Part 6 The %>% forward-pipe operator

Let’s review the steps from the previous hands-on:

df_w2 <- pivot_longer(df_w, 
                    cols=c("Weight_day0", "Weight_day10"),
                    values_to="weight", 
                    names_to="day"
                    )
df_w3 <- separate(df_w2, col = "day", 
                  sep="_", 
                  into=c(NA, "day")
                  )

If we are only interested in the final tidy data frame (and not in the intermediate object df_w2), there is a simple and clean way to combine all steps.

The %>% (forward-pipe) operator from the magritrrr package (the tidyverse packages automatically load this operator):

The pipe allows to process the output of a function as the input of the following function: it makes the code easier to read and understand:

  • It pipes the output of a function as the first argument of the next function (from left to right):
    • mytibble %>% function1 is equivalent to function1(mytibble)
    • mytibble %>% function2(y) is equivalent to function1(mytibble, y)
    • mytibble %>% function1 %>% function2 is equivalent to function2(function1(mytibble))

If we go back to the previous exercise, we can simplify the code the following way:

df_w_clean <- pivot_longer(df_w,
                           cols=c("Weight_day0", "Weight_day10"),
                           values_to="weight", 
                           names_to="day") %>%
              separate(col = "day",
                        into=c(NA, "day"))

# This is equivalent to:
df_w_clean <- df_w %>% pivot_longer(cols=c("Weight_day0", "Weight_day10"),
                           values_to="weight", 
                           names_to="day") %>%
                       separate(col = "day",
                            into=c(NA, "day"))

HANDS-ON

Simplify the following piece of code using the %>% operator:

table5_1 <- separate(data=table5, col=rate, into=c("cases", "population"), sep="/")
table5_2 <- unite(data=table5_1, col=year, c("century", "year"), sep="")
Answer 
table5_1 <- table5 %>% 
        separate(col=rate, into=c("cases", "population"), sep="/") %>%
        unite(col=year, c("century", "year"), sep="")