Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
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Analyzing Board Games & Predicting Ratings ## Q2 When was it published? March 15, 2019 ## Q3 Describe the data 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? Data set comes from The Board Game Geek database. The data represents games with at least 50 ratings from 1950-2016. The Variables consist of game id, descriprion, image, maximum players, maximum play time, minimum age, minimum players, minimum playtime, name, play time, thumbnail, year published, artist, category, compiliation, designer, expansion, family, mechanic, publisher, average rating and users rated. There are 10,532 observations. The row represents each game observed. ## Q4-Q5 Describe how Dave approached the analysis each step. Hint: For example, importing data, understanding the data, data exploration, etc. He imports the data much like the way we have been doing in class. He begins by organizing the data by publisher to determine how many there are. From there he begins to look at the years of publication and how many games are published per year. From this he generates a line plot to display the data. He discovers that most of the data is recent and there is a peak in 2015-2016. To better understand the data he goes back to look at what each variable represents. He then makes a histogram to look at the average ratings. He then makes another graph to look at the distribution of ratings by users. ## Q6 Did you see anything in the video that you learned in class? Describe. In class we have been using the data sets to create graphs to analyze. In this video he does this several times in order to better understand and to better explain what the numbers of the data set mean. We have also been learning how to sort the data in order to see the the specific data that we want. He uses predictors and from that data he creates histrograms that represent the data. We have also been working a lot with what variables are significant. Dave discusses the p value a great deal throughout this analysis. ## Q7 What is a major finding from the analysis. A major finding within this analysis was that the majority of these games seem to be card games and war games. From the data we can predict whether or not a game by a specific creator will be successful or not. ## Q8 What is the most interesting thing you really liked about the analysis. I think it was interesting to see that he could take this large data set and sort it in a way that he could see 10,532 board games. The data set gave the game name, a description and so much more. It was very interesting to see the different board games that were created and how many people play these games. It was interesting to see that there was a peak from 2015-2016 for the publication of board games. ## Q9 Display the title and your name correctly at the top of the webpage.