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

Original


Source: Freeman, M. A., Staudenmaier, P. J., Zisser, M. R., & Andresen, L. A. (2018). The prevalence and co-occurrence of psychiatric conditions among entrepreneurs and their families. Small Business Economics, 53(2), 323–342.


Objective

Evaluating the prevalence of mental health conditions among entrepreneurs in comparison to the general population is intended to increase awareness of the possible mental health issues that business people may encounter, as well as to point out the necessity of solving mental health concerns in the business society.

Starting your own business seems like a joyful and gratifying experience, but with great benefits come great obstacles and stress. Starting a business involves a lot of long hours, hard-labour, and uncertainty. Many research papers have been published in recent years addressing the issue of mental health in entrepreneurs, and statistics show that business people are at a higher risk of experiencing mental health problems than the general population. According to studies, most company owners encounter ADHD (Attention-deficit hyperactivity disorder) issues, which is why this graphic focuses on how mental health is becoming a critical issue in the globe.

Target Audience

This problem is mainly focusing on people who are always involved in some form of company; as we all know, the fast-paced nature of business may frequently result in a high degree of worry and stress. Mental health professionals who use this report to understand the various stress levels experienced by people might use this knowledge to build more effective treatments for those who are dealing with stress-related issues. The general population may help promote awareness of mental health among their employers by proposing work-life balance and stress-reduction methods.

The visualisation chosen had the following three main issues:

  • Readability Issue:- The infographic looks to contain deceptive information, since the legend implies five groups yet the actual dataset only contains two, implying a possible difference between the visual representation and the underlying data.

  • Color encoding Issue:- As we can see from the graph , contrast between the colors used in the graph makes it difficult for readers to discern which colors correlate to which legends. This might lead to misunderstanding or trouble evaluating the facts supplied, which can lead to incorrect conclusions. To address this issue, use colors that have high contrast and are clearly identifiable from one another, ensuring that all viewers can appropriately see and comprehend the information offered.

  • Data integrity (Poor labeling of data legends):- It is clearly evident that, visual’s lack of clear labeling for the legends makes it difficult for the viewer to grasp and evaluate the data displayed. The observer may struggle to relate the colors or symbols in the legend to the relevant areas of the graph if there is no obvious identification. This may cause confusion or misinterpretation of the facts supplied, resulting in an incorrect perception of the associations between entrepreneurship and mental health disorders. As a result, it is critical to develop visual aids such as infographics with clear and simple labeling to assist readers quickly grasp and facts being presented.

Reference

Code

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

#install.packages("ggplot2")
library(ggplot2)
#install.packages("readlx")
library(readxl)
#install.packages("ggsci")
library(ggsci)

health_df = read_excel("C:/RMIT_Course_Work/DV/health_condition.xlsx")

'In the code below, we have plotted a stacked bar graph illustrating the proportion of entrepreneurs and general individuals by mental state. We have used the sprintf() method to display data labels for a clear comprehension of the percentage on the plot. we have also use ggsci package also know as scientific color palettes to solve color encoding issue.'
## [1] "In the code below, we have plotted a stacked bar graph illustrating the proportion of entrepreneurs and general individuals by mental state. We have used the sprintf() method to display data labels for a clear comprehension of the percentage on the plot. we have also use ggsci package also know as scientific color palettes to solve color encoding issue."
p1 = ggplot(health_df, aes(x = Health_Condition)) +
  geom_bar(aes(y = Entrepreneurs, fill = "Entrepreneurs"),  position = "stack", stat="identity") +
  geom_bar(aes(y = General_Sample_Group, fill = "General Sample Group"), position = "stack", stat="identity") +
  scale_fill_npg(name = "Groups") +
  labs(x = "Mental Condition", y = "Percentage") +
  ggtitle("Percentage of Enterpreneurs and General People by Mental Condition") +
  geom_text(aes(y = Entrepreneurs, label = sprintf("%.1f%%", Entrepreneurs)), vjust = -0.5, position = position_stack()) +
  geom_text(aes(y = General_Sample_Group, label = sprintf("%.1f%%", General_Sample_Group)), vjust = -0.5, position = position_stack()) +
  ylim(c(0,50))+
  theme_bw()

Data Reference

Reconstruction

The following plot fixes the main issues in the original.