You must have watched the introductory videos for the course in order to start with this case study.
The case will show you how to download data from Google Analytics, make some basic transformations and will create a report visualizing the data to support on the content creation decisions of the website.
Fork this repository and load its content into R.
Follow the instructions in RunStudy.R. If you have access to a Google Analytics account, use your own data. If not, just load the file GACaseStudyData.csv in the data folder.
Make the needed transformations of the data in RunStudy.R
Open GAReport.Rmd in the folder doc, understand what it does by creating a html report (through RunStudy.R) and create the visualizations/analysis to answer the following questions within the report.
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What are the pages with more visits? Are them the same when medium is not 'cpc'? Can you make a recommendation on the content that creates more awareness?
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What are the exit rates for the pages with the most number of exits? Is there any of them that should not be there? How would you improve the content of these pages to reduce exit rate?
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What are the pages with the highest average time on page? Would you recommend the author to write more on these topics?
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What combinations of sources and mediums attract the more amount of new visitors? Is their exit rate different to the existing visitors?
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When medium is referral, which are the sources that attracted the highest number of pageviews? Based on this, should the author promote the content in any of these websites? Would it be better to invest in visitors coming from the medium 'cpc'?