Consolidate capture history data

Combines duplicate capture histories by summing their counts. It can also add all observable histories having zero count and remove non-informative capture lists.

Usage

tidy_lists(zdat, includezerocounts = FALSE, remove_noninformative = FALSE)

Arguments

zdat

A matrix or data frame with \(t+1\) columns. The first \(t\) columns are binary list-membership indicators and the final column contains the capture history counts. Histories not included explicitly are assumed to have zero count.

includezerocounts

If TRUE, include every observable capture history, including those with zero count. The all-zero history representing the unobserved population is never included. If FALSE, return only positive-count histories.

remove_noninformative

If TRUE, remove lists containing all or none of the observed cases and remove duplicate list columns, retaining the first copy.

Value

A data frame with one row for each retained capture history and one column for each retained list, followed by the count column.

Examples

# Two rows have the same capture history: A only.
zdat <- data.frame(
  A = c(1, 1, 0, 1),
  B = c(0, 0, 1, 1),
  C = c(0, 0, 0, 1),
  count = c(3, 5, 4, 2)
)
zdat
#>   A B C count
#> 1 1 0 0     3
#> 2 1 0 0     5
#> 3 0 1 0     4
#> 4 1 1 1     2

# Consolidate rows with the same capture history by summing their counts.
# The two A-only rows become one row with count 3 + 5 = 8.
tidy_lists(zdat)
#>   A B C count
#> 1 1 0 0     8
#> 2 0 1 0     4
#> 3 1 1 1     2

# Include all seven observable histories, filling missing counts with zero.
# The unobserved history (0, 0, 0) is excluded.
tidy_lists(zdat, includezerocounts = TRUE)
#>   A B C count
#> 1 1 0 0     8
#> 2 0 1 0     4
#> 3 0 0 1     0
#> 4 1 1 0     0
#> 5 1 0 1     0
#> 6 0 1 1     0
#> 7 1 1 1     2

# Add a duplicate list, an empty list and a list containing every case.
# Keep the count column last.
redundant <- data.frame(
  zdat[c("A", "B", "C")],
  A_copy = zdat$A,
  empty = 0,
  everyone = 1,
  count = zdat$count
)
redundant
#>   A B C A_copy empty everyone count
#> 1 1 0 0      1     0        1     3
#> 2 1 0 0      1     0        1     5
#> 3 0 1 0      0     0        1     4
#> 4 1 1 1      1     0        1     2

# Remove the three redundant lists and consolidate repeated histories.
tidy_lists(redundant, remove_noninformative = TRUE)
#>   A B C count
#> 1 1 0 0     8
#> 2 0 1 0     4
#> 3 1 1 1     2