One Story Type

Seven Story Types

1. Overcoming the monster
2. Rags to riches
3. The quest
4. Voyage and return
5. Comedy
6. Tragedy
7. Rebirth

Twenty Story Types

Data Story Types

Data: key crop yields

# devtools::install_github("etc5523-2022/data", ref = "tut9")
cwdata::key_crop_yields

# A tibble: 143,825 × 5
   entity      code   year crop     yield
   <chr>       <chr> <dbl> <chr>    <dbl>
 1 Afghanistan AFG    1961 wheat     1.02
 2 Afghanistan AFG    1961 rice      1.52
 3 Afghanistan AFG    1961 maize     1.4 
 4 Afghanistan AFG    1961 soybeans NA   
 5 Afghanistan AFG    1961 potatoes  8.67
 6 Afghanistan AFG    1961 beans    NA   
 7 Afghanistan AFG    1961 peas     NA   
 8 Afghanistan AFG    1961 cassava  NA   
 9 Afghanistan AFG    1961 barley    1.08
10 Afghanistan AFG    1961 cocoa    NA   
# ℹ 143,815 more rows

Change over time

Drill down

Contrast

Factors

Intersections

Outliers

Narrative structure

Elements of statistical persuasion

• Magnitude of effects: the strength of a statistical argument is enhanced in accord with the quantitative magnitude of support for its qualitative claim.
• Articulation of results: the degree of comprehensible detail in which conclusions are phrased.
• Generality of effects: the replicability of the results.
• Interestingness of argument: the potential to change people believe.
• Credibility of argument: the believability of a claim.

Narrative techniques

• Metaphor, simile, analogy or anecdotes, e.g. “one in three households is wasting the equivalent of a shopping bag full of food each week.”
• Comparisons, e.g. “While the average Australian man has $52,786 in their savings account right now, the average woman sits at around half that figure – $26,132.”
• Flow of information, e.g. new information before old, simple before complex information.

Feedback loops

• Recall there is no single, ideal way to communicate.
• Get in a habit to seek feedback from others about:
  • Reach: Did the audience even receive your message at all? Who did and who didn’t?
  • Understanding: Did the audience interpret the data message in the way you intended?
  • Impact: Did the audience react in the way you wanted them to react?

Anscombe’s Quartet

Anscombe’s Quartet

Same Stats, Different Graphs

Uncovering Simpson’s paradox

Advantages of visualisation

• Ability to “see” large amounts of data
• Reveals properties or structures in data that are not apparent in numerical summaries
• Facilitates understanding of both large-scale and small-scale features of the data

A model of visual information processing

Visual encodings

Effectiveness of visual encoding

Effectiveness of visual encoding by data type

Affectiveness by color

User Interactions

User Inputs

Overview

Interactive plots with JS

• State-of-the-art for interactive plots is JS
• Some of the most popular and free JS library for plotting are:

• Rising JS libraries that are based on the Grammar of Graphics:

Plotly

• Plotly is a high-level, declarative library for plotting build on d3.js and stack.gl (open software ecosystem for WebGL).

Plotly for ggplot2

library(ggplot2)
g <- ggplot(ggplot2::diamonds, aes(x = cut, fill = clarity)) +
    geom_bar(position = "dodge") +
    scale_fill_brewer(palette = "Accent")

ggplotly(g)

Plotly with drop down menu

Code for Plotly with drop down menu

yields <- na.omit(cwdata::key_crop_yields)
crops <- sort(unique(yields$crop))

map(crops, ~ {
    yields %>%
        filter(crop == .x) %>%
        plot_ly(
            x = ~year,
            y = ~yield,
            type = "scatter",
            mode = "lines",
            color = ~crop,
            transforms = list(
                list(
                    type = "filter",
                    target = ~entity,
                    operation = "=",
                    value = "Afghanistan"
                )
            )
        )
}) %>%
    subplot(nrows = 3) %>%
    layout(
        showlegend = FALSE,
        annotations = imap(crops, ~ {
            list(
                x = 0 + 1 / 4 * (.y - 1) - 1 / 4 * 4 * (.y > 4) - 1 / 4 * 4 * (.y > 8),
                y = 1 - 0.05 - 1 / 3 * (.y > 4) - 1 / 3 * (.y > 8),
                font = list(size = 18),
                text = .x,
                xref = "paper",
                yref = "paper",
                xanchor = "center",
                yanchor = "bottom",
                showarrow = FALSE
            )
        }),
        updatemenus = list(list(
            type = "dropdown",
            active = 0,
            buttons = map(
                unique(yields$entity),
                ~ list(
                    method = "restyle",
                    args = list("transforms[0].value", .x),
                    label = .x
                )
            )
        ))
    )

Difference to Shiny

cwdata::run_app()

Code for Shiny app

library(shiny)
library(ggplot2)
library(dplyr)
library(cwdata)

ui <- fluidPage(
    br(),
    sidebarLayout(
        sidebarPanel(
            selectizeInput("entity",
                "Select a country or region:",
                choices = sort(unique(key_crop_yields$entity)),
                selected = "Australia"
            )
        ),
        mainPanel(
            plotOutput("tsplot")
        )
    )
)

server <- function(input, output, session) {
    output$tsplot <- renderPlot({
        key_crop_yields %>%
            filter(entity == input$entity) %>%
            ggplot(aes(year, yield)) +
            geom_line() +
            geom_point() +
            facet_wrap(~crop, scale = "free_y")
    })
}

shinyApp(ui, server)

Week 10 Lesson