From 637e012372396103a964a6d2f3d61d4393b7e71f Mon Sep 17 00:00:00 2001
From: Fefe Alves <monitore.falves@gmail.com>
Date: Tue, 29 Aug 2023 12:50:32 -0300
Subject: [PATCH] commit files

---
 .gitignore                                    |  4 ++
 ... dataset and working on ordinary methods.R | 52 +++++++++++++++++++
 ordinary_methods.Rproj                        | 13 +++++
 3 files changed, 69 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 Preparing dataset and working on ordinary methods.R
 create mode 100644 ordinary_methods.Rproj

diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
diff --git a/Preparing dataset and working on ordinary methods.R b/Preparing dataset and working on ordinary methods.R
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index 0000000..cf537f1
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+++ b/Preparing dataset and working on ordinary methods.R	
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+### Fifa Dataset
+# https://www.kaggle.com/general/307245
+
+### Libraries that will be used (and that you should install them)
+library(dplyr)
+
+######## PART 1
+### Preparing dataset to analyse
+
+# Transforming Fifa's csv to dataframe
+fifa = read.csv('players_22.csv', encoding = 'UTF-8')
+
+# Updating the players's ages to 2023
+fifa$dob_y = substr(fifa$dob, 1, 4)
+fifa = fifa %>% mutate(age_2023 = 2023 - as.numeric(dob_y))
+
+# Separating the columns that I want to use, filtering for only Brazilian ST players below 23 years old
+cols = c(1, 3, 5:9, 112, 12:13, 15:18, 24, 26, 31, 28:30, 38:43, 32:33, 36:37)
+fifa_br = fifa %>% filter(nationality_name == 'Brazil',
+                          age_2023 <= 23,
+                          grepl('ST', player_positions)) %>%
+                    select(all_of(cols))
+
+# As ordinary methods demands e preview ranking for each column, I reorder them all
+fifa_br = fifa_br %>% arrange(age_2023, desc(height_cm), weight_kg, desc(weak_foot),
+                              league_level, desc(skill_moves), desc(pace), desc(shooting),
+                              desc(passing), desc(dribbling), desc(defending), desc(physic))
+
+# Creating a matrix with only the columns that I should use in the analysis
+cols = c(1, 9, 19:26)
+matrix_df = fifa_br %>% select(all_of(cols))
+rownames(matrix_df) = matrix_df$sofifa_id
+sofifa_id = matrix_df$sofifa_id
+
+matrix_players = as.matrix(matrix_df[, c(2:10)])
+
+######## PART 2
+### Borda Count Method
+
+# Calculating the player's scores by column
+borda = apply(matrix_players, 2, function(column) rank(-column))
+
+# Adding up the score of each row
+borda = apply(borda, 1, sum)
+
+# Assigning the Borda's score to each player
+borda_df = data.frame(cbind(sofifa_id, borda))
+fifa_om = left_join(fifa_br, borda_df, by = 'sofifa_id')
+
+### Condorcet Count Method
+
+
diff --git a/ordinary_methods.Rproj b/ordinary_methods.Rproj
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+++ b/ordinary_methods.Rproj
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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX