The Mad Hatter’s Guide to Data Viz and Stats in R
  1. Group 2 — Risk factors
  • 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 2 — Risk factors

Studio wall

Author

Group names here

1 Spine

How do bodies carry risk, how do class and smoke talk, and where do children still die young?

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) — cardioRiskFactors: BMI, lipids, blood pressure, uric acid. sex and smok have already been recoded from integers to factors.
  • Panel B (panel-B.csv) — Scottish Health Survey 1998: sex, social class, smoking, diagnosed CVD. area is a code, not a map.
  • Panel C (panel-C.csv) — SOWC child / infant / neonatal mortality by country. Join by country name.

If a brick needs a histogram, it lives in A. If it needs a mosaic, it lives in B.

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

bmi, choles, sys, dia, Uric are Quant. Do not average smok.

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

smoke × sex, cvd × sc (social class). area is forbidden as geography.

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

under5_mortality_2018 and infant_mortality_2018 are rates. Join on country.

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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