Atp Assignment

library(tidyverse)
library(dplyr)
library(ggplot2)

1. Data Import

url<-url("https://github.com/pjournal/mef03-demirefe91/blob/master/atp_tennis_data_2017.RData?raw=TRUE")
atp_data<-load(url)
glimpse(atp_data)

##  chr [1:6] "tourney_df" "player_df" "stats_df" "score_df" "rank_df" ...

1.2. Flag Code Rankings

singles_winners<-left_join(tourney_df,player_df,by=c("singles_winner_player_id"="player_id"))
champ_flags_df<-singles_winners%>%select(singles_winner_player_id,flag_code)%>%count(flag_code,sort=T)
champ_flags_df

## # A tibble: 21 x 2
##    flag_code     n
##    <chr>     <int>
##  1 ESP          11
##  2 USA           9
##  3 SUI           8
##  4 FRA           7
##  5 GER           7
##  6 BUL           4
##  7 ARG           2
##  8 BEL           2
##  9 BIH           2
## 10 CRO           2
## # … with 11 more rows

1.3. Rank countries which did not get any singles championships by the games won when they win the match

nonchamp_players<- player_df %>%select(player_id, flag_code) %>%anti_join(., champ_flags_df,by="flag_code")
nonchamp_players %>% left_join(.,score_df, by= c("player_id"="winner_player_id")) %>%group_by(flag_code) %>%summarise(total_won= sum(winner_games_won, na.rm=TRUE)) %>%arrange(desc(total_won))

## # A tibble: 93 x 2
##    flag_code total_won
##    <chr>         <dbl>
##  1 AUS            1989
##  2 CZE            1209
##  3 CAN            1190
##  4 SVK             889
##  5 BRA             873
##  6 POR             621
##  7 RSA             566
##  8 KAZ             495
##  9 KOR             438
## 10 GEO             377
## # … with 83 more rows

1.3. Hand Details of Champions

prize_money <- inner_join(tourney_df, player_df, by=c("singles_winner_player_id" = "player_id")) %>% 
  count(handedness, backhand) %>% arrange(desc(n))
prize_money

## # A tibble: 4 x 3
##   handedness   backhand                n
##   <chr>        <chr>               <int>
## 1 Right-Handed Two-Handed Backhand    41
## 2 Right-Handed One-Handed Backhand    17
## 3 Left-Handed  Two-Handed Backhand     8
## 4 Left-Handed  One-Handed Backhand     1

1.4. Total Match Counts And Average Match Durations of Tournaments

stats_df <- stats_df %>% mutate(num_tourney_id=as.numeric(tourney_id))
tournaments <- inner_join(tourney_df, stats_df, by=c("tourney_id" ="num_tourney_id")) %>%
  group_by(tourney_name) %>% summarise(total_duration = sum(match_duration)) %>% arrange(desc(total_duration))
tournaments

## # A tibble: 67 x 2
##    tourney_name                 total_duration
##    <chr>                                 <dbl>
##  1 Australian Open                       29548
##  2 Roland Garros                         29516
##  3 Wimbledon                             28437
##  4 US Open                               18702
##  5 Miami Open presented by Itaú          12732
##  6 BNP Paribas Open                      12551
##  7 Coupe Rogers                           7747
##  8 Rolex Monte-Carlo Masters              7542
##  9 Mutua Madrid Open                      7531
## 10 Internazionali BNL d’Italia            7523
## # … with 57 more rows