orderfast: Fast Ordering Function

In cgmguru: Advanced Continuous Glucose Monitoring Analysis with High-Performance C++ Backend

Fast Ordering Function

Description

Orders a dataframe by id and time columns using a C++ std::sort backend. Optimized for large CGM datasets, it returns the input with rows sorted by subject then timestamp while preserving all columns.

Orders a dataframe by id and time columns using the C++ backend

Usage

orderfast(df)

Arguments

df	A dataframe with 'id' and 'time' columns

Value

A dataframe ordered by id and time

A dataframe ordered by id and time

Examples

# Load sample data
library(iglu)
data(example_data_5_subject)
data(example_data_hall)

# Shuffle without replacement, then order and compare to baseline
set.seed(123)
shuffled <- example_data_5_subject[sample(seq_len(nrow(example_data_5_subject)),
                                          replace = FALSE), ]
baseline <- orderfast(example_data_5_subject)
ordered_shuffled <- orderfast(shuffled)

# Compare results
print(paste("Identical after ordering:", identical(baseline, ordered_shuffled)))
head(baseline[, c("id", "time", "gl")])
head(ordered_shuffled[, c("id", "time", "gl")])

# Order larger dataset
ordered_large <- orderfast(example_data_hall)
print(paste("Ordered", nrow(ordered_large), "rows in larger dataset"))
df <- data.frame(id = c("b", "a", "a"), time = as.POSIXct(
  c("2024-01-01 01:00:00", "2024-01-01 00:00:00", "2024-01-01 01:00:00"), tz = "UTC"
))
orderfast(df)

cgmguru documentation built on July 8, 2026, 9:06 a.m.