Before you start

There are many publicly available spatial datasets that can be downloaded using R. Programming data downloading using R instead of manually downloading data from websites can save lots of time and also enhances the reproducibility of your analysis. In this section, we will introduce some of such datasets and show how to download and process those data.

Direction for replication

Datasets

No datasets to download for this Chapter.

Packages

• Run the following code to install or load (if already installed) the pacman package, and then install or load (if already installed) the listed package inside the pacman::p_load() function.

if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  stars, # spatiotemporal data handling
  terra, # raster data handling
  raster, # raster data handling
  sf, # vector data handling
  dplyr, # data wrangling
  stringr, # string manipulation
  lubridate, # dates handling
  data.table, # data wrangling
  tidyr, # reshape
  tidyUSDA, # download USDA NASS data
  keyring, # API key management
  FedData, # download Daymet data
  daymetr, # download Daymet data
  ggplot2, # make maps
  tmap, # make maps
  future.apply, # parallel processing
  CropScapeR, # download CDL data
  prism, # download PRISM data
  exactextractr # extract raster values to sf
)  

• Run the following code to define the theme for map:

theme_set(theme_bw())

theme_for_map <- theme(
  axis.ticks = element_blank(),
  axis.text= element_blank(), 
  axis.line = element_blank(),
  panel.border = element_blank(),
  panel.grid.major = element_line(color='transparent'),
  panel.grid.minor = element_line(color='transparent'),
  panel.background = element_blank(),
  plot.background = element_rect(fill = "transparent",color='transparent')
)