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

Original


Source: information is beautiful (2017)


Objective

The main objective of this data visualization is to showcase the movies that have the largest difference between critics and viewers ‘rotten tomatoes’ score, from 2007 to 2017. The target audience is likely for those who are involved or interested in the film industry.

The visualisation chosen had the following three main issues:

  • The data visualization uses a type of graph which is not suited for the data and objective. To show which movies have the largest difference between scores, a bar chart would be a much better representation. In this visualization, we are unable to easily compare two data points i.e, which movie has a larger difference in scores, especially if they are close together on the x–axis.

  • The data visualization contains variables that are unnecessary and unrelated to the objective. Variables as year, budget, and genre of movie all do not show the difference between scores. This makes the visualization take longer to process and ultimately more confusing to the viewer.

  • The data visualization has deceptive features. The author highlights the largest data point on the visualization which could be misinterpreted as the movie with the largest difference in scores. However, size only represents the budget which is not related to the objective. The author has strangely labelled the x-axis as “Audience really hates”. This leads us to believe the higher the percentage, the more the audience dislikes the movie. However, this is not the case. The x-axis resembles the difference between critics scores and the audience.

Reference

Information is Beautiful 2017, Movies Critics Loved, But Audiences Really Didn’t, Information is Beautiful, viewed 29 July 2021, <https://informationisbeautiful.net/visualizations/star-wars-last-jedi-one-of-the-biggest-rotten-tomatoes-audience-vs-critics-score-splits-ever/>

Code

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

library(ggplot2)
library(readxl)
movies3 <- read_excel("movies3.xlsx")
movieplot <- ggplot(data = movies3, aes(x = reorder(Film, Gap), y = Gap))
p1 <- movieplot + geom_bar(stat="identity", width = 0.7, colour = "#FFFFFF", fill = "#FFAAAA")+
  theme_minimal() +
  geom_text(aes(x = Film, y = Gap, label = Gap, hjust = -0.2)) +
  coord_flip() + 
  labs(title = "Movies Critics Loved, But Viewers Didn't (2007 - 2017)", subtitle = "Top 10 movies with the largest rating difference between Critics and Viewers") +
  labs(y = "Difference Between Critic and Viewer Ratings (%)") +
  labs(x = "") +
  labs(caption = "Source: RottenTomatoes.com") + 
  theme(
  plot.title = element_text(size = 16, hjust = -8, vjust = 2),
  plot.subtitle = element_text(size = 11, hjust = -2.7, vjust = 3),
  axis.title.x = element_text(size = 9),
  plot.caption = element_text(size = 7))

Data Reference

Information is Beautiful 2017, Movies Critics Loved, But Audiences Really Didn’t, Information is Beautiful, viewed 29 July 2021, <https://docs.google.com/spreadsheets/d/1IGd2ELYiHrknHBFJI58MTnEBIFcvyJYHct0zu98Pdog/edit#gid=2121555861/>

Reconstruction

The following plot fixes the main issues in the original.