DATA

primary <- read.csv(file = "primary_results.csv")

LIBRERIAS

library(dplyr)
library(ggplot2)

CODIGO

¿Cuantos candidatos estaban en las primarias?

#--------------------------------------------------------------------------------------------------
pimary %>%
  select(candidate) %>%
  group_by(candidate) %>%
  summarise() %>%
  nrow()
[1] 16

————————————————————————————————–

¿Cuantos de los candidatos eran republicanos?

pimary %>%
  select(candidate, party) %>%
  filter(party == "Republican") %>%
  group_by(candidate) %>%
  summarise() %>%
  nrow()
[1] 11
#--------------------------------------------------------------------------------------------------

¿Que partido obtuvo la mayor cantidad de votos en Florida?

pimary %>%
  select(state, party, votes) %>%
  filter(state == "Florida") %>%
  group_by(party) %>%
  summarise(x =sum(votes)) %>%
  arrange(desc(x)) %>%
  head(1)
#-------------------------------------------------------------------------------------------------

¿Que condado de Florida es el que tiene la mayor cantidad de votantes??

pimary %>%
  select(state, county, votes) %>%
  filter(state == "Florida") %>%
  group_by(county) %>%
  summarise(y = sum(votes)) %>%
  arrange(desc(y)) %>%
  head(1)
  
#--------------------------------------------------------------------------------------------------

En el condado de florida que tuvo la mayor cantidad de votantes,

¿Que candidato tuvo la mayor cantidad de votos y de que partido era?

pimary %>%
  select(state, county, votes, party, candidate) %>%
  filter(state == "Florida" & county == "Miami-Dade") %>%
  group_by(candidate, party) %>%
  summarise(y = sum(votes)) %>%
  arrange(desc(y)) %>%
  select(candidate, party) %>%
  head(1)
#--------------------------------------------------------------------------------------------------

Cuantas personas Votaron por Hillary Clinton y cuantas por Donald Trump en estados unidos?

pimary %>%
  select(votes, candidate) %>%
  filter(candidate == "Hillary Clinton" | candidate == "Donald Trump") %>%
  group_by(candidate) %>%
  summarise(z = sum(votes)) %>%
  arrange(desc(z)) 
  
#--------------------------------------------------------------------------------------------------

¿Cual es la probabilidad de que si alguien sea republicano en florida haya votado por Jeb Bush

pimary %>%
  select(votes, candidate, party, state) %>%
  filter(state == "Florida", party == "Republican", candidate == "Jeb Bush") %>%
  summarise(e = sum(votes))
primary %>%
  filter(!trimws(candidate) %in% c("No Preference", "Uncommitted") ) %>%
  ggplot(aes(x=party, y=votes, col=candidate)) +
  geom_point() +
  geom_jitter() +
  facet_wrap(~state, scales = "free")

primary %>%
  group_by(state, party, candidate) %>%
  summarise(tot_votos = sum(votes, na.rm = TRUE)) %>%
  ggplot(aes(x=party, y=tot_votos)) +
  geom_point() +
  facet_grid(state~candidate, scales = "free")+
  ggtitle("Intension de voto por partido por estado") +
  ylab("Total de votos") +
  xlab("Partido")

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