library(tidyverse)
library(openintro)

The Data for New York flight

data(nycflights)    #view the nycflights data frame
                   
nycflights          #  #view the nycflights data frame
names(nycflights)
##  [1] "year"      "month"     "day"       "dep_time"  "dep_delay" "arr_time" 
##  [7] "arr_delay" "carrier"   "tailnum"   "flight"    "origin"    "dest"     
## [13] "air_time"  "distance"  "hour"      "minute"
?nycflights         #The codebook (description of the variables) can be accessed by pulling up the help file:
## starting httpd help server ... done
glimpse(nycflights)  #Remember that you can use glimpse to take a quick peek at your data to understand its contents better.
## Rows: 32,735
## Columns: 16
## $ year      <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 201...
## $ month     <int> 6, 5, 12, 5, 7, 1, 12, 8, 9, 4, 6, 11, 4, 3, 10, 1, 2, 8,...
## $ day       <int> 30, 7, 8, 14, 21, 1, 9, 13, 26, 30, 17, 22, 26, 25, 21, 2...
## $ dep_time  <int> 940, 1657, 859, 1841, 1102, 1817, 1259, 1920, 725, 1323, ...
## $ dep_delay <dbl> 15, -3, -1, -4, -3, -3, 14, 85, -10, 62, 5, 5, -2, 115, -...
## $ arr_time  <int> 1216, 2104, 1238, 2122, 1230, 2008, 1617, 2032, 1027, 154...
## $ arr_delay <dbl> -4, 10, 11, -34, -8, 3, 22, 71, -8, 60, -4, -2, 22, 91, -...
## $ carrier   <chr> "VX", "DL", "DL", "DL", "9E", "AA", "WN", "B6", "AA", "EV...
## $ tailnum   <chr> "N626VA", "N3760C", "N712TW", "N914DL", "N823AY", "N3AXAA...
## $ flight    <int> 407, 329, 422, 2391, 3652, 353, 1428, 1407, 2279, 4162, 2...
## $ origin    <chr> "JFK", "JFK", "JFK", "JFK", "LGA", "LGA", "EWR", "JFK", "...
## $ dest      <chr> "LAX", "SJU", "LAX", "TPA", "ORF", "ORD", "HOU", "IAD", "...
## $ air_time  <dbl> 313, 216, 376, 135, 50, 138, 240, 48, 148, 110, 50, 161, ...
## $ distance  <dbl> 2475, 1598, 2475, 1005, 296, 733, 1411, 228, 1096, 820, 2...
## $ hour      <dbl> 9, 16, 8, 18, 11, 18, 12, 19, 7, 13, 9, 13, 8, 20, 12, 20...
## $ minute    <dbl> 40, 57, 59, 41, 2, 17, 59, 20, 25, 23, 40, 20, 9, 54, 17,...

The nycflights data frame is a massive trove of information. Let’s think about some questions we might want to answer with these data:

How delayed were flights that were headed to Los Angeles?
How do departure delays vary by month?
Which of the three major NYC airports has the best on time percentage for departing flights?

Analysis

Departure delays

# examing the distribution of departure delays of all flights with a histogram.
ggplot(data = nycflights, aes(x = dep_delay)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# This function says to plot the dep_delay variable from the nycflights data frame on the x-axis. It also defines a geom (short for geometric object), which describes the type of plot you will produce.
# 
# Histograms are generally a very good way to see the shape of a single distribution of numerical data, but that shape can change depending on how the data is split between the different bins. You can easily define the binwidth you want to use:

ggplot(data = nycflights, aes(x = dep_delay)) +
  geom_histogram(binwidth = 15)

ggplot(data = nycflights, aes(x = dep_delay)) +
  geom_histogram(binwidth = 150)

Exercise 1

Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?

# If you want to visualize only on delays of flights headed to Los Angeles, you need to first filter the data for flights with that destination (dest == "LAX") and then make a histogram of the departure delays of only those flights.
lax_flights <- nycflights %>%
  filter(dest == "LAX")
ggplot(data = lax_flights, aes(x = dep_delay)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Command 1: Take the nycflights data frame, filter for flights headed to LAX, and save the result as a new data frame called lax_flights.
# 
#     == means “if it’s equal to”.
#     LAX is in quotation marks since it is a character string.

# You can also obtain numerical summaries for these flights
lax_flights %>%
  summarise(mean_dd   = mean(dep_delay), 
            median_dd = median(dep_delay), 
            n         = n())
# Note that in the summarise function you created a list of three different numerical summaries that you were interested in. The names of these elements are user defined, like mean_dd, median_dd, n, and you can customize these names as you like (just don’t use spaces in your names). Calculating these summary statistics also requires that you know the function calls. Note that n() reports the sample size.
#Summary statistics: Some useful function calls for summary statistics for a single numerical variable are as follows:
#mean,    median,    sd,    var,    IQR,    min,  max....each of these functions takes a single vector as an argument and returns a single value

#interested in flights headed to San Francisco (SFO) in February:
  sfo_feb_flights <- nycflights %>%
  filter(dest == "SFO", month == 2)
  
#you can separate the conditions using commas if you want flights that are both headed to SFO and in February. If you are interested in either flights #headed to SFO or in February, you can use the | instead of the comma.

Exercise 2

Create a new data frame that includes flights headed to SFO in February, and save this data frame as sfo_feb_flights. How many flights meet these criteria? Answer: 68 flights

  sfo_feb_flights <- nycflights %>%
  filter(dest == "SFO", month == 2)
sfo_feb_flights
view(sfo_feb_flights) # view sfo_feb_flights like in excel sheet

Exercise 3

Describe the distribution of the arrival delays of these flights using a histogram and appropriate summary statistics. Hint: The summary statistics you use should depend on the shape of the distribution. Based on my summary using group_by (carrier), and mean or average departure delay time, I observe that 5 companies (American Airlines Inc.,JetBlue Airways, Delta Air Lines Inc., United Air Lines Inc, Virgin America) served SF with American Airlines carried out most of delay about 2.14 time delay per flights. Jetblue airways was the top company carrying out their flight in time.

#Another useful technique is quickly calculating summary statistics for various groups in your data frame. For example, we can modify the above command #using the group_by function to get the same summary stats for each origin airport:
sfo_feb_flights %>%
  group_by(origin) %>%
  summarise(median_dd = median(dep_delay), iqr_dd = IQR(dep_delay), n_flights = n())
## `summarise()` ungrouping output (override with `.groups` argument)
#group by carrier, find mean, and number of flight
sfo_feb_flights %>%
  group_by(carrier) %>%
  summarise(mean_dd = mean(dep_delay), iqr_dd = IQR(dep_delay), n_flights = n())
## `summarise()` ungrouping output (override with `.groups` argument)
# 
# nycflights %>%
#   group_by(month) %>%
#   summarise(mean_dd = mean(dep_delay)) %>%
#   arrange(desc(mean_dd))
# 
# 
# lax_flights %>%
#   summarise(mean_dd   = mean(dep_delay), 
#             median_dd = median(dep_delay), 
#             n         = n())

Exercise 4

Calculate the median and interquartile range for arr_delays of flights in in the sfo_feb_flights data frame, grouped by carrier. Which carrier has the most variable arrival delays? I actualy answered this question in exercise 3, in SFO…. American Airlines carried out most of delay about 2.14 time delay per flights. Jetblue airways was the top company carrying out their flight in time. July is the month with highest departure delay

nycflights %>%
  group_by(month) %>%
  summarise(mean_dd = mean(dep_delay)) %>%
  arrange(desc(mean_dd))
## `summarise()` ungrouping output (override with `.groups` argument)

Exercise 5

Suppose you really dislike departure delays and you want to schedule your travel in a month that minimizes your potential departure delay leaving NYC. One option is to choose the month with the lowest mean departure delay. Answer: October will be the month to flight out of NYC with flight on time.

Another option is to choose the month with the lowest median departure delay. Answer: September and October are will be the month to flight out of NYC with flight on time.

What are the pros and cons of these two choices? I think people don’t like to look at report with room for interpretation, Knowing October is the best month for on time flight is more appreciable for decision making than having September and October. So mean gives more data understanding: Median is like considering a second option. Beside, mean is unique Vs. median that is not.

On time departure rate for NYC airports

# option one: pick the month with the lowest departure delay time  by mean

# nycflights %>%
#   group_by(month) %>%
#   summarise(mean_dd = mean(dep_delay)) %>%
#   arrange(mean_dd)

# option one: pick the month with the lowest departure delay time  by median

# nycflights %>%
#   group_by(month) %>%
#   summarise(median_dd = median(dep_delay)) %>%
#   arrange(median_dd)

# Suppose you will be flying out of NYC and want to know which of the three major NYC airports has the best on time departure rate of departing flights. Also supposed that for you, a flight that is delayed for less than 5 minutes is basically “on time.”" You consider any flight delayed for 5 minutes of more to be “delayed”.
# 
# In order to determine which airport has the best on time departure rate, you can
# 
#     first classify each flight as “on time” or “delayed”,
#     then group flights by origin airport,
#     then calculate on time departure rates for each origin airport,
#     and finally arrange the airports in descending order for on time departure percentage.
# 
# Let’s start with classifying each flight as “on time” or “delayed” by creating a new variable with the mutate function.

nycflights
view(nycflights)
nycflights <- nycflights %>%
  mutate(dep_type = ifelse(dep_delay < 5, "on time", "delayed"))
view(nycflights)
# 
# The first argument in the mutate function is the name of the new variable we want to create, in this case dep_type. Then if dep_delay < 5, we classify the flight as "on time" and "delayed" if not, i.e. if the flight is delayed for 5 or more minutes.
# 
# Note that we are also overwriting the nycflights data frame with the new version of this data frame that includes the new dep_type variable.
# 
# We can handle all of the remaining steps in one code chunk:

nycflights %>%
  group_by(origin) %>%
  summarise(ot_dep_rate = sum(dep_type == "on time") / n()) %>%
  arrange(desc(ot_dep_rate))
## `summarise()` ungrouping output (override with `.groups` argument)

Exercise 6

If you were selecting an airport simply based on on time departure percentage, which NYC airport would you choose to fly out of? It is bit hard to conclude: EWR

# ot <- select(nycflights, origin, dep_type)
# ot
# ot <- select(ot, origin = "LGA" && dep_type = "on time")
# filter(select(ot,origin, dep_type), origin == "LGA" & dep_type == "on time") # 7328 rows
# filter(select(ot,origin, dep_type), origin == "JFK" & dep_type == "on time") #7558 rows
# filter(select(ot,origin, dep_type), origin == "EWR" & dep_type == "on time") # 7498 rows

ggplot(data = nycflights, aes(x = origin, fill = dep_type))  +
  geom_bar()

Exercise 7

Mutate the data frame so that it includes a new variable that contains the average speed, avg_speed traveled by the plane for each flight (in mph). Hint: Average speed can be calculated as distance divided by number of hours of travel, and note that air_time is given in minutes.

nycflights <- nycflights %>%
  mutate(avg_speed = (distance / air_time))
nycflights

Exercise 8

Make a scatterplot of avg_speed vs. distance. Describe the relationship between average speed and distance. Hint: Use geom_point(). Answer: Look like right skewed distribution

x <- nycflights$distance
y <- nycflights$avg_speed


plot(x, y, main="Travel Times",
   xlab="distance", ylab="avg_speed", pch=19) +
  geom_point()

## NULL

Exercise 9

Replicate the following plot. Hint: The data frame plotted only contains flights from American Airlines, Delta Airlines, and United Airlines, and the points are colored by carrier. Once you replicate the plot, determine (roughly) what the cutoff point is for departure delays where you can still expect to get to your destination on time. Answer: I found a strong positive linear between the departure delay and arrival delay for the 03 carriers AA, DL, UA. As departure delay time goes higher, arrival delay time also goes higher too

#delays <- select(nycflights,carrier, dep_delay, arr_delay)
#delays

# filter(select(nycflights,origin, dep_delay, arr_delay), origin == "AA" | origin == "DL" | origin == "UA")
#filter(delays, carrier == "AA", carrier == "DL", carrier == "UA", dep_delay > 0, arr_delay > 0)

delays <- nycflights %>% 
  select(carrier, dep_delay, arr_delay) %>%
  filter(carrier %in% c("UA", "AA", "DL"), dep_delay > 0, arr_delay > 0)
delays
ggplot(delays, aes(x=dep_delay, y=arr_delay, color=carrier)) +
  geom_point() + geom_rug()