#' ---
#' title: Beware the Canadians!
#' week: 5
#' type: Case Study
#' subtitle: Working with Spatial Data and the sf package
#' reading:
#' - The [Spatial Features Package (sf)](https://r-spatial.github.io/sf/){target='blank'}
#' tasks:
#' - Reproject spatial data using `st_transform()`
#' - Perform spatial operations on spatial data (e.g. intersection and buffering)
#' - Generate a polygon that includes all land in NY that is within 10km of the Canadian border and calculate the area
#' - Save your script as a .R or .Rmd in your course repository
#' ---
#'
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#' # Reading
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#' # Background
#' Up to this point, we have dealt with data that fits into the tidy format without much effort. Spatial data has many complicating factors that have made handling spatial data in R complicated. Big strides are being made to make spatial data tidy in R.
#'
#'
#' # Objective
#'
#' You woke up in the middle of the night terrified of the Canadians after a bad dream. You decide you need to set up military bases to defend the Canada-NY border. After you tweet your plans, you realize you have no plan. What will you do next?
#'
#' > 1) Generate a polygon that includes all land in NY that is within 10km of the Canadian border and 2) calculate it's area in km^2. How much land will you need to defend from the Canadians?
#'
#'
#' # Tasks
#'
#'
#' [ Download starter R script (if desired)](`r output_nocomment`){target="_blank"}
#'
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#'
#' The details below describe one possible approach.
#'
#' ## Libraries
#' You will need to load the following packages
## ----warning=FALSE, message=FALSE----------------------------------------
library(spData)
library(sf)
library(tidyverse)
# library(units) #this one is optional, but can help with unit conversions.
#'
#' ## Data
## ----message=F-----------------------------------------------------------
#load 'world' data from spData package
data(world)
# load 'states' boundaries from spData package
data(us_states)
# plot(world[1]) #plot if desired
# plot(us_states[1]) #plot if desired
#'
#' ## Steps
#' 1. `world` dataset
#' 1. transform to the albers equal area projection:
## ------------------------------------------------------------------------
albers="+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
#' 2. use `st_set_geometry()` to specify that the `geom` column contains the `geometry`. This will also rename the column from `geom` to `geometry` to make it easier to use `ggplot()`
#' 3. filter the world dataset to include only `name_long=="Canada"`
#' 4. buffer canada to 10km (10000m)
#' 2. `us_states` object
#' 1. transform to the albers equal area projection defined above as `albers`
#' 2. filter the `us_states` dataset to include only `NAME == "New York"`
#' 3. Create a 'border' object
#' 1. use `st_intersection()` to intersect the canada buffer with New York (this will be your final polygon)
#' 2. Plot the border area using `ggplot()` and `geom_sf()`.
#' 3. use `st_area()` to calculate the area of this polygon.
#' 4. Convert the units to km^2. You can use `set_units(km^2)` (from the `units` library) or some other method.
#' 4. Do not worry about small waterways, etc. Just use the two datasets listed above.
#'
#'
#'
#'
#' Your final result should look something like this:
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#'
#' Important note: This is a crude dataset meant simply to illustrate the use of intersections and buffers. The two datasets are not adequate for a highly accurate analysis. Please do not use these data for real military purposes.
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#' Build a leaflet map of the same dataset.
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