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 four 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.

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(), ___, ___)
```
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 ___ ___)

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(_____)

Create a new logical variable that indicates if the flight arrived on time. Convert it to a useful text, e.g. for later plotting.
```
flights %>%
  mutate(
    on_time = (___ <= 0),
    on_time_desc = if_else(___, "On time", ___)
  )
```
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(___)
```
Find more exercises in Section 5.5.2 of r4ds.

Summarize

Compute the mean arrival and departure delay overall, and per origin airport. What is the standard deviation of these variables? What is New York City’s busiest airport?
```
flights %>%
  summarize(mean(___, na.rm = ___))

flights %>%
  group_by(___) %>%
  summarize(___)

flights %>%
  count(___) %>%
  arrange(___)
```

Which carriers had the longest accumulated air time, excluding cancelled flights?

flights %>%
  group_by(___) %>%
  summarize(acc_air_time = sum(_____)) %>% 
  ungroup()

Which carriers specialize on long-distance routes?

flights
  _____ %>%
  _____ %>%
  _____ %>%
  arrange(___)

Which plane had the most failed departure attempts? Can you find a solution without filter()?

Hint: Use the idiom sum(___) to count the rows where a predicate is true.

flights %>%
  filter(is.na(dep_time)) %>%
  group_by(tailnum) %>%
  _____ %>%
  _____ %>%
  filter(!is.na(tailnum)) %>%
  arrange(desc(___)) %>% 
  head(1)

flights %>%
  group_by(tailnum) %>%
  _____ %>%
  _____ %>%
  arrange(_____) %>%
  head(1)

Compute the ratio of short-distance routes (less than 300 miles) for each airline.

Hint: Use the idiom mean(___) to compute the share of rows where a predicate is true.
```
flights %>%
  group_by(carrier) %>%
  _____ %>% 
  ungroup()
```
Find more exercises in item 1 of Section 5.6.7 of r4ds.

Summarize with multiple variables

Which relation is serviced by the largest number of distinct airlines? Find a solution using summarize(), one using count(), and one using tally(). Which is more elegant?

flights %>%
  group_by(___, ___, airline) %>%
  summarize(n = n()) %>%
  summarize(n_airlines = ___) %>%
  ungroup() %>%
  arrange(___) %>%
  head(1)

flights %>%
  count(_____) %>%
  count(_____) %>%
  _____ %>%
  _____

flights %>%
  group_by(_____) %>%
  tally() %>%
  tally(wt = NULL) %>%
  _____ %>%
  _____

Compute the share of cancelled flights per month per airline.

flights %>% 
  group_by(_____) %>% 
  summarize(share_of_cancelled = _____) %>%
  ungroup()

Find more exercises in Section 5.6.7 of r4ds.

Putting it all together: Preparing a time series for plotting

flights %>%
  mutate(date = lubridate::make_date(year, month, day)) %>%
  group_by(origin, date) %>% 
  summarize(
    dep_delay = median(dep_delay, na.rm = TRUE)
  ) %>% 
  ungroup() %>%
  tsbox::ts_ggplot()

## [time]: 'date' [value]: 'dep_delay'

flights %>%
  mutate(date = lubridate::make_date(year, month, day)) %>%
  group_by(date) %>% 
  summarize(
    dep_delay = median(dep_delay, na.rm = TRUE),
    arr_delay = median(arr_delay, na.rm = TRUE),
  ) %>% 
  ungroup() %>%
  tsbox::ts_long() %>%
  tsbox::ts_ggplot()

## [time]: 'date' 
## [time]: 'date'

dplyr exercises part 1

Kirill Müller

Putting it all together: Preparing a time series for plotting