Below is a description of the necessary files to run this visualization. This isn't comprehensive of everything in the folder. I kept my old work to give an idea of my thought process while attempting this assignment.
This is the main html file. On my github.io, the visualization is viewable at charleyferrari.github.io/together.html. This file pulls in the relevant javascript libraries (d3.min.js, nv.d3.min.js, and the CSS file nv.d3.css), as well as the javascript file that controls the visualization: together.js.
This is the main javascript file, which controls the drawing and revealing of the text and graphs as the user makes their way through the visualization.
econdata.csv: This data came from the UK Office for National Statistics (http://www.ons.gov.uk/ons/index.html). Their data is available in premade excel tables that I had to manually search through. Unfortunately, there was no automatic way to parse this data directly from the source (it's what keeps my company's economic data business alive!) I copied, pasted, and manipulated this data in excel. You can see some of the raw data in economic data.xlsx. This data is used to feed the first economic data graph.
agents.csv: This data was created ultimately from the Bank of England source file: agents.xlsx. Taking the data tab as a CSV, I was able to use agentscreate.R to shape the data into something workable for my visualization. Most notably, I combined the future and current score columns, and added another column that defines the score type as current or future. This raw data is used to feed the histogram and the google visualization. Together.js filters this data depending on what the user selects for CurrentOrFuture, Score(title), and Sector.
meancvs.csv: This data was created using meancvscreator.R. Because the second meanCVS graph was always going to display the mean CVS score (by sector, or total), I thought it would be quicker to have this calculated beforehand. This is filtered by sec