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< section id="introduction" class="level1">
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Introduction
So I was challanged by Adrian Antico to learn data.table, so yesterday I started with a single function from my package {TidyDensity} called tidy_bernoulli().
So let’s see how I did (hint, works but needs a lot of improvement, so I’ll learn it.)
< section id="function" class="level1">Function
Let’s see the function in data.table
library(data.table)
library(tidyr)
library(stats)
library(purrr)
new_func <- function(num_sims, n, pr) {
# Create a data.table with one row per simulation
sim_data <- data.table(sim_number = factor(seq(1, num_sims, 1)))
# Group the data by sim_number and add columns for x and y
sim_data[, `:=` (
x = list(1:n),
y = list(stats::rbinom(n = n, size = 1, prob = pr))
), by = sim_number]
# Compute the density of the y values and add columns for dx and dy
sim_data[, `:=` (
d = list(density(unlist(y), n = n)[c("x", "y")] |>
set_names("dx", "dy") |>
as_tibble())
), by = sim_number]
# Compute the p-values for the y values and add a column for p
sim_data[, `:=` (
p = list(stats::pbinom(unlist(y), size = 1, prob = pr))
), by = sim_number]
# Compute the q-values for the p-values and add a column for q
sim_data[, `:=` (
q = list(stats::qbinom(unlist(p), size = 1, prob = pr))
), by = sim_number]
# Unnest the columns for x, y, d, p, and q
sim_data <- sim_data[,
unnest(
.SD,
cols = c("x", "y", "d", "p", "q")
),
by = sim_number]
# Remove the grouping
sim_data[, sim_number := as.factor(sim_number)]
return(sim_data)
}
Example
Now, let’s see the output of the original function tidy_bernoulli() and new_func().
library(TidyDensity) n <- 50 pr <- 0.1 sims <- 5 set.seed(123) tb <- tidy_bernoulli(.n = n, .prob = pr, .num_sims = sims) set.seed(123) nf <- new_func(n = n, num_sims = sims, pr = pr) print(tb)
# A tibble: 250 × 7 sim_number x y dx dy p q <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> 1 1 1 0 -0.405 0.0292 0.9 0 2 1 2 0 -0.368 0.0637 0.9 0 3 1 3 0 -0.331 0.129 0.9 0 4 1 4 0 -0.294 0.243 0.9 0 5 1 5 1 -0.258 0.424 1 1 6 1 6 0 -0.221 0.688 0.9 0 7 1 7 0 -0.184 1.03 0.9 0 8 1 8 0 -0.147 1.44 0.9 0 9 1 9 0 -0.110 1.87 0.9 0 10 1 10 0 -0.0727 2.25 0.9 0 # … with 240 more rows
print(nf)
sim_number x y dx dy p q 1: 1 1 0 -0.4053113 0.029196114 0.9 0 2: 1 2 0 -0.3683598 0.063683226 0.9 0 3: 1 3 0 -0.3314083 0.129227066 0.9 0 4: 1 4 0 -0.2944568 0.242967496 0.9 0 5: 1 5 1 -0.2575054 0.424395426 1.0 1 --- 246: 5 46 0 1.2575054 0.057872104 0.9 0 247: 5 47 0 1.2944568 0.033131931 0.9 0 248: 5 48 1 1.3314083 0.017621873 1.0 1 249: 5 49 1 1.3683598 0.008684076 1.0 1 250: 5 50 0 1.4053113 0.003981288 0.9 0
Ok so at least the output is identical which is a good sign. Now let’s benchmark the two solutions.
library(rbenchmark)
library(dplyr)
benchmark(
"original" = {
tidy_bernoulli(.n = n, .prob = pr, .num_sims = sims)
},
"data.table" = {
new_func(n = n, pr = pr, num_sims = sims)
},
replications = 100,
columns = c("test","replications","elapsed","relative","user.self","sys.self" )
) |>
arrange(relative)
test replications elapsed relative user.self sys.self 1 original 100 3.29 1.00 2.51 0.08 2 data.table 100 4.64 1.41 3.34 0.04
Yeah, needs some work but it’s a start.
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