transform.scdf: Transform every single case of a single case data frame

In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Transform every single case of a single case data frame

Description

Takes an scdf and applies transformations to each individual case. This is useful to calculate or modify new variables.

Usage

moving_median(x, lag = 1)

moving_mean(x, lag = 1)

local_regression(x, mt = 1:length(x), f = 0.2)

set_na_at(x, first_of, positions = 0)

center_at(x, at = TRUE, shift = 0, part = 0)

first_of(x, positions = 0)

across_cases(...)

all_cases(...)

rowwise(...)

## S3 method for class 'scdf'
transform(`_data`, ...)

Arguments

x	A logical vector.
lag	Number of values surrounding a value to calculate the average
mt	A vector with measurement times.
f	the proportion of surrounding data influencing each data point.
first_of	A logical vector
positions	A numeric vector with relative positions to the first appearance of a TRUE value in x.
at	A logical vector. E.g. phase == "A". The first TRUE value of that vector is the target position for centring. By default, this is the first position.
shift	A value indicating a shift in measurement times for centring. E.g. shift = 4 will centre four measurement-times after the position defined by the at and part arguments.
part	A numeric value between 0 and 1. 0 refers to the first TRUE in the at vector, 1 to the last, and 0.5 to the midpoint of the sequence of TRUE values. E.g. if you want to centre at the middle of phase A, set ⁠at = phase == A, part = 0.5⁠. Note: decimals are rounded to integers.
...	Expressions.
_data	An scdf.

Details

This function is a method of the generic transform() function. Unlike the generic version, expressions are evaluated serially: the result of one expression is used as the basis for subsequent computations.

Several helper functions can be used inside expressions:

• n(): returns the number of measurements in a case.

• all_cases(): extracts the values of a variable across all cases. Takes an expression as argument. For example:

  • mean(all_cases(values)) calculates the mean of values across all cases.

  • mean(all_cases(values[phase == "A"])) calculates the mean of all values where phase == "A".

• rowwise(): applies a calculation separately to each row. Example: rowwise(sum(values, mt, na.rm = TRUE)).

• across_cases(): creates new variables or replaces existing ones across all cases. Example: across_cases(values_ranked = rank(values, na.last = "keep"))

Value

An scdf.

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), print.sc_outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## Creates a single-case with frequency distributions. The proportion and
## percentage of the frequencies are calculated with transform:
design <- design(
 n = 3,
 level = 5,
 distribution = "binomial",
 n_trials = 20,
 start_value = 0.5
)
study <- random_scdf(design)
transform(study, proportion = values/trials, percentage = proportion * 100)

## Z standardize the dependent variable and add two new variables:
exampleAB |>
  transform(
    values = scale(values),
    mean_values = mean(values),
    sd_values = sd(values)
  )

## Use `all` to calculate global variables.
exampleAB |>
  transform(
    values_center_case = values - mean(values[phase == "A"]),
    values_center_global = values - mean(all(values[phase == "A"])),
    value_dif = values_center_case - values_center_global
  )

## Use `across_cases` to calculate or replace a variable with values from
## all cases. E.g., standardize the dependent variable:
exampleABC |>
  transform(
    across_cases(values = scale(values))
  )

## Rank transform the values based on all cases vs. within each case:
exampleABC |>
  transform(
    across_cases(values_across = rank(values, na.last="keep")),
    value_within = rank(values, na.last="keep")
  )

## Three helper functions to smooth the data
Huber2014$Berta |>
transform(
  "compliance (moving median)" = moving_median(compliance),
  "compliance (moving mean)" = moving_mean(compliance),
  "compliance (local regression)" = local_regression(compliance, mt)
)

## Function first_of() helps to set NAs for specific phases.
## E.g., you want to replace the first two values of phase A and the first
## value of phase B and its preceding value.

byHeart2011 |>
  transform(
    values = set_na_at(values, phase == "A", 0:1),
    values = set_na_at(values, phase == "B", -1:0)
  )

scan documentation built on Sept. 11, 2025, 5:12 p.m.