dplyr exercises part 1

Filtering

View the flights dataset in RStudio’s data pane. Look up the meaning of the variables in the help.

Hint: You need to load the nycflights13 package.
```
View(___)
```
Find all flights that departed between 8:00 AM and 10:00 PM.
```
flights %>%
  filter(between(dep_time, ___, ___))
```
Find all flights that departed in the three winter months.
```
flights %>%
  filter(month ___ c(___)))
```
Are there any flights where departure time is later than arrival time? What does this mean?
```
flights %>%
  filter(_____)
```
Find all flights that departed today 5 years ago.
```
flights %>%
  filter(month ___, day ___)
```

Arrange

On what day did the flight with the shortest airtime take place?

Hint: Use head() to restrict your result to one row only.
```
flights %>% 
  arrange(___) %>%
  head(1)
```
Which flights had the heaviest delays? Can you use the tail() verb to obtain this information?
```
flights %>% 
  arrange(___) %>%
  tail(1)

flights %>% 
  arrange(desc(___)) %>%
  ___(1)
```
On what day did the flight with the longest airtime take place?
```
flights %>% 
  arrange(___ - ___) %>%
  head(1)
```
Find two equivalent ways to select the six "UA" flights with the lowest delay. Which is faster? Why?

Hint: RStudio has shortcuts for swapping the current line with the next or previous line.
```
flights %>%
  filter(___) %>%
  arrange(___)

flights %>%
  arrange(___) %>%
  filter(___)
```
Which flights were best in recovering from delay in the air?

Assignment, the pipe

View all flights that arrived after 10:00 PM. Use an intermediate variable, a nested expression, and the pipe. Which appeals more to you?
```
flights_after_10 <- filter(flights, ___)
View(flights_after_10)
```
```
View(filter(flights, ___))
```
```
flights %>%
  filter(___) %>%
  View()
```

Extend the four solutions to view all "UA" flights that arrived after 10:00 PM.

flights_after_10 <- filter(flights, ___)
ua_flights_after_10 <- ...
View(___)

View(filter(filter(flights, ___)))

flights %>%
  filter(___) %>%
  filter(___) %>%
  View()

Extend the four solutions to view all "UA" flights that departed before 6:00 AM and arrived after 10:00 PM.
Extend the four solutions to view all "UA" flights that departed before 6:00 AM and arrived after 10:00 PM and had a delay of more than two hours.
Extend the four solutions to view all "UA" flights that departed before 6:00 AM and arrived after 10:00 PM and had a delay of more than two hours, originating in one of New York City’s airports.
Extend the four solutions to view all "UA" flights that departed before 6:00 AM and arrived after 10:00 PM and had a delay of more than two hours, originating in one of New York City’s airports but excluding Honolulu International airport.

Hint: Consult the airports dataset, use a filter with the predicate stringr::str_detect(name, "^Honolulu") .
Sort the result by distance.

Filtering and plotting

Plot a histogram of the air time of all flights. Exclude Honolulu International Airport in Hawaii to get rid of the peak at the right-hand side. Zoom into the flights that have an air time between 400 and 500 minutes.

Hint: Start with flights %>% ggplot() + ...
```
flights %>% 
  ggplot(___) +
    ___()

flights %>% 
  filter(___) %>%
  ggplot(___) +
    ___()

flights %>% 
  filter(___) %>%
  filter(___) %>%
  ___
```
Plot a heat map for all relations with an air time shorter than one hour.

Hint: Use geom_bin2d().
```
flights %>% 
  filter(___) %>%
  ggplot(___) +
    ___()
```
Think of other plots of the flights data that would not work if applied on the full dataset but are useful when applying a filter beforehand.

Combining filters

Look at the “Details” section in the help page for | with help("|") to understand predicate logic in R. (We need element-wise comparisons.)
Find all flights that departed today x years ago, flown by "US". Two simple solutions exist, which appeals most to you?
```
flights %>%
  filter(___, ___) %>%
  filter(___)

flights %>%
  filter(_____)
```
Find all flights that departed before 6:00 AM or after 10:00 PM.
```
flights %>%
  filter(___ | ___)
```
Find all flights not flown by either "UA" or "WN". Can you think of three different solutions? Which appeals more to you?
```
flights %>%
  filter(___ ___ ___)

flights %>%
  filter(!(___) ___ !(___))

flights %>%
  filter(!(_____))
```
Which flights have a missing departure or arrival time? Which have both missing? Can the number of flights that have a missing arrival but not departure time correspond to lost or crashed flights?
```
flights %>%
  filter(is.na(___))

flights %>%
  filter(___(___) ___ _____)

flights %>%
  filter(_____ ___ !_____)
```
Find two equivalent ways to select the six "UA" flights with the lowest delay. Which is faster? Why?

Hint: RStudio has shortcuts for swapping the current line with the next or previous line.
```
flights %>%
  filter(___) %>%
  arrange(___)

flights %>%
  arrange(___) %>%
  filter(___)
```
Find more exercises in items 1 and 4 of Section 5.2.4, and in Section 5.3.1, of r4ds.

Select and rename

Find three ways to select the first five variables from the flights dataset.

flights %>% 
  select(___, ___, ________)

flights %>% 
  select(___:___)

flights %>% 
  select(___:___)

Find three ways to exclude the date of the flight.

flights %>% 
  select(___, ___, ______________________)

flights %>% 
  select(-___, -___, -___)

flights %>% 
  select(-___:-___)

Select all variables related to departure.

flights %>% 
  select(___, ___, _______)

flights %>% 
  select(starts_with("___"))

Move the variables related to scheduled time to the end of the table.
```
flights %>% 
  select(-___, -___, _______, everything(), ___, ___)
```
Create a contour plot of departure and arrival time. Rename the columns to show prettily in the plot. Restrict the plot to all flights that arrive before 5:00 AM.
```
flights %>%
  select(`___` = ___, `___` = ___, ___) %>%
  filter(___) %>%
  ggplot(aes(x = `___`, y = `___`)) +
  geom_density2d()
```
Find more exercises in Section 5.4.1 of r4ds.

Mutate

Store the speed for each flight as miles per hour in a new variable.

flights %>% 
  mutate(miles_per_hour = air_time ___ distance ___ ___) %>%
  ggplot(aes(___)) +
  _____

Can you use an intermediate variable to clarify the intent? How do you remove the intermediate variable?

flights %>% 
  mutate(miles_per_minute = _____) %>% 
  mutate(miles_per_hour = _____) %>% 
  select(_____)

Visualize the speed distribution as a histogram. Would this visualization work without involving mutate()?

flights %>% 
  ______ %>%
  ggplot(aes(___)) +
  _____

# Alternative:
flights %>%
  ggplot(aes(___)) +
  _____

Create a new logical variable that indicates if the flight arrived on time.
```
flights %>%
  mutate(on_time = (___ <= 0))
```

Visualize the aggregated on-time status per airline with a useful text.

flights %>%
  flights %>%
  mutate(
    on_time = _____,
    on_time_desc = if_else(___, "On time", ___)
  ) %>%
  ggplot(aes(___)) +
  geom_bar()

Can you detect a difference in the speed distributions of on-time vs. delayed flights? Ose color of faceting.

speed_and_on_time_info <-
  _____

speed_and_on_time_info %>%
  ggplot() +
  geom_freqpoly(
    aes(x = ___, y = ..density.., color = ___),
    na.rm = TRUE,
    binwidth = 20
  )

speed_and_on_time_info %>%
  ggplot() +
  geom_histogram(
    aes(x = ___),
    na.rm = TRUE,
    binwidth = 20
  ) +
  facet_wrap(~___, ncol = 1)

Create two new variables date_hour and date_ymd, using as.Date() or lubridate::make_date(), respectively. Are the two values the same for all observations? What happens if we omit the tz argument to as.Date()?
```
flights %>%
  mutate(
    ___ = as.Date(___, tz = "EST"),
    ___ = lubridate::make_date(_____)
  ) %>% 
  filter(___)
```
Visualize the deviation from the overall average departure delay for the three airports of New York City. Consider using a violin plot.
```
flights %>%
  mutate(dep_delay_dev = ___ - mean(___)) %>%
  ggplot(aes(___)) +
  _____ +
  _____
```
Find more exercises in Section 5.5.2 of r4ds.