library(dplyr)

library(tidyr)

library(nycflights13)

head(flights)

glimpse(flights)

select(flights,year, month, day)

select(flights,-tailnum)

select(flights,contains("time"))

select(flights,year,carrier,destination=dest)

filter(flights, month == 1, day == 1)

flights[flights$month == 1 & flights$day == 1, ]

filter(flights, month == 1 | month == 2)

slice(flights, 1:10)

arrange(flights, year, month, day)

arrange(flights, desc(arr_delay))

distinct(

select(flights,carrier)

)

select(

mutate(flights,ave_speed=distance/(air_time/60)),

distance, air_time,ave_speed)

a1 <- group_by(flights, year, month, day)

a2 <- select(a1, arr_delay, dep_delay)

a3 <- summarise(a2,

arr = mean(arr_delay, na.rm = TRUE),

dep = mean(dep_delay, na.rm = TRUE))

a4 <- filter(a3, arr > 30 | dep > 30)

head(a4)

filter(

summarise(

select(

group_by(flights, year, month, day),

arr_delay, dep_delay

),

arr = mean(arr_delay, na.rm = TRUE),

dep = mean(dep_delay, na.rm = TRUE)

),

arr > 30 | dep > 30

)

flights %>%

group_by(year, month, day) %>%

select(arr_delay, dep_delay) %>%

summarise(

arr = mean(arr_delay, na.rm = TRUE),

dep = mean(dep_delay, na.rm = TRUE)

) %>%

filter(arr > 30 | dep > 30)

flights %>%

group_by(origin) %>%

summarise(meanDelay = mean(dep_delay,na.rm=T))

flights %>%

group_by(carrier) %>%

summarise(meanDelay = mean(dep_delay,na.rm=T),

sdDelay = sd(dep_delay,na.rm=T))

flights%>%

select(-year,-month,-day,-hour,-minute,-dep_time,-dep_delay)%>%

glimpse()

glimpse(airports)

library(geosphere)

library(maps)

library(ggplot2)

library(sp)

library(rgeos)

select(airports,

dest=faa,

destName=name,

destLat=lat,

destLon=lon)%>%

right_join(flights)%>%

group_by(dest,

destLon,

destLat,

distance)%>%

summarise(count=n())%>%

ungroup()%>%

select(destLon,

destLat,

count,

distance)%>%

mutate(id=row_number())%>%

na.omit()

NYCll=airports%>%filter(faa=="JFK")%>%select(lon,lat) # get NYC coordinates

rts <- gcIntermediate(as.matrix(NYCll),

as.matrix(select(data,destLon,destLat)),

1000,

addStartEnd=TRUE,

sp=TRUE)

rts.ff <- fortify(

as(rts,"SpatialLinesDataFrame")) # convert into something ggplot can plot

rts.ff$id=as.integer(rts.ff$id)

gcircles <- left_join(rts.ff,

data,

by="id") # join attributes, we keep them all, just in case

base = ggplot()

worldmap <- map_data("world",

ylim = c(10, 70),

xlim = c(-160, -80))

wrld <- c(geom_polygon(

aes(long, lat, group = group),

size = 0.1,

colour = "grey",

fill = "grey",

alpha = 1,

data = worldmap

))

base + wrld +

geom_path(

data = gcircles,

aes(

long,

lat,

col = count,

group = group,

order = as.factor(distance)

),

alpha = 0.5,

lineend = "round",

lwd = 1

) +

coord_equal() +

scale_colour_gradientn(colours = c("blue", "orange", "red"),

guide = "colourbar") +

theme(panel.background = element_rect(fill = 'white', colour = 'white')) +

labs(y = "Latitude", x = "Longitude",

title = "Count of Flights from New York in 2013")