Activity - convert back to dates

# Create weekD variable in GI data set
GI$weekD = as.Date(GI$weekC) # could have used mutate
str(GI$weekD)
##  Date[1:21244], format: "2005-02-28" "2005-02-28" "2005-02-28" "2005-02-28" "2005-02-28" ...

Activity - weekly GI cases by ageC

# Construct a data set aggregated by week and age category
GI_wa <- GI %>%
  group_by(week, ageC) %>%
  summarize(count = n())

# Construct a plot to look at weekly GI cases by age category
ggplot(GI_wa, aes(x = week, y = count, color = ageC)) + geom_point()

ggplot(GI_wa, aes(x = week, y = count, shape = ageC)) + geom_point()

ggplot(GI_wa, aes(x = week, y = count, shape = ageC, color = ageC)) + geom_point()

Activity - weekly GI counts by zip3 and ageC

Construct a plot of weekly GI counts by zip3 and ageC.

# Construct data set 
GI_za <- GI %>%
  group_by(week, zip3, ageC) %>%
  summarize(count = n())

# Construct plot of weekly GI counts by zip3 and ageC
ggplot(GI_za, aes(x = week, y = count)) + 
  geom_point() + 
  facet_grid(ageC ~ zip3)

Activity - filtered graphs

Construct a plot for those in zipcode 206xx in 2008.

# Filter the data to zipcode 206xx in 2008
zip206_w <- GI %>%
  mutate(year = as.numeric(format(date, "%Y"))) %>%
  filter(zip3 == 206,
         year == 2008) %>%
  group_by(week) %>% 
  summarize(count = n())


# Construct the plot of weekly GI counts in zipcode 206xx.
ggplot(zip206_w, aes(x = week, y = count)) + 
  geom_point()