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)

first_of(x, positions = 0)

across_cases(...)

all_cases(...)

## 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.
positions	A numeric vector with relative positions to the first appearance of a TRUE value in x.
...	Expressions.
_data	An scdf.

Details

This function is a method of the generic transform function. Unlike the generic function, it calculates expressions serially. This means that the results of the calculation of one expression are the basis for the following computations. The n function returns the number of measurements in a case. The all_cases function is a helper function that extracts the values of a variable from all cases. It takes an expression as an argument. For example, mean(all_cases(values)) calculates the mean of the values from all cases. mean(all_cases(values[phase == "A"])) will calculate the mean of all values where phase is A. The function across_cases allows to calculate new variables or replace existing variables across all cases. E.g., across_cases(values_ranked = rank(values, na.last = "keep")) will calculate a new variable with values ranked across all cases.

Value

An scdf.

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), outlier(), ranks(), 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 = replace(values, first_of(phase == "A", 0:1), NA),
    values = replace(values, first_of(phase == "B", -1:0), NA)
  )

scan documentation built on July 1, 2024, 9:07 a.m.