Google Trends Analysis using R

Coursera Project Network

Get Google trends data into the RStudio workspace

Manipulate Google trends data using R libraries

Analyze Google trends data using R libraries

Clock2 hours
Comment Dots英語

Welcome to this project-based course Google Trends Analysis using R. In this project, you will learn how to perform extensive exploratory data analysis on Google trends data using different R packages. By the end of this 2-hour long project, you will understand how to get data from Google trends into your RStudio workspace. Also, you will learn how to use different dplyr verbs such as the select verb, filter verb, arrange verb, and mutate verb to manipulate the Google trends data about the word “Covid.” By extension, you will learn how to use the ggplot2 package and other advanced R plotting libraries to render beautiful plots and maps from the data returned from using the dplyr verbs. You will learn how to use the R markdown file to organize your work and how to knit your code into an HTML document for publishing. Although you do not need to be a data analyst expert or data scientist to succeed in this guided project, it requires an intermediate knowledge of using R, especially working with the dplyr and ggplot2 packages. Therefore, to complete this project, it is required that you have prior experience with using R dplyr and ggplot2 packages. Please don’t get discouraged; I’ve got you covered. If you are not familiar with working with these packages I have mentioned, then you have to take my projects on “Data Manipulation with dplyr in R” and “Data Visualization using dplyr and ggplot2 in R”. So, taking these projects will give the needed requisite to go ahead with this project on Google Trends Analysis using R. However, if you are comfortable with working with the dplyr and ggplot2 packages, please join me on this wonderful ride! Let’s get our hands dirty!


Data ManipulationData AnalysisGgplot2dplyrData Visualization (DataViz)



  1. Getting Started

  2. Getting Google trends data

  3. Interest Over Time Data

  4. Visualize the Interest Over Time Data

  5. Related Queries Data

  6. Interest by Country Data

  7. Interest by City Data

  8. Wrap Up