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---
title: "Demo of the bit package"
author: "Dr. Jens Oehlschlägel"
date: '`r Sys.Date()`'
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Demo of the bit package}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, echo = FALSE, results = "hide", message = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(bit)
.ff.is.available = requireNamespace("ff", quietly=TRUE) && packageVersion("ff") >= "4.0.0"
if (.ff.is.available) library(ff)
#tools::buildVignette("vignettes/bit-demo.Rmd")
#devtools::build_vignettes()
```
---
## bit type
Create a huge boolean vector (no NAs allowed)
```{r}
n <- 1e8
b1 <- bit(n)
b1
```
It costs only one bit per element
```{r}
object.size(b1) / n
```
A couple of standard methods work
```{r}
b1[10:30] <- TRUE
summary(b1)
```
Create a another boolean vector with TRUE in some different positions
```{r}
b2 <- bit(n)
b2[20:40] <- TRUE
b2
```
fast boolean operations
```{r}
b1 & b2
```
fast boolean operations
```{r}
summary(b1 & b2)
```
## bitwhich type
Since we have a very skewed distribution we may coerce to an even sparser representation
```{r}
w1 <- as.bitwhich(b1)
w2 <- as.bitwhich(b2)
object.size(w1) / n
```
and everything
```{r}
w1 & w2
```
works as expected
```{r}
summary(w1 & w2)
```
even mixing
```{r}
summary(b1 & w2)
```
## processing chunks
Many bit functions support a range restriction,
```{r}
summary(b1, range=c(1, 1000))
```
which is useful
```{r}
as.which(b1, range=c(1, 1000))
```
for filtered chunked looping
```{r}
lapply(chunk(from=1, to=n, length=10), function(i) as.which(b1, range=i))
```
over large ff vectors
```{r, eval=.ff.is.available}
options(ffbatchbytes=1024^3)
x <- ff(vmode="single", length=n)
x[1:1000] <- runif(1000)
lapply(chunk(x, length.out = 10), function(i) sum(x[as.hi(b1, range=i)]))
```
and wrap-up
```{r, eval=.ff.is.available}
delete(x)
rm(x, b1, b2, w1, w2, n)
```
for more info check the usage vignette
|
