03_piping.md

Piping

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%>%

Steps

Notice how each dplyr function takes a data frame as input and returns a data frame as output. This makes the functions easy to use in a step by step fashion. For example, you could:

1. Filter spotify to just Rap songs, then
2. Select the loudness and energy columns from the result

rap <- filter(spotify, genre == "Rap")
rap <- select(rap, loudness, energy)
rap

## # A tibble: 1,000 x 2
##    loudness energy
##       <dbl>  <dbl>
##  1    -6.71  0.570
##  2   -13.1   0.297
##  3    -5.05  0.638
##  4    -6.58  0.705
##  5   -10.6   0.612
##  6    -8.97  0.762
##  7    -4.57  0.580
##  8    -7.00  0.656
##  9    -5.76  0.503
## 10    -8.42  0.593
## # … with 990 more rows

Redundancy

The result shows us the loudest rap songs and their energy levels in our data set. But take a look at the code. Do you notice how we re-create rap at each step so we will have something to pass to the next step? This is an inefficient way to write R code.

You could avoid creating rap by nesting your functions inside of each other, but this creates code that is hard to read:

select(filter(spotify, genre == "Rap"), loudness, energy)

The dplyr package provides a third way to write sequences of functions: the pipe.

%>%

The pipe operator %>% performs an extremely simple task: it passes the result on its left into the first argument of the function on its right. Or put another way, x %>% f(y) is the same as f(x, y). This piece of code punctuation makes it easy to write and read series of functions that are applied in a step by step way. For example, we can use the pipe to rewrite our code above:

spotify %>% 
  filter(genre == "Rap") %>% 
  select(loudness, energy)

## # A tibble: 1,000 x 2
##    loudness energy
##       <dbl>  <dbl>
##  1    -6.71  0.570
##  2   -13.1   0.297
##  3    -5.05  0.638
##  4    -6.58  0.705
##  5   -10.6   0.612
##  6    -8.97  0.762
##  7    -4.57  0.580
##  8    -7.00  0.656
##  9    -5.76  0.503
## 10    -8.42  0.593
## # … with 990 more rows

As you read the code, pronounce %>% as “then”. You’ll notice that dplyr makes it easy to read pipes. Each function name is a verb, so our code resembles the statement, “Take spotify, then filter it by genre, then select the loudness and energy.”

dplyr also makes it easy to write pipes. Each dplyr function returns a data frame that can be piped into another dplyr function, which will accept the data frame as its first argument. In fact, dplyr functions are written with pipes in mind: each function does one simple task. dplyr expects you to use pipes to combine these simple tasks to produce sophisticated results.

Exercise 1

We’ll use pipes for the remainder of the tutorial. Let’s practice a little by writing a new pipe. The pipe should:

1. Filter spotify to just the songs that are above 0.50 danceability
2. Select the tempo and energy columns

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Answers

Exercise 1

spotify %>% 
  filter(danceability > 0.50) %>% 
  select(tempo, energy) 

## # A tibble: 16,727 x 2
##    tempo energy
##    <dbl>  <dbl>
##  1  79.8  0.647
##  2 141.   0.917
##  3  93.1  0.606
##  4 126.   0.973
##  5 108.   0.919
##  6 132.   0.889
##  7 119.   0.761
##  8  79.6  0.611
##  9 120.   0.613
## 10 100.   0.816
## # … with 16,717 more rows

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