--- title: "Exercise _Spatial data_" author: "Maximilian H.K. Hesselbarth" date: 2022/10/24 editor_options: chunk_output_type: console --- ```{r, setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE ) library(downloadthis) ``` ```{r tidyfigure, echo = FALSE, fig.align = "center", out.width = '65%'} knitr::include_graphics("img/sf.png", auto_pdf = FALSE) ``` ```{r download, echo = FALSE} download_link(link = "https://raw.githubusercontent.com/mhesselbarth/advanced-r-workshop/main/exercise-spatial.Rmd", button_label = "Download .Rmd file", button_type = "danger") ``` <br><br> Make sure you can install and load all packages. This includes `terra` and `sf`, but also the `tidyverse`. ```{r load_libs} # Insert code here # End ``` Next, go to [https://www.naturalearthdata.com](https://www.naturalearthdata.com) and download the "Small scale data, 1:110m" > "Cultural" > "Admin 1 – States, Provinces" data set. Additionally, download the "NLCD 2019 Land Cover (CONUS)" data set from [https://www.mrlc.gov](https://www.mrlc.gov). Once you downloaded all the data, read it into your R Session using the corresponding packages. ```{r read} # Insert code here # End ``` Make sure both the vector and the raster data have the same CRS (Hint: It's often faster to project vectors instead of raster. If projecting the raster, have a look at the 'method' argument). ```{r crs} # Insert code here # End ``` Next, remove Alaska and Hawaii from the states vector because there is no NLCD data for these states. Next select only the 5 largest states in area ```{r states_size} # Insert code here # End ``` First plot the NLCD data and add the largest states to the map. Try to use the region as shape fill. ```{r plot} # Insert code here # End ``` Now, pick one state (your home state, a state you recently visited, a state you want to visit, ...) and get the NLCD data for that state only. ```{r crop} # Insert code here # End ``` Next, get all values of the cropped NLCD data and remove all `NA` and `NaN` values. Calculate the relative amount of all remaining values. Which one is the most dominant land-cover class in your state? ```{r rel_class} # Insert code here # End ``` Last, try to reclassify the raster into less classes (e.g., use the bigger classification found at[NLCD classes](https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description)) ```{r classification} # Insert code here # End ```