YifanYang_A4_notebook

1.Data preparation part.

In this part, I built the dataframe using data from the csv files I have created.

SMO <- data.frame(label = 'SMO', lon=-118.456667, lat=34.010167)
smo <- c(SMO$lon, SMO$lat)
data_0426_day<-read.csv('RTL150426_day.csv')
names(data_0426_day) <- c('number','timestamp','flight','code', 'track','id', 'lon','lat', 'alt','dist' )
data_0426_day$number <- NULL
data_0426_night<-read.csv('RTL150426_night.csv')
data_1129_day<-read.csv('RTL151129_day.csv')
data_1129_night<-read.csv('RTL151129_night.csv')

1.Airline numbers comparation.

In those coxcomb charts, each sector shows an airline during that period. The first and second coxcomb chart respectively represent the airlines in day-time and night-time of 04/26/2015. The third and forth coxcomb chart represent the airlines in day-time and night-time of 11/29/2015. Those chart shows that November is much busier than April for this airport.

#ggplot(data_0426_day, aes(code)) + geom_bar() + #counts of cars by code
#  coord_polar()
ggplot(data_0426_day, aes(code, fill = code)) + #color the bars
  geom_bar(width = 1) + coord_polar()

ggplot(data_0426_night, aes(code, fill = code)) + #color the bars
  geom_bar(width = 1) + coord_polar()

ggplot(data_1129_day, aes(code)) + geom_bar(width = 1) + coord_polar()

ggplot(data_1129_night, aes(code, fill = code)) + #color the bars
  geom_bar(width = 1) + coord_polar()

2.Airplane landing timeline.

In this chart, different color represents different flights, the chart shows the airplanes’ landing, first two charts shows the day-time and night-time of landing in 04/26/2015. The last two shows the day-time and night-time of landing in 11/29/2015. This chart shows day-time is always busier than night-time, and november is busier than April.

p <- ggplot(data_0426_day, aes(timestamp, alt)) 
p + geom_point(aes(color = factor(flight)))

q <- ggplot(data_0426_night, aes(timestamp, alt)) 
q + geom_point(aes(color = factor(flight)))

r <- ggplot(data_1129_day, aes(timestamp, alt)) 
r + geom_point()

#r + geom_point(aes(color = factor(flight)))
s <- ggplot(data_1129_night, aes(timestamp, alt)) 
s + geom_point(aes(color = factor(flight)))