Homework 3 — Dates, Factors + EDA
Malki On
This homework has two parts. Part 1 uses lubridate and
factors on NYC flight data. Part 2 does a full EDA on the built-in
airquality dataset.
Part 1 — NYC Flights: Dates and Factors
# install.packages(c("nycflights13", "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)
data(flights)Quick look:
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># Q1. Create a column dep_datetime by combining year, month, day, and dep_time into a
# POSIXct datetime using lubridate's make_datetime().
# Hint: hour = dep_time %/% 100, minute = dep_time %% 100
# Show the first 5 rows with year, month, day, dep_time, and dep_datetime.
flights <- flights %>%
mutate(
dep_datetime = make_datetime(
year,
month,
day,
hour = dep_time %/% 100,
min = dep_time %% 100
)
)
head(flights)## # A tibble: 6 × 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
## 6 2013 1 1 554 558 -4 740 728
## # 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># Q2. After creating dep_datetime, use lubridate's month() to filter flights that
# departed in JUNE 2013. How many flights are there?
# (Hint: filter(month(dep_datetime) == 6))
flights_june <- flights %>%
filter(month(dep_datetime) == 6)
nrow(flights_june)## [1] 27234# Q3. The carrier column is a character. Convert it to a factor and check its levels.
flights <- flights %>%
mutate(carrier = as.factor(carrier))
nlevels(flights$carrier)## [1] 16# Q4. Use fct_collapse() to keep "UA", "AA", and "DL" as their own levels and lump
# everything else into "Other". Then count flights per recoded carrier level.
# (Hint: see the fct_collapse demo from the Wrangling Activity Part H)
flights %>%
mutate(
carrier_group = fct_collapse(
carrier,
UA = "UA",
AA = "AA",
DL = "DL",
Other = setdiff(levels(carrier), c("UA", "AA", "DL"))
)
) %>%
count(carrier_group)## # A tibble: 4 × 2
## carrier_group n
## <fct> <int>
## 1 Other 197272
## 2 AA 32729
## 3 DL 48110
## 4 UA 58665# Q5. Missing data: how many flights have NA for dep_delay? Filter them out and report
# the remaining row count.
sum(is.na(flights$dep_delay))## [1] 8255flights_no_na <- flights %>%
filter(!is.na(dep_delay))
nrow(flights_no_na)## [1] 328521Part 2 — Airquality EDA
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ ggplot2 4.0.3 ✔ stringr 1.6.0
## ✔ purrr 1.2.2 ✔ tibble 3.3.1
## ✔ readr 2.2.0 ✔ tidyr 1.3.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata("airquality")# Q6. For Ozone, Temp, and Wind: compute mean, median, sd, min, max
# (use na.rm = TRUE where needed).
airquality |>
summarise(
across(
c(Ozone, Temp, Wind),
list(
mean = ~mean (.x, na.rm =TRUE),
median = ~median(.x, na.rm = TRUE),
sd = ~sd (.x, na.rm =TRUE),
min = ~min(.x, na.rm =TRUE),
max =~max(.x, na.rm = TRUE)
)
)
)## Ozone_mean Ozone_median Ozone_sd Ozone_min Ozone_max Temp_mean Temp_median
## 1 42.12931 31.5 32.98788 1 168 77.88235 79
## Temp_sd Temp_min Temp_max Wind_mean Wind_median Wind_sd Wind_min Wind_max
## 1 9.46527 56 97 9.957516 9.7 3.523001 1.7 20.7Q7. Compare the mean and median for each variable. What does the relationship between mean and median suggest about distribution skewness? What does the standard deviation tell you about variability?
# Q8. Make a histogram of Ozone.
hist(airquality$Ozone)
Q9. Describe the shape of the Ozone distribution. Any outliers or unusual features? The bar all the way to the right is an outlier.
# Q10. Create a new column month_name with full month names (May–September) using case_when.
# Then make a boxplot of Ozone by month_name.
# (Hint: case_when was covered in the Wrangling Activity.)
airquality_months <- airquality |>
mutate(
month_name = case_when(
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September"
)
)
ggplot(airquality_months, aes(x = month_name, y = Ozone)) +
geom_boxplot()## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
**Q11. Which month has the highest median Ozone? Are there outliers in any month?**
August has the highest median Ozone. June, May, August and September have outliers.
# Q12. Scatterplot of Temp vs Ozone, colored by Month.
ggplot(airquality, aes(x = Temp, y = Ozone, color = factor(Month))) +
geom_point()## Warning: Removed 37 rows containing missing values or values outside the scale range
## (`geom_point()`).
Q13. Is there a visible relationship between temperature and ozone? The scatterplot shows how Ozone levels and Temp levels are related. It seems that higher Temp levels are correlated with higher Ozone levels.
# Q14. Compute the correlation matrix for Ozone, Temp, and Wind.
# (Hint: cor(airquality[, c("Ozone","Temp","Wind")], use = "complete.obs"))
cor(airquality[, c("Ozone", "Temp", "Wind")], use = "complete.obs")## Ozone Temp Wind
## Ozone 1.0000000 0.6983603 -0.6015465
## Temp 0.6983603 1.0000000 -0.5110750
## Wind -0.6015465 -0.5110750 1.0000000Q15. Which pair has the strongest correlation? What does that suggest? Ozone and Temp have the strongest correlation (o.69), suggesting that higher Ozone levels also mean higher Temp levels.
# Q16. Generate a summary table grouped by Month with: count, mean Ozone, mean Temp,
# mean Wind for each month.
airquality %>%
group_by(Month) %>%
summarise(
count = n(),
mean_Ozone = mean(Ozone, na.rm = TRUE),
mean_Temp = mean(Temp, na.rm = TRUE),
mean_Wind = mean(Wind, na.rm = TRUE)
)## # A tibble: 5 × 5
## Month count mean_Ozone mean_Temp mean_Wind
## <int> <int> <dbl> <dbl> <dbl>
## 1 5 31 23.6 65.5 11.6
## 2 6 30 29.4 79.1 10.3
## 3 7 31 59.1 83.9 8.94
## 4 8 31 60.0 84.0 8.79
## 5 9 30 31.4 76.9 10.2Q17. Which month has the highest average ozone? How do temperature and wind vary across months? Month 8 has the highest Ozone mean (59.96). mean_Temp varies between 65.5-83.9. Mean_Wind 8.7-11.6