Numărul și mijloacele grupate rapid în r

# We run the code in a fresh session
library(tidyverse)
library(duckdb)
library(data.table)
library(collapse)
library(polars)

polars_info() # 8 threads
setDTthreads(8)
con <- dbConnect(duckdb(config = list(threads = "8")))

set.seed(1)

N <- 10^(5:8)
m_queries <- 3
results <- vector("list", length(N) * m_queries)

for (i in seq_along(N)) {
  n <- N(i)

  # Create data
  y <- rexp(n)
  w <- runif(n)
  g <- factor(sample(LETTERS, n, TRUE))

  df <- tibble(y = y, g = g, w = w)
  dt <- data.table(df)
  dfp <- as_polars_df(df)
  duckdb_register(con, name = "df", df = df, overwrite = TRUE)

  # Grouped counts
  results((1 + (i - 1) * m_queries)) <- bench::mark(
    base = tabulate(g),
    dplyr = dplyr::count(df, g),
    data.table = dt(, .N, by = g),
    polars = dfp$get_column("g")$value_counts(),
    collapse = fcount(g),
    duckdb = dbGetQuery(con, "SELECT g, COUNT(*) N FROM df GROUP BY g"),
    check = FALSE,
    min_iterations = 3,
  ) |>
    bind_cols(n = n, query = "counts")

  results((2 + (i - 1) * m_queries)) <- bench::mark(
    base = rowsum(y, g) / tabulate(g),
    dplyr = df |> group_by(g) |> summarize(mean(y)),
    data.table = dt(, mean(y), by = g),
    polars = dfp$select(c("g", "y"))$group_by("g")$mean(),
    collapse = fmean(y, g = g),
    duckdb = dbGetQuery(con, "SELECT g, AVG(y) AS mean FROM df GROUP BY g"),
    check = FALSE,
    min_iterations = 3
  ) |>
    bind_cols(n = n, query = "means")

  results((3 + (i - 1) * m_queries)) <- bench::mark(
    base = {
      ws <- rowsum(data.frame(y = y * w, w), g)
      ws(, 1L) / ws(, 2L)
    },
    dplyr = df |> group_by(g) |> summarize(sum(w * y) / sum(w)),
    data.table = dt(, sum(w * y) / sum(w), by = g),
    polars = (
      dfp
      $with_columns(pl$col("y") * pl$col("w"))
      $group_by("g")
      $sum()
      $with_columns(pl$col("y") / pl$col("w"))
      $drop("w")
    ),
    collapse = fmean(y, g = g, w = w),
    duckdb = dbGetQuery(
      con,
      "SELECT g, SUM(y * w) / sum(w) as wmean FROM df GROUP BY g"
    ),
    check = FALSE,
    min_iterations = 3
  ) |>
    bind_cols(n = n, query = "weighted means")
}

results_df <- bind_rows(results) |>
  group_by(n, query) |>
  mutate(
    time = median,
    approach = as.character(expression),
    relative = as.numeric(time / min(time))
  ) |>
  ungroup()

ggplot(results_df, aes(y = relative, x = query, group = approach, color = approach)) +
  geom_point() +
  geom_line() +
  facet_wrap("n", scales = "free_y") +
  labs(x = element_blank(), y = "Relative timings") +
  theme_gray(base_size = 14)

ggplot(results_df, aes(y = time, x = query, group = approach, color = approach)) +
  geom_point() +
  geom_line() +
  facet_wrap("n", scales = "free_y") +
  labs(x = element_blank(), y = "Absolute time in seconds") +
  theme_gray(base_size = 14)