# Stringdist 0.9.2: dist objects, string similarities and some deprecated arguments

**Mark van der Loo » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

On 24-06-2015 stringdist 0.9.2 was accepted on CRAN. A summary of new features can be found in the NEWS file; here I discuss the changes with some examples.

## Computing ‘dist’ objects with ‘stringdistmatrix’

The R `dist`

object is used as input for many clustering algorithms such as `cluster::hclust`

. It is stores the lower triangle of a matrix of distances between a vector of objects. The function `stringdist::stringdistmatrix`

now takes a variable number of `character`

arguments. If two vectors are given, it behaves the same as it used to.

> x <- c("fu","bar","baz","barb") > stringdistmatrix(x,x,useNames="strings") fu bar baz barb fu 0 3 3 4 bar 3 0 1 1 baz 3 1 0 2 barb 4 1 2 0

However, we’re doing more work then necessary. Feeding `stringdistmatrix`

just a single `character`

argument yields the same information, but at half the computational and storage cost.

> stringdistmatrix(x,useNames="strings") fu bar baz bar 3 baz 3 1 barb 4 1 2

The output is a `dist`

object storing only the subdiagonal triangle. This makes it particularly easy to cluster texts using any algorithm that takes a `dist`

object as argument. Many such algorithms available in R do, for example:

d <- stringdistmatrix(x,useNames="strings") h <- stats::hclust(d) plot(h)

(by the way, parallelizing the calculation of a lower triangle of a matrix poses an interesting exercise in index calculation. For those interested, I wrote it down)

## Better labeling of distance matrices

Distance matrices can be labeled with the input strings by setting the `useNames`

argument in `stringdistmatrix`

to `TRUE`

or `FALSE`

(the default). However, if you're computing distances between looooong strings, like complete texts it is more convenient to use the `names`

attribute of the input vector. So, the `useNames`

arguments now takes three different values.

> x <- c(one="fu",two="bar",three="baz",four="barb") > y <- c(a="foo",b="fuu") > # the default: > stringdistmatrix(x,y,useNames="none") [,1] [,2] [1,] 2 1 [2,] 3 3 [3,] 3 3 [4,] 4 4 > # like useNames=TRUE > stringdistmatrix(x,y,useNames = "strings") foo fuu fu 2 1 bar 3 3 baz 3 3 barb 4 4 > # use labels > stringdistmatrix(x,y,useNames="names") a b one 2 1 two 3 3 three 3 3 four 4 4

## String similarities

Thanks to Jan van der Laan, a string similarity convenience function has been added. It computes the distance metric between two strings and then rescales it as , where the maximum possible distance depends on the type of distance metric and (depending on the metric) the length of the strings.

# similarity based on the damerau-levenshtein distance > stringsim(c("hello", "World"), c("Ola", "Mundo"),method="dl") [1] 0.2 0.0 # similarity based on the jaro distance > stringsim(c("hello", "World"), c("Ola", "Mundo"),method="jw") [1] 0.5111111 0.4666667

Here a similarity of 0 means completely different and 1 means exactly the same (within the chosen metric).

## Deprecated arguments

The `stringdistmatrix`

function had to option to be computed in parallel based on facilities of the `parallel`

package. However, as of stringdist 0.9.0, all distance calculations are multicored by default.

Therefore, I'm phasing out the following options in `stringdistmatrix`

:

`ncores`

(how many R-sessions should be started by parallel to compute the matrix?)`cluster`

(optionally, provide your own cluster, created by`parallel::makeCluster`

.

These argument are now ignored with a message but they'll be available untill somewhere in 2016 so users have time to adapt their code. Please mail me if you have any trouble doing so.

**leave a comment**for the author, please follow the link and comment on their blog:

**Mark van der Loo » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.