Best Plot Assignment

GRAPHS:

Version 1)

final_data3 <- read_excel("~/Desktop/Data Visual/final_data3.xlsx")

final_data3$GDP <- as.numeric(final_data3$GDP)
final_data3$Electricity <- as.numeric(final_data3$Electricity)
final_data3$Year <- as.numeric(final_data3$Year)

final_data3 <- final_data3 %>% 
  filter(Year == 2022)

plot0 <- plot_ly(final_data3, 
  x = ~Electricity, 
  y = ~GDP_data,
  type = 'scatter', 
  mode = "markers"
  )

plot0

Version 2)

#Okabe Ito Palette
custom_colors <- c("Eastern Mediterranean" = "#CC79A7",
                   "Europe" = "#009E73",
                   "Africa" = "#D55E00",
                   "Americas" = "#0072B2",
                   "Asia" = "#F0E442",
                   "Western Pacific" = "#000000")

plot2 <- plot_ly(final_data3, x = ~Electricity, y = ~GDP,
        color = ~ParentLocation, colors = custom_colors,type = 'scatter', mode = "markers", marker = list(symbol = 'diamond', size = 10)) %>%
  layout(
    title = list(
      text = "Access to Electricity vs. GDP",  
      x = 0.28,  # Center the title horizontally
      y = .98,  # Adjust the vertical position of the title
      font = list(size = 15)  # Set the font size
    ),
    xaxis = list(title = "Electricity Access (%)"),
    yaxis = list(title = "GDP (USD)")
  )
plot2

##1) color palette ##2) shape of point ##3) size of point ##4) group by region

Version 3)

#Okabe Ito Palette (1 aesthetic change)
custom_colors <- c("Eastern Mediterranean" = "#CC79A7",
                   "Europe" = "#009E73",
                   "Africa" = "#D55E00",
                   "Americas" = "#0072B2",
                   "Asia" = "#F0E442",
                   "Western Pacific" = "#000000")

plot3 <- plot_ly(final_data3, x = ~Electricity, y = ~GDP_data,
        color = ~ParentLocation, colors = custom_colors, text = ~paste("Region: ", Country, "\n", 
  "Electricity Access: ", round(Electricity, 2), "%", "\n", 
  "GDP: $", format(round(GDP_data / 1e9, 2), big.mark = ","), "B"),
        hoverinfo = "text", type = 'scatter', mode = "markers", marker = list(symbol = 'diamond', size = 10, opacity = 0.6)) %>%
  layout(
    title = list(
      text = "Access to Electricity vs. GDP",  
      x = 0.28,  # Center the title horizontally
      y = .98,  # Adjust the vertical position of the title
      font = list(size = 15)  # Set the font size
    ),
    xaxis = list(title = "Electricity Access (%)"),
    yaxis = list(title = "GDP (USD)")
  )

plot3

##5) add information to show the points GDP and Electricity ##6) opacity

Version 4)

#Okabe Ito Palette (1 aesthetic change)
custom_colors <- c("Eastern Mediterranean" = "#CC79A7",
                   "Europe" = "#009E73",
                   "Africa" = "#D55E00",
                   "Americas" = "#0072B2",
                   "Asia" = "#F0E442",
                   "Western Pacific" = "#000000")

plot4 <- plot_ly(final_data3, x = ~Electricity, y = ~GDP_data, color = ~ParentLocation,
  colors = custom_colors, text = ~paste("Country: ", Country, "\n", 
  "Electricity Access: ", round(Electricity, 2), "%", "\n", 
  "GDP: $", format(round(GDP_data/ 1e9, 2), big.mark = ","), "B"),
  hoverinfo = "text", 
  type = 'scatter', 
  mode = "markers", 
  marker = list(symbol = 'diamond', size = 10, opacity = 0.5)
) %>%
  layout(
    title = list(
      text = "Percentage of Population with Access to Electricity vs. GDP",  
      x = 0.14,  # Center the title horizontally
      y = .98,  # Adjust the vertical position of the title
      font = list(size = 15)  # Set the font size
    ),
    xaxis = list(title = "Electricity Access (%)"),
    yaxis = list(title = "GDP (USD)"),
    legend = list(
      title = list(
        text = "Region"  # Title text for the legend
      ))
  )
plot4

##7) title / added a legend title

Version 5)

library(plotly)
library(dplyr)

# Okabe-Ito Palette
custom_colors <- c(
  "Eastern Mediterranean" = "#CC79A7",
  "Europe" = "#009E73",
  "Africa" = "#D55E00",
  "Americas" = "#0072B2",
  "Asia" = "#F0E442",
  "Western Pacific" = "#000000"
)

# Define regions
regions <- c("Eastern Mediterranean", "Europe", "Africa", "Americas", "Asia", "Western Pacific")


# Generate individual plots for each region
plots <- lapply(regions, function(region) {
  region_data <- final_data3 %>% filter(ParentLocation == region)
  
  plot_ly(
    region_data, 
    x = ~Electricity, 
    y = ~GDP_data,
    text = ~paste("Country: ", Country, "\n",
                  "Electricity Access: ", round(Electricity, 2), "%", "\n",
                  "GDP: $", format(round(GDP_data / 1e9, 2), big.mark = ","), "B"),
    hoverinfo = "text",
    type = 'scatter',
    mode = "markers",
    marker = list(
      symbol = 'diamond', 
      size = 10, 
      opacity = 0.6,
      color = custom_colors[region]
    ),
    showlegend = FALSE
  ) %>%
    layout(
      xaxis = list(title = "Access to Electricity (%)", dtick = 20),
      yaxis = list(title = "GDP (USD)")  # Y-axis label
    )
})

# Combine all plots into a single subplot layout
facet_plot <- subplot(
  plots,
  nrows = 3,   # Number of rows
  titleX = TRUE, 
  titleY = TRUE, 
  shareX = TRUE, 
  shareY = TRUE
)

# Add individual headers (region titles) for each plot
annotations <- lapply(seq_along(regions), function(i) {
  row <- ceiling(i / 2)  # Compute row number (2 plots per row)
  col <- ifelse(i %% 2 == 1, 0.25, 0.75)  # Left or right column position
  
  list(
    x = col,  # Horizontal position of the annotation
    y = 1 - (row - 1) / 3,  # Vertical position (based on row)
    text = regions[i],
    xref = 'paper',
    yref = 'paper',
    showarrow = FALSE,
    font = list(size = 14, color = "black"),
    align = "center"
  )
})

# Add annotations and title to the layout
facet_plot <- facet_plot %>%
  layout(
    title = list(
      text = "Access to Electricity vs. GDP by Region",
      x = 0.5,
      font = list(size = 18)
    ),
    annotations = annotations
  )

facet_plot

#8) Facet groups

QUESTION 1)

In my plot, I want to highlight the differences that exist between and amongst various continental regions specifically related to their access to electricity and national GDP. There are 189 total countries in this dataset, and each of them is classified further by region. These regions include Africa, Americas, Asia, Eastern Mediterranean, Europe, and Western Pacific as defined by the World Health Organization.

QUESTION 2)

The first plot that I created was a scatterplot showing percentage of individuals with access to electricity on the x-axis and GDP measured in US dollars on the y-axis. The data is from the year 2022 and all the points are a default blue color. In the next graph, the first aesthetic change (1) that made was to group the points by the regions listed above. In doing so, I was able to better see the trends of each region. Next, (2) I created a custom palette based on the Okabe Ito colors so that they would be more visible to better accommodate those with different forms of colorblindness. Then, (3) I changed the shape of each point to be diamond shaped. I felt that this made each point feel more precise and easier to read in comparison to the circle points which existed in the first graph. Further, (4) I increased the size of the points so that they could be more visible to the readers relative to those in the first graph. After completing this first iteration, I like the second graph better because I feel that it provides more regional data and is easier to visually digest.

In my third graph, I incorporated the previously listed modifications and (5) also changed the opacity of the points. By making this change, I was better able to see the points, as there is substantial overlap amongst them. Additionally, (6) I added hover text which provided precise information for each of the points. The text included the country that the point corresponds to, as well as the relative GDP and electricity access percentage. By adding these details, the viewer can gain additional crucial details which would otherwise not be visible. By the completion of this graph, I liked this one better as it provided greater information that helps the viewer to understand the differences between the various continental regions, as I had been striving to achieve as my main message.

In my fourth graph, (7) I added a title for the legend to explain that they are showing regions. I also made the title more descriptive so that viewers can more quickly understand what the graph is showing. At this point, I liked this graph better than the others as it included additional text which made it easier to understand.

For the fifth, and final plot, (8) I decided to use faceted plots to better see all of the data present. I created six separate graphs, each one including all countries within the particular region. I liked this change as I could better see all of the points and the regional titles made it very easy to observe trends across the different groups. In the prior graphs, there was a lot of overlap between the points, and it made it very difficult to observe trends. This version allows the viewer to clearly see trends within each region and compare them to the others.

QUESTION 3)

The fifth graph that I have created is the best one as it incorporates multiple principles from the course and most effectively displays the data. Firstly, it uses elements that make the data more visually accessible including a distinct diamond shape, lower opacity, and a color pallet which caters to a broader audience. Secondly, its structure helps to reduce the viewer’s mental strain. More specifically, the hover text helps them to quickly identify which points corresponds with which country, and the specific statistics for it. Further, more of the countries are visible which allows them to draw greater conclusions about the data points and observe. By grouping the plots by different regions, it allows the viewer to quickly scan to see trends both within regions and between them. In addition, it is well labeled and intuitive to understand. Being that the main message that I wanted to show was the difference between and amongst regions with the electricity and GDP variables. I feel that this best illustrates this idea.

References)

##Used ChatGPT for: formatting / alignment questions, how to do the faceted scatterplot, and adding annotations.