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Deconstruct

Original

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


Source: Statista. (2025). Where Do Emissions Come From? Data adapted from UN Emissions Gap Report 2025.


Objective and Audience

The data visualization represents the reader’s overall understanding of major sectors’ contribution to total global net emissions as a whole for the year 2024:

Objective
Original data visualizations show how various sectors contribute to the relative contribution of total global net greenhouse gas emission (measured in terms of their percentage) as per the report for the same. It will help readers to understand what sectors contribute to the highest proportionate shares of greenhouse gases and provide a basic overall picture of total global emissions.

Audience
The intended audience appears to be general members of the public who would be reading a high-quality news-style infographic; it does not appear to be an audience that comprises technical/specialist readers. This conclusion comes from the fact that the chart is presented in an easy to look at visual format and has limited explanatory technical detail; therefore, it is intended for those who want to quickly look at and see how much global emissions come from various sectors rather than technical/specialist readers requiring a full analytical comparison or detailed analysis.

Critique

The visualization chosen had the following three main issues:

  • The donuts chart makes accurate comparison difficult.
    The chart requires readers to compare angles and arc lengths rather than values on a common scale. This is a weaker method for precise comparison, especially when several categories have similar proportions, such as industry, agriculture, and fuel production. As a result, the visualisation reduces the audience’s ability to quickly and accurately judge which sectors contribute more emissions.

  • The information is fragmented across the visualization.
    The visualization splits data into pieces, with leftover pieces at the right side of the page labelled. As a result, audiences must refer to different parts of the visualisation multiple times for colours, labels and percentages of slices, rather than presenting a complete view of data, the design gives audiences added work to understand this visualisation.

  • The visualization does not fully answer the practical question for the audience.
    Although the title asks about the source of emissions, its not easy to identify which particular sector is in ranking order so that audiences can determine if their sector of interest produces large amounts of emissions. The viewers will have to guess how much larger the biggest emission-producing slices are than the smaller emission-producing slices from only visually comparing the sizes of those slices. In order to better answer this question for an audience, present the emissions-producing sectors in a ordered manner using a bar chart. The audience will be able to see immediately which sectors produce the largest amounts of emissions.

Reconstruct

Code

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

library(ggplot2)
library(dplyr)
library(forcats)
library(tibble)

emissions <- tibble(
  sector = c(
    "Power",
    "Transport",
    "Industry",
    "Agriculture",
    "Fuel production",
    "Industrial processes",
    "Land-use change",
    "Buildings and other",
    "Waste"
  ),
  pct = c(27, 15, 11, 11, 10, 9, 8, 6, 4)
)

p1 <- emissions %>%
  mutate(sector = fct_reorder(sector, pct)) %>%
  ggplot(aes(x = pct, y = sector)) +
  geom_col(fill = "#4C78A8", width = 0.7) +
  geom_text(aes(label = paste0(pct, "%")), hjust = -0.2, size = 4) +
  scale_x_continuous(
    limits = c(0, 30),
    breaks = seq(0, 30, 5),
    expand = expansion(mult = c(0, 0.08))
  ) +
  labs(
    title = "Global net greenhouse gas emissions by sector, 2024",
    subtitle = "Share of total worldwide net greenhouse gas emissions",
    x = "Share of total emissions (%)",
    y = NULL,
    caption = "Source: UNEP Emissions Gap Report 2025; original infographic adapted from Statista"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 11),
    axis.text.y = element_text(size = 11),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )

Reconstruction

The following plot fixes the main issues in the original.

The reconstructed visualisation above addresses the three primary issues identified in the original chart as follows:

1. Improved comparison of sector shares
The interpretation and visual comparison of sector shares using a bar chart versus a donut chart are vastly improved through a redesign of the original donut chart into a horizontal bar graph. Specifically, because the donut chart requires readers to compare angles and arc lengths to determine sector share amounts and some of the sector percentages are very close in size; it is not an effective method of conveying a sector share value compared to the bar chart format that utilizes a common value scale.

2. Better integration of labels and values
The reconstructed graph is easier to read because the labels and values are presented together. In the original visualization, readers had to move back and forth between the donut chart and several labelled boxes to match colors, categories and percentages. In the revised graph, everything is shown in one place, which makes the information quicker and easier to understand.

3. Clearer answer to the practical question
The new chart provides a much more direct answer to the pragmatics of the original chart’s design. The original chart wanted to demonstrate the magnitude of where the world’s emissions are coming from, but the original layout did not provide any delineation regarding how much of the overall total came from each particular sector. By placing the sectors in order by percentage, the redesigned chart allows for the immediate recognition of which sectors account for the largest portions. This will allow the viewer to determine quickly what the main message is.

In conclusion, the redesigned chart continues to fulfill the original goal of providing information to the viewer, but providing that same information in a much more concise and useful manner. It is now much easier to compare, much easier to read, and much more accessible to the general reader.

References

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