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

Original


Source: ACMA Research and Analysis Section (2015).


Objective

Explain the objective of the original data visualisation and the targetted audience.

The visualisation chosen had the following three main issues:

  • Graph is not properly aligned.
  • User cannot find the values of each applications.
  • The values of some of the applications should not be determined as it will confuse the user.

Reference

Code

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

library(ggplot2)
library(dplyr)
library(forcats)
Applications=data.frame(Apps=c("Amazon Prime","Kanopy","Epix","Fandor","ConTV","Dove Channel","ShoutfactoryTV","Netflix","Docurama","Hulu Plus","UpFaithFamily","Shudder","Tribeca Shortlist","Qello Concerts","Sundancenow","Starz","The Criterion Channel","Full Moon","Hiyah","Hallmark Movies Now","Gaia","Dox","HBO","AMEBA"),Count=c(5834,898,774,504,378,233,233,179,179,179,153,144,127,112,101,95,77,76,63,59,50,36,34,24),Perc=c(52.2,8,6.9,4.5,3.4,2.1,2.1,1.6,1.6,1.6,1.4,1.3,1.1,1,0.9,0.8,0.7,0.7,0.6,0.5,0.4,0.3,0.3,0.2))
p1 <- ggplot(data = Applications, aes( x = reorder(Apps,+Perc),y = Perc,group=2))+geom_point(aes(color=Apps),size=4)+geom_line(stat="identity",colour="turquoise3")+geom_text(aes(label=paste(Perc,"%",sep="")),nudge_y = -1,nudge_x = .05)+labs(y="Percentage",x="Applications",title = "Comparison of movies between Applications")+theme(axis.title.x = element_text(size=40))+theme(axis.title.y = element_text(size=40))+theme(plot.title = element_text(size = 50))+theme(legend.text = element_text(size=25))+theme(legend.title = element_text(size=40))

Data Reference

Reconstruction

The following plot fixes the main issues in the original.