Homework 3 — Dates, Factors + EDA
Cheslav Lukashanets
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"))
library(nycflights13)## Warning: package 'nycflights13' was built under R version 4.5.3library(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)## Warning: package 'lubridate' was built under R version 4.5.3##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(zoo)## Warning: package 'zoo' was built under R version 4.5.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(forcats)## Warning: package 'forcats' was built under R version 4.5.3data(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)
# Then, show the first 5 rows with 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))
flights |>
select(year, month, day, dep_time, dep_datetime) |>
head()## # A tibble: 6 × 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 05:54:00
## 6 2013 1 1 554 2013-01-01 05:54:00# 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 |> filter(month(dep_datetime) == 6) |> nrow()## [1] 27234# number of flights: 27,234
# Q3. The carrier column is a character. Convert it to a factor and check its levels.
flights$carrier <- as.factor(flights$carrier)
levels(flights$carrier)## [1] "9E" "AA" "AS" "B6" "DL" "EV" "F9" "FL" "HA" "MQ" "OO" "UA" "US" "VX" "WN"
## [16] "YV"# Q4. Use fct_collapse() to keep "UA", "AA", and "DL" as their own levels and lump
# everything else into "Other". Then count flights per recoded carrier level.
# (Hint: see the fct_collapse demo from the Wrangling Activity Part H)
flights$carrier <- fct_collapse(flights$carrier,
other = c("9E", "AS", "B6", "EV", "F9", "FL", "HA", "MQ", "OO", "US", "VX", "WN", "YV"))
# Q5. Missing data: how many flights have NA for dep_delay? Filter them out and report the remaining row count.
flights |> filter(!is.na(dep_delay)) |> nrow()## [1] 328521# remaining rows: 328521Part 2 — Airquality EDA
library(tidyverse)## Warning: package 'ggplot2' was built under R version 4.5.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ ggplot2 4.0.1 ✔ stringr 1.5.1
## ✔ purrr 1.1.0 ✔ tibble 3.3.0
## ✔ readr 2.1.5 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata("airquality")# Q6. For Ozone, Temp, and Wind: compute mean, median, sd, min, max
# (use na.rm = TRUE where needed).
summary(airquality$Ozone)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 18.00 31.50 42.13 63.25 168.00 37sd(airquality$Ozone, na.rm = TRUE)## [1] 32.98788summary(airquality$Temp)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 56.00 72.00 79.00 77.88 85.00 97.00sd(airquality$Temp)## [1] 9.46527summary(airquality$Wind)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.700 7.400 9.700 9.958 11.500 20.700sd(airquality$Wind)## [1] 3.523001Q7. 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? Ozone levels: mean is higher than median, suggests distribution is skewed right. Standard deviation is high, suggests high variability.
Temperature: mean is slightly lower than median, suggests normal distribution. Standard deviation is moderate, suggests moderate variability.
Wind levels: mean is slightly higher than median, suggests normal distribution. Standard deviation is low, suggests low variability.
# Q8. Make a histogram of Ozone.
ozone <- airquality |>
ggplot(aes(x = Ozone)) +
geom_histogram()
ozone## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_bin()`).
Q9. Describe the shape of the Ozone distribution. Any outliers or unusual features?
Skewed right, with two outliers (> 131.125).
# 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 <- airquality |>
mutate(
month_name = case_when(
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September"))
airquality |>
ggplot(aes(x = month_name, y = Ozone, order)) +
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?
July has the highest median ozone levels. August, June, and May each have a single outlier, whilst September has four.
# Q12. Scatterplot of Temp vs Ozone, colored by Month.
airquality |>
ggplot(aes(x = Temp, y = Ozone, colour = month_name)) +
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?
Yes, a positive correlation.
# 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 temperature, suggesting that they are correlated.
# Q16. Generate a summary table grouped by Month with: count, mean Ozone, mean Temp,
# mean Wind for each month.
airquality |>
group_by(month_name) |>
summarize(n(), mean(Ozone, na.rm = TRUE), mean(Temp), mean(Wind))## # A tibble: 5 × 5
## month_name `n()` `mean(Ozone, na.rm = TRUE)` `mean(Temp)` `mean(Wind)`
## <chr> <int> <dbl> <dbl> <dbl>
## 1 August 31 60.0 84.0 8.79
## 2 July 31 59.1 83.9 8.94
## 3 June 30 29.4 79.1 10.3
## 4 May 31 23.6 65.5 11.6
## 5 September 30 31.4 76.9 10.2Q17. Which month has the highest average ozone? How do temperature and wind vary across months?
August has the highest average ozone levels. Average temperature increases from May, peaks in August, and starts to decrease in September. Wind levels are inverse to temperature: highest in May, reach a low in August, and increase in September.