Project Title:

Revealing Unseen Data: A 2020 U.S. Election Visualisation

Project Description: This project focused on visualising the 2020 U.S. presidential election results with a special emphasis on accessibility for colour vision-impaired individuals. The goal was to create clear and accessible visualisations that effectively communicate the election dynamics across different states.

Key Responsibilities:

  1. Data Collection and Preparation:

    • Utilised election data from the 2020 U.S. presidential election, including state-wise vote counts and party distributions.

    • Cleaned and prepared the data using R to ensure accuracy and usability in visualisation tools.

  2. Coding and Visualisation:

    • R Programming:

      • Used R for data preparation and employed the 'colorblind' package to create a colour-blind-friendly electoral map.

      • Developed a custom palette with red, blue, and gold to represent Republican, Democrat, and Swing States, respectively.

      • Created visual representations, including bar charts and bubble maps, to display the total number of votes and party shares across states.

      • Example code snippet for creating a colour-blind-friendly map:

        library(tidyverse)

        library(sf)

        library(ggplot2

        library(colorBlindness)

        usdata <- read.csv("POTUS_votes_2020.csv")

        us_states <- st_read("States_shapefile.shp") joined_data <- left_join(usdata, us_states, by = c("state_po" = "State_Code"))

        joined_data <- joined_data %>% mutate(vote_gap = abs(DEM_votes - REP_votes) / totalvotes, swing_state = vote_gap <= 0.05,

        party_color = case_when( swing_state ~ "#FFFFBF", DEM_votes > REP_votes & DEM_votes > OTH_votes ~ "#290AD8", REP_votes > DEM_votes & REP_votes > OTH_votes ~ "#A50021", TRUE ~ NA_character_ ))

        joined_data_sf <- st_as_sf(joined_data, sf_column_name = "geometry")

        ggplot(data = joined_data_sf) + geom_sf(aes(fill = party_color)) + scale_fill_identity() + theme_minimal() + labs(title = "Clear Vision for All: The 2020 U.S. Presidential Election Map by State", subtitle = "Redesigned for Colorblind Accessibility")

    • Tableau:

      • Utilised Tableau to create additional visualisations such as bar charts and bubble charts to supplement the electoral map.

      • Ensured that all visual elements adhered to accessibility standards for colour vision deficiencies.

  3. Validation and Testing:

    • Tested visualisations using Color Oracle to simulate different types of colour vision deficiencies (deuteranopia, protanopia, tritanopia, and grayscale).

    • Ensured that all visualisations were clear and informative regardless of the viewer's colour vision capabilities​​ .

Project Outcomes:

  • Produced a set of accessible visualisations that clearly conveyed the 2020 U.S. election results, making them usable for individuals with colour vision impairments.

  • Demonstrated the effectiveness of using tailored colour palettes and alternative visual variables to enhance data accessibility.

  • Contributed to the broader field of accessible data visualisation, providing insights for future projects aimed at inclusivity.

Skills and Tools:

  • Data Collection and Analysis: R, Tableau

  • Data Visualisation: ggplot2, ColorBlindness package in R

  • Accessibility Testing: Color Oracle