Position in vector based on approximate matching

I have the sorted vector

m<-c(1.1, 3.2, 3.6, 4, 4.6, 4.6, 5.6, 5.7, 6.2, 8.9)

I want to find 开发者_StackOverflowthe position of a value based on approximate matching. If the value does not exist in the vector i would like the position of the immediately previous value

for exact matching I would use

> match(4,m)
[1] 4

But if I do

> match(6,m)
[1] NA

What i would like to get in this example is 8 (the position of the immidiately previous value of 6 which is the position of 5.7 which is 8)

Thank you in advance

---

There is a built-in function that does precisely what you want: findInterval ...It's vectorized as well, so you can give it several values to find in one go which is much more efficient.

m <- c(1.1, 3.2, 3.6, 4, 4.6, 4.6, 5.6, 5.7, 6.2, 8.9)
# Find nearest (lower) index for both 4 and 6
findInterval(c(4,6), m) 
# [1] 4 8

---

Use which.max in combination with vector subsetting, a solution of 17 characters:

which.max(m[m<=6]) # Edited to specify <=
[1] 8

Since your vector is sorted, you can use the even shorter:

sum(m<=6) # Edited to specify <=
[1] 8

This works because the value TRUE is implicitly converted to 1 in the sum

---

This should do it for you.

#v is a sorted vector and x is an item for which we want the exact or nearest lower match
lowSideMatch <- function(x, v) max(which(v <= x))

lowSideMatch(6, m)
[1] 8

lowSideMatch(4, m)
[1] 4

---

you could use the which() function to get the index of the element with the smallest deviation from your searched value. Also works with unordered vectors.

x <- c(8,4,1,2,3,6,9)
find <- 6
pos <- which(abs(x-find) == min(abs(x-find)))

---

Using c++ upper_bound with Rcpp might be fast.

Rcpp::cppFunction(
  "int upper_bound(double v, const Rcpp::NumericVector& x) {
     return std::distance(x.begin(), std::upper_bound(x.begin(), x.end(), v));
}")

upper_bound(4, m)
#[1] 4

upper_bound(6, m)
#[1] 8

Benchmark:

set.seed(42)
n <- 1e6
m <- sort(rnorm(n, 0, 2))

bench::mark(which_min = c(which(abs(m-4) == min(abs(m-4))), which(abs(m-6) == min(abs(m-6))))
          , which.max = c(which.max(m[m<=4]), which.max(m[m<=6]))
          , length = c(length(m[m<=4]), length(m[m<=6]))
          , max_which = c(max(which(m <= 4)), max(which(m <= 6)))
          , sum = c(sum(m<=4), sum(m<=6))
          , findIntervall = findInterval(c(4,6), m)
          , CPP_upper_bound = c(upper_bound(4, m), upper_bound(6, m)))
#  expression           min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc
#  <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>
#1 which_min        16.33ms  18.33ms      51.1        NA     55.0    26    28
#2 which.max         9.38ms  11.46ms      73.1        NA    101.     37    51
#3 length            8.41ms  10.06ms      79.7        NA    112.     40    56
#4 max_which         5.89ms   7.48ms     136.         NA     67.8    68    34
#5 sum               2.68ms   3.33ms     274.         NA     45.9   137    23
#6 findIntervall   679.33µs  788.1µs    1214.         NA      0     607     0
#7 CPP_upper_bound   2.31µs   2.75µs  307024.         NA     30.7 10000     1

---

Something like:

> m<-c(1.1, 3.2, 3.6, 4, 4.6, 4.6, 5.6, 5.7, 6.2, 8.9)

> ( index = length(m[m<=4]) )
[1] 4

> ( m[index] )
[1] 4

> ( index = length(m[m<=6]) )
[1] 8

> ( m[index] )
[1] 5.7