library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readr)
library(ggplot2)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
VR <- read_csv('Voter_Registration_Data_(oregon_2024).csv')
## Rows: 7920 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): COUNTY, SR Code, SR Desc, SS Code, SS Desc, Cong Code, Cong Desc, P...
## dbl (1): SUM(PARTYCOUNT)
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
VR <- VR %>% select(-'SR Code', -'SS Code', -'SS Desc')
max_date <- max(VR$Date)
VR <- VR %>% filter(Date == max_date)
oregon_totals <-VR %>% group_by(PARTY) %>% summarise(Total_Registered = sum(`SUM(PARTYCOUNT)`, na.rm = TRUE)) %>% mutate(Percent_Registered = round((Total_Registered/sum(Total_Registered)) * 100,2) )
oregon_county_totals <-VR %>% group_by(COUNTY,PARTY) %>% summarise(Total_Registered = sum(`SUM(PARTYCOUNT)`, na.rm = TRUE)) %>% mutate(Percent_Registered = round((Total_Registered/sum(Total_Registered)) * 100,2) )
## `summarise()` has grouped output by 'COUNTY'. You can override using the
## `.groups` argument.
most_repub_counties <- oregon_county_totals %>% select(COUNTY, PARTY, Percent_Registered) %>% filter(PARTY == 'Republican')
most_repub_counties <- most_repub_counties %>% arrange(desc(Percent_Registered)) %>% head(10)
most_demo_counties <- oregon_county_totals %>% select(COUNTY, PARTY, Percent_Registered) %>% filter(PARTY == 'Democrat')
most_demo_counties <- most_demo_counties %>% arrange(desc(Percent_Registered)) %>% head(10)
most_indy_counties <- oregon_county_totals %>% select(COUNTY, PARTY, Percent_Registered) %>% filter(PARTY == 'Independent')
most_indy_counties <- most_indy_counties %>% arrange(desc(Percent_Registered)) %>% head(10)
oregon_totals_pie <- ggplot(data = oregon_totals, aes(x="", y=Percent_Registered, fill=PARTY)) +
geom_bar(stat="identity", width=1) +
coord_polar("y", start=0) + theme_void()
oregon_totals_bar <- ggplot(data = oregon_totals, aes( x = PARTY, y = Percent_Registered)) + geom_bar(stat = 'identity', aes(fill=PARTY)) + labs( y = "Percent Registered Voters (%)")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
#vector of smaller percentage parties. These will be grouped together into a single group 'other'
other_parties <- c('Constitution', 'Libertarian', 'No Labels', 'Other', 'Pacific Green', 'Progressive', 'Working Families Party of Oregon')
#A copy of the Oregon county totals DF where smaller parties are aggregated into a single category
OCT_other_agg <- oregon_county_totals
OCT_other_agg <- OCT_other_agg %>% mutate(PARTY = case_when(
PARTY %in% other_parties ~ "Other",
TRUE ~ PARTY
))
OCT_other_agg <- OCT_other_agg %>% group_by(COUNTY,PARTY) %>% summarise(Total_Registered = sum(`Total_Registered`, na.rm = TRUE)) %>% mutate(Percent_Registered = round((Total_Registered/sum(Total_Registered)) * 100,2) )
## `summarise()` has grouped output by 'COUNTY'. You can override using the
## `.groups` argument.
OT_other_agg <- oregon_totals
OT_other_agg <- OT_other_agg %>% mutate(PARTY = case_when(
PARTY %in% other_parties ~ "Other",
TRUE ~ PARTY
))
OT_other_agg <- OT_other_agg %>% group_by(PARTY) %>% summarise(Total_Registered = sum(`Total_Registered`, na.rm = TRUE)) %>% mutate(Percent_Registered = round((Total_Registered/sum(Total_Registered)) * 100,2) )
oregon_totals_agg_pie <- ggplot(data = OCT_other_agg, aes(x="", y=Percent_Registered, fill=PARTY)) +
geom_bar(stat="identity", width=1) +
coord_polar("y", start=0) + theme_void()
oregon_totals_agg_bar <- ggplot(data = OCT_other_agg, aes( x = PARTY, y = Percent_Registered)) + geom_bar(stat = 'identity', aes(fill=PARTY)) + labs( y = "Percent Registered Voters (%)")
other_parties_df <- oregon_totals %>% filter(PARTY %in% other_parties)
main_parties_df <- OCT_other_agg %>% filter(!PARTY %in% other_parties)
other_parties_bar <- ggplot(data = other_parties_df, aes( x = PARTY, y = Percent_Registered)) + geom_bar(stat = 'identity', width = 0.8, aes(fill=PARTY)) + labs( y = "Percent Registered Voters (%)") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
oregon_totals
## # A tibble: 11 × 3
## PARTY Total_Registered Percent_Registered
## <chr> <dbl> <dbl>
## 1 Constitution 3868 0.13
## 2 Democrat 998171 32.8
## 3 Independent 144005 4.74
## 4 Libertarian 20322 0.67
## 5 No Labels 2325 0.08
## 6 Nonaffiliated 1111398 36.6
## 7 Other 14444 0.48
## 8 Pacific Green 7872 0.26
## 9 Progressive 3767 0.12
## 10 Republican 725407 23.9
## 11 Working Families Party of Oregon 8347 0.27
oregon_county_totals
## # A tibble: 396 × 4
## # Groups: COUNTY [36]
## COUNTY PARTY Total_Registered Percent_Registered
## <chr> <chr> <dbl> <dbl>
## 1 BAKER Constitution 26 0.2
## 2 BAKER Democrat 1844 14.1
## 3 BAKER Independent 745 5.71
## 4 BAKER Libertarian 77 0.59
## 5 BAKER No Labels 8 0.06
## 6 BAKER Nonaffiliated 4445 34.1
## 7 BAKER Other 30 0.23
## 8 BAKER Pacific Green 20 0.15
## 9 BAKER Progressive 4 0.03
## 10 BAKER Republican 5820 44.6
## # ℹ 386 more rows
most_repub_counties
## # A tibble: 10 × 3
## # Groups: COUNTY [10]
## COUNTY PARTY Percent_Registered
## <chr> <chr> <dbl>
## 1 GRANT Republican 49.5
## 2 LAKE Republican 49.4
## 3 HARNEY Republican 48.7
## 4 WHEELER Republican 48.4
## 5 SHERMAN Republican 47.7
## 6 WALLOWA Republican 46.2
## 7 BAKER Republican 44.6
## 8 CROOK Republican 43.7
## 9 GILLIAM Republican 42.2
## 10 UNION Republican 41.9
most_demo_counties
## # A tibble: 10 × 3
## # Groups: COUNTY [10]
## COUNTY PARTY Percent_Registered
## <chr> <chr> <dbl>
## 1 MULTNOMAH Democrat 49.7
## 2 BENTON Democrat 41.6
## 3 HOOD RIVER Democrat 38.3
## 4 LANE Democrat 36.3
## 5 WASHINGTON Democrat 36.0
## 6 LINCOLN Democrat 33.0
## 7 CLACKAMAS Democrat 32.4
## 8 CLATSOP Democrat 31.6
## 9 DESCHUTES Democrat 29.8
## 10 TILLAMOOK Democrat 28.6
most_indy_counties
## # A tibble: 10 × 3
## # Groups: COUNTY [10]
## COUNTY PARTY Percent_Registered
## <chr> <chr> <dbl>
## 1 CURRY Independent 6.14
## 2 DESCHUTES Independent 5.93
## 3 BAKER Independent 5.71
## 4 LINCOLN Independent 5.65
## 5 GRANT Independent 5.53
## 6 CROOK Independent 5.44
## 7 CLATSOP Independent 5.39
## 8 COOS Independent 5.37
## 9 DOUGLAS Independent 5.25
## 10 KLAMATH Independent 5.25
oregon_totals_agg_pie
oregon_totals_agg_bar
other_parties_bar
oregon_totals_bar
OT_other_agg
## # A tibble: 5 × 3
## PARTY Total_Registered Percent_Registered
## <chr> <dbl> <dbl>
## 1 Democrat 998171 32.8
## 2 Independent 144005 4.74
## 3 Nonaffiliated 1111398 36.6
## 4 Other 60945 2
## 5 Republican 725407 23.9
OCT_other_agg
## # A tibble: 180 × 4
## # Groups: COUNTY [36]
## COUNTY PARTY Total_Registered Percent_Registered
## <chr> <chr> <dbl> <dbl>
## 1 BAKER Democrat 1844 14.1
## 2 BAKER Independent 745 5.71
## 3 BAKER Nonaffiliated 4445 34.1
## 4 BAKER Other 194 1.49
## 5 BAKER Republican 5820 44.6
## 6 BENTON Democrat 25436 41.6
## 7 BENTON Independent 2760 4.52
## 8 BENTON Nonaffiliated 19506 31.9
## 9 BENTON Other 1160 1.9
## 10 BENTON Republican 12207 20.0
## # ℹ 170 more rows
#plots, tables and graphs
party_colors <- c("Republican" = "red", "Democrat" = "blue", "Nonaffiliated" = "green", "Other" = "yellow", "Independent" = "brown", "Constitution" = "violet", "Libertarian" = "black", "No Labels" = "darkgreen", "Pacific Green" = "tan", "Progressive" = "lightblue", "Working Families Party of Oregon" = "darkred")
OT_other_agg <- OT_other_agg %>%
arrange(desc(PARTY)) %>%
mutate(cumulative = cumsum(Percent_Registered),
midpoint = cumulative - Percent_Registered / 2,
label = paste0(Percent_Registered, "%"))
OT_pie <- ggplot(data = OT_other_agg, aes(x="", y=Percent_Registered, fill=PARTY)) +
geom_bar(stat="identity", width=1) +
coord_polar("y", start=0) + theme_void() + scale_fill_manual(values = party_colors) + geom_text(aes(label = label, y = midpoint), color = "black", fontface = "bold") + ggtitle(" Registered Voters in Orgeon by Party (2024)")
OT_bar <- ggplot(data = OT_other_agg, aes( x = PARTY, y = Percent_Registered)) + geom_bar(stat = 'identity', aes(fill=PARTY)) + labs( y = "Percent Registered Voters (%)", x = "Part Affiliation") + scale_fill_manual(values = party_colors) + ggtitle(" Registered Voters in Orgeon by Party (2024)")
OCT_scatter <- ggplot(data = OCT_other_agg, aes(x = COUNTY, y = Percent_Registered,)) + geom_histogram(stat = 'identity', aes(fill = PARTY)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values = party_colors) + labs(y = "Registered Voters (%)", x = "County") + ggtitle(" Party Affiliation of Oregon Voters by County (2024)")
## Warning in geom_histogram(stat = "identity", aes(fill = PARTY)): Ignoring
## unknown parameters: `binwidth`, `bins`, and `pad`
oregon_totals_bar <- ggplot(data = oregon_totals, aes( x = PARTY, y = Percent_Registered)) + geom_bar(stat = 'identity', aes(fill=PARTY)) + labs( y = "Percent Registered Voters (%)", x = "Party Affiliation")+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values = party_colors) + ggtitle(" Registered Voters in Orgeon by Party (2024)")
other_parties_df <- oregon_totals %>% filter(PARTY %in% other_parties)
other_parties_bar <- ggplot(data = other_parties_df, aes( x = PARTY, y = Percent_Registered)) + geom_bar(stat = 'identity', width = 0.8, aes(fill=PARTY)) + labs( y = "Percent Registered Voters (%)", x = "Party Affiliation") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values = party_colors) + ggtitle(" Oregon Voter Affiliation for Minor Political Parties (2024)")
most_repub_counties <- oregon_county_totals %>% select(COUNTY, PARTY, Percent_Registered) %>% filter(PARTY == 'Republican')
most_repub_counties <- most_repub_counties %>% arrange(desc(Percent_Registered)) %>% head(10)
most_demo_counties <- oregon_county_totals %>% select(COUNTY, PARTY, Percent_Registered) %>% filter(PARTY == 'Democrat')
most_demo_counties <- most_demo_counties %>% arrange(desc(Percent_Registered)) %>% head(10)
party_change <- data.frame(
Party = c("Democrat", "Republican", "Independent", "Other", "Nonaffiliated"),
Percent_change = c(-2.89, -1.97, 0.17, -0.1, 4.8)
)
percent_change_bar <- ggplot(data = party_change, aes( x = Party, y = Percent_change)) + geom_bar(stat = 'identity', aes(fill=Party)) + labs( y = "Chane in Party Membership (%)", x = "Party") + scale_fill_manual(values = party_colors) + ggtitle(" Percent Change in Party Membership from 2020 to 2024")
OT_pie
OT_bar
OCT_scatter
oregon_totals_bar
other_parties_bar
most_repub_counties
## # A tibble: 10 × 3
## # Groups: COUNTY [10]
## COUNTY PARTY Percent_Registered
## <chr> <chr> <dbl>
## 1 GRANT Republican 49.5
## 2 LAKE Republican 49.4
## 3 HARNEY Republican 48.7
## 4 WHEELER Republican 48.4
## 5 SHERMAN Republican 47.7
## 6 WALLOWA Republican 46.2
## 7 BAKER Republican 44.6
## 8 CROOK Republican 43.7
## 9 GILLIAM Republican 42.2
## 10 UNION Republican 41.9
most_demo_counties
## # A tibble: 10 × 3
## # Groups: COUNTY [10]
## COUNTY PARTY Percent_Registered
## <chr> <chr> <dbl>
## 1 MULTNOMAH Democrat 49.7
## 2 BENTON Democrat 41.6
## 3 HOOD RIVER Democrat 38.3
## 4 LANE Democrat 36.3
## 5 WASHINGTON Democrat 36.0
## 6 LINCOLN Democrat 33.0
## 7 CLACKAMAS Democrat 32.4
## 8 CLATSOP Democrat 31.6
## 9 DESCHUTES Democrat 29.8
## 10 TILLAMOOK Democrat 28.6
party_change
## Party Percent_change
## 1 Democrat -2.89
## 2 Republican -1.97
## 3 Independent 0.17
## 4 Other -0.10
## 5 Nonaffiliated 4.80
percent_change_bar
