PFW: Filtering and Processing Data from Project FeederWatch

Overview

Installation

install.packages("PFW")

Vignette

Citation

citation("PFW")

Quick Start

library(PFW)# Load in the included example datasetdata<-pfw_example# If you were using your own selection of PFW data,# this would be pfw_import() instead.# pfw_import() creates and defaults to "/data-raw" in a local directory,# but will accept a different filepath.# Create a list of study speciesspecies_list<- c("Song Sparrow","Dark-eyed Junco","Spotted Towhee")# Create a list of study regionsregion_list<- c("Washington","Oregon")# Filter data by species and region, from 2022–2024 during November-Februarydata_filtered<- pfw_filter(data,region=region_list,species=species_list,year=2022:2024,month=11:2,# pfw_date(), which is called within pfw_filter(),# will appropriately wrap this around the end of the year.valid=TRUE,# TRUE by defaultrollup=TRUE# TRUE by default)# Output:# 2 regions successfully filtered.# Date filtering complete.# Species roll-up complete. 36 ambiguous records removed.# 3 species successfully filtered.# Filtering complete. 23538 records remaining.# View the filters that were appliedpfw_attr(data_filtered)# Output:# Filters applied to this dataset:## - Filter type: region#  Values: Washington, Oregon## - Filter type: date#   year : 2022, 2023, 2024#   month : 11, 12, 1, 2## - Filter type: rollup#  Values: TRUE## - Filter type: valid#  Values: TRUE## - Filter type: species#  Values: song sparrow, dark-eyed junco, spotted towhee# Zerofill missing species/survey instance combosdata_zf<- pfw_zerofill(data_filtered)# Attach site description metadata# Replace "path/sitedata.csv" with the actual path to the downloaded filedata_full<- pfw_sitedata(data_zf,path="path/sitedata.csv")# If the file does not exist at that path, pfw_sitedata will# download it there from the Project FeederWatch website.# Alternatively, you can manually download the site description file from:# https://feederwatch.org/explore/raw-dataset-requests/

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Acknowledgements