In nicholasjclark/BBS.occurrences: BBS community composition and co-occurrence analysis

knitr::opts_chunk$set(echo = TRUE)
library(knitr)
opts_chunk$set(tidy.opts=list(width.cutoff=50),tidy=TRUE)

This appendix describes how to extract data on storm disturbances by county across time. These storm occurrences can be accessed in the BBS.occurrences library using data('Storm.data').

Download the NOAA storm dataset and unzip into a .csv file

if(!file.exists("StormData.csv")) {
  url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url= url,
              destfile = "StormData.csv.bz2")
  R.utils::bunzip2(filename = "StormData.csv.bz2", destname = "StormData.csv", remove = TRUE)
  }

Read in the downloaded .csv file

 Storm.data.raw <- data.table::fread('StormData.csv')

Because the dataset has over 900,000 rows in its original form, we process the data by categorising storm events into recognised categories, cleaning state and county names and then returning data only on the most damaging storms (controlled by the argument damage.threshold). Note, this function also only returns data on weather events that are likely to cause widespread disturbances to natural communities (including thunderstorm, heave winds, hail, tornados, heave rain, fire, drought, and tropical storms). Here, we return the top 33% of the most damaging storms that fall into these categories

library(BBS.occurrences)
Storm.data <- cleanStormData(Stormdata.raw = Storm.data.raw, damage.threshold = .33)

Save the processed storm data in the Analysis folder. Note, these storm occurrences can be accessed in the BBS.occurrences library using data('Storm.data').

save(Storm.data,
     file = './Analysis_data/Storm.data.Rdata')

nicholasjclark/BBS.occurrences documentation built on July 19, 2020, 8:31 p.m.