class: center, middle, inverse, title-slide # ATIVIDADE 6 ## ⚔<br/>with xaringan ### VITÓRIA MONTEIRO ### UNIRIO ### 12/04/2021 --- --- class: center, middle ### Diagrama de dispersão ### coeficiente de correlação --- class: inverse, center, middle **CARREGAR BASE DE DADOS** ```r library(readr) FifaData <- read_csv("C:/Users/ziin/Documents/FACULDADE/estatistica/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. ``` ```r View(FifaData) names(FifaData) ``` ``` ## [1] "Name" "Nationality" "National_Position" ## [4] "National_Kit" "Club" "Club_Position" ## [7] "Club_Kit" "Club_Joining" "Contract_Expiry" ## [10] "Rating" "Height" "Weight" ## [13] "Preffered_Foot" "Birth_Date" "Age" ## [16] "Preffered_Position" "Work_Rate" "Weak_foot" ## [19] "Skill_Moves" "Ball_Control" "Dribbling" ## [22] "Marking" "Sliding_Tackle" "Standing_Tackle" ## [25] "Aggression" "Reactions" "Attacking_Position" ## [28] "Interceptions" "Vision" "Composure" ## [31] "Crossing" "Short_Pass" "Long_Pass" ## [34] "Acceleration" "Speed" "Stamina" ## [37] "Strength" "Balance" "Agility" ## [40] "Jumping" "Heading" "Shot_Power" ## [43] "Finishing" "Long_Shots" "Curve" ## [46] "Freekick_Accuracy" "Penalties" "Volleys" ## [49] "GK_Positioning" "GK_Diving" "GK_Kicking" ## [52] "GK_Handling" "GK_Reflexes" ``` --- class: inverse, middle, center #DISPERSÃO --- **DIAGRAMA DE DISPERSÃO** <!-- --> --- class: center, middle #CORRELAÇÕES --- **CORRELAÇÕES ENTRE SPEED(RAPIDEZ) E MARKING (MARCAÇÃO)** ```r cor(FifaData$Speed,FifaData$Marking) ``` ``` ## [1] 0.1632428 ``` --- ``` ## ## 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 ``` ``` ## [1] "Name" "Nationality" "National_Position" ## [4] "National_Kit" "Club" "Club_Position" ## [7] "Club_Kit" "Club_Joining" "Contract_Expiry" ## [10] "Rating" "Height" "Weight" ## [13] "Preffered_Foot" "Birth_Date" "Age" ## [16] "Preffered_Position" "Work_Rate" "Weak_foot" ## [19] "Skill_Moves" "Ball_Control" "Dribbling" ## [22] "Marking" "Sliding_Tackle" "Standing_Tackle" ## [25] "Aggression" "Reactions" "Attacking_Position" ## [28] "Interceptions" "Vision" "Composure" ## [31] "Crossing" "Short_Pass" "Long_Pass" ## [34] "Acceleration" "Speed" "Stamina" ## [37] "Strength" "Balance" "Agility" ## [40] "Jumping" "Heading" "Shot_Power" ## [43] "Finishing" "Long_Shots" "Curve" ## [46] "Freekick_Accuracy" "Penalties" "Volleys" ## [49] "GK_Positioning" "GK_Diving" "GK_Kicking" ## [52] "GK_Handling" "GK_Reflexes" ``` ``` ## Speed Marking ## Speed 1.0000000 0.1632428 ## Marking 0.1632428 1.0000000 ``` --- ```r library(corrplot) ``` ``` ## Warning: package 'corrplot' was built under R version 4.0.5 ``` ``` ## corrplot 0.84 loaded ``` ```r MCorr <- cor(FifaData_quanti) corrplot(MCorr,addCoef.col=TRUE,number.cex=0.7) ``` <!-- --> --- ***CONCLUSÃO*** **Como o Diagrama de Dispersão e a correlação, analisaremos a relação e a intensidade em que ocorre, entre as variáveis : Speed(Rapidez) e Marking(Marcação).** **Podemos observar que, a correlação entre as variáveis é positiva e fraca,pois o valor da correlação está em torno de 0,1, mas não deixa de ser positiva , pois, os pontos tendem a crescer simultaneamente e próximos um dos outros.**