Join dataframes

# Make sure you use vroom() as it is significantly faster than read.csv()
# Load datasets
CA_contributors_2016 <- vroom::vroom(here::here("data","CA_contributors_2016.csv"))

zipcodes <- read_csv(here::here("data", "zip_code_database.csv"))

# Glimpse datasets
glimpse(CA_contributors_2016)

## Rows: 1,292,843
## Columns: 4
## $ cand_nm           <chr> "Clinton, Hillary Rodham", "Clinton, Hillary Rodham"…
## $ contb_receipt_amt <dbl> 50.0, 200.0, 5.0, 48.3, 40.0, 244.3, 35.0, 100.0, 25…
## $ zip               <dbl> 94939, 93428, 92337, 95334, 93011, 95826, 90278, 902…
## $ contb_date        <date> 2016-04-26, 2016-04-20, 2016-04-02, 2016-11-21, 201…

glimpse(zipcodes)

## Rows: 42,522
## Columns: 16
## $ zip                  <chr> "00501", "00544", "00601", "00602", "00603", "006…
## $ type                 <chr> "UNIQUE", "UNIQUE", "STANDARD", "STANDARD", "STAN…
## $ primary_city         <chr> "Holtsville", "Holtsville", "Adjuntas", "Aguada",…
## $ acceptable_cities    <chr> NA, NA, NA, NA, "Ramey", "Ramey", NA, NA, NA, NA,…
## $ unacceptable_cities  <chr> "I R S Service Center", "Irs Service Center", "Co…
## $ state                <chr> "NY", "NY", "PR", "PR", "PR", "PR", "PR", "PR", "…
## $ county               <chr> "Suffolk County", "Suffolk County", "Adjuntas", N…
## $ timezone             <chr> "America/New_York", "America/New_York", "America/…
## $ area_codes           <dbl> 631, 631, 787939, 787, 787, NA, NA, 787939, 787, …
## $ latitude             <dbl> 40.8, 40.8, 18.2, 18.4, 18.4, 18.4, 18.4, 18.2, 1…
## $ longitude            <dbl> -73.0, -73.0, -66.7, -67.2, -67.2, -67.2, -67.2, …
## $ world_region         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country              <chr> "US", "US", "US", "US", "US", "US", "US", "US", "…
## $ decommissioned       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ estimated_population <dbl> 384, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ notes                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "no NWS data,…

# Join datasets (note that zip types are different across both datasets)
# Therefore we change them to be the same so that the datasets can be joined
CA_contributors_2016$zip<-as.character(CA_contributors_2016$zip)
df <- left_join(CA_contributors_2016, zipcodes, "zip")

Create summary statistics

# Summarise total contributions raised by the top 10 candidates across every city
top_10_cand_overall <- df %>% 
  group_by(cand_nm) %>%
  summarize(total = sum(contb_receipt_amt)) %>%
  arrange(desc(total)) %>% 
  top_n(10)

# Create long form data to incude only the top 10 candidates
long_form_top10_cand_all_cities <- df %>% 
  filter(cand_nm %in% top_10_cand_overall$cand_nm) %>% 
  group_by(cand_nm, primary_city) %>%
  summarise(total_contrib = sum(contb_receipt_amt))

# From previous dataset, create a table for the top 10 candidates across the top 10 cities
# Incorporate the use of reorder_within to reorder within a group
top_10_cities_per_cand <- long_form_top10_cand_all_cities %>% 
  group_by(cand_nm) %>% 
  top_n(10, total_contrib) %>% 
  ungroup() %>%
  mutate(cand_nm = as.factor(cand_nm),
         primary_city = tidytext::reorder_within(primary_city, total_contrib, cand_nm)) 

Plot political contributions raised by top 10 candidates in top 10 cities

# Plot faceted plots for each of the top 10 candidates across their respective top 10 cities
ggplot(top_10_cities_per_cand , aes(total_contrib, primary_city))+
  geom_col(aes(fill = cand_nm), show.legend = FALSE) +
  facet_wrap(~ cand_nm, ncol = 2, scales = "free") +
  tidytext::scale_y_reordered() +
  labs(title = "Comparisonn of contribution amounts raised", subtitle = "In which cities did the top 10 candidates raise the most money?", x = "Amount raised", y = "City") +
  theme(axis.title = element_text(size = 14),
          axis.text = element_text(size = 8),
          strip.text = element_text(size = 5),
          strip.background = element_rect(fill="lightblue", colour="black", size=1))+
  scale_x_continuous(labels = ~ format(.x, scientific = FALSE),
                     sec.axis = sec_axis(~ . , name = "Candidates", breaks = NULL, labels = NULL))