## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ----packages, message=FALSE, eval=FALSE--------------------------------------
# # Firstly, load BAwiR and other packages that will be used in the paper:
# library(BAwiR)
# library(tidyverse)
# library(FSA)
# library(gridExtra)
## ----figure 1, eval=FALSE-----------------------------------------------------
# # Code for Figure 1:
# # Load the data_app_acb file with the ACB games from the 1985-1986 season to the 2017-2018 season:
# load(url("http://www.uv.es/vivigui/softw/data_app_acb.RData"))
# title <- " Number of Spanish and foreign players along the ACB seasons \n Data from www.acb.com"
# get_pop_pyramid(data_app_acb, title, "eng")
## ----data, message=FALSE, eval=FALSE------------------------------------------
# # Create the data with games and players' info, add the advanced stats and compute the total numbers:
# df0 <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
# df1 <- do_add_adv_stats(df0)
# df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season")
## ----figure 2, eval=FALSE-----------------------------------------------------
# # Code for Figure 2:
# df3 <- df2[which(df2$Position == "Center"), c("MP", "PTS", "Name", "CombinID")]
# df3 <- df3[df3$MP > 100,]
# ggplot(df3, aes(x = c(df3[,1])[[1]], y = c(df3[,2])[[1]], group = Name)) +
# geom_point() +
# geom_text(aes(label = Name), size = 2, vjust = -0.8) +
# labs(x = colnames(df3)[1], y = colnames(df3)[2],
# title = "ACB 2017-2018, Regular Season. Total stats. Centers.")
## ----table 2, eval=FALSE------------------------------------------------------
# # Code for Table 2:
# df4 <- df3 %>%
# mutate(Player_info = paste("http://www.acb.com/jugador.php?id=", CombinID, sep = "")) %>%
# select(-CombinID)
# df5 <- df4 %>%
# arrange(-MP)
# headtail(df5, 3)
## ----figure 3, eval=FALSE-----------------------------------------------------
# # Code for Figure 3:
# stats <- c("GP", "MP", "PTS", "FGPerc", "FTPerc", "TRB", "AST", "TOV", "PlusMinus", "PIR")
# descr_stats <- c("Games played", "Minutes played", "Points", "Field goals percentage",
# "Free throws percentage", "Total rebounds", "Assists", "Turnovers",
# "Plus/minus", "Performance index rating")
# df2_1 <- df2 %>%
# select(1:5, stats, 46:49)
#
# perc_plot_doncid <- get_bubble_plot(df2_1, "Doncic, Luka", descr_stats, 3, 7, 8) +
# theme(strip.text.x = element_blank()) +
# ggtitle(label = "Doncic, Luka",
# subtitle = "ACB 2017-2018, Regular Season. Total stats.") +
# theme(plot.title = element_text(size = 20))
#
# perc_plot_abalde <- get_bubble_plot(df2_1, "Abalde, Alberto", descr_stats, 3, 7, 8) +
# theme(strip.text.x = element_blank()) +
# ggtitle(label = "Abalde, Alberto",
# subtitle = "ACB 2017-2018, Regular Season. Total stats.") +
# theme(plot.title = element_text(size = 20))
#
# grid.arrange(perc_plot_doncid, perc_plot_abalde, ncol = 2)
## ----figure 4, message=FALSE, eval=FALSE--------------------------------------
# # Code for Figure 4:
# months <- c(df0 %>% distinct(Month))$Month
# months_order <- c("septiembre", "octubre", "noviembre", "diciembre", "enero")
# months_plot <- match(months_order, months)
# months_plot1 <- months_plot[!is.na(months_plot)]
# months_plot2 <- months[months_plot1]
#
# df1_m <- df1 %>%
# filter(Player.x %in% c("Doncic, Luka", "Abalde, Alberto")) %>%
# group_by(Month) %>%
# do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%
# ungroup() %>%
# mutate(Month = factor(Month, levels = months_plot2)) %>%
# arrange(Month)
#
# df1_m1 <- df1_m %>%
# select(1:5, stats, 46:50) %>%
# select(-EPS) %>%
# mutate(Month = plyr::mapvalues(Month,
# from = c("octubre", "noviembre", "diciembre", "enero"),
# to = c("October", "November", "December", "January")))
#
# max_val <- max(df1_m1[,colnames(df1_m1) %in% stats])
# min_val <- min(df1_m1[,colnames(df1_m1) %in% stats])
# get_barplot_monthly_stats(df1_m1, "ACB 2017-2018, Regular Season. Monthly average stats.", 3) +
# scale_y_continuous(limits = c(min_val - 10, max_val + 10))
## ----figure 5, message=FALSE, eval=FALSE--------------------------------------
# # Code for Figure 5:
# df0$Compet <- "ACB"
# plot_yearly <- get_stats_seasons(df0, "ACB", c("Doncic, Luka", "Abalde, Alberto"),
# stats[1:4], "Regular Season", TRUE, FALSE)
# plot_yearly$gg +
# labs(title = "ACB 2017-2018, Regular Season. Yearly average stats.") +
# theme(strip.text.x = element_text(size = 15))
## ----figure 6, message=FALSE, eval=FALSE--------------------------------------
# # Code for Figure 6:
# levels_stats <- list("Offensive" = c("PTS", "FG", "FGA", "FGPerc",
# "TwoP", "TwoPA", "TwoPPerc",
# "ThreeP", "ThreePA", "ThreePPerc",
# "FT", "FTA", "FTPerc", "ORB", "AST"),
# "Defensive" = c("DRB", "STL", "PF"),
# "Other" = c("GP", "MP", "TRB", "PlusMinus", "PIR"),
# "Advanced" = c("EFGPerc", "PPS"))
# get_heatmap_bb(df2, "Real_Madrid", levels_stats, "PlusMinus", 9,
# paste("ACB", "2017-2018, Regular Season.", "Total stats.", sep = " "))
## ----figure 7, eval=FALSE-----------------------------------------------------
# # Code for Figure 7:
# get_shooting_plot(df2, "Real_Madrid", 3, 1, "ACB 2017-2018, Regular Season.", "en") +
# theme(plot.title = element_text(size = 15))
## ----figure 8, eval=FALSE-----------------------------------------------------
# # Code for Figure 8:
# df1_10 <- df1 %>%
# filter(Day <= 10)
# teams <- as.character(rev(sort(unique(df2$Team))))
# df_four_factors <- do_four_factors_df(df1_10, teams)
# get_four_factors_plot(df_four_factors$df_rank, df_four_factors$df_no_rank,
# c("Real_Madrid", "Valencia"), "en") +
# ggtitle("ACB 2017-2018, Regular Season.")
## ----figure 9, eval=FALSE-----------------------------------------------------
# # Code for Figure 9:
# df0$Compet <- "ACB"
# gg <- get_table_results(df0, "ACB", "2017-2018")
# gg$plot_teams
## ----figure 10, eval=FALSE----------------------------------------------------
# # Code for Figure 10:
# get_map_nats(df2) +
# ggtitle("ACB 2017-2018, Regular Season.")
## ----session info-------------------------------------------------------------
sessionInfo()