Este trabalho tem por finalidade analisar duas variáveis quantitativas, através da elaboração de um diagrama de dispersão e de uma matriz de correlação, baseando-se pelo banco de dados “FifaData.csv”
library(readr)
FifaData <- read_csv("C:/Users/19801926775/Desktop/Base_de_dados-master/FifaData.csv")
View(FifaData)
library(readxl)
library(flextable)
library(dplyr)
library(ggplot2)
head(FifaData) %>% flextable() %>% theme_tron_legacy()
Name | Nationality | National_Position | National_Kit | Club | Club_Position | Club_Kit | Club_Joining | Contract_Expiry | Rating | Height | Weight | Preffered_Foot | Birth_Date | Age | Preffered_Position | Work_Rate | Weak_foot | Skill_Moves | Ball_Control | Dribbling | Marking | Sliding_Tackle | Standing_Tackle | Aggression | Reactions | Attacking_Position | Interceptions | Vision | Composure | Crossing | Short_Pass | Long_Pass | Acceleration | Speed | Stamina | Strength | Balance | Agility | Jumping | Heading | Shot_Power | Finishing | Long_Shots | Curve | Freekick_Accuracy | Penalties | Volleys | GK_Positioning | GK_Diving | GK_Kicking | GK_Handling | GK_Reflexes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cristiano Ronaldo | Portugal | LS | 7 | Real Madrid | LW | 7 | 07/01/2009 | 2,021 | 94 | 185 cm | 80 kg | Right | 02/05/1985 | 32 | LW/ST | High / Low | 4 | 5 | 93 | 92 | 22 | 23 | 31 | 63 | 96 | 94 | 29 | 85 | 86 | 84 | 83 | 77 | 91 | 92 | 92 | 80 | 63 | 90 | 95 | 85 | 92 | 93 | 90 | 81 | 76 | 85 | 88 | 14 | 7 | 15 | 11 | 11 |
Lionel Messi | Argentina | RW | 10 | FC Barcelona | RW | 10 | 07/01/2004 | 2,018 | 93 | 170 cm | 72 kg | Left | 06/24/1987 | 29 | RW | Medium / Medium | 4 | 4 | 95 | 97 | 13 | 26 | 28 | 48 | 95 | 93 | 22 | 90 | 94 | 77 | 88 | 87 | 92 | 87 | 74 | 59 | 95 | 90 | 68 | 71 | 85 | 95 | 88 | 89 | 90 | 74 | 85 | 14 | 6 | 15 | 11 | 8 |
Neymar | Brazil | LW | 10 | FC Barcelona | LW | 11 | 07/01/2013 | 2,021 | 92 | 174 cm | 68 kg | Right | 02/05/1992 | 25 | LW | High / Medium | 5 | 5 | 95 | 96 | 21 | 33 | 24 | 56 | 88 | 90 | 36 | 80 | 80 | 75 | 81 | 75 | 93 | 90 | 79 | 49 | 82 | 96 | 61 | 62 | 78 | 89 | 77 | 79 | 84 | 81 | 83 | 15 | 9 | 15 | 9 | 11 |
Luis Suárez | Uruguay | LS | 9 | FC Barcelona | ST | 9 | 07/11/2014 | 2,021 | 92 | 182 cm | 85 kg | Right | 01/24/1987 | 30 | ST | High / Medium | 4 | 4 | 91 | 86 | 30 | 38 | 45 | 78 | 93 | 92 | 41 | 84 | 83 | 77 | 83 | 64 | 88 | 77 | 89 | 76 | 60 | 86 | 69 | 77 | 87 | 94 | 86 | 86 | 84 | 85 | 88 | 33 | 27 | 31 | 25 | 37 |
Manuel Neuer | Germany | GK | 1 | FC Bayern | GK | 1 | 07/01/2011 | 2,021 | 92 | 193 cm | 92 kg | Right | 03/27/1986 | 31 | GK | Medium / Medium | 4 | 1 | 48 | 30 | 10 | 11 | 10 | 29 | 85 | 12 | 30 | 70 | 70 | 15 | 55 | 59 | 58 | 61 | 44 | 83 | 35 | 52 | 78 | 25 | 25 | 13 | 16 | 14 | 11 | 47 | 11 | 91 | 89 | 95 | 90 | 89 |
De Gea | Spain | GK | 1 | Manchester Utd | GK | 1 | 07/01/2011 | 2,019 | 90 | 193 cm | 82 kg | Right | 11/07/1990 | 26 | GK | Medium / Medium | 3 | 1 | 31 | 13 | 13 | 13 | 21 | 38 | 88 | 12 | 30 | 68 | 60 | 17 | 31 | 32 | 56 | 56 | 25 | 64 | 43 | 57 | 67 | 21 | 31 | 13 | 12 | 21 | 19 | 40 | 13 | 86 | 88 | 87 | 85 | 90 |
summary(FifaData)
## Name Nationality National_Position National_Kit
## Length:17588 Length:17588 Length:17588 Min. : 1.00
## Class :character Class :character Class :character 1st Qu.: 6.00
## Mode :character Mode :character Mode :character Median :12.00
## Mean :12.22
## 3rd Qu.:18.00
## Max. :36.00
## NA's :16513
## Club Club_Position Club_Kit Club_Joining
## Length:17588 Length:17588 Min. : 1.00 Length:17588
## Class :character Class :character 1st Qu.: 9.00 Class :character
## Mode :character Mode :character Median :18.00 Mode :character
## Mean :21.29
## 3rd Qu.:27.00
## Max. :99.00
## NA's :1
## Contract_Expiry Rating Height Weight
## Min. :2017 Min. :45.00 Length:17588 Length:17588
## 1st Qu.:2017 1st Qu.:62.00 Class :character Class :character
## Median :2019 Median :66.00 Mode :character Mode :character
## Mean :2019 Mean :66.17
## 3rd Qu.:2020 3rd Qu.:71.00
## Max. :2023 Max. :94.00
## NA's :1
## Preffered_Foot Birth_Date Age Preffered_Position
## Length:17588 Length:17588 Min. :17.00 Length:17588
## Class :character Class :character 1st Qu.:22.00 Class :character
## Mode :character Mode :character Median :25.00 Mode :character
## Mean :25.46
## 3rd Qu.:29.00
## Max. :47.00
##
## Work_Rate Weak_foot Skill_Moves Ball_Control
## Length:17588 Min. :1.000 Min. :1.000 Min. : 5.00
## Class :character 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:53.00
## Mode :character Median :3.000 Median :2.000 Median :63.00
## Mean :2.934 Mean :2.303 Mean :57.97
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:69.00
## Max. :5.000 Max. :5.000 Max. :95.00
##
## Dribbling Marking Sliding_Tackle Standing_Tackle Aggression
## Min. : 4.0 Min. : 3.00 Min. : 5.00 Min. : 3.00 Min. : 2.00
## 1st Qu.:47.0 1st Qu.:22.00 1st Qu.:23.00 1st Qu.:26.00 1st Qu.:44.00
## Median :60.0 Median :48.00 Median :51.00 Median :54.00 Median :59.00
## Mean :54.8 Mean :44.23 Mean :45.57 Mean :47.44 Mean :55.92
## 3rd Qu.:68.0 3rd Qu.:64.00 3rd Qu.:64.00 3rd Qu.:66.00 3rd Qu.:70.00
## Max. :97.0 Max. :92.00 Max. :95.00 Max. :92.00 Max. :96.00
##
## Reactions Attacking_Position Interceptions Vision
## Min. :29.00 Min. : 2.00 Min. : 3.00 Min. :10.00
## 1st Qu.:55.00 1st Qu.:37.00 1st Qu.:26.00 1st Qu.:43.00
## Median :62.00 Median :54.00 Median :52.00 Median :54.00
## Mean :61.77 Mean :49.59 Mean :46.79 Mean :52.71
## 3rd Qu.:68.00 3rd Qu.:64.00 3rd Qu.:64.00 3rd Qu.:64.00
## Max. :96.00 Max. :94.00 Max. :93.00 Max. :94.00
##
## Composure Crossing Short_Pass Long_Pass Acceleration
## Min. : 5.00 Min. : 6.00 Min. :10.00 Min. : 7.0 Min. :11.00
## 1st Qu.:47.00 1st Qu.:38.00 1st Qu.:52.00 1st Qu.:42.0 1st Qu.:57.00
## Median :57.00 Median :54.00 Median :62.00 Median :56.0 Median :68.00
## Mean :55.85 Mean :49.74 Mean :58.12 Mean :52.4 Mean :65.29
## 3rd Qu.:66.00 3rd Qu.:64.00 3rd Qu.:68.00 3rd Qu.:64.0 3rd Qu.:75.00
## Max. :94.00 Max. :91.00 Max. :92.00 Max. :93.0 Max. :96.00
##
## Speed Stamina Strength Balance
## Min. :11.00 Min. :10.00 Min. :20.00 Min. :10.00
## 1st Qu.:58.00 1st Qu.:57.00 1st Qu.:57.00 1st Qu.:56.00
## Median :68.00 Median :66.00 Median :66.00 Median :65.00
## Mean :65.48 Mean :63.48 Mean :65.09 Mean :64.01
## 3rd Qu.:75.00 3rd Qu.:74.00 3rd Qu.:74.00 3rd Qu.:74.00
## Max. :96.00 Max. :95.00 Max. :98.00 Max. :97.00
##
## Agility Jumping Heading Shot_Power
## Min. :11.00 Min. :15.00 Min. : 4.00 Min. : 3.00
## 1st Qu.:55.00 1st Qu.:58.00 1st Qu.:45.00 1st Qu.:45.00
## Median :65.00 Median :65.00 Median :56.00 Median :59.00
## Mean :63.21 Mean :64.92 Mean :52.39 Mean :55.58
## 3rd Qu.:74.00 3rd Qu.:73.00 3rd Qu.:65.00 3rd Qu.:69.00
## Max. :96.00 Max. :95.00 Max. :94.00 Max. :93.00
##
## Finishing Long_Shots Curve Freekick_Accuracy
## Min. : 2.00 Min. : 4.0 Min. : 6.00 Min. : 4.00
## 1st Qu.:29.00 1st Qu.:32.0 1st Qu.:34.00 1st Qu.:31.00
## Median :48.00 Median :52.0 Median :48.00 Median :42.00
## Mean :45.16 Mean :47.4 Mean :47.18 Mean :43.38
## 3rd Qu.:61.00 3rd Qu.:63.0 3rd Qu.:62.00 3rd Qu.:57.00
## Max. :95.00 Max. :91.0 Max. :92.00 Max. :93.00
##
## Penalties Volleys GK_Positioning GK_Diving
## Min. : 7.00 Min. : 3.00 Min. : 1.00 Min. : 1.00
## 1st Qu.:39.00 1st Qu.:30.00 1st Qu.: 8.00 1st Qu.: 8.00
## Median :50.00 Median :44.00 Median :11.00 Median :11.00
## Mean :49.17 Mean :43.28 Mean :16.61 Mean :16.82
## 3rd Qu.:61.00 3rd Qu.:57.00 3rd Qu.:14.00 3rd Qu.:14.00
## Max. :96.00 Max. :93.00 Max. :91.00 Max. :89.00
##
## GK_Kicking GK_Handling GK_Reflexes
## Min. : 1.00 Min. : 1.00 Min. : 1.0
## 1st Qu.: 8.00 1st Qu.: 8.00 1st Qu.: 8.0
## Median :11.00 Median :11.00 Median :11.0
## Mean :16.46 Mean :16.56 Mean :16.9
## 3rd Qu.:14.00 3rd Qu.:14.00 3rd Qu.:14.0
## Max. :95.00 Max. :91.00 Max. :90.0
##
plot(FifaData$Age, FifaData$Rating, col="#52de9d",
pch=19, main="Diagrama de Dispersão",
xlab = "Idade",
ylab = "Avaliação")
abline(lsfit(FifaData$Age, FifaData$Rating), col="#4f3620")
cor(FifaData$Age,FifaData$Rating)
## [1] 0.4582763
library(corrplot)
FifaData %>% select(Age, Rating, Speed, Agility, Dribbling, Marking) %>%
filter(!is.na(Age)) %>%
cor() %>% corrplot(method = "circle")
Este banco de dados apresenta 53 variáveis. As duas variáveis quantitativas que resolvi analisar neste trabalho são Idade e Avaliação. A hipótese levantada é se a idade dos jogadores interfere na avaliação dos mesmos. O diagrama de dispersão mostra que correlação entre a idade e a avaliação dos jogadores é de 0,45, ou seja, é uma correlação fraca.
Já a matriz de correlação mostrou que a variável que possui maior grau de relação e está mais positivamente correlacionada com a idade é “Rating”, com índices acima de 0,6. As variáveis com menor correlação são “Marking” e “Dribbling”.