Some Visualization

Data Source

Has this data set been seasonally adjusted?

I have no clue

Let’s get some packages loaded first!

library(forecast)
library(bsts)
library(ggplot2)
library(dplyr)
library(ggthemes)
library(tseries)
library(pracma)
library(seasonal)

df0$Housing.estimates<-factor(df0$Housing.estimates)
df0$Type.of.unit<-factor(df0$Type.of.unit)
df0$REF_DATE<-as.Date(paste0(as.character(df0$REF_DATE), "-01"), format="%Y-%m-%d")

#df0

df0%>%select(REF_DATE,VALUE,Housing.estimates,Type.of.unit)%>%
  filter(Housing.estimates == "Housing starts")%>%
  ggplot(aes(x=REF_DATE,y=VALUE,col=Type.of.unit))+geom_line()+
  geom_point()+labs(x="Year",y="Units Started",col="Types")+ggtitle("Units Started in Canada from 2014-09 to 2022-12")+
  theme_stata(scheme = "s1color")+scale_color_stata()+
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

df0%>%select(REF_DATE,VALUE,Housing.estimates,Type.of.unit)%>%
  filter(Housing.estimates == "Housing completions")%>%
  ggplot(aes(x=REF_DATE,y=VALUE,col=Type.of.unit))+geom_line()+
  geom_point()+labs(x="Year",y="Units Completed",col="Types")+ggtitle("Units Completed in Canada from 2014-09 to 2022-12")+
  theme_stata(scheme = "s1color")+scale_color_stata()+
  scale_x_date(date_breaks = "4 month", date_labels = "%Y-%m")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

By different types

df0%>%select(REF_DATE,VALUE,Housing.estimates,Type.of.unit)%>%
  filter(Type.of.unit == "Total units")%>%
  ggplot(aes(x=REF_DATE,y=VALUE,col=Housing.estimates))+geom_line()+
  geom_point()+labs(x="Year",y="Units Completed",col="Types")+ggtitle("Units Completed in Canada from 2014-09 to 2024-01")+
  theme_stata(scheme = "s1color")+scale_color_stata()+
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

df0$Year<-format(df0$REF_DATE, "%Y") 
yearly_data<-df0%>%
  group_by(Year, Housing.estimates) %>%
  summarise(total_v = sum(VALUE, na.rm = TRUE), .groups = 'drop') %>%
  ungroup() 
yearly_data_filtered<-yearly_data %>%
  filter(!Year %in% c(2014, 2023, 2024))

yearly_data_filtered%>%
  ggplot(aes(x = Year, y = total_v, fill = Housing.estimates)) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_stata(scheme = "s1color")+scale_color_stata()+
  labs(x="Year", 
       y="Estimated Total Units", 
       title="Total Housing Estimates by Year", 
       fill = "Housing Estimates")