# Aggregating local election results in R

### Richard Sprague / 2016-12-11

National and local news outlets report the results of the Presidential election at the state and county level, but that doesn’t tell me how my *neighborhood* voted. For that I’ll need the precinct-by-precinct results, which in my area is not reported. Can I calculate it myself? In `R`

the answer is of course a big *YES*. Here’s how I did it.

First load the following R packages:

```
require(dplyr)
require(tidyr)
require(knitr)
```

Next obtain the full King County election results as a precinct-by-precint CSV file.

Filter the results to look for just the Mercer Island precincts, and further narrow down to just the US Presidential results.

```
election2016<-read.csv("../../data/November_2016_ECanvass.csv")
mercerIsland2016<- filter(election2016,startsWith(as.character(Precinct),"M-I"))
mi.president.all<-filter(mercerIsland2016,Race=="US President & Vice President") %>%
select(precinct=Precinct,candidate=CounterType,sum=SumOfCount)
#mi.president.all %>% group_by(candidate) %>% summarize(sum=sum(sum)) %>% arrange(desc(sum))
mi.president.all %>% filter(candidate!="Times Counted" &
candidate!="Registered Voters") %>%
group_by(candidate) %>%
summarize(sum=sum(sum)) %>%
arrange(desc(sum)) %>%
mutate(pct=sum/sum(sum)*100) %>%
kable(digits=3)
```

candidate | sum | pct |
---|---|---|

Hillary Clinton & Tim Kaine | 10721 | 69.244 |

Donald J. Trump & Michael R. Pence | 3272 | 21.133 |

Gary Johnson & Bill Weld | 578 | 3.733 |

Write-In | 438 | 2.829 |

Times Blank Voted | 290 | 1.873 |

Jill Stein & Ajamu Baraka | 139 | 0.898 |

Darrell L. Castle & Scott N. Bradley | 31 | 0.200 |

Gloria Estela La Riva & Eugene Puryear | 7 | 0.045 |

Times Over Voted | 4 | 0.026 |

Alyson Kennedy & Osborne Hart | 3 | 0.019 |

# Top Candidates

Sometimes it’s nice to compare just among votes case for the “top candidates”, like this:

```
topCandidates<-c("Donald J. Trump & Michael R. Pence",
"Gary Johnson & Bill Weld",
"Jill Stein & Ajamu Baraka",
"Hillary Clinton & Tim Kaine")
mi.president<-mi.president.all %>% filter(candidate %in% topCandidates)
mi.president.all$candidate<-factor(mi.president$candidate)
mi.president.top<-mi.president %>% group_by(candidate) %>%
summarize(sum=sum(sum)) %>%
arrange(desc(sum)) %>%
mutate(pct=sum/sum(sum)*100)
kable(mi.president.top,digits=3)
```

candidate | sum | pct |
---|---|---|

Hillary Clinton & Tim Kaine | 10721 | 72.882 |

Donald J. Trump & Michael R. Pence | 3272 | 22.243 |

Gary Johnson & Bill Weld | 578 | 3.929 |

Jill Stein & Ajamu Baraka | 139 | 0.945 |

and that’s it!