uploading climate data

BirdsCanada · Nov 14, 2024 · d5943d4 · d5943d4
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diff --git a/RELEASE.R b/RELEASE.R
@@ -83,6 +83,7 @@ pkgdown::build_article("region-areas")
 pkgdown::build_article("articles/region-spatial")
 pkgdown::build_article("articles/2.1-SpatialDataExploration")
 pkgdown::build_article("articles/2.2-ConservationAreas")
+pkgdown::build_article("articles/2.3-ClimateData")
 
 # Don't push resources
 unlink("vignettes/articles/BCR_Terrestrial/", recursive = TRUE)

diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -72,6 +72,7 @@ articles:
   contents:
   - '`articles/2.1-SpatialDataExploration`'
   - '`articles/2.2-ConservationAreas`'
+  - '`articles/2.3-ClimateData`'
 - title: Additional Tutorials
   desc: How-to articles describing common tasks
   contents:

diff --git a/figure/average_temp-1.png b/figure/average_temp-1.png
diff --git a/figure/cedarwaxwing_map-1.png b/figure/cedarwaxwing_map-1.png
diff --git a/figure/ontario_kbas-1.png b/figure/ontario_kbas-1.png
diff --git a/figure/scatterplot_bio1-1.png b/figure/scatterplot_bio1-1.png
diff --git a/figure/unnamed-chunk-16-1.png b/figure/unnamed-chunk-16-1.png
diff --git a/figure/unnamed-chunk-17-1.png b/figure/unnamed-chunk-17-1.png
diff --git a/figure/unnamed-chunk-22-1.png b/figure/unnamed-chunk-22-1.png
diff --git a/figure/unnamed-chunk-28-1.png b/figure/unnamed-chunk-28-1.png
diff --git a/vignettes/articles/2.1-SpatialDataExploration.Rmd b/vignettes/articles/2.1-SpatialDataExploration.Rmd
@@ -1,7 +1,6 @@
 ---
 title: "Module 1 - Spatial Data Exploration"
-author: Dimitrios Markou
-date: "2024-11-11"
+date: "2024-11-14"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Module 1 - Spatial Data Exploration}
@@ -183,6 +182,21 @@ ggplot(data = cedar_waxwing_june_sf) +
 
 ```
 ## Zoom: 5
+```
+
+```
+## Fetching 2 missing tiles
+```
+
+```
+##   |                                                                                                                                      |                                                                                                                              |   0%  |                                                                                                                                      |===============================================================                                                               |  50%  |                                                                                                                                      |==============================================================================================================================| 100%
+```
+
+```
+## ...complete!
+```
+
+```
 ## Zoom: 5
 ## Zoom: 5
 ## Zoom: 5

diff --git a/vignettes/articles/2.2-ConservationAreas.Rmd b/vignettes/articles/2.2-ConservationAreas.Rmd
@@ -1,7 +1,6 @@
 ---
 title: "Module 2 - Conservation Areas"
-author: Dimitrios Markou
-date: "2024-11-11"
+date: "2024-11-14"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Module 2 - Conservation Areas}
@@ -62,10 +61,6 @@ getwd()
 It helps if you assign these files a meaningful name! We can read in our KBA polygons using the `sf` package once it is in your working directory.
 
 
-``` r
-ontario_kba <- sf::st_read("C:/Users/dimit/Birds Canada/Data/kba/provontario_kba/ontario_kba.shp")
-```
-
 ```
 ## Reading layer `ontario_kba' from data source `C:\Users\dimit\Birds Canada\Data\kba\provontario_kba\ontario_kba.shp' using driver `ESRI Shapefile'
 ## Simple feature collection with 198 features and 8 fields
@@ -111,21 +106,13 @@ kba_attributes <- kba_attributes %>%
          "Species")
 ```
 
-```
-## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'select': object 'kba_attributes' not found
-```
-
 Both dataframes now contain unique columns, after our selection. We apply the `full_join()` function to hold all attributes within one dataframe.
 
 
 ``` r
 ontario_kba <- full_join(ontario_kba, kba_attributes, by = "SiteCode")
 ```
 
-```
-## Error in eval(expr, envir, enclos): object 'kba_attributes' not found
-```
-
 To visualize the **ontario_kba** data we can use `ggplot()`.
 
 
@@ -150,17 +137,31 @@ ggplot() +
 ## Zoom: 4
 ```
 
-![plot of chunk ontario_kbas](figure/ontario_kbas-1.png)
+```
+## Fetching 2 missing tiles
+```
+
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 
-![plot of chunk ontario_kbas](figures/spatdatatutorial_ontario_kbas-1.png)
 
-We can also visualize the **ontario_kba** data with an interactive map, using the `leaflet` package.
 
 
-``` r
-leaflet(width = "100%") %>%
-  addTiles() %>%
-  addPolygons(data=ontario_kba,color = "black", weight = 2, smoothFactor = 1,opacity = 1.0, fillOpacity = 0.5, fillColor = "red") %>% addFullscreenControl() %>%
   addLegend(colors = c("red"),labels = c("Ontario KBAs"),position = "bottomright")
 ```
 
@@ -250,6 +251,10 @@ atlas_on_sf <- sf::st_as_sf(atlas_on,
                         coords = c("longitude", "latitude"), crs = 4326)
 ```
 
+```
+## Error in eval(expr, envir, enclos): object 'atlas_on' not found
+```
+
 Now let's ensure that the conversion was successful. You'll notice a new geometry column where each observation is a point.
 
 
@@ -271,6 +276,10 @@ It can also be used to ensure that the CRS of our spatial objects match.
 atlas_on_sf <- st_transform(atlas_on_sf, crs = st_crs(ontario_kba))
 ```
 
+```
+## Error in eval(expr, envir, enclos): object 'atlas_on_sf' not found
+```
+
 To identify Wood Ducks observed across Ontario KBA's, we can apply the `st_intersection()` function.
 
 
@@ -279,7 +288,7 @@ wood_ducks_kba <- sf::st_intersection(ontario_kba, atlas_on_sf)
 ```
 
 ```
-## Warning: attribute variables are assumed to be spatially constant throughout all geometries
+## Error in eval(expr, envir, enclos): object 'atlas_on_sf' not found
 ```
 
 You will get a `warning` message, which you can safely ignore.
@@ -291,6 +300,10 @@ ontario_kba <- st_transform(ontario_kba, crs = 4326)
 wood_ducks_kba <- st_transform(wood_ducks_kba, crs = 4326)
 ```
 
+```
+## Error in eval(expr, envir, enclos): object 'wood_ducks_kba' not found
+```
+
 Using `ggplot`, we can visualize the polygon data and point data.
 
 
@@ -316,11 +329,9 @@ ggplot() +
 ```
 
 ```
-## Zoom: 9
+## Error in eval(expr, envir, enclos): object 'ontario_kba_LP' not found
 ```
 
-![plot of chunk longpoint_kba](figure/longpoint_kba-1.png)
-
 ![plot of chunk longpoint_kba](figures/spatdatatutorial_longpoint_kba-1.png)
 
 You can also apply `leaflet` to create an interactive map, using the `addPolygons()` `addCircleMarkers()` arguments.
@@ -351,9 +362,6 @@ After geoprocessing our data in R, we can write out any sf objects to Shapefiles
 First, lets create a path to our downloaded Priority Place file.
 
 
-``` r
-gdb_path <- "C:/Users/dimit/Birds Canada/Data/priority_places/priorityplaces.gdb"
-```
 
 
 ``` r
@@ -407,6 +415,10 @@ Then reproject the Wood Duck data to match our Priority Place using `st_transfor
 atlas_on_sf <- st_transform(atlas_on_sf, crs = st_crs(long_point_polygon))
 ```
 
+```
+## Error in eval(expr, envir, enclos): object 'atlas_on_sf' not found
+```
+
 Next, we'll apply our geoprocessing function to find the Wood Duck observations that intersect with our chosen Priority Place.
 
 
@@ -415,7 +427,7 @@ wood_ducks_longpoint <- sf::st_intersection(long_point_polygon, atlas_on_sf)
 ```
 
 ```
-## Warning: attribute variables are assumed to be spatially constant throughout all geometries
+## Error in eval(expr, envir, enclos): object 'atlas_on_sf' not found
 ```
 
 Finally, we'll transform our spatial objects one more time before visualizing them.
@@ -430,6 +442,10 @@ long_point_polygon <- st_transform(long_point_polygon, crs = 4326)
 wood_ducks_longpoint <- st_transform(wood_ducks_longpoint, crs = 4326)
 ```
 
+```
+## Error in eval(expr, envir, enclos): object 'wood_ducks_longpoint' not found
+```
+
 Use `ggplot` to visualize the polygon and point data.
 
 
@@ -457,11 +473,9 @@ ggplot() +
 ```
 
 ```
-## Zoom: 9
+## Error in eval(expr, envir, enclos): object 'wood_ducks_longpoint' not found
 ```
 
-![plot of chunk longpoint_prioriplace](figure/longpoint_prioriplace-1.png)
-
 ![plot of chunk longpoint_prioriplace](figures/spatdatatutorial_longpoint_prioriplace-1.png)
 
 After geoprocessing our data in R, we can write out any sf objects to Shapefiles on a disk, where the argument delete_layer = TRUE is used to overwrite a file.