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New Nation Votes is a collection of US election returns from 1787-1826. The span of elections is

min(nnv$date)

## [1] "1787"

to

max(nnv$date)

## [1] "1826-11"

and it covers the following types of elections:

nnv %>% 
  count(office)

## Source: local data frame [136 x 2]
## 
##                                             office     n
##                                              (chr) (int)
## 1                                         Alderman   394
## 2                                        Appraiser    22
## 3                                         Assembly 89400
## 4                                         Assessor   272
## 5                                        Assistant   114
## 6                               Assistant Alderman    48
## 7                               Assistant Assessor    98
## 8  Assistant Clerk of the House of Representatives    30
## 9                    Assistant Clerk of the Senate    26
## 10                                 Associate Judge    20
## ..                                             ...   ...

in the following states:

unique (nnv$state)

##  [1] "Vermont"        "Missouri"       NA               "New Hampshire" 
##  [5] "Tennessee"      "New York"       "NY"             "Alabama"       
##  [9] "Maryland"       "Indiana"        "Georgia"        "New Jersey"    
## [13] "Massachusetts"  "Rhode Island"   "Maine"          "North Carolina"
## [17] "Kentucky"       "Mississippi"    "Illinois"       "Louisiana"     
## [21] "Ohio"           "South Carolina" "Virginia"       "Delaware"      
## [25] "Connecticut"    "Pennsylvania"   "null"

The coroner’s elections caught my eye. The data base contains the following information on coroner candidates.

coroner_elections <- nnv %>%
filter (office == "Coroner")

I’m interested in finding a set of state data on coroner elections to examine. A quick bar chart tells me where my best candidates lay:

ggplot(coroner_elections, aes(x=state))+
geom_bar(stat="count")

|
New Jersey is famous for interesting and mysterious corpses. There’s also a large data set to take a look at. So let’s start with creating a data set for New Jersey and then decide if there any variables that can be safely deleted from the table:

nj_coroners <- nnv %>%
filter (office == "Coroner", state == "New Jersey")

A quick check of a few variables….

unique(nj_coroners$`role scope`)

## [1] "County"

unique(nj_coroners$iteration)

## [1] "First Ballot"

unique(nj_coroners$affiliation)

## [1] "null"                  "Republican"            "Federalist"           
## [4] "Union"                 "Opposition Republican"

tells me that all coroners in New Jersey were elected at the county level. Therefore, I’ll delete geographic variables below the county level. I also see that they were elected on the first ballot, so I don’t need that variable either. I do see that some coroners were elected through party affiliation, so I’ll keep that.

nj_coroners_county <- nnv %>%
filter (office == "Coroner", state == "New Jersey")%>%
  filter(!is.na(county))
  
nj_coroners_county %>%
select(date, name, `name id`, affiliation, `affiliation id`, vote)

## Source: local data frame [13,112 x 6]
## 
##     date             name name id affiliation affiliation id  vote
##    (chr)            (chr)   (chr)       (chr)          (chr) (int)
## 1   1796      James Craig  CJ0326        null           null    57
## 2   1796       John Price  PJ0474        null           null     5
## 3   1796 Jacob Covenhoven  CJ0488        null           null     5
## 4   1796      James Craig  CJ0326        null           null    55
## 5   1796       John Price  PJ0474        null           null     4
## 6   1796 Jacob Covenhoven  CJ0488        null           null     4
## 7   1796      James Craig  CJ0326        null           null     1
## 8   1796       John Price  PJ0474        null           null    NA
## 9   1796 Jacob Covenhoven  CJ0488        null           null    NA
## 10  1796      James Craig  CJ0326        null           null     1
## ..   ...              ...     ...         ...            ...   ...

Now that I’ve narrowed down my interests to New Jersey and some useful variables, I want to take a look at election returns by county. I wonder when political affiliation first started to be associated with the position of coroner.

To do that, I need to find out who the winners were and focus on them. (A lesson learned in the first draft of this analysis is that retaining the unique identifier for each person makes the analysis more efficient. In the first draft, failure to retain the name id meant that additional queries had to be made to filter out people in the coroner list who shared the same name, but not the same identity, with people in the NNV data set.)

elected_nj_coroners <- nj_coroners %>% 
  group_by(date, county) %>% 
  mutate(won = vote == max(vote)) %>% 
  filter (won == TRUE)%>%
  filter (!is.na(county))%>%
  select(county, name, `name id`, vote, won, affiliation)
elected_nj_coroners

## Source: local data frame [220 x 7]
## Groups: date, county [199]
## 
##     date     county               name name id  vote   won affiliation
##    (chr)      (chr)              (chr)   (chr) (int) (lgl)       (chr)
## 1   1823 Burlington        John Forman  FJ0006  2235  TRUE  Federalist
## 2   1823  Hunterdon         Jonas Lake  LJ0537  1121  TRUE        null
## 3   1823   Monmouth      David Newbury  ND0001  1877  TRUE        null
## 4   1823   Somerset           John Cox  CJ0315  1071  TRUE        null
## 5   1823 Cumberland        Reuben Hunt  HR0111   411  TRUE        null
## 6   1823 Gloucester      Lewis M. Wall  WL0058   740  TRUE        null
## 7   1823      Essex     Richard Sweazy  SR0216  1186  TRUE        null
## 8   1823     Sussex Francis MacCormick  MF0008  3447  TRUE        null
## 9   1823  Middlesex        David Smith  SD0024   930  TRUE        null
## 10  1823     Morris William Hader, Jr.  HW0438   957  TRUE        null
## ..   ...        ...                ...     ...   ...   ...         ...

Now I want to see when political affiliation first popped up in elections:

coroners_party <- elected_nj_coroners%>%
#filter(affiliation != "null") %>%   I'm including the nulls in order to better compare with listed party affiliations.
arrange (date, county)
coroners_party

## Source: local data frame [220 x 7]
## Groups: date, county [199]
## 
##     date     county             name name id  vote   won affiliation
##    (chr)      (chr)            (chr)   (chr) (int) (lgl)       (chr)
## 1   1789 Gloucester     Joel Wescott  WJ0853    64  TRUE        null
## 2   1791 Burlington    Job Lippencot  LJ0149   132  TRUE        null
## 3   1791 Gloucester    Samuel Cosens  CS0193   250  TRUE        null
## 4   1792 Gloucester    Richard Price  PR0069   264  TRUE        null
## 5   1792 Gloucester   Thomas Wilkins  WT0120   264  TRUE        null
## 6   1792   Monmouth Samuel P. Forman  FS0069     2  TRUE        null
## 7   1792   Monmouth      James Lloyd  LJ0176     2  TRUE        null
## 8   1792   Monmouth       John Price  PJ0474     2  TRUE        null
## 9   1792   Monmouth Israel Penington  PI0044     2  TRUE        null
## 10  1792   Monmouth  William Brinley  BW0491     2  TRUE        null
## ..   ...        ...              ...     ...   ...   ...         ...

Next, I want to see if one party dominated the other in the race for coroner during this era:

ggplot(coroners_party, aes(x=affiliation))+
geom_bar(stat="count")+
facet_wrap(~ county)+
  labs (title = "Party Affiliation By County in NJ Coroner Races",
     x = "Year",
     y = "Votes")

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A county by county examination shows variance in party domination. Some counties show a balance between two parties, while others were dominated by a single party. In the counties where a single party dominated, it was always the Republican party. The Union party only makes a strong show in Burlington County; the Republican Party is absent there.

ggplot (coroners_party, aes (x=date, y=vote, color=affiliation))+
 geom_line(aes(group=affiliation)) +
    geom_count()+
  facet_wrap(~affiliation)+
labs (title = "Statewide Party Affiliations in the Race for Coroner",
     x = "Year",
     y = "Votes")

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

This plot looks at the statewide party affiliation (including null) of the winners in each election, along with their vote counts. It more clearly illustrates that party affiliation is still spotty during this period. However, where a party affiliation is recorded, the dominance of the Republican party is evident.

Now I’m curious to know how much turnover there was in this office. Did incumbents tend to stay for many years?

incumbent_count <-coroners_party %>%
count (`name id`) 

incumbent_count

## Source: local data frame [163 x 2]
## 
##    name id     n
##      (chr) (int)
## 1   AJ0180     1
## 2   AJ0240     2
## 3   AS0034     2
## 4   BA0049     1
## 5   BD0210     1
## 6   BJ0518     1
## 7   BJ1142     2
## 8   BJ1148     2
## 9   BJ1160     1
## 10  BJ1344     1
## ..     ...   ...

The answer appears to be no. Out of 163 individuals, only 46 served more than one term as coroner. Just ten served more than two terms as coroner. John Hoar (name id HJ0400), of Middlesex County, served a heroic six terms. However, perhaps it’s possible that some of them went on to serve in other offices. So let’s go back an compare the list of incumbents with the main database to see if they show up elsewhere:

career_coroners <- incumbent_count%>%
left_join(nnv) %>%
select (name, `name id`, role, year)

## Joining by: "name id"

Having joined my list back to the main data set to compare them, I want to get a sense of the distinct numbers and combinations of roles these men held:

political_coroners <- career_coroners %>%
 filter(`name id` != "null")%>%
 distinct(`name id`, role, year)%>%
  arrange(`name id`, year)

political_coroners

## Source: local data frame [644 x 4]
## 
##                name name id        role  year
##               (chr)   (chr)       (chr) (int)
## 1   James W. Andrew  AJ0180     Coroner  1814
## 2  James W. Andrews  AJ0180     Coroner  1815
## 3  James W. Andrews  AJ0180     Coroner  1816
## 4  James W. Andrews  AJ0180 Assemblyman  1821
## 5  James W. Andrews  AJ0180     Coroner  1821
## 6  James W. Andrews  AJ0180 Assemblyman  1823
## 7  James W. Andrews  AJ0180 Assemblyman  1824
## 8       John Alling  AJ0240     Coroner  1806
## 9       John Alling  AJ0240     Coroner  1808
## 10      John Alling  AJ0240     Coroner  1813
## ..              ...     ...         ...   ...

Most notably, a very few rose to national office:

political_coroners %>%
  filter(role == "U.S. Congressman")

## Source: local data frame [8 x 4]
## 
##                 name name id             role  year
##                (chr)   (chr)            (chr) (int)
## 1      Aaron Bennett  BA0049 U.S. Congressman  1806
## 2       John Clement  CJ0437 U.S. Congressman  1820
## 3       John Clement  CJ0437 U.S. Congressman  1822
## 4       John Clement  CJ0437 U.S. Congressman  1824
## 5  Richard M. Cooper  CR0058 U.S. Congressman  1813
## 6 Ephraim Green, Jr.  GE0075 U.S. Congressman  1824
## 7   John M. Hynneman  HJ0988 U.S. Congressman  1812
## 8   Freedom L. Shinn  SF0060 U.S. Congressman  1822

Summation: The office of coroner in the state of New Jersey was a stepping off point for some, albeit not many, politicians during this era.

This glimpse of New Jersey coroner elections raises a few questions for further exploration: Is Republican dominance of party affiliation results a reflection of genuine party dominance, or did Republicans more actively pursue a strategy of partisan control of local offices? Did party affiliation support re-election in other low-level offices? Did any other low-level offices serve as stepping stones to higher office? If so, which were the best offices from which to launch a career in politics? How strongly does the party affiliation of local contest winners correlate to that of state or national level office winners?