MATH2270 Assignment 1

Student Details

Arjun Balaji (s3629999)

Part I - Data Visualisation

Taxes on Oil and how much did each country made

Graph taken from: Organization of the Petroleum Exporting Country Source Data: Organization of the Petroleum Exporting Country

Part II - Deconstruct

According to the Kaiser’s Trifecta check-up a data visualisation must ensure the three parameters namely;

Q Checkup: the visual has a purpose as it wants to bring out how much the major oil consuming countries made by selling oil to its customers. The data visualisation has passed this checkup
D Checkup: As a viewer it is quiet confusing to understand based on what data they came up with these percentage figures as the original data does not show any trace on how this has been computed. The data on the visual makes the viewer to raise a question about the source or quality of the data, the data visualisation failed on the data question of the check-up
V Checkup: The stacked bar chart used does not convey the message appropriately. It gives you an overall picture of average price of oil that each country sold, but the visual would have been suited better if presented on a grouped bar chart. The reason being stacked bar chart misleads the viewer’s attention on comparison. Upon that, both the axes labels are not being mentioned and it is always best to keep the legends outside the plot so it gives a clear picture

Critique rating: DV - Good question, but issues with data and visuals

Part III - Reconstruct

# Reading the dataset
OPEC <- read.csv("C:/MS242 - Master of Analytics/2nd Semester/MATH2270 - Data Visualisation/Assignment/T81_draft_meltt.csv", check.names = FALSE)

# Layered approach of plotting 
oil <- ggplot(data = OPEC, aes(x=factor(Country), y=Price, fill=factor(Category))) +
  geom_bar(stat = "identity", position=position_dodge()) +
  facet_grid(Category ~., margins = TRUE) +  
  scale_fill_discrete(name="Retail Prices types",
                      labels=c("Crude Price", "Industry Margin", "Tax", 
                                "Composite Barrel")) +
  labs(title = "Does Tax plays a pivotal role on how much a Country earns by selling Oil? ",
       x = "Country",
       y = "Price(in $/barrel)",
       caption = "Composite Barrel-consumption weighted average of retail prices(including taxes)") + 
  coord_flip() +
  theme_light() 

oil

The above data visualisation gives a whole new perspective of how we were seeing Oil prices. We are all aware that Oil prices are playing a major part in the World. But it is just majority of the people are not aware of how the Oil price is determined (at least me).

Firstly, the purpose of this data visualisation is to give an overview on the components that decides the Oil Price. Secondly, the data I have chosen supports and helps me in giving a visual perspective on the variables. Finally, the reason for choosing grouped bar chart is that it helps me in answering the question effectively and gives the reader a view on the comparison between the different Retail prices used.

The graph above illustrates the variations in the different retail price types used to derive the Composite barrel price of Oil. The data chosen is only amongst the major oil consuming countries along with G7 countries as well as the OECD during the year 2016. By looking at the graph the various components such as Crude Price & Industry margin does not have much variations in its price across different countries. However, it is important to note that these price variations are mainly due to the widely varying levels of taxes imposed by major oil consuming nations. These can range from relatively modest levels - like in the USA - to very high levels in Europe and Asia/Pacific.

References