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Created with R and Gephi.

Scraping Google Trends with R

These R functions will allow you to programmatically download Google Trends data and importing it to R.

Step 1: Install the Google Trends functions from my Github account.
Step 2: Sign in to Google Trends in your main browser
Step 3: define the keywords you need

keywords=c("Samsung", "Apple", "Xiaomi")

Step 4: create list of URL:s (in this example, we'll have only one URL)

url=URL_GT(keywords)

Step 5: specify your browser download directory and set it as your working directory

downloadDir="C:/downloads"
setwd(downloadDir)

Step 6: download the csv:s. The function outputs the file name.

filePath=downloadGT(url, downloadDir)

Step 7: import the csv to R

googletrends_data=readGT(filePath)

In this post, I write about how to merge daily data from Google Trends into longer time series using R.

Creating your own Google Trends plots in R

With this post, I want to demonstrate how you can use the R functions I've built to create your own Google Trends graphs.



First, install the functions by pasting this code into R.

You now have the following functions at your disposal:

• URL_GT(keywords)
• downoloadGT(url, downloadDir)
• readGT(file path)

By running the following code, you will get chart above.

#downloadDir=Where you save the csv from Google Trends
downloadDir="C:/downloads"
setwd(downloadDir)
keywords=c("Samsung", "Apple", "Nokia")
url=URL_GT(keywords)
filePath=downloadGT(url, downloadDir)
smartphones=readGT(filePath)

library(ggplot2)

ggplot(smartphones, aes(x=Date, y=SVI, color=Keyword))+geom_line()

Analysis of daily, weekly and monthly Google Trends data

How will our analysis of search behavior differ if we look at short or long time spans? Here's and example of the search volumes for Supercell's games Boom Beach, Hay Day and Clash of Clans.

Monthly, 2004-present

Boom Beach and Clash of Clans seems to be doing well.

Weekly, past 12 months

When looking ag the weekly data, we see that only Clash of Clans seems to have made any significant gains during the past 12 months.

Daily, past 90 days

The daily data reveals a weekday pattern, where searches during the weekends are higher. There is no increasing trend for either of the games.

Conclusion

The choice of time horizon when when analyzing Google Trends data can make a big difference. It is important to have a clear idea of why a certain time horizon has been chosen for analysis.