Assignment 2

Assessment declaration checklist

Please carefully read the statements below and check each box if you agree with the declaration. If you do not check all boxes, your assignment will not be marked. If you make a false declaration on any of these points, you may be investigated for academic misconduct. Students found to have breached academic integrity may receive official warnings and/or serious academic penalties. Please read more about academic integrity here. If you are unsure about any of these points or feel your assessment might breach academic integrity, please contact your course coordinator for support. It is important that you DO NOT submit any assessment until you can complete the declaration truthfully.

By checking the boxes below, I declare the following:

I have not impersonated, or allowed myself to be impersonated by, any person for the purposes of this assessment
This assessment is my original work and no part of it has been copied from any other source except where due acknowledgement is made.
No part of this assessment has been written for me by any other person except where such collaboration has been authorised by the lecturer/teacher concerned.
Where this work is being submitted for individual assessment, I declare that it is my original work and that no part has been contributed by, produced by or in conjunction with another student.
I give permission for my assessment response to be reproduced, communicated compared and archived for the purposes of detecting plagiarism.
I give permission for a copy of my assessment to be retained by the university for review and comparison, including review by external examiners.

I understand that:

Plagiarism is the presentation of the work, idea or creation of another person as though it is your own. It is a form of cheating and is a very serious academic offence that may lead to exclusion from the University. Plagiarised material can be drawn from, and presented in, written, graphic and visual form, including electronic data and oral presentations. Plagiarism occurs when the origin of the material used is not appropriately cited.
Plagiarism includes the act of assisting or allowing another person to plagiarise or to copy my work.

I agree and acknowledge that:

I have read and understood the Declaration and Statement of Authorship above.
If I do not agree to the Declaration and Statement of Authorship in this context and all boxes are not checked, the assessment outcome is not valid for assessment purposes and will not be included in my final result for this course.

Deconstruct

Original

The original data visualisation selected for the assignment was as follows::

How Much Money People Take Home After Paying Taxes (2018)

Objective and Audience

Objective:

The objective of the data visulisation is show how much money people take home after paying taxes in different The Organization for Economic Cooperation and Development (OECD).

The visulisation shows the avarage gross wage earnings and net wage earnings after taxes for a single worker in 35 countries. Countries are ranked from highest to lowest in terms of net wage earnings. Additionally, each country’s income tax and social security contributions are represented as a percentage of gross wage earnings in the visualization.

Audience:

The target audience is people who are interested in international comparisons of wages and taxes. Economists, researchers, and research organizations focused on researching living standards, inequality in income, the effects of taxes, and economic policies in developed countries.

Public and media audiences with an interest in learning about the variations in take-home pay and personal taxes in OECD nations.

Code

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

# Loading the necessary packages:

library(readr)
library(dplyr)
library(ggplot2)

setwd("E:/SEM-2/DV/As-2")
data <- read.csv("data.csv")
head(data)

##       Country Total.payment..as...of.gross.wage.earnings.
## 1 Switzerland                                        16.9
## 2  Luxembourg                                        29.1
## 3     Iceland                                        28.7
## 4     Germany                                        39.9
## 5 Netherlands                                        30.4
## 6     Belgium                                        40.5
##   Income.tax..as...of.gross.wage.earnings.
## 1                                     10.7
## 2                                     16.7
## 3                                     28.3
## 4                                     19.1
## 5                                     17.3
## 6                                     26.5
##   Employee.social.security.contributions..as...of.gross.wage.earnings.
## 1                                                                  6.2
## 2                                                                 12.3
## 3                                                                  0.3
## 4                                                                 20.8
## 5                                                                 13.1
## 6                                                                 14.0
##   Gross.wage.earnings..US.dollars.with.equal.purchasing.power.
## 1                                                        70835
## 2                                                        65716
## 3                                                        63661
## 4                                                        63551
## 5                                                        62981
## 6                                                        58545
##   Wages.earning.After.Taxes
## 1                     58864
## 2                     46593
## 3                     45390
## 4                     38194
## 5                     43835
## 6                     34834

# Define new column names

column_names <- c("Country", "Total_payment", "Income_tax", "Emp_contro", "Gross_earnings", "Wages_aftertax")
colnames(data) <- column_names
head(data)

##       Country Total_payment Income_tax Emp_contro Gross_earnings Wages_aftertax
## 1 Switzerland          16.9       10.7        6.2          70835          58864
## 2  Luxembourg          29.1       16.7       12.3          65716          46593
## 3     Iceland          28.7       28.3        0.3          63661          45390
## 4     Germany          39.9       19.1       20.8          63551          38194
## 5 Netherlands          30.4       17.3       13.1          62981          43835
## 6     Belgium          40.5       26.5       14.0          58545          34834

g1 <- ggplot(data, aes(x = reorder(data$Country, data$Wages_aftertax), y = data$Wages_aftertax)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Countries by Net Wage Earnings After Taxes",
       x = "Country", y = "Net Wage Earnings (US$)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))



g2 <- ggplot(data, aes(x = reorder(data$Country, data$Total_payment), y = data$Total_payment)) +
  geom_line(color = "steelblue", linetype = "dashed") +
  geom_point(color = "steelblue", size = 3) +
  labs(title = "Total Tax paid by Country",
       x = "Country", y = "Total Tax (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Reconstruction

The following plot fixes the main issues in the original.

Technology

The reconstruction makes use of ggplot2 to create scatter plots that indicate the relationship between given data. Before plotting, the reconstruction uses another tidyverse package called dplyr to filter the data to identify specific car types.

The reconstruction includes titles, labels, and a legend that explains the color coding for car classes, going beyond simple visualizations. For improved readability, it might also add lines and alter the axis scales.

Reference:

Raul. (May 24, 2018). How Much Money People Take Home After Paying Taxes. https://howmuch.net/articles/money-people-take-home-after-taxes