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Commit 1b3f5794 authored by Mario Garrido Tapias's avatar Mario Garrido Tapias
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All variables contemplated

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BivariateAnalysis.R
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......@@ -3,6 +3,24 @@ setwd("/home/mariogt/TFGs/Estadistica/")
#####################################
# FUNCIONES #
#####################################
puntos2d <- function(variableEnX, variableEnY, datos, temporada, color) {
# Variables AUXILIARES
maxX <- which.max(eval(parse(text = paste("datos$",variableEnX,".",temporada, sep = ""))))
maxY <- which.max(eval(parse(text = paste("datos$",variableEnY,".",temporada, sep = ""))))
xMean <- mean(eval(parse(text = paste("datos$",variableEnX,".",temporada, sep = ""))))
ggplot(datos, aes(eval(parse(text = paste(variableEnX,".",temporada, sep = ""))),
eval(parse(text = paste(variableEnY,".",temporada, sep = ""))))) +
labs(x = titleForYAxis(variableEnX), y = titleForYAxis(variableEnY)) +
geom_point(colour = color) +
geom_label_repel(data = subset(datos,
eval(parse(text = paste(variableEnX,".",temporada, sep = ""))) >
eval(parse(text = paste(variableEnX,".",temporada, "[", maxX, "]", sep = ""))) - xMean),
aes(label = player),
box.padding = 0.35,
point.padding = 0.5,
segment.color = 'grey50')
}
#############
# LIBRERIAS #
......@@ -10,31 +28,57 @@ setwd("/home/mariogt/TFGs/Estadistica/")
library(dplyr)
library(ggplot2)
library(ggcorrplot) # Correlation matrix visualization
library(correlation) # Correlation matrix table
library(RColorBrewer)
library(yarrr)
library(ggrepel)
library(PerformanceAnalytics)
# Seleccion de las varibles de ESTUDIO
variables <- paste(c("age.", "height.", "goals_assists_per90.", "xg.", "npxg.", "xa.", "shots_on_target_pct.", "passes_pct.",
"assisted_shots.", "passes_switches.", "sca_passes_dead.", "gca_passes_dead.", "passes_intercepted.",
"pressure_regains.", "dribbles_completed_pct.", "fouls.", "ball_recoveries.", "aerials_won_pct.",
"pens_att.", "goals_against_per90_gk.", "save_pct."), "1718", sep = "")
# variables <- paste(c("age.", "height.", "goals_assists_per90.", "xg.", "npxg.", "xa.", "shots_on_target_pct.", "passes_pct.",
# "assisted_shots.", "passes_switches.", "sca_passes_dead.", "gca_passes_dead.", "passes_intercepted.",
# "pressure_regains.", "dribbles_completed_pct.", "fouls.", "ball_recoveries.", "aerials_won_pct.",
# "pens_att.", "goals_against_per90_gk.", "save_pct."), "1718", sep = "")
# variables <- paste(c("age.", "height.", "goals_assists_per90.", "xg.", "npxg.", "xa.", "shots_on_target_pct.", "passes_pct.",
# "assisted_shots.", "passes_switches.", "sca_passes_dead.", "gca_passes_dead.", "passes_intercepted.",
# "pressure_regains.", "dribbles_completed_pct.", "fouls.", "ball_recoveries.", "aerials_won_pct.",
# "pens_att.", "goals_against_per90_gk.", "save_pct.", "passes_received_pct.", "goals_per_shot.", "passes_pressure."), "1718", sep = "")
variables <- paste(c("age.", "height.", "goals_assists_per90.", "xg.", "npxg.", "xa.", "shots_on_target_pct.", "passes_pct.",
"assisted_shots.", "passes_switches.", "sca_passes_dead.", "gca_passes_dead.", "passes_intercepted.",
"pressure_regains.", "dribbles_completed_pct.", "fouls.", "ball_recoveries.", "aerials_won_pct.",
"pens_att.", "goals_against_per90_gk.", "save_pct.",
"sca_passes_live.", "gca_passes_live.", "sca_dribbles.", "gca_dribbles.", "sca_fouled.", "gca_fouled."), "1718", sep = "")
trainStudy <- train %>% select(contains(variables))
trainStudy <- (trainStudy)
trainStudy <- data.frame(lapply(trainStudy, as.numeric))
variables <- paste(c("age.", "height.",
"xa.", "passes_completed.", "passes.", "passes_pct.", "passes_total_distance.", "assisted_shots.", "passes_switches.",
"pass_targets.", "passes_received.", "passes_received_pct.", "passes_pressure.",
"xg.", "npxg.", "shots_total.", "shots_on_target.", "shots_on_target_pct.", "goals_per_shot.", "pens_made.", "pens_att.",
"dribbles.", "dribbles_completed.", "dribbles_completed_pct.", "goals_assists_per90.",
"sca_passes_dead.", "gca_passes_dead.", "sca_passes_live.", "gca_passes_live.", "sca_dribbles.", "gca_dribbles.", "sca_fouled.", "gca_fouled.",
"passes_intercepted.", "ball_recoveries.", "pressure_regains.", "fouls.", "tackles_won", "tackles", "aerials_won.", "aerials_lost.", "aerials_won_pct.",
"goals_against_per90_gk.", "pens_saved.", "pens_allowed.", "shots_on_target_against.", "saves.", "save_pct."), "1718", sep = "")
correlacion <- round(cor(trainStudy), 1)
p.mat <- cor_pmat(trainStudy)
trainStudy <- train %>% select(starts_with(variables))
trainStudy <- data.frame(lapply(trainStudy, as.numeric))
trainStudyStand <- as.data.frame(scale(trainStudy))
correlacion <- round(cor(trainStudyStand), 1)
p.mat <- cor_pmat(trainStudyStand)
mypalette <- brewer.pal(n = 3, name = 'BuPu')
ggcorrplot(correlacion, type = "upper",
title = "Matriz de correlaciones ordenada",
hc.order = TRUE, lab = TRUE,
pch.cex = 1.2, lab_size = 2.5,
hc.order = FALSE, lab = TRUE,
pch.cex = 1.2, lab_size = 2.5, tl.cex = 9,
colors = mypalette, ggtheme = ggplot2::theme_gray, p.mat = p.mat, insig = "blank")
correlation::correlation(trainStudyStand,
include_factors = TRUE, method = "pearson")
# Porcentaje SI/NO
passes <- trainStudy[, 4:6]
chart.Correlation(passes, histogram = TRUE, pch = 18)
#######################
# ANALISIS BIVARIANTE #
#######################
# Paradas contra Tiros a puerta en contra
goalkeepers <- laLigaPlayers[which(laLigaPlayers$position.1718 == "GK"),]
datos <- laLigaPlayers[which(laLigaPlayers$position.1718 == "GK")]
puntos2d("saves",
"shots_on_target_against",
goalkeepers, "1718", "firebrick3")
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