Tidy Parallel Processing in R with furrr
Want to share your content on Rbloggers? click here if you have a blog, or here if you don't.
Parallel processing in the tidyverse
couldn’t be easier with the furrr
package. If you are familiar with the purrr::map()
function, then you’ll love furrr::future_map()
, which we’ll use in this FREE RTip training to get a 2.6X speedup in our code.
RTips Weekly
This article is part of RTips Weekly, a weekly video tutorial that shows you stepbystep how to do common R coding tasks.
Here are the links to get set up. 👇
Video Tutorial
Follow along with our Full YouTube Video Tutorial.
Learn how to use furrr
in our 5minute YouTube video tutorial.
Parallel Processing [furrr
Tutorial]
This tutorial showcases the awesome power of furrr
for parallel processing. We’ll get a 2.6X speed boost.
R Package Author Credits
This tutorial wouldn’t be possible without the excellent work of Davis Vaughan, creator of furrr
. For more information beyond the tutorial, check out the furrr package here.
Before we get started, get the R Cheat Sheet
furrr
is great for parallel processing. But, you’ll need to learn purrr
to take full advantage. For these topics, I’ll use the Ultimate R Cheat Sheet to refer to purrr
code in my workflow.
Quick Example:
Step 1. Download the Ultimate R Cheat Sheet.
Step 2. Then Click the “CS” hyperlink to “purrr”.
Step 3. Reference the purrr
cheat sheet.
Onto the tutorial.
Load the Libraries
Get the Data
We’ll use the walmart_sales_weekly
dataset from timetk
. We will do a bit of data manipulation using dplyr
.
select()
: Used to select columnsset_names()
: Used to update the column names
The output is a “tidy” dataset in long format where there are:
 7 ID’s: Each ID represents a walmart store department
 Date and Value: The date and value combination represents the sales in a given week
Purrr: Nest + Mutate + Map
Next, we’ll use a common sequence of operations to iteratively apply an “expensive” modeling function to each ID (Store Deparment) that models the sales data as a function of it’s trend and month of the year.
ProTip 1: Use the R cheat sheet to refer to purrr
functions.
ProTip 2: If you need to master R, then I’ll talk about my 5Course RTrack at the end of the tutorial. It’s a way to upskill yourself with the data science skills that organizations demand. I teach purrr
iteration and nested structures in the RTrack.
Purrr Model Nested Data
We’ll first perform our “expensive modeling” with purrr
, which runs each operation sequentially:

nest()
: To convert the data to a “Nested” structure, where columns contain our data as a list structure. This generates a column called “data”, with our nested data. 
mutate()
andmap()
: We use the combination ofmutate()
to first add a column called “model” andpurrr::map()
to iteratively apply an expensive function. 
“Expensive Function”: The function that we apply is a linear regression using the
lm()
function. We useSys.sleep(1)
to simulate an expensive calculation that takes 1second during each iteration.
Purrr Nested Models and Timing
The output is our nested data now with a column called “model” that contains the 7 Linear Regression Models we just made.
Purrr operations can be expensive. In our case the operation took 7.14 seconds, mainly because we told the “Expensive Function” to sleep for 1second before making the model.
Furrr: Nest + Mutate + Future Map
Now, we’ll redo the calculation, this time changing purrr::map()
out for furrr::future_map()
, which will let us run each calculation in parallel for a speed boost.
Furrr Set Plan and Model Nested Data
The furrr
code is the same as before using purrr
with two important changes:

Add a
plan()
: This allows you to set the number of CPU cores to use when parallel processing. I have 6cores available on my computer, so I’ll use all 6. 
future_map()
: We swappurrr::map()
out forfurrr::future_map()
, which let’s the iterative process run in parallel.
Furrr Nested Models and Timing
The output is the same nested data structure as previously. But we got a 2.6X Speed up (2.57seconds with furrr vs 7.14seconds with purrr)
Summary
We learned how to parallel process with furrr
. But, there’s a lot more to modeling and data science. And, if you are just starting out, then this tutorial was probably difficult. That’s OK because I have a solution.
If you’d like to learn data visualizations, data wrangling, shiny
apps, and data science for business with R, then read on. 👇
My Struggles with Learning Data Science
It took me a long time to learn data science. I made a lot of mistakes as I fumbled through learning R. I specifically had a tough time navigating the ever increasing landscape of tools and packages, trying to pick between R and Python, and getting lost along the way.
If you feel like this, you’re not alone. Coding is tough, data science is tough, and connecting it all with the business is tough.
If you feel like this, you’re not alone.
The good news is that, after years of learning, I was able to become a highlyrated business consultant working with Fortune 500 clients and my career advanced rapidly. More than that, I was able to help others in the community by developing open source software that has been downloaded over 1,000,000 times, and I found a real passion for coding.
In fact, that’s the driving reason that I created Business Science to help people like you and me that are struggling to learn data science for business (You can read about my personal journey here).
What I found out is that:

Data Science does not have to be difficult, it just has to be taught smartly

Anyone can learn data science fast provided they are motivated.
How I can help
If you are interested in learning R and the ecosystem of tools at a deeper level, then I have a streamlined program that will get you past your struggles and improve your career in the process.
It’s called the 5Course RTrack System. It’s an integrated system containing 5 courses that work together on a learning path. Through 5+ projects, you learn everything you need to help your organization: from data science foundations, to advanced machine learning, to web applications and deployment.
The result is that you break through previous struggles, learning from my experience & our community of 2000+ data scientists that are ready to help you succeed.
Ready to take the next step? Then let’s get started.
👇 Top RTips Tutorials you might like:
 mmtable2: ggplot2 for tables
 ggdist: Make a Raincloud Plot to Visualize Distribution in ggplot2
 ggside: Plot linear regression with marginal distributions
 DataEditR: Interactive Data Editing in R
 openxlsx: How to Automate Excel in R
 officer: How to Automate PowerPoint in R
 DataExplorer: Fast EDA in R
 esquisse: Interactive ggplot2 builder
 gghalves: Halfplots with ggplot2
 rmarkdown: How to Automate PDF Reporting
 patchwork: How to combine multiple ggplots
 Geospatial Map Visualizations in R
Want these tips every week? Join RTips Weekly.
Rbloggers.com offers daily email updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/datascience job.
Want to share your content on Rbloggers? click here if you have a blog, or here if you don't.