Course 1 · Week 1 — Toolchain & data hygiene

Cheatsheet — biostats_courses

Author

R. Heller

The research workflow

Question → Measurements → Design → Acquisition → Description → Analysis → Interpretation → Validation → Knowledge

Biological vs statistical significance: “p < 0.05” ≠ “effect matters”.

R / RStudio / Quarto / renv

Task Command
Install packages install.packages("pkg")
Pin project renv::init() / renv::snapshot() / renv::restore()
Render one file quarto render path/to/file.qmd
Preview with live reload quarto preview
Render only slides quarto render file.qmd --to revealjs

Data types and scales

Scale Example Statistic
Nominal sex, site counts, proportions
Ordinal pain 0–10 median, IQR, rank tests
Interval °C mean, SD
Ratio kg, time mean, SD, ratios

Accuracy ≠ precision. Bias shifts the target; variance spreads the shots.

Tidy data

  1. One variable per column.
  2. One observation per row.
  3. One observational unit per table.

dplyr verbs

df |>
  filter(age >= 18) |>
  select(id, age, sbp) |>
  mutate(sbp_z = (sbp - mean(sbp)) / sd(sbp)) |>
  group_by(arm) |>
  summarise(mean_sbp = mean(sbp, na.rm = TRUE), n = n())

Joins: left_join, inner_join, anti_join by a key.

Missingness: is.na(), drop_na(), tidyr::complete().

ggplot2 grammar

ggplot(data, aes(x, y, colour = group)) +
  geom_point() +
  facet_wrap(~ factor) +
  labs(x = "x label", y = "y label") +
  theme_minimal()

Layer order: data → aes → geom → facet → scales → labels → theme.

Plot families

  • One variable continuous → histogram, density.
  • Two continuous → scatter, sometimes binned / smoothed.
  • One categorical + one continuous → boxplot, violin, strip.
  • Two categorical → bar with fill, mosaic.

Decision rule for Week 1

  • Cannot read the data into a tibble → fix data entry first.
  • Tibble reads but has missing or mixed types → clean before modelling.
  • Plot is ugly or cluttered → trust the plot, not the summary statistic.

Common pitfalls

  • Running statistics on untyped / partially-missing data.
  • Adjusting colour scales instead of simplifying the chart.
  • Reporting mean + SD for a skewed variable.
  • Forgetting set.seed() in any simulation chunk.

Further reading

  • Wickham, R for Data Science (2e).
  • Posit ggplot2 and dplyr cheatsheets.