Longitudinal and Panel Sequence Workflows

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Scope

Panel sequences are repeated ordered-state records from the same independent unit. The workflow preserves the panel identifier, occasion, sequence identity, and preprocessing decisions. Distance between occasions is a structural change measure; it is not evidence of learning, adaptation, or causality by itself.

Synthetic data

base <- data.frame(
  participant_id = rep(paste0("p", 1:4), each = 8L),
  occasion = rep(rep(c(1, 2), each = 4L), times = 4L),
  sequence_id = rep(paste0("s", 1:8), each = 4L),
  sequence_order = rep(1:4, times = 8L),
  state = c(
    "A", "B", "C", "D", "A", "B", "D", "D",
    "A", "C", "C", "D", "A", "C", "D", "D",
    "D", "C", "B", "A", "D", "C", "A", "A",
    "D", "B", "B", "A", "D", "B", "A", "A"
  ),
  stringsAsFactors = FALSE
)
head(base)
#>   participant_id occasion sequence_id sequence_order state
#> 1             p1        1          s1              1     A
#> 2             p1        1          s1              2     B
#> 3             p1        1          s1              3     C
#> 4             p1        1          s1              4     D
#> 5             p1        2          s2              1     A
#> 6             p1        2          s2              2     B

Prepare and audit the panel

panel <- prepare_sequence_panel(
  base,
  panel_id_col = "participant_id",
  occasion_col = "occasion"
)
panel$index
#>   sequence_id panel_id occasion occasion_rank sequence_length transition_count
#> 1          s1       p1        1             1               4                3
#> 2          s2       p1        2             2               4                3
#> 3          s3       p2        1             1               4                3
#> 4          s4       p2        2             2               4                3
#> 5          s5       p3        1             1               4                3
#> 6          s6       p3        2             2               4                3
#> 7          s7       p4        1             1               4                3
#> 8          s8       p4        2             2               4                3

A unique panel/occasion combination is required by default. This prevents two sequences from being silently treated as the same repeated observation.

Summarise occasions and states

panel_summary <- summarise_sequence_panel(panel)
panel_summary$occasions
#>   occasion n_panels n_sequences mean_length median_length mean_transitions
#> 1        1        4           4           4             4                3
#> 2        2        4           4           4             4                3
head(panel_summary$states)
#>   occasion state occurrence_count occurrence_share sequence_count
#> 1        1     A                4            0.250              4
#> 2        1     B                4            0.250              3
#> 3        1     C                4            0.250              3
#> 4        1     D                4            0.250              4
#> 5        2     A                6            0.375              4
#> 6        2     B                2            0.125              2
#>   sequence_prevalence
#> 1                1.00
#> 2                0.75
#> 3                0.75
#> 4                1.00
#> 5                1.00
#> 6                0.50

Quantify within-panel change

changes <- compare_sequence_panel_changes(
  panel,
  method = "levenshtein",
  normalise = "max_length"
)
changes
#>   panel_id from_sequence_id to_sequence_id from_occasion to_occasion from_rank
#> 1       p1               s1             s2             1           2         1
#> 2       p2               s3             s4             1           2         1
#> 3       p3               s5             s6             1           2         1
#> 4       p4               s7             s8             1           2         1
#>   to_rank distance length_change transition_change
#> 1       2     0.25             0                 0
#> 2       2     0.25             0                 0
#> 3       2     0.25             0                 0
#> 4       2     0.25             0                 0

Alternative distance methods use the same explicit arguments as compute_sequence_distance(). The result compares consecutive occasions within each panel only.

plot_sequence_panel_changes(changes, metric = "distance", type = "individual")

plot_sequence_panel_changes(changes, metric = "distance", type = "summary")

Reporting

Report the panel unit, occasion ordering, distance method, normalisation, sequence counts at each occasion, and any missing occasions. Treat change as a structural description unless a separate design supports stronger inference.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.