Here, we install the package in R and instantiate the library:
>install.packages('nycflights13')
...
>library('nycflights13')
>dim(flights)
[1] 336776 16
>head(flights,3)
year month day dep_timedep_delayarr_timearr_delay carrier tailnum flight
1 2013 1 1 517 2 830 11 UA N14228 1545
2 2013 1 1 533 4 850 20 UA N24211 1714
3 2013 1 1 542 2 923 33 AA N619AA 1141
origindestair_time distance hour minute
1 EWR IAH 227 1400 5 17
2 LGA IAH 227 1416 5 33
3 JFK MIA 160 1089 5 42
> flights.data=na.omit(flights[,c('year','month','dep_delay','arr_delay','distance')])
>flights.sample<- flights.data[sample(1:nrow(flights.data),100,replace=FALSE),]
>head(flights.sample,5)
year month dep_delayarr_delay distance
155501 2013 3 2 5 184
2410 2013 1 0 4 762
64158 2013 11 -7 -27 509
221447 2013 5 -5 -12 184
281887 2013 8 -1 -10 937
The ddply function enables us to summarize the departure delays (mean and standard deviation) by year and month:
>ddply(flights.sample,.(year,month),summarize, mean_dep_delay=round(mean(dep_delay),2), s_dep_delay=round(sd(dep_delay),2))
year month mean_dep_delaysd_dep_delay
1 2013 1 -0.20 2.28
2 2013 2 23.85 61.63
3 2013 3 10.00 34.72
4 2013 4 0.88 12.56
5 2013 5 8.56 32.42
6 2013 6 58.14 145.78
7 2013 7 25.29 58.88
8 2013 8 25.86 59.38
9 2013 9 -0.38 10.25
10 2013 10 9.31 15.27
11 2013 11 -1.09 7.73
12 2013 12 0.00 8.58
Let's save the flights.sample dataset to a CSV file so that we can use the data to show us how to do the same thing in pandas:
>write.csv(flights.sample,file='nycflights13_sample.csv', quote=FALSE,row.names=FALSE)