tidy_pipeline.R in R

A full tidyverse pipeline: wrangle, summarize, and plot.

#!/usr/bin/env Rscript
# A full tidyverse pipeline: wrangle, summarise, and plot mtcars.

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

cars <- mtcars %>%
  as_tibble(rownames = "model") %>%
  mutate(
    cylinders = factor(cyl),
    transmission = if_else(am == 1, "manual", "automatic"),
    efficiency = case_when(
      mpg < 15 ~ "thirsty",
      mpg < 25 ~ "average",
      TRUE ~ "efficient"
    )
  )

summary_table <- cars %>%
  group_by(cylinders, transmission) %>%
  summarise(n = n(), avg_mpg = round(mean(mpg), 1), .groups = "drop") %>%
  pivot_wider(names_from = transmission, values_from = c(n, avg_mpg))

print(summary_table)

plot <- ggplot(cars, aes(x = wt, y = mpg, colour = cylinders)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.6) +
  labs(title = "Efficiency by weight", x = "Weight", y = "MPG") +
  theme_minimal()

ggsave("efficiency.png", plot, width = 7, height = 5, dpi = 150)
cat("wrote efficiency.png\n")

How it works

1. mutate with case_when derives labeled columns.
2. group_by/summarize then pivot_wider build a table.
3. ggplot plots it and ggsave writes the figure.

Keywords and builtins used here

• aes
• as_tibble
• c
• case_when
• cat
• factor
• geom_point
• geom_smooth
• ggplot
• ggsave
• group_by
• if_else
• labs
• library
• mean
• mutate
• n
• pivot_wider
• print
• round
• summarise
• suppressPackageStartupMessages
• theme_minimal

The run, in numbers

Lines

36

Characters to type

956

Tokens

225

Three-star pace

110 tpm

At the three-star pace of 110 tokens a minute, this run takes about 123 seconds.

Type this snippet

Step 1 of 1 in Encore, step 27 of 27 in dplyr.

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