Merging datasets

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

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…

CA_zipcodes <- vroom::vroom(here::here("data","zip_code_database.csv"))

glimpse(CA_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,…

#code from the workshop 1 slides
CA_contributors_2016 <- CA_contributors_2016 %>% 
  mutate(zip = as.character(zip))

CA_contributors_2016 <- left_join(CA_contributors_2016, CA_zipcodes, by="zip")

glimpse(CA_contributors_2016)

## Rows: 1,292,843
## Columns: 19
## $ cand_nm              <chr> "Clinton, Hillary Rodham", "Clinton, Hillary Rodh…
## $ contb_receipt_amt    <dbl> 50.0, 200.0, 5.0, 48.3, 40.0, 244.3, 35.0, 100.0,…
## $ zip                  <chr> "94939", "93428", "92337", "95334", "93011", "958…
## $ contb_date           <date> 2016-04-26, 2016-04-20, 2016-04-02, 2016-11-21, …
## $ type                 <chr> "STANDARD", "STANDARD", "STANDARD", "STANDARD", "…
## $ primary_city         <chr> "Larkspur", "Cambria", "Fontana", "Livingston", "…
## $ acceptable_cities    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Laguna Hills…
## $ unacceptable_cities  <chr> NA, NA, NA, NA, NA, "Walsh Station", NA, NA, NA, …
## $ state                <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "…
## $ county               <chr> "Marin County", "San Luis Obispo County", "San Be…
## $ timezone             <chr> "America/Los_Angeles", "America/Los_Angeles", "Am…
## $ area_codes           <dbl> 4.16e+05, 8.05e+02, 9.10e+05, 2.09e+02, 8.05e+02,…
## $ latitude             <dbl> 37.9, 35.6, 34.0, 37.3, 34.2, 38.5, 33.9, 33.9, 3…
## $ longitude            <dbl> -123, -121, -117, -121, -119, -121, -118, -118, -…
## $ 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> 5652, 5624, 27988, 11811, 1879, 26728, 33427, 334…
## $ notes                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "no NWS data,…

Transforming data

Hillary <- CA_contributors_2016 %>% 
  filter(cand_nm == "Clinton, Hillary Rodham") %>% 
  group_by(primary_city, cand_nm) %>% 
  summarise(total_contributions = sum(contb_receipt_amt)) %>% 
  arrange(desc(total_contributions)) %>% 
  head(10)
    
Hillary_plot <- ggplot(Hillary, 
       aes(x = total_contributions,
           y = fct_reorder(primary_city, total_contributions))) +
  geom_bar(stat='identity', fill = "royalblue2") +
  labs(
    y = NULL,
    x = NULL,
  ) +
  theme_bw() +
  theme(legend.position="none") +
  facet_wrap(~ cand_nm)

Donald <- CA_contributors_2016 %>% 
  filter(cand_nm == "Trump, Donald J.") %>% 
  group_by(primary_city, cand_nm) %>% 
  summarise(total_contributions = sum(contb_receipt_amt)) %>% 
  arrange(desc(total_contributions)) %>% 
  head(10)
    
Donald_plot <- ggplot(Donald, 
       aes(x = total_contributions,
           y = fct_reorder(primary_city, total_contributions))) +
  geom_bar(stat='identity', fill = "brown2") +
  labs(
    y = NULL,
    x = NULL,
  ) +
  theme_bw() +
  theme(legend.position="none") +
  facet_wrap(~ cand_nm)

Joining plots together

library(patchwork)

plots <- Hillary_plot + Donald_plot + plot_annotation(title = "Where did candidates raise most money?") + plot_layout(widths = 2000)  #joining two plots using patchwork package


# code taken from https://github.com/thomasp85/patchwork/issues/150 to add label on the x-axis between two plots
gt <- patchwork::patchworkGrob(plots)
gridExtra::grid.arrange(gt, bottom = "Amounts raised")