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DIC LAB 2 Problem 2 B : Repurpose Data : European Soccer Data





In [25]:



    


library("XML")
library("methods")
library("dplyr")
library("tidyr")

Read the player data





In [26]:



    


playersFinalData <- read.csv(file="player.csv", header=TRUE, sep=",")

Read the team data





In [27]:



    


teamFinalData <- read.csv(file="team.csv", header=TRUE, sep=",")

Display the data





In [28]:



    


head(playersFinalData)



    


















    









player_api_idplayer_nameoverall_ratinggk_ratingappearancesleague_appearances


	
505942            Aaron Appindangoye67                 43                 8               1                 

	
155782            Aaron Cresswell   74                121                75               1                 

	
162549            Aaron Doran       71                125               104               1                 

	
 30572            Aaron Galindo     75                111                10               1                 

	
 23780            Aaron Hughes      78                129               162               1                 

	
 27316            Aaron Hunt        79                132               158               1

Display the data





In [29]:



    


head(teamFinalData)



    


















    









team_api_idteam_long_namechance_creationbuild_up_playdefenseawayGoalshomeGoalsgoals


	
 9987            KRC Genk         182              148              205              143              205              348              

	
 9993            Beerschot AC     185               NA              210               62              112              174              

	
10000            SV Zulte-Waregem 153              147              200              130              176              306              

	
 9994            Sporting Lokeren 154              159              200              130              149              279              

	
 9984            KSV Cercle Brugge200              145              195               94              119              213              

	
 8635            RSC Anderlecht   182              149              190              180              247              427

Qn) Which are the top 10 players by overall rating?





In [30]:



    


topPlayer = tbl_df(playersFinalData) %>% select(player_name,overall_rating) %>% top_n(10) %>% arrange(desc(overall_rating))



    


















    






Selecting by overall_rating

















In [31]:



    


head(topPlayer,n=10)



    


















    









player_nameoverall_rating


	
Lionel Messi     94               

	
Cristiano Ronaldo93               

	
Gianluigi Buffon 93               

	
Wayne Rooney     93               

	
Gregory Coupet   92               

	
Xavi Hernandez   92               

	
Alessandro Nesta 91               

	
Andres Iniesta   91               

	
Fabio Cannavaro  91               

	
Iker Casillas    91

Qn) Which are the top 10 goalkeepers by sum of gk attributes?





In [32]:



    


topGoalKeeper = tbl_df(playersFinalData) %>% select(player_name,gk_rating) %>% top_n(10) %>% arrange(desc(gk_rating))



    


















    






Selecting by gk_rating

















In [33]:



    


head(topGoalKeeper,n=10)



    


















    









player_namegk_rating


	
Gianluigi Buffon 449              

	
Gregory Coupet   447              

	
Petr Cech        445              

	
Iker Casillas    442              

	
Manuel Neuer     440              

	
Julio Cesar      438              

	
Sebastian Frey   437              

	
Edwin van der Sar436              

	
David De Gea     429              

	
Pepe Reina       428

Qn) Which are the top 10 players by number of appearances (with any team they played with)?





In [34]:



    


topAppearances = select(playersFinalData,player_name,appearances) %>% top_n(10) %>% arrange(desc(appearances))



    


















    






Selecting by appearances

















In [35]:



    


head(topAppearances,n=10)



    


















    









player_nameappearances


	
Steve Mandanda      300                 

	
Stephane Ruffier    294                 

	
Gorka Iraizoz Moreno286                 

	
Samir Handanovic    286                 

	
Hugo Lloris         282                 

	
Tim Howard          282                 

	
Joe Hart            275                 

	
Cedric Carrasso     268                 

	
Daniel Congre       262                 

	
Moussa Sissoko      260

Qn) Which are the top 10 players by number of leagues they played in?





In [36]:



    


topLeagueAppearances = select(playersFinalData,player_name,league_appearances) %>% top_n(10) %>% arrange(desc(league_appearances))



    


















    






Selecting by league_appearances

















In [37]:



    


head(topLeagueAppearances,n=10)



    


















    









player_nameleague_appearances


	
Gelson Fernandes     6                    

	
Filip Djuricic       5                    

	
Ricky van Wolfswinkel5                    

	
Aleksandar Tonev     4                    

	
Alfred Finnbogason   4                    

	
Almen Abdi           4                    

	
Aly Cissokho         4                    

	
Angel Di Maria       4                    

	
Bojan Krkic          4                    

	
Bryan Ruiz           4

Qn) Which are the top 10 teams by sum of build up play attributes?





In [38]:



    


topBuildUpTeam = tbl_df(teamFinalData) %>% select(team_long_name,build_up_play) %>% top_n(10) %>% arrange(desc(build_up_play))



    


















    






Selecting by build_up_play

















In [39]:



    


head(topBuildUpTeam,n=10)



    


















    









team_long_namebuild_up_play


	
Torino                    209                       

	
Udinese                   205                       

	
Fortuna Düsseldorf        198                       

	
Podbeskidzie Bielsko-Biała193                       

	
Korona Kielce             192                       

	
Carpi                     190                       

	
Widzew Łódź               190                       

	
Widzew Łódź               190                       

	
FC Nantes                 189                       

	
Lazio                     188

Qn) Which are the top 10 teams by sum of chance creation attributes?





In [40]:



    


topChanceCreationTeam = tbl_df(teamFinalData) %>% select(team_long_name,chance_creation) %>% top_n(10) %>% arrange(desc(chance_creation))



    


















    






Selecting by chance_creation

















In [41]:



    


head(topChanceCreationTeam,n=10)



    


















    









team_long_namechance_creation


	
Lazio                  220                    

	
SV Werder Bremen       216                    

	
Napoli                 215                    

	
Manchester City        210                    

	
Tottenham Hotspur      210                    

	
Birmingham City        210                    

	
Wolverhampton Wanderers210                    

	
Burnley                210                    

	
Swansea City           210                    

	
Queens Park Rangers    210

Qn) Which are the top 10 teams by sum of defense attributes?





In [42]:



    


topDefenseTeam = tbl_df(teamFinalData) %>% select(team_long_name,defense) %>% top_n(10) %>% arrange(desc(defense))



    


















    






Selecting by defense

















In [43]:



    


head(topDefenseTeam,n=10)



    


















    









team_long_namedefense


	
Beerschot AC           210                    

	
KVC Westerlo           210                    

	
Sporting Charleroi     210                    

	
West Bromwich Albion   210                    

	
Hull City              210                    

	
Birmingham City        210                    

	
Wolverhampton Wanderers210                    

	
Blackpool              210                    

	
Swansea City           210                    

	
Queens Park Rangers    210

Qn) Which are the top 10 teams by number of scored goals?





In [44]:



    


topGoalScoringTeams = teamFinalData %>% select(team_long_name,goals) %>% top_n(10) %>% arrange(desc(goals))



    


















    






Selecting by goals

















In [45]:



    


head(topGoalScoringTeams,n=10)



    


















    









team_long_namegoals


	
FC Barcelona     849              

	
Real Madrid CF   843              

	
Celtic           695              

	
FC Bayern Munich 653              

	
PSV              652              

	
Ajax             647              

	
FC Basel         619              

	
Manchester City  606              

	
Chelsea          583              

	
Manchester United582

References

  1. https://cran.r-project.org/web/packages/dplyr/dplyr.pdf
  2. https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html
  3. https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html
  4. https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
  5. https://blog.rstudio.org/2014/07/22/introducing-tidyr/
  6. https://rpubs.com/bradleyboehmke/data_wrangling
  7. https://rdrr.io/cran/dplyr/man/join.tbl_sql.html
  8. http://stat545.com/bit001_dplyr-cheatsheet.html




In [ ]:

Content source: vk3105/Data-Intensive-Programming-CSE-587

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