Check if a row's columns' values are present in a set of known values

row_in(.data, match_type = "none", sets = list(), negate = FALSE, ...)

# S3 method for matrix
row_in(.data, match_type = "none", sets = list(), negate = FALSE, ...)

# S3 method for data.frame
row_in(.data, match_type = "none", sets = list(), negate = FALSE, ...)

Arguments

.data	A two-dimensional data structure.
match_type	One of ("all", "any", "none", "which_first", "count"). Possibly abbreviated.
sets	The list of sets to compare against. See details.
negate	Logical. If `TRUE`, values that are not in the `sets` are sought.
...	Arguments passed on to `op_ctrl` `output_class` One of ("vector", "list", "data.frame", "matrix"), possibly abbreviated. `cols` A vector indicating which columns to consider for the operation. If `NULL`, all columns are used. If its length is 0, no columns are considered. Negative numbers, logical values, character vectors representing column names, and tidyselect::select_helpers are supported. `rows` Like `cols` but for row indices, and without `tidyselect` support. `factor_mode` One of ("character", "integer"), possibly abbreviated. If a column is a factor, this determines whether the operation uses its internal integer values, or the character values from its levels.

Details

The sets are recycled in order to match the number of columns in .data.

Type promotion/conversion will follow normal R rules.

Note

Note that string comparison follows C++ rules.

Examples


mat <- matrix(sample(letters, 10), nrow = 5L, ncol = 2L)

# count how many columns are in a chosen subset
row_in(mat, "count", list(c("a", "b")))
#> [1] 0 0 0 0 0

# careful if complex numbers are involved
row_in(data.frame(-1 + 0i, 1/3 + 0i), "all", list(-1L, 1/3))
#> [1] TRUE
row_in(data.frame(-1 + 1i, 1/3 - 1i), "none", list(-1L, 1/3))
#> [1] TRUE