Furkan Oktay Sevimli
library(tidyverse)
## -- Attaching packages --------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --
## <U+221A> ggplot2 3.2.1 <U+221A> purrr 0.3.2
## <U+221A> tibble 2.1.3 <U+221A> dplyr 0.8.3
## <U+221A> tidyr 1.0.0 <U+221A> stringr 1.4.0
## <U+221A> readr 1.3.1 <U+221A> forcats 0.4.0
## -- Conflicts ------------------------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(kableExtra)
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## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
url<-url("https://github.com/pjournal/mef03-FurkanSevimli/raw/master/atp_tennis_data_2017.RData?raw=TRUE")
atp_tennis<-load(url)
Rank countries (Flag codes) by the single champions.
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)
kable(champ_flags_df) %>%
kable_styling("striped", full_width = F) %>%
scroll_box(width = "30%", height = "400px")
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flag_code
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n
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ESP
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11
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USA
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9
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SUI
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8
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FRA
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7
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GER
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7
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BUL
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4
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ARG
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2
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BEL
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2
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BIH
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2
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CRO
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2
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LUX
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2
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SRB
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2
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AUT
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1
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DOM
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1
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GBR
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1
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ITA
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1
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JPN
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1
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RUS
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1
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UKR
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1
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URU
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1
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UZB
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1
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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
Best country according to tourney surface at single player
Champflag_tourney_surface <- inner_join(player_df,tourney_df, by= c("player_id"="singles_winner_player_id")) %>% group_by(tourney_surface) %>% count(flag_code) %>% top_n(1,n)
kable(Champflag_tourney_surface) %>%
kable_styling("striped", full_width = F) %>%
scroll_box(width = "50%", height = "200px")
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tourney_surface
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flag_code
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n
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Clay
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ESP
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6
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Grass
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SUI
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2
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Hard
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USA
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7
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Total Match Durations of Winners
match_dur <- inner_join(player_df,score_df, by=c("player_id"="winner_player_id"))
match_dur <- inner_join(stats_df,match_dur, by = c("match_id"="match_id")) %>% group_by(player_slug)%>% summarise(total_match_duration=sum(match_duration, na.rm=TRUE)) %>% top_n(3,total_match_duration) %>% arrange(desc(total_match_duration))
kable(match_dur) %>%
kable_styling("striped", full_width = F) %>%
scroll_box(width = "50%", height = "200px")
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player_slug
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total_match_duration
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rafael-nadal
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7445
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david-goffin
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5483
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alexander-zverev
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5218
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