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

Original


Source: https://cdn.howmuch.net/articles/fastest-growing-occupations-in-the-US-0263.jpg


Objective

The data visualization is extracted from U.S. Bureau of Labor Statistics which shows the top 20 occupations growing in US based on their real salary in the year 2019. But they also represent the prediction growth rate of each occupation from 2019-2029 in percentage. The main aim is to compare different occupations and to predict their expected future growth based on their salary. The visualization shows the top 20 occupations, their median pay salary(yearly) and growth rate from 2019-2029 in US. The targeted audience can be taken as the people who are waiting for the employment opportunities in specific professions.

The visualization chosen had the following three main issues:

  • Issue 1: In this visualization they used an irregular shape which is difficult to understand.There is no proper axis division between the variables to define different professions based on their median pay and their percentage of growth.

  • Issue 2: Color gradations can occasionally have a significant impact on how a visualization looks.The visual uses different colors to represent the growth rate. This methods reduces the accuracy and makes it challenging to differentiate the growth rate percentage between different occupations in US.

  • Issue 3: Visual bombardment is an issue that does not provide the real information in the visualization which misleads the audience. In this visual the three variables such as occupation, growth rate, median pay all are overloaded that is all the information at once.

Reference

Top 20 Fastest Growing Occupation in US. Retrieved from HowMuch website: https://howmuch.net/articles/fastest-growing-occupations-in-the-US

Code

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

library(ggplot2)
library(readr)
library(tidyr) 
library(dplyr)
occupations <- data.frame(
  Occupation = c("Wind turbine service technicians", 
                 "Nurse practitioners",
                 "Solar photovoltaic installers", 
                  "Statisticians",
                  "Occupational therapy assistants", 
                  "Home health & personal care aides",
                  "Physical therapist assistants",
                  "Medical & health services managers",
                  "Physician assistants", 
                  "Information security analysts", 
                  "Data scientists & mathematical science occupations",
                  "Derrick operators, oil & gas",
                  "Rotary drill operators, oil & gas",
                  "Operations research analysts",
                  "Speech-language pathologists",
                  "Substance abuse,
                 behavioral disorder,
                 mental health counselors",
                  "Roustabouts, oil & gas",
                  "Forest fire inspectors & prevention specialists", 
                  "Cooks", 
                  "Animal caretakers"),
  Median_Pay = c(52910,109820,44890, 91160, 61510, 25280, 58790,100980, 112260, 99730,
                 94280, 46990, 54980, 84810, 79120, 46240, 38910, 45270, 27790, 24780),
  Growth_Rate = c(61, 52,51,35,35,34,33,32,31,31,
                  31,31,27,25,25,25,25,24,23,23)
)
graph <- ggplot(occupations, aes(x = reorder(Occupation, Median_Pay), y = Median_Pay, fill =Growth_Rate)) +
  geom_bar(
  stat="identity",position="dodge",width =1 , color = "black",fill = "steelblue")+
  coord_flip() +
  ggtitle("Top 20 Occupations Growth Rate in US") +
  
theme(axis.text.x = element_text(angle = 90, hjust = 1))
  

result<-graph +
  geom_text(aes(label = paste(Growth_Rate,"%",sep="")),nudge_y = -2, nudge_x = .05,color = "black") +
  theme_classic()

Data Reference

Charting the 20 Top Growing U.S. Careers Based on Real Salary Projections. Retrieved 2019, from HowMuch website: https://howmuch.net/articles/fastest-growing-occupations-in-the-US

Reconstruction

The following plot fixes the main issues in the original. Reconstructed visual is a barchart which helps to compare different occupation growth rate based on their median pay salary that is more understandable and convenient. The occupation is given in y-axis, median salary amount in x-axis and their percentage growth rate as labels on corresponding bars of each occupation which makes the chart not overloaded with more information.The chart is ordered in descending order so that we can easily visualize which occupation is having highest growth rate and lowest by comparing each other.