In liaaaaran/mcrcoral:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(mcrcoral)
library(tidyverse)
library(janitor)
library(tidyr)
library(plotly)

head(bes_birds)

Cleaning the Data

bes_birds <- bes_birds %>%
  clean_names() %>%
  select(-c(visit, datej:startmin, x0_5m:x40_m, ft:notes)) %>%
  mutate(wind_mph = wind_mph*10)
bes_birds

# count of birds per species 
species_ct <- bes_birds %>%
  na.omit() %>%
  group_by(species) %>%
#  na.omit() %>%
  summarise(across(total, sum)) %>%
  arrange(desc(total))

species_ct

sub <- species_ct[1:10,]
species_sub <- bes_birds %>%
  group_by(species) %>%
  filter(species %in% c('EUST', 'HOSP', 'RODO', 'CHSW', 'AMRO', 'COGR', 'CAGO', 'MODO', 'NOCA', 'HOFI'))

Exploratory Analysis:: Species Count

sub %>%
  ggplot(aes(x = reorder(species, -total), y = total)) + 
  geom_bar(stat = 'identity', fill = colors()[128]) + 
  labs(title = 'Most Common Birds Spotted') +
  xlab('Species')

Under what conditions are the most birds observed?

p <- species_sub %>%
  filter(species != 'CAGO') %>%
  group_by(species, temp_f) %>%
  summarise(avg_total = mean(total, na.rm = TRUE)) %>%
  ggplot(aes(x = temp_f, y = avg_total, group = species, color = species)) + 
  geom_line() +
  labs(title = 'Average Total Birds Spotted by Temperature')
p

bes_birds %>%
  group_by(wind_mph) %>%
  summarise(total_ct = sum(total, na.rm = TRUE)) %>%
  na.omit() %>%
  ggplot(aes(x = wind_mph, y = total_ct)) + 
  geom_bar(stat = 'identity', fill = colors()[128]) +
  labs(title = 'Total Birds Spotted by Wind Speed')

liaaaaran/mcrcoral documentation built on Dec. 21, 2021, 10:46 a.m.