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## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(leaflet)
library(sf)## Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUE#install.packages("rgbif")
library(rgbif)
library(terra)## terra 1.8.93
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## Attaching package: 'terra'
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## The following object is masked from 'package:tidyr':
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## extractConnect with data housed in gbif (https://www.gbif.org/) to examine migration patterns of a specific species of bird (4 pts). This selection might require some research! This is citizen science data, so you may want to select a species that is known to migrate, easy for humans to identify, and with enough observations in the database. A great resource for looking at GBIF data - [[https://poldham.github.io/abs/gbif.html\\](https://poldham.github.io/abs/gbif.html\){.uri}
TurkeyVulture_gbi <- readr::read_delim("N_TurkeyVulture.csv", delim = "\t", escape_double = FALSE,
col_names = TRUE, na = c("", "NA"))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 1727 Columns: 50
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (32): datasetKey, occurrenceID, kingdom, phylum, class, order, family, ...
## dbl (13): gbifID, individualCount, decimalLatitude, decimalLongitude, coord...
## lgl (3): depth, depthAccuracy, typeStatus
## dttm (2): dateIdentified, lastInterpreted
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. head(TurkeyVulture_gbi)## # A tibble: 6 × 50
## gbifID datasetKey occurrenceID kingdom phylum class order family genus species
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 9.19e8 22a66350-… urn:catalog… Animal… Chord… Aves Acci… Catha… Cath… Cathar…
## 2 9.19e8 22a66350-… urn:catalog… Animal… Chord… Aves Acci… Catha… Cath… Cathar…
## 3 9.19e8 22a66350-… urn:catalog… Animal… Chord… Aves Acci… Catha… Cath… Cathar…
## 4 8.64e8 4bfac3ea-… MCZ:Orn:354… Animal… Chord… Aves Acci… Catha… Cath… Cathar…
## 5 8.64e8 4bfac3ea-… MCZ:Orn:354… Animal… Chord… Aves Acci… Catha… Cath… Cathar…
## 6 8.64e8 4bfac3ea-… MCZ:Orn:333… Animal… Chord… Aves Acci… Catha… Cath… Cathar…
## # ℹ 40 more variables: infraspecificEpithet <chr>, taxonRank <chr>,
## # scientificName <chr>, verbatimScientificName <chr>,
## # verbatimScientificNameAuthorship <chr>, countryCode <chr>, locality <chr>,
## # stateProvince <chr>, occurrenceStatus <chr>, individualCount <dbl>,
## # publishingOrgKey <chr>, decimalLatitude <dbl>, decimalLongitude <dbl>,
## # coordinateUncertaintyInMeters <dbl>, coordinatePrecision <dbl>,
## # elevation <dbl>, elevationAccuracy <dbl>, depth <lgl>, …Display this information (bird observations and
migration) on a map via leaflet - include layers for temperature and
elevation (10 pts). Add additional information that you think
might be for this question. You can find inspiration in the same
location here: https://poldham.github.io/abs/mapgbif.html (as well as
many other sources online)
TV_occurrence <- dplyr::rename(TurkeyVulture_gbi, latitude = decimalLatitude,
longitude = decimalLongitude)
TV_occurrence%>%
group_by(countryCode)## # A tibble: 1,727 × 50
## # Groups: countryCode [21]
## gbifID datasetKey occurrenceID kingdom phylum class order family genus
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 919415692 22a66350-7947… urn:catalog… Animal… Chord… Aves Acci… Catha… Cath…
## 2 919406224 22a66350-7947… urn:catalog… Animal… Chord… Aves Acci… Catha… Cath…
## 3 919404310 22a66350-7947… urn:catalog… Animal… Chord… Aves Acci… Catha… Cath…
## 4 863555909 4bfac3ea-8763… MCZ:Orn:354… Animal… Chord… Aves Acci… Catha… Cath…
## 5 863555908 4bfac3ea-8763… MCZ:Orn:354… Animal… Chord… Aves Acci… Catha… Cath…
## 6 863509449 4bfac3ea-8763… MCZ:Orn:333… Animal… Chord… Aves Acci… Catha… Cath…
## 7 863509427 4bfac3ea-8763… MCZ:Orn:333… Animal… Chord… Aves Acci… Catha… Cath…
## 8 863389017 4bfac3ea-8763… MCZ:Orn:102… Animal… Chord… Aves Acci… Catha… Cath…
## 9 863389008 4bfac3ea-8763… MCZ:Orn:429… Animal… Chord… Aves Acci… Catha… Cath…
## 10 863388984 4bfac3ea-8763… MCZ:Orn:429… Animal… Chord… Aves Acci… Catha… Cath…
## # ℹ 1,717 more rows
## # ℹ 41 more variables: species <chr>, infraspecificEpithet <chr>,
## # taxonRank <chr>, scientificName <chr>, verbatimScientificName <chr>,
## # verbatimScientificNameAuthorship <chr>, countryCode <chr>, locality <chr>,
## # stateProvince <chr>, occurrenceStatus <chr>, individualCount <dbl>,
## # publishingOrgKey <chr>, latitude <dbl>, longitude <dbl>,
## # coordinateUncertaintyInMeters <dbl>, coordinatePrecision <dbl>, … TV_obv_summer <- TV_occurrence %>% filter(countryCode=="US" | countryCode== "CA")
TV_obv_winter <- TV_occurrence %>% filter(countryCode !="US")%>%
filter(countryCode != "CA")
nc_data <- rast("subset.nc")
print(crs(nc_data))## [1] "GEOGCRS[\"WGS 84 (CRS84)\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n CS[ellipsoidal,2],\n AXIS[\"geodetic longitude (Lon)\",east,\n ORDER[1],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n AXIS[\"geodetic latitude (Lat)\",north,\n ORDER[2],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n USAGE[\n SCOPE[\"unknown\"],\n AREA[\"World\"],\n BBOX[-90,-180,90,180]],\n ID[\"OGC\",\"CRS84\"]]"writeRaster(nc_data, filename = "temp.tif", overwrite = TRUE)
temperature_raster <- rast("temp.tif")
downsampled_temp <- aggregate(temperature_raster, fact = 4)
leaflet() %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap)%>%
addProviderTiles(providers$OpenTopoMap) %>%
addRasterImage(downsampled_temp, opacity = .7,color="cyan", group = "Temperature") %>%
addCircleMarkers(data=TV_obv_winter,~longitude, ~latitude, radius=.5,fillOpacity = 0.5, color = "grey",popup = ~paste("Turkey Vulture: Winter", "<br>", eventDate),
group = "Turkey Vulture Winter Observation")%>%
addCircleMarkers(data=TV_obv_summer,~longitude, ~latitude, radius=.5,fillOpacity = 0.5, color = "purple", popup = ~paste("Turkey Vulture", "<br>", eventDate),
group = "Turkey Vulture: Observation")%>%
addLayersControl(overlayGroups = c("Turkey Vulture Winter Observation", "Turkey Vulture: Observation", "Temperature"),
options = layersControlOptions(collapsed = FALSE)
) %>%
addLegend(position = "bottomright",
colors = c("grey", "purple","cyan"),
labels = c("Turkey Vulture: Winter", "Turkey Vulture","Temperature"),
opacity = 1)%>%
addLegend("bottomleft", title = "Elevation",
pal = colorNumeric(palette = c("lightgreen", "green", "darkgreen", "yellow", "orange", "red"), domain = c(0, 2000)),
values = c(0, 2000),
labels = c("0-200m", "201-400m", "401-600m", "601-800m", "801-1000m", "1001-2000m"),opacity = 1) ## Warning in validateCoords(lng, lat, funcName): Data contains 65 rows with
## either missing or invalid lat/lon values and will be ignored## Warning in validateCoords(lng, lat, funcName): Data contains 29 rows with
## either missing or invalid lat/lon values and will be ignoredLabel the three most populous cities near the largest set
of observations for your target species (4 pts)
TV_occurrence%>%
group_by(locality)%>%
count(locality)%>%
arrange(desc(n))## # A tibble: 490 × 2
## # Groups: locality [490]
## locality n
## <chr> <int>
## 1 <NA> 829
## 2 Brownsville 16
## 3 Coronado National Forest (AZ) 14
## 4 Whittier 14
## 5 MANGLAR EL CONCHALITO 12
## 6 San Juan 12
## 7 CHAMETLA 1 11
## 8 ENSENADA DE LA PAZ - AREA 11 11
## 9 Sorrento 11
## 10 Alpine Valley 10
## # ℹ 480 more rows Tucson <- data.frame(lat = 32.2540, lng = -110.9742, name = "Tucson, AZ")
LA <- data.frame(lat = 34.0549, lng = -118.2426, name = "Los Angeles, CA")
Brown <- data.frame(lat = 25.9017, lng = -97.4975, name = "Brownsville, TX")
map<- leaflet() %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap)%>%
addProviderTiles(providers$OpenTopoMap) %>%
addRasterImage(downsampled_temp, opacity = .7,color="cyan", group = "Temperature") %>%
addMarkers(data=Tucson, label = ~name, popup= ~paste(), group= "Cities Near the Most Observations")%>%
addMarkers(data=LA, label = ~name, popup= ~paste(), group= "Cities Near the Most Observations")%>%
addMarkers(data=Brown, label = ~name, popup= ~paste(), group= "Cities Near the Most Observations")%>%
addCircleMarkers(data=TV_obv_winter,~longitude, ~latitude, radius=.5,fillOpacity = 0.5, color = "grey",popup = ~paste("Turkey Vulture: Winter", "<br>", eventDate),
group = "Turkey Vulture Winter Observation")%>%
addCircleMarkers(data=TV_obv_summer,~longitude, ~latitude, radius=.5,fillOpacity = 0.5, color = "purple", popup = ~paste("Turkey Vulture", "<br>", eventDate),
group = "Turkey Vulture: Observation")%>%
addLayersControl(overlayGroups = c("Turkey Vulture Winter Observation", "Turkey Vulture: Observation", "Temperature", "Cities Near the Most Observations"),
options = layersControlOptions(collapsed = FALSE)
) %>%
addLegend(position = "bottomright",
colors = c("grey", "purple","cyan"),
labels = c("Turkey Vulture: Winter", "Turkey Vulture","Temperature"),
opacity = 1)%>%
addLegend("bottomleft", title = "Elevation",
pal = colorNumeric(palette = c("lightgreen", "green", "darkgreen", "yellow", "orange", "red"), domain = c(0, 2000)),
values = c(0, 2000),
labels = c("0-200m", "201-400m", "401-600m", "601-800m", "801-1000m", "1001-2000m"),opacity = 1) ## Warning in validateCoords(lng, lat, funcName): Data contains 65 rows with
## either missing or invalid lat/lon values and will be ignored## Warning in validateCoords(lng, lat, funcName): Data contains 29 rows with
## either missing or invalid lat/lon values and will be ignored## Assuming "lng" and "lat" are longitude and latitude, respectively
## Assuming "lng" and "lat" are longitude and latitude, respectively
## Assuming "lng" and "lat" are longitude and latitude, respectivelymap
Provide appropriate legends to describe the data (2 pts). Describe the choices you made on the map design and representation I chose purple and grey to represent the Turkey Vulture observation because they’re cool colors that don’t blend in with the warm colors of the map and they’re colorblind friendly. I chose cyan for the temperature because it doesn’t blend in with the elevation colors and doesn’t hide the observation points allowing them to still be visible. The elevation colors I chose after setting up map as the colorsin the legion represent the one’s on the map the best. I also made it possible to hide each of the points and visuals so viewers had the option to only see what they need.