2023_01_31_pet_cats_uk.Rmd

---
title: "Pet Cats UK"
date: 2023-02-01
output: html_document
---

# TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event!
Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data.
While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format.
The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community!
As such we encourage everyone of all skills to participate!

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(lubridate)
library(tidytuesdayR)

library(scales)
theme_set(theme_light())
```

# Load the weekly Data

Download the weekly data and make available in the `tt` object.

```{r Load}

tt <- tt_load("2023-01-31")

```


# Readme

Take a look at the readme for the weekly data to get insight on the dataset.
This includes a data dictionary, source, and a link to an article on the data.

```{r Readme, eval = interactive()}

tt

```


# Glimpse Data

Take an initial look at the format of the data available.

```{r Glimpse}

tt %>% 
  map(glimpse)

```

# Wrangle

Explore the data and process it into a nice format for plotting! Access each dataset by name by using a dollarsign after the `tt` object and then the name of the data set.

```{r Wrangle}
cats_uk <- tt$cats_uk
cats_uk_reference <- tt$cats_uk_reference

```


# Visualize

Using your processed dataset, create your unique visualization.

```{r Visualize}
cats_uk |> 
  count(tag_id, sort = TRUE)

cats_uk |> 
#  filter(tag_id == "Gracie_2-Tag") |> 
  ggplot(aes(location_long, location_lat)) +
  geom_point(alpha = 1/10)
```

Cats are concentrated in the South West in the UK.

```{r}
map_data("world", "uk") |> 
  ggplot(aes(long, lat)) +
  geom_polygon(aes(group = group), fill = "gray90") +
  geom_point(aes(location_long, location_lat), data = cats_uk, alpha = 1/5) +
  coord_map()
```

Most cats don't move much.

```{r}
cats_uk |> 
  group_by(tag_id) |> 
  summarize(
    max_long = max(location_long),
    min_long = min(location_long),
    max_lat = max(location_lat),
    min_lat = min(location_lat),
    .groups = "drop"
  ) |> 
  mutate(max_area = (max_long - min_long) * (max_lat - min_lat)) |> 
  arrange(desc(max_area))
```

Follow "Mifty-Tag"in long and lat, and find one time travel.

```{r}
cats_uk |> 
  filter(tag_id == "Mifty-Tag") |> 
  pivot_longer(location_long:location_lat, names_to = "long_lat") |> 
  ggplot(aes(timestamp, value)) +
  geom_line() +
  facet_wrap(vars(long_lat), ncol = 1, scales = "free_y")
```

Height

```{r}
cats_uk |> 
  group_by(tag_id) |> 
  summarize(
    height_move = max(height_above_ellipsoid) - min(height_above_ellipsoid),
    .groups = "drop") |> 
  arrange(desc(height_move))
```

Follow "Gracie_2-Tag", and find her fly. Or record error?

```{r}
cats_uk |> 
  filter(tag_id == "Gracie_2-Tag") |> 
  ggplot(aes(timestamp, height_above_ellipsoid)) +
  geom_line()

cats_uk_reference |> 
  filter(tag_id == "Gracie_2-Tag") |> View()
```

Filter visible, and no more 8000 meters high.

```{r}
cats_uk |> 
  filter(visible) |> 
  group_by(tag_id) |> 
  summarize(
    height_move = max(height_above_ellipsoid) - min(height_above_ellipsoid),
    .groups = "drop") |> 
  arrange(desc(height_move))
```

Follow visible "Dexter2-Tag", but still dubious

```{r}
cats_uk |> 
  filter(visible, tag_id == "Dexter2-Tag") |> 
  ggplot(aes(timestamp, height_above_ellipsoid)) +
  geom_line()

cats_uk_reference |> 
  filter(tag_id == "Dexter2-Tag") |> View()
```

Ground speed. Very dubious.
1000 m/s = 1 km/s = 60 km/m = 3600 km/h

```{r}
cats_uk |> 
  filter(visible) |> 
  arrange(desc(ground_speed))

cats_uk |> 
  filter(visible) |> 
  ggplot(aes(ground_speed)) +
  geom_histogram() +
  scale_x_log10()
```

Reference

Felis catus only
```{r}
cats_uk_reference |> 
  count(animal_taxon, sort = TRUE)
```

81 out of 101 are allowed to hunt.

```{r}
cats_uk_reference |> 
  count(hunt)
```

```{r}
cats_uk_reference |> 
  filter(hunt) |> 
  ggplot(aes(prey_p_month)) +
  geom_histogram()
```

Most are fixed.

```{r}
cats_uk_reference |> 
  count(animal_reproductive_condition)
```

Male and female almost half.

```{r}
cats_uk_reference |> 
  count(animal_sex)
```

Male stays less indoors than female.

```{r}
cats_uk_reference |> 
  ggplot(aes(hrs_indoors)) +
  geom_histogram(aes(fill = animal_sex))
```

```{r}
cats_uk_reference |> 
  ggplot(aes(n_cats)) +
  geom_histogram(aes(fill = animal_sex))
```

```{r}
cats_uk_reference |> 
  count(food_dry)

cats_uk_reference |> 
  count(food_wet)

cats_uk_reference |> 
  count(food_other)
```

UK only for sure.
```{r}
cats_uk_reference |> 
  count(study_site)
```

Male tends to be younger.

```{r}
cats_uk_reference |> 
  filter(!is.na(age_years)) |> 
  ggplot(aes(age_years)) +
  geom_histogram(aes(fill = animal_sex), binwidth = 1,
                 position = position_dodge(width = 0.8)) +
  scale_y_continuous(breaks = 0:10) +
  theme(panel.grid.minor.y = element_blank())
```

Among 3 yr old male, allowed to hunt,
"Indie-Tag" and "Tigger-Tag" are lazy
"Carrots-Tag" and "Ginge-Tag" are eager to hunt.

```{r}
cats_uk_reference |> 
  filter(age_years == 3, hunt) |> 
  ggplot(aes(prey_p_month)) +
  geom_histogram(aes(fill = animal_sex))

lazy_eager_3yr_male <- cats_uk_reference |> 
  filter(age_years == 3, hunt, animal_sex == "m") |> 
  filter(prey_p_month == 0.5 | prey_p_month == 17.5) |> 
  pull(tag_id)

```

"Carrots-Tag" and "Ginge-Tag" may live in higher floor.

```{r}
cats_uk |> 
  filter(tag_id %in% lazy_eager_3yr_male, visible) |> 
  ggplot(aes(timestamp, height_above_ellipsoid)) +
  geom_line(aes(color = tag_id))
```

All four cats not move much.

```{r}
p <- cats_uk |> 
  filter(tag_id %in% lazy_eager_3yr_male, visible) |> 
  group_by(tag_id) |> 
  mutate(
    long = location_long - min(location_long),
    lat = location_lat - min(location_lat)
  ) |> 
  ungroup() |> 
  left_join(cats_uk_reference, by = "tag_id") |> 
  mutate(tag_id = glue::glue("{tag_id} ({prey_p_month})")) |> 
  ggplot(aes(long, lat)) +
  geom_point(alpha = 1/5) +
  facet_wrap(vars(tag_id)) +
  labs(x = "Relative longitude", y = "Relative latitude",
       title = "Eager hunters move in wider area",
       subtitle = "( ) denotes # of prey caught per month\nmost and least eager hunters among 3 yr old male",
     caption = "Source: Movebank for Animal Tracking Data via Data is Plural")
```

# Save Image

Save your image for sharing. Be sure to use the `#TidyTuesday` hashtag in your post on twitter! 

```{r}

# This will save your most recent plot
ggsave("image/pet_cats_uk.png", p, width = 5, height = 4)

```