In TrashBirdEcology/USAvian: A Pilot Mapping Tool for Project USAvian

knitr::opts_chunk$set(echo = TRUE, eval = TRUE, warning=FALSE, message=FALSE)

# install and load packages
pkgs<- c( "ggplot2", "dplyr", "ggplot2", "magrittr", "here", "usmap", "sbtools", "devtools", "USAvian", "taxize")
new.packages <- pkgs[!(pkgs %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
suppressMessages(lapply(pkgs, library, character.only = TRUE))
rm(new.packages,pkgs)

Data Sources

We are curating a list of data sources, currently located here. Import this file as an object to easily retrieve the URL for relevant information:

# Available abbreviations can be found using:
data("data_sources")

We want to define, based on the column abbrev in the data frame data sources, which data sets we want to import:

# abbrevs<-"mbjv" # choose a single data
abbrevs <- c("bcr_persistent", "iba", "flyways", "mbjv") # or multiple data
# make sure that each abbreviation we specified is listed in the `data sources`:
if(any(abbrevs %in% unique(data_sources$abbrev))==FALSE){ "some abbrevs do not appear in data_sources$abbrev. please check."}

Download and decompress the desired data sources

Next we can download the data. This function will not overwrite existing files with the same names.

download_data(abbrevs=abbrevs, dir= here("data/data-raw")) # this will create a new directory if it does not already exist
zip_files <- unzip_data(abbrevs)

Unzip the files associated with 'abbrevs'. For some reason this isn't working inside an RMD chunk, but works as expected here..... r zip_files <- unzip_data(abbrevs)

Categorize the available files

categorized_files <- categorize_data(zip_files)

Next, import selected data into memory.

data <- import_data(abbrevs = abbrevs)

USA Base Map

Create the base map for adding layers

# in 2019 package usmap used US Census county boundaries
## state boundaries
usa_states <- plot_usmap(regions="states")+
  labs(title="US States, US Census Bureau (YYYY)")

## county boundaries
usa_counties <- plot_usmap(regions="counties")+
  labs(title="US Counties, US Census Bureau (YYYY)")

# for specific regions, see https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf 
## southeast
usa_se <- plot_usmap(include = c("FL", "GA", "MS", "SC", "NC", "AL", "AR", "LA", "TN")) +
  labs(title = "Southeastern US",
       subtitle = "States in the Southeastern US")

## or use the usmap to grab predefined census regions
# usa_sa <- usmap::plot_usmap(include = .south_atlantic)
# usa_s <- usmap::plot_usmap(include = .south_region)

str(usmap::us_map())

View Conservation Groups

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