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

Original


Source:“Homosexuality in India” : What data shows(2018) - livemint. https://www.livemint.com/Politics/nLQiPpl5UICajLDXETU3EO/Homosexuality-in-India-What-data-shows.html , https://rigorousthemes.com/blog/bad-data-visualization-examples/.


Objective

The understanding of this visualization is the support for the same-sex relationships (Homosexuality) being opposed and accepted in India from the year 1990 to 2014. The graph shows the change in number of broadly supportive respondents and number of broadly opposed respondents over 24 years.

Target Audience

The targeted audience are the Indian citizens who are becoming more accepting towards same-gender relationships . This helps in understanding the growth of human nature towards accepting the individual choices so as to maintain unity and peace.

The visualization chosen had the following three main issues:

  • Failed to highlight the cause of the survey: The graph explains the support for the growth of homosexuality in India whereas the responses of the opposed respondents are much highlighted and graphically given more importance , suppressing the poll of support responses.

  • Inappropriate usage of graph: The area graph used to represent the survey does not do justice to the audience in understanding the cause. It is hard to determine the comparisons and exact values.

  • Color Issue: It also shows the wrong color choices for both supportive and opposed respondents. The darker shade representing the opposed respondents are eye-catching to the audience than the lighter shade used for supportive respondents which deviates from the cause.

Reference

“Homosexuality in India” : What data shows(2018) - livemint. https://www.livemint.com/Politics/nLQiPpl5UICajLDXETU3EO/Homosexuality-in-India-What-data-shows.html Source : https://rigorousthemes.com/blog/bad-data-visualization-examples/

Code

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

library(readr)
library(readxl)
library(tidyr) 
library(dplyr)
library(ggplot2)

data_india <- read_excel("C:/Users/ekant/Desktop/data-india.xlsx")

data<- data_india %>% gather("Respondents", "Percentage", 2:3)

data$Year<- as.factor(data$Year)

data$Respondents<- as.factor(data$Respondents)

p1 <- ggplot(data = data, aes(x=Year, y=Percentage, fill=`Respondents`)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  geom_text (aes (label = `Percentage`), size = 2, position = position_dodge(0.9),
             vjust = 0) +
  scale_fill_hue(name="Respondents") +     
  xlab("Year") + ylab("Percentage of Respondents") + 
  ggtitle("Growth of homosexuality in India") +    
  theme_bw()

Data Reference

“Homosexuality in India” : What data shows(2018) - livemint. https://www.livemint.com/Politics/nLQiPpl5UICajLDXETU3EO/Homosexuality-in-India-What-data-shows.html Source : https://rigorousthemes.com/blog/bad-data-visualization-examples/

Reconstruction

The following plot fixes the main issues in the original.