Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.

Original


Source:https://www.kaggle.com/saml324/fifa-players-radar-spider-chart


Objective

Part-1:Choosing Data Visualisation and its Objective.

The main objective of the visualisation is to visualise ceratin attributes of top football players whose overall rating is higher than 90. The target audience of the visualisation are the people who play and watch football and also for the sport journalist

Part-2:Deconstructing the Data Visualisation.

The visualisation is mainly about displaying key attributes of the top fifa 18 players whose overall ratings is higher than 90.The data is visualised using spider chart.The publisher has mainly tried to visualise top fifa players by comparing certain attributes rating of the players.

Three main issues with the visualisation are as follows-

1)Deception. The use of spider chart in the visualisation has lead to deception. Due to use of spider chart it is difficult to know an exact value of a particular attribute of a player. Eg-If a user wants to know the Neymar’s acceleartion rating it is difficult to calculate because the type of representation. Also the overlapping represenataion of players data make it difficult for a visualisation.

2)Perceptual or Colour issues Some players are represented using the same colour. Using same colour for same player is decepting as the visualiser gets confused regarding attribute data of those players. Eg-There is same colour representation for Neymar and Lewandowski so its difficult to visualise which attribute rating belongs to which player. Also due to overlapping of colours of similar shades creates an unwanted delusion.

3)Ehtical issues- There is a lack of accuracy in the given visualisation. The variables choosen for visualisation(Acceleration,Agression and others) are ratings of players attributes out of 100 as per the original dataset while in the visualisation its represented on the scale of percentage which is highly misguiding. The visualisation is created in such a way that it may misguide or confuse the viewers.

Reference The Data Visualisation is retrieved based on the folowing link. https://www.kaggle.com/saml324/fifa-players-radar-spider-chart

Code

The following code was used to fix the issues identified in the original.

#The following libraries are used in the code:
library(knitr)
library(ggplot2)
library(RColorBrewer)

#Loading the dataset:
football_data <- read.csv("/Users/christangelfargose/CompleteDataset.csv", header = TRUE, stringsAsFactors = FALSE)

#Creating a subset of a subset of a dataset:
sub1 <- subset(football_data,football_data$Overall>90,select =c(14,15,29,37,40))

#Changing data types of variables:
sub1$Acceleration = as.numeric(sub1$Acceleration)
sub1$Aggression = as.numeric(sub1$Aggression)
sub1$GK.reflexes = as.numeric(sub1$GK.reflexes)
sub1$Positioning = as.numeric(sub1$Positioning)
sub1$Shot.power = as.numeric(sub1$Shot.power)

#Converting sub1 to a dataframe:
df1 <- t(as.matrix(sub1))

#Ploting a Multiple bar plot:
bp <- barplot(df1,beside=TRUE,col=1:5,legend = colnames(sub1),
              names.arg =c("C.Ronaldo","L.Messi","Neymar","L.Suarez","M.Neuer","Lewandowski"),
              las=2,ylab = "Attributes Rating",
              ylim = c(0,150),
              args.legend = list(x="top",ncol=3,title="Attributes"))

Data Reference

The dataset for the visualisation was downloaded from the following link- https://www.kaggle.com/thec03u5/fifa-18-demo-player-dataset

Reconstruction

The data visualisation was reconstructed with the help of multiple bar graph which is clearly defining the attributes rating of each player as shown in the graphs below. The x-axis refers to the player name and the y-axis refers to the attributes ratings.Each colour refers to a diffrent attribute of a particular player which is indicated clearly in a legend placed above the graph. Also, here the names of the players are provided in a vertical positon to avoid overlapping of the names.