# Summarize data by facility and age category
fa_l <- GI %>%
  group_by(facility, ageC) %>%
  summarize(count = n())

# Reshape data from long to wide format
fa_w <- fa_l %>%
  spread(ageC, count)

# Create HTML table
tab = xtable(fa_w,
             caption = "Total GI cases by Facility and Age Category",
             label   = "myHTMLanchor",
             align   = "ll|rrrrr") # rownames gets a column

# Save HTML to file
print(tab, file="table.html", type="html", include.rownames=FALSE)

# Copy-and-paste table into Word

# Here is some code to get you started
states = map_data("state")

# Read in the data from http://www.google.org/flutrends/us/data.txt
# ** you will need to figure this part out **
# ** it is NOT trivial                     **
fluTrends = read.csv(file="http://www.google.org/flutrends/about/data/flu/us/data.txt", 
                     skip = 11, 
                     check.names=FALSE)

# Keep only the last row for the states
flu_w = fluTrends[nrow(fluTrends), c(1,3:53)]

# Reshape to long format
flu_l <- flu_w %>%
  gather(region, index, -Date)

# Merge flutrends data with map_data
flu_l$region = tolower(flu_l$region)
states_merged = merge(states, flu_l, sort=FALSE, by='region')

# Find the date
states_merged$Date = as.Date(states_merged$Date)
mx_date = max(states_merged$Date)

# Construct plot
ggplot(states_merged, aes(x=long, y=lat, group=group, fill=index)) + 
  geom_polygon() + 
  labs(title=paste('Google Flu Trends on', mx_date), x='', y='') +
  theme_minimal() + 
  theme(legend.title = element_blank()) +
  coord_map("cylindrical") 

install.packages("surveillance")
help(package=surveillance)