Week 4-HW
Ayan Elmi
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:
#install.packages(c("nycflights13", "dplyr", "lubridate", "zoo", "forcats")) # If needed
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) # For factor recoding; base R alternatives are acceptable
data(flights) # Load the datasetExplore 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 #removes
remainderrr, minute = 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(hour= dep_time %/% 100,
minute = dep_time %% 100,
dep_datetime = make_datetime(year, month, day, hour, minute)
)
flights|>
head(5)## # A tibble: 5 × 20
## 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
## # ℹ 12 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>Question 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$weekday) #gets the value for that day ##
## Sun Mon Tue Wed Thu Fri Sat
## 45643 49468 49273 48858 48654 48703 37922Question 3: Time Series with zoo
Filter for flights from JFK (origin == “JFK”) and create a zoo time series of departure delays (dep_delay) by dep_datetime. Plot the time series (use plot()). (Hint: Use a subset to avoid memory issues, e.g., first 1000 JFK flights.)
Output: The time series plot.
JFK<- flights |>
filter(origin == "JFK") |>
arrange(dep_datetime) |>
#GIVE THE ROWS WHERE ORIGIN IS = TO JFK USE = ASSIGN TO RESULT COMPARE ==
slice_head(n=1000)
JFK_ts<-zoo(JFK$dep_delay, JFK$dep_datetime)## Warning in zoo(JFK$dep_delay, JFK$dep_datetime): some methods for "zoo" objects
## do not work if the index entries in 'order.by' are not uniqueplot(JFK_ts, main = "Departure Delays (JFK)", ylab = "Departure delays (minutes)", xlab = "Date")
Question 4: Working with Factors
Convert the origin column (airports: “JFK”, “LGA”, “EWR”) to a factor called origin_factor. Show the factor levels with levels() and create a frequency table with table(). Make a bar plot of flights by airport using barplot().
Output: The levels, frequency table, and bar plot.
flights<- flights|>
mutate(origin_factor = factor(origin))
levels(flights$origin_factor)## [1] "EWR" "JFK" "LGA"origin_table <- table(flights$origin_factor)
origin_table##
## EWR JFK LGA
## 120835 111279 104662barplot(origin_table,
main="Flights From Each Airports",
xlab= "Airport",
ylab= "Number of flights")
Question 5: Recoding Factors
Recode origin_factor from Question 4 into a new column origin_recoded with full names: “JFK” to “Kennedy”, “LGA” to “LaGuardia”, “EWR” to “Newark” using fct_recode() or base R. Create a bar plot of the recoded factor.
Output: The new levels and bar plot.
flights<- flights |>
mutate(origin_recoded=fct_recode(origin_factor,
"Kennedy"="JFK",
"LaGuardia"="LGA",
"Newark"="EWR"))
levels(flights$origin_recoded)## [1] "Newark" "Kennedy" "LaGuardia"recoded_factor_table=table(flights$origin_recoded)
recoded_factor_table##
## Newark Kennedy LaGuardia
## 120835 111279 104662barplot(recoded_factor_table,
main="Flights From Each Airports",
xlab= "Airport",
ylab= "Number of flights")
Question 6: Handling Missing Data
Count missing values in dep_delay and arr_delay using colSums(is.na(flights)). Impute missing dep_delay values with 0 (assuming no delay for cancelled flights) in a new column dep_delay_imputed. Create a frequency table of dep_delay_imputed for delays between -20 and 20 minutes (use filter() to subset).
Output: NA counts, and the frequency table for imputed delays.
colSums(is.na(flights[, c("dep_delay", "arr_delay")])) ## dep_delay arr_delay
## 8255 9430colSums(is.na(flights))## year month day dep_time sched_dep_time
## 0 0 0 8255 0
## dep_delay arr_time sched_arr_time arr_delay carrier
## 8255 8713 0 9430 0
## flight tailnum origin dest air_time
## 0 2512 0 0 9430
## distance hour minute time_hour dep_datetime
## 0 8255 8255 0 8255
## weekday origin_factor origin_recoded
## 8255 0 0flights<- flights |>
mutate(dep_delay_imputed=ifelse(is.na(dep_delay),0,dep_delay))
table(flights$dep_delay_imputed[flights$dep_delay_imputed >= -20 &
flights$dep_delay_imputed <= 20])##
## -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 1704Question 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)
I found that working with the NYC dataset was both interesting and a great learning experience, but also challenging.
The easiest part of working with the dataset was getting the structure and head to see the dataset. Although the hardest part of working with this data set was making the table, which required using the lubridate library functions, such as weekday and make_datetime. However, once I learnt how it works, it was easy to extract the minutes and hours. Also, I found it a bit difficult to understand how to use zoo time series for departure delays.
Furthermore, in question 6, using 0 for missing delays may make the data misleading because it will appear as though the flight is more punctual and has fewer delays, but in reality, it just has missing values.
In conclusion, I learnt that the 2013 NYC dataset had a lot of variety. Constrastingly, it still had a noticeable pattern with weekdays and airports. This assignment helped me learn more about cleaning data and visualizing data in R.