# funique

[![Travis build status](https://travis-ci.org/mkearney/funique.svg?branch=master)](https://travis-ci.org/mkearney/funique) [![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) > ⌚️ A faster `unique()` function ## Installation You can install the released version of funique from Github with: ``` r ## install remotes pkg if not already if (!requireNamespace("remotes", quietly = TRUE)) { install.packages("remotes") } ## install funique from github remotes::install_github("mkearney/funique") ``` ## Usage There’s one function `funique()`, which is the same as `base::unique()` only optimized to be faster when data contain date-time variables. ## Speed test: `funique()` vs. `base::unique()` The code below creates a data frame with several duplicate rows and then compares performance (in time) of `funique()` versus `base::unique()`. ``` r ## set seed set.seed(20180812) ## generate data d <- data.frame( x = rnorm(1000), y = seq.POSIXt(as.POSIXct("2018-01-01"), as.POSIXct("2018-12-31"), length.out = 10)) ## create data frame with duplicate rows d <- d[c(1:1000, sample(1:1000, 500, replace = TRUE)), ] row.names(d) <- NULL ## check the output against base::unique identical(unique(d), funique(d)) #> [1] TRUE ## bench mark (m <- microbenchmark::microbenchmark(unique(d), funique(d), times = 200, unit = "relative")) #> Unit: relative #> expr min lq mean median uq max neval #> unique(d) 4.98663 4.92073 4.94704 5.14552 5.38573 2.02836 200 #> funique(d) 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 200 ## plot plot(drop_hl(m, n = 4)) + ggplot2::ggsave("/images/funique/r1.png", width = 8, height = 4.5, units = "in") ```

Here’s another test this time using duplicate-infested Twitter data. ``` r ## search for data on 100 tweets rt <- rtweet::search_tweets("lang:en", verbose = FALSE) ## create duplicates rt2 <- rt[sample(1:nrow(rt), 1000, replace = TRUE), ] ## benchmarks (mb <- microbenchmark::microbenchmark( unique(rt2), funique(rt2), unit = "relative")) #> Unit: relative #> expr min lq mean median uq max neval #> unique(rt2) 1.56861 1.57622 1.35692 1.5524 1.35336 0.393617 100 #> funique(rt2) 1.00000 1.00000 1.00000 1.0000 1.00000 1.000000 100 ## make sure the output is the same identical(unique(rt2), funique(rt2)) #> [1] TRUE ## plot plot(drop_hl(m, n = 4)) + ggplot2::ggsave("/images/funique/r2.png", width = 8, height = 4.5, units = "in") ```

funique0.0.1 package

Functions

Readme

Dependencies

Versions

funique