Week 4-HW
Youssef mezni
Homework Assignment: Analyzing NYC Flight Data
This homework assignment uses the flights dataset from
the nycflights13 package, which contains real-world data on
over 336,000 flights departing from New York City airports (JFK, LGA,
EWR) in 2013. The dataset includes variables such as departure and
arrival times (with date components), airline carrier (categorical),
origin and destination airports (categorical), delays (with missing
values for cancelled flights), distance, and more. It is sourced from
the US Bureau of Transportation Statistics.
Objectives
This assignment reinforces the Week 4 topics:
- Parsing and manipulating dates/times using
lubridate. - Creating and analyzing time series with
zoo. - Working with factors, inspecting levels, and recoding them.
- Identifying and handling missing data (e.g., removal, imputation).
All questions (except the final reflection) require you to write and run R code to solve them. Submit your URL for your RPubs. Make sure to comment your code, along with key outputs (e.g., summaries, plots, or tables). Use the provided setup code to load the data.
Setup
Install and load the necessary packages if not already done:
library(nycflights13)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(forcats)
data(flights) Explore the data briefly with str(flights) and
head(flights) to understand the structure. Note: Dates are
in separate year, month, day
columns; times are in dep_time and arr_time
(as integers like 517 for 5:17 AM).
#Explore your data here
str(flights)## tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame)
## $ year : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ month : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ day : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ dep_time : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ...
## $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ...
## $ dep_delay : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
## $ arr_time : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ...
## $ sched_arr_time: int [1:336776] 819 830 850 1022 837 728 854 723 846 745 ...
## $ arr_delay : num [1:336776] 11 20 33 -18 -25 12 19 -14 -8 8 ...
## $ carrier : chr [1:336776] "UA" "UA" "AA" "B6" ...
## $ flight : int [1:336776] 1545 1714 1141 725 461 1696 507 5708 79 301 ...
## $ tailnum : chr [1:336776] "N14228" "N24211" "N619AA" "N804JB" ...
## $ origin : chr [1:336776] "EWR" "LGA" "JFK" "JFK" ...
## $ dest : chr [1:336776] "IAH" "IAH" "MIA" "BQN" ...
## $ air_time : num [1:336776] 227 227 160 183 116 150 158 53 140 138 ...
## $ distance : num [1:336776] 1400 1416 1089 1576 762 ...
## $ hour : num [1:336776] 5 5 5 5 6 5 6 6 6 6 ...
## $ minute : num [1:336776] 15 29 40 45 0 58 0 0 0 0 ...
## $ time_hour : POSIXct[1:336776], format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...head(flights)## # A tibble: 6 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>Question 1: Creating Dates with lubridate
Create a column dep_datetime by combining year, month, day, and
dep_time into a POSIXct datetime using lubridate. (Hint: Use
make_datetime function to combine: year, month, day, for
hour and min use division, e.g., hour = dep_time %/% 100, min = dep_time
%% 100.)
Show the first 5 rows of flights with dep_datetime.
Output: First 5 rows showing year, month, day, dep_time, and dep_datetime.
flights <- flights |>
mutate(dep_datetime = make_datetime(
year = year,
month = month,
day = day,
hour = dep_time %/% 100,
min = dep_time %% 100,
sec = 0
))
flights |>
select(year, month, day, dep_time, dep_datetime) |>
head(5)## # A tibble: 5 × 5
## year month day dep_time dep_datetime
## <int> <int> <int> <int> <dttm>
## 1 2013 1 1 517 2013-01-01 05:17:00
## 2 2013 1 1 533 2013-01-01 05:33:00
## 3 2013 1 1 542 2013-01-01 05:42:00
## 4 2013 1 1 544 2013-01-01 05:44:00
## 5 2013 1 1 554 2013-01-01 05:54:00Question 2: Simple Date Manipulations with
lubridate
Using dep_datetime from Question 1, create a column weekday with the day of the week (e.g., “Mon”) using wday(dep_datetime, label = TRUE). Use table() to show how many flights occur on each weekday.
Output: The table of flight counts by weekday.
flights <- flights |>
mutate(weekday = wday(dep_datetime, label = TRUE))
table(flights$weekday)##
## Sun Mon Tue Wed Thu Fri Sat
## 45643 49468 49273 48858 48654 48703 37922Question 3: Time Series with zoo
Filter for flights from JFK (origin == “JFK”) and create a zoo time series of departure delays (dep_delay) by dep_datetime. Plot the time series (use plot()). (Hint: Use a subset to avoid memory issues, e.g., first 1000 JFK flights using `slice_head().)
Output: The time series plot.
library(dplyr)
library(lubridate)
library(zoo)
library(nycflights13)
jfk <- flights |>
filter(origin == "JFK") |>
arrange(dep_datetime) |>
slice_head(n = 1000)
head(jfk)## # A tibble: 6 × 21
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 542 540 2 923 850
## 2 2013 1 1 544 545 -1 1004 1022
## 3 2013 1 1 557 600 -3 838 846
## 4 2013 1 1 558 600 -2 849 851
## 5 2013 1 1 558 600 -2 853 856
## 6 2013 1 1 558 600 -2 924 917
## # ℹ 13 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>, dep_datetime <dttm>,
## # weekday <ord>delay_ts <- zoo(jfk$dep_delay, jfk$dep_datetime)## Warning in zoo(jfk$dep_delay, jfk$dep_datetime): some methods for "zoo" objects
## do not work if the index entries in 'order.by' are not uniqueplot(
delay_ts,
main = "Departure Delays for JFK Flights",
ylab = "Departure Delay (minutes)",
xlab = "Date/Time"
)
Question 4: Working with Factors
Convert the origin column (airports: “JFK”, “LGA”, “EWR”) to a factor called origin_factor. Show the factor levels with levels() and create a frequency table with table(). Make a bar plot of flights by airport using barplot().
Output: The levels, frequency table, and bar plot.
library(forcats)
flights <- flights |>
mutate(
origin_factor = as_factor(origin),
origin_recoded = fct_recode(
origin_factor,
"Newark" = "EWR",
"Kennedy" = "JFK",
"LaGuardia" = "LGA"
)
)
levels(flights$origin_factor)## [1] "EWR" "LGA" "JFK"table(flights$origin_recoded)##
## Newark LaGuardia Kennedy
## 120835 104662 111279barplot(
table(flights$origin_recoded),
main = "Flights by Airport (Recoded)",
xlab = "Airport",
ylab = "Count",
col = "skyblue"
)
Question 5: Recoding Factors
Recode origin_factor from Question 4 into a new column origin_recoded with full names: “JFK” to “Kennedy”, “LGA” to “LaGuardia”, “EWR” to “Newark” using fct_recode() or base R. Create a bar plot of the recoded factor.
Output: The new levels and bar plot.
library(forcats)
flights <- flights |>
mutate(
origin_recoded = fct_recode(origin_factor,
"Kennedy" = "JFK",
"LaGuardia" = "LGA",
"Newark" = "EWR")
)
levels(flights$origin_recoded)## [1] "Newark" "LaGuardia" "Kennedy"table(flights$origin_recoded)##
## Newark LaGuardia Kennedy
## 120835 104662 111279barplot(table(flights$origin_recoded),
main = "Flights by Airport (Recoded)",
xlab = "Airport",
ylab = "Count",
col = c("skyblue", "lightgreen", "orange"))
Question 6: Handling Missing Data
Count missing values in dep_delay and arr_delay using colSums(is.na(flights)). Impute missing dep_delay values with 0 (assuming no delay for cancelled flights) in a new column dep_delay_imputed. Create a frequency table of dep_delay_imputed for delays between -20 and 20 minutes (use filter() to subset).
Output: NA counts, and the frequency table for imputed delays.
colSums(is.na(flights[, c("dep_delay", "arr_delay")]))## dep_delay arr_delay
## 8255 9430library(dplyr)
colSums(is.na(flights[, c("dep_delay", "arr_delay")]))## dep_delay arr_delay
## 8255 9430missing_prop <- colSums(is.na(flights[, c("dep_delay", "arr_delay")])) / nrow(flights)
missing_prop## dep_delay arr_delay
## 0.02451184 0.02800081flights_no_na <- flights |>
filter(!is.na(dep_delay))
nrow(flights) - nrow(flights_no_na)## [1] 8255flights <- flights |>
mutate(dep_delay_imputed = ifelse(is.na(dep_delay), 0, dep_delay))
flights_filtered <- flights |>
filter(dep_delay_imputed >= -20, dep_delay_imputed <= 20)
table(flights_filtered$dep_delay_imputed)##
## -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8
## 37 19 81 110 162 408 498 901 1594 2727 5891 7875 11791
## -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
## 16752 20701 24821 24619 24218 21516 18813 24769 8050 6233 5450 4807 4447
## 6 7 8 9 10 11 12 13 14 15 16 17 18
## 3789 3520 3381 3062 2859 2756 2494 2414 2256 2140 2085 1873 1749
## 19 20
## 1730 1704hist(flights_filtered$dep_delay_imputed,
main = "Distribution of Imputed Departure Delays (-20 to 20 min)",
xlab = "Departure Delay (minutes)",
ylab = "Frequency",
col = "lightblue",
breaks = 20)
Question 7: Reflection (No Coding)
Reflect on the assignment: What was easy or hard about working with flight dates or missing data? How might assuming zero delay for missing values (Question 6) affect conclusions about flight punctuality? What did you learn about NYC flights in 2013? (150-200 words)
This assignment was quite challenging for me, especially when working with flight dates and missing data. I struggled with several concepts, including converting and manipulating date-time variables, using functions like mutate(), wday(), and zoo() for time series, and understanding how factors actually work in R. It took me a while and I Had to watch several youtube videos so i can truly understand how important it is to structure data properly before plotting or analyzing it. Handling missing data was also confusing — deciding whether to remove or impute values required understanding the impact on the dataset. Assuming a zero delay for missing values made me think critically about data interpretation: it simplifies analysis but could make flights look more punctual than they really were. Overall, I learned that NYC flights in 2013 experienced frequent delays, and cleaning and transforming the data properly is crucial for getting accurate insights. This assignment really pushed me to understand R more deeply and think like a data analyst.