README.md

In unimi-dse/8554ac27: Compute UEFA Financial Fair Play Break-Even requirement

Computing the Financial Fair Play Break-Even Requirement on a 3-year monitoring period

by Silvia Fratarcangeli

Install

library(devtools)
library(plotly)
library(dplyr)
library(ggplot2)
library(shiny)

Uploading and formatting table

upload()
frmtb(df, vct_date)

Creating distinct and Relevant dataframes

rel_var(df,vct_var)
df1$AggrX <- trnsfR(df1)
df2$AggrY <- trnsfR(df2)

WARNING: for subject matters, assign two distinct dataframes, e.g. RR=Relevant Revenues and RE=Relevant Expenses to rel_var(df,vct_var) so as to make BE(defined as RR+RE) computations easier.

Computing break-even numeric result for 3-years monitoring period and printing as logical the final evaluation according to threshold established by the UEFA regulations

sum_be(df1,df2)
be_judgf(b)

The break-even result for the 3-years monitoring period was be <-sum_be(df1,df2). The evaluation to be taken into account from the UEFA Commission was be_judgf(be).

Creating a vector with yearly break-even resuts and plotting the results, together with others variable of interest, into an interactive bars graph using plotly library

rel_var(df,vct_var)
z <-c(beyR(df2,df2,x,y))
grph(z)

Running the functions included in the FFP package in the correct order so to obtain the final results printed as logical, together with the interactive bars graph

completefunc()

Launching the ShinyApp interface. The page will plot bar graphs on a 3-years comparison scale, for any relevant account we choose from the sidebar.

shinyapp_asr(server,ui)

unimi-dse/8554ac27 documentation built on Feb. 20, 2020, 10:13 p.m.