Introduction

This project was done on Tableau and inserted to RMarkdown

Most-visited places sorted

This plot helps determine top five most visited group of places.

Most-visited brands sorted (without null value)

As we can see, most visited places belong to recreational purposes. Full-Service Restaurants offer full-dining package, Lessors of Nonresidential Buildings are malls, Limited-Service Restaurants are mainly fastfood chains, Snack and Nonalcoholic Beverage Bars offer quick sit-down visits. Natural Parks and other Institution don’t provide any brands/specific description so can’t be included here

Customers’ behavior throughout the month of July

This plot demonstrates customers’ habits for checking-in these recreational places through out the month of July. If we pay closer attention to the date, since 4th of July is the public holiday, we see the steepest drop of the month. It’s looking like people spend more time hanging out on the 3rd and use the 4th to relax.

Customers’ behavior based on weekdays

It is understandable that people tend to spend more time to go out beginning Wednesday. Peak time is on Friday and the trend dramatically drops on Saturday and reaches the lowest point on Sunday

Unidentified Brands Percentage of the Top-five Titles

However, the amount of data collected offers a great percentage of null value for brands. Therefore, it would be difficult to use this data for analyzing specific brands’ analysis. For example, we have no idea which national parks are popular but we do know that people like to visit them.

Unidentified Brands Percentage of the whole data set

Unidentifiable brands values do not only appear in the top-visited areas but throughout the table. As we can see, only less than 15% of the records happen to have a specific location attached to it. Hence, we need to ask ourselves if we should rely on this dataset.