Relaxed Value Matching

These functions offer relaxed matching of one vector in another. In contrast to the similar base::match() and base::%in% functions they just accept numeric arguments but have an additional tolerance argument that allows relaxed matching.

Usage

closest(
  x,
  table,
  tolerance = Inf,
  ppm = 0,
  duplicates = c("keep", "closest", "remove"),
  nomatch = NA_integer_,
  .check = TRUE
)

common(
  x,
  table,
  tolerance = Inf,
  ppm = 0,
  duplicates = c("keep", "closest", "remove"),
  .check = TRUE
)

join(
  x,
  y,
  tolerance = 0,
  ppm = 0,
  type = c("outer", "left", "right", "inner"),
  .check = TRUE,
  ...
)

Arguments

x: numeric, the values to be matched. In contrast to base::match() x has to be sorted in increasing order and must not contain any NA.
table: numeric, the values to be matched against. In contrast to base::match() table has to be sorted in increasing order and must not contain any NA.
tolerance: numeric, accepted tolerance. Could be of length one or the same length as x.
ppm: numeric(1) representing a relative, value-specific parts-per-million (PPM) tolerance that is added to tolerance.
duplicates: character(1), how to handle duplicated matches. Has to be one of c("keep", "closest", "remove"). No abbreviations allowed.
nomatch: integer(1), if the difference between the value in x and table is larger than tolerance nomatch is returned.
.check: logical(1) turn off checks for increasingly sorted x and y. This should just be done if it is ensured by other methods that x and y are sorted, see also closest().
y: numeric, the values to be joined. Should be sorted.
type: character(1), defines how x and y should be joined. See details for join.
...: ignored.

Value

closest returns an integer vector of the same length as x giving the closest position in table of the first match or nomatch if there is no match.

common returns a logical vector of length x that is TRUE if the element in x was found in table. It is similar to base::%in%.

join returns a matrix with two columns, namely x and y, representing the index of the values in x matching the corresponding value in y (or NA if the value does not match).

Details

For closest/common the tolerance argument could be set to 0 to get the same results as for base::match()/base::%in%. If it is set to Inf (default) the index of the closest values is returned without any restriction.

It is not guaranteed that there is a one-to-one matching for neither the x to table nor the table to x matching.

If multiple elements in x match a single element in table all their corresponding indices are returned if duplicates="keep" is set (default). This behaviour is identical to base::match(). For duplicates="closest" just the closest element in x gets the corresponding index in table and for duplicates="remove" all elements in x that match to the same element in table are set to nomatch.

If a single element in x matches multiple elements in table the closest is returned for duplicates="keep" or duplicates="closest" (keeping multiple matches isn't possible in this case because the return value should be of the same length as x). If the differences between x and the corresponding matches in table are identical the lower index (the smaller element in table) is returned. There is one exception: if the lower index is already returned for another x with a smaller difference to this index the higher one is returned for duplicates = "closer" (but only if there is no other x that is closer to the higher one). For duplicates="remove" all multiple matches are returned as nomatch as above.

.checks = TRUE tests among other input validation checks for increasingly sorted x and table arguments that are mandatory assumptions for the closest algorithm. These checks require to loop through both vectors and compare each element against its precursor. Depending on the length and distribution of x and table these checks take equal/more time than the whole closest algorithm. If it is ensured by other methods that both arguments x and table are sorted the tests could be skipped by .check = FALSE. In the case that .check = FALSE is used and one of x and table is not sorted (or decreasingly sorted) the output would be incorrect in the best case and result in infinity loop in the average and worst case.

join: joins two numeric vectors by mapping values in x with values in y and vice versa if they are similar enough (provided the tolerance and ppm specified). The function returns a matrix with the indices of mapped values in x and y. Parameter type allows to define how the vectors will be joined: type = "left": values in x will be mapped to values in y, elements in y not matching any value in x will be discarded. type = "right": same as type = "left" but for y. type = "outer": return matches for all values in x and in y. type = "inner": report only indices of values that could be mapped.

Note

join is based on closest(x, y, tolerance, duplicates = "closest"). That means for multiple matches just the closest one is reported.

Author

Sebastian Gibb, Johannes Rainer

Examples

## Define two vectors to match
x <- c(1, 3, 5)
y <- 1:10

## Compare match and closest
match(x, y)
#> [1] 1 3 5
closest(x, y)
#> [1] 1 3 5

## If there is no exact match
x <- x + 0.1
match(x, y) # no match
#> [1] NA NA NA
closest(x, y)
#> [1] 1 3 5

## Some new values
x <- c(1.11, 45.02, 556.45)
y <- c(3.01, 34.12, 45.021, 46.1, 556.449)

## Using a single tolerance value
closest(x, y, tolerance = 0.01)
#> [1] NA  3  5

## Using a value-specific tolerance accepting differences of 20 ppm
closest(x, y, ppm = 20)
#> [1] 1 3 5

## Same using 50 ppm
closest(x, y, ppm = 50)
#> [1] 1 3 5

## Sometimes multiple elements in `x` match to `table`
x <- c(1.6, 1.75, 1.8)
y <- 1:2
closest(x, y, tolerance = 0.5)
#> [1] 2 2 2
closest(x, y, tolerance = 0.5, duplicates = "closest")
#> [1] NA NA  2
closest(x, y, tolerance = 0.5, duplicates = "remove")
#> [1] NA NA NA

## Are there any common values?
x <- c(1.6, 1.75, 1.8)
y <- 1:2
common(x, y, tolerance = 0.5)
#> [1] TRUE TRUE TRUE
common(x, y, tolerance = 0.5, duplicates = "closest")
#> [1] FALSE FALSE  TRUE
common(x, y, tolerance = 0.5, duplicates = "remove")
#> [1] FALSE FALSE FALSE

## Join two vectors
x <- c(1, 2, 3, 6)
y <- c(3, 4, 5, 6, 7)

jo <- join(x, y, type = "outer")
jo
#> $x
#> [1]  1  2  3 NA NA  4 NA
#> 
#> $y
#> [1] NA NA  1  2  3  4  5
#> 
x[jo$x]
#> [1]  1  2  3 NA NA  6 NA
y[jo$y]
#> [1] NA NA  3  4  5  6  7

jl <- join(x, y, type = "left")
jl
#> $x
#> [1] 1 2 3 4
#> 
#> $y
#> [1] NA NA  1  4
#> 
x[jl$x]
#> [1] 1 2 3 6
y[jl$y]
#> [1] NA NA  3  6

jr <- join(x, y, type = "right")
jr
#> $x
#> [1]  3 NA NA  4 NA
#> 
#> $y
#> [1] 1 2 3 4 5
#> 
x[jr$x]
#> [1]  3 NA NA  6 NA
y[jr$y]
#> [1] 3 4 5 6 7

ji <- join(x, y, type = "inner")
ji
#> $x
#> [1] 3 4
#> 
#> $y
#> [1] 1 4
#> 
x[ji$x]
#> [1] 3 6
y[ji$y]
#> [1] 3 6