This is an extension of the tidytuesday assignment you have already done. Complete the questions below, using the screencast you chose for the tidytuesday assigment.

Import data

library(tidyverse)
library(lubridate)
theme_set(theme_light())
seattle_pets <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-26/seattle_pets.csv") %>%
  mutate(license_issue_date = mdy(license_issue_date)) %>%
  rename(animal_name = animals_name)

Description of the data and definition of variables

The data is pet names In Seattle which accounts to 52,519 pets. Pets meaing dog, cats, 38 goats and 6 pigs.The data shows common animal names which are Lucy, Charlie, Luna, Bella, Max, Daisy, Molly and Jack. The data also shows primary breed. ## Visualize data Hint: One graph of your choice.

seattle_pets %>%
  filter(license_issue_date >= "2017-01-01") %>%
  count(species, primary_breed, sort = TRUE) %>%
  filter(species %in% c("Cat", "Dog")) %>%
  mutate(percent = n / sum(n)) %>%
  group_by(species) %>%
  top_n(10, percent) %>%
  ungroup() %>%
  mutate(primary_breed = fct_reorder(primary_breed, percent)) %>%
  ggplot(aes(primary_breed, percent, fill = species)) +
  geom_col(show.legend = FALSE) +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap(~ species, scales = "free_y", ncol = 1) +
  coord_flip() +
  labs(x = "Primary breed",
       y = "% of this species",
       title = "Most common cat and dog breeds",
       subtitle = "Of licensed pets in Seattle 2017-2018")

What is the story behind the graph?

The story behind the graph shows the most common cat and dog breeds of licensed pets in Seattle 2017-2018. Y-axise is primary breed and the x-axis shows the percentage of species in seattle. For example, there is about %9 of

Hide the messages, but display the code and its results on the webpage.

Write your name for the author at the top.

Use the correct slug.