FOODWORLDCUPURL <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/food-world-cup/food-world-cup-data.csv"
#read.csv(FOODWORLDCUPURL)# load data
### Research question You should phrase your research question in a way that matches up with the scope of inference your dataset allows for.** I want to clean and transform my data first to answer this question: who is most likely to have the blandest taste in America? A rank of 3 corresponds to bland. After I’ve removed all 1’s, 2’s, 4’s, 5’s and N/As then I will agreggate 3’s accross columns
These are my features: RespondentID Generally speaking, how would you rate your level of knowledge of cuisines from different parts of the world?
How much, if at all, are you interested in cuisines from different parts of the world? Please rate how much you like the traditional cuisine of [countryName]: Gender Age Household Income
Education
Location (Census Region)
What are the cases, and how many are there? Each case represents a contestant at the 2014 food world cup 1374
Describe the method of data collection. A survey was given at the food world cup, and then I curled it.
The transformation will be a sum of rank 3 foods.
What type of study is this (observational/experiment)? This is an observational study
If you collected the data, state self-collected. If not, provide a citation/link.
https://github.com/fivethirtyeight/data/blob/master/food-world-cup/food-world-cup-data.csv
What is the response variable? Is it quantitative or qualitative? Blandness, which is a quantatative intepretation of a food ranking [qualitative.]
You should have two independent variables, one quantitative and one qualitative. Education: qualitative Location: quantatitative Maybe also add Age, and other demographic features
Provide summary statistics for each the variables. Also include appropriate visualizations related to your research question (e.g. scatter plot, boxplots, etc). This step requires the use of R, hence a code chunk is provided below. Insert more code chunks as needed.
After doing the transformation, will have summaries for blandness groupings, which will be filtered by location etc. Will represent box plots and normal plots interactively using ShinyR and Dplyr