The Mad Hatter’s Guide to Data Viz and Stats in R
  1. Group 6 — Acute coronary syndrome and Africa
  • Data Viz and Stats
    • Tools
      • Introduction to R and RStudio
    • Data Kits
      • Healthcare Data Kits
    • Descriptions
      • Data
      • Inspect Data
      • Graphs
      • Summaries
      • Counts
      • Quantities
      • Groups
      • Distributions
      • Groups and Distributions
      • Change
      • Proportions
      • Hierarchy
      • Evolution and Flow
      • Ratings and Rankings
      • Surveys
      • Time
      • Space
        • Introduction to Maps
        • What is Vector Data?
        • The Grammar of Maps
        • Interactive Maps with leaflet
      • Networks
      • Miscellaneous Graphing Tools, and References
    • Inference
      • Basics
      • 🎲 Samples
      • Randomization
      • One Mean
      • Two Independent Means
      • Two Paired Means
      • Multiple Means (ANOVA)
      • Correlation
      • One Proportion
      • Two Proportions
    • Modelling
      • Modelling with Linear Regression
      • Modelling with Logistic Regression
      • 🕔 Modelling and Predicting Time Series
    • Workflow
      • Facing the Abyss
      • I Publish, therefore I Am
      • Data Carpentry
    • Arts
      • Colours
      • Fonts
      • Annotations
      • More Annotations
      • Highlighting
      • Scales
    • AI Tools
      • Using gander and ellmer
      • Using Github Copilot and other AI tools to generate R code
      • Using LLMs to Explain Stat models
    • Case Studies
      • Demo:Product Packaging and Elderly People
      • Ikea Furniture
      • Movie Profits
      • Gender at the Work Place
      • Heptathlon
      • School Scores
      • Children’s Games
      • Valentine’s Day Spending
      • Women Live Longer?
      • Hearing Loss in Children
      • California Transit Payments
      • Seaweed Nutrients
      • Coffee Flavours
      • Legionnaire’s Disease in the USA
      • Antarctic Sea ice
      • William Farr’s Observations on Cholera in London
    • Projects
      • No Free Hunch

On this page

  • 1 Spine
  • 2 Introduction — the tables we own
  • 3 Panel A — body / measure
  • 4 Panel B — category / talk
  • 5 Panel C — place
  • 6 Conclusion

Group 6 — Acute coronary syndrome and Africa

Studio wall

Author

Group names here

1 Spine

Who arrives in acute coronary crisis, which habits travel with that crisis, and where in Africa is life short?

This sentence stays at the top. Every panel must attach to it. If a plot cannot be introduced in a clause that hangs off this line, it is off the wall.

2 Introduction — the tables we own

This folder is ours for the ten days. Three tables, three shapes.

  • Panel A (panel-A.csv) — ACS clinic file: age, height, weight, BMI, ejection fraction, lipids. sex and obesity for grouping.
  • Panel B (panel-B.csv) — same people, Quals: shock, diagnosis, diabetes, high blood pressure, smoking, obesity.
  • Panel C (panel-C.csv) — gapminder 2007, Africa only. Join by country name.

A is the body in crisis. B is the label set. C is not a ward map.

We load from this folder:

library(readr)
library(tidyverse)

A <- read_csv("panel-A.csv")
B <- read_csv("panel-B.csv")
C <- read_csv("panel-C.csv")

Found tables train the mark. A campus Free Hunch, if we collect one, is a made panel of the same pair type — new people, old grammar. It does not replace A, B, or C.

3 Panel A — body / measure

age, BMI, EF, TC, LDLC, HDLC, TG are Quant. obesity and sex are Quals for split.

3.0.1 1. See the table

What is a case? Which columns are Quant / Qual? What is missing?

# glimpse() / inspect() / skim()

3.0.2 2. Ask

One question this table can answer. One it cannot. Write both.

3.0.3 3. Choose a mark

Geom, map mark, or (later) test that matches the pair type. Name it before you code.

3.0.4 4. Predict

What do we think we will see? One sentence, before Run.

3.0.5 5. Make

Human brick first. Optional: one AI prompt, pasted below, only for what section 3 named.

# human chunk

# ## Prompt we actually used
# ai_draft
# ours

3.0.6 6. Say

Caption + one sentence we will stand behind. Title is a sentence, not a column name.

3.0.7 7. Limit

What this panel cannot do. Which kit table we refused to abuse.

4 Panel B — category / talk

smoking × sex, DM × HBP, Dx × cardiogenicShock. Do not histogram smoking.

4.0.1 1. See the table

What is a case? Which columns are Quant / Qual? What is missing?

# glimpse() / inspect() / skim()

4.0.2 2. Ask

One question this table can answer. One it cannot. Write both.

4.0.3 3. Choose a mark

Geom, map mark, or (later) test that matches the pair type. Name it before you code.

4.0.4 4. Predict

What do we think we will see? One sentence, before Run.

4.0.5 5. Make

Human brick first. Optional: one AI prompt, pasted below, only for what section 3 named.

# human chunk

# ## Prompt we actually used
# ai_draft
# ours

4.0.6 6. Say

Caption + one sentence we will stand behind. Title is a sentence, not a column name.

4.0.7 7. Limit

What this panel cannot do. Which kit table we refused to abuse.

5 Panel C — place

Africa-only lifeExp / gdpPercap / pop. Same join brick as other map panels.

Geometry is not in the CSV. Join in R, then use the same seven headings.

# library(sf)
# library(rnaturalearth)
# world <- ne_countries(scale = "medium", returnclass = "sf")
# # join on country name; list the unmatched labels

5.0.1 1. See the table

What is a case? Which columns are Quant / Qual? What is missing?

# glimpse() / inspect() / skim()

5.0.2 2. Ask

One question this table can answer. One it cannot. Write both.

5.0.3 3. Choose a mark

Geom, map mark, or (later) test that matches the pair type. Name it before you code.

5.0.4 4. Predict

What do we think we will see? One sentence, before Run.

5.0.5 5. Make

Human brick first. Optional: one AI prompt, pasted below, only for what section 3 named.

# human chunk

# ## Prompt we actually used
# ai_draft
# ours

5.0.6 6. Say

Caption + one sentence we will stand behind. Title is a sentence, not a column name.

5.0.7 7. Limit

What this panel cannot do. Which kit table we refused to abuse.

6 Conclusion

What the spine looks like after three panels.

  • The question we will defend in ninety seconds
  • What we will not claim
  • Which lines were human, which were machine
  • Limits: sample, missingness, the test we did not run
  • Gift for the steal-this strip (palette, annotation, join trick)
# sessionInfo()
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