06_r-packages-to-download-open-spatial-data.Rmd

# R packages to download open spatial data

**Learning objectives:**

- find the right package for downloading spatial data

## Packages

- rnaturalearth (boundaries)
- geodata (climate, elevation, land use, soil, crop, species occurrence, administrative boundaries, and other data)
- chirps (daily high-resolution precipitation, as well as daily maximum and minimum temperatures)
- elevatr (elevation data from Amazon Web Services)
- osmdata (roads, shops, railway stations, and much more)
- wbstats (global socio-economic data spanning several decades)
- spocc (species occurrence data sources including Global Biodiversity Information)
- wopr (estimates of population sizes for specific geographic areas)
- rdhs (Demographic and Health Survey (DHS))
- malariaAttlas (global malaria data)
- openair (air quality data and other atmospheric composition data)
- spatstat (point pattern data)
- spData (includes diverse spatial datasets )
- ggmap (geocode place names or addresses)
- opencage (convert names to geographic coordinates)
- tidycensus
- tigris
- mapSpain
- goebr
- giscoR

### Example with rnaturalearth
```{r eval=FALSE}
# install.packages("devtools")
# devtools::install_github("ropensci/rnaturalearthhires")

library(rnaturalearth)
library(sf)
library(ggplot2)
library(viridis)
library(patchwork)

map1 <- ne_countries(type = "countries", 
                     country = "Germany",
                     scale = "medium", 
                     returnclass = "sf")
map2 <- rnaturalearth::ne_states("Germany", 
                                 returnclass = "sf")
p1 <- ggplot(map1) + geom_sf()
p2 <- ggplot(map2) + geom_sf()
p1 + p2
```

## geodata
```{r eval=FALSE}
library(geodata)
d <- worldclim_country(country = "Jamaica", 
                       var = "tmin",
                       path = tempdir())
terra::plot(mean(d), 
            plg = list(title = "Min. temperature (C)"))
```


## chirps
```{r eval=FALSE}
library("chirps")
location <- data.frame(long = 100.523186, lat = 13.736717)
d <- get_chirps(location, dates = c("2020-01-01", "2022-12-31"),
                server = "ClimateSERV")
ggplot(d, aes(x = date, y = chirps)) + geom_line() +
  labs(y = "Precipitation (mm)")
```

## elevatr
```{r eval=FALSE}
library(rnaturalearth)
library(elevatr)
library(terra)
map <- ne_countries(type = "countries", country = "Switzerland",
                    scale = "medium", returnclass = "sf")
d <- get_elev_raster(locations = map, z = 9, clip = "locations")
terra::plot(rast(d), plg = list(title = "Elevation (m)"))
```


## osmdata

```{r eval=FALSE}
library(osmdata)
head(available_features())
head(available_tags("amenity"))
placebb <- getbb("Barcelona")
placebb
```


```{r eval=FALSE}
hospitals <- placebb %>% opq() %>%
  add_osm_feature(key = "amenity", value = "hospital") %>%
  osmdata_sf()
```

```{r eval=FALSE}
motorways <- placebb %>% opq() %>%
  add_osm_feature(key = "highway", value = "motorway") %>%
  osmdata_sf()
```

### with leaflet
```{r eval=FALSE}
library(leaflet)
leaflet() %>%  addTiles() %>%
  addPolylines(data = motorways$osm_lines, color = "black") %>%
  addPolygons(data = hospitals$osm_polygons,
              label = hospitals$osm_polygons$name)
```

## wbstats
```{r eval=FALSE}
library(wbstats)
indicators <- wb_search(pattern = "poverty|unemployment")
# print(indicators)
```

```{r eval=FALSE}
d <- wb_data(indicator = "MO.INDEX.HDEV.XQ",
             start_date = 2011, end_date = 2011)
print(head(d))
```


### with mapview
```{r eval=FALSE}
library(rnaturalearth)
library(mapview)
map <- ne_countries(continent = "Africa", returnclass = "sf")
map <- dplyr::left_join(map, d, by = c("iso_a3" = "iso3c"))
mapview(map, zcol = "MO.INDEX.HDEV.XQ")
```


## spocc
```{r eval=FALSE}
library('spocc')
df <- occ(query = "Bradypus variegatus", from = "gbif",
          date = c("2000-01-01", "2019-12-31"),
          gbifopts = list(country = "CR"),
          has_coords = TRUE, limit = 1000)
d <- occ2df(df)
```


```{r eval=FALSE}
library(sf)
d <- st_as_sf(d, coords = c("longitude", "latitude"))
st_crs(d) <- 4326
mapview(d)
```



## Meeting Videos {-}

### Cohort 1 {-}

`r knitr::include_url("https://www.youtube.com/embed/URL")`

<details>
<summary> Meeting chat log </summary>

```
LOG
```
</details>