Week 2: Discussion Section - Answer Key

Read in Vector Data

library(here)
library(tidyverse)
library(stars)
library(sf)
library(tmap)

col <- st_read(here::here("data", "week2-discussion", "Colombia", "Colombia.shp"))

roads <- st_read(here::here("data", "week2-discussion", "RDLINE_colombia", "RDLINE_colombia.shp"))

aves <- readr::read_csv(here::here("data", "week2-discussion", "dataves.csv")) %>%
  dplyr::as_tibble() %>%
  dplyr::rename(long = decimal_longitude) %>%
  dplyr::mutate(lat = decimal_latitude) %>%
  st_as_sf(coords = c("long", "lat"), crs = 4326)

Check Class and Geometry Type

class(col)
class(roads)
class(aves)

unique(st_geometry_type(col))
unique(st_geometry_type(roads))
unique(st_geometry_type(aves))

Select Macro Region of Interest

col_andean <- col %>%
  # Set categorical "levels" in attribute N1_MacroBi (subregions of Colombia)
  dplyr::mutate(N1_MacroBi = as.factor(N1_MacroBi)) %>%
  # Subset to Andean region of Colombia
  dplyr::filter(N1_MacroBi == "Andean")

tm_shape(col_andean) +
  tm_polygons() +
  tm_layout(main.title = "Andean Region of Colombia",
            main.title.size = 1)

Play with Coordinate Reference System

# Print the CRS of each spatial object
st_crs(col)
st_crs(roads)
st_crs(aves)

# Print units of each CRS 
st_crs(col)$units
st_crs(roads)$units
st_crs(aves)$units

There are several ways to extract the longitude and latitude from the geometry column.

purr Approach

aves_df_purrr <- aves %>%
  # Extract lat & long from geometry column
  mutate(lon = unlist(purrr::map(aves$geometry, 1)), # longitude = first component (x)
         lat = unlist(purrr::map(aves$geometry, 2))) %>% # latitude = second component (y)
  st_drop_geometry() # Remove geometry column now that it's redundant

st_coordinates Approach

aves_df_st_coords <- aves %>%
  dplyr::mutate(lon = sf::st_coordinates(.)[,1],# Assign first matrix item to "lon"
                lat = sf::st_coordinates(.)[,2]) %>% # Assign second matrix item to "lat"
  st_drop_geometry() # Remove geometry column now that it's redundant

Next, convert to sf object again.

st_as_sf Approach

aves_df_purrr <- aves %>%
  st_as_sf(coords = c("lon", "lat"), crs = 4326)

Bring All Vector Data Types Together

# Boolen check if CRS match between 2 datasets
st_crs(col) == st_crs(roads)

# Transform bird data into same CRS as other Colombia data
aves <- st_transform(aves, crs = st_crs(col))

# Simple plot with all 3 data layers
tm_shape(col) +
  tm_polygons() +
  tm_shape(roads) +
  tm_lines() +
  tm_shape(aves) +
  tm_dots() +
  tm_layout(main.title = "Colombia ecoregions, roads,\nand bird observations",
            main.title.size = 1)