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Pivot a rollout schedule from wide to long format with local time calculation

Source: R/data_generation.R
pivot_schedule_longer.Rd

Transforms a wide-format rollout schedule into a long-format schedule, extracting chronological time from column names, converting condition columns to factors, and adding local time within each cohort if desired.

Usage

pivot_schedule_longer(
  schedule,
  time_cols,
  names_to = "chron_time",
  names_pattern = ".*(\\d+)",
  names_transform = as.numeric,
  values_to = "condition",
  values_transform = as.factor,
  cohort_name = cohort,
  local_time = TRUE
)

Arguments

schedule

A data frame containing the rollout schedule in wide format.

time_cols

Columns containing time-specific condition assignments (tidyselect syntax).

names_to

Name of the new column to store extracted chronological time (default "chron_time").

names_pattern

Regular expression to extract the numeric time from column names (default ".*(\\d+)").

names_transform

Function to transform extracted time values (default as.numeric).

values_to

Name of the new column to store condition values (default "condition").

values_transform

Function to transform condition values (default as.factor).

cohort_name

The column indicating cohort membership for local time calculation (default cohort).

local_time

Logical; if TRUE, adds a local_time column indicating time since rollout start for each cohort and condition (default TRUE).

Value

A long-format tibble with columns for cohort, condition, chronological time, and optionally local time.

Examples

library(dplyr)
library(tidyr)
#> 
#> Attaching package: ‘tidyr’
#> The following objects are masked from ‘package:Matrix’:
#> 
#>     expand, pack, unpack
schedule <- tibble::tibble(
  site = c("A", "B"),
  cohort = c(1, 2),
  t1 = c("control", "intervention"),
  t2 = c("intervention", "intervention")
)
pivot_schedule_longer(schedule, time_cols = starts_with("t"))
#> # A tibble: 4 × 5
#>   site  cohort chron_time condition    local_time
#>   <chr>  <dbl>      <dbl> <fct>             <dbl>
#> 1 A          1          1 control               0
#> 2 A          1          2 intervention          0
#> 3 B          2          1 intervention          0
#> 4 B          2          2 intervention          1