What do positive values of the
gain variable in flights correspond to? What about negative values? And what about a zero value?
Section 3.5 mutate existing variables
mutate() columns.Another common transformation of data is to create/compute new variables based on existing ones as shown in Figure 3.5.1. For example, say you are more comfortable thinking of temperature in degrees Celsius (\({}^{\circ}\text{C}\)) instead of degrees Fahrenheit (\({}^{\circ}\text{F}\)). The formula to convert temperatures from \({}^{\circ}\text{F}\) to \({}^{\circ}\text{C}\) is
\begin{equation*}
\text{temp in C} = \frac{\text{temp in F} - 32}{1.8}
\end{equation*}
We can apply this formula to the
temp variable using the mutate() function from the dplyr package, which takes existing variables and mutates them to create new ones.
weather <- weather |>
mutate(temp_in_C = (temp - 32) / 1.8)
In this code, we
mutate() the weather data frame by creating a new variable temp_in_C = (temp - 32) / 1.8, and then we overwrite the original weather data frame. Why did we overwrite the data frame weather, instead of assigning the result to a new data frame like weather_new?
As a rough rule of thumb, as long as you are not losing original information that you might need later, it’s acceptable practice to overwrite existing data frames with updated ones, as we did here. On the other hand, why did we not overwrite the variable
temp, but instead created a new variable called temp_in_C? Because if we did this, we would have erased the original information contained in temp of temperatures in Fahrenheit that may still be valuable to us.
Let’s now compute monthly average temperatures in both \({}^{\circ}\text{F}\) and \({}^{\circ}\text{C}\) using the
group_by() and summarize() code we saw in Section 3.4:
summary_monthly_temp <- weather |>
group_by(month) |>
summarize(mean_temp_in_F = mean(temp, na.rm = TRUE),
mean_temp_in_C = mean(temp_in_C, na.rm = TRUE))
summary_monthly_temp
# A tibble: 12 Ă— 3 month mean_temp_in_F mean_temp_in_C <int> <dbl> <dbl> 1 1 42.8 6.02 2 2 40.3 4.62 3 3 43.9 6.61 4 4 56.0 13.3 5 5 62.4 16.9 6 6 69.6 20.9 7 7 79.2 26.2 8 8 75.3 24.1 9 9 70.1 21.2 10 10 61.0 16.1 11 11 47.0 8.31 12 12 43.9 6.62
Let’s consider another example. Passengers are often frustrated when their flight departs late, but aren’t as annoyed if, in the end, pilots can make up some time during the flight. This is known in the airline industry as gain, and we will create this variable using the
mutate() function:
flights <- flights |>
mutate(gain = dep_delay - arr_delay)
Let’s take a look at only the
dep_delay, arr_delay, and the resulting gain variables for the first 5 rows in our updated flights data frame in Table 3.5.2.
dep_delay |
arr_delay |
gain |
|---|---|---|
| 203 | 205 | -2 |
| 78 | 53 | 25 |
| 47 | 34 | 13 |
| 173 | 166 | 7 |
| 228 | 211 | 17 |
The flight in the first row departed 203 minutes late but arrived 205 minutes late, so its “gained time in the air” is a gain of -2 minutes, hence its
gain is \(203 - 205 = -2\text{,}\) which is a loss of 2 minutes. On the other hand, the flight in the third row departed late (dep_delay of 47) but arrived 34 minutes late (arr_delay of 34), so its “gained time in the air” is \(47 - 34 = 13\) minutes, hence its gain is 13.
Let’s look at some summary statistics of the
gain variable by considering multiple summary functions at once in the same summarize() code:
gain_summary <- flights |>
summarize(
min = min(gain, na.rm = TRUE),
q1 = quantile(gain, 0.25, na.rm = TRUE),
median = quantile(gain, 0.5, na.rm = TRUE),
q3 = quantile(gain, 0.75, na.rm = TRUE),
max = max(gain, na.rm = TRUE),
mean = mean(gain, na.rm = TRUE),
sd = sd(gain, na.rm = TRUE),
missing = sum(is.na(gain))
)
gain_summary
# A tibble: 1 Ă— 8
min q1 median q3 max mean sd missing
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 -321 1 11 20 101 9.35 18.4 12534
We see for example that the median gain is 11 minutes, while the largest is +101 minutes and the largest negative gain (or loss) at -321 minutes! However, this code would take some time to type out in practice. We’ll see later on in Subsection 5.1.1 that there is a much more succinct way to compute a variety of common summary statistics using the
tidy_summary() function from the moderndive package.
Recall from Section 2.5 that since
gain is a numerical variable, we can visualize its distribution using a histogram.
ggplot(data = flights, mapping = aes(x = gain)) +
geom_histogram(color = "white", bins = 20)
The resulting histogram in Figure 3.5.3 provides additional perspective on the
gain variable beyond the summary statistics computed earlier. For example, note that most values of gain are right around 0.
To close out our discussion on the
mutate() function to create new variables, note that we can create multiple new variables at once in the same mutate() code. Furthermore, within the same mutate() code we can refer to new variables we just created. As an example, consider the mutate() code Hadley Wickham and Garrett Grolemund show in R for Data Science [1]:
flights <- flights |>
mutate(
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)
Checkpoint 3.5.4. Learning Check 3.10.
Checkpoint 3.5.5. Learning Check 3.11.
Could we create the
dep_delay and arr_delay columns by simply subtracting dep_time from sched_dep_time and similarly for arrivals? Try the code out and explain any differences between the result and what actually appears in flights.
Checkpoint 3.5.6. Learning Check 3.12.
What can we say about the distribution of
gain? Describe it in a few sentences using the plot and the gain_summary data frame values.
