Desvio Padrão e Correação
load("C:/Users/manue/Base_de_dados-master/df_pokemon.RData")
load("C:/Users/manue/Base_de_dados-master/CARROS.RData")
CARROS$Tipodecombustivel = ifelse(CARROS$Tipodecombustivel==0,"Gas", "Alc")
CARROS$TipodeMarcha = ifelse(CARROS$TipodeMarcha==0,"Auto",
ifelse(CARROS$TipodeMarcha==1, "Manual",""))
plot(CARROS$Kmporlitro,CARROS$Preco,pch=16,col="red",
main = "Gráfico 1 - Diagrama de dispersão",
ylab = "preço", xlab = "km/l")
abline(lsfit(CARROS$Kmporlitro,CARROS$Preco),col="darkred")
par(bg="lightyellow")
par(cex=1.4)
plot(CARROS$HP, CARROS$Preco, pch=16, col = "royalblue",
main = "grafico 2 - HP por preço",
ylab = "preço", xlab = "HP")
abline(lsfit(CARROS$HP,CARROS$Preco),col="darkblue")
par(bg="lightblue")
par(cex=1.4)
plot(CARROS$Peso, CARROS$Preco, pch=16, col = "purple",
main = "grafico 3 - Peso por preço",
ylab = "preço", xlab = "Peso")
abline(lsfit(CARROS$Peso,CARROS$Preco),col="purple")
par(bg="lightyellow")
plot(df$speed, df$attack, pch=16, col = "darkblue",
main = "grafico 4 - velocidade e ataque pokemons",
ylab = "ataque", xlab = "velocidade")
abline(lsfit(df$speed,df$attack),col="black")
par(bg="lightyellow")
plot(df$speed, df$defense, pch=20, col = "darkblue",
main = "grafico 5 - velocidade e defesa pokemons",
ylab = "defesa", xlab = "velocidade")
abline(lsfit(df$speed,df$defense),col="black")
dados <-data.frame(x=c(2,3,4,5,5,6,7,8),
y=c(4,7,9,10,11,11,13,15))
cor(dados$x,dados$y)
## [1] 0.980871
cor(df$speed,df$defense)
## [1] -0.00597676
cor(df$speed,df$attack)
## [1] 0.3356615
cor(CARROS$Kmporlitro,CARROS$Preco)
## [1] -0.8475514
cor(CARROS$Peso,CARROS$Preco)
## [1] 0.8879799
cor(CARROS$HP,CARROS$Preco)
## [1] 0.7909486
names(CARROS)
## [1] "Kmporlitro" "Cilindros"
## [3] "Preco" "HP"
## [5] "Amperagem_circ_eletrico" "Peso"
## [7] "RPM" "Tipodecombustivel"
## [9] "TipodeMarcha" "NumdeMarchas"
## [11] "NumdeValvulas"
names(df)
## [1] "id" "pokemon" "species_id" "height"
## [5] "weight" "base_experience" "type_1" "type_2"
## [9] "attack" "defense" "hp" "special_attack"
## [13] "special_defense" "speed" "color_1" "color_2"
## [17] "color_f" "egg_group_1" "egg_group_2" "url_image"
## [21] "x" "y"
selecao = c("Kmporlitro","Cilindros","Preco",
"HP","Amperagem_circ_eletrico", "Peso",
"RPM","NumdeMarchas","NumdeValvulas")
selecao2 = c("height","weight","base_experience",
"attack","defense","hp","special_attack" ,
"special_defense", "speed")
cor_carros = cor(CARROS[,selecao])
cor_carros
## Kmporlitro Cilindros Preco HP
## Kmporlitro 1.0000000 -0.8521620 -0.8475514 -0.7761684
## Cilindros -0.8521620 1.0000000 0.9020329 0.8324475
## Preco -0.8475514 0.9020329 1.0000000 0.7909486
## HP -0.7761684 0.8324475 0.7909486 1.0000000
## Amperagem_circ_eletrico 0.6811719 -0.6999381 -0.7102139 -0.4487591
## Peso -0.8676594 0.7824958 0.8879799 0.6587479
## RPM 0.4186840 -0.5912421 -0.4336979 -0.7082234
## NumdeMarchas 0.4802848 -0.4926866 -0.5555692 -0.1257043
## NumdeValvulas -0.5509251 0.5269883 0.3949769 0.7498125
## Amperagem_circ_eletrico Peso RPM
## Kmporlitro 0.68117191 -0.8676594 0.41868403
## Cilindros -0.69993811 0.7824958 -0.59124207
## Preco -0.71021393 0.8879799 -0.43369788
## HP -0.44875912 0.6587479 -0.70822339
## Amperagem_circ_eletrico 1.00000000 -0.7124406 0.09120476
## Peso -0.71244065 1.0000000 -0.17471588
## RPM 0.09120476 -0.1747159 1.00000000
## NumdeMarchas 0.69961013 -0.5832870 -0.21268223
## NumdeValvulas -0.09078980 0.4276059 -0.65624923
## NumdeMarchas NumdeValvulas
## Kmporlitro 0.4802848 -0.5509251
## Cilindros -0.4926866 0.5269883
## Preco -0.5555692 0.3949769
## HP -0.1257043 0.7498125
## Amperagem_circ_eletrico 0.6996101 -0.0907898
## Peso -0.5832870 0.4276059
## RPM -0.2126822 -0.6562492
## NumdeMarchas 1.0000000 0.2740728
## NumdeValvulas 0.2740728 1.0000000
cor_pokemon = cor(df[,selecao2])
cor_pokemon
## height weight base_experience attack defense
## height 1.0000000 0.6465813 0.4804062 0.4088367 0.35995909
## weight 0.6465813 1.0000000 0.4874248 0.4605428 0.48171259
## base_experience 0.4804062 0.4874248 1.0000000 0.5891011 0.50577735
## attack 0.4088367 0.4605428 0.5891011 1.0000000 0.43177454
## defense 0.3599591 0.4817126 0.5057773 0.4317745 1.00000000
## hp 0.4401011 0.4314012 0.6849912 0.4298658 0.23521065
## special_attack 0.3330252 0.2793074 0.6630866 0.3278213 0.19828306
## special_defense 0.3239820 0.3403940 0.6723205 0.2008107 0.48187370
## speed 0.2249439 0.1081207 0.5021202 0.3356615 -0.00597676
## hp special_attack special_defense speed
## height 0.4401011 0.3330252 0.3239820 0.22494390
## weight 0.4314012 0.2793074 0.3403940 0.10812069
## base_experience 0.6849912 0.6630866 0.6723205 0.50212021
## attack 0.4298658 0.3278213 0.2008107 0.33566149
## defense 0.2352107 0.1982831 0.4818737 -0.00597676
## hp 1.0000000 0.3678422 0.3838715 0.16941766
## special_attack 0.3678422 1.0000000 0.4867300 0.44706699
## special_defense 0.3838715 0.4867300 1.0000000 0.23825607
## speed 0.1694177 0.4470670 0.2382561 1.00000000
library(corrplot)
## corrplot 0.92 loaded
par(cex=0.5)
corrplot(cor_carros)
par(cex=0.8)
corrplot(cor_pokemon)
corrplot(cor_pokemon, method="number")
