The Mad Hatter’s Guide to Data Viz and Stats in R
  1. Group 7 — ERCP trial and the Americas
  • 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 7 — ERCP trial and the Americas

Studio wall

Author

Group names here

1 Spine

Does a simple pill change post-procedure pancreatitis, who is in that trial, and where in the Americas is life long?

This sentence stays at the top. Every panel must attach to it.

2 Introduction — the tables we own

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

  • Panel A (panel-A.csv) — medicaldata::indo_rct: age and risk score, plus gender, treatment (rx: indomethacin vs placebo), and outcome.
  • Panel B (panel-B.csv) — same trial, Quals: treatment, outcome, PEP, outpatient status, site.
  • Panel C (panel-C.csv) — gapminder 2007, Americas only. Join by country name.

This is an RCT. rx is assigned, not a lifestyle. Do not talk about it as if people chose the pill.

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 is a made panel of the same pair type.

3 Panel A — body / measure

age and risk are Quant. gender, rx, outcome 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

3.0.6 6. Say

Caption + one sentence we will stand behind.

3.0.7 7. Limit

What this panel cannot do.

4 Panel B — category / talk

rx × outcome is the trial mosaic. site × outcome. Do not average rx.

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

4.0.6 6. Say

Caption + one sentence we will stand behind.

4.0.7 7. Limit

What this panel cannot do.

5 Panel C — place

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

# library(sf)
# library(rnaturalearth)
# world <- ne_countries(scale = "medium", returnclass = "sf")

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

5.0.6 6. Say

Caption + one sentence we will stand behind.

5.0.7 7. Limit

What this panel cannot do.

6 Conclusion

  • The question we will defend in ninety seconds
  • What we will not claim
  • Which lines were human, which were machine
  • Limits
  • Gift for the steal-this strip
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