diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000..4bbce75 --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,50 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + release: + types: [published] + workflow_dispatch: + +name: pkgdown.yaml + +permissions: read-all + +jobs: + pkgdown: + runs-on: ubuntu-latest + # Only restrict concurrency for non-PR jobs + concurrency: + group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + permissions: + contents: write + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.5.0 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index 8044035..0000000 --- a/docs/404.html +++ /dev/null @@ -1,160 +0,0 @@ - - - -
- - - - -Version 3, 29 June 2007
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Also add information on how to contact you by electronic and paper mail.
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The hypothetical commands show w
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-This vignette provides a comparison of r2r with the same-purpose CRAN package {hash}
, which also offers an implementation of hash tables based on R environments. We first describe the features offered by both packages, and then perform some benchmark timing comparisons. The package versions referred to in this vignette are:
-library(hash)
-library(r2r)
-packageVersion("hash")
-#> [1] '2.2.6.1'
-packageVersion("r2r")
-#> [1] '0.1.1'
Both r2r and {hash}
hash tables are built on top of the R built-in environment
data structure, and have thus a similar API. In particular, hash table objects have reference semantics for both packages. r2r hashtable
s are S3 class objects, whereas in {hash}
the data structure is implemented as an S4 class.
Hash tables provided by r2r
support arbitrary type keys and values, arbitrary key comparison and hash functions, and have customizable behaviour (either throw an exception or return a default value) upon query of a missing key.
In contrast, hash tables in hash
currently support only string keys, with basic identity comparison (the hashing is performed automatically by the underlying environment
objects); values can be arbitrary R objects. Querying missing keys through non-vectorized [[
-subsetting returns the default value NULL
, whereas queries through vectorized [
-subsetting result in an error. On the other hand, hash
also offers support for inverting hash tables (an experimental feature at the time of writing).
The table below summarizes the features of the two packages
-Feature | -r2r | -hash | -
---|---|---|
Basic data structure | -R environment | -R environment | -
Arbitrary type keys | -X | -- |
Arbitrary type values | -X | -X | -
Arbitrary hash function | -X | -- |
Arbitrary key comparison function | -X | -- |
Throw or return default on missing keys | -X | -- |
Hash table inversion | -- | X | -
We will perform our benchmark tests using the CRAN package microbenchmark
.
We start by timing the insertion of:
-
-N <- 1e4
random key-value pairs (with possible repetitions). In order to perform a meaningful comparison between the two packages, we restrict to string (i.e. length one character) keys. We can generate random keys as follows:
-
-chars <- c(letters, LETTERS, 0:9)
-random_keys <- function(n) paste0(
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE)
- )
-
-set.seed(840)
-keys <- random_keys(N)
-values <- rnorm(N)
We test both the non-vectorized ([[<-
) and vectorized ([<-
) operators:
-microbenchmark(
- `r2r_[[<-` = {
- for (i in seq_along(keys))
- m_r2r[[ keys[[i]] ]] <- values[[i]]
- },
- `r2r_[<-` = { m_r2r[keys] <- values },
- `hash_[[<-` = {
- for (i in seq_along(keys))
- m_hash[[ keys[[i]] ]] <- values[[i]]
- },
- `hash_[<-` = m_hash[keys] <- values,
-
- times = 30,
- setup = { m_r2r <- hashmap(); m_hash <- hash() }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_[[<- 111.54835 130.37921 193.59728 201.5787 246.5056 312.6756 30
-#> r2r_[<- 103.44057 146.71259 175.29195 176.1595 196.2209 281.5497 30
-#> hash_[[<- 97.69138 140.48700 177.92566 182.8497 203.2853 259.5250 30
-#> hash_[<- 52.65147 61.11784 89.96423 81.7027 113.0783 167.0759 30
As it is seen, r2r
and hash
have comparable performances at the insertion of key-value pairs, with both vectorized and non-vectorized insertions, hash
being somewhat more efficient in both cases.
We now test key query, again both in non-vectorized and vectorized form:
-
-microbenchmark(
- `r2r_[[` = { for (key in keys) m_r2r[[ key ]] },
- `r2r_[` = { m_r2r[ keys ] },
- `hash_[[` = { for (key in keys) m_hash[[ key ]] },
- `hash_[` = { m_hash[ keys ] },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_[[ 124.81017 175.76404 228.40115 218.11206 262.34639 363.20552 30
-#> r2r_[ 113.27040 172.00862 210.85097 211.59312 250.79167 333.21509 30
-#> hash_[[ 14.26790 16.28593 22.85595 21.99028 24.30694 48.34391 30
-#> hash_[ 82.63965 109.64820 141.58864 135.93963 159.63201 268.81355 30
For non-vectorized queries, hash
is significantly faster (by one order of magnitude) than r2r
. This is likely due to the fact that the [[
method dispatch is handled natively by R in hash
(i.e. the default [[
method for environment
s is used ), whereas r2r
suffers the overhead of S3 method dispatch. This is confirmed by the result for vectorized queries, which is comparable for the two packages; notice that here a single (rather than N
) S3 method dispatch occurs in the r2r
timed expression.
As an additional test, we perform the benchmarks for non-vectorized expressions with a new set of keys:
-
-set.seed(841)
-new_keys <- random_keys(N)
-microbenchmark(
- `r2r_[[_bis` = { for (key in new_keys) m_r2r[[ key ]] },
- `hash_[[_bis` = { for (key in new_keys) m_hash[[ key ]] },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_[[_bis 86.50014 118.93648 157.65807 153.16882 163.77239 365.71789 30
-#> hash_[[_bis 14.05082 16.57323 23.19929 21.53846 30.16298 39.63644 30
The results are similar to the ones already commented. Finally, we test the performances of the two packages in checking the existence of keys (notice that here has_key
refers to r2r::has_key
, whereas has.key
is hash::has.key
):
-set.seed(842)
-mixed_keys <- sample(c(keys, new_keys), N)
-microbenchmark(
- r2r_has_key = { for (key in mixed_keys) has_key(m_r2r, key) },
- hash_has_key = { for (key in new_keys) has.key(key, m_hash) },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_has_key 83.2514 109.7505 131.4229 120.3173 149.7155 291.3949 30
-#> hash_has_key 233.2610 304.9865 367.8324 364.3668 408.9261 539.3776 30
The results are comparable for the two packages, r2r
being slightly more performant in this particular case.
Finally, we test key deletion. In order to handle name collisions, we will use delete()
(which refers to r2r::delete()
) and del()
(which refers to hash::del()
).
-microbenchmark(
- r2r_delete = { for (key in keys) delete(m_r2r, key) },
- hash_delete = { for (key in keys) del(key, m_hash) },
- hash_vectorized_delete = { del(keys, m_hash) },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq
-#> r2r_delete 429.794784 522.802137 661.172206 639.401653 758.826645
-#> hash_delete 227.654058 288.144309 364.531141 362.392011 439.393266
-#> hash_vectorized_delete 4.533082 5.389375 6.431943 5.769315 7.656032
-#> max neval
-#> 1179.17381 30
-#> 557.73474 30
-#> 11.19358 30
The vectorized version of hash
significantly outperforms the non-vectorized versions (by roughly two orders of magnitude in speed). Currently, r2r
does not support vectorized key deletion 1.
The two R packages r2r
and hash
offer hash table implementations with different advantages and drawbacks. r2r
focuses on flexibility, and has a richer set of features. hash
is more minimal, but offers superior performance in some important tasks. Finally, as a positive note for both parties, the two packages share a similar API, making it relatively easy to switch between the two, according to the particular use case needs.
This is due to complications introduced by the internal hash collision handling system of r2r
.↩︎
Hash tables are among the most useful data structures for efficient coding. An hash table can be abstractly thought as a map from a set of unique keys (which may be strings, numbers, or even more complicated objects) to a set of values. From a computational viewpoint, its most distinctive feature is that its read/write operations (i.e. storing, retrieving or deleting a particular key or key-value pair) have average \(O(1)\) time complexity, independent of the table size.
-Many programming languages come with their own native implementation of hash tables (for instance std::unordered_map/set
s in C++, or dict
s and set
s in Python); in base R, the objects which come closest to hash tables are environment
s. These, however, can be somewhat cumbersome to handle from the user point of view, and only support string type keys. The purpose of r2r is to provide a more flexible implementation of hash tables in R, building on top of base R environment
s.
In particular, r2r
hash tables support:
This document provides a quick hands-on introduction to r2r
hash tables.
We create an empty hash map with:
-
-m <- hashmap()
We can insert key-value pairs in m
in several different ways:
-m[["key"]] <- "value"
-m[c(1, 2, 3)] <- c("a", "b", "c") # Vectorized over keys and values
-m[[c(4, 5, 6)]] <- c("d", "e", "f") # Not vectorized
The following queries explain the differences between the [[
and [
operator mentioned in the comments above:
-m[["key"]]
-#> [1] "value"
-
-m[c(1, 2, 3)]
-#> [[1]]
-#> [1] "a"
-#>
-#> [[2]]
-#> [1] "b"
-#>
-#> [[3]]
-#> [1] "c"
-m[[c(1, 2, 3)]]
-#> NULL
-
-m[c(4, 5, 6)]
-#> [[1]]
-#> NULL
-#>
-#> [[2]]
-#> NULL
-#>
-#> [[3]]
-#> NULL
-m[[c(4, 5, 6)]]
-#> [1] "d" "e" "f"
Single element insertions and queries can also be performed through the generics insert()
and query()
There is no restriction on the type of object you can use as keys and values. For instance:
- -You can set default values for missing keys. For instance:
-
-m <- hashmap(default = 0)
which is useful for creating a counter:
-
-objects <- list(1, 1, "1", FALSE, "1", 1)
-for (object in objects)
- m[[object]] <- m[[object]] + 1
-m[["1"]]
-#> [1] 2
Alternatively, you may throw an exception upon querying a missing key:
- -hashmap
s and hashmap
s use by default base::identical()
to compare keys. For instance:
-m <- hashmap()
-m[[1]] <- "double"
-m[["1"]] <- "character"
-m[[1]]
-#> [1] "double"
This behavior can be changed by explicitly providing a key comparison function. For this to work correctly, one must also explicitly provide an hash function which produces the same hashes for equivalent keys. A simple way to do this is to apply a preprocessing function to keys, as illustrated by the following example.
-We assume that keys are length one complex numbers, and consider two keys equivalent when they have the same direction in the complex plane. The direction of a complex vector can be found applying the R function Arg()
, which is thus a sensible key preprocessing function. We can instruct an hashmap to preprocess its keys in this way through the constructor’s key_preproc_fn
argument:
-m <- hashmap(key_preproc_fn = Arg)
Let us check that everything works as intended:
- -Version 3, 29 June 2007
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-for details type 'show w'.
- This program comes with ABSOLUTELY NO WARRANTY;
- This is free software, and you are welcome to redistribute it'show c' for details. under certain conditions; type
The hypothetical commands show w
and show c
should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.
-The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
-This vignette provides a comparison of r2r with the same-purpose CRAN package {hash}
, which also offers an implementation of hash tables based on R environments. We first describe the features offered by both packages, and then perform some benchmark timing comparisons. The package versions referred to in this vignette are:
-library(hash)
-library(r2r)
-packageVersion("hash")
-#> [1] '2.2.6.1'
-packageVersion("r2r")
-#> [1] '0.1.1.9000'
Both r2r and {hash}
hash tables are built on top of the R built-in environment
data structure, and have thus a similar API. In particular, hash table objects have reference semantics for both packages. r2r hashtable
s are S3 class objects, whereas in {hash}
the data structure is implemented as an S4 class.
Hash tables provided by r2r
support arbitrary type keys and values, arbitrary key comparison and hash functions, and have customizable behaviour (either throw an exception or return a default value) upon query of a missing key.
In contrast, hash tables in hash
currently support only string keys, with basic identity comparison (the hashing is performed automatically by the underlying environment
objects); values can be arbitrary R objects. Querying missing keys through non-vectorized [[
-subsetting returns the default value NULL
, whereas queries through vectorized [
-subsetting result in an error. On the other hand, hash
also offers support for inverting hash tables (an experimental feature at the time of writing).
The table below summarizes the features of the two packages
-Feature | -r2r | -hash | -
---|---|---|
Basic data structure | -R environment | -R environment | -
Arbitrary type keys | -X | -- |
Arbitrary type values | -X | -X | -
Arbitrary hash function | -X | -- |
Arbitrary key comparison function | -X | -- |
Throw or return default on missing keys | -X | -- |
Hash table inversion | -- | X | -
We will perform our benchmark tests using the CRAN package microbenchmark
.
We start by timing the insertion of:
-
-N <- 1e4
random key-value pairs (with possible repetitions). In order to perform a meaningful comparison between the two packages, we restrict to string (i.e. length one character) keys. We can generate random keys as follows:
-
-chars <- c(letters, LETTERS, 0:9)
-random_keys <- function(n) paste0(
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE),
- sample(chars, n, replace = TRUE)
- )
-
-set.seed(840)
-keys <- random_keys(N)
-values <- rnorm(N)
We test both the non-vectorized ([[<-
) and vectorized ([<-
) operators:
-microbenchmark(
- `r2r_[[<-` = {
- for (i in seq_along(keys))
- m_r2r[[ keys[[i]] ]] <- values[[i]]
- },
- `r2r_[<-` = { m_r2r[keys] <- values },
- `hash_[[<-` = {
- for (i in seq_along(keys))
- m_hash[[ keys[[i]] ]] <- values[[i]]
- },
- `hash_[<-` = m_hash[keys] <- values,
-
- times = 30,
- setup = { m_r2r <- hashmap(); m_hash <- hash() }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_[[<- 120.47568 182.86525 244.1387 243.1772 304.0422 411.0924 30
-#> r2r_[<- 123.38350 182.98655 214.5312 215.8607 232.2120 385.5852 30
-#> hash_[[<- 121.26634 186.95017 223.9272 230.6890 251.0618 338.8484 30
-#> hash_[<- 62.37829 79.40925 115.5532 116.2670 146.9353 207.5767 30
As it is seen, r2r
and hash
have comparable performances at the insertion of key-value pairs, with both vectorized and non-vectorized insertions, hash
being somewhat more efficient in both cases.
We now test key query, again both in non-vectorized and vectorized form:
-
-microbenchmark(
- `r2r_[[` = { for (key in keys) m_r2r[[ key ]] },
- `r2r_[` = { m_r2r[ keys ] },
- `hash_[[` = { for (key in keys) m_hash[[ key ]] },
- `hash_[` = { m_hash[ keys ] },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_[[ 140.39407 176.18373 224.13309 226.82090 283.59444 317.34049 30
-#> r2r_[ 108.70052 177.57796 204.80297 205.84184 238.23730 285.59978 30
-#> hash_[[ 15.57094 17.46111 22.46317 21.36017 25.60891 34.91532 30
-#> hash_[ 86.42241 114.73289 145.53935 142.52136 171.77681 250.76570 30
For non-vectorized queries, hash
is significantly faster (by one order of magnitude) than r2r
. This is likely due to the fact that the [[
method dispatch is handled natively by R in hash
(i.e. the default [[
method for environment
s is used ), whereas r2r
suffers the overhead of S3 method dispatch. This is confirmed by the result for vectorized queries, which is comparable for the two packages; notice that here a single (rather than N
) S3 method dispatch occurs in the r2r
timed expression.
As an additional test, we perform the benchmarks for non-vectorized expressions with a new set of keys:
-
-set.seed(841)
-new_keys <- random_keys(N)
-microbenchmark(
- `r2r_[[_bis` = { for (key in new_keys) m_r2r[[ key ]] },
- `hash_[[_bis` = { for (key in new_keys) m_hash[[ key ]] },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_[[_bis 94.11585 118.54886 163.74233 156.59081 188.45233 294.04927 30
-#> hash_[[_bis 14.27472 17.64294 22.75982 21.47824 28.38551 34.33601 30
The results are similar to the ones already commented. Finally, we test the performances of the two packages in checking the existence of keys (notice that here has_key
refers to r2r::has_key
, whereas has.key
is hash::has.key
):
-set.seed(842)
-mixed_keys <- sample(c(keys, new_keys), N)
-microbenchmark(
- r2r_has_key = { for (key in mixed_keys) has_key(m_r2r, key) },
- hash_has_key = { for (key in new_keys) has.key(key, m_hash) },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq max neval
-#> r2r_has_key 87.96082 116.9253 143.7786 142.9583 158.4250 242.7802 30
-#> hash_has_key 256.93287 325.4338 410.0415 381.8220 483.4683 793.3913 30
The results are comparable for the two packages, r2r
being slightly more performant in this particular case.
Finally, we test key deletion. In order to handle name collisions, we will use delete()
(which refers to r2r::delete()
) and del()
(which refers to hash::del()
).
-microbenchmark(
- r2r_delete = { for (key in keys) delete(m_r2r, key) },
- hash_delete = { for (key in keys) del(key, m_hash) },
- hash_vectorized_delete = { del(keys, m_hash) },
-
- times = 30,
- setup = {
- m_r2r <- hashmap(); m_r2r[keys] <- values
- m_hash <- hash(); m_hash[keys] <- values
- }
-)
-#> Unit: milliseconds
-#> expr min lq mean median uq
-#> r2r_delete 447.042418 545.606133 627.458179 606.035055 718.133232
-#> hash_delete 216.297967 265.433382 309.916893 319.151273 354.446717
-#> hash_vectorized_delete 4.070514 4.670905 5.598708 5.507212 6.282724
-#> max neval
-#> 1063.144923 30
-#> 421.433838 30
-#> 7.630347 30
The vectorized version of hash
significantly outperforms the non-vectorized versions (by roughly two orders of magnitude in speed). Currently, r2r
does not support vectorized key deletion 1.
The two R packages r2r
and hash
offer hash table implementations with different advantages and drawbacks. r2r
focuses on flexibility, and has a richer set of features. hash
is more minimal, but offers superior performance in some important tasks. Finally, as a positive note for both parties, the two packages share a similar API, making it relatively easy to switch between the two, according to the particular use case needs.
This is due to complications introduced by the internal hash collision handling system of r2r
.↩︎
Hash tables are among the most useful data structures for efficient coding. An hash table can be abstractly thought as a map from a set of unique keys (which may be strings, numbers, or even more complicated objects) to a set of values. From a computational viewpoint, its most distinctive feature is that its read/write operations (i.e. storing, retrieving or deleting a particular key or key-value pair) have average \(O(1)\) time complexity, independent of the table size.
-Many programming languages come with their own native implementation of hash tables (for instance std::unordered_map/set
s in C++, or dict
s and set
s in Python); in base R, the objects which come closest to hash tables are environment
s. These, however, can be somewhat cumbersome to handle from the user point of view, and only support string type keys. The purpose of r2r is to provide a more flexible implementation of hash tables in R, building on top of base R environment
s.
In particular, r2r
hash tables support:
This document provides a quick hands-on introduction to r2r
hash tables.
We create an empty hash map with:
-
-m <- hashmap()
We can insert key-value pairs in m
in several different ways:
-m[["key"]] <- "value"
-m[c(1, 2, 3)] <- c("a", "b", "c") # Vectorized over keys and values
-m[[c(4, 5, 6)]] <- c("d", "e", "f") # Not vectorized
The following queries explain the differences between the [[
and [
operator mentioned in the comments above:
-m[["key"]]
-#> [1] "value"
-
-m[c(1, 2, 3)]
-#> [[1]]
-#> [1] "a"
-#>
-#> [[2]]
-#> [1] "b"
-#>
-#> [[3]]
-#> [1] "c"
-m[[c(1, 2, 3)]]
-#> NULL
-
-m[c(4, 5, 6)]
-#> [[1]]
-#> NULL
-#>
-#> [[2]]
-#> NULL
-#>
-#> [[3]]
-#> NULL
-m[[c(4, 5, 6)]]
-#> [1] "d" "e" "f"
Single element insertions and queries can also be performed through the generics insert()
and query()
There is no restriction on the type of object you can use as keys and values. For instance:
- -You can set default values for missing keys. For instance:
-
-m <- hashmap(default = 0)
which is useful for creating a counter:
-
-objects <- list(1, 1, "1", FALSE, "1", 1)
-for (object in objects)
- m[[object]] <- m[[object]] + 1
-m[["1"]]
-#> [1] 2
Alternatively, you may throw an exception upon querying a missing key:
- -hashmap
s and hashmap
s use by default base::identical()
to compare keys. For instance:
-m <- hashmap()
-m[[1]] <- "double"
-m[["1"]] <- "character"
-m[[1]]
-#> [1] "double"
This behavior can be changed by explicitly providing a key comparison function. For this to work correctly, one must also explicitly provide an hash function which produces the same hashes for equivalent keys. A simple way to do this is to apply a preprocessing function to keys, as illustrated by the following example.
-We assume that keys are length one complex numbers, and consider two keys equivalent when they have the same direction in the complex plane. The direction of a complex vector can be found applying the R function Arg()
, which is thus a sensible key preprocessing function. We can instruct an hashmap to preprocess its keys in this way through the constructor’s key_preproc_fn
argument:
-m <- hashmap(key_preproc_fn = Arg)
Let us check that everything works as intended:
- -r2r
provides a flexible implementation of hash tables in R, allowing for:
You can install the released version of r2r
from CRAN with:
-install.packages("r2r")
and the development version from my R-universe repository, with:
-
-install.packages("r2r", repos = "https://vgherard.r-universe.dev")
-library(r2r)
-m <- hashmap()
-
-# Insert and query a single key-value pair
-m[[ "user" ]] <- "vgherard"
-m[[ "user" ]]
-#> [1] "vgherard"
-
-# Insert and query multiple key-value pairs
-m[ c(1, 2, 3) ] <- c("one", "two", "three")
-m[ c(1, 3) ]
-#> [[1]]
-#> [1] "one"
-#>
-#> [[2]]
-#> [1] "three"
-
-# Keys and values can be arbitrary R objects
-m[[ lm(mpg ~ wt, mtcars) ]] <- c(TRUE, FALSE, TRUE)
-m[[ lm(mpg ~ wt, mtcars) ]]
-#> [1] TRUE FALSE TRUE
For further details, including an introductory vignette illustrating the features of r2r
hash maps, you can consult the r2r
website. If you encounter a bug, want to suggest a feature or need further help, you can open a GitHub issue.
hash
-CRAN package {hash}
also offers an implementation of hash tables based on R environments. The two tables below offer a comparison between r2r and {hash}
(for more details, see the benchmarks Vignette)
Feature | -r2r | -hash | -
---|---|---|
Basic data structure | -R environment | -R environment | -
Arbitrary type keys | -X | -- |
Arbitrary type values | -X | -X | -
Arbitrary hash function | -X | -- |
Arbitrary key comparison function | -X | -- |
Throw or return default on missing keys | -X | -- |
Hash table inversion | -- | X | -
Features supported by {r2r} and {hash}.
-Task | -Comparison | -
---|---|
Key insertion | -{r2r} ~ {hash} | -
Key query | -{r2r} < {hash} | -
Key deletion | -{r2r} << {hash} | -
Performances of {r2r} and {hash} for basic hash table operations.
-R/generics.R
, R/shared_methods.R
- compare_fn.Rd
Returns the key comparison function of an hash table
-(hashset
or hashmap
).
compare_fn(x) - -# S3 method for r2r_hashtable -compare_fn(x)- -
x | -an |
-
---|
a function.
-Valerio Gherardi
- --#> function (x, y) -#> compare_fn(key_preproc_fn(x), key_preproc_fn(y)) -#> <bytecode: 0x562a8aad7120> -#> <environment: 0x562a8aad6908>
These generics are used to get or set the default value of an
-hashmap
, optionally returned upon query of a missing key.
default(x) - -default(x) <- value - -# S3 method for r2r_hashmap -default(x) - -# S3 method for r2r_hashmap -default(x) <- value- -
x | -an |
-
---|---|
value | -an arbitrary R object. Default value to be associated to missing
-keys in the |
-
an arbitrary R object.
-For more details, see the hashtable documentation page.
-Valerio Gherardi
- --#> NULLdefault(m) <- 840 -
generates string hashes for arbitrary R objects as follows.
-This is the default hash function used by hashset
s and hashmap
s
-objects.
default_hash_fn(key)- -
key | -an arbitrary R object. |
-
---|
a character vector of length one. Hash digest of key
.
If key
is an atomic vector (as tested by
-is.atomic(key)
) of length one, default_hash_fn(key)
simply
-coerces the input to character. For more complex inputs, the function calls
-digest(key)
from the digest package.
Valerio Gherardi
- -R/generics.R
, R/hashmap.R
, R/hashset.R
- delete.Rd
These generics are used for deleting a single key or key/value
-pair from an hashset
or hashmap
, respectively.
delete(x, key) - -# S3 method for r2r_hashmap -delete(x, key) - -# S3 method for r2r_hashset -delete(x, key)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be deleted from the hash table. |
-
NULL
, invisibly.
Valerio Gherardi
- --#> [1] FALSE
This generics are used to check whether a key exists in a given
-hashset
or hashmap
.
has_key(x, key) - -x %has_key% key - -# S3 method for r2r_hashmap -has_key(x, key) - -# S3 method for r2r_hashset -has_key(x, key)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be checked for existence in the -hash table. |
-
TRUE
or FALSE
.
Valerio Gherardi
- --#> [1] TRUEm %has_key% "b" -#> [1] TRUE
Returns the hash function used for key hashing in an hash table
-(hashset
or hashmap
).
hash_fn(x) - -# S3 method for r2r_hashtable -hash_fn(x)- -
x | -an |
-
---|
a function.
-Valerio Gherardi
- --#> function (x) -#> hash_fn(key_preproc_fn(x)) -#> <bytecode: 0x562a8aad73c0> -#> <environment: 0x562a8a91a7d0>
Objects of class hashmap
and hashset
store
-collections of key/value pairs (hashmap
), or just keys
-(hashset
), providing constant time read and write operations. Both
-the keys and the optional values can be arbitrary R objects. hashmap
s
-and hashset
s provide an R implementation of
-hash tables.
See hashtable_methods for an overview of the available methods
-for hashmap
and hashset
class objects. Note that both these
-classes have a common parent class hashtable
, from which they can also
-inherit S3 methods.
hashmap( - ..., - hash_fn = default_hash_fn, - compare_fn = identical, - key_preproc_fn = identity, - on_missing_key = "default", - default = NULL -) - -hashset( - ..., - hash_fn = default_hash_fn, - compare_fn = identical, - key_preproc_fn = identity -)- -
... | -these arguments can be used to specify a set of initial elements
-to be inserted in the |
-
---|---|
hash_fn | -the (string valued) hash function applied to keys. -Required for advanced use only; see Details. |
-
compare_fn | -the (boolean valued) comparison function used for -testing key equality. Required for advanced use only; see Details. |
-
key_preproc_fn | -key pre-processing function applied to keys before -hashing and comparison. Required for advanced use only; see Details. |
-
on_missing_key | -either |
-
default | -default value associated with missing keys. This will be
-returned only if |
-
a hashmap
and a hashset
class object for
-hashmap()
and hashset()
, respectively.
hashmap
s and hashset
s implement hash tables,
-building on top of base R built-in environment
s,
-which by themselves are, essentially, string -> R object hash maps.
-In order to handle keys of non-string type, a string valued hash function
-default_hash_fn()
is provided, which leverages on
-digest()
for handling arbitrary R object keys.
By default, key equality is tested through identical()
.
-For some use cases, it may be sensible to employ a different comparison
-function, which can be assigned through the compare_fn
argument. In this
-case, one must also make sure that equal (in the sense of
-compare_fn()
)
-keys get also assigned the same hashes by hash_fn()
. A simple way to
-ensure this is to use to use a key pre-processing function, to be applied
-before both key hashing and comparison. The key_preproc_fn
-argument provides a short-cut to this, by automatically composing both the
-provided hash_fn()
and compare_fn()
functions with
-key_preproc_fn()
function. This is illustrated in an example below.
One might also want to set set specific hash and/or key comparison functions
-for efficiency reasons, e.g. if the default_hash_fn()
function produces
-many collisions between inequivalent keys.
When on_missing_key
is equal to "throw"
, querying a missing
-key will cause an error. In this case, an rlang abort
-condition of class "r2r_missing_key"
is returned, which can be useful
-for testing purposes.
Valerio Gherardi
- --m <- hashmap( - list("foo", 1), - list("bar", 1:5), - list(data.frame(x = letters, y = LETTERS), "baz") - ) -m[[ data.frame(x = letters, y = LETTERS) ]] -#> [1] "baz"-# Set of character keys, case insensitive. -s <- hashset("A", "B", "C", key_preproc = tolower) -s[["a"]] -#> [1] FALSE-
This page provides an overview of the available methods for
-hashmap
and hashset
objects (and for their common parent class
-hashtable
). We list methods based on the general type of task
-addressed.
Valerio Gherardi
- -
- All functions- - |
- |
---|---|
- - | -Get key comparison function of an hash table |
-
- - | -Default |
-
- - | -String hashes for arbitrary R objects |
-
- - | -Delete keys or key/value pairs from an hash table. |
-
- - | -Key existence in hash tables |
-
- - | -Get hash function of an hash table |
-
- - | -Hash maps and sets |
-
- - | -Methods for S3 classes |
-
- - | -Insert keys or key/value pairs into an hash table. |
-
- - | -List all keys from an hash table |
-
- - | -Size of hash tables |
-
- - | -On missing key behaviour |
-
- - | -Query keys from an hash table. |
-
-
|
- Subsetting |
-
- - | -List all values from an hash map |
-
R/generics.R
, R/hashmap.R
, R/hashset.R
- insert.Rd
These generics are used for inserting a single key or key/value
-pair into an hashset
or hashmap
, respectively. For vectorized
-insertions, see the subsetting_hashtables documentation page.
insert(x, key, ...) - -# S3 method for r2r_hashmap -insert(x, key, value, ...) - -# S3 method for r2r_hashset -insert(x, key, ...)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be inserted into the hash table. |
-
... | -further arguments passed to or from other methods. |
-
value | -an arbitrary R object. Value associated to |
-
key
for the hashset
method,
-value
for the hashmap
method.
Valerio Gherardi
- --#> [1] TRUE
These generics are used for listing all keys registered in an
-hashset
or hashmap
, respectively.
keys(x) - -# S3 method for r2r_hashtable -keys(x)- -
x | -an |
-
---|
a list. Registered keys in the hash table x
.
Valerio Gherardi
- --#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 2 -#> -#> [[3]] -#> [1] 3 -#>
Returns the total number of keys in an hash table.
-# S3 method for r2r_hashtable -length(x)- -
x | -an |
-
---|
an integer. Number of keys in the hash table (or elements in a set).
-Valerio Gherardi
- --#> [1] 1
These generics are used to get or set the behaviour of an
-hashmap
upon query of a missing key (currently, only an
-hashmap
method is implemented).
on_missing_key(x) - -on_missing_key(x) <- value - -# S3 method for r2r_hashmap -on_missing_key(x) - -# S3 method for r2r_hashmap -on_missing_key(x) <- value- -
x | -an |
-
---|---|
value | -a string, either |
-
a string, either "throw"
or "default"
.
For more details, see the hashtable documentation page.
-Valerio Gherardi
- --#> [1] "default"on_missing_key(m) <- "throw" -
These generics are used for querying a single key from an
-hashset
or hashmap
, respectively. For vectorized queries,
-see the subsetting_hashtables documentation page.
query(x, key) - -# S3 method for r2r_hashmap -query(x, key) - -# S3 method for r2r_hashset -query(x, key)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be queried from the hash table. |
-
TRUE
or FALSE
, for hashset
s. For
-hashmap
s, if the queried key exists in the hash table, returns the
-associated value (an a priori arbitrary R object); otherwise, behaves as
-specified by on_missing_key(x)
-(see also hashtable).
Valerio Gherardi
- --#> [1] TRUE
Implementation of hash tables (hash sets and hash maps) in R, - featuring arbitrary R objects as keys, - arbitrary hash and key-comparison functions, - and customizable behaviour upon queries of missing keys.
-Useful links:
Report bugs at https://github.com/vgherard/r2r/issues
Maintainer: Valerio Gherardi vgherard@sissa.it (ORCID)
- -hashset
s and hashmap
sR/docs.R
, R/hashmap.R
, R/hashset.R
- subsetting_hashtables.Rd
Subsetting operators `[[`
and `[`
for
-hashset
s and hashmap
s provide an equivalent synthax for the
-basic read/write operations performed by insert()
,
-delete()
and query()
.
# S3 method for r2r_hashmap -[[(x, i) - -# S3 method for r2r_hashmap -[(x, i) - -# S3 method for r2r_hashmap -[[(x, i) <- value - -# S3 method for r2r_hashmap -[(x, i) <- value - -# S3 method for r2r_hashset -[[(x, i) - -# S3 method for r2r_hashset -[(x, i) - -# S3 method for r2r_hashset -[[(x, i) <- value - -# S3 method for r2r_hashset -[(x, i) <- value- -
x | -an |
-
---|---|
i | -for |
-
value | -for |
-
the replacement forms ([[<-
and [<-
) always return value
.
-`[[`
returns TRUE
or FALSE
if
-x
is an hashset
, an arbitrary R object if x
is an
-hashmap
and i
is a valid key; when i
is not a key, the
-behaviour for hashmap
s depends on the value of
-on_missing_key(x)
.
-The `[`
operator returns a list of the same length of i
, whose
-k-th element is given by x[[ i[[k]] ]]
(the remark on missing keys for
-hashmaps applies also here).
Valerio Gherardi
- -This function is used to list all values associated to keys in
-an hashmap
. Implemented as a generic, but currently only the
-hashmap
method is defined.
values(x) - -# S3 method for r2r_hashmap -values(x)- -
x | -an |
-
---|
a list. Values associated to keys in the hash map x
.
Valerio Gherardi
- --#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 2 -#>
r2r
provides a flexible implementation of hash tables in R, allowing for:
You can install the released version of r2r
from CRAN with:
-install.packages("r2r")
and the development version from my R-universe repository, with:
-
-install.packages("r2r", repos = "https://vgherard.r-universe.dev")
-library(r2r)
-m <- hashmap()
-
-# Insert and query a single key-value pair
-m[[ "user" ]] <- "vgherard"
-m[[ "user" ]]
-#> [1] "vgherard"
-
-# Insert and query multiple key-value pairs
-m[ c(1, 2, 3) ] <- c("one", "two", "three")
-m[ c(1, 3) ]
-#> [[1]]
-#> [1] "one"
-#>
-#> [[2]]
-#> [1] "three"
-
-# Keys and values can be arbitrary R objects
-m[[ lm(mpg ~ wt, mtcars) ]] <- c(TRUE, FALSE, TRUE)
-m[[ lm(mpg ~ wt, mtcars) ]]
-#> [1] TRUE FALSE TRUE
For further details, including an introductory vignette illustrating the features of r2r
hash maps, you can consult the r2r
website. If you encounter a bug, want to suggest a feature or need further help, you can open a GitHub issue.
hash
-CRAN package {hash}
also offers an implementation of hash tables based on R environments. The two tables below offer a comparison between r2r and {hash}
(for more details, see the benchmarks Vignette)
Feature | -r2r | -hash | -
---|---|---|
Basic data structure | -R environment | -R environment | -
Arbitrary type keys | -X | -- |
Arbitrary type values | -X | -X | -
Arbitrary hash function | -X | -- |
Arbitrary key comparison function | -X | -- |
Throw or return default on missing keys | -X | -- |
Hash table inversion | -- | X | -
Features supported by {r2r} and {hash}.
-Task | -Comparison | -
---|---|
Key insertion | -{r2r} ~ {hash} | -
Key query | -{r2r} < {hash} | -
Key deletion | -{r2r} << {hash} | -
Performances of {r2r} and {hash} for basic hash table operations.
-R/generics.R
, R/shared_methods.R
- compare_fn.Rd
Returns the key comparison function of an hash table
-(hashset
or hashmap
).
compare_fn(x) - -# S3 method for r2r_hashtable -compare_fn(x)- -
x | -an |
-
---|
a function.
-Valerio Gherardi
- --#> function (x, y) -#> compare_fn(key_preproc_fn(x), key_preproc_fn(y)) -#> <bytecode: 0x55a10d88e100> -#> <environment: 0x55a10d891718>
These generics are used to get or set the default value of an
-hashmap
, optionally returned upon query of a missing key.
default(x) - -default(x) <- value - -# S3 method for r2r_hashmap -default(x) - -# S3 method for r2r_hashmap -default(x) <- value- -
x | -an |
-
---|---|
value | -an arbitrary R object. Default value to be associated to missing
-keys in the |
-
an arbitrary R object.
-For more details, see the hashtable documentation page.
-Valerio Gherardi
- --#> NULLdefault(m) <- 840 -
generates string hashes for arbitrary R objects as follows.
-This is the default hash function used by hashset
s and hashmap
s
-objects.
default_hash_fn(key)- -
key | -an arbitrary R object. |
-
---|
a character vector of length one. Hash digest of key
.
If key
is an atomic vector (as tested by
-is.atomic(key)
) of length one, default_hash_fn(key)
simply
-coerces the input to character. For more complex inputs, the function calls
-digest(key)
from the digest package.
Valerio Gherardi
- -R/generics.R
, R/hashmap.R
, R/hashset.R
- delete.Rd
These generics are used for deleting a single key or key/value
-pair from an hashset
or hashmap
, respectively.
delete(x, key) - -# S3 method for r2r_hashmap -delete(x, key) - -# S3 method for r2r_hashset -delete(x, key)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be deleted from the hash table. |
-
NULL
, invisibly.
Valerio Gherardi
- --#> [1] FALSE
This generics are used to check whether a key exists in a given
-hashset
or hashmap
.
has_key(x, key) - -x %has_key% key - -# S3 method for r2r_hashmap -has_key(x, key) - -# S3 method for r2r_hashset -has_key(x, key)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be checked for existence in the -hash table. |
-
TRUE
or FALSE
.
Valerio Gherardi
- --#> [1] TRUEm %has_key% "b" -#> [1] TRUE
Returns the hash function used for key hashing in an hash table
-(hashset
or hashmap
).
hash_fn(x) - -# S3 method for r2r_hashtable -hash_fn(x)- -
x | -an |
-
---|
a function.
-Valerio Gherardi
- --#> function (x) -#> hash_fn(key_preproc_fn(x)) -#> <bytecode: 0x55a10d88e3a0> -#> <environment: 0x55a10d8157d8>
Objects of class hashmap
and hashset
store
-collections of key/value pairs (hashmap
), or just keys
-(hashset
), providing constant time read and write operations. Both
-the keys and the optional values can be arbitrary R objects. hashmap
s
-and hashset
s provide an R implementation of
-hash tables.
See hashtable_methods for an overview of the available methods
-for hashmap
and hashset
class objects. Note that both these
-classes have a common parent class hashtable
, from which they can also
-inherit S3 methods.
hashmap( - ..., - hash_fn = default_hash_fn, - compare_fn = identical, - key_preproc_fn = identity, - on_missing_key = "default", - default = NULL -) - -hashset( - ..., - hash_fn = default_hash_fn, - compare_fn = identical, - key_preproc_fn = identity -)- -
... | -these arguments can be used to specify a set of initial elements
-to be inserted in the |
-
---|---|
hash_fn | -the (string valued) hash function applied to keys. -Required for advanced use only; see Details. |
-
compare_fn | -the (boolean valued) comparison function used for -testing key equality. Required for advanced use only; see Details. |
-
key_preproc_fn | -key pre-processing function applied to keys before -hashing and comparison. Required for advanced use only; see Details. |
-
on_missing_key | -either |
-
default | -default value associated with missing keys. This will be
-returned only if |
-
a hashmap
and a hashset
class object for
-hashmap()
and hashset()
, respectively.
hashmap
s and hashset
s implement hash tables,
-building on top of base R built-in environment
s,
-which by themselves are, essentially, string -> R object hash maps.
-In order to handle keys of non-string type, a string valued hash function
-default_hash_fn()
is provided, which leverages on
-digest()
for handling arbitrary R object keys.
By default, key equality is tested through identical()
.
-For some use cases, it may be sensible to employ a different comparison
-function, which can be assigned through the compare_fn
argument. In this
-case, one must also make sure that equal (in the sense of
-compare_fn()
)
-keys get also assigned the same hashes by hash_fn()
. A simple way to
-ensure this is to use to use a key pre-processing function, to be applied
-before both key hashing and comparison. The key_preproc_fn
-argument provides a short-cut to this, by automatically composing both the
-provided hash_fn()
and compare_fn()
functions with
-key_preproc_fn()
function. This is illustrated in an example below.
One might also want to set set specific hash and/or key comparison functions
-for efficiency reasons, e.g. if the default_hash_fn()
function produces
-many collisions between inequivalent keys.
When on_missing_key
is equal to "throw"
, querying a missing
-key will cause an error. In this case, an rlang abort
-condition of class "r2r_missing_key"
is returned, which can be useful
-for testing purposes.
Valerio Gherardi
- --m <- hashmap( - list("foo", 1), - list("bar", 1:5), - list(data.frame(x = letters, y = LETTERS), "baz") - ) -m[[ data.frame(x = letters, y = LETTERS) ]] -#> [1] "baz"-# Set of character keys, case insensitive. -s <- hashset("A", "B", "C", key_preproc = tolower) -s[["a"]] -#> [1] FALSE-
This page provides an overview of the available methods for
-hashmap
and hashset
objects (and for their common parent class
-hashtable
). We list methods based on the general type of task
-addressed.
Valerio Gherardi
- -
- All functions- - |
- |
---|---|
- - | -Get key comparison function of an hash table |
-
- - | -Default |
-
- - | -String hashes for arbitrary R objects |
-
- - | -Delete keys or key/value pairs from an hash table. |
-
- - | -Key existence in hash tables |
-
- - | -Get hash function of an hash table |
-
- - | -Hash maps and sets |
-
- - | -Methods for S3 classes |
-
- - | -Insert keys or key/value pairs into an hash table. |
-
- - | -List all keys from an hash table |
-
- - | -Size of hash tables |
-
- - | -On missing key behaviour |
-
- - | -Query keys from an hash table. |
-
-
|
- Subsetting |
-
- - | -List all values from an hash map |
-
R/generics.R
, R/hashmap.R
, R/hashset.R
- insert.Rd
These generics are used for inserting a single key or key/value
-pair into an hashset
or hashmap
, respectively. For vectorized
-insertions, see the subsetting_hashtables documentation page.
insert(x, key, ...) - -# S3 method for r2r_hashmap -insert(x, key, value, ...) - -# S3 method for r2r_hashset -insert(x, key, ...)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be inserted into the hash table. |
-
... | -further arguments passed to or from other methods. |
-
value | -an arbitrary R object. Value associated to |
-
key
for the hashset
method,
-value
for the hashmap
method.
Valerio Gherardi
- --#> [1] TRUE
These generics are used for listing all keys registered in an
-hashset
or hashmap
, respectively.
keys(x) - -# S3 method for r2r_hashtable -keys(x)- -
x | -an |
-
---|
a list. Registered keys in the hash table x
.
Valerio Gherardi
- --#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 2 -#> -#> [[3]] -#> [1] 3 -#>
Returns the total number of keys in an hash table.
-# S3 method for r2r_hashtable -length(x)- -
x | -an |
-
---|
an integer. Number of keys in the hash table (or elements in a set).
-Valerio Gherardi
- --#> [1] 1
These generics are used to get or set the behaviour of an
-hashmap
upon query of a missing key (currently, only an
-hashmap
method is implemented).
on_missing_key(x) - -on_missing_key(x) <- value - -# S3 method for r2r_hashmap -on_missing_key(x) - -# S3 method for r2r_hashmap -on_missing_key(x) <- value- -
x | -an |
-
---|---|
value | -a string, either |
-
a string, either "throw"
or "default"
.
For more details, see the hashtable documentation page.
-Valerio Gherardi
- --#> [1] "default"on_missing_key(m) <- "throw" -
These generics are used for querying a single key from an
-hashset
or hashmap
, respectively. For vectorized queries,
-see the subsetting_hashtables documentation page.
query(x, key) - -# S3 method for r2r_hashmap -query(x, key) - -# S3 method for r2r_hashset -query(x, key)- -
x | -an |
-
---|---|
key | -an arbitrary R object. Key to be queried from the hash table. |
-
TRUE
or FALSE
, for hashset
s. For
-hashmap
s, if the queried key exists in the hash table, returns the
-associated value (an a priori arbitrary R object); otherwise, behaves as
-specified by on_missing_key(x)
-(see also hashtable).
Valerio Gherardi
- --#> [1] TRUE
Implementation of hash tables (hash sets and hash maps) in R, - featuring arbitrary R objects as keys, - arbitrary hash and key-comparison functions, - and customizable behaviour upon queries of missing keys.
-Useful links:
Report bugs at https://github.com/vgherard/r2r/issues
Maintainer: Valerio Gherardi vgherard@sissa.it (ORCID)
- -hashset
s and hashmap
sR/docs.R
, R/hashmap.R
, R/hashset.R
- subsetting_hashtables.Rd
Subsetting operators `[[`
and `[`
for
-hashset
s and hashmap
s provide an equivalent synthax for the
-basic read/write operations performed by insert()
,
-delete()
and query()
.
# S3 method for r2r_hashmap -[[(x, i) - -# S3 method for r2r_hashmap -[(x, i) - -# S3 method for r2r_hashmap -[[(x, i) <- value - -# S3 method for r2r_hashmap -[(x, i) <- value - -# S3 method for r2r_hashset -[[(x, i) - -# S3 method for r2r_hashset -[(x, i) - -# S3 method for r2r_hashset -[[(x, i) <- value - -# S3 method for r2r_hashset -[(x, i) <- value- -
x | -an |
-
---|---|
i | -for |
-
value | -for |
-
the replacement forms ([[<-
and [<-
) always return value
.
-`[[`
returns TRUE
or FALSE
if
-x
is an hashset
, an arbitrary R object if x
is an
-hashmap
and i
is a valid key; when i
is not a key, the
-behaviour for hashmap
s depends on the value of
-on_missing_key(x)
.
-The `[`
operator returns a list of the same length of i
, whose
-k-th element is given by x[[ i[[k]] ]]
(the remark on missing keys for
-hashmaps applies also here).
Valerio Gherardi
- -This function is used to list all values associated to keys in
-an hashmap
. Implemented as a generic, but currently only the
-hashmap
method is defined.
values(x) - -# S3 method for r2r_hashmap -values(x)- -
x | -an |
-
---|
a list. Values associated to keys in the hash map x
.
Valerio Gherardi
- --#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 2 -#>