A bit more tricky, the group_by() function allows for custom aggregation
in order to make other dplyr functions even more powerful.
library('tidyverse')
library('nycflights13')
Group By
summarize() becomes extremely useful when paired with the final
primary dplyr function, group_by(). group_by() changes the unit of
analysis from the complete dataset to an individual group, thus changing
the scope of summarize. In the above example, the flights dataset is
grouped by the dest variable, destination. Grouping by destination by
itself does nothing to the data frame, so the then pipe, %>%, is used
to push the output to the summarize() function. Summarize first
creates a count variable that is equivalent to the function n(). n()
is another one of those secondary dplyr functions that often comes in
handy; n() is a function that finds the number of observations in the
current group. Next, summarize() creates a variable named distance
based off the mean distance travelled by each observation, as grouped by
destination. Finally, summarize() creates a variable named delay,
based off the mean arrival delay each observation experienced, as
grouped by destination. The resulting data frame gives excellent insight
into each of the 105 destinations present in the flights dataset.
group_by(flights, dest) %>%
summarize(count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
)
## # A tibble: 105 x 4
## dest count dist delay
## <chr> <int> <dbl> <dbl>
## 1 ABQ 254 1826 4.38
## 2 ACK 265 199 4.85
## 3 ALB 439 143 14.4
## 4 ANC 8 3370 -2.5
## 5 ATL 17215 757. 11.3
## 6 AUS 2439 1514. 6.02
## 7 AVL 275 584. 8.00
## 8 BDL 443 116 7.05
## 9 BGR 375 378 8.03
## 10 BHM 297 866. 16.9
## # … with 95 more rows
Really take a look into group_by(), often it’s your data wrangling solution, and you didn’t
even know it!
- Fisher
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