Transform every single case of a single case data frame
Source:R/transform-helper-functions.R, R/transform.scdf.R
transform.scdf.RdTakes an scdf and applies transformations to each individual case. This is useful to calculate or modify new variables.
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.
See also
Other data manipulation functions:
add_l2(),
as.data.frame.scdf(),
as_scdf(),
fill_missing(),
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)
#> #A single-case data frame with three cases
#>
#> Case1: phase values mt trials proportion percentage
#> A 9 1 20 0.45 45
#> A 9 2 20 0.45 45
#> A 10 3 20 0.5 50
#> A 13 4 20 0.65 65
#> A 12 5 20 0.6 60
#> B 20 6 20 1 100
#> B 20 7 20 1 100
#> B 20 8 20 1 100
#> B 20 9 20 1 100
#> B 20 10 20 1 100
#> B 20 11 20 1 100
#> B 20 12 20 1 100
#> B 20 13 20 1 100
#> B 20 14 20 1 100
#> B 20 15 20 1 100
#> # ... up to five more rows
#> # two more cases
## Z standardize the dependent variable and add two new variables:
exampleAB |>
transform(
values = scale(values),
mean_values = mean(values),
sd_values = sd(values)
)
#> #A single-case data frame with three cases
#>
#> Johanna: values mt phase mean_values sd_values
#> -1.31 1 A 0 1
#> -1.4 2 A 0 1
#> -1.14 3 A 0 1
#> -0.97 4 A 0 1
#> -1.48 5 A 0 1
#> -0.71 6 B 0 1
#> -0.62 7 B 0 1
#> 0.15 8 B 0 1
#> -0.28 9 B 0 1
#> -0.45 10 B 0 1
#> 0.75 11 B 0 1
#> 0.06 12 B 0 1
#> 0.41 13 B 0 1
#> 1.1 14 B 0 1
#> 0.67 15 B 0 1
#> # ... up to five more rows
#> # two more cases
## 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
)
#> #A single-case data frame with three cases
#>
#> Johanna: values mt phase values_center_case values_center_global value_dif
#> 54 1 A -0.6 0.67 -1.27
#> 53 2 A -1.6 -0.33 -1.27
#> 56 3 A 1.4 2.67 -1.27
#> 58 4 A 3.4 4.67 -1.27
#> 52 5 A -2.6 -1.33 -1.27
#> 61 6 B 6.4 7.67 -1.27
#> 62 7 B 7.4 8.67 -1.27
#> 71 8 B 16.4 17.67 -1.27
#> 66 9 B 11.4 12.67 -1.27
#> 64 10 B 9.4 10.67 -1.27
#> 78 11 B 23.4 24.67 -1.27
#> 70 12 B 15.4 16.67 -1.27
#> 74 13 B 19.4 20.67 -1.27
#> 82 14 B 27.4 28.67 -1.27
#> 77 15 B 22.4 23.67 -1.27
#> # ... up to five more rows
#> # two more cases
## 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))
)
#> #A single-case data frame with three cases
#>
#> Marie: values mt phase | Rosalind: values mt phase | Lise: values mt phase |
#> -0.24 1 A | -1.02 1 A | -0.81 1 A |
#> -0.39 2 A | -1.45 2 A | -1.16 2 A |
#> -0.1 3 A | -1.02 3 A | 0.11 3 A |
#> 0.11 4 A | -0.67 4 A | -0.6 4 A |
#> -0.74 5 A | -0.53 5 A | 0.32 5 A |
#> -1.16 6 A | 0.25 6 A | -0.32 6 A |
#> -1.23 7 A | -0.46 7 A | -1.87 7 A |
#> -0.17 8 A | -1.73 8 A | -1.16 8 A |
#> -1.16 9 A | -0.74 9 A | 0.88 9 A |
#> -1.59 10 A | -0.1 10 A | 0.11 10 A |
#> 1.52 11 B | -0.1 11 A | -1.02 11 A |
#> 0.25 12 B | 0.25 12 A | -1.16 12 A |
#> 0.6 13 B | -0.46 13 A | -1.02 13 A |
#> 1.52 14 B | -1.09 14 A | -1.8 14 A |
#> 0.6 15 B | -0.88 15 A | -0.74 15 A |
#> # ... up to 15 more rows
## 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")
)
#> #A single-case data frame with three cases
#>
#> Marie: values mt phase values_across value_within
#> 58 1 A 40 11
#> 56 2 A 37.5 9.5
#> 60 3 A 44.5 13
#> 63 4 A 50 14.5
#> 51 5 A 23.5 6.5
#> 45 6 A 10 3.5
#> 44 7 A 7 2
#> 59 8 A 41.5 12
#> 45 9 A 10 3.5
#> 39 10 A 5 1
#> 83 11 B 82 27
#> 65 12 B 55 16
#> 70 13 B 65.5 20.5
#> 83 14 B 82 27
#> 70 15 B 65.5 20.5
#> # ... up to 15 more rows
#> # two more cases
## 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)
)
#> #A single-case data frame with one case
#>
#> Berta: mt compliance phase compliance (moving median) compliance (moving mean)
#> 1 25 A 25 25
#> 2 20.8 A 25 28.47
#> 3 39.6 A 39.6 47.69
#> 4 75 A 45 55.9
#> 5 45 A 45 38.5
#> 6 14.6 A 45 32.97
#> 7 45.8 A 45 37.36
#> 8 33.3 A 33.3 33.99
#> 9 31.3 A 32.5 32.6
#> 10 32.5 A 32.5 23.1
#> 11 4.2 B 32.5 24.37
#> 12 45.8 B 32.5 33.82
#> 13 31.3 B 31.3 24.84
#> 14 9.4 B 9.4 13.51
#> 15 6.3 B 6.3 8
#> compliance (local regression)
#> 22.02
#> 28.81
#> 39.6
#> 42.3
#> 45
#> 45.4
#> 45.8
#> 36.07
#> 32.2
#> 32.5
#> 39.15
#> 45.8
#> 29.41
#> 15.05
#> 6.58
#> # ... up to 14 more rows
## 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)
)
#> #A single-case data frame with 11 cases
#>
#> Lisa (Turkish): values mt phase | Patrick (Spanish): values mt phase |
#> <NA> 1 A | <NA> 1 A |
#> <NA> 2 A | <NA> 2 A |
#> 0 3 A | 3 3 A |
#> 0 4 A | 0 4 A |
#> <NA> 5 A | <NA> 5 A |
#> <NA> 6 B | <NA> 6 B |
#> 5 7 B | 8 7 B |
#> 6 8 B | 8 8 B |
#> 7 9 B | 8 9 B |
#> 10 10 B | 12 10 B |
#> 10 11 B | 13 11 B |
#> 15 12 B | 13 12 B |
#> 16 13 B | 15 13 B |
#> 14 14 B | 14 14 B |
#> 17 15 B | 15 15 B |
#> # ... up to 11 more rows
#> # nine more cases