Historical

Resources:

• https://github.com/rfordatascience/tidytuesday/tree/master/data/2023/2023-01-10
• https://feederwatch.org/explore/raw-dataset-requests/

library(tidyverse)
tuesdata <- tidytuesdayR::tt_load(2023, week = 02)

tuesdata$PFW_2021_public%>%head

tuesdata$PFW_count_site_data_public_2021%>%head

d <- tuesdata$PFW_2021_public %>% janitor::clean_names()


d%>% 
  count(year)

world <- map_data("world")
states <- map_data("state")

prop_df <- d %>%
  filter(longitude < -50) %>%
  group_by(longitude, latitude, year) %>%
  reframe(prop_many = how_many / sum(how_many) * 100)


ggplot(data = world, mapping = aes(long, lat, group = group)) +
  geom_polygon(color = "grey", fill = NA) +
  geom_polygon(data = states,
               linewidth=0.1,
               color = "grey",
               fill = NA) +
  geom_point(
    data = prop_df,
    mapping = aes(x = longitude, y = latitude, fill = factor(year)),
    inherit.aes = FALSE,
    key_glyph = "rect",
    size=0.5,
    shape = 21,
    stroke = 0.3,
    color = "black"
  ) +
  scale_fill_manual(values=c("#fc1c3c","#9189c1"))+
  coord_sf(xlim = c(-157.94932, -50),
           ylim = c(20 , 65.51989)) +
  #facet_wrap(vars(year),nrow = 2) +
  labs(title = "Maximum number of individuals seen at one time during observation period",
       subtitle="total proportion (%)",
       caption="DataSource: #TidyTuesday 2023 week2 - BFW data\nDataViz: Federica Gazzelloni #30DayChartChallenge 2023 Day4 - historical\n",
       fill = "Year") +
  ggthemes::theme_map(base_size = 10, base_family = "Roboto Condensed") +
  theme(plot.background = element_rect(color="black",fill="black"),
        panel.background = element_rect(color="black",fill="black"),
        plot.title = element_text(color="#9189c1",size=14),
        plot.subtitle = element_text(color="#9189c1",size=12),
        plot.caption = element_text(color="#fc1c3c"),
        legend.position = c(0.85,0.1))

ggsave("images/map.png",
       width = 7,height = 4)

d%>%count(species_code,sort=TRUE)

library(fpp3)
data <- d %>%
  mutate(date=paste0(month,"-",day,"-",year),.after =obs_id )%>%
  mutate(date=as.Date(date,"%m-%d-%Y"))%>%
  select(year,month,day,date,how_many)%>%
  distinct()%>%
  group_by(date)%>%
  reframe(how_many=sum(how_many))

ts <- data%>%
  as_tsibble()

ts %>%
  #autoplot()
  gg_season(y = how_many,period = "week")+
  scale_colour_steps2() +
  labs(title="Feeder Birds seasonality",
       subtitle="Exploring Daily Seasonal Patterns of Feeder Bird Sightings",
       y="number of individuals seen",x="Day of the week (2020-2021)" )+
  theme_linedraw(base_size = 12,
                 base_family = "Roboto Condensed")+
  theme(plot.title = element_text(size=18),
        plot.subtitle = element_text(size=14))

ggsave("images/daily.png",width = 7,height = 4)

ts %>%
  autoplot()+
  scale_colour_steps2() +
  labs(title="Feeder Birds seasonality",
       subtitle="Exploring Monthly Seasonal Patterns of Feeder Bird Sightings",
       y="number of individuals seen",x="Months (2020-2021)" )+
  theme_linedraw(base_size = 12,
                 base_family = "Roboto Condensed")+
  theme(plot.title = element_text(size=18),
        plot.subtitle = element_text(size=14))

ggsave("images/monthly.png",width = 7,height = 4)

library(cowplot)

ggdraw()+
  draw_image("images/map.png")+
  draw_image("images/daily.png",scale = 0.25,x=-0.36,y=-0)+
  draw_image("images/monthly.png",scale = 0.25,x=-0.36,y=-0.27)+
  draw_label("Map of locations from which Project FeederWatch participants have submitted data\nbetween 2020 and 2021. The inset plots provides detail of an example of seasonality.",x=0.02,y=0.07,size=6,color="grey",fontfamily = "Roboto Condensed",hjust = 0)

ggsave("w2_BFWd.png",width = 7,height = 4,dpi=320)