knitr::opts_chunk$set(warning = FALSE,
message = FALSE)library(tidyverse)
library(lubridate)
theme_set(theme_classic(18) +
theme(legend.position = "bottom"))
# download.file(url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-29/animal_rescues.csv', destfile = "./animal_rescues.csv")
animal_rescues <- readr::read_csv(file = "./animal_rescues.csv")skimr::skim(animal_rescues)| Name | animal_rescues |
| Number of rows | 7544 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| character | 26 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| date_time_of_call | 0 | 1 | 16 | 16 | 0 | 7533 | 0 |
| fin_year | 0 | 1 | 7 | 7 | 0 | 14 | 0 |
| type_of_incident | 0 | 1 | 15 | 15 | 0 | 1 | 0 |
| pump_count | 0 | 1 | 1 | 4 | 0 | 5 | 0 |
| pump_hours_total | 0 | 1 | 1 | 4 | 0 | 13 | 0 |
| incident_notional_cost | 0 | 1 | 1 | 4 | 0 | 72 | 0 |
| final_description | 0 | 1 | 4 | 100 | 0 | 4556 | 0 |
| animal_group_parent | 0 | 1 | 3 | 55 | 0 | 28 | 0 |
| originof_call | 0 | 1 | 6 | 21 | 0 | 8 | 0 |
| property_type | 0 | 1 | 3 | 78 | 0 | 180 | 0 |
| property_category | 0 | 1 | 4 | 17 | 0 | 7 | 0 |
| special_service_type_category | 0 | 1 | 23 | 31 | 0 | 4 | 0 |
| special_service_type | 0 | 1 | 26 | 58 | 0 | 24 | 0 |
| ward_code | 9 | 1 | 4 | 9 | 0 | 710 | 0 |
| ward | 9 | 1 | 3 | 37 | 0 | 1254 | 0 |
| borough_code | 9 | 1 | 9 | 9 | 0 | 37 | 0 |
| borough | 9 | 1 | 5 | 22 | 0 | 70 | 0 |
| stn_ground_name | 0 | 1 | 4 | 16 | 0 | 107 | 0 |
| uprn | 0 | 1 | 4 | 11 | 0 | 1453 | 0 |
| street | 0 | 1 | 4 | 94 | 0 | 5804 | 0 |
| usrn | 0 | 1 | 4 | 8 | 0 | 5070 | 0 |
| postcode_district | 0 | 1 | 2 | 4 | 0 | 272 | 0 |
| easting_m | 0 | 1 | 4 | 6 | 0 | 3431 | 0 |
| northing_m | 0 | 1 | 4 | 6 | 0 | 3381 | 0 |
| latitude | 0 | 1 | 1 | 11 | 0 | 3633 | 0 |
| longitude | 0 | 1 | 1 | 12 | 0 | 3634 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| incident_number | 3478 | 0.54 | 91854662.33 | 54836667.38 | 4149 | 49306118 | 89438626 | 131567118 | 233284091 | ▆▇▇▅▂ |
| cal_year | 0 | 1.00 | 2014.88 | 3.68 | 2009 | 2012 | 2015 | 2018 | 2021 | ▇▅▇▅▇ |
| hourly_notional_cost | 0 | 1.00 | 301.26 | 33.99 | 255 | 260 | 298 | 333 | 352 | ▇▂▃▃▇ |
| easting_rounded | 0 | 1.00 | 531242.84 | 10528.50 | 500050 | 524750 | 531650 | 537750 | 571350 | ▁▅▇▂▁ |
| northing_rounded | 0 | 1.00 | 180724.52 | 8348.27 | 157050 | 175150 | 181250 | 186750 | 200750 | ▁▃▇▇▂ |
animal_rescues$animal_group_parent %>% table## .
## Bird
## 1530
## Budgie
## 2
## Bull
## 1
## cat
## 17
## Cat
## 3649
## Cow
## 8
## Deer
## 130
## Dog
## 1194
## Ferret
## 8
## Fish
## 2
## Fox
## 349
## Goat
## 2
## Hamster
## 16
## Hedgehog
## 2
## Horse
## 193
## Lamb
## 2
## Lizard
## 3
## Pigeon
## 4
## Rabbit
## 14
## Sheep
## 6
## Snake
## 13
## Squirrel
## 65
## Tortoise
## 1
## Unknown - Animal rescue from below ground - Farm animal
## 1
## Unknown - Animal rescue from water - Farm animal
## 3
## Unknown - Domestic Animal Or Pet
## 191
## Unknown - Heavy Livestock Animal
## 49
## Unknown - Wild Animal
## 89subdata = animal_rescues %>%
dplyr::transmute(
incident_number,
date_time_of_call = lubridate::dmy_hm(date_time_of_call),
animal_type = animal_group_parent %>% tolower,
animal_type = case_when(
str_detect(animal_type, "farm animal") ~ "farm animal",
str_detect(animal_type, "heavy livestock animal") ~ "farm animal",
animal_type == "bull" ~ "cow",
animal_type == "budgie" ~ "bird",
animal_type == "pigeon" ~ "bird",
# str_detect(animal_type, "unknown") ~ "unknown",
TRUE ~ animal_type),
animal_type = fct_lump(f = animal_type, n = 8),
animal_type_int = animal_type %>% as.factor() %>% as.integer(),
latitude = as.numeric(latitude),
longitude = as.numeric(longitude)) %>%
dplyr::filter(complete.cases(latitude), complete.cases(longitude), latitude > 0)
# subdata
# glimpse(subdata)
subdata$animal_type %>% table## .
## bird cat
## 804 1388
## deer dog
## 122 788
## fox horse
## 211 193
## unknown - domestic animal or pet unknown - wild animal
## 78 35
## Other
## 81catdata = subdata %>%
dplyr::filter(animal_type == "cat")plotlyClick legend to select for specific type of animals.
library(plotly)
Sys.setenv('MAPBOX_TOKEN' = Sys.getenv('MAPBOX_AUTH'))
fig = subdata %>%
plot_mapbox(lat = ~latitude, lon = ~longitude,
split = ~animal_type,
size = 2,
mode = 'scattermapbox',
hoverinfo = 'animal_type')
fig = fig %>%
layout(title = 'Rescue animal types',
font = list(color='white'),
plot_bgcolor = '#191A1A',
paper_bgcolor = '#191A1A',
mapbox = list(style = 'dark',
zoom = 7,
center = list(
lat = median(subdata$latitude),
lon = median(subdata$longitude))),
legend = list(orientation = 'h',
font = list(size = 8)),
margin = list(l = 25, r = 25,
b = 25, t = 25,
pad = 2))
figmapdecklibrary(mapdeck)
set_token(Sys.getenv("MAPBOX_AUTH"))
# mapdeck(token = mapdeck(token = 'your_token'))
mapdeck::mapdeck(style = mapdeck_style('dark')) %>%
mapdeck::add_scatterplot(
data = subdata,
lat = "latitude",
lon = "longitude",
tooltip = "animal_type",
radius = 200,
fill_colour = "animal_type",
palette = "viridis",
legend = TRUE
)