Dog Movements: a tidyTuesday

Adoptable Dogs

# devtools::install_github("thebioengineer/tidytuesdayR", force=TRUE)
tuesdata51 <- tidytuesdayR::tt_load(2019, week = 51)
dog_moves <- tuesdata51$dog_moves
dog_des <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-17/dog_descriptions.csv')
library(tidyverse); library(scatterpie)
library(rgeos)
library(maptools)
library(rgdal); library(usmap); library(ggthemes)

The Base Map

My.Map <- us_map(regions = "states")
Base.Plot <- ggplot() + geom_polygon(data=My.Map, aes(x=x, y=y, group=group), fill="white", color="black") + theme_map()
Base.Plot

A fifty state map to plot this information on.

New.Dat <- left_join(My.Map, dog_moves, by= c("full" = "location"))
ggplot() + geom_polygon(data=New.Dat, aes(x=x, y=y, group=group, fill=total), color="black") + scale_fill_viridis_c() + labs(title = "Dogs Available for Adoption")

That’s a start. Now for the scatterpie. After a bit of work, I needed to translate the coordinates from most common maps. So I started with a ggmap for easier to work with coordinates.

library(sf); library(ggmap)
states <- maps::map("state", plot = FALSE, fill=TRUE)
states <- st_as_sf(states)
statesC <- cbind(states, st_coordinates(st_centroid(states)))
dog_moves <-dog_moves %>% mutate(ID = tolower(location))
statesC <- left_join(statesC, dog_moves)
statesC %>% filter(!is.na(imported) & !is.na(exported) & !is.na(total)) %>% select(X, Y, exported, imported, total) %>% data.frame() -> ShortCD
My.SM <- get_map(c(-124.6813, 25.12993, -67.00742, 49.00508), source="osm")
ggmap(My.SM) + geom_scatterpie(data=ShortCD, aes(x=X, y=Y, r=total/3000), cols=c("exported","imported")) + labs(fill="Dog Movements") + theme_map()

Dogs

library(readr)
USZCLL <- read_delim(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/us-zip-code-latitude-and-longitude.csv"), ";", escape_double = FALSE, trim_ws = TRUE)
## Parsed with column specification:
## cols(
##   Zip = col_character(),
##   City = col_character(),
##   State = col_character(),
##   Latitude = col_double(),
##   Longitude = col_double(),
##   Timezone = col_double(),
##   `Daylight savings time flag` = col_double(),
##   geopoint = col_character()
## )
dog_des %>% group_by(contact_zip) %>% summarise(Total = n()) %>% mutate(zip = contact_zip) -> Dog.zips
Dog.zips <- Dog.zips %>% mutate(Zip = contact_zip)
Plot.Zips <- left_join(Dog.zips, USZCLL)
## Joining, by = "Zip"
ggmap(My.SM) + geom_point(data=Plot.Zips, aes(x=Longitude, y=Latitude, color=Total), alpha = 0.5)
## Warning: Removed 40 rows containing missing values (geom_point).

That’s not right. Let’s try something else.

library(leaflet)

mybins <- seq(0, 600, by=100)
mypalette <- colorBin( palette="viridis", domain=Plot.Zips$Total, na.color="transparent", bins=mybins)

# Prepare the text for the tooltip:
mytext <- paste(
   "Total: ", Plot.Zips$Total, "<br/>", 
   "Zip: ", Plot.Zips$Zip, "<br/>", sep="") %>%
  lapply(htmltools::HTML)

Plot.Zips <- Plot.Zips %>% filter(!is.na(Latitude))
# Final Map
map.l <- leaflet(Plot.Zips) %>% 
  addTiles() %>% 
  addCircleMarkers(lng=~Longitude, lat=~Latitude, fillColor = ~mypalette(Total), fillOpacity = 0.7, color="white", radius=~Total/100, stroke=FALSE, label = mytext, labelOptions = labelOptions( style = list("font-weight" = "normal", padding = "3px 8px"), textsize = "13px", direction = "auto")) %>% addLegend( pal=mypalette, values=~Total, opacity=0.9, title = "Pets Listed", position = "bottomright" )
map.l
Avatar
Robert W. Walker
Associate Professor of Quantitative Methods

My research interests include causal inference, statistical computation and data visualisation.

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