Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.
Objective
The visualisation used in this assignment is regarding the deduction of taxes in the Gross Wage in USD while keeping the equal purchasing power, in different countries in the year 2017, regulated by the Organisation for Economic Co-Operation and Development (OECD). The data shows the percentage of taxes in each country and the wage a person takes home after paying all the taxes. The visualisation and the dataset were obtained from the How Much website.
The main problems analysed in the visualisation are:
Too confusing: The data is not in order all the values are haphazard, so it gets confusing in comparing one country with other. The visualisation looks very eye catchy but there is too much going on, all the values are plotted all together which is causing a lot of confusion. No need to put country flags with their labels.
Poor Choice of Visualisation: The best way to visualise these types of data is through bar chart in which it is easy to compare the difference between the values of different countries.
Unnecessary use of colours: Use of too many colours in one visualisation, three different colours for three different categories and all in one visualisation which causes confusion.
The targeted audience for this visualisation is the normal working people who can have an idea of how much are they paying taxes from their gross wage as compared to the other countries. And in those taxes what is the percentage of with tax.
Reference
The following code was used to fix the issues identified in the original visualisation.
library(ggplot2)
library(tidyverse)
library(readxl)
taxes <- read_excel("C:/Users/hassa/Downloads/RMIT/4th Semester/Data Visualisation/Assignment 2/assign 2/taxes.xlsx")
View(taxes)
taxes_formatted1 <- taxes %>%
gather(Types,Values,`Income Tax (% of Gross Wage Earnings)`,`Employee SSC (% of Gross Wage Earnings)`)
taxes_formatted2 <- taxes %>% gather(Types,Wages,`Gross Wage (USD with equal purchasing power)`,`Wages Earning After Taxes`)
taxes_formatted1.1 <- taxes %>% gather(Labels,Values, `Income tax`,`Employee SSC`)
taxes_formatted2.1 <- taxes %>% gather(Labels,Values, `Gross Wage Earnings`,`Wages After Taxes`)
p1 <- ggplot(taxes_formatted2, aes(x=reorder(Country, Wages), y=Wages, fill=Types))+
geom_bar(stat="identity", color="black",position="identity")+
scale_fill_manual(values=c("#56B4E9","#00CC33"))+
geom_text(aes(label=taxes_formatted2.1$Values), hjust= 1 ,position = position_identity(), color="black", size=2.5) +
geom_hline(yintercept = c(30000,50000,70000), colour = "red", show.legend =TRUE) +
labs(title = "How Much Money People Take Home After Paying Taxes",
y = "Wages in USD ($)" , x = "OECD Countries",
caption = "Source: How Much. (24 May 2018) by Raul.
*See How Your Take Home Pay Compares to Workers Around the World*. Retrieved May 5, 2020,
How Much website: https://howmuch.net/articles/money-people-take-home-after-taxes") +
theme_minimal()+ coord_flip()+ theme(legend.position = c(0.8, 0.2),
legend.direction = "vertical", legend.title = element_text(color = "black", size = 7),
legend.text = element_text(color = "black", size = 5),
title = element_text(face="bold"),
plot.background = element_rect(fill = "#EFF1F0"),
panel.background = element_rect(fill = "#EFF1F0"),
legend.background = element_rect(fill = "#EFF1F0"))
p2 <- ggplot(data=taxes_formatted1, aes(x=reorder(Country,Values), fill=Types, y=Values)) +
geom_bar(stat="identity", color="black", position="stack")+
scale_fill_manual(values=c("pink","#CC0066")) +
geom_text(aes(label=taxes_formatted1.1$Values), hjust=0, position = position_stack(0.5), color="black", size=2.5) +
labs(title = "Percentage of Gross Wage Earnings Paid in Taxes",
y = "Percentage of Taxes (%)" , x = "OECD Countries",
caption = "Source: How Much. (24 May 2018) by Raul.
*See How Your Take Home Pay Compares to Workers Around the World*. Retrieved May 5, 2020,
How Much website: https://howmuch.net/articles/money-people-take-home-after-taxes") +
theme_minimal()+ coord_flip() + theme(legend.position = c(0.8, 0.2),
legend.direction = "vertical", legend.title = element_text(color = "black", size = 7),
legend.text = element_text(color = "black", size = 5),
title = element_text(face="bold"),
plot.background = element_rect(fill = "#EFF1F0"),
panel.background = element_rect(fill = "#EFF1F0"),
legend.background = element_rect(fill = "#EFF1F0"))
Data Reference
The reconstruction was done on the original visualisation and it looks like the graphs plotted below.
That one visualisation was divided into two bar plots, one showing the difference between the actual pay (gross wage) and the pay (wage) a person actually takes home after paying taxes among all the OECD countries, all the wages are in US dollars while keeping the equal purchasing power The red line in the first bar plot represents the bands of income there are three bands at $30000, $50000 and $70000.
While the other one shows that from the total tax paid, it consists of two parts the income tax and the employee social security contributions. So, the second bar plot shows those two types of taxes and their difference while comparing it with all the OECD countries.