With friends like that, who needs enemies?

Analysis on contributor data to each presidential campaign committee.

Overview

Goals:

Give users a fresh perspective on candidates based on their contributors
Find loopholes in reporting guidelines and FEC data availability
Challenge users to test their own biases against certain candidates

How we do that:

Start with the latest FEC data available
Clean it and make CEO also equal Chief Executive Officer
Provide the user with an easy to use search function on raw data

The Dataset

ggplot(data = dataset, aes(x=candidate_name, y=contributor_money)) +scale_y_continuous("", labels=dollar) + geom_bar(stat="identity",fill="blue") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  ggtitle("Monetary contributions per candidate in Millions") + labs(x="",y="")

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Navigation

In order to explore the dataset, users can choose a specific candidate.

Summary: A Wikipedia-introduction to each candidate
Occupations: The top occupations provided by campaign contributors
Employers - The top companies provided by campaign contributors
States - The top states where campaign contributions were received
Raw - Perform your own searches on the data

On candidate dropdown change, each plot is generated on the fly using Shiny's reactive function.

Observations

One of the biggest media spotlights is on Donald Trump, however, his data is non-existent in this data due to timing.
Getting into the race early is usually seen as a way to get ahead when it comes to fundraising, however, it doesn't appear to impact fundraising, nor performance in the polls.
Rolling over Senate fundraising helps with reporting, but does not necessarily contain the right data

User challenges

Which candidate do you think received the most contributions from pastors?
Which candidate received contributions from a writer on The Simpsons?
Who received the most contributions from actresses?
Which candidate received the most contributions from Google staff?