NYCFlightsDataModel
Alan Hitch
2019-06-04
Summarize Data
daily <- flights %>%
mutate(date = make_date(year, month, day)) %>%
group_by(date) %>%
summarize(n = n())
ggplot(daily, aes(date, n)) +
geom_line()
Investigate Daily-Weekly Pattern
daily <- daily %>%
mutate(wday = wday(date, label = TRUE))
ggplot(daily, aes(wday,n)) +
geom_boxplot()
mod = lm(n ~ wday, data = daily)
grid <- daily %>%
data_grid(wday) %>%
add_predictions(mod, "n")
ggplot(daily, aes(wday, n)) +
geom_boxplot() +
geom_point(data = grid, color = "orange", size = 4)
Investigate residuals
daily <- daily %>%
add_residuals(mod)
daily %>%
ggplot(aes(date, resid)) +
geom_ref_line(h = 0) +
geom_line()
ggplot(daily, aes(date, resid, color = wday)) +
geom_ref_line(h = 0, colour = "red") +
geom_line()
daily %>%
filter(resid < -100)## # A tibble: 11 x 4
## date n wday resid
## <date> <int> <ord> <dbl>
## 1 2013-01-01 842 Tue -109.
## 2 2013-01-20 786 Sun -105.
## 3 2013-05-26 729 Sun -162.
## 4 2013-07-04 737 Thu -229.
## 5 2013-07-05 822 Fri -145.
## 6 2013-09-01 718 Sun -173.
## 7 2013-11-28 634 Thu -332.
## 8 2013-11-29 661 Fri -306.
## 9 2013-12-24 761 Tue -190.
## 10 2013-12-25 719 Wed -244.
## 11 2013-12-31 776 Tue -175.daily %>%
ggplot(aes(date, resid)) +
geom_ref_line(h = 0, colour = "red", size = 1) +
geom_line(color = "grey50") +
geom_smooth(se = FALSE, span = 0.20)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Seasonal Saturday effect
daily %>%
filter(wday == "Sat") %>%
ggplot(aes(date, n)) +
geom_point()+
geom_line() +
scale_x_date(
NULL,
date_breaks = "1 month",
date_labels = "%b"
)
Add Seasonal Variable
term <- function(date) {
cut(date,
breaks = ymd(20130101, 20130605, 20130825, 20140101),
labels = c("spring", "summer", "fall")
)
}
daily <- daily %>%
mutate(term = term(date))
daily %>%
filter(wday == "Sat") %>%
ggplot(aes(date, n, color = term)) +
geom_point(alpha = 1/3)+
geom_line() +
scale_x_date(
NULL,
date_breaks = "1 month",
date_labels = "%b"
)
daily %>%
ggplot(aes(wday, n, color = term)) +
geom_boxplot()
mod1 <- lm(n ~ wday, data = daily)
mod2 <- lm(n ~ wday * term, data = daily)
daily %>%
gather_residuals(without_term = mod1, with_term = mod2) %>%
ggplot(aes(date, resid, color = model)) +
geom_line(alpha = 0.75)
grid <- daily %>%
data_grid(wday, term) %>%
add_predictions(mod2, "n")
ggplot(daily, aes(wday, n)) +
geom_boxplot() +
geom_point(data = grid, color = "red") +
facet_wrap(~ term)
Better model for outliers (Robust regression)
mod3 <- MASS::rlm(n ~ wday * term, data = daily)
daily %>%
add_residuals(mod3, "resid") %>%
ggplot(aes(date, resid)) +
geom_hline(yintercept = 0, size = 2, color = "red") +
geom_line()
Computed Variables
# If you are creating variables it might be a good idea to bundle the creation of the variables up into a function
compute_vars <- function(data) {
data %>%
mutate(term = term(date),
wday = wday(date, label = TRUE)
)
}
# Another option would be to put the transformations directly in the model formula:
wday2 <- function(x) wday(x, label = TRUE)
mod3 <- lm(n ~ wday2(date) * term(date), data = daily)Time of Year: An Alternative Approach
# We could use a more flexible model to capture the pattern of school term in the data
library(splines)
mod <- MASS::rlm(n ~ wday * ns(date, 5), data = daily)
daily %>%
data_grid(wday, date = seq_range(date, n = 13)) %>%
add_predictions(mod) %>%
ggplot(aes(date, pred, color = wday)) +
geom_line() +
geom_point()
# We see a strong pattern in the numbers of Sat flights. This is reassuring, because we also saw that pattern in the raw data. It's a good sign when you get the same signal from different approaches.Question #1
Why are there fewer than expected flights on January 20, May 26 and September 1? (Hint: they all have the same explanation.) How would these days generalize into another year?
They are all Sundays before USA holidays, which are Martin Luther King Jr. Day, Memorial Day, and Labor Day. We cannot generalized them into another years as those dates are dependent on weekdays.
Question #2
What do the three days with high positive residuals represent? How would these days generalize to another year?
daily %>%
top_n(3, resid)## # A tibble: 3 x 5
## date n wday resid term
## <date> <int> <ord> <dbl> <fct>
## 1 2013-11-30 857 Sat 112. fall
## 2 2013-12-01 987 Sun 95.5 fall
## 3 2013-12-28 814 Sat 69.4 fallNov 30th and Dec 1st are the weekends after thanksgiving; dec 28 is the Saturday after Christmas. The travel needs will be high as people are travelling back to where they came from after the festival. We would be able to generalize these days for other years as the holidays are on a fixed date.
Question #3
Create a new variable that splits the “wday” variable into terms, but only for Saturdays, i.e., it should have Thurs, Fri, but Sat-summer, Sat-spring, Sat-fall. How does this model compare with the model with every combination of “wday” and “term”?
daily = daily %>%
mutate(term = term(date)) %>%
mutate(term2 = ifelse(wday == 'Sat',paste0(wday,"-",term),as.character(term) ))
mod3 = lm(n~ wday * term2, data = daily)
daily %>%
gather_residuals(mod3,mod2)%>%
arrange(date)%>%
ggplot(aes(date,resid,color = model))+
geom_line(alpha = 0.75)## Warning in predict.lm(model, data): prediction from a rank-deficient fit
## may be misleading
There is not much difference in the prediction result.
Question #4
Create a new “wday” variable that combines the day of week, term(for Saturdays), and public holidays. What do the residuals of the model look like?
daily_holidays =
daily %>%
mutate(holidays = case_when(date %in% ymd(c(20130101, # new years
20130121, # mlk
20130218, # presidents
20130527, # memorial
20130704, # independence
20130902, # labor
20131028, # columbus
20131111, # veterans
20131128, # thanksgiving
20131225)) ~ "holiday",
TRUE ~ "None")) %>%
unite(new_term, term2, holidays)
mod2 <- lm(n ~ new_term, data = daily_holidays)
daily_holidays %>%
add_residuals(mod2) %>%
ggplot(aes(date, resid)) +
geom_line()
There is not much change as well.
Question #5
What happens if you fit a day-of-week effect that varies by month (i.e.m n ~ wday*month)? Why is this not very helpful?
mod4 = lm(n ~ wday * month(date), data = daily_holidays)
daily_holidays %>%
add_residuals(mod4) %>%
ggplot(aes(date, resid)) +
geom_line()
It has became even worse.Since month observation is part of data observation, it has decrese the observation in each cell, leading it ot more uncertainty.
Question #6
What would you expect the model n ~ wday + ns(date,5) to look like? Knowing what you know about the data, why would you expect it not to be particularly effective?
mod5 = lm(n ~ wday + splines::ns(date, 5), data = daily)
daily %>%
add_residuals(mod5)%>%
ggplot(aes(date,resid))+
geom_line()
It looks like there is an overestimation during the fall semester.
Question #7
We hypothesized that people leaving on Sundays are more likely to be business travelers who need to be somewhere on Monday. Explore the hypothesis by seeing how if breaks down based on distance and time: if it’s true, you’d expect to see more Sunday evening flights to places that are far away.
dist = function(distance) {
cut(distance,
breaks = seq(min(flights$distance,na.rm = TRUE),max(flights$distance,na.rm = TRUE),length.out = 10),
labels = letters[1:9]
)
}
time = function(air_time){
cut(air_time,
breaks = seq(min(flights$air_time,na.rm = TRUE),max(flights$air_time,na.rm = TRUE),length.out = 10),
labels = letters[1:9]
)
}
dep = function(dep_time){
cut(dep_time,
breaks = seq(0000,2400,length.out = 9),
labels = c("0-3","3-6","6-9","9-12","12-15","15-18","18-21","21-24")
)
}
flights = flights %>%
mutate(dist = dist(distance)) %>%
mutate(time = time(air_time)) %>%
mutate(dep = dep(dep_time))%>%
mutate(wday= wday(make_date(year, month, day), label = TRUE))
flights %>%
ggplot() +
geom_bar(aes(dep,color = dist),position = "dodge") +
facet_wrap(~wday, nrow=3)
Hypothesis is not true.