Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
Analyzing Pizza Ratings.
October 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 Pizza Ratings from Pizza Jared, Pizza Barstool and Pizza Datafiniti. The data is based off of the best pizza places in New York. The variables are questions, votes, answeres, place, time. The orginial data set is 375x10, it has been broken down to the pizza places with at least 29 votes, leaning out to 9 total pizza places. Dave decided to narrow the data down to the top 9 pizza places with the most responednts. These 9 being Prince Street Pizza, NY Pizza Suprema, Rivoli Pizza, Bella Napoli, Saluggi’s, Little Italy Pizza, Fiore’s, Rocky’s, and Williamsburg Pizza. Dave wasnt happy with the data for the top 9, so he had decided to make it the top 16 to get a better understanding this added Previti Pizza, Highline Pizza, Mariella, Pomodoro, Rocco’s Pizza Joint, Pizza Mercato.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave was able to break down the data by gathering an understanding for how many votes each place had. He was able to narrow down the pizza places by most votes. Once Dave had narrowed the places down to a better number to work with, being 9 places, he was able to make a bar graph for each of these places, labeling the y- axis the percent of respondents and the x- axis the answers being Never Again, Poor, Average, Good and Excellent. After Dave decides to change his data to the to a larger scale he used t value to help find the mean and then was able to create scatter charts to see if there was any correalation.
Some things that I have seen in this video that ive seen in class, is when Dave was able to use ggplot and make bar graphs. This helped dave gather a better undersatnding for the data he was analyzing. Another thing that was familiar was studying the t value, this gave Dave more specific infromation which ultimetly led him to create a better alalysis. When Dave was reveiewing the barstool data he has created a scattergraph to see if there would be any correalation, there was not, however I vividly remeber creating and analyzing scattergraphs in class.
A major finding from the analysis is that towards the end of the video, Dave broke down the barstool reviews. He has made a box graph breaking down the top cities that barstool had the most reviews in. The top 3 cities were Boston, Brooklyn and NYC. With NYC being claimed as the “pizza capital of the worl” I was interested to see that Brooklyn was in fact slightly higher based off of the data and statistics.
The most interestling thing that I really liked about this analysis is that I thought it was a very interesting topic. I never would have imagined there being so much information about pizza, I thought Dave had done a phenomenal job of breaking down all of the data, I was able to stay of course and follow him throughout the video due to his clear explanations of what he was doing and why he was doing it. I thought it was a great idea to break down the data and then express the anaylsis throughout several graphs/ charts. It helped me recieve the infromation becaus of the visuals.