15  Summarising

Counting with summarise()

Now we are moving into real data analysis - which often is a case of counting things in your data.

The verb summarise will take all the rows in your data and run some function on them to produce a desired summary. It looks a little like mutate(): first we provide the new column name for the summary, followed by the instruction.

We should consider two types of counting: we could count the number of rows, or we could count some kind of value.

Examples should help. To calculate the total number of rows, we can use a function called n(), which does exactly that:

What if we want to calculate the total amount of Fare paid? In that case, we can use a function called sum():

Note

Notice that as well as putting the Fare column within the brackets of the sum() function, there is also some extra text after a comma: na.rm = TRUE? This is an extra instruction to the sum function, telling it to exclude values where there is no data (NA). Otherwise the result will return NA as the value.

Here is a list of some of the most useful summarise functions:

• Center: mean(), median()

• Spread: sd(), IQR(), mad()

• Range: min(), max(),

• Position: first(), last(), nth(),

• Count: n(), n_distinct()

• Logical: any(), all()

Save some typing by using tally()

Because summarise combined with n() or sum() is used so frequently, there is a shorter way of writing it, in a single command. To calculate the total rows you can use the verb tally(). You don’t even need to specify a new column name, as it will call it n by default. In other words, tally() is exactly the same as writing summarise(n = n()).

tally(Fare) is the same as writing summarise(n = sum(Fare, na.rm = TRUE))

Exercises:

What is the average age of all the passengers? Average can be calculated using a function called mean().

What is the maximum Fare paid? You can use the function max() for this.