Introdução

Aqui vou colocar os primeiros gráficos.

FASE 1 - Importar os bancos de dados

# Carregar do excel
library(readxl)
BasesEstados <- read_excel("C:/Users/Hp/Desktop/Base_de_dados-master/BasesEstados.xlsx", 
                           sheet = "dados")


# Carregar do CSV
library(readr)
## Warning: package 'readr' was built under R version 4.0.3
Fifa <- read_csv("C:/Users/Hp/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.
# Carregar do formato R
load("C:/Users/Hp/Desktop/Base_de_dados-master/CARROS.RData")

Aqui vou continuar escrevendo.

Fase 2 - 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$TipodeMarcha)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4062  1.0000  1.0000
class(CARROS$Tipodecombustivel)
## [1] "numeric"
class(CARROS$TipodeMarcha)
## [1] "numeric"
class(Fifa$Nationality)
## [1] "character"
# Com transformar de quantitativa para qualitativa nominal?

CARROS$Tipodecombustivel <- as.factor(CARROS$Tipodecombustivel)
CARROS$TipodeMarcha <- as.factor(CARROS$TipodeMarcha)
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$Tipodecombustivel=="0","Gas","Alc")
CARROS$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

Aqui vou continuar escrevendo.

Fase 3 - Construção das estatísticas

tabela_combustivel <- table(CARROS$Tipodecombustivel_2)

tabela_combustivel # Abs
## 
## Alc Gas 
##  14  18
prop.table(tabela_combustivel)*100 # Rel
## 
##   Alc   Gas 
## 43.75 56.25
pie(tabela_combustivel,col = c("red","blue"))

pie(tabela_combustivel,col = c("#2e6163","#eddb98"),
    main = "Meu primeiro gráfico no R!")

barplot(tabela_combustivel,col = c("#2e6163","#eddb98"),
        main = "Meu segundo gráfico no R!",ylim = c(0,20))