In mstrimas/auklet: Analysis and Visualization of Your eBird Sightings

knitr::opts_chunk$set(
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auklet: Analysis and visualization of your eBird sightings

auklet provides tools for analyzing and visualizing your personal eBird data. Your personal sightings can be downloaded as a CSV file from the Download My Data page on the eBird website.

Installation

Install auklet from GitHub using:

# install.packages("devtools")
devtools::install_github("mstrimas/auklet")

Usage

All functions in auklet begin with eb_ (for eBird) to aid tab completion. Import your eBird sightings data into a data frame with eb_sightings():

library(auklet)
library(dplyr)
# load example data inclued with the package
ebird_data <- system.file("extdata/MyEBirdData.csv", package = "auklet") %>%
  eb_sightings()

Once your eBird data are imported, you can begin summarizing and visualizing them. The most basic functionality is generating your life list.

eb_lifelist(ebird_data) %>% 
  select(species_common, date, country) %>% 
  head()

Life lists can, of course, be viewed directly on the eBird website; however, other functions produce summaries or visualizations not available in eBird. For example, use eb_lifelist_day() to creat daily life lists, i.e. a data frame of species seen on each day of the year.

day_lists <- eb_lifelist_day(ebird_data)
# species seen on feb 14
filter(day_lists, month == 2, day == 14) %>% 
  select(month, day, species_common)

These day lists can be summarized to daily counts with summary() or visualized with plot().

summary(day_lists) %>% 
  head()
plot(day_lists)

Acknowledgments

This package, and some of the specific functionality, was inspired by conversations with Drew Weber, Taylor Long, and Tom Auer.

mstrimas/auklet documentation built on May 29, 2019, 2:57 p.m.