We can modify our code above to look at the average wind speed and its spread, keeping the na.rm = TRUE set just in case any missing values are stored in the column:
# A tibble: 1 × 2
mean std_dev
<dbl> <dbl>
1 9.43 5.27
Say instead of a single mean wind speed for the whole year, we would like 12 mean wind speeds, one for each of the 12 months separately. In other words, we would like to compute the mean wind speed split by month as shown via generic diagram in Figure 3.4.1. We can do this by “grouping” wind speed observations by the values of another variable, in this case by the 12 values of the variable month:
This code is identical to the previous code that created summary_windspeed, but with an extra group_by(month) added before the summarize(). Grouping the weather dataset by month and then applying the summarize() functions yields a data frame that displays the mean and standard deviation wind speed split by the 12 months of the year.
It is important to note that the group_by() function doesn’t change data frames by itself. Rather it changes the meta-data, or data about the data, specifically the grouping structure. Only after applying the summarize() function does the data frame change.
# A tibble: 53,940 × 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
# ℹ 53,930 more rows
Observe that the first line of the output reads # A tibble: 53940 x 10. This is an example of meta-data, in this case the number of observations/rows and variables/columns in diamonds. The actual data itself are the subsequent table of values. Now let’s pipe the diamonds data frame into group_by(cut):
# A tibble: 53,940 × 10
# Groups: cut [5]
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
# ℹ 53,930 more rows
Observe that now there is additional meta-data: # Groups: cut [5] indicating that the grouping structure meta-data has been set based on the 5 possible levels of the categorical variable cut: "Fair", "Good", "Very Good", "Premium", and "Ideal". On the other hand, observe that the data has not changed: it is still a table of \(53940 imes 10\) values. Only by combining a group_by() with another data-wrangling operation, in this case summarize(), will the data actually be transformed.
Let’s now revisit the n() counting summary function we briefly introduced previously. Recall that the n() function counts rows. This is opposed to the sum() summary function that returns the sum of a numerical variable. For example, suppose we’d like to count how many flights departed each of the three airports in New York City:
We see that LaGuardia ("LGA") had the most flights departing in 2023 followed by Newark ("EWR") and lastly by "JFK". Note there is a subtle but important difference between sum() and n(); while sum() returns the sum of a numerical variable, n() returns a count of the number of rows/observations.
You are not limited to grouping by one variable. Say you want to know the number of flights leaving each of the three New York City airports for each month. We can also group by a second variable month using group_by(origin, month):
`summarise()` has grouped output by 'origin'. You can override using the
`.groups` argument.
Note that an additional message appears here specifying the grouping done. The .groups argument to summarize() has four options: drop_last, drop, keep, and rowwise:
In most circumstances, the default is drop_last which drops the last grouping variable. The message is informing us that the default behavior is to drop the last grouping variable, which in this case is month.
Observe that there are 36 rows to by_origin_monthly because there are 12 months for 3 airports (EWR, JFK, and LGA). Why do we group_by(origin, month) and not group_by(origin) and then group_by(month)? Let’s investigate:
What happened here is that the second group_by(month) overwrote the grouping structure meta-data of the earlier group_by(origin), so that in the end we are only grouping by month. The lesson here is if you want to group_by() two or more variables, you should include all the variables at the same time in the same group_by() adding a comma between the variable names.
Recall from Chapter 2 when we looked at wind speeds by months in NYC. What does the standard deviation column in the summary_monthly_windspeed data frame tell us about wind speeds in NYC throughout the year?
Recreate by_monthly_origin, but instead of grouping via group_by(origin, month), group variables in a different order group_by(month, origin). What differs in the resulting dataset?
