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()))Week 15 : Tennis Tournaments
TidyTuesday
2019
{{% tweet "1115652786862153729" %}}
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
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")
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