Academic Honesty Statement (fill your name in the blank)

I, Sang Dao, 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

# load required packages here
library(tidyverse)
library(openintro)
library(nycflights13)

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.
wb_tidy <- world_bank_pop %>%
  pivot_longer(cols = -c(country, indicator), 
               names_to = "year", 
               values_to = "value") %>%
  pivot_wider(names_from = indicator, 
              values_from = value)

head(wb_tidy, 10)

Answer:

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

wb_joined <- wb_tidy %>%
  left_join(who_codes, by = c("country" = "iso3")) %>%
  select(country = full_name, year, SP.URB.TOTL, SP.URB.GROW, SP.POP.TOTL, SP.POP.GROW)

head(wb_joined, 10)

Answer:

  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.
urban_change_data <- wb_joined %>%
  filter(year %in% c("2000", "2017")) %>%
  mutate(urban_prop = SP.URB.TOTL / SP.POP.TOTL) %>%
  select(country, year, urban_prop) %>%
  pivot_wider(names_from = year, 
              values_from = urban_prop, 
              names_prefix = "yr_",
              values_fn = mean) %>% 
  mutate(urbanization_change = yr_2017 - yr_2000) %>%
  filter(!is.na(urbanization_change)) %>%
  arrange(desc(urbanization_change))

head(urban_change_data)

Answer: So there are top 6 countries has the trongest undergone significant urbanization between 2000 and 2017.

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_cleaned <- planes %>%
  group_by(manufacturer) %>%
  filter(n() > 10) %>%
  ungroup() %>%
  mutate(manufacturer = factor(manufacturer)) %>%
  mutate(manufacturer = fct_collapse(manufacturer,
    AIRBUS = c("AIRBUS", "AIRBUS INDUSTRIE"),
    MCDONNELL = c("MCDONNELL DOUGLAS", "MCDONNELL DOUGLAS AIRCRAFT CO", "MCDONNELL DOUGLAS CORPORATION")
  ))

head(planes_cleaned, 10)

Answer:

  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.
flights_planes <- flights %>%
  inner_join(planes_cleaned, by = "tailnum")
model_distance_summary <- flights_planes %>%
  group_by(model) %>%
  summarise(
    flight_count = n(),
    avg_distance = mean(distance, na.rm = TRUE),
    median_distance = median(distance, na.rm = TRUE)
  ) %>%
  arrange(desc(avg_distance))

head(model_distance_summary, 10)
top_15_models <- model_distance_summary %>%
  arrange(desc(flight_count)) %>%
  slice(1:15) %>%
  pull(model)

flights_planes %>%
  filter(model %in% top_15_models) %>%
  ggplot(aes(x = fct_reorder(model, distance, .fun = median), y = distance)) +
  geom_boxplot(fill = "steelblue", outlier.alpha = 0.3) +
  coord_flip()

Answer: From the charts it is easy to see that different planes have different jobs. Small planes mostly just do short trips. They usually fly less than 700 miles and stick to the same short routes. But the big Airbus and Boeing planes fly much further. They often go 1,200 or 2,000 miles and do all kinds of long trips across the country.

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") %>%
  mutate(datetime = make_datetime(year, month, day, hour))

ggplot(jfk_weather, aes(x = datetime, y = temp)) +
  geom_line(color = "steelblue", alpha = 0.7)

Answer:

  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).
largest_temp_diff_day <- weather %>%
  group_by(year, month, day) %>%
  summarise(
    max_temp = max(temp, na.rm = TRUE),
    min_temp = min(temp, na.rm = TRUE),
    temp_diff = max_temp - min_temp,
    .groups = "drop" 
  ) %>%
  arrange(desc(temp_diff))

head(largest_temp_diff_day, 1)

Answer:

  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_updated <- flights %>%
  mutate(
    overnight_flag = ifelse((dep_time >= 2200 | dep_time <= 100) & air_time > 240, "YES", "NO")
  )

overnight_flights <- flights_updated %>%
  filter(overnight_flag == "YES")

overnight_flights %>%
  select(tailnum, dep_time, air_time, overnight_flag) %>%
  head(10)

Answer:

  1. Someone says that most overnight flights use relatively small planes. Verify whether this is true with the data frame obtained in
    1. and the planes data set.
flights_with_planes <- flights_updated %>%
  inner_join(planes, by = "tailnum")
size_verification <- flights_with_planes %>%
  filter(!is.na(overnight_flag)) %>% 
  group_by(overnight_flag) %>%
  summarise(
    flight_count = n(),
    avg_seats = mean(seats, na.rm = TRUE),
    median_seats = median(seats, na.rm = TRUE)
  )

print(size_verification)
## # A tibble: 2 × 4
##   overnight_flag flight_count avg_seats median_seats
##   <chr>                 <int>     <dbl>        <dbl>
## 1 NO                   279298      137.          149
## 2 YES                     639      200.          200

Answer:

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.
gss_cat %>%
  filter(!is.na(rincome) & !is.na(tvhours)) %>%
  ggplot(aes(x = fct_reorder(rincome, tvhours, .fun = median, .na_rm = TRUE), y = tvhours)) +
  geom_boxplot(fill = "steelblue") +
  coord_flip() 

oneway.test(tvhours ~ rincome, data = gss_cat)
## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  tvhours and rincome
## F = 53.509, num df = 15, denom df = 1004, p-value < 2.2e-16

Answer: From the chart we can see difference. Who make less money usually watch way more TV than people with high pay. Basically, the more money someone makes, the less time they spend watching TV.

  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.
smokers_data <- smoking %>%
  filter(smoke == "Yes" & !is.na(amt_weekdays) & !is.na(age))
ggplot(smokers_data, aes(x = age, y = amt_weekdays)) +
  geom_point(position = "jitter", alpha = 0.5, color = "darkred") + 
  geom_smooth(method = "lm")

cor(smokers_data$age, smokers_data$amt_weekdays)
## [1] 0.1927826

Answer: The test result show that is was close to zero so the link is pretty weak. If we look at the dot chart the line goes up just a little bit. => Older people might smoke a few more cigarettes during the week, but the dots are spread out all over.