PEAT Project: Randomisation Process

Generate all unique permutations and convert to data frame

# Loading necessary libraries
library(tidyverse)

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library(combinat)

## 
## Attaching package: 'combinat'
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##     combn

# Setting seed for reproducibility
set.seed(1234)

# Defining the conditions
conditions <- c("NS", "S", "SN")

# Generating all unique permutations
perm_unique <- permn(conditions)

# Converting the list of permutations to a transposed dataframe and naming columns
perm_df <- map_dfr(perm_unique, ~as.data.frame(t(.x))) %>%
  rename_with(~paste0("condition_", seq_along(.))) %>%
  mutate(permutation = row_number()) %>%
  select(permutation, contains("condition"))

# Viewing the generated permutations
print(perm_df)

##   permutation condition_1 condition_2 condition_3
## 1           1          NS           S          SN
## 2           2          NS          SN           S
## 3           3          SN          NS           S
## 4           4          SN           S          NS
## 5           5           S          SN          NS
## 6           6           S          NS          SN

Randomize the Order of the Rows (Permutations)

# Randomizing the order of the rows (permutations)
perm_random <- perm_df %>%
  slice_sample(n = nrow(perm_df)) %>%
  mutate(participant = row_number()) %>%
  select(participant, contains("condition"))

# Viewing the randomized permutations
print(perm_random)

##   participant condition_1 condition_2 condition_3
## 1           1          SN           S          NS
## 2           2          NS          SN           S
## 3           3           S          SN          NS
## 4           4          NS           S          SN
## 5           5           S          NS          SN
## 6           6          SN          NS           S

Assigning 30 (fully counterbalanced) Participants to the Randomised Sequences

# Number of participants
n_participants <- 30

# Number of unique sequences generated (6 permutations)
n_sequences <- nrow(perm_df)

# Verify the number of sequences
print(n_sequences)

## [1] 6

# Randomly assigning participants to sequences
assignments <- sample(rep(1:n_sequences, each = n_participants / n_sequences))

# Creating the final dataframe with assignments
final_assignments <- data.frame(
  Participant = 1:n_participants,
  Sequence = assignments,
  Condition1 = perm_random[assignments, "condition_1"],
  Condition2 = perm_random[assignments, "condition_2"],
  Condition3 = perm_random[assignments, "condition_3"]
)

# Viewing the final assignments
print(final_assignments)

##    Participant Sequence Condition1 Condition2 Condition3
## 1            1        2         NS         SN          S
## 2            2        1         SN          S         NS
## 3            3        2         NS         SN          S
## 4            4        4         NS          S         SN
## 5            5        1         SN          S         NS
## 6            6        1         SN          S         NS
## 7            7        2         NS         SN          S
## 8            8        5          S         NS         SN
## 9            9        6         SN         NS          S
## 10          10        3          S         SN         NS
## 11          11        3          S         SN         NS
## 12          12        4         NS          S         SN
## 13          13        6         SN         NS          S
## 14          14        4         NS          S         SN
## 15          15        6         SN         NS          S
## 16          16        2         NS         SN          S
## 17          17        4         NS          S         SN
## 18          18        5          S         NS         SN
## 19          19        1         SN          S         NS
## 20          20        4         NS          S         SN
## 21          21        2         NS         SN          S
## 22          22        6         SN         NS          S
## 23          23        5          S         NS         SN
## 24          24        5          S         NS         SN
## 25          25        3          S         SN         NS
## 26          26        3          S         SN         NS
## 27          27        6         SN         NS          S
## 28          28        5          S         NS         SN
## 29          29        1         SN          S         NS
## 30          30        3          S         SN         NS