#cargar librerias

library(modeest)

1 Como generaria un arreglo aletorio de 250 elementos con los colores primarios?

sample(rainbow(250))
##   [1] "#0200FF" "#00FF6C" "#00FFA9" "#00FFFF" "#F7FF00" "#009DFF" "#FF7400"
##   [8] "#FFAB00" "#FF0081" "#E400FF" "#FF001F" "#FF0600" "#FFE900" "#89FF00"
##  [15] "#7CFF00" "#FF0000" "#A100FF" "#00DAFF" "#00E7FF" "#A700FF" "#0066FF"
##  [22] "#83FF00" "#FF9300" "#FF1F00" "#00FF0A" "#00FF66" "#2700FF" "#C0FF00"
##  [29] "#00AFFF" "#64FF00" "#D2FF00" "#00FFF3" "#FFD000" "#00F3FF" "#0800FF"
##  [36] "#0060FF" "#5200FF" "#FFB100" "#00FFA3" "#6A00FF" "#FF7A00" "#1400FF"
##  [43] "#FFFB00" "#0023FF" "#000AFF" "#76FF00" "#B400FF" "#00FF29" "#00C2FF"
##  [50] "#006CFF" "#2100FF" "#00FF10" "#4BFF00" "#FFB800" "#8FFF00" "#00FFCE"
##  [57] "#A1FF00" "#C6FF00" "#00BCFF" "#FF8700" "#F700FF" "#00FFBC" "#0E00FF"
##  [64] "#FF00BE" "#00FFD4" "#3FFF00" "#FF00FB" "#6400FF" "#FF3100" "#00FF97"
##  [71] "#0072FF" "#FF00E9" "#D800FF" "#FF008D" "#FFC400" "#005AFF" "#FF0037"
##  [78] "#00B6FF" "#0047FF" "#FDFF00" "#FF002B" "#FF00AB" "#00FF04" "#FF0068"
##  [85] "#CC00FF" "#9500FF" "#8900FF" "#CCFF00" "#0029FF" "#00FF41" "#F100FF"
##  [92] "#FF009F" "#DE00FF" "#39FF00" "#FF0043" "#EB00FF" "#33FF00" "#0016FF"
##  [99] "#3F00FF" "#58FF00" "#FF8100" "#003BFF" "#FF0C00" "#FF00F5" "#0085FF"
## [106] "#00FF85" "#0010FF" "#FFE200" "#FF00DC" "#3900FF" "#B4FF00" "#FF5000"
## [113] "#FFA500" "#FFBE00" "#00FF78" "#002FFF" "#00FFB6" "#00EDFF" "#C000FF"
## [120] "#EBFF00" "#70FF00" "#4B00FF" "#F1FF00" "#7000FF" "#00FFE7" "#00CEFF"
## [127] "#FF0049" "#008BFF" "#00FFE0" "#02FF00" "#00FFC2" "#2DFF00" "#00FF60"
## [134] "#00FF7E" "#FF9F00" "#00A3FF" "#FF5600" "#FF00C4" "#1B00FF" "#00D4FF"
## [141] "#FF0006" "#004EFF" "#FF6200" "#00FFDA" "#00FF1D" "#FF0031" "#FF2B00"
## [148] "#FF0050" "#001DFF" "#D200FF" "#5E00FF" "#0054FF" "#00FF47" "#FF0099"
## [155] "#00FF16" "#FF0087" "#FFF500" "#BA00FF" "#00FF5A" "#FF3D00" "#FFDC00"
## [162] "#A7FF00" "#FFCA00" "#00FF35" "#FF005C" "#00FF2F" "#FF1800" "#FF0012"
## [169] "#FF8D00" "#7600FF" "#FD00FF" "#FF0025" "#ADFF00" "#1BFF00" "#00E0FF"
## [176] "#00FF72" "#FF0062" "#8F00FF" "#4500FF" "#9B00FF" "#95FF00" "#00FFC8"
## [183] "#45FF00" "#E4FF00" "#6AFF00" "#FF4300" "#FF0074" "#27FF00" "#FF00D6"
## [190] "#00FF3B" "#0035FF" "#0EFF00" "#FF5C00" "#FF9900" "#00A9FF" "#5EFF00"
## [197] "#FF2500" "#5800FF" "#AD00FF" "#FF1200" "#FF0018" "#00FF9D" "#8300FF"
## [204] "#FF0056" "#FF4900" "#D8FF00" "#FF006E" "#FF6E00" "#FF6800" "#0041FF"
## [211] "#007EFF" "#00FF4E" "#FF00CA" "#21FF00" "#9BFF00" "#00F9FF" "#00FFAF"
## [218] "#BAFF00" "#0097FF" "#FF003D" "#FFEF00" "#7C00FF" "#FF000C" "#0078FF"
## [225] "#08FF00" "#00FF54" "#FF3700" "#FF00B8" "#FF007A" "#FF00B1" "#FF0093"
## [232] "#0004FF" "#00C8FF" "#FF00A5" "#FFD600" "#00FF8B" "#52FF00" "#FF00D0"
## [239] "#C600FF" "#00FF91" "#3300FF" "#FF00EF" "#00FFED" "#00FF23" "#00FFF9"
## [246] "#0091FF" "#DEFF00" "#FF00E2" "#14FF00" "#2D00FF"

2 Desv Std de Numeros mayores a 55 y menores a 64

#1 Generacion de Valores entre 55 a 64 120K Elementos
Edades<-sample(40:70,120000,replace = T)
#1.1 Filtrar Edades
filtro_1 <-((Edades >=55) & (Edades <=64)) 
#1.2 Determinar la SD de la Muestra Edades Mayores a 55 y menores a 64
Desvest_Muestra <- sd(filtro_1, na.rm = TRUE)
Desvest_Muestra
## [1] 0.4671051

#3 Defina el Vector y=(1,3,5,7) utilizando la funcion c()

#3.1 Definir el Vector y
y1<- c(1,3,5,7)
y1
## [1] 1 3 5 7
#3.2 Como lo harias con la funcion seq()
y2<-seq(1,7,length=4)
y2
## [1] 1 3 5 7

#4 Calcular Media, mediana, moda

x<- c(8,7,6,5)
y<-c(3,3,3,3,3,3,3,3,2,2)
z<-c(1,1.75,2.5,3.25,4)
xyz<-c(x,y,z)
xyz
##  [1] 8.00 7.00 6.00 5.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 2.00 2.00 1.00
## [16] 1.75 2.50 3.25 4.00
media<-mean(xyz)
mediana<-median(xyz)
moda<-mfv(xyz)
sumatoria_xyz<-sum(xyz)
media
## [1] 3.5
mediana
## [1] 3
moda
## [1] 3
sumatoria_xyz
## [1] 66.5

#5 Sumar los Vectores

x<-c(1,2,3,4,5,6)
y<-c(7,8)
z<-c(9,10,11,12)

#Suma x+x
xx<-sum(x,x)
xx
## [1] 42
#suma x+y
xy<-sum(x,y)
xy
## [1] 36

##Parte 2 #Carga de la Data

library(readr)
primary_results <- read_csv("C:\\Users\\SandyMa\\OneDrive\\Documents\\Maestria BI\\DATA MINIMG\\Lab1\\primary_results.csv")
## Rows: 24611 Columns: 8
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (5): state, state_abbreviation, county, party, candidate
## dbl (3): fips, votes, fraction_votes
## 
## 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.
head(primary_results)

#carga de librerias

library(ggplot2)
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

#Cuantos Candidatos estaban en las primarias

n_distinct(primary_results$candidate)
## [1] 16

#Que partido obtuvo la mayor cantidad de votos en Florida

Votes_bystates <- primary_results %>%
    filter(state=="Florida")%>%
    group_by(party) %>%
    summarize(sumbyparty=sum(votes))
Votes_bystates

#4 Realice una grafica Top 10 de los condados de Colorado

Votosxstate<- primary_results%>%
            filter(state == "Colorado")
Top10Colorado<-Votosxstate%>%
          group_by(county)%>%
          summarise(Votos= sum(votes))%>%
          top_n(10, Votos)%>%
          arrange(Votos)
Top10Colorado
Top10Colorado %>%
  ggplot(aes(x=Votos, y=county))+
  geom_col(fill="steelblue")+
   labs(title = "Top 10 por Condado en Colorado")

#5 En California que Condado tuvo la mayor cantidad de Votantes

Votosxstate<- primary_results%>%
            filter(state == "California")
Top1California<-Votosxstate%>%
          group_by(county)%>%
          summarise(Votos= sum(votes))%>%
          top_n(1, Votos)%>%
          arrange(Votos)
Top1California

#6 Grafica Donald Trump and Hillary Clinton

VotesXCandidate <- primary_results %>%
        filter(candidate %in% c("Donald Trump","Hillary Clinton"))%>%
        group_by(candidate)%>%
        summarise(Voto= sum(votes))
       VotesXCandidate
VotesXCandidate %>%
  ggplot(aes(x=Voto, y=candidate))+
  geom_col(fill="blue")+
   labs(title = "Distribucion Votos Hillary Clinton - Donald Trump")

# 7 Top 10 por Condado

Top10XCounty<- primary_results%>%
          group_by(county)%>%
          summarise(Votos= sum(votes))%>%
          top_n(10, Votos)%>%
          arrange(Votos)
Top10XCounty
Top10XCounty %>%
  ggplot(aes(x=Votos, y=county))+
  geom_col(fill="blue")+
   labs(title = "Top 10 por Condado")