Many models, many years later

A few days ago, a friend and I remembered Hadley Wickham’s 2016 talk, Managing many models with R. Today, it feels like a classic!

This talk sold me on purrr. Before map(), many years ago, I used for loops because the apply family never felt intuitive to me. I created a list with the final length and saved each result in results[[i]]. It gave me the same result in a more explicit way, but it could be slower when I did not allocate the output in advance. With map(), the action became the important part.

Here is a small version of the Gapminder example from that talk. First, we nest the data and fit one linear model for each country. The example uses year - 1950, so the intercept has a clear meaning: estimated life expectancy in 1950.

models <- gapminder |>
  mutate(year1950 = year - 1950) |>
  nest(.by = c(country, continent)) |>
  mutate(
    model = map(data, \(df) lm(lifeExp ~ year1950, data = df)),
    coefficients = map(model, tidy),
    metrics = map(model, glance),
    beta0 = map_dbl(coefficients, \(x) x$estimate[x$term == "(Intercept)"]),
    beta1 = map_dbl(coefficients, \(x) x$estimate[x$term == "year1950"]),
    r_squared = map_dbl(metrics, "r.squared")
  )

The chart below puts the intercept on the x-axis and the slope on the y-axis. Bubble size represents the model’s R². Hover over a country to see its data and fitted line.

chart_data <- models |>
  mutate(
    observed = map(
      data,
      \(df) transmute(df, x = year, y = lifeExp) |> list_parse()
    ),
    fitted = map2(
      data,
      model,
      \(df, fit) tibble(x = df$year, y = predict(fit, newdata = df)) |>
        list_parse()
    ),
    label = sprintf("%s · R² = %.3f"f"countryryr_squared)d)
  ) |>
  select(country, continent, beta0, beta1, r_squared, label, observed, fitted)

hc <- hchart(
  chart_data,
  "bubble",
  hcaes(
    x = beta0,
    y = beta1,
    z = r_squared,
    name = country,
    group = continent
  )
) |>
  hc_chart(zooming = list(type = "xy")) |>
  hc_title(text = "One model for each country") |>
  hc_subtitle(text = "Bubble size represents R²"))|>>
  hc_xAxis(title = list(text = "Estimated life expectancy in 1950 (beta0)")) |>
  hc_yAxis(title = list(text = "Estimated change per year (beta1)")) |>
  hc_plotOptions(
    bubble = list(minSize = 6, maxSize = 28),
    series = list(marker = list(lineWidth = 0), opacity = 0.75)
  ) |>
  hc_tooltip(
    useHTML = TRUE,
    pointFormatter = tooltip_chart(
      accesor = "observed",
      width = 300,
      height = 190,
      hc_opts = list(
        chart = list(type = "scatter"),
        title = list(text = "point.label", style = list(fontSize = "12px")),
        credits = list(enabled = FALSE),
        xAxis = list(title = list(text = NULL)),
        yAxis = list(title = list(text = "Life expectancy")),
        plotOptions = list(
          scatter = list(marker = list(radius = 3)),
          series = list(animation = FALSE)
        ),
        series = list(
          list(name = "Observed", type = "scatter"),
          list(
            name = "Linear fit",
            type = "line",
            data = "point.fitted",
            color = "#31546d",
            lineWidth = 2,
            marker = list(enabled = FALSE)
          )
        )
      )
    )
  ) |>
  hc_credits(enabled = FALSE)

hc |> 
  hc_size(height = 700)

As Hadley points out, countries with slopes close to zero also tend to have lower R² values. In these cases, a straight line may not be the best way to represent how life expectancy changed over time.

I still use purrr almost every day—or at least my AI assistant does these days. Other languages have tools for iteration, but in my experience, nothing feels as natural and simple as this.