Construct Indices

These functions construct flat or dimensional indices.

• ci_flat() constructs an integer vector flat indices.

• ci_margin() constructs an integer vector of indices for one particular dimension margin.

• ci_sub() constructs a list of integer subscripts.

• ci_df() is the same as ci_margin(), except it is specifically designed for data.frame-like objects.
  It is a separate function, because things like dimnames(x)[1] and rownames(x) do not always return the same output for certain data.frame-like objects.

• ci_obs() and ci_vars() construct row and column indices, respectively, data.frame-like objects.
  

Usage

ci_flat(
  x,
  i,
  inv = FALSE,
  chkdup = FALSE,
  uniquely_named = FALSE,
  .abortcall = sys.call()
)

ci_margin(
  x,
  slice,
  margin,
  inv = FALSE,
  chkdup = FALSE,
  uniquely_named = FALSE,
  .abortcall = sys.call()
)

ci_sub(
  x,
  s,
  d,
  inv = FALSE,
  chkdup = FALSE,
  uniquely_named = FALSE,
  .abortcall = sys.call()
)

ci_df(
  x,
  slice,
  margin,
  inv = FALSE,
  chkdup = FALSE,
  uniquely_named = TRUE,
  .abortcall = sys.call()
)

ci_obs(
  x,
  obs,
  inv = FALSE,
  chkdup = FALSE,
  uniquely_named = TRUE,
  .abortcall = sys.call()
)

ci_vars(
  x,
  vars,
  inv = FALSE,
  chkdup = FALSE,
  uniquely_named = TRUE,
  .abortcall = sys.call()
)

Arguments

x

the object for which the indices are meant.

i, s, d, slice, margin, obs, vars, inv

See squarebrackets_indx_args.

chkdup

see squarebrackets_options.

uniquely_named

Boolean, indicating if the user knows a-priori that the relevant names of x are unique.
If set to TRUE, speed may increase.
But specifying TRUE when the relevant names are not unique will result in incorrect output.

.abortcall

environment where the error message is passed to.

Value

An integer vector of constructed indices.

Examples

x <- matrix(1:25, 5, 5)
colnames(x) <- c("a", "a", "b", "c", "d")
print(x)
#>      a  a  b  c  d
#> [1,] 1  6 11 16 21
#> [2,] 2  7 12 17 22
#> [3,] 3  8 13 18 23
#> [4,] 4  9 14 19 24
#> [5,] 5 10 15 20 25

bool <- sample(c(TRUE, FALSE), 5, TRUE)
int <- 1:4
chr <- c("a", "a")
cplx <- 1:4 * -1i
tci_bool(bool, nrow(x))
#> [1] 2
tci_int(int, ncol(x), inv = TRUE)
#> [1] 5
tci_chr(chr, colnames(x))
#> [1] 1 2 1 2
tci_cplx(cplx, nrow(x))
#> [1] 5 4 3 2

ci_flat(x, 1:10 * -1i)
#>  [1] 25 24 23 22 21 20 19 18 17 16
ci_margin(x, 1:4, 2)
#> [1] 1 2 3 4
ci_sub(x, n(1:5 * -1i, 1:4), 1:2)
#> [[1]]
#> [1] 5 4 3 2 1
#> 
#> [[2]]
#> [1] 1 2 3 4
#>