Create a list-column data.frame

Let's end our chapter with an implementation of our links extractor, but using a list-column. The idea when using a nested dataframe (i.e., dataframe with a list column) is to keep everything inside a dataframe so that the workflow stays tidy.

You have been provided a tibble called df, which has a column urls with the four URLs you've been using since the beginning of this chapter. If you want to have a look at this dataframe, feel free to print it in the console.

We are going to create a new column called links, which contains the results of the get_links() function (available in your workspace). As the outputs of this function have different lengths, the output will be a list column that you will then need to unnest() to get back a standard dataframe.

This exercise is part of the course

Intermediate Functional Programming with purrr

Exercise instructions

Load the three necessary packages: dplyr, tidyr, and purrr
Take the df element, and run mutate() on it. mutate() will map the get_links() function on the urls column.
Print the result.
Unnest the result.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load dplyr, tidyr, and purrr




# Create a "links" columns, by mapping get_links() on urls
df2 <- df %>%
  mutate(___ = map(___, get_links)) 

# Print df2 to see what it looks like


# unnest() df2 to have a tidy dataframe
df2 %>%
  ___(cols=c(links))

Edit and Run Code

This exercise is part of the course

Intermediate Functional Programming with purrr

IntermediateSkill Level

4.9+

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Do lambda functions, mappers, and predicates sound scary to you? Fear no more! After refreshing your purrr memory, we will dive into functional programming 101, discover anonymous functions and predicates, and see how we can use them to clean and explore data.

Exercise 1: purrr basics - a refresher Exercise 2: Refreshing your purrr memory Exercise 3: Another purrr refresher Exercise 4: Introduction to mappers Exercise 5: Creating lambda functions Exercise 6: Lambda functions Exercise 7: Using mappers to clean up your data Exercise 8: Clean up your data with keep Exercise 9: Split up with keep() and discard()Exercise 10: Predicates Exercise 11: What is a predicate?Exercise 12: Exploring data with predicates

Ready to go deeper with functional programming and purrr? In this chapter, we'll discover the concept of functional programming, explore error handling using including safely() and possibly(), and introduce the function compact() for cleaning your code.

Exercise 1: Functional programming in R Exercise 2: Everything that happens is a function call Exercise 3: Identifying pure functions Exercise 4: Tools for functional programming in purrr Exercise 5: Safe iterations Exercise 6: Create a function Exercise 7: Using possibly()Exercise 8: A possibly() version of read_lines()Exercise 9: Everything in one call Exercise 10: Handling adverb results Exercise 11: Purrrfecting our function Exercise 12: Extracting status codes with GET()

In this chapter, we'll use purrr to write code that is clearer, cleaner, and easier to maintain. We'll learn how to write clean functions with compose() and negate(). We'll also use partial() to compose functions by "prefilling" arguments from existing functions. Lastly, we'll introduce list-columns, which are a convenient data structure that helps us write clean code using the Tidyverse.

Exercise 1: Why cleaner code?Exercise 2: How to write compose()Exercise 3: Back to the office Exercise 4: Building functions with compose() and negate()Exercise 5: Build a function Exercise 6: Count the NA Exercise 7: Prefilling functions Exercise 8: A content extractor Exercise 9: Another extractor Exercise 10: List columns Exercise 11: About list-columns Exercise 12: Create a list-column data.frame

Current Exercise

We'll wrap up everything we know about purrr in a case study. Here, we'll use purrr to analyze data that has been scraped from Twitter. We'll use clean code to organize the data and then we'll identify Twitter influencers from the 2018 RStudio conference.

Exercise 1: Discovering the dataset Exercise 2: Playing with tweets, round 1 Exercise 3: Identify profiles Exercise 4: Extracting information from the dataset Exercise 5: Counting favorites Exercise 6: Extracting mentions Exercise 7: Manipulating URLs Exercise 8: Analyzing URLs Exercise 9: Playing with URLs Exercise 10: Identifying influencers Exercise 11: Splitting the dataset Exercise 12: We have a winner!Exercise 13: Congratulations!