This RMarkdown document presents a preliminary analysis of Seattleโs pet licenses dataset. It interleaves text, code, and code output.
The following code chunk loads the tidyverse library, reads the CSV file containing the Seattle pet license dataset, and saves that dataset under the name seattle_pets. The code assumes that the CSV file is loaded in a sub-directory, data, of the directory that contains this document.
library(tidyverse)
seattle_pets <- read_csv("data/Seattle_Pet_Licenses.csv")We can now view the dataset as it is stored in R.
seattle_pets## # A tibble: 46,062 x 7
## `License Issue Date` `License Number` `Animal's Name` Species `Primary Breed`
## <chr> <chr> <chr> <chr> <chr>
## 1 November 12 2015 819997 Dixie Dog Terrier
## 2 March 24 2016 900605 Chloe Dog Chihuahua, Sho~
## 3 May 21 2018 21081 Molly Dog Retriever, Lab~
## 4 May 27 2018 283603 Whidbey Dog Terrier
## 5 June 18 2018 359079 Chinook Dog Retriever, Lab~
## 6 June 19 2018 S144848 Penny Dog Retriever, Lab~
## 7 June 21 2018 215454 Peggy Sue Dog Bulldog, French
## 8 July 08 2018 S144996 Summer Dog Australian She~
## 9 July 10 2018 S112107 Tess Dog Border Collie
## 10 July 10 2018 S116838 Emmy Dog Schnauzer, Min~
## # ... with 46,052 more rows, and 2 more variables: `Secondary Breed` <chr>,
## # `ZIP Code` <dbl>Based on the information provided in the previous section, we can see that the pets dataset contains how many of the following:
We can now count the number of each species using the count() function.
count(seattle_pets, Species, sort=TRUE)## # A tibble: 4 x 2
## Species n
## <chr> <int>
## 1 Dog 31893
## 2 Cat 14134
## 3 Goat 31
## 4 Pig 4๐ง Replace this line with a description of what the output of the last code chunk tells us.
๐ง Finally, add one more code chunk that computes the most popular names in the dataset and describes the results.