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kickR 1.0.0 |
kickR is a comprehensive R package designed for football analytics and web scraping of football metrics from FBRef. Whether you're an analyst, data scientist, or a football enthusiast, kickR provides you with the tools to access player and team statistics from various football leagues. This package includes features to scrape data from websites like FBRef and other sources, making it easy to gather football metrics for your analysis and/or data vizzes.
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Scraping Data: Easily retrieve football statistics, player data, and team metrics from websites like FBref.
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League Coverage:
- Premier League
- Championship
- Serie A
- Ligue 1
- La Liga
- Segunda División
- Serie B
- Bundesliga
- Eredivisie
- Campeonato Brasileiro Série A
- Liga MX
- Major League Soccer
- Primeira Liga
- Bundesliga 2
- Belgian Pro League
- Ligue 2
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Data Analysis: FBRef has metrics grouped under 9 categories which are listed with the top metrics for each category below:
- Standard: General team metrics, xG, npxG, xG performance
- Goalkeeping: Clean sheets, save percentage, goals conceded
- Advanced Goalkeeping: Free kick and corner kick goals conceded, post shot expected goals(PSxG), PSxG performance, average passing length, goal kicks breakdown, crosses faced and crosses stopped an sweeping metrics.
- Shooting: Goals, shots and shots on target, average shot distance, xG and npxG performance.
- Passing: Total passes, pass blength breakdown, total passing distance, total progressive passing distance, pass length breakdown(attempted, completed and completion rates of shor, medium and long passes), final third passes, expected assisted goals, expected assists, key passes, final third passes and crosses and passes into the penalty area.
- Pass Types: Switches, throw ins, through balls, crosses, live in-play and dead passes, in and out-swinging corner kicks and passes offside.
- Goal & Shot Creation: Goal & Shot creating actions from live and dead passes, take-ons, shots, fouls drawn and defensive actions.
- Defensive Actions: tackles, challenges, blocks, interceptions, clearances and errors leading to an opponent shot.
- Possession: Touches, take-ons, carries and passes received.
- Playing Time: Matches played, minutes played, minutes per matches played, starts, points per match, goals scored and goals conceded when playing, xg performance when playing
- Miscellaneous: disciplinary record, fouls made and drawn, ball recoveries and offsides
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Data Export: Export your data to common formats for further analysis or sharing.
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Upcoming Features: With our next version we will introduce features such as modelling metrics for enhanced scouting and performance analysis and player similarity identification using performance metrics.
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Open Source: kickR is open-source software distributed under the MIT License.
To install, run the code below:
# Install latest development version of kickR.
if (!requireNamespace('devtools', quietly = T)) {
install.packages('devtools')
}
devtools::install_github('jeffreyohene/kickR')
To scrape statistics for the top 5 football leagues (English Premier League, La Liga, Bundesliga, Serie A, and Ligue 1) using kickR, follow these steps:
- Load the
kickRpackage in your R environment.
library(kickR)
# The fbref_team_stats() function supports all leagues except the MLS
# To scrape premier league goalkeeping data for 2020/2021
epl_2020_2021 <- fbref_team_stats(league = 'Premier League', season = '2020/2021', type = 'goalkeeping')
# Expected output as at 12/10/2023
# A tibble: 20 × 21
club league matches_played squad total_minutes_played mins_per_90 goals_against goals_against_per90
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Arse… 3 38 38 3,417 38.0 39 1.03
2 Asto… 1 38 38 3,420 38.0 46 1.21
3 Brig… 2 38 38 3,420 38.0 46 1.21
4 Burn… 3 38 38 3,420 38.0 55 1.45
5 Chel… 3 38 38 3,420 38.0 36 0.95
6 Crys… 2 38 38 3,420 38.0 66 1.74
7 Ever… 3 38 38 3,420 38.0 48 1.26
8 Fulh… 2 38 38 3,420 38.0 53 1.39
9 Leed… 2 38 38 3,420 38.0 54 1.42
10 Leic… 1 38 38 3,420 38.0 50 1.32
11 Live… 3 38 38 3,420 38.0 42 1.11
12 Manc… 3 38 38 3,420 38.0 32 0.84
13 Manc… 2 38 38 3,420 38.0 44 1.16
14 Newc… 2 38 38 3,420 38.0 62 1.63
15 Shef… 1 38 38 3,420 38.0 63 1.66
16 Sout… 2 38 38 3,420 38.0 68 1.79
17 Tott… 1 38 38 3,420 38.0 45 1.18
18 West… 2 38 38 3,420 38.0 76 2.00
19 West… 2 38 38 3,420 38.0 47 1.24
20 Wolv… 2 38 38 3,420 38.0 52 1.37
# ℹ 13 more variables: shots_on_target_against <chr>, saves <chr>, save_percentage <chr>, wins <chr>,
# draws <chr>, losses <chr>, clean_sheets <chr>, clean_sheet_percentage <chr>,
# penalties_attempted <chr>, penalty_kicks_allowed <chr>, penalty_kicks_saved <chr>,
# penalty_kicks_missed <chr>, penalty_kicks_save_percentage <chr>
# Passing data for La Liga
# If you want latest statistics for a league you can always leave the season parameter out like this
la_liga_passing_latest <- fbref_team_stats(league = 'La Liga', type = 'passing')
# Expected Output as at 12/10/2023
la_liga_passing_latest
# A tibble: 20 × 26
club number_of_players_used mins_per_90 total_passes_completed total_passes_attempted
<chr> <chr> <chr> <chr> <chr>
1 Alavés 22 9.0 2578 3617
2 Almería 26 9.0 3405 4238
3 Athletic Club 22 9.0 3416 4434
4 Atlético Madrid 22 8.0 3231 4016
5 Barcelona 23 9.0 5506 6261
6 Betis 27 9.0 3786 4586
7 Cádiz 25 9.0 2034 2971
8 Celta Vigo 22 9.0 3064 3996
9 Getafe 23 9.0 2146 3152
10 Girona 22 9.0 3945 4680
11 Granada 26 9.0 2588 3477
12 Las Palmas 23 9.0 4424 5245
13 Mallorca 21 9.0 2354 3252
14 Osasuna 22 9.0 2942 4005
15 Rayo Vallecano 22 9.0 2835 3818
16 Real Madrid 21 9.0 5118 5836
17 Real Sociedad 24 9.0 3748 4673
18 Sevilla 27 8.0 2783 3743
19 Valencia 24 9.0 3060 3987
20 Villarreal 23 9.0 3618 4361
# ℹ 21 more variables: pass_completion_percentage <chr>, total_passing_distance <chr>,
# total_progressive_distance <chr>, short_passes_completed <chr>, short_passes_attempted <chr>,
# short_pass_completion_percentage <chr>, medium_passes_completed <chr>,
# medium_passes_attempted <chr>, medium_pass_completion_percentage <chr>,
# long_passes_completed <chr>, long_passes_attempted <chr>, long_pass_completion_percentage <chr>,
# assists <chr>, xAG <chr>, xA <chr>, xag_performance <chr>, key_passes <chr>,
# passes_into_final_third <chr>, passes_into_penalty_box <chr>, crosses_into_penalty_box <chr>, …
# kickR also supports these non european leagues: Liga MX and Campeonato Brasileiro Série A
# To get possession stats of the Campeonato Brasileiro Série A
br_serie_a_possession <- fbref_team_stats(league = 'Campeonato Brasileiro Serie A', season = '2022/2023', type = 'possession')
# Expected output
# A tibble: 20 × 26
club number_of_players_used possession mins_per_90 total_touches touches_defensive_pen…¹
<chr> <chr> <chr> <chr> <chr> <chr>
1 América (MG) 41 41.5 26.0 13067 1475
2 Ath Paranaense 36 47.4 26.0 14197 1488
3 Atlético Mineiro 31 53.8 26.0 16389 1575
4 Bahia 34 50.7 26.0 15195 1547
5 Botafogo (RJ) 34 45.7 26.0 14527 1492
6 Bragantino 34 56.2 26.0 16180 1308
7 Corinthians 35 49.0 26.0 16003 1797
8 Coritiba 42 43.5 26.0 13593 1612
9 Cruzeiro 35 52.5 25.0 15199 1477
10 Cuiabá 29 44.5 25.0 13314 1320
11 Flamengo 32 58.5 26.0 17899 1507
12 Fluminense 34 61.1 26.0 18010 1870
13 Fortaleza 31 47.0 26.0 14391 1248
14 Goiás 37 44.4 26.0 13865 1776
15 Grêmio 41 45.8 26.0 14651 1514
16 Internacional 34 52.4 26.0 16120 1469
17 Palmeiras 31 53.8 26.0 15495 1415
18 Santos 45 44.1 26.0 13489 1515
19 São Paulo 35 60.5 26.0 17832 1626
20 Vasco da Gama 39 47.5 26.0 14282 1550
# ℹ abbreviated name: ¹touches_defensive_penalty_box
# ℹ 20 more variables: touches_defensive_third <chr>, touches_middle_third <chr>,
# touches_final_third <chr>, touches_attacking_penalty_box <chr>, inplay_touches <chr>,
# take_ons_attempted <chr>, successful_take_ons <chr>, successful_take_on_percentage <chr>,
# tackles_during_take_on <chr>, tackles_during_take_on_percentage <chr>, carries <chr>,
# total_carrying_distance <chr>, progressive_carrying_distance <chr>, progressive_carries <chr>,
# carries_into_final_third <chr>, carries_into_penalty_box <chr>, miscontrols <chr>, …
# MLS is a year long league not split over seasons. We can get standardMLS team data by running this line of code
mls_2020 <- fbref_mls_team_stats(season = '2020', type = 'standard')
# Expected output
# A tibble: 26 × 32
club number_of_players_used average_age possession matches_played starts minutes mins_per_90 goals
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Atlan… 27 25.6 54.7 23 253 2,070 23.0 23
2 Chica… 25 26.7 51.2 23 253 2,070 23.0 33
3 Color… 22 25.8 52.0 18 198 1,620 18.0 30
4 Colum… 26 27.4 52.0 23 253 2,070 23.0 34
5 D.C. … 26 26.6 46.0 23 253 2,070 23.0 24
6 Dynamo 26 27.7 48.7 23 253 2,070 23.0 30
7 FC Ci… 30 27.5 49.3 23 253 2,070 23.0 11
8 FC Da… 27 27.0 51.2 22 242 1,980 22.0 28
9 Inter… 28 26.8 47.3 23 253 2,070 23.0 24
10 LA Ga… 25 27.2 45.3 22 242 1,980 22.0 26
# ℹ 16 more rows
# ℹ 23 more variables: assists <chr>, goals_and_assists <chr>, non_penalty_goals <chr>,
# penalties <chr>, penalty_kick_attempts <chr>, yellow_cards <chr>, red_cards <chr>, xG <chr>,
# npxG <chr>, xAG <chr>, `npxG+xAG` <chr>, progressive_carries <chr>, progressive_passes <chr>,
# goals_per90 <chr>, assists_per90 <chr>, goals_and_assists_per90 <chr>,
# non_penalty_goals_per90 <chr>, non_penalty_goals_and_assists_per90 <chr>, xg_per90 <chr>,
# xag_per90 <chr>, xg_and_xag_per90 <chr>, npxg_per90 <chr>, npxg_and_xag_per90 <chr>
# ℹ Use `print(n = ...)` to see more rows
# Also every league has a separate function for scraping team statistics but slightly differs from the MLS code
# To scrape advanced goalkeeping statistics for Belgium Pro League for example, this code should do the trick
belge_adv_goalkeeping <- fbref_belgian_pro_league_team_stats(type = 'advanced_goalkeeping')
# Expected output
# A tibble: 16 × 28
club number_of_players_used mins_per_90 goals_against penalty_kicks_allowed free_kick_goals_agai…¹
<chr> <chr> <chr> <chr> <chr> <chr>
1 Ander… 2 10.0 10 2 0
2 Antwe… 1 10.0 5 3 0
3 Cercl… 1 10.0 11 0 0
4 Charl… 1 10.0 15 3 0
5 Club … 1 10.0 11 0 1
6 Eupen 2 10.0 22 3 0
7 Genk 1 10.0 10 0 0
8 Gent 2 10.0 10 1 0
9 Kortr… 1 10.0 22 0 0
10 Meche… 1 10.0 12 1 0
11 OH Le… 2 10.0 19 5 0
12 RWD M… 1 10.0 20 1 0
13 Sint-… 2 10.0 14 1 0
14 Stand… 1 10.0 9 1 0
15 Union… 2 10.0 12 0 0
16 Weste… 2 10.0 19 1 0
# ℹ abbreviated name: ¹free_kick_goals_against
# ℹ 22 more variables: corner_kick_goals_against <chr>, own_goals_against <chr>, PSxG <chr>,
# PSxG_per_shot_on_target <chr>, PSxG_minus_goals_allowed <chr>,
# PSxG_minus_goals_allowed_per90 <chr>, launched_passes_completed <chr>,
# launched_passes_attempted <chr>, launched_passes_completion_pct <chr>, passes_attempted <chr>,
# throws_attempted <chr>, percentage_passes_launched <chr>, avg_pass_length <chr>, goal_kicks <chr>,
# percentage_goal_kicks_launched <chr>, avg_goal_kick_length <chr>, crosses_faced <chr>, …
With this version you can access player data of every single player playing in Europe's top 5 leagues.
Due to standardization, this version gives you access to get player statistics data for only these 5 leagues for now. In the next release which will feature enhanced scouting programme and a player similarity model, we will extend the access to more players in other leagues
# If you want to run an analysis on some of the most influential players/metronomes
# in possession in Europe's top 5 leagues, you could scrape the data first with this
possession <- fbref_big5_player_stats(season = '2022/2023', type = 'possession')
# Expected output
# A tibble: 2,889 × 30
player nation position club league age birth_year mins_per_90 total_touches
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Brenden Aaronson us USA MF,FW Leeds United eng Pr… 21 2000 26.4 1143
2 Paxten Aaronson us USA MF,DF Eint Frankfurt de Bun… 18 2003 1.9 99
3 James Abankwah ie IRL DF Udinese it Ser… 18 2004 0.7 39
4 George Abbott eng ENG MF Tottenham eng Pr… 16 2005 0.0 2
5 Yunis Abdelhamid ma MAR DF Reims fr Lig… 34 1987 37.0 2459
6 Himad Abdelli dz ALG MF,FW Angers fr Lig… 22 1999 23.7 1516
7 Salis Abdul Samed gh GHA MF Lens fr Lig… 22 2000 32.2 2224
8 Laurent Abergel fr FRA MF Lorient fr Lig… 29 1993 26.0 1779
9 Oliver Abildgaard dk DEN MF Hellas Verona it Ser… 26 1996 6.6 257
10 Matthis Abline fr FRA FW,MF Auxerre fr Lig… 19 2003 9.0 210
# ℹ 2,879 more rows
# ℹ 21 more variables: touches_defensive_penalty_box <chr>, touches_defensive_third <chr>,
# touches_middle_third <chr>, touches_final_third <chr>, touches_attacking_penalty_box <chr>,
# inplay_touches <chr>, take_ons_attempted <chr>, successful_take_ons <chr>,
# successful_take_on_percentage <chr>, tackles_during_take_on <chr>,
# tackles_during_take_on_percentage <chr>, carries <chr>, total_carrying_distance <chr>,
# progressive_carrying_distance <chr>, progressive_carries <chr>, carries_into_final_third <chr>, …
# ℹ Use `print(n = ...)` to see more rows
# Imagine you wanted to do the same analysis but only for Ligue 1 players
# You can run this
library(dplyr)
possession_fr <- possession |> filter(league == "fr Ligue 1")
# Expected Output
# A tibble: 606 × 30
player nation position club league age birth_year mins_per_90 total_touches
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Yunis Abdelhamid ma MAR DF Reims fr Ligue 1 34 1987 37.0 2459
2 Himad Abdelli dz ALG MF,FW Angers fr Ligue 1 22 1999 23.7 1516
3 Salis Abdul Samed gh GHA MF Lens fr Ligue 1 22 2000 32.2 2224
4 Laurent Abergel fr FRA MF Lorient fr Ligue 1 29 1993 26.0 1779
5 Matthis Abline fr FRA FW,MF Auxerre fr Ligue 1 19 2003 9.0 210
6 Matthis Abline fr FRA FW,MF Rennes fr Ligue 1 19 2003 1.2 37
7 Zakaria Aboukhlal ma MAR FW,MF Toulouse fr Ligue 1 22 2000 27.5 1163
8 Mohamed Achi fr FRA FW Nantes fr Ligue 1 20 2002 0.4 25
9 Martin Adeline fr FRA MF,FW Reims fr Ligue 1 18 2003 1.2 59
10 Emmanuel Agbadou ci CIV DF Reims fr Ligue 1 25 1997 26.5 1798
# If you work with a national team and want to see the performances of your country's players
# Provided they play in Europe's T5 leagues and they are for example Ghanaian, you can do this
gha_players_europe <- possession |> filter(nation == "gh GHA")
# Expected output
# A tibble: 26 × 30
player nation position club league age birth_year mins_per_90 total_touches
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Salis Abdul Samed gh GHA MF Lens fr Li… 22 2000 32.2 2224
2 Felix Afena-Gyan gh GHA FW,MF Cremonese it Se… 19 2003 9.1 318
3 Joseph Aidoo gh GHA DF Celta Vigo es La… 26 1995 34.7 2082
4 Daniel Amartey gh GHA DF Leicester… eng P… 27 1994 17.9 1279
5 Christopher Antwi-Adjei gh GHA FW,MF Bochum de Bu… 28 1994 22.0 996
6 André Ayew gh GHA FW,MF Nott'ham … eng P… 32 1989 3.5 106
7 Jordan Ayew gh GHA FW,MF Crystal P… eng P… 30 1991 30.2 1327
8 Iddrisu Baba gh GHA MF,DF Mallorca es La… 26 1996 18.9 773
9 Kevin-Prince Boateng gh GHA MF Hertha BSC de Bu… 35 1987 5.2 320
10 Alexander Djiku gh GHA DF Strasbourg fr Li… 27 1994 29.7 1873
# ℹ 16 more rows
# ℹ 21 more variables: touches_defensive_penalty_box <chr>, touches_defensive_third <chr>,
# touches_middle_third <chr>, touches_final_third <chr>, touches_attacking_penalty_box <chr>,
# inplay_touches <chr>, take_ons_attempted <chr>, successful_take_ons <chr>,
# successful_take_on_percentage <chr>, tackles_during_take_on <chr>,
# tackles_during_take_on_percentage <chr>, carries <chr>, total_carrying_distance <chr>,
# progressive_carrying_distance <chr>, progressive_carries <chr>, carries_into_final_third <chr>, …
# ℹ Use `print(n = ...)` to see more rows
This package was built on rvest, jsonlite and openxlsx. Since the first release is a purely scraping package release, you would have to load dplyr into your R environment for helpful data manipulation functions like renaming columns and also changing column data types from character to numeric for example.
It is also worth noting that the scraping package cleans the column names into more descriptive names for easier analysis.
# We can change the column data type from character to double for example for a single column or multiple columns in our br_serie_a_possession dataframe.
# We want to convert every column apart from the only actual character column, club to do that we can run
br_serie_a_possession <- br_serie_a_possession |> mutate_at(-1, as.numeric)
# Expected output
# A tibble: 20 × 26
club number_of_players_used possession mins_per_90 total_touches touches_defensive_pen…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 América (MG) 41 41.5 26 13067 1475
2 Ath Paranaense 36 47.4 26 14197 1488
3 Atlético Mineiro 31 53.8 26 16389 1575
4 Bahia 34 50.7 26 15195 1547
5 Botafogo (RJ) 34 45.7 26 14527 1492
6 Bragantino 34 56.2 26 16180 1308
7 Corinthians 35 49 26 16003 1797
8 Coritiba 42 43.5 26 13593 1612
9 Cruzeiro 35 52.5 25 15199 1477
10 Cuiabá 29 44.5 25 13314 1320
11 Flamengo 32 58.5 26 17899 1507
12 Fluminense 34 61.1 26 18010 1870
13 Fortaleza 31 47 26 14391 1248
14 Goiás 37 44.4 26 13865 1776
15 Grêmio 41 45.8 26 14651 1514
16 Internacional 34 52.4 26 16120 1469
17 Palmeiras 31 53.8 26 15495 1415
18 Santos 45 44.1 26 13489 1515
19 São Paulo 35 60.5 26 17832 1626
20 Vasco da Gama 39 47.5 26 14282 1550
# ℹ abbreviated name: ¹touches_defensive_penalty_box
# ℹ 20 more variables: touches_defensive_third <dbl>, touches_middle_third <dbl>,
# touches_final_third <dbl>, touches_attacking_penalty_box <dbl>, inplay_touches <dbl>,
# take_ons_attempted <dbl>, successful_take_ons <dbl>, successful_take_on_percentage <dbl>,
# tackles_during_take_on <dbl>, tackles_during_take_on_percentage <dbl>, carries <dbl>,
# total_carrying_distance <dbl>, progressive_carrying_distance <dbl>, progressive_carries <dbl>,
# carries_into_final_third <dbl>, carries_into_penalty_box <dbl>, miscontrols <dbl>, …
# Renaming columns
# If we wanted to rename the possession column above, we could do this
br_serie_a_possession <- br_serie_a_possession |> rename(avg_possession = possession)
# Or if you want to use colnames() but you hate to manually find the index
# Find the index of the 'possession' column
possession_index <- which(colnames(df) == "possession")
# Rename the 'possession' column to 'avg_possession'
colnames(df)[possession_index] <- 'avg_possession'
# If you actually enjoy pain, you can do it manually like this
colnames(br_serie_a_possession)[3] <- 'avg_possession'
# Expected output of all the methods above
# A tibble: 20 × 26
club number_of_players_used avg_possession mins_per_90 total_touches touches_defensive_pe…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 América (MG) 41 41.5 26 13067 1475
2 Ath Paranaen… 36 47.4 26 14197 1488
3 Atlético Min… 31 53.8 26 16389 1575
4 Bahia 34 50.7 26 15195 1547
5 Botafogo (RJ) 34 45.7 26 14527 1492
6 Bragantino 34 56.2 26 16180 1308
7 Corinthians 35 49 26 16003 1797
8 Coritiba 42 43.5 26 13593 1612
9 Cruzeiro 35 52.5 25 15199 1477
10 Cuiabá 29 44.5 25 13314 1320
11 Flamengo 32 58.5 26 17899 1507
12 Fluminense 34 61.1 26 18010 1870
13 Fortaleza 31 47 26 14391 1248
14 Goiás 37 44.4 26 13865 1776
15 Grêmio 41 45.8 26 14651 1514
16 Internacional 34 52.4 26 16120 1469
17 Palmeiras 31 53.8 26 15495 1415
18 Santos 45 44.1 26 13489 1515
19 São Paulo 35 60.5 26 17832 1626
20 Vasco da Gama 39 47.5 26 14282 1550
# ℹ abbreviated name: ¹touches_defensive_penalty_box
# ℹ 20 more variables: touches_defensive_third <dbl>, touches_middle_third <dbl>,
# touches_final_third <dbl>, touches_attacking_penalty_box <dbl>, inplay_touches <dbl>,
# take_ons_attempted <dbl>, successful_take_ons <dbl>, successful_take_on_percentage <dbl>,
# tackles_during_take_on <dbl>, tackles_during_take_on_percentage <dbl>, carries <dbl>,
# total_carrying_distance <dbl>, progressive_carrying_distance <dbl>, progressive_carries <dbl>,
# carries_into_final_third <dbl>, carries_into_penalty_box <dbl>, miscontrols <dbl>, …
# Nation and league columns in dataframe
# If you want to see nation and league in the three letter code format
possession$nation <- gsub("^.+\\s", "", possession$nation)
possession$league <- gsub("^.+\\s", "", possession$league)The save_table function is designed to save a given data frame in various formats such as JSON, CSV, XLSX, or RDS. It offers flexibility for choosing the desired format.
The save_table function saves a specified dataframe to your working directory provided you want to store it locally or work with it later. If we want to save our La Liga passing table from early on to make a viz that proves why Real Madrid is the most potent passing team in La Liga for example we can do that below with the following code.
# Initialize variables
df <- la_liga_passing_latest
filename <- 'la_liga_passing_latest'
format <- 'csv'
save_table(df, filename, format)
# Expected output:
Table saved as ' la_liga_passing_latest.csv '
kickR relies on the following R packages:
- jsonlite
- rvest
- openxlsx
Additionally, it's recommended to use testthat for testing.
- Author: Jeffrey Ohene
- Maintainer: Jeffrey Ohene (jeffrey.ohene@aol.com)
This package is released under the MIT License. See the LICENSE file for details.
If you would like to contribute to this project, please check the contributions file for this package.
We regularly monitor the packages' functions' performance and functionality and release updates as needed to ensure its reliability. If you encounter any issues or have suggestions for improvements, please don't hesitate to open an issue on our GitHub repository and provide as much detail as possible to help us understand and address the issue.
Explore football analytics and data scraping with kickR, your go-to R package for football enthusiasts, analysts, and data scientists.
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