Week 15 : Tennis Tournaments

TidyTuesday

Week 15 : Tennis Tournaments

TidyTuesday 
library(readr)
library(tidyverse)
library(lubridate)
library(gganimate)

player_dob <- read_csv("player_dob.csv", 
                        col_types = cols(date_of_birth = col_date(format = "%Y-%m-%d"), 
                        date_of_first_title = col_date(format = "%Y-%m-%d")))
grand_slams <- read_csv("grand_slams.csv", 
                        col_types = cols(gender = col_factor(levels = c("Female","Male")), 
                        rolling_win_count = col_integer(), 
                        tournament_date = col_date(format = "%Y-%m-%d"), 
                        year = col_integer()))
grand_slam_timeline <- read_csv("grand_slam_timeline.csv", 
                                col_types = cols(gender = col_factor(levels = c("Female","Male")), 
                                year = col_integer()))

GitHub Code

Player Information

Decade of Birth vs First Grand Slam Title Won

player_dob %>% 
   remove_missing() %>%
   mutate(grand_slam=recode_factor(grand_slam,
                                  'Wimbledon'="Wimbledon",
                                  'US Open'="US Open",
                                  'French Open'="French Open",
                                  'Australian Open'="Aus Open",
                                  'Australian Open (January)'="Aus Open",
                                  'Australian Open (Jan)'="Aus Open",       
                                  'Australian Open (December)'="Aus Open",  
                                  'Australian Open (Jan.)'="Aus Open"                                  
                                  )) %>%
  mutate(Birth=year(date_of_birth)) %>%
  mutate(Birth=cut(Birth,breaks = c(1929,1939,1949,1959,1969,1979,1989,1999),
                   labels = c(1930,1940,1950,1960,1970,1980,1990)
                   )) %>%
  group_by(Birth,grand_slam) %>%
  count()  %>%
ggplot(.,aes(x=factor(Birth),y=n,fill=grand_slam))+
      geom_col(position = "dodge")+
      scale_y_continuous(breaks=seq(1,11,1),labels=seq(1,11,1))+
      labs(fill="Grand Slam")+
      geom_text(aes(label=n),position = position_dodge(width = 1),vjust=1)+
      xlab("Decade of Birth")+ylab("Count")+
      ggtitle("How Decade of Birth and First Win of Grand Slam changes")

Decade of Birth vs First Grand Slam with Age

player_dob %>% 
  remove_missing() %>%
  mutate(grand_slam=recode_factor(grand_slam,
                                  'Wimbledon'="Wimbledon",
                                  'US Open'="US Open",
                                  'French Open'="French Open",
                                  'Australian Open'="Aus Open",
                                  'Australian Open (January)'="Aus Open",
                                  'Australian Open (Jan)'="Aus Open",       
                                  'Australian Open (December)'="Aus Open",  
                                  'Australian Open (Jan.)'="Aus Open"                                  
                                  )) %>%
  mutate(Birth=year(date_of_birth)) %>%
  mutate(Birth=cut(Birth,breaks = c(1929,1939,1949,1959,1969,1979,1989,1999),
                   labels = c(1930,1940,1950,1960,1970,1980,1990)
                   )) %>%
ggplot(.,aes(x=grand_slam,size=round(age/365),y=Birth))+
       geom_jitter()+
       xlab("Grand Slam")+ylab("Decade of Birth")+
       labs(color="Age",size="Age in Years")+
       ggtitle("Birth Decade vs First Grand Slam with Age")

Grand Slams

Gender vs Grand Slam with Name

grand_slams %>%
      group_by(name,gender) %>%
      count(sort = TRUE) %>%
      head(25) %>%
      ggplot(.,aes(x=fct_inorder(name),y=n,
                   fill=gender,label=n))+
      geom_col()+xlab("Name")+ylab("Count")+
      labs(fill="Gender")+
      coord_flip()+geom_text(hjust =1)+
      ggtitle("Who won most with Gender")

Gender vs Grand Slam with Year

p<-grand_slams %>%
      ggplot(.,aes(x=name,y=rolling_win_count,
                   shape=gender,color=grand_slam))+
      geom_point()+
      xlab("Name")+ylab("Cumulative Count")+
      labs(color="Grand Slam",shape="Gender")+
      transition_time(tournament_date)+ease_aes("linear")+
      coord_flip()+shadow_mark()+
      ggtitle("Cumulative progress with Year: {year(frame_time)}")

animate(p,nframes = 52,fps=1)

Grand Slam Timeline

Top 10 players and their Outcomes

top10<-grand_slams %>%
       group_by(name) %>%
       count(sort = TRUE) %>%
       head(10) %>%
       select(name)

grand_slam_timeline %>%
      rename(name="player") %>%
      inner_join(top10,"name") %>%
      group_by(name,outcome) %>%
      count() %>%
ggplot(.,aes(name,n,fill=outcome))+
      geom_col(position=position_dodge(width = 0.95))+
      geom_text(aes(label=n),position = position_dodge(width = 0.95),hjust=1)+
      coord_flip()+labs(fill="Outcome")+
      xlab("Name")+ylab("Count")+
      ggtitle("Top 10 Tennis Players and their Outcomes")

Top 10 Players and Tournament with outcomes of activeness

grand_slam_timeline %>%
      rename(name="player") %>%
      inner_join(top10,"name") %>%
      group_by(tournament,outcome) %>%
      count() %>%
ggplot(.,aes(x=tournament,y=n,fill=outcome))+
      geom_col(position =position_dodge(width=0.95))+
      geom_text(aes(label=n),position =position_dodge(width=0.95),vjust=1)+
      labs(fill="Outcome")+
      xlab("Tournament")+ylab("Count")+
      ggtitle("Tournament perspective of Top 10 Tennis Players and their Outcomes")

Top 10 Players Winning based on Tournament

grand_slam_timeline %>%
      rename(name="player") %>%
      inner_join(top10,"name") %>%
      subset(outcome=="Won") %>%
      group_by(name,tournament) %>%
      count() %>%
ggplot(.,aes(x=name,y=n,fill=tournament))+
      geom_col(position =position_dodge(width=0.95))+
      geom_text(aes(label=n),position =position_dodge(width=0.95),hjust=1)+
      coord_flip()+
      labs(fill="Tournament")+
      xlab("Name")+ylab("Count")+
      ggtitle("Winning, Top 10 Tennis Players with related to Tournament")

Top 10 Players Finalist based on Tournament

grand_slam_timeline %>%
      rename(name="player") %>%
      inner_join(top10,"name") %>%
      subset(outcome=="Finalist") %>%
      group_by(name,tournament) %>%
      count() %>%
ggplot(.,aes(x=name,y=n,fill=tournament))+
      geom_col(position =position_dodge(width=0.95))+
      geom_text(aes(label=n),position =position_dodge(width=0.95),hjust=1)+
      coord_flip()+
      labs(fill="Tournament")+
      xlab("Name")+ylab("Count")+
      ggtitle("Finalist, Top 10 Tennis Players with related to Tournament")

Top 10 Players Semi-Finalist based on Tournament

grand_slam_timeline %>%
      rename(name="player") %>%
      inner_join(top10,"name") %>%
      subset(outcome=="Semi-finalist") %>%
      group_by(name,tournament) %>%
      count() %>%
ggplot(.,aes(x=name,y=n,fill=tournament))+
      geom_col(position =position_dodge(width=0.95))+
      geom_text(aes(label=n),position =position_dodge(width=0.95),hjust=1)+
      coord_flip()+
      labs(fill="Tournament")+
      xlab("Name")+ylab("Count")+
      ggtitle("Semi-Finalist, Top 10 Tennis Players with related to Tournament")

Top 10 Players Retired based on Tournament

grand_slam_timeline %>%
      rename(name="player") %>%
      inner_join(top10,"name") %>%
      subset(outcome=="Retired") %>%
      group_by(name,tournament) %>%
      count() %>%
ggplot(.,aes(x=name,y=n,fill=tournament))+
      geom_col(position =position_dodge(width=0.95))+
      geom_text(aes(label=n),position =position_dodge(width=0.95),hjust=1)+
      coord_flip()+
      labs(fill="Tournament")+
      xlab("Name")+ylab("Count")+
      ggtitle("Retired, Top 10 Tennis Players with related to Tournament")

THANK YOU

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