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

Original


Source: https://techjury.net/blog/anime-stats/ (2023).


Objective

the visualisation was originaly made as par of a larger article showing interesting facts and statistics about anime around the world

this visualisation in particular is meant to communicate the generational breakdown of people that responded positively to a Morning Consult survey asking if they like to watch anime

The visualisation chosen had the following three main issues:

  • the title doesn’t actually line up with the data that is being shown in the visualisation. the percentages provided in the visualisation are actually the percentage of positive respondents that belong to each generational group
  • the question the visualisation is answering would be much clearer with a properly labelled bar graph
  • the question the title is asking would be better answered if there were other visualisations to for the information in the article that follows this particular visualisation

Reference

Dejan C & Girlie D (2023). 21 Eye-Opening Anime Stats To Show Its State in 2023. Retrieved July 24, 2023, from https://techjury.net/blog/anime-stats/

Code

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

library(ggplot2)

anime <- data.frame(Gen = c("Gen Z Adults", "Millenial", "Gen X", "Baby Boomer"),
                    Viewers = c(25,42,21,12)) 

plot_colours <- c("#1f78b4","#33a02c","#33a02c","#1f78b4")

p1 <- ggplot(data = anime, aes(group = 1, x = Gen, y = Viewers))
p1 <- p1 + geom_bar(stat="identity", fill=plot_colours) +
    geom_text(aes(label = paste(Viewers,"%",sep="")), nudge_y = -2, colour = "white")+
    labs(title = "Anime Viewership by Generation",
    subtitle = "Percentage of Total Anime Viewers by Generation",
    x = "Generation",
    y = "% of Viewers from each Generation")+
    scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0,50))

Data Reference

Dejan C & Girlie D (2023). 21 Eye-Opening Anime Stats To Show Its State in 2023. Retrieved July 24, 2023, from https://techjury.net/blog/anime-stats/

citation("ggplot2")
## To cite ggplot2 in publications, please use
## 
##   H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
##   Springer-Verlag New York, 2016.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     author = {Hadley Wickham},
##     title = {ggplot2: Elegant Graphics for Data Analysis},
##     publisher = {Springer-Verlag New York},
##     year = {2016},
##     isbn = {978-3-319-24277-4},
##     url = {https://ggplot2.tidyverse.org},
##   }
citation("knitr")
## To cite package 'knitr' in publications use:
## 
##   Xie Y (2023). _knitr: A General-Purpose Package for Dynamic Report
##   Generation in R_. R package version 1.43, <https://yihui.org/knitr/>.
## 
##   Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition.
##   Chapman and Hall/CRC. ISBN 978-1498716963
## 
##   Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible
##   Research in R. In Victoria Stodden, Friedrich Leisch and Roger D.
##   Peng, editors, Implementing Reproducible Computational Research.
##   Chapman and Hall/CRC. ISBN 978-1466561595
## 
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.

Reconstruction

The following plot fixes the main issues in the original.