Assignment 2
Deconstruct, Reconstruct Web Report
Joheb Shaikh (s3823492)
Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.
Original
Objective
In recent years sports personalities have gained a huge popularity not only in terms of their brand value but also in terms of money they get paid. The top-notch athletes who are paid big fat pay cheques mostly play sports like football, basketball, tennis. The original data visualisation tells us that their goal is to illustrate how these sportsmen from various sports have fared in terms of money they’ve earned in the year 2017.
The original visualisation caters to the target audience of sports analysts on various news channels and newspapers to show how much these sportsmen are earning from various endorsements. Sponsors with these different sports leagues also get benefited with the stardom of the players and they can also be viewed as a target audience in this visualisation.
The visualisation chosen had the following three main issues: -
In this graph the first issue is visual bombardment, we see there are lots of unnecessary data and visuals provided which make it difficult to visualise the graph easily. Also, in this data there are so many graphic cues that take away the attention from their intended audience. It can be seen in the visualisation that players face and information of them are irrelevant in the graph also graph includes grid line which makes difficult to read the earnings of the players.
The second issue is poor colour scheme,the data visualisation looks ambiguous and lacks clarification because the modulation of colour is not used very effectively. As we can see, identical colours are used, making the users hard to identify. For example, Beneath the graph, we could see several details that might cause a colour distinguishing issue to the audience.
The third issue is inaccurate selection of graph, here the choice of the data visualisation is very substandard. When we see those, who earn more have a wide circle while those who earn less have a tiny circle, we just could not differentiate whose incomes are more from the graph. Hence this visualisation is not self-explanatory.
Reference
- The world’s Highest-paid athletes in Top sports 2017. Restored October 26, from Howmuch.net website: - https://howmuch.net/articles/wolds-highest-paid-athletes-in-top-sports
Code
The following code was used to fix the issues identified in the original.
library(ggplot2)
library(dplyr)
library(tidyr)
### Reading dataset
highest_paid_athlete<-read.csv("athletes1.csv")
# using trimws function to remove spacing or unnecessary tabs
highest_paid_athlete$Sport<-trimws(highest_paid_athlete$Sport)
highest_paid_athlete$Name<-trimws(highest_paid_athlete$Name)
### selecting top 20 highest paid athelete
hpa_1<-head(highest_paid_athlete,20)
str(hpa_1)## 'data.frame': 20 obs. of 6 variables:
## $ Rank : num 1 2 3 4 5 6 6 8 9 10 ...
## $ Name : chr "CRISTIANO RONALDO" "LEBRON JAMES" "LIONEL MESSI" "ROGER FEDERER" ...
## $ X.Pay....mln. : num 93 86 80 64 61 50 50 47 47 46 ...
## $ Salary.Winnings....mln.: num 58 31 53 6 27 16 47 12 27 38 ...
## $ Endorsements....mln. : num 35 55 27 58 34 34 3 35 20 8 ...
## $ Sport : chr "SOCCER" "BASKETBALL" "SOCCER" "TENNIS" ...# changing column names for better understanding
names(hpa_1)[names(hpa_1) == "Rank"] <- "Rank_of_Players"
names(hpa_1)[names(hpa_1) == "Name"] <- "Name_of_Players"
names(hpa_1)[names(hpa_1) == "X.Pay....mln."] <- "Payment"
names(hpa_1)[names(hpa_1) == "Salary.Winnings....mln."] <- "Salary/winnings"
names(hpa_1)[names(hpa_1) == "Endorsements....mln."] <- "Endorsements"
names(hpa_1)[names(hpa_1) == "Sport"] <- "Sport"
# created new column
hpa_1_new=hpa_1
hpa_1_new<-hpa_1%>%mutate(Percent_Earnings_from_endorsements= Endorsements/Payment*100)
hpa_1_new$Percent_Earnings_from_endorsements=round(hpa_1_new$Percent_Earnings_from_endorsements,0)
hpa_1_new## Rank_of_Players Name_of_Players Payment Salary/winnings Endorsements
## 1 1 CRISTIANO RONALDO 93 58 35
## 2 2 LEBRON JAMES 86 31 55
## 3 3 LIONEL MESSI 80 53 27
## 4 4 ROGER FEDERER 64 6 58
## 5 5 KEVIN DURANT 61 27 34
## 6 6 RORY MCILROY 50 16 34
## 7 6 ANDREW LUCK 50 47 3
## 8 8 STEPHEN CURRY 47 12 35
## 9 9 JAMES HARDEN 47 27 20
## 10 10 LEWIS HAMILTON 46 38 8
## 11 11 DREW BREES 45 31 14
## 12 12 PHIL MICKELSON 44 4 40
## 13 13 RUSSELL WESTBROOK 39 27 12
## 14 14 SEBASTIAN VETTEL 39 38 1
## 15 15 DAMIAN LILLARD 38 24 14
## 16 16 NOVAK DJOKOVIC 38 10 28
## 17 17 TIGER WOODS 37 0 37
## 18 18 NEYMAR 37 15 22
## 19 19 DWYANE WADE 36 23 13
## 20 20 FERNANDO ALONSO 36 34 2
## Sport Percent_Earnings_from_endorsements
## 1 SOCCER 38
## 2 BASKETBALL 64
## 3 SOCCER 34
## 4 TENNIS 91
## 5 BASKETBALL 56
## 6 GOLF 68
## 7 FOOTBALL 6
## 8 BASKETBALL 74
## 9 BASKETBALL 43
## 10 AUTO RACING 17
## 11 FOOTBALL 31
## 12 GOLF 91
## 13 BASKETBALL 31
## 14 AUTO RACING 3
## 15 BASKETBALL 37
## 16 TENNIS 74
## 17 GOLF 100
## 18 SOCCER 59
## 19 BASKETBALL 36
## 20 AUTO RACING 6# plot
plot_bar<- ggplot(hpa_1_new,aes(x=reorder(Name_of_Players, Payment), y=Payment, fill= Sport)) +
geom_bar(stat = "identity")+
coord_flip()+
geom_text(aes(label=paste0(Payment,"M {", Percent_Earnings_from_endorsements,"%}")),hjust=1,size = 2.5,fontface = "bold")+
labs(title = "2017, Highest-paid athletes ", subtitle = "with regard to percentage earnings from endorsements" , x="Players", y = "Athletes Payment-USD Millions (Percentage earnings from endorsements)",fill = "Sports")+
scale_fill_manual(values =rev(c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2")))+
theme_minimal()+
theme(panel.grid.major = element_blank(),
plot.title = element_text(hjust = 0.4, size = 15, face = "bold" ), plot.subtitle = element_text(hjust = 0.4, size = 15, face = "bold" ), axis.title.x = element_text(vjust = -1, size = 11, face = "bold"),axis.title.y = element_text(size = 11, face = "bold"),legend.title = element_text(face = "bold"))Data Reference
- The dataset of world’s Highest-paid athletes in Top sports 2017 is taken from Link
Reconstruction
The following plot corrects the key problems with the original plot as the information are now in a horizontal bar chart. This data visualisation displays players overall profits in terms of the percentage profits from the endorsement. We can see players who don’t earn much yet are profiting from endorsement. This means that players have to be recognised and have huge personalities in their competitions. So that they will gain endorsement even though the players are not in their great form.
