## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----packages, message=FALSE, eval=FALSE-------------------------------------- # # Firstly, load BAwiR and other packages that will be used in the paper: # library(BAwiR) # 1.3 # library(tidyverse) # 1.3.2 # library(FSA) # 0.8.22 # library(gridExtra) # 2.3 ## ----figure 1, eval=FALSE----------------------------------------------------- # # Code for Figure 1: # # Load the data_app_acb file with the ACB games from the 1985-1986 season to the 2017-2018 season: # load(url("http://www.uv.es/vivigui/softw/data_app_acb.RData")) # title <- " Number of Spanish and foreign players along the ACB seasons \n Data from www.acb.com" # get_pop_pyramid(data_app_acb, title, "eng") ## ----data, message=FALSE, eval=FALSE------------------------------------------ # # Create the data with games and players' info, add the advanced stats and compute the total numbers: # df0 <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718) # df1 <- do_add_adv_stats(df0) # df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season") ## ----figure 2, eval=FALSE----------------------------------------------------- # # Code for Figure 2: # df3 <- df2[which(df2$Position == "Center"), c("MP", "PTS", "Name", "CombinID")] # df3 <- df3[df3$MP > 100,] # ggplot(df3, aes(x = c(df3[,1])[[1]], y = c(df3[,2])[[1]], group = Name)) + # geom_point() + # geom_text(aes(label = Name), size = 2, vjust = -0.8) + # labs(x = colnames(df3)[1], y = colnames(df3)[2], # title = "ACB 2017-2018, Regular Season. Total stats. Centers.") ## ----table 2, eval=FALSE------------------------------------------------------ # # Code for Table 2: # df4 <- df3 %>% # mutate(Player_info = paste("http://www.acb.com/jugador.php?id=", CombinID, sep = "")) %>% # select(-CombinID) # df5 <- df4[order(df4[,1][[1]], decreasing = TRUE),] # headtail(df5, 3) ## ----figure 3, eval=FALSE----------------------------------------------------- # # Code for Figure 3: # stats <- c("GP", "MP", "PTS", "FGPerc", "FTPerc", "TRB", "AST", "TOV", "PlusMinus", "PIR") # descr_stats <- c("Games played", "Minutes played", "Points", "Field goals percentage", # "Free throws percentage", "Total rebounds", "Assists", "Turnovers", # "Plus/minus", "Performance index rating") # df2_1 <- df2 %>% # select(1:5, stats, 46:49) # # perc_plot_doncid <- get_bubble_plot(df2_1, "Doncic, Luka", descr_stats, 3, 7, 8) + # theme(strip.text.x = element_blank()) + # ggtitle(label = "Doncic, Luka", # subtitle = "ACB 2017-2018, Regular Season. Total stats.") + # theme(plot.title = element_text(size = 20)) # # perc_plot_abalde <- get_bubble_plot(df2_1, "Abalde, Alberto", descr_stats, 3, 7, 8) + # theme(strip.text.x = element_blank()) + # ggtitle(label = "Abalde, Alberto", # subtitle = "ACB 2017-2018, Regular Season. Total stats.") + # theme(plot.title = element_text(size = 20)) # # grid.arrange(perc_plot_doncid, perc_plot_abalde, ncol = 2) ## ----figure 4, message=FALSE, eval=FALSE-------------------------------------- # # Code for Figure 4: # months <- c(df0 %>% distinct(Month))$Month # months_order <- c("September", "October", "November", "December", "January", # "February", "March", "April", "May", "June") # 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) # 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()