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This is a template file. The following example included is not considered a good example to follow for Assignment 2. Remove this warning prior to submitting.

Deconstruct

Original

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


Source: Visual Capitalist (2024).


Objective

The objective of the visualization was to show the projected inflation rates of various countries around the world for the year 2024. Generally, the visualization displays the inflation rates to highlight economic conditions across different regions and to indicate potential economic challenges or stability in those areas. The ideal target audience of the visualization includes economists, policymakers, investors, business leaders, and readers interested in global economic trends and inflation-related topics.

Audience

The intended audience for this visualization includes:

1. Economists and Financial Analysts: The chart provides detailed inflation forecasts for various countries, which is crucial for these professionals to analyze economic trends and predict future conditions. The specific inflation rates for 2024, shown by country, help economists understand the broader global economic landscape.

2. Policymakers and Government Officials: This audience uses inflation data to guide economic policy decisions. The chart depicting inflation rates across different regions enables policymakers to compare economic conditions and adjust their strategies accordingly. For example, countries with high inflation rates like Venezuela (230.0%) and Zimbabwe (190.2%) may need urgent policy interventions.

3. Investors and Business Leaders: Investors and business leaders can use this chart to decide where to invest or expand business operations. Understanding which countries face high inflation can help them assess risks and opportunities. For instance, high inflation may signal economic instability, influencing investment decisions.

4. Academics and Researchers: Researchers studying global economic trends will find the detailed country-specific data helpful for comparative analysis. The chart provides a snapshot of inflation expectations that can be used in academic studies on economic development, globalization, and market behavior.

5. General Public and Media: The chart is visually engaging and provides accessible information on a complex topic, making it easier for the general public and media to understand the implications of inflation. Including graphics and clear labels helps convey the message quickly, making it suitable for media reporting and public education.

Critique

The visualization chosen had the following three main issues:

1. Clarity and Readability

Using small text and complex visual elements, such as the circular bar chart, can make the visualization difficult to interpret quickly. For example, smaller countries or those with overlapping text and data points (e.g., Europe) may not be easily readable, reducing clarity.

2. Colour Scheme Accessibility

The colour scheme uses similar shades to represent different inflation rates, which may not be easily distinguishable, particularly for viewers with colour vision deficiencies. This makes it difficult to quickly differentiate between countries with varying inflation rates, such as distinguishing between moderate (e.g., Canada at 2.1%) and high (e.g., Brazil at 3.9%) inflation rates.

3. Lack of Detailed Context

The visualization provides limited context or explanation regarding the factors driving high or low inflation rates in specific countries. There is also a lack of historical comparison, which would help better understand how these projections relate to past trends or events. For instance, while countries like Venezuela (230.0%) and Zimbabwe (190.2%) show extreme inflation, the underlying economic conditions leading to these rates are not fully explained.

Code

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

library(tidyverse)
# Create a data frame with the growth projections
growth_data <- tibble(
  Region = c("World Output", "Advanced Economies", "United States", "Euro Area", "Germany", 
             "France", "Italy", "Spain", "Japan", "United Kingdom", "Canada", 
             "Other Advanced Economies", "Emerging Market and Developing Economies", 
             "Emerging and Developing Asia", "China", "India", "Emerging and Developing Europe", 
             "Russia", "Latin America and the Caribbean", "Brazil", "Mexico", 
             "Middle East and Central Asia", "Saudi Arabia", "Sub-Saharan Africa", 
             "Nigeria", "South Africa", "Emerging Market and Middle-Income Economies", 
             "Low-Income Developing Countries"),
  `2023` = c(3.3, 1.7, 2.5, 0.5, -0.2, 1.1, 0.9, 2.5, 1.9, 0.1, 1.2, 1.8, 4.4, 5.7, 5.2, 8.2, 3.2, 
             3.6, 2.3, 2.9, 3.2, 2.0, -0.8, 3.4, 2.9, 0.7, 4.4, 3.9),
  `2024` = c(3.2, 1.7, 2.6, 0.9, 0.2, 0.9, 0.7, 2.4, 0.7, 0.7, 1.3, 2.0, 4.3, 5.4, 5.0, 7.0, 3.2, 
             3.2, 1.9, 2.1, 2.2, 2.4, 1.7, 3.7, 3.1, 0.9, 4.2, 4.4),
  `2025` = c(3.3, 1.8, 1.9, 1.5, 1.3, 1.3, 0.9, 2.1, 1.0, 1.5, 2.4, 2.2, 4.3, 5.1, 4.5, 6.5, 2.6, 
             1.5, 2.7, 2.4, 1.6, 4.0, 4.7, 4.1, 3.0, 1.2, 4.2, 5.3)
)

# Convert data to long format for visualization
growth_data_long <- growth_data %>%
  pivot_longer(cols = starts_with("20"), names_to = "Year", values_to = "GrowthRate")

# Plot the growth projections
ggplot(growth_data_long, aes(x = Year, y = GrowthRate, color = Region, group = Region)) +
  geom_line() +
  geom_point() +
  theme_minimal() +
  labs(
    title = "Global Economic Growth Projections",
    x = "Year",
    y = "Real GDP Growth Rate (%)",
    color = "Region"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Reconstruction

The following plot fixes the main issues in the original.

References

The reference to the original data visualisation choose, the data source(s) used for the reconstruction and any other sources used for this assignment are as follows: