The flights data frame in the nycflights23 package contains many different variables. You can identify the names of these variables by running the glimpse() function from the dplyr package:
# A tibble: 435,352 Ă— 2
carrier flight
<chr> <int>
1 UA 628
2 DL 393
3 B6 371
4 B6 1053
5 UA 219
6 AA 499
7 B6 996
8 AA 981
9 UA 206
10 NK 225
# â„ą 435,342 more rows
This function makes it easier to explore large datasets since it allows us to limit the scope to only those variables we care most about. For example, if we select() only a smaller number of variables as shown in Figure 3.8.1, it will make viewing the dataset in RStudio’s spreadsheet viewer more digestible.
Let’s say instead you want to drop, or de-select, certain variables. For example, consider the variable year in the flights data frame. This variable isn’t quite a “variable” because it is always 2023 and hence doesn’t change. Say you want to remove this variable from the data frame. We can deselect year by using the - sign:
Lastly, the select() function can also be used to reorder columns when used with the everything() helper function. For example, suppose we want the hour, minute, and time_hour variables to appear immediately after the year, month, and day variables, while not discarding the rest of the variables. In the following code, everything() will pick up all remaining variables:
Another (usually shorter) way to reorder variables is by using the relocate() function. This function allows you to move variables to a new position in the data frame. For example, if we want to move the hour, minute, and time_hour variables to appear immediately after the year, month, and day variables, we can use the following code:
One more useful function is rename(), which as you may have guessed changes the name of variables. Suppose we want to only focus on dep_time and arr_time and change dep_time and arr_time to be departure_time and arrival_time instead in the flights_time_new data frame:
Note that in this case we used a single = sign within the rename(). For example, departure_time = dep_time renames the dep_time variable to have the new name departure_time. This is because we are not testing for equality like we would using ==. Instead we want to assign a new variable departure_time to have the same values as dep_time and then delete the variable dep_time. Note that new dplyr users often forget that the new variable name comes before the equal sign.
We can also return the top \(n\) values of a variable using the top_n() function. For example, we can return a data frame of the top 10 destination airports using the example from Subsection 3.7.2. Observe that we set the number of values to return to n = 10 and wt = num_flights to indicate that we want the rows corresponding to the top 10 values of num_flights. See the help file for top_n() by running ?top_n for more information.
# A tibble: 10 Ă— 9
dest num_flights airport_name lat lon alt tz dst tzone
<chr> <int> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 BOS 19036 General Edward Lawren… 42.4 -71.0 20 -5 A Amer…
2 ORD 18200 Chicago O'Hare Intern… 42.0 -87.9 672 -6 A Amer…
3 MCO 17756 Orlando International… 28.4 -81.3 96 -5 A Amer…
4 ATL 17570 Hartsfield Jackson At… 33.6 -84.4 1026 -5 A Amer…
5 MIA 16076 Miami International A… 25.8 -80.3 8 -5 A Amer…
6 LAX 15968 Los Angeles Internati… 33.9 -118. 125 -8 A Amer…
7 FLL 14239 Fort Lauderdale Holly… 26.1 -80.2 9 -5 A Amer…
8 CLT 12866 Charlotte Douglas Int… 35.2 -80.9 748 -5 A Amer…
9 DFW 11675 Dallas Fort Worth Int… 32.9 -97.0 607 -6 A Amer…
10 SFO 11651 San Francisco Interna… 37.6 -122. 13 -8 A Amer…
Let’s further arrange() these results in descending order of num_flights:
# A tibble: 10 Ă— 9
dest num_flights airport_name lat lon alt tz dst tzone
<chr> <int> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 BOS 19036 General Edward Lawren… 42.4 -71.0 20 -5 A Amer…
2 ORD 18200 Chicago O'Hare Intern… 42.0 -87.9 672 -6 A Amer…
3 MCO 17756 Orlando International… 28.4 -81.3 96 -5 A Amer…
4 ATL 17570 Hartsfield Jackson At… 33.6 -84.4 1026 -5 A Amer…
5 MIA 16076 Miami International A… 25.8 -80.3 8 -5 A Amer…
6 LAX 15968 Los Angeles Internati… 33.9 -118. 125 -8 A Amer…
7 FLL 14239 Fort Lauderdale Holly… 26.1 -80.2 9 -5 A Amer…
8 CLT 12866 Charlotte Douglas Int… 35.2 -80.9 748 -5 A Amer…
9 DFW 11675 Dallas Fort Worth Int… 32.9 -97.0 607 -6 A Amer…
10 SFO 11651 San Francisco Interna… 37.6 -122. 13 -8 A Amer…
Checkpoint3.8.2.Learning Check 3.16.
What are some ways to select all three of the dest, air_time, and distance variables from flights? Give the code showing how to do this in at least three different ways.
How could one use starts_with(), ends_with(), and contains() to select columns from the flights data frame? Provide three different examples in total: one for starts_with(), one for ends_with(), and one for contains().
