In this project you should analyse data to attempt answering the following questions:

Which are the best football teams according to individual player's Overall score?

Can you identify relevant skills for each position (Attacker, Midfielder, Defender)?

How are skills affected by age? Can you identify a skill that increases with age and another that decreases with age?

Which player has the best combination of Dribbling and Finishing?

The FIFA 2019 datasets combine attributes from La Liga football players, including age, club, speed, dribbling, ball control, etc. The dataset is split into two files. The first CSV file is called fifa_performance_statistics.csv and contains performance statistics. The second CSV file is called fifa_player_information.csv. The original data files are available here

You should use the following two datasets:

1. The CSV file fifa_performance_statistics.csv downloaded from here contains performance statistics, combining data on players speed, dribbling, ball control, etc. from La Liga.

2. The CSV file fifa_player_information.csv downloaded from here contains information about football players, including the club for which they play, age, height, weight, etc. from La Liga.

The GitHub repo for this project should contain, at least, the following files:

• index.Rmd: R Markdown script with the R code doing the analysis of the data and the corresponding text explaining those analysis steps.
• index.html: Resulting HTML output from processing (knitting) the file index.Rmd.
• The CSV files employed during the analysis.
• This README.md file.

The analysis of the data described in the HTML file should contain the following sections:

• Abstract: Summary of the question and the findings (max. 200 words).
• Introduction: Description of the question and the data employed to answer it. Description of any steps taken, if any, previous to this R Markdown document, to prepare the data that is being analyzed.
• Results: R code intertwined with text, descriping the analysis steps and the display items with the results, which should consist, at least, of one table and one plot.
• Conclusions: summary of the findings, limitations of the study, ways in which this type of study could be improved in the future.
• References: bibliographic references.

The analysis of the data should be carried out at least using R, but you can also use shell or Python scripts to transform or prepare the data for the analysis with R. If those prior steps using shell or Python scripts are included, they should be described in the introduction section of the R Markdown document and, ideally, made readily reproducible using a Makefile.

The rubric to evaluate this project consists of the following items:

• Have all members of the group made a sizeable number of commits to the GitHub repo?

• Does the GitHub repo contain at least the analysed CSV files along with the index.Rmd file and the resulting index.html?

• Does the R Markdown file index.Rmd run the analysis without errors and generates the expected index.html file?

• Does the analysis described in the resulting index.html file conform to the requested sectioning.

• Does the introduction explain clearly what is the question addressed, the data employed and the number of observations and variables involved?

• Do the plots show some meaningful summary of the data? Are axes in plots labeled in plain language and large enough to read?

• Does the GitHub repo include a Makefile that automatizes the entire analysis pipeline and generation of the final report in the index.html file?