# load data from the database
# drv <- JDBC("org.postgresql.Driver", "/sw/share/java/postgresql-java/postgresql.jar");
# conn <- dbConnect(drv, "jdbc:postgresql://localhost/acd", "acd", "");
# dbListTables(conn);
# kite <- dbReadTable(conn, "r");

# load data from an r data file
load('kite.r.data');

summary(kite);

# split the data up by site and exclude days where the wind never exceeded 15
kailua <- kite[kite$site == 'Kailua' & kite$skunk == 'f',];
laniakea <- kite[kite$site == 'Laniakea' & kite$skunk == 'f',];
moluleia <- kite[kite$site == 'Mokuleia' & kite$skunk == 'f',];

mygraph <- function(df, title='avg speed', ...) {
	
	# calculate the mean and stddev for each hour of the day -- like group by in SQL
	df.mean <- aggregate(df[c(3)], list(Hour = df$hr), mean);
	df.sd <- aggregate(df[c(3)], list(Hour = df$hr), sd);

	# for stupid reasons, the data.frame is alphabetically, so reorder it so that it is sorted numerically 
	df.mean$Hour <- reorder(df.mean$Hour, c(1,2,3,4,5,6,7,8,9,10,11,12,13,14));
	
	# make nice lables for the x-axis
	levels(df.mean$Hour) <- c('6am', '7am', '8am', '9am', '10am', '11am', 'noon', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm');

	# plot the data 
	plot(df.mean, ylab='avg speed', xlab='time of day', main=title, ylim=c(0,23));

	# add the stddev error bars 
	arrows(as.numeric(df.mean$Hour), df.mean$speed, as.numeric(df.mean$Hour), df.mean$speed + df.sd$speed, .05, 90 , col="orange");
	arrows(as.numeric(df.mean$Hour), df.mean$speed, as.numeric(df.mean$Hour), df.mean$speed - df.sd$speed, .05, 90 , col="orange");
};


mygraph(laniakea, 'Laniakea: avg speed');
quartz.save('lanikea.png');
mygraph(kailua, 'Kailua: avg speed');
quartz.save('kailua.png');
mygraph(moluleia, 'Moluleia: avg speed');
quartz.save('moluleia.png');