INTRODUÇÃO

1º FASE-IMPORTAR DADOS

library(readxl)
BasesEstados <- read_excel("C:/Users/welington/Desktop/MESTRADO_2021/Base_de_dados-master/BasesEstados.xlsx", 
    sheet = "dados")

library(readr)
Fifa <- read_csv("C:/Users/welington/Desktop/MESTRADO_2021/Base_de_dados-master/FifaData.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Name = col_character(),
##   Nationality = col_character(),
##   National_Position = col_character(),
##   Club = col_character(),
##   Club_Position = col_character(),
##   Club_Joining = col_character(),
##   Height = col_character(),
##   Weight = col_character(),
##   Preffered_Foot = col_character(),
##   Birth_Date = col_character(),
##   Preffered_Position = col_character(),
##   Work_Rate = col_character()
## )
## i Use `spec()` for the full column specifications.
load("C:/Users/welington/Desktop/MESTRADO_2021/Base_de_dados-master/CARROS.RData")

2ª FASE-MANIPULAR O BANCO DE DADOS

summary(CARROS$Kmporlitro)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.40   15.43   19.20   20.09   22.80   33.90
summary(Fifa$Nationality)
##    Length     Class      Mode 
##     17588 character character
summary(BasesEstados$PIB)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 7.314e+06 2.945e+07 8.083e+07 1.627e+08 1.695e+08 1.409e+09
summary(CARROS$Tipodecombustivel)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4375  1.0000  1.0000
summary(CARROS$TipodeMarcha)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4062  1.0000  1.0000
summary(Fifa$Nationality)
##    Length     Class      Mode 
##     17588 character character
class(CARROS$Tipodecombustivel)
## [1] "numeric"
class(CARROS$TipodeMarcha)
## [1] "numeric"
class(Fifa$Nationality)
## [1] "character"
CARROS$Tipodecombustivel<-as.factor(CARROS$Tipodecombustivel)
CARROS$TipodeMarcha<-as.factor(CARROS$TipodeMarcha)
Fifa$Nationality<-as.factor(Fifa$Nationality)


CARROS$Tipodecombustivel_2 <- ifelse(CARROS$Tipodecombustivel=="0","Gas","Alc")
CARROS$TipodeMarcha_2 <- ifelse(CARROS$TipodeMarcha=="0","Auto","Manual")
CARROS$Tipodecombustivel_2<- as.factor(CARROS$Tipodecombustivel_2)
CARROS$TipodeMarcha_2<- as.factor(CARROS$TipodeMarcha_2)
summary(CARROS$TipodeMarcha_2)
##   Auto Manual 
##     19     13

tabela_combustivel<-table(CARROS$Tipodecombustivel_2)
tabela_combustivel
## 
## Alc Gas 
##  14  18
prop.table(tabela_combustivel)*100 #Abs
## 
##   Alc   Gas 
## 43.75 56.25
pie(tabela_combustivel)

vet1<-c(1,2,3,4,5)
vet1
## [1] 1 2 3 4 5
vet2<-c(T,T,T,F,F)
vet2
## [1]  TRUE  TRUE  TRUE FALSE FALSE
vet3<-c("carlos","paulo","carla","paula")
vet3
## [1] "carlos" "paulo"  "carla"  "paula"
vet4<-c("red","yellow","blue","green")

pie(tabela_combustivel,col =c("red","blue"))

pie(tabela_combustivel,col =c("#60a397" , "#28249c"),main ="Meu Primeiro Gráfico no R!")

tabela_fifa<-table(Fifa$Nationality)
pie(tabela_fifa)

barplot(tabela_combustivel,col = c("#60a397","#28249c"),main ="Meu Primeiro Gráfico no R!",ylim = c(0,20))