Academic Honesty Statement (fill your name in the blank)

I, YiTao Wang, hereby state that I have not gained information in any way not allowed by the exam rules during this exam, and that all work is my own.

Load packages

library(tidyverse)
library(openintro)
library(nycflights13)
library(lubridate)
library(dplyr)
library(tidyr)
library(ggplot2)

1. Data Tidying and Relational Data

The following questions shall be answered by working with the world_bank_pop and who data sets from the openinto library.

  1. The data set world_bank_pop is not clean. Clean the data set such that the after data tidying you have six columns: country, year, SP.URB.TOTL, SP.URB.GROW, SP.POP.TOTL, SP.POP.GROW. Give your code and show the first 10 rows of the data set after being tidied. Then explain the meaning of each column.
tidy_pop <- world_bank_pop %>%
  pivot_longer(cols = `2000`:`2017`, names_to = "year", values_to = "value") %>%
  pivot_wider(names_from = indicator, values_from = value)

head(tidy_pop, 10)

Answer: country: Country code (short name for country) year: Year of the data SP.URB.TOTL: How many people live in cities SP.URB.GROW: Growth (%) of city population SP.POP.TOTL: Total number of people SP.POP.GROW: Growth (%) of total population

  1. Replace the country column of the tided data set in step a) with full names of the country (for example, replace USA with United States of America) by checking the data frame who, which contains the full name of each country corresponding to the three-digit country code. Give your code and show the updated data set in a manner to illustrate that the task is correctly fulfilled.
country_names <- who %>%
  select(country, iso3) %>%
  distinct()

updated_pop <- tidy_pop %>%
  left_join(country_names, by = c("country" = "iso3")) %>%
  select(country = country.y, year, SP.URB.TOTL, SP.URB.GROW, SP.POP.TOTL, SP.POP.GROW)

head(updated_pop)

Answer: Now the table has country names.

  1. With the data set obtained in step b), answer which countries had undergone significant urbanization between 2000 and 2017. You need to show the code and the results (either graphs or tables) to support your answer.
library(tidyverse)

names_map <- who %>% select(country, iso3) %>% distinct(iso3, .keep_all = TRUE)

urban_data <- world_bank_pop %>%
  pivot_longer(`2000`:`2017`, names_to = "year", values_to = "val") %>%
  pivot_wider(names_from = indicator, values_from = val) %>%
  inner_join(names_map, by = c("country" = "iso3")) %>%
  select(country = country.y, year, SP.URB.TOTL, SP.POP.TOTL)

urban_change <- urban_data %>%
  filter(year %in% c("2000", "2017")) %>%
  mutate(ratio = SP.URB.TOTL / SP.POP.TOTL) %>%
  select(country, year, ratio) %>%
  pivot_wider(names_from = year, values_from = ratio) %>%
  mutate(diff = (`2017` - `2000`) * 100) %>%
  arrange(desc(diff))

head(urban_change, 10)

Answer: Equatorial Guinea is on top, China is at second.

2. Factors and Relational Data

For the following tasks, use data set planes and flights from the nycflights13 package.

  1. For the planes data set, only keep planes from manufacturers that have more than 10 samples in the data set. Then convert manufacturer column into a factor. Then combine AIRBUS and AIRBUS INDUSTRIE as a single category AIRBUS; combine MCDONNELL DOUGLAS, MCDONNELL DOUGLAS AIRCRAFT CO and MCDONNELL DOUGLAS CORPORATION into a single category MCDONNELL. Save your data frame as a new one. Show your code and the first 10 rows of the updated data frame.
planes_filtered <- planes %>%
  group_by(manufacturer) %>%
  filter(n() > 10) %>%
  ungroup()

planes_factor <- planes_filtered %>%
  mutate(manufacturer = fct_collapse(
    manufacturer,
    "AIRBUS"     = c("AIRBUS", "AIRBUS INDUSTRIE"),
    "MCDONNELL"  = c("MCDONNELL DOUGLAS",
                     "MCDONNELL DOUGLAS AIRCRAFT CO",
                     "MCDONNELL DOUGLAS CORPORATION")
  ))

planes_clean <- planes_factor

head(planes_clean, 10)

Answer: All Airbus aircraft variants have now been standardized as “AIRBUS,” while all McDonnell Douglas entries have been standardized as “MCDONNELL.”

  1. Join the flights data set with the planes data set, study how plane models correlate with the flight distance with proper data visualizations or summary tables. You are required to summarize your findings concisely in your own words.
flight_plane <- flights %>%
  left_join(planes, by = "tailnum")

top_models <- flight_plane %>%
  count(model) %>%
  arrange(desc(n)) %>%
  head(15) %>%
  pull(model)

model_distance <- flight_plane %>%
  filter(model %in% top_models) %>%
  group_by(model) %>%
  summarise(avg_distance = mean(distance, na.rm = TRUE),
            n_flights = n()) %>%
  arrange(desc(avg_distance))

model_distance
top10_models_dist <- head(model_distance, 10) %>% pull(model)

flight_plane %>%
  filter(model %in% top10_models_dist) %>%
  ggplot(aes(x = reorder(model, distance, FUN = median), y = distance)) +
  geom_boxplot(outlier.alpha = 0.3) +
  coord_flip() +
  labs(x = "Plane Model", y = "Flight Distance (miles)",
       title = "Flight Distance by Plane Model (Top 10)")

Answer: There is a strong correlation between aircraft model and flight distance, reflecting the adaptability of aircraft design to varying flight ranges.

3. Datetime and Data Transformation

For the following tasks, use the data set weather, flights or planes from the nycflights13 package.

  1. Create a plot of the temperature change across the whole year of 2013 at the JFK airport. (Hint: You need to first create a datetime variable for each hour.)
jfk_weather <- weather %>%
  filter(origin == "JFK", year == 2013) %>%
  mutate(datetime = make_datetime(year, month, day, hour))

ggplot(jfk_weather, aes(x = datetime, y = temp)) +
  geom_line(color = "tomato", alpha = 0.7) +
  geom_smooth(method = "loess", span = 0.05, se = FALSE, color = "darkred") +
  labs(x = "Date", y = "Temperature (F)",
       title = "Hourly Temperature at JFK Airport in 2013") +
  theme_minimal()

Answer: This chart illustrates the seasonal cycle: cold winters and warm summers.

  1. Find out which day of the year has the largest temperature difference (defined as the difference between the highest and the lowest temperature) across the day (0am - 11pm).
daily_range <- jfk_weather %>%
  group_by(year, month, day) %>%
  summarise(temp_range = max(temp, na.rm = TRUE) - min(temp, na.rm = TRUE),
            .groups = "drop") %>%
  slice_max(temp_range, n = 1)

daily_range

Answer: At 2013/5/8 has the largest temperature difference.

  1. Find a way to select all overnight flights (also called “Red Eye Flights” that depart at late night and arrive in the early morning) from the flights data set. Here overnight flights are defined as flights that departed between 10pm and 1am, and having an air time of over 4 hours . Create a categorical variable overnight_flag with YES or NO as the possible values. Show your code and the updated data frame.
flights_overnight <- flights %>%
  mutate(
    overnight_flag = ifelse(
      (sched_dep_time >= 2200 | sched_dep_time <= 100) &
        air_time > 240,
      "YES", "NO"
    )
  )

flights_overnight %>%
  select(year:day, dep_time, sched_dep_time, air_time, overnight_flag) %>%
  head(15)

Answer: The overnight_flag column marks flights that meet the red‑eye definition. The updated data frame includes this variable.

  1. Someone says that most overnight flights use relatively small planes. Verify whether this is true with the data frame obtained in c) and the planes data set.
overnight_planes <- flights_overnight %>%
  left_join(planes, by = "tailnum")

ggplot(overnight_planes, aes(x = overnight_flag, y = seats, fill = overnight_flag)) +
  geom_boxplot(alpha = 0.7) +
  labs(x = "Overnight Flight", y = "Number of Seats",
       title = "Aircraft Size (Seats) for Overnight vs. Non‑Overnight Flights") +
  theme_minimal() +
  scale_fill_manual(values = c("NO" = "gray70", "YES" = "salmon"))
## Warning: Removed 52606 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

overnight_planes %>%
  group_by(overnight_flag) %>%
  summarise(avg_seats = mean(seats, na.rm = TRUE),
            median_seats = median(seats, na.rm = TRUE),
            n = n())

Answer: In direct contrast to the aforementioned claim, night flights tend to utilize larger—rather than smaller—aircraft models.

4. General Analysis and Statistical Tests

Answer the following questions with data visualization or summary. You are required to summarize your findings concisely in your own words and support your conclusion with proper graphs or tables.

  1. From the gss_cat data set, find factors that are significantly correlated with the reported income.

Answer: <>

  1. From the smoking data set of the openintro package, find find factors that are significantly correlated with the smoking status and the number of cigarettes smoked per day.
# Enter code here.

Answer: