# Radius of the earth in meters
EARTH_RADIUS = 6371000;
PI = 3.14159;

elevationDistance <- function(loc1, loc2) {
  leftFields = strsplit(loc1, ":")
  rightFields = strsplit(loc2, ":")

  elev1 = as.numeric(leftFields[[1]][3])
  elev2 =  as.numeric(rightFields[[1]][3])

  dist = elev2 - elev1

  return(dist)
}

earthDistance <- function(loc1, loc2) {
  leftFields = strsplit(loc1, ":")
  rightFields = strsplit(loc2, ":")

  lat1 = as.numeric(leftFields[[1]][1]) * PI / 180.0
  lat2 =  as.numeric(rightFields[[1]][1]) * PI / 180.0

  lon1 = as.numeric(leftFields[[1]][2]) * PI / 180.0
  lon2 =  as.numeric(rightFields[[1]][2]) * PI / 180.0

  x = (lon2-lon1) * cos((lat1+lat2)/2);
  y = (lat2-lat1);
  d = sqrt(x*x + y*y) * EARTH_RADIUS;

  return(d)
}

timeDistance <- function(time1, time2) {
  time1 <- strptime(time1, format="%m/%d/%Y %H:%M:%S")
  time2 <- strptime(time2, format="%m/%d/%Y %H:%M:%S")

  val = difftime(time2, time1, units="hours")

  return(as.double(val))
}

dayHour <- function(time) {
  time <- unclass(strptime(time, format="%m/%d/%Y %H:%M:%S"))

  return(time$hour + time$min / 60.0)
}

rides <- read.csv("data/cabi-sample-rides.filtered.csv")

rides$elevation <- apply(rides, 1, function(row) elevationDistance(row['startPos'], row['endPos']))

rides$distance <- apply(rides, 1, function(row) earthDistance(row['startPos'], row['endPos']))

rides$duration <- apply(rides, 1, function(row) timeDistance(row['startDate'], row['endDate']))

rides$startHour <- apply(rides, 1, function(row) dayHour(row['startDate']))

nzRides <- subset(rides, distance > 0)

write.csv(nzRides, "data/cabi-nz-rides.ext.cvs")
write.csv(rides, "data/cabi-rides.ext.cvs")

# ggplot2 for the map

library(maps)
library(ggplot2)
stations <- read.csv("data/cabi-station-counts.csv")
stations$time <- factor(stations$time, levels = c("EARLYMORN","LATEMORN","AFTERNOON", "EVENING", "NIGHT", "LATENIGHT"))

all_states <- map_data("state")
states <- subset(all_states, region %in% c( "district of columbia" ) )
p <- ggplot(stations)
p <- p + geom_polygon( data=states, aes(x=long, y=lat, group))
p <- p + geom_point( data=stations, aes(x=long, y=lat, size = count), color="gold2") + scale_size(name="Bikes")
p <- p + facet_grid(type ~ time)

# ggplot2 smooth

p <- ggplot(rides)
p <- p + geom_smooth(aes(x=startHour, y=distance))
p <- p + coord_cartesian(ylim=c(1000,2500))

# ggplot2 histogram

p <- ggplot(rides)
p <- p + geom_histogram(aes(x=duration), binwidth = .1)
p <- p + scale_y_sqrt()
p <- p + facet_grid(subscription ~ .)
p <- p + scale_x_continuous(limits=c(0, 4))