Atividade 3
Vitoria Monteiro
19/03/2021
##INTRODUÇÃO
Iremos analisar a base de dados FIFA.
library(readr)
FifaData <- read_csv("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.View(FifaData)
head(FifaData)## # A tibble: 6 x 53
## Name Nationality National_Positi~ National_Kit Club Club_Position Club_Kit
## <chr> <chr> <chr> <dbl> <chr> <chr> <dbl>
## 1 Cristi~ Portugal LS 7 Real~ LW 7
## 2 Lionel~ Argentina RW 10 FC B~ RW 10
## 3 Neymar Brazil LW 10 FC B~ LW 11
## 4 Luis S~ Uruguay LS 9 FC B~ ST 9
## 5 Manuel~ Germany GK 1 FC B~ GK 1
## 6 De Gea Spain GK 1 Manc~ GK 1
## # ... with 46 more variables: Club_Joining <chr>, Contract_Expiry <dbl>,
## # Rating <dbl>, Height <chr>, Weight <chr>, Preffered_Foot <chr>,
## # Birth_Date <chr>, Age <dbl>, Preffered_Position <chr>, Work_Rate <chr>,
## # Weak_foot <dbl>, Skill_Moves <dbl>, Ball_Control <dbl>, Dribbling <dbl>,
## # Marking <dbl>, Sliding_Tackle <dbl>, Standing_Tackle <dbl>,
## # Aggression <dbl>, Reactions <dbl>, Attacking_Position <dbl>,
## # Interceptions <dbl>, Vision <dbl>, Composure <dbl>, Crossing <dbl>,
## # Short_Pass <dbl>, Long_Pass <dbl>, Acceleration <dbl>, Speed <dbl>,
## # Stamina <dbl>, Strength <dbl>, Balance <dbl>, Agility <dbl>, Jumping <dbl>,
## # Heading <dbl>, Shot_Power <dbl>, Finishing <dbl>, Long_Shots <dbl>,
## # Curve <dbl>, Freekick_Accuracy <dbl>, Penalties <dbl>, Volleys <dbl>,
## # GK_Positioning <dbl>, GK_Diving <dbl>, GK_Kicking <dbl>, GK_Handling <dbl>,
## # GK_Reflexes <dbl>Resumo dos dados
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
## #transformação dos dados
FifaData$Age<- ifelse(FifaData$Age ==0,"17","47")##Analise grafico de barras
barplot(table(FifaData$Age),col = c("yellow","pink"),main ="Distribuição da Fifa por idade")
Nesse gráfico podemos analisar, a quantidade de jogadores em função de suas idades. De primeiro modo ,podemos ver que, a idade em que predomina o maior número de jogadores é a de 25 anos. De outro modo, podemos ver que há crescimento a partir dos 19 anos, a partir disso há um oscilamento e após os 25 anos decresce. Podemos concluir, que maioria dos jogadores tem em torno de 19 a 25 anos. OBS:Infelizmente imagem,nao foi por completo, mas irei disponibilizar.
##estudo das variaveis quantitativas
summary(FifaData$Penalties)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.00 39.00 50.00 49.17 61.00 96.00hist(FifaData$Penalties, col="red",
main = "histograma dos penaltis",
xlab = "Penalties")
Nesse segundo gráfico, podemos perceber a frequência de determinadas quantidades de pênaltis. Em primeiro instante, nota se como destaque e de maior frequência é de 60 pênaltis . Percebe - se que entre 40 e 60 pênaltis, a frequência se manteve quase constantes. E a partir de 20 a 60 penaltis houve crescimento, da frequencia, só que a partir 60 a 100 penaltis a fequencia decresceu.
##boxplot
boxplot(FifaData$Penalties, col="green",
main="Boxplot Penaltis")
Nesse terceiro gráfico , também relacionado aos pênaltis podemos analisar, alguns pontos especiais e interpreta lo. De início, seu ponto máximo que encontra se após 80, que vimos gráfico anterior que é 100 pênaltis. Já No terceiro quartil, valor de 60 pênaltis, Em segundo modo , a mediana que está entre em 40 e 60 pênaltis e o primeiro quartel, que esta sobre valor de 40 pênaltis. Seu valor mínimo que esta abaixo de 20 pênaltis.