This data includes Political Party donations from people in the Cincinnati area. It also has data on where the person lives and works, which provides for interesting analysis!
library(tidyverse) #installs the tidyverse world of commands for our use!
library(DT) #making pretty javascript data tables
library(skimr)
download.file("https://myxavier-my.sharepoint.com/:x:/g/personal/largentm_xavier_edu/ERYKo_XmeLZBixoyOKFdvfEBK1TZZ5yf9Z4T6IDUFJcm0w?download=1","cincy_politics.csv")
cincy_politics <- read.csv("cincy_politics.csv")
| Variable | Description |
|---|---|
| contributor_last_name | Last name of contributor |
| contributor_first_name | First name of contributor |
| contributor_street_1 | Street address of contributor |
| contributor_employer | Current employer of contributor |
| contributor_occupation | Occupation of contributor |
| contributition_receipt_date | Day contribution was received |
| contributition_receipt_amount | Amount of contribution |
| contribution_aggregate_ytd | Amount given by contributor on aggregate |
| committee_name | Committee benefitting from contribution |
| committee_type | Type of committee benefitting |
| committee_party_affiliation | Political Party affiliation of committee |
## # A tibble: 6 x 2
## committee_party_affiliation percent_of_donations
## <fct> <dbl>
## 1 DEMOCRATIC PARTY 46.0
## 2 DEMOCRATIC-FARMER-LABOR 0.104
## 3 GREEN PARTY 0.0377
## 4 INDEPENDENT 0.118
## 5 LIBERTARIAN PARTY 0.0591
## 6 REPUBLICAN PARTY 53.6
I summed the contribution amounts for each committee type, and it is clear that most of them are just for a party itself, not necessarily a specific candidate. It would be more interesting to see this data in a non-election year when the Presidential race is not taking up such a large majority of the donations.