library(readr)
library(tidyverse)
library(gganimate)
library(splitstackshape)
library(forcats)
library(ggthemr)
ggthemr("flat dark")
seattle_pets <- read_csv("seattle_pets.csv")Daisy is a name no longer used after 2016 even though it is popular before that and a sudden surge from 2017 towards registering pets. #TidyTuesday pic.twitter.com/bnYSgnkhac
— Amalan Mahendran (@Amalan_Con_Stat) March 28, 2019
Yearly Change
Zipcode Counts Over the Years
p<-seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(zip_code,Year) %>%
group_by(Year) %>%
count(zip_code) %>%
remove_missing() %>%
subset(Year >=2010) %>%
top_n(25) %>%
ggplot(.,aes(x= fct_infreq(zip_code),y=n,fill=factor(Year)))+
geom_col()+transition_time(Year)+ease_aes("linear") +
coord_flip()+xlab("Zip code")+ylab(" Count")+
labs(fill="Year")+
scale_y_continuous(breaks=seq(0,3000,250),labels = seq(0,3000,250))+
ggtitle("Zip Code Over the Years",subtitle="Year : {frame_time}")
animate(p,fps=1,nframes=7)
Species Counts Over the Years
seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(species,Year) %>%
subset(Year >=2016) %>%
group_by(Year) %>%
count(species) %>%
ggplot(.,aes(x= species,y=n,fill=factor(Year),label=n))+
geom_col()+geom_text()+
transition_states(Year,transition_length = 2,state_length = 2)+
enter_fade()+exit_shrink()+ease_aes("back-in")+
xlab("Species")+ylab("Count")+ labs(fill="Year")+
scale_y_continuous(breaks=seq(0,23000,1000),labels=seq(0,23000,1000))+
ggtitle("Species Over the Years",subtitle = "Year: {closest_state}")
Primary Breed Over the Years
p<-seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(primary_breed,Year) %>%
group_by(Year) %>%
count(primary_breed) %>%
remove_missing() %>%
subset(Year >=2010) %>%
top_n(15) %>%
ggplot(.,aes(x= str_wrap(primary_breed,20),y=n,label=n,fill=factor(Year)))+
geom_col()+transition_time(Year)+ease_aes("linear") +
coord_flip()+geom_text()+labs(fill="Year")+
xlab("Primary Breed")+ylab("Count")+
scale_y_continuous(breaks=seq(0,6000,500),labels=seq(0,6000,500))+
ggtitle("Primary Breed Over the Years",subtitle = "Year : {frame_time}")
animate(p,fps=1,nframes=8)
Animals Name Over the Years
p<-seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(animals_name,Year) %>%
group_by(Year) %>%
count(animals_name) %>%
remove_missing() %>%
subset(Year >=2010) %>%
top_n(5) %>%
ggplot(.,aes(x= fct_infreq(animals_name),y=n,fill=factor(Year),label=n))+
geom_col()+transition_time(Year)+ease_aes("linear") +
coord_flip()+geom_text()+labs(fill="Year")+
xlab("Animals Name")+ylab("Count")+
scale_y_continuous(breaks = seq(0,275,25),labels=seq(0,275,25))+
ggtitle("Animales N ame Over the Years",
subtitle = "Year : {frame_time}")
animate(p,fps=1,nframes=8)
ZipCode and Choices
Zipcode with choices of Species
p<-seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(zip_code,species,Year,Month) %>%
mutate(Month = as.integer(Month)) %>%
cSplit("zip_code",sep = "-") %>%
remove_missing() %>%
unite("zip_code",c("zip_code_1","zip_code_2")) %>%
ggplot(.,aes(x=factor(Month),y=factor(zip_code),
shape=species,color=factor(Year)))+geom_jitter()+
transition_time(Year)+ ease_aes("linear")+
ggtitle("Zipcode vs Species of Choice ",
subtitle = "Year : {frame_time}")+
shadow_mark()+xlab("Month")+ylab("Zip Code")+
labs(color="Year",shape="Species")
animate(p,nframes=4,fps=1)
a<-seattle_pets %>%
count(zip_code) %>%
top_n(10)
seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(zip_code,species,Year,Month) %>%
mutate(Month = as.integer(Month)) %>%
subset(zip_code %in% c(a$zip_code)) %>%
ggplot(.,aes(x=species,y=zip_code,color=Month))+geom_jitter()+
transition_states(Year,transition_length = 2,state_length = 3)+
enter_fade()+exit_shrink()+ease_aes("back-in")+
xlab("Species")+ylab("Zip Code")+
ggtitle("Top 10 Zipcodes vs Species of Choice ",
subtitle = "Year : {closest_state}")
Zipcode with choices of Primary Breed
b<-seattle_pets %>%
count(primary_breed) %>%
top_n(10)
seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(zip_code,primary_breed,Year,Month) %>%
mutate(Month = as.integer(Month)) %>%
subset(zip_code %in% c(a$zip_code)) %>%
subset(primary_breed %in% c(b$primary_breed)) %>%
ggplot(.,aes(x=str_wrap(primary_breed,10),y=zip_code,color=Month))+
geom_jitter()+
transition_states(Year,transition_length = 2,state_length = 3)+
enter_fade()+exit_shrink()+ease_aes("back-in")+
xlab("Primary Breed")+ylab("Zip Code")+
ggtitle("Top 10 Zipcode and Primary Breed of Choice ",
subtitle = "Year : {closest_state}")
Zipcode with choices of Animals Name
d<-seattle_pets %>%
count(animals_name) %>%
top_n(10)
e<-seattle_pets %>%
count(zip_code) %>%
top_n(15)
seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(zip_code,animals_name,Year,Month) %>%
mutate(Month = as.integer(Month)) %>%
subset(zip_code %in% c(e$zip_code)) %>%
subset(animals_name %in% c(d$animals_name)) %>%
ggplot(.,aes(x=animals_name,y=zip_code,color=Month))+geom_jitter()+
transition_states(Year,transition_length = 2,state_length = 3)+
enter_fade()+exit_shrink()+ease_aes("back-in")+
xlab("Animals Name")+ylab("Zip Code")+
ggtitle("Top 10 Zipcode and Top 15 Animals Name of Choice ",
subtitle = "Year : {closest_state}")
Primary and Secondary Breed Choices Over the Years
a<-seattle_pets %>%
count(primary_breed) %>%
top_n(10)
b<-seattle_pets %>%
count(secondary_breed) %>%
top_n(10)
seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(primary_breed,secondary_breed,Year,Month) %>%
remove_missing() %>%
mutate(Month = as.integer(Month)) %>%
subset(primary_breed %in% c(a$primary_breed)) %>%
subset(secondary_breed %in% c(b$secondary_breed)) %>%
ggplot(.,aes(x=str_wrap(primary_breed,12),y=str_wrap(secondary_breed,12),
color=Month))+
geom_jitter()+xlab("Primary Breed")+ylab("Second Breed")+
transition_states(Year,transition_length = 2,state_length = 3)+
enter_fade() + exit_shrink() +ease_aes("back-in")+
#theme(axis.text.x = element_text(angle = 270))+
ggtitle("Top 10 Primary and Secondary Breeds",
subtitle = "Year : {closest_state}")
Species and Name choices for Animals
b<-seattle_pets %>%
count(animals_name) %>%
top_n(15)
seattle_pets %>%
cSplit("license_issue_date",sep = " ") %>%
rename(Month =license_issue_date_1) %>%
rename(Day = license_issue_date_2) %>%
rename(Year = license_issue_date_3) %>%
select(species,animals_name,Year,Month) %>%
remove_missing() %>%
mutate(Month = as.integer(Month)) %>%
subset(animals_name %in% c(b$animals_name)) %>%
ggplot(.,aes(y=animals_name,x=species,color=Month))+geom_jitter()+
transition_states(Year,transition_length = 2,state_length = 3)+
enter_fade() + exit_shrink() +ease_aes("back-in")+
ylab("Animals Name")+xlab("Species")+
ggtitle("Animals Name and Species",
subtitle = "Year : {closest_state}")
THANK YOU