| itle: “Tidytuesday Screencast” |
| ubtitle: "Tidy Tuesday Screencast: Analyzing Thanksgiving dinners in R |
| uthor: “Ryan Masingill” |
| utput: |
| html_document: |
| toc: true |
Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
You must follow the instructions below to get credits for this assignment.
The title of the Screencast is: “Tidy Tuesday Screencast: Analyzing Thanksgiving dinners in R”
This was published on November 21, 2018
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 a thanksgiving survey. The rows represent who celebrates thanksgiving, what type of desert, vegetables, dinner, and sides the people eat on thanksgiving. There was about a thousand respondents to the survey. There are multiple different options for people to choose from in the survey and in the data, it shows this by saying side1, side2, etc.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave uses a lot of graphs in order for the listener to better understand how many people are eating each of the observations. In the beginning Dave grabs the raw data and puts this raw data into an r markdown file by inserting the link. Dave then goes through to understand the data by graphing groups like age, the amount of people that celebrate, etc. Dave just glances through all of the data to see what will be relevant later. Dave then goes through each of the questions he came up with to see how many people ate pie, dessert, turkey, etc. Throughout the exploration of the data Dave maps out what foods are linked with one another ex) if you have Pecan Pie, yam casserole, green beans, rolls, mashed potatoes you are most like to find people having Pumpkin pie with that.
One of the first things that I noticed was that he used GGplot to analyize some of the data. I forget when we learned this, but it is extremely useful when analyzing data. Another thing that I remember learning about was the group_by code (not positive if it was business stats or stats). He also used filter and summarize which I remember learning about to organize data. Most of the other stuff that he did was new to me.
About half of the surveyors had canned cranberry sauce and the other half had homemade cranberry sauce or no cranberry.
Most popular side: mashed potato Most popular pie: Pumpkin Most popular desert: Ice cream
I liked this analysis because it showed us how we can analyze data from surveys. It could be beneficial if for a company you ever do a survey you could go through r markdown and analyze the data. It was also interesting to see what different people have for thanksgiving especially with thanksgiving coming up I also liked at the end with all of the data graphed with multiple links to understand what each meal for different individuals is on thanksgiving. It’s amazing also just how much you can do with Rstudio to analyze your data.