library(nycflights13) library(ggplot2) library(knitr) library(lubridate) library(tidyr) library(tidyverse) library(plotrix)

Newark_January <- flights %>% filter(origin == “EWR” & month == 1 & day <= 5) %>% group_by(day)%>% summarize(mean=mean(air_time, na.rm=TRUE), std_dev=sd(air_time, na.rm = TRUE))

ggplot(Newark_January, aes(x=day, y=mean))+ geom_bar(stat = “identity”, color=“black”, fill=“gray100”, width = 0.4)+ geom_errorbar(aes(ymax = mean + std_err, ymin = mean - std_err), width=.05)+ labs(y=“Mean Flight Duration (minutes) from Newark Jan 2013”)

flites_dest_names <- flights %>% inner_join(airports, by = c(“dest” = “faa”))%>% rename(dest_airport=name)

JFK_Miami_Orlando <- flites_dest_names %>% filter(origin == “JFK” & (dest_airport == “Miami Intl” | dest_airport == “Orlando Intl”) & month == 2) %>% group_by(carrier) %>% summarize(number_of_flights = n())

flites_day <- flites_dest_names %>% mutate(weekday = wday(time_hour))

flites_day\(weekday<-factor(flites_day\)weekday, labels = c(“Sunday”, “Monday”, “Tuesday”, “Wednesday”,“Thursday”, “Friday”, “Saturday”))

flites_per_weekday <- flites_day %>% group_by(weekday) %>% summarize(number_of_flights = n())

summary_dep_delay <- flites_day %>% group_by(origin, weekday) %>% summarize(mean = mean(dep_delay, na.rm = TRUE), std_dev = sd(dep_delay, na.rm = TRUE), std_err = std.error(dep_delay, na.rm = TRUE))

ggplot(summary_dep_delay, aes(x=weekday, y=mean)) + geom_bar(state=“identity”, color=“black”,fill=“gray65”, width=.03)+ geom_errorbar(aes(ymax=mean+std_err,ymin=mean-std_err),width=.05)+ facet_grid(origin.~ )+ labs(y=“Mean Departure Delay(min)”)