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Updated readme.
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@NicChr
NicChr committed Oct 18, 2024
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2 changes: 0 additions & 2 deletions README.Rmd
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Expand Up @@ -23,8 +23,6 @@ knitr::opts_chunk$set(
[![R-CMD-check](https://github.com/NicChr/cheapr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/NicChr/cheapr/actions/workflows/R-CMD-check.yaml)
[![CRAN
status](https://www.r-pkg.org/badges/version/cheapr)](https://CRAN.R-project.org/package=cheapr)
[![Codecov test
coverage](https://codecov.io/gh/NicChr/cheapr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/NicChr/cheapr?branch=main)
[![Codecov test coverage](https://codecov.io/gh/NicChr/cheapr/graph/badge.svg)](https://app.codecov.io/gh/NicChr/cheapr)
<!-- badges: end -->

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107 changes: 55 additions & 52 deletions README.md
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Expand Up @@ -9,8 +9,6 @@
[![CRAN
status](https://www.r-pkg.org/badges/version/cheapr)](https://CRAN.R-project.org/package=cheapr)
[![Codecov test
coverage](https://codecov.io/gh/NicChr/cheapr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/NicChr/cheapr?branch=main)
[![Codecov test
coverage](https://codecov.io/gh/NicChr/cheapr/graph/badge.svg)](https://app.codecov.io/gh/NicChr/cheapr)
<!-- badges: end -->

Expand Down Expand Up @@ -59,6 +57,11 @@ Let’s first load the required packages

``` r
library(cheapr)
#>
#> Attaching package: 'cheapr'
#> The following objects are masked from 'package:base':
#>
#> round, trunc
library(bench)
```

Expand All @@ -76,16 +79,16 @@ mark(num_na(x), sum(is.na(x)))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 num_na(x) 987µs 1.04ms 958. 2.41KB 0
#> 2 sum(is.na(x)) 776µs 1.65ms 601. 3.81MB 48.6
#> 1 num_na(x) 983µs 1.01ms 973. 2.41KB 0
#> 2 sum(is.na(x)) 780µs 1.84ms 543. 3.81MB 44.8
# 4 cores
options(cheapr.cores = 4)
mark(num_na(x), sum(is.na(x)))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 num_na(x) 266µs 340.9µs 2821. 0B 0
#> 2 sum(is.na(x)) 764µs 1.67ms 563. 3.81MB 46.7
#> 1 num_na(x) 258µs 346.5µs 2666. 0B 0
#> 2 sum(is.na(x)) 812µs 1.93ms 493. 3.81MB 40.4
options(cheapr.cores = 1)
```

Expand All @@ -99,16 +102,16 @@ mark(row_na_counts(m),
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 row_na_counts(m) 1.89ms 2.14ms 473. 9.14KB 0
#> 2 rowSums(is.na(m)) 3.42ms 3.56ms 274. 3.82MB 23.9
#> 1 row_na_counts(m) 1.89ms 2.21ms 455. 9.14KB 0
#> 2 rowSums(is.na(m)) 2.63ms 3.71ms 267. 3.82MB 25.8
# Number of NA values by col
mark(col_na_counts(m),
colSums(is.na(m)))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 col_na_counts(m) 2.48ms 2.6ms 385. 9.14KB 0
#> 2 colSums(is.na(m)) 2.62ms 2.77ms 357. 3.82MB 34.0
#> 1 col_na_counts(m) 2.49ms 2.55ms 384. 9.14KB 0
#> 2 colSums(is.na(m)) 1.77ms 2.85ms 353. 3.82MB 31.0
```

`is_na` is a multi-threaded alternative to `is.na`
Expand All @@ -120,8 +123,8 @@ mark(is.na(x), is_na(x))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 is.na(x) 724.9µs 1.77ms 559. 3.81MB 89.5
#> 2 is_na(x) 1.31ms 2.44ms 417. 3.82MB 37.4
#> 1 is.na(x) 818µs 2.01ms 481. 3.81MB 61.6
#> 2 is_na(x) 1.33ms 2.6ms 391. 3.82MB 36.2

### posixlt method is much faster
hours <- as.POSIXlt(seq.int(0, length.out = 10^6, by = 3600),
Expand All @@ -134,8 +137,8 @@ mark(is.na(hours), is_na(hours))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 is.na(hours) 1.23s 1.23s 0.813 61MB 0.813
#> 2 is_na(hours) 13.38ms 15.96ms 53.7 13.9MB 5.97
#> 1 is.na(hours) 1.19s 1.19s 0.843 61MB 0.843
#> 2 is_na(hours) 13.26ms 15.35ms 64.3 13.9MB 7.80
```

It differs in 2 regards:
Expand Down Expand Up @@ -204,7 +207,7 @@ mark(overview(df, hist = FALSE))
#> # A tibble: 1 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 overview(df, hist = FALSE) 1.35s 1.35s 0.740 2.09KB 0
#> 1 overview(df, hist = FALSE) 1.33s 1.33s 0.754 2.09KB 0
```

## Cheaper and consistent subsetting with `sset`
Expand Down Expand Up @@ -239,9 +242,9 @@ mark(sset(x, x %in_% y), sset(x, x %in% y), x[x %in% y])
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sset(x, x %in_% y) 82.2µs 122µs 8390. 83.2KB 6.71
#> 2 sset(x, x %in% y) 160.9µs 243µs 3935. 285.4KB 8.87
#> 3 x[x %in% y] 136µs 235µs 3664. 324.5KB 9.03
#> 1 sset(x, x %in_% y) 81.9µs 119µs 8606. 83.2KB 6.54
#> 2 sset(x, x %in% y) 156.4µs 238µs 4096. 285.4KB 8.90
#> 3 x[x %in% y] 131.2µs 210µs 4802. 324.5KB 13.8
```

`sset` uses an internal range-based subset when `i` is an ALTREP integer
Expand All @@ -252,8 +255,8 @@ mark(sset(df, 0:10^5), df[0:10^5, , drop = FALSE])
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sset(df, 0:10^5) 133µs 511.6µs 1989. 1.53MB 27.1
#> 2 df[0:10^5, , drop = FALSE] 6.18ms 13.8ms 85.3 4.83MB 2.30
#> 1 sset(df, 0:10^5) 137.5µs 527.1µs 1899. 1.53MB 27.5
#> 2 df[0:10^5, , drop = FALSE] 6.01ms 7.14ms 141. 4.83MB 7.43
```

It also accepts negative indexes
Expand All @@ -267,8 +270,8 @@ mark(sset(df, -10^4:0),
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sset(df, -10^4:0) 49.1ms 61ms 14.6 152MB 9.14
#> 2 df[-10^4:0, , drop = FALSE] 923.3ms 923ms 1.08 776MB 4.33
#> 1 sset(df, -10^4:0) 50.8ms 65.1ms 13.1 152MB 9.34
#> 2 df[-10^4:0, , drop = FALSE] 796.2ms 796.2ms 1.26 776MB 5.02
```

The biggest difference between `sset` and `[` is the way logical vectors
Expand Down Expand Up @@ -411,13 +414,13 @@ mark(gcd(x))
#> # A tibble: 1 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 gcd(x) 1.3µs 2.6µs 429812. 0B 0
#> 1 gcd(x) 1.3µs 1.4µs 658788. 0B 0
x <- seq(0, 10^6, 0.5)
mark(gcd(x))
#> # A tibble: 1 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 gcd(x) 52.5ms 54.2ms 18.5 0B 0
#> 1 gcd(x) 51.6ms 51.9ms 19.2 0B 0
```

## Creating many sequences
Expand Down Expand Up @@ -505,41 +508,41 @@ mark(cheapr_which = which_(x),
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_which 3.17ms 4.26ms 230. 3.81MB 2.09
#> 2 base_which 623.1µs 2.78ms 360. 7.63MB 12.5
#> 1 cheapr_which 2.52ms 3.92ms 252. 3.81MB 4.31
#> 2 base_which 654.2µs 2.68ms 379. 7.63MB 12.0
x <- rep(FALSE, 10^6)
mark(cheapr_which = which_(x),
base_which = which(x))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_which 750µs 797µs 1214. 0B 0
#> 2 base_which 459µs 476µs 2025. 3.81MB 26.0
#> 1 cheapr_which 741µs 753µs 1311. 0B 0
#> 2 base_which 453µs 464µs 2081. 3.81MB 28.7
x <- c(rep(TRUE, 5e05), rep(FALSE, 1e06))
mark(cheapr_which = which_(x),
base_which = which(x))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_which 2.04ms 2.83ms 341. 1.91MB 2.04
#> 2 base_which 1.26ms 1.97ms 467. 7.63MB 14.2
#> 1 cheapr_which 2.02ms 2.71ms 364. 1.91MB 2.05
#> 2 base_which 776.9µs 1.77ms 558. 7.63MB 17.3
x <- c(rep(FALSE, 5e05), rep(TRUE, 1e06))
mark(cheapr_which = which_(x),
base_which = which(x))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_which 4.03ms 5.07ms 193. 3.81MB 2.07
#> 2 base_which 894µs 3.07ms 302. 9.54MB 12.7
#> 1 cheapr_which 3.63ms 4.93ms 203. 3.81MB 2.07
#> 2 base_which 913.1µs 2.96ms 332. 9.54MB 11.8
x <- sample(c(TRUE, FALSE), 10^6, TRUE)
x[sample.int(10^6, 10^4)] <- NA
mark(cheapr_which = which_(x),
base_which = which(x))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_which 2.47ms 3.09ms 323. 1.89MB 2.06
#> 2 base_which 3.15ms 4.13ms 239. 5.7MB 6.57
#> 1 cheapr_which 2.42ms 3.01ms 331. 1.89MB 2.08
#> 2 base_which 3.16ms 4.09ms 245. 5.7MB 6.68
```

### factor
Expand All @@ -552,29 +555,29 @@ mark(cheapr_factor = factor_(x),
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_factor 9.21ms 9.72ms 102. 4.59MB 0
#> 2 base_factor 654.68ms 654.68ms 1.53 27.84MB 0
#> 1 cheapr_factor 9.19ms 9.91ms 97.6 4.59MB 0
#> 2 base_factor 529.85ms 529.85ms 1.89 27.84MB 0
mark(cheapr_factor = factor_(x, order = FALSE),
base_factor = factor(x, levels = unique(x)))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_factor 4.46ms 5ms 200. 1.53MB 2.20
#> 2 base_factor 1.04s 1.04s 0.959 22.79MB 0
#> 1 cheapr_factor 4.34ms 4.93ms 202. 1.53MB 2.02
#> 2 base_factor 839.96ms 839.96ms 1.19 22.79MB 0
mark(cheapr_factor = factor_(y),
base_factor = factor(y))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_factor 227.85ms 231.52ms 4.31 5.23MB 0
#> 2 base_factor 3.07s 3.07s 0.326 54.35MB 0
#> 1 cheapr_factor 221.21ms 224.83ms 4.44 5.23MB 0
#> 2 base_factor 3.09s 3.09s 0.324 54.35MB 0
mark(cheapr_factor = factor_(y, order = FALSE),
base_factor = factor(y, levels = unique(y)))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_factor 5.17ms 6.45ms 154. 3.49MB 2.20
#> 2 base_factor 56.78ms 63.28ms 16.2 39.89MB 0
#> 1 cheapr_factor 4.88ms 6.5ms 152. 3.49MB 2.21
#> 2 base_factor 54.53ms 60.2ms 16.7 39.89MB 0
```

### intersect & setdiff
Expand All @@ -587,15 +590,15 @@ mark(cheapr_intersect = intersect_(x, y, dups = FALSE),
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_intersect 2.68ms 3.57ms 279. 1.18MB 2.18
#> 2 base_intersect 4.82ms 6.65ms 149. 5.16MB 2.19
#> 1 cheapr_intersect 2.71ms 3.37ms 292. 1.18MB 2.17
#> 2 base_intersect 4.17ms 5.01ms 196. 5.16MB 2.18
mark(cheapr_setdiff = setdiff_(x, y, dups = FALSE),
base_setdiff = setdiff(x, y))
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_setdiff 2.63ms 3.39ms 269. 1.76MB 0
#> 2 base_setdiff 4.42ms 5.41ms 186. 5.71MB 2.18
#> 1 cheapr_setdiff 2.99ms 3.12ms 312. 1.76MB 0
#> 2 base_setdiff 4.45ms 5.43ms 185. 5.71MB 2.17
```

### `%in_%` and `%!in_%`
Expand All @@ -606,15 +609,15 @@ mark(cheapr = x %in_% y,
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr 1.63ms 1.84ms 529. 781.34KB 2.18
#> 2 base 2.93ms 3.17ms 307. 2.53MB 0
#> 1 cheapr 1.75ms 1.81ms 535. 781.34KB 2.17
#> 2 base 2.5ms 2.96ms 336. 2.53MB 2.18
mark(cheapr = x %!in_% y,
base = !x %in% y)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr 1.59ms 1.88ms 518. 787.84KB 2.17
#> 2 base 2.63ms 3.28ms 297. 2.91MB 2.19
#> 1 cheapr 1.75ms 1.83ms 533. 787.84KB 0
#> 2 base 2.67ms 3.14ms 313. 2.91MB 2.17
```

### `as_discrete`
Expand All @@ -631,6 +634,6 @@ mark(cheapr_cut = as_discrete(x, b, left = FALSE),
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cheapr_cut 215ms 216ms 4.63 38.2MB 0
#> 2 base_cut 513ms 513ms 1.95 267.1MB 1.95
#> 1 cheapr_cut 210ms 211ms 4.73 38.2MB 0
#> 2 base_cut 487ms 509ms 1.96 267.1MB 2.95
```

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