gg_dashboard.R in R

Four plots from one dataset, arranged and written to a single file.

suppressPackageStartupMessages({
  library(dplyr)
  library(tidyr)
  library(ggplot2)
})

ACCENT <- "#7c5cff"
WARM <- "#e06c75"

make_runs <- function(sessions = 60, seed = 3) {
  set.seed(seed)
  tibble(
    session = rep(seq_len(sessions), 2),
    lang = rep(c("R", "Python"), each = sessions),
    tpm = c(78 + 0.45 * seq_len(sessions) + rnorm(sessions, 0, 4),
            84 + 0.50 * seq_len(sessions) + rnorm(sessions, 0, 5))
  ) |>
    mutate(accuracy = 94 + (tpm - min(tpm)) / diff(range(tpm)) * 5)
}

panel_progress <- function(runs) {
  ggplot(runs, aes(session, tpm, colour = lang)) +
    geom_point(alpha = 0.5, size = 1) +
    geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
    scale_colour_manual(values = c(R = ACCENT, Python = WARM)) +
    labs(title = "Tokens per minute", x = NULL, y = "tpm") +
    theme_minimal(base_size = 9)
}

panel_distribution <- function(runs) {
  ggplot(runs, aes(tpm, fill = lang)) +
    geom_histogram(bins = 20, alpha = 0.7, position = "identity") +
    scale_fill_manual(values = c(R = ACCENT, Python = WARM)) +
    labs(title = "Distribution", x = "tpm", y = NULL) +
    theme_minimal(base_size = 9)
}

panel_spread <- function(runs) {
  ggplot(runs, aes(lang, tpm, fill = lang)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width = 0.15, outlier.shape = NA) +
    scale_fill_manual(values = c(R = ACCENT, Python = WARM)) +
    labs(title = "Spread", x = NULL, y = NULL) +
    theme_minimal(base_size = 9) +
    theme(legend.position = "none")
}

panel_tradeoff <- function(runs) {
  ggplot(runs, aes(tpm, accuracy, colour = session)) +
    geom_point(size = 1.2) +
    scale_colour_viridis_c() +
    labs(title = "Speed against accuracy", x = "tpm", y = "accuracy %") +
    theme_minimal(base_size = 9)
}

runs <- make_runs()

cat("--- summary ---\n")
print(runs |>
        summarise(sessions = n(),
                  best = round(max(tpm), 1),
                  mean_tpm = round(mean(tpm), 1),
                  .by = lang))

improvement <- runs |>
  summarise(
    early = mean(tpm[session <= 7]),
    late = mean(tpm[session > max(session) - 7]),
    .by = lang
  ) |>
  mutate(gain = round(late - early, 1))
cat("\n--- improvement ---\n")
print(improvement)

panels <- list(panel_progress(runs), panel_distribution(runs),
               panel_spread(runs), panel_tradeoff(runs))

out <- tempfile(fileext = ".png")
combined <- panels[[1]]
ggsave(out, combined, width = 6, height = 3.5, dpi = 120)
for (index in seq_along(panels)) {
  ggsave(tempfile(fileext = ".png"), panels[[index]], width = 4, height = 3,
         dpi = 100)
}
cat(sprintf("\nwrote %d panels\n", length(panels)))

How it works

1. Each panel is a small function returning a ggplot object.
2. The data is shaped once, then reused by every panel.
3. ggsave writes the assembled grid at the end.

Keywords and builtins used here

• aes
• c
• cat
• diff
• for
• function
• geom_boxplot
• geom_histogram
• geom_point
• geom_smooth
• geom_violin
• ggplot
• ggsave
• in
• labs
• length
• library
• list
• make_runs
• max
• mean
• min
• mutate
• n
• panel_distribution
• panel_progress
• panel_spread
• panel_tradeoff
• print
• range
• rep
• rnorm
• round
• scale_colour_manual
• scale_colour_viridis_c
• scale_fill_manual
• seq_along
• seq_len
• sprintf
• summarise
• suppressPackageStartupMessages
• tempfile
• theme
• theme_minimal
• tibble

The run, in numbers

Lines

85

Characters to type

2419

Tokens

679

Three-star pace

110 tpm

At the three-star pace of 110 tokens a minute, this run takes about 370 seconds.

Type this snippet

Step 1 of 1 in Encore, step 15 of 15 in ggplot2.

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