{"Package":"dtplyr","Title":"Data Table Back-End for 'dplyr'","Version":"1.3.1","Authors@R":"c(\nperson(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"cre\", \"aut\")),\nperson(\"Maximilian\", \"Girlich\", role = \"aut\"),\nperson(\"Mark\", \"Fairbanks\", role = \"aut\"),\nperson(\"Ryan\", \"Dickerson\", role = \"aut\"),\nperson(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"))\n)","Description":"Provides a data.table backend for 'dplyr'. The goal of\n'dtplyr' is to allow you to write 'dplyr' code that is automatically\ntranslated to the equivalent, but usually much faster, data.table\ncode.","License":"MIT + file LICENSE","URL":"https://dtplyr.tidyverse.org, https://github.com/tidyverse/dtplyr","BugReports":"https://github.com/tidyverse/dtplyr/issues","Depends":"R (>= 3.3)","Imports":"cli (>= 3.4.0), data.table (>= 1.13.0), dplyr (>= 1.1.0),\nglue, lifecycle, rlang (>= 1.0.4), tibble, tidyselect (>=\n1.2.0), vctrs (>= 0.4.1)","Suggests":"bench, covr, knitr, rmarkdown, testthat (>= 3.1.2), tidyr (>=\n1.1.0), waldo (>= 0.3.1)","VignetteBuilder":"knitr","Config/Needs/website":"tidyverse/tidytemplate","Config/testthat/edition":"3","Encoding":"UTF-8","RoxygenNote":"7.2.3","NeedsCompilation":"no","Packaged":"2023-03-21 14:31:40 UTC; hadleywickham","Author":"Hadley Wickham [cre, aut],\nMaximilian Girlich [aut],\nMark Fairbanks [aut],\nRyan Dickerson [aut],\nPosit Software, PBC [cph, fnd]","Maintainer":"Hadley Wickham ","Repository":"CRAN","Date/Publication":"2023-03-22 13:10:05 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/dtplyr_1.3.1.tar.gz","jsonAuthors":[{"name":"Hadley Wickham","email":"hadley@posit.co","maintainer":true},{"name":"Maximilian Girlich","email":null,"maintainer":false},{"name":"Mark Fairbanks","email":null,"maintainer":false},{"name":"Ryan Dickerson","email":null,"maintainer":false},{"name":"Posit Software, PBC ","email":null,"maintainer":false}],"readme":"\n\n\n# dtplyr

\n\n\n\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/dtplyr)](https://cran.r-project.org/package=dtplyr)\n[![R-CMD-check](https://github.com/tidyverse/dtplyr/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverse/dtplyr/actions)\n[![Codecov test\ncoverage](https://codecov.io/gh/tidyverse/dtplyr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/dtplyr?branch=main)\n\n\n## Overview\n\ndtplyr provides a [data.table](http://r-datatable.com/) backend for\ndplyr. The goal of dtplyr is to allow you to write dplyr code that is\nautomatically translated to the equivalent, but usually much faster,\ndata.table code.\n\nSee `vignette(\"translation\")` for details of the current translations,\nand [table.express](https://github.com/asardaes/table.express) and\n[rqdatatable](https://github.com/WinVector/rqdatatable/) for related\nwork.\n\n## Installation\n\nYou can install from CRAN with:\n\n``` r\ninstall.packages(\"dtplyr\")\n```\n\nOr try the development version from GitHub with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"tidyverse/dtplyr\")\n```\n\n## Usage\n\nTo use dtplyr, you must at least load dtplyr and dplyr. You may also\nwant to load [data.table](http://r-datatable.com/) so you can access the\nother goodies that it provides:\n\n``` r\nlibrary(data.table)\nlibrary(dtplyr)\nlibrary(dplyr, warn.conflicts = FALSE)\n```\n\nThen use `lazy_dt()` to create a “lazy” data table that tracks the\noperations performed on it.\n\n``` r\nmtcars2 <- lazy_dt(mtcars)\n```\n\nYou can preview the transformation (including the generated data.table\ncode) by printing the result:\n\n``` r\nmtcars2 %>% \n filter(wt < 5) %>% \n mutate(l100k = 235.21 / mpg) %>% # liters / 100 km\n group_by(cyl) %>% \n summarise(l100k = mean(l100k))\n#> Source: local data table [3 x 2]\n#> Call: `_DT1`[wt < 5][, `:=`(l100k = 235.21/mpg)][, .(l100k = mean(l100k)), \n#> keyby = .(cyl)]\n#> \n#> cyl l100k\n#> \n#> 1 4 9.05\n#> 2 6 12.0 \n#> 3 8 14.9 \n#> \n#> # Use as.data.table()/as.data.frame()/as_tibble() to access results\n```\n\nBut generally you should reserve this only for debugging, and use\n`as.data.table()`, `as.data.frame()`, or `as_tibble()` to indicate that\nyou’re done with the transformation and want to access the results:\n\n``` r\nmtcars2 %>% \n filter(wt < 5) %>% \n mutate(l100k = 235.21 / mpg) %>% # liters / 100 km\n group_by(cyl) %>% \n summarise(l100k = mean(l100k)) %>% \n as_tibble()\n#> # A tibble: 3 × 2\n#> cyl l100k\n#> \n#> 1 4 9.05\n#> 2 6 12.0 \n#> 3 8 14.9\n```\n\n## Why is dtplyr slower than data.table?\n\nThere are two primary reasons that dtplyr will always be somewhat slower\nthan data.table:\n\n- Each dplyr verb must do some work to convert dplyr syntax to\n data.table syntax. This takes time proportional to the complexity of\n the input code, not the input *data*, so should be a negligible\n overhead for large datasets. [Initial\n benchmarks](https://dtplyr.tidyverse.org/articles/translation.html#performance)\n suggest that the overhead should be under 1ms per dplyr call.\n\n- To match dplyr semantics, `mutate()` does not modify in place by\n default. This means that most expressions involving `mutate()` must\n make a copy that would not be necessary if you were using data.table\n directly. (You can opt out of this behaviour in `lazy_dt()` with\n `immutable = FALSE`).\n\n## Code of Conduct\n\nPlease note that the dtplyr project is released with a [Contributor Code\nof Conduct](https://dtplyr.tidyverse.org/CODE_OF_CONDUCT.html). By\ncontributing to this project, you agree to abide by its terms.","jobInfo":{"package":"dtplyr","version":"1.3.1","parsingStatus":"success","parserVersion":2,"parsedAt":"2023-03-22T14:30:00+0000"}}

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