How to Write SQL From R
Written by Matt Dancho
The problem with SQL is that there are so many “flavors” (every database has it’s own version of SQL and they are different). And, remembering how to do simple things in SQL can be a huge pain. What if I told you there was an easier way? There is!
Table of Contents
Today I’m going to show you how you can write SQL from R. Here’s what you’re learning today:
- Tutorial: Write SQL from R and query the database (without writing any SQL code)
- Bonus: Formatting SQL like a SQL expert
Get the code.
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R-Tips Weekly
This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Pretty cool, right?
Here are the links to get set up. 👇
This Tutorial is Available in Video
I have a companion video tutorial that shows even more secrets (plus mistakes to avoid). And, I’m finding that a lot of my students prefer the dialogue that goes along with coding. So check out this video to see me running the code in this tutorial. 👇
Writing SQL from R
Get the code.
We’re going to generate SQL from R using 3 key packages:
DBI - For connecting to & working with databasesRSQLite - For working with the SQLite-variety of databasetidyverse - Gives us dbplyr - the database-backened to dplyr - FOR FREE!! OMG!
Step 1: Connect to database
Use DBI to make a connection
DBI has the dbConnect() function. Let’s use this to establish a connection with our SQL database.
Get the code.
Examine the connection. You should see a connection object has been created and the SQL database has one table named “MPG”.
Get the code.
Step 2: Write Dplyr, Generate SQL
Use the dbplyr backend to auto-magically create SQL for us.
DBI has the show_query() function we can use to generate SQL…
Get the code.
…and here’s the result! See how the SQL query using show_query() is automatically formatted. Amazing.
(And now your boss thinks you’re an expert in SQL.)
đź’ˇ Conclusions
You learned how to write SQL from R, connect to a database and query it all from the compfort of R and dplyr. Great work! But, there’s a lot more to becoming a data scientist.
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