Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
analyzing pizza ratings
oct 1,2019
Hint: What’s the source of the data; what does the row represent; how many observations?; what are the variables; and what do they mean?
The source of the data is from Jared Lander.The data sets that are used are from the top pizza places from over the world from Barstool sports analysis. The observations shown are showing which pizza places are better from Barstool sports perspectives. Variables that could make it change are different peoples likes and dislikes of pizza and who tries what and makes the reviews just based on their tastes. It shows maps and locations of the pizza shops and reviews that are already online about the pizza. The data sets show that the people who try the pizza in large quantities get a better oportunity to have their place seen if they share videos to Barstool sports.
Hint: For example, importing data, understanding the data, data exploration, etc. Dave first imports the data to R studio from the pizza places and loads up Tidyverse. He sorts though them, and incorporates packages and datasets to show the pizza places on the graphs correctly. He shows where there might be a problem and how to fix the data sets which helps people understand why problems may occur and how to fix their own data sets.
We have learned how to make and depict graphs to show the data which is easier to understand which Dave shows how to in this video. He goes over how to and where to insert the R functions which is how the data will run when you insert it.
That there were no overlaps of the ones with community views than the critic reviews. Also Dave from Barstool sports agrees with customer reviews rather than critics.
How to find weighted averages and how to score the pizza places based on the percentage of respondace.