSE_FORE… 3994067      NA 1120935 <NA>    86          0.6     0.6       0 MAT     NAT     <NA>   
-#> 4 WHSE_FORE… 3994009 1404558 1248495 A       AA         22.1    22.1       0 IMM     ART     <NA>   
-#> 5 WHSE_FORE… 3994057 1211894 1120935 B       2B         16.9    16.9       0 IMM     ART     <NA>   
-#> 6 WHSE_FORE… 3994063      NA 1120935 <NA>    82          0.1     0.1       0 MAT     NAT     <NA>   
-#> # … with 148 more variables: SILV_RESERVE_CODE <chr>, SILV_RESERVE_OBJECTIVE_CODE <chr>,
+#>   id         FOREST_COVER_ID STOCKING_STANDARD_UN…¹ OPENING_ID STANDARDS_UNIT_ID SILV_POLYGON_NUMBER
+#>   <chr>                <int>                  <int>      <int> <chr>             <chr>              
+#> 1 WHSE_FORE…         4177991                2243439    1724262 1                 C                  
+#> 2 WHSE_FORE…         3994007                     NA    1248495 <NA>              91                 
+#> 3 WHSE_FORE…         3994067                     NA    1120935 <NA>              86                 
+#> 4 WHSE_FORE…         3994009                1404558    1248495 A                 AA                 
+#> 5 WHSE_FORE…         3994057                1211894    1120935 B                 2B                 
+#> 6 WHSE_FORE…         3994063                     NA    1120935 <NA>              82                 
+#> # ℹ abbreviated name: ¹​STOCKING_STANDARD_UNIT_ID
+#> # ℹ 154 more variables: SILV_POLYGON_AREA <dbl>, SILV_POLYGON_NET_AREA <dbl>,
+#> #   SILV_NON_MAPPED_AREA <int>, STOCKING_STATUS_CODE <chr>, STOCKING_TYPE_CODE <chr>,
+#> #   STOCKING_CLASS_CODE <chr>, SILV_RESERVE_CODE <chr>, SILV_RESERVE_OBJECTIVE_CODE <chr>,
 #> #   TREE_COVER_PATTERN_CODE <chr>, REENTRY_YEAR <chr>, REFERENCE_YEAR <int>, SITE_INDEX <int>,
 #> #   SITE_INDEX_SOURCE_CODE <chr>, BGC_ZONE_CODE <chr>, BGC_SUBZONE_CODE <chr>, BGC_VARIANT <chr>,
-#> #   BGC_PHASE <chr>, BEC_SITE_SERIES <chr>, BEC_SITE_TYPE <chr>, BEC_SERAL <chr>,
-#> #   IS_SILV_IMPLIED_IND <chr>, FOREST_COVER_SILV_TYPE <chr>, S_FOREST_COVER_LAYER_ID <int>,
-#> #   S_TOTAL_STEMS_PER_HA <chr>, S_TOTAL_WELL_SPACED_STEMS_HA <int>,
-#> #   S_WELL_SPACED_STEMS_PER_HA <int>, S_FREE_GROWING_STEMS_PER_HA <int>, …
+#> #   BGC_PHASE <chr>, BEC_SITE_SERIES <chr>, BEC_SITE_TYPE <chr>, BEC_SERAL <chr>, …
 ```
 
 This query shows that this data set has many features and over 150 fields.  Each feature is a treatment unit within a harvested opening, and contains information on the leading five tree species that are present in each treatment unit, including stems per hectare, age, and height.
@@ -128,7 +128,7 @@ To address our question, we need the treatment data (1) from the Prince George N
 First, we can use the `bcdata` package to download the spatial boundary for the Prince George Natural Resource District&mdash;`DPG` is the `ORG_UNIT` for Prince George Natural Resource District:
 
 
-```r
+``` r
 ## Create a spatial feature object named dpg
 dpg <- bcdc_query_geodata("natural-resource-nr-district") %>%
   filter(ORG_UNIT=="DPG") %>% # filter for Prince George Natural Resource District
@@ -138,21 +138,24 @@ dpg <- bcdc_query_geodata("natural-resource-nr-district") %>%
 Let's plot this spatial object and double check we have we what we need:
 
 
-```r
+``` r
 dpg %>%
   ggplot() +
   geom_sf() +
   theme_minimal()
 ```
 
+<div class="figure">
 <img src="vignette-fig-plot-dpg-1.png" alt="plot of chunk plot-dpg" width="100%" />
+<p class="caption">plot of chunk plot-dpg</p>
+</div>
 
 Now we have a spatial object that we can use as a bounding box to filter and download records in the RESULTS - silviculture layer from the Prince George Natural Resource District.
 
 We only need to download the treatments that have western larch planted. We can use the `bcdc_describe_feature()` helper function to examine the column names and types of the layer. In this case, we want to keep rows where the five `S_SPECIES_CODE_*` columns contain `"LW"`, the code for western larch.
 
 
-```r
+``` r
 # Make a vector of tree species we are interested in
 # (in this case only LW for western larch)
 spp_list = c("LW")
@@ -177,23 +180,26 @@ trees_dpg <-
 Let's look at the dimensions of this now much more manageable data object we have downloaded from the B.C. Data Catalogue:
 
 
-```r
+``` r
 dim(trees_dpg)
-#> [1] 212 160
+#> [1] 261 160
 ```
 
 We can see there are several treatment units planted with western larch, and we can make a quick map of these harvested openings for the Prince George Natural Resource District:
 
 
-```r
+``` r
 trees_dpg %>%
   ggplot() +
   geom_sf() +
   geom_sf(data = dpg, fill = NA) + #add the DPG spatial boundary
   theme_minimal()
 ```
 
+<div class="figure">
 <img src="vignette-fig-map-larch-plantations-dpg-1.png" alt="plot of chunk map-larch-plantations-dpg" width="100%" />
+<p class="caption">plot of chunk map-larch-plantations-dpg</p>
+</div>
 
 
 We can also create some quick descriptive summaries of the data, treating the geospatial attribute table as a data frame in R, and answer our original question---how much western larch has been planted in the Prince George Natural Resource District?
@@ -202,7 +208,7 @@ We can also create some quick descriptive summaries of the data, treating the ge
 #### What is the size and age distribution of larch plantations in the Prince George Natural Resource District in the year 2020?
 
 
-```r
+``` r
 trees_dpg %>%
   mutate(age = 2020 - REFERENCE_YEAR + S_SPECIES_AGE_1) %>% #create a plantation age column
   ggplot() +  #start a plot
@@ -220,15 +226,18 @@ trees_dpg %>%
   theme(legend.position = "none")
 ```
 
+<div class="figure">
 <img src="vignette-fig-unnamed-chunk-1-1.png" alt="plot of chunk unnamed-chunk-1" width="100%" />
+<p class="caption">plot of chunk unnamed-chunk-1</p>
+</div>
 
 #### What is the Biogeoclimatic Ecosystem Classification (BEC) distribution of western larch plantations in the Prince George Natural Resource District?
 
 We can download [British Columbia biogeoclimatic (BEC) data](https://catalogue.data.gov.bc.ca/dataset/bec-map) from the B.C. Data Catalogue using `bcdata` and join to our existing `trees_dpg` geospatial dataset using the `st_join` function from the `sf` package. You can learn more about `sf` [here](https://r-spatial.github.io/sf/).
 
 
 
-```r
+``` r
 library(sf) #load the sf package
 
 # Load the BEC data for Prince George Natural Resource District
@@ -245,7 +254,7 @@ trees_bec_dpg <- trees_dpg %>%
 Now, we can summarize the area planted with western larch by biogeoclimatic unit:
 
 
-```r
+``` r
 trees_bec_dpg %>%
   group_by(MAP_LABEL) %>% # group polygons by biogeoclimatic unit
   summarise(Area = sum(FEATURE_AREA_SQM)/10000) %>%
@@ -262,4 +271,7 @@ trees_bec_dpg %>%
   theme(legend.position = "none")
 ```
 
+<div class="figure">
 <img src="vignette-fig-unnamed-chunk-3-1.png" alt="plot of chunk unnamed-chunk-3" width="100%" />
+<p class="caption">plot of chunk unnamed-chunk-3</p>
+</div>

vignettes/local-filter.Rmd

@@ -1,6 +1,6 @@
 ---
 title: "Update to `filter()` behaviour in bcdata v0.4.0"
-date: "2022-10-28"
+date: "2024-12-11"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Update to `filter()` behaviour in bcdata v0.4.0}
@@ -33,7 +33,7 @@ bounding box.
 
 
 
-```r
+``` r
 library(sf)
 library(bcdata)
 
@@ -46,49 +46,50 @@ Previously, we could just do this, with `sf::st_bbox()`
 embedded in the call:
 
 
-```r
+``` 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()'.
+## Error: Error : Cannot translate a <sfc_POINT> object to SQL.
+## ℹ Do you want to force evaluation in R with (e.g.) `!!x` or `local(x)`?
 ```
 
 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
+``` 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
+## • Using collect() on this object will return 1158 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
+## Bounding box:  xmin: 1200113 ymin: 385903.5 xmax: 1202130 ymax: 388026
 ## 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…˟
-##   <chr>        <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>     <dbl>   <dbl>
-## 1 WHSE_BASE…    3347 Konuks… Local   Green … Capital Distri… <NA>    <NA>    <NA>       48.5   -123.
-## 2 WHSE_BASE…    3304 <NA>    Local   Trail   Capital Distri… <NA>    <NA>    <NA>       48.5   -123.
-## 3 WHSE_BASE…    3380 <NA>    Local   Water … Capital Distri… <NA>    <NA>    <NA>       48.5   -123.
-## 4 WHSE_BASE…    3369 <NA>    Local   Water … Capital Distri… <NA>    <NA>    <NA>       48.5   -123.
-## 5 WHSE_BASE…    3453 <NA>    Local   Water … Capital Distri… <NA>    <NA>    <NA>       48.5   -123.
-## 6 WHSE_BASE…    3361 <NA>    Local   Trail   Capital Distri… <NA>    <NA>    <NA>       48.5   -123.
-## # … with 8 more variables: WHEN_UPDATED <date>, WEBSITE_URL <chr>, LICENCE_COMMENTS <chr>,
-## #   FEATURE_AREA_SQM <dbl>, FEATURE_LENGTH_M <dbl>, OBJECTID <int>, SE_ANNO_CAD_DATA <chr>,
-## #   geometry <POLYGON [m]>, 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
+##   id      LOCAL_REG_GREENSPACE…¹ PARK_NAME PARK_TYPE PARK_PRIMARY_USE REGIONAL_DISTRICT MUNICIPALITY
+##   <chr>                    <int> <chr>     <chr>     <chr>            <chr>             <chr>       
+## 1 WHSE_B…                    689 Cranford… Local     Water Access     Capital           District of…
+## 2 WHSE_B…                    634 <NA>      Local     Water Access     Capital           District of…
+## 3 WHSE_B…                    725 <NA>      Local     Water Access     Capital           District of…
+## 4 WHSE_B…                    665 Konukson… Local     Green Space      Capital           District of…
+## 5 WHSE_B…                    622 <NA>      Local     Trail            Capital           District of…
+## 6 WHSE_B…                    698 <NA>      Local     Water Access     Capital           District of…
+## # ℹ abbreviated name: ¹​LOCAL_REG_GREENSPACE_ID
+## # ℹ 13 more variables: CIVIC_NUMBER <chr>, CIVIC_NUMBER_SUFFIX <chr>, STREET_NAME <chr>,
+## #   LATITUDE <dbl>, LONGITUDE <dbl>, WHEN_UPDATED <date>, WEBSITE_URL <chr>,
+## #   LICENCE_COMMENTS <chr>, FEATURE_AREA_SQM <dbl>, FEATURE_LENGTH_M <dbl>, OBJECTID <int>,
+## #   SE_ANNO_CAD_DATA <chr>, geometry <POLYGON [m]>
 ```
 
 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).