INTRODUCTION:

Dog attacks are of a particular interest to me personally, as someone who has been attacked by a vicious doberman. However, the data is very messy and untidy. I will attempt to clean it up by removing missing data, and converting all dog ages into years. The data analysis will focus on looking for a relationship between dog attacks and gender. As well as dog attacks and status of spay neuter.

To call in knitr.

To call in the libraries.

library(tidyverse)

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## v tibble  3.1.8      v dplyr   1.0.10
## v tidyr   1.3.0      v stringr 1.5.0 
## v readr   2.1.2      v forcats 0.5.2

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library(ggplot2)
library(lubridate)

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## 
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To read in the data.

dog_bite <- read_csv("DOHMH_Dog_Bite_Data.csv")

## Rows: 22663 Columns: 9
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): DateOfBite, Species, Breed, Age, Gender, Borough, ZipCode
## dbl (1): UniqueID
## lgl (1): SpayNeuter
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

dog_bite <- as_tibble(dog_bite)

Cleaning the dataset

If the the data contains a just a number, we assume it is already in years. If the data contains “M”, its months, and similarly if the data contains a “W” its in weeks.

dog_bite$Age <- gsub("Y", "", dog_bite$Age)

for(i in 1:nrow(dog_bite)){
  #print(i)
#dealing with months that were given as a range.
  if( grepl("[0-9]-[0-9]M", dog_bite[i, "Age"], ignore.case = TRUE)){
temp = gsub("-[0-9]M", "", dog_bite[i, "Age"])
temp = as.numeric(temp)
dog_bite[i, "Age"] <- as.character(round(temp/12, 3))
}
#dealing with the remaining months.
if( grepl("M", dog_bite[i, "Age"], ignore.case = TRUE)){
temp = gsub("M", "", dog_bite[i, "Age"])
temp = as.numeric(temp)
dog_bite[i, "Age"] <- as.character(round(temp/12, 3))}
#dealing with weeks.
if( grepl("W", dog_bite[i, "Age"], ignore.case = TRUE)){
temp = gsub("W", "", dog_bite[i, "Age"])
temp = as.numeric(temp)
dog_bite[i, "Age"] <- as.character(round(temp/52, 3))
}}

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#Visualization Is there a relationship between age, gender assigned at birth, Spayed status.

ggplot(dog_bite, aes(y = as.numeric(Age), x = Gender, fill = Gender)) +
geom_boxplot() + ylab("Dog Age in Years")

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## Warning: Removed 11558 rows containing non-finite values (stat_boxplot).

ggplot(dog_bite, aes(y = as.numeric(Age), x = SpayNeuter, fill = Gender)) +
geom_boxplot() + ylab("Dog Age in Years")

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

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## Warning: Removed 11558 rows containing non-finite values (stat_boxplot).

#CONCLUSION In conclusion the data the there seems to be a relationship between gender and attacks. More male dogs a responsible for more attacks then female dogs, however no clear relationship exists between status of spay neuter an attacks.

DATA_607 PROJECT_2 DOg_Bite_Data

Alex Khaykin

3/1/2023

INTRODUCTION:

Cleaning the dataset