meteo_df = read.csv("meteo.csv")

#inspiration
#http://stackoverflow.com/questions/8214303/conditional-replacement-of-values-in-a-data-frame

meteo_df$Season = 0
meteo_df$Week = 0
meteo_df$Pollution = -1

#Need to load and aggregate PM10 by days: use getData() from package
data = getData(..., predict_at=0) #TODO:

for (i in 1:nrow(meteo_df))
{
	pm10_level = data$getLevel(i)
	#Fill Pollution column: -1 if no info, 0 to 2 for pollution level
	if (!is.nan(pm10_level))
	{
		if (pm10_level < 30)
			meteo_df$Pollution[i] = 0
		else if (pm10_level <= 50)
			meteo_df$Pollution[i] = 1
		else #pm10 > 50
			meteo_df$Pollution[i] = 2
	}

	#Also fill season + days of week variables
	meteo_df$Season[i] = ifelse(
		strsplit(as.character(meteo_df$Date[i]),'/')[[1]][1] %in% c("4","5","6","7","8"),
		1, 0)
	current_datetime = strptime(as.character(meteo_df$Date[i]), "%m/%d/%Y", tz="GMT")
	meteo_df$Week[i] = ifelse(current_datetime$wday %in% c(6,0), 0, 1)
}

#Finally write new data
write.csv(meteo_df, file="meteo_extra.csv", row.names=FALSE)