Fase 1
library(readxl)
BasesEstados <- read_excel("C:/Users/Kariny/Desktop/Base_de_dados-master/BasesEstados.xlsx")
load("C:/Users/Kariny/Desktop/Base_de_dados-master/CARROS.rdata")
library(readr)
Fifa <- read_csv("C:/Users/Kariny/Desktop/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.
Fase 2
# Fase 2 - Manipular 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$TipodeMarcha)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4062 1.0000 1.0000
class(CARROS$Tipodecombustivel)
## [1] "numeric"
class(CARROS$TipodeMarcha)
## [1] "numeric"
class(Fifa$Nationality)
## [1] "character"
#Como transformar de quantitativa para qualitativa nominal
CARROS$Tipodecombustivel<-as.factor(CARROS$Tipodecombustivel)
summary(CARROS$Tipodecombustivel)
## 0 1
## 18 14
Fifa$Nationality<-as.factor(Fifa$Nationality)
summary(Fifa$Nationality)
## England Argentina Spain France
## 1618 1097 1008 974
## Brazil Italy Germany Colombia
## 921 751 689 592
## Japan Republic of Ireland Netherlands Chile
## 471 442 426 398
## Sweden Portugal Saudi Arabia Denmark
## 378 360 354 342
## Norway Mexico United States Poland
## 342 341 332 328
## Korea Republic Russia Scotland Turkey
## 321 309 292 292
## Austria Belgium Australia Switzerland
## 266 265 234 210
## Uruguay Serbia Nigeria Wales
## 153 136 122 122
## Ghana Senegal Croatia Cameroon
## 119 119 116 96
## Ivory Coast Greece Northern Ireland South Africa
## 90 86 83 78
## Paraguay Morocco Romania Slovakia
## 75 74 61 61
## Finland Canada Ukraine DR Congo
## 60 59 59 58
## Slovenia Czech Republic Bosnia Herzegovina Algeria
## 58 57 52 50
## Iceland Mali Venezuela Hungary
## 47 46 42 41
## Albania Jamaica Bulgaria Tunisia
## 37 36 35 35
## Ecuador Guinea Peru Kosovo
## 34 34 34 31
## Bolivia China PR Costa Rica Egypt
## 30 30 30 30
## India New Zealand Georgia Montenegro
## 30 30 28 23
## Cape Verde Congo FYR Macedonia Guinea Bissau
## 22 18 18 17
## Belarus Benin Honduras Gabon
## 16 16 16 15
## Lithuania Burkina Faso Gambia Israel
## 15 14 14 14
## Haiti Angola Curacao Iran
## 12 11 11 11
## Panama Togo Zimbabwe Comoros
## 11 10 10 9
## Luxembourg Armenia Azerbaijan Equatorial Guinea
## 9 8 8 8
## Estonia Iraq Latvia (Other)
## 8 8 8 171
CARROS$Tipodecombustivel_2<-ifelse(CARROS$Tipodecombustivel=="0","gas","alc")
Fase 3
# Fase 3 Construção das Estatisticas
tabela_combustivel<-table(CARROS$Tipodecombustivel_2)
tabela_combustivel
##
## alc gas
## 14 18
prop.table(tabela_combustivel)
##
## alc gas
## 0.4375 0.5625
prop.table(tabela_combustivel)*100#rel
##
## alc gas
## 43.75 56.25
pie(tabela_combustivel)
pie(tabela_combustivel,col=c("green","blue"))
pie(tabela_combustivel,col=c("red","green"),main = "Meu Primeiro Gráfico no R!")
pie(tabela_combustivel,col = c("#307860","#e3591d"),
main = "Meu primeiro gráfico no R!")
barplot(tabela_combustivel,col = c("#307860","#e3591d"),
main = "Meu segundo gráfico no R!",ylim = c(0,50))
