How to work with Google n-gram data sets in R using MySQL
Introduction
Google Ngram is a corpus of n-grams compiled from data from Google Books. Here I’m going to show how to analyze individual word counts from Google 1-grams in R using MySQL. I’ve also written an R script to automatically extract and plot multiple word counts. To read more about the datasets go to: http://books.google.com/ngrams/datasets. Of course, one could just use Google Ngram Viewer but what’s the fun in that? And it won’t really give the output that I’m looking for. Since it’s case sensitive queries like “psychotherapy” and “Psychotherapy” will give different results. Using R one can combine match counts regardless of case lettering and display the results in a more intuitive way using ggplot2. If you’re not interested in the technical aspects of this post, you could just jump to the end of it to view an example of different applications of the n-gram database.
Setting up MySQL
Get MySQL
First you need to install and setup MySQL on your system. I’m on Mac OS and it was really straightforward to get MySQL up and running. Here’s the documentation on how to do it on Mac OS.
Download the raw data
Go to http://books.google.com/ngrams/datasets and get the data files for Google 1-gram [highlight]files 0-9[/highlight]. After you’ve downloaded the files unzip them.
Import Google 1-gram into a MySQL database
Since I figured it would take a couple of hours to build the database I first combined all 10 files into one csv-file using cat in Terminal:
Since I’m not really well versed in working with MySQL I used a free GUI (Sequel Pro) to create and import the data. I setup my DB like this:
And imported newly created CSV-file into this structure. I figured it took about 8 hours to build it on my 2,4 GHz Core 2 Duo iMac from 2009, but I didn’t time it. The resulting database contained 470 million rows and landed at 24 GB using InnoDB indexing.
Querying MySQL from R
I’m using the RMySQL-package to get data from MySQL into R. I wrote a function that accepts search terms and fetches the matching results from my Google 1-gram database. I’ve masked my user and password, so you’ve got to change ‘user=”*”, password=”*”` to your own user name and password.
Optimization
MySQL is well optimized to handle OR statements, and it’s a lot faster to send all terms in the same query then to send new queries for each term. Consequently I needed a function that would write out my MySQL query combining the different search terms used. Like this:
Which I then put into the function that connects to MySQL. Using system.time() [highlight]I clocked the run time to about 15 minutes[/highlight] independent of how many search terms I used. I would say that’s pretty decent considering it’s ~470 million rows of data, that I’m hosting on an external FireWire 800 drive.
Cleaning up the data
The MySQL query created a data frame with “n_gram”, “year” and “match_count”. However, since the raw data is case sensitive there are a lot of duplicates with just different lower- and uppercase configurations. Therefore I created a function to combine all 1-grams regardless of letter casing. Google’s n-gram database is not perfect, so sometimes you fetch OCR-errors with your query. I had to add some code to get rid of those erroneous words otherwise tolower() would return an error and the script would stop.
Creating the final data frame
To create the final data frame I run CreateDf() for each query term and combine them into one data frame with ldply(). Lastly I import data containing total counts for each year, which allows me to calculate relative values for each n-gram.
Results
Without smoothing
This is what the raw data look like.
With smoothing
Here I added a smoothing function and ran some more queries.
Women vs Men
Sweden vs Norway vs Denmark vs Finland
Psychodynamic vs Psychoanalysis vs Psychoanalytic vs Psychotherapy
Jesus vs Christ
Gay vs Lesbian vs Homosexual vs Heterosexual
Socialism vs Capitalism
But isn’t this exactly like using Google Ngram Viewer except a lot sexier?
Well, yes, this is exactly like using Google Ngram Viewer except with sexier graphics. However, you could do much more with this data than with Google Ngram Viewer. One could, for instance, aggregate the data with another data set. For example I could combine “socialism” and “capitalism” with data about which US political party were in power at that time. If you have more computer power than I do you could work with 2-9-grams and generate much cooler data.
Ggplot2 R code used here
Smoothed plots
I actually had to write a function to get direct.labels() to display annotations after the smoothed curve instead of after the line of the raw data.
Aggregated plot
I added party data using an example in the book ggplot2: elegant graphics for data analysis. Which I just added to the previous syntax already saved in PLOT
Written by Kristoffer Magnusson, a researcher in clinical psychology. You should follow him on Twitter and come hang out on the open science discord Git Gud Science.
Published April 12, 2012 (View on GitHub)
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Archived Comments (10)
I have been trying to get your code to run but am wondering if there is something I am doing wrong. It appears that I'm having a problem loading the google_n-gram_total_table.txt file. I get an error that the function is deprecated and I have not been able to figure out an alternative to load the database and use it. Any thoughts or ways the code can be modified to work again? Thanks!
Amazing!
Grate work
Hi Kris,
Thanks for your R/MySQL advice. It answered a question that I'd been struggling with for some time.
Thanks,
Ron Wates.
Hi Kris,
I am trying to recreate this experiment (as part of my thesis) on my 2.6 GHz Thinkpad which runs Windows. I've pretty much followed your instructions so far, albeit I've used the command line to set up the DB and the table instead of a GUI. I'm trying to load the giant .csv file directly through the command line and I'm unsure if it's working or not. I actually did try to use a GUI at one point, HeidiSQL, to import the .csv but it froze/stopped responding once it realized how large the file was. What was this process like for you? Am I not being patient enough? Also, do you have any tips on how to get RMySQL to work for Windows? This data is so darn cool but the size of the files makes working with them unwieldy!
Hi Andrew! If I remember correctly it took about 8 hours to import to 1-gram csv-file into MySQL (I'm guessing that's dependent on what indexing that's being used). After I finished this article I began working with the 2-gram data files, but my computer crashed after the import had been loading for 8 days, so I gave it up. So some patience is needed :). Unfortunately, I've never worked with RMySQL on Windows so I can't really help you there.
Hope this helps!
Thanks for the tutorial, but maybe you could set the length of the Year, Match_count, Page_count, Volume_Count to a shorter value than 11, that needs less storage especially with a big table.
Excellent article, it inspires me to use some of your ideas especially as I am just starting tio research natural language propcessing, information retrieval etc.
best wishes and thanks
Fantastic post Kris!
Thank you!