socceranalysisR

socceranalysisR is a powerful R package designed to make it easy to analyze and understand soccer statistics. With its set of functions, you can quickly obtain summary statistics for a particular team, identify outliers based on market value, rank players by goals per game and display different plots. The package is built in a way that allows user to easily customize the functions to their own interests, giving them the flexibility to analyze the data in a way that is most meaningful to them. Whether you’re a coach, a sports journalist or an analyst, socceranalysisR will help you unlock the insights hidden in your soccer data and make more informed decisions.

Functions

1. soc_find_team_stat: provides a quick and easy way to understand the descriptive statistics of a team.

2. soc_rankingplayers: Ranks players based on specific attributes

3. soc_get_outliers: Identifes outliers using statistical methods (interquartile range or standard deviations)

4. soc_viz_stats : Generates meaningful visualizations to help users understand and interpret the numerical data (scatter plots or histograms)

R ecosystem

socceranalysisR can be used in conjunction with other popular R packages such as dplyr and ML_for_Hackers to perform more advanced data analysis and machine learning tasks. For example, users can use dplyr to manipulate and clean their soccer data, and then use this package to perform specific soccer-related analysis on the cleaned data. Additionally, socceranalysisR can be used in conjunction with ML_for_Hackers for machine learning tasks on soccer data. They are designed to be a higher-level, more user-friendly and declarative interface based on ggplot2 for performing specific soccer-related analysis and visualization tasks. Users can perform similar visualization using shiny. Overall, socceranalysisR is a valuable addition to the R ecosystem as it provides a specialized tool for analyzing and understanding soccer data without the need for writing complex code, this can be especially useful for users who may not have extensive experience with data analysis.

Installation

You can install the development version of socceranalysisR from GitHub with:

# install.packages("devtools")
devtools::install_github("UBC-MDS/socceranalysisR")

Use this link to download the dataset: https://github.com/UBC-MDS/socceranalysis_python/blob/main/soccer_data.xlsx

Example

This is a basic example which shows you how to solve a common problem:

library(socceranalysisR)
# Visualization 
## toy data
small_data <- data.frame(age = - c(18, 20, 20, 25, 25, 24, 25, 30, 33, 32, 44, 30, 17),
                         Wages_Euros = c(300000, 575000,    150000, 475000,375000,  225000, 225000, 200000, 225000, 250000, 250000, 375000, 200000))
## scatter plots
soc_viz_stats('age', 'Wages_Euros', T , small_data)
#> Warning: `label_number_si()` was deprecated in scales 1.2.0.
#> ℹ Please use the `scale_cut` argument of `label_number()` instead.
#> ℹ The deprecated feature was likely used in the socceranalysisR package.
#>   Please report the issue to the authors.

## histograms
soc_viz_stats('age', 'Wages_Euros', FALSE , small_data)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## getting outliers
soc_get_outliers(small_data,Wages_Euros,"SD",2)
#>   age Wages_Euros
#> 1 -20      575000

## toy data set for ranking
 my_data <- data.frame (Name  = c("Flora", "Mary", "Sarah", "Ester", "Sophie", "Maria"),
second_column = c(1, 4, 3, 5, 1, 6),
third_column  = c(7, 30, 12, 17, 34,8))

 ## ranking players by their names based on attributes
 rankingplayers(my_data, "second_column")
#>     Name second_column
#> 1  Maria             6
#> 2  Ester             5
#> 3   Mary             4
#> 4  Sarah             3
#> 5  Flora             1
#> 6 Sophie             1
 
 
## toy dataset to test soc_find_team_stat
small_df <- data.frame(Team=  c("A", "B", "B", "B", "C", "C") ,
                       Market_Value_Euros = c(375000,   225000, 225000, 200000, 225000, 250000),
                       age = c(18, 20, 20, 25, 25, 24)) 

## a team's descriptive statistics table and box-plot of selected feature
soc_find_team_stat(small_df, "B", Market_Value_Euros)
#> [[1]]
#>      Team           Market_Value_Euros      age       
#>  Length:3           Min.   :200000     Min.   :20.00  
#>  Class :character   1st Qu.:212500     1st Qu.:20.00  
#>  Mode  :character   Median :225000     Median :20.00  
#>                     Mean   :216667     Mean   :21.67  
#>                     3rd Qu.:225000     3rd Qu.:22.50  
#>                     Max.   :225000     Max.   :25.00  
#> 
#> [[2]]

Contributors

Core contributor	Github.com username
Flora Ouedraogo	@florawendy19
Gaoxiang Wang	@louiewang820
Manvir Kohli	@manvirsingh96
Vincent Ho	@vincentho32

Contributing

Authors: Vincent Ho, Manvir Singh Kohli, Gaoxiang Wang, Flora Ouedraogo

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

socceranalysisR was created by Gaoxiang Wang, Manvir Kohli, Vincent Ho and Flora Ouedraogo. It is licensed under the terms of the MIT license.