R Notebook

Document set-up

1.) Import data and format data

library(tidyverse)
library(lubridate)
library(DT)
library(stringr)

'%!in%' <- function(x,y)!('%in%'(x,y))

# Set working directory to file location
setwd("~/Documents/GitHub/2020-10-campaign-contributions/workspace")

#import candidates from primary and general
candidates <- read_csv('data/candidates.csv')

#candidates not on ballot
not_on_ballot <- read_csv('committees_not_on_ballot.csv')

# import 2020 contributions from individuals to state races.
contributions.raw <- read_csv('data/all_contributions_2020.csv')

  
# convert names to snake_case
colnames(contributions.raw) <- gsub(' ', '_', tolower(colnames(contributions.raw)))

contributions.noParty <- contributions.raw %>% 
    mutate(contribution_date = as.Date(contribution_date, format='%m/%d/%Y') )  %>% 
    # removes records where office is NA, leaving only contributions to committees supporting candidates
    # removes candidate laons
    filter(
      !is.na(office),
      contribution_type != 'Candidate Loan'
    )


# join party data from candidate list
contributions <- left_join(
  contributions.noParty %>% 
    filter(
      receiving_committee %!in% not_on_ballot$committee
    ) %>% 
    mutate(
      committeeJoin = gsub(' ','', receiving_committee)
    ),
  candidates %>% 
    select(committee, party, ballotName, inGeneral) %>% 
    mutate(
      committeeJoin = gsub(' ','', committee)
    ),
  by = 'committeeJoin'
) %>% 
  filter(!is.na(party)) %>%
  select(-filing_period) %>% 
  distinct()
# original      17,704
# not on ballot 17,311
# no party join 17,117
# distinct      15,873











contributions.individuals <- contributions %>% 
  filter(
      contributor_type %in% c('Individual', "Total of Contributions not exceeding $100"),
      contribution_type %in% c("Check", "Electronic Fund Transfer", "Credit Card", "Cash", "Total of Contributions not exceeding $100")
    )

---

Data overview

key	value
contributions	all non-candidate-loan contributions to any candidate committee (where is.na(Office) & contribution_type != 'Candidate Loan' )
contributions.individuals	direct monetary contributions from individuals to any candidate committee (where contribution_type %in% c("Check", "Electronic Fund Transfer", "Credit Card", "Cash", "Total of Contributions not exceeding $100") & is.na(Office) & contributor_type == 'Individual' )
candidates	all candidates in the Delaware primary and general elections
contributions.raw	all contributions to any committee

---

Questions

Total

How much money has been contributed total?

What groups or individuals are the top donors to each party?

`summarise()` regrouping output by 'contributor_name' (override with `.groups` argument)

What groups or people donated to candidates from both parties?

`summarise()` ungrouping output (override with `.groups` argument)

Contributions where contributor_name contains “Police”

Individuals

How much money has been contributed by individuals total?

How much money has been contributed by individuals to each party

contributions.individuals %>% 
  group_by(party) %>% 
  summarise(total = sum(contribution_amount)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` ungrouping output (override with `.groups` argument)

Who the top donors among individuals?

contributions.individuals %>% 
  group_by(contributor_name) %>% 
  summarise(total = sum(contribution_amount)) %>% 
  arrange(desc(total)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` ungrouping output (override with `.groups` argument)

Who the top donors by party among individuals?

contributions.individuals %>% 
  group_by(contributor_name, party) %>% 
  summarise(total = sum(contribution_amount)) %>% 
  arrange(desc(total)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` regrouping output by 'contributor_name' (override with `.groups` argument)

How much money has been contributed to each office (primary and general) by individuals?

contributions.individuals %>% 
  group_by(office, party) %>% 
  summarise(total = sum(contribution_amount)) %>% 
  arrange(desc(total)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` regrouping output by 'office' (override with `.groups` argument)

How much money has been contributed to each candidate by individuals?

contributions.individuals %>% 
  group_by(ballotName, party) %>% 
  summarise(total = sum(contribution_amount)) %>% 
  arrange(desc(total)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` regrouping output by 'ballotName' (override with `.groups` argument)

How much money has been contributed to each general election candidate by individuals?

contributions.individuals %>%
  filter(inGeneral == 1) %>% 
  group_by(ballotName) %>% 
  summarise(total = sum(contribution_amount)) %>% 
  arrange(desc(total)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` ungrouping output (override with `.groups` argument)

How many people donated to multiple candidates?

contributions.individuals %>% 
  select(
    contributor_name, committee
  ) %>% 
  distinct() %>% 
  group_by(contributor_name) %>%
  summarise(numCandidates = n()) %>% 
  ungroup() %>% 
  group_by(numCandidates) %>% 
  summarise(group_count = n()) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)

How many people donated to candidates from both parties?

contributions.individuals %>% 
  select(
    contributor_name, party 
  ) %>% 
  distinct() %>% 
  group_by(contributor_name) %>%
  summarise(numParties = n()) %>% 
  ungroup() %>% 
  group_by(numParties) %>% 
  summarise(group_count = n()) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)

---

How much money John Carney and Julianne Murray received in donations from individuals

`summarise()` ungrouping output (override with `.groups` argument)

How much money John Carney and Julianne Murray received in donations from PACS, business groups, labor uniions, and non-profit organizations

`summarise()` ungrouping output (override with `.groups` argument)

John Carney and Julianne Murray donations from PACS, business groups, labor uniions, and non-profit organizations broken down

`summarise()` regrouping output by 'receiving_committee' (override with `.groups` argument)

How much money John Carney and Julianne Murray received in donations from individuals, PACS, business groups, labor uniions, and non-profit organizations (This is everything except Candidate Committee, Self (Candidate), Political Committee, , and Dem or Rep National Sub-Committees)

`summarise()` ungrouping output (override with `.groups` argument)

---

How much did legislative candidates receive?

contributions %>% 
  filter(
    str_detect(office, 'State'),
    contribution_type %in% c("Check", "Electronic Fund Transfer", "Credit Card", "Cash", "Total of Contributions not exceeding $100")
  ) %>% 
  select(
    contributor_name,
    contributor_type,
    contribution_type,
    contribution_amount,
    ballotName
  ) %>% 
  mutate(
    contributor_type_group = case_when(
      contributor_type %in% c("Individual","Total of Contributions not exceeding $100") ~ 'individual',
      contributor_type == 'Candidate Committee' ~ 'Candidate Committee',
      contributor_type %in% outside_groups ~ 'outside group',
      contributor_type == 'Self (Candidate)' ~ 'self',
      TRUE ~ 'other'
    )
  ) %>% 
  group_by(
    ballotName,
    contributor_type_group
  ) %>% 
  summarise(
    total = round(sum(contribution_amount), 0)
  ) %>%
  pivot_wider(
    id_cols = ballotName,
    names_from = contributor_type_group,
    values_from = total
  ) %>%
  mutate(
    total = round(sum(c(individual, `Candidate Committee`, `outside group`, other, self), na.rm=T), 0)
  ) %>% 
  arrange(desc(total)) %>% 
  datatable(options = list(pageLength = 10))

`summarise()` regrouping output by 'ballotName' (override with `.groups` argument)