{"Type":"Package","Package":"dplyr","Title":"A Grammar of Data Manipulation","Version":"0.7.5","Authors@R":"c(\nperson(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4757-117X\")),\nperson(\"Romain\", \"Fran\\u00e7ois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")),\nperson(\"Lionel\", \"Henry\", role = \"aut\"),\nperson(\"Kirill\", \"M\\u00fcller\", role = \"aut\", comment = c(ORCID = \"0000-0002-1416-3412\")),\nperson(\"RStudio\", role = c(\"cph\", \"fnd\"))\n)","Description":"A fast, consistent tool for working with data frame like objects,\nboth in memory and out of memory.","License":"MIT + file LICENSE","URL":"http://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr","BugReports":"https://github.com/tidyverse/dplyr/issues","Depends":"R (>= 3.1.2)","Imports":"assertthat (>= 0.2.0), bindrcpp (>= 0.2.0.9000), glue (>=\n1.1.1), magrittr (>= 1.5), methods, pkgconfig (>= 2.0.1), R6\n(>= 2.2.2), Rcpp (>= 0.12.15), rlang (>= 0.2.0), tibble (>=\n1.3.1), tidyselect (>= 0.2.3), utils","Suggests":"bit64 (>= 0.9.7), callr, covr (>= 3.0.1), DBI (>= 0.7.14),\ndbplyr (>= 1.2.0), dtplyr (>= 0.0.2), ggplot2 (>= 2.2.1), hms\n(>= 0.4.1), knitr (>= 1.19), Lahman (>= 3.0-1), lubridate, mgcv\n(>= 1.8.23), microbenchmark (>= 1.4.4), nycflights13 (>=\n0.2.2), rmarkdown (>= 1.8), RMySQL (>= 0.10.13), RPostgreSQL\n(>= 0.6.2), RSQLite (>= 2.0), testthat (>= 2.0.0), withr (>=\n2.1.1)","LinkingTo":"BH (>= 1.58.0-1), bindrcpp (>= 0.2.0.9000), plogr (>=\n0.1.10), Rcpp (>= 0.12.15)","VignetteBuilder":"knitr","Encoding":"UTF-8","LazyData":"yes","RoxygenNote":"6.0.1.9000","NeedsCompilation":"yes","Packaged":"2018-05-09 07:53:55 UTC; kirill","Author":"Hadley Wickham [aut, cre] (),\nRomain Franois [aut] (),\nLionel Henry [aut],\nKirill Mller [aut] (),\nRStudio [cph, fnd]","Maintainer":"Hadley Wickham ","Repository":"CRAN","Date/Publication":"2018-05-19 09:06:10","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/dplyr_0.7.5.tar.gz","jsonAuthors":[{"name":"Hadley Wickham","email":"hadley@rstudio.com","maintainer":true},{"name":"Romain François","email":null,"maintainer":false},{"name":"Lionel Henry","email":null,"maintainer":false},{"name":"Kirill Müller","email":null,"maintainer":false},{"name":"RStudio ","email":null,"maintainer":false}],"readme":"\n\n\n# dplyr

\n\n[![Build\nStatus](https://travis-ci.org/tidyverse/dplyr.svg?branch=master)](https://travis-ci.org/tidyverse/dplyr)\n[![AppVeyor Build\nStatus](https://ci.appveyor.com/api/projects/status/github/tidyverse/dplyr?branch=master&svg=true)](https://ci.appveyor.com/project/tidyverse/dplyr)\n[![CRAN\\_Status\\_Badge](http://www.r-pkg.org/badges/version/dplyr)](http://cran.r-project.org/package=dplyr)\n[![Coverage\nStatus](https://codecov.io/gh/tidyverse/dplyr/branch/master/graph/badge.svg)](https://codecov.io/gh/tidyverse/dplyr?branch=master)\n\n## Overview\n\ndplyr is a grammar of data manipulation, providing a consistent set of\nverbs that help you solve the most common data manipulation challenges:\n\n - `mutate()` adds new variables that are functions of existing\n variables\n - `select()` picks variables based on their names.\n - `filter()` picks cases based on their values.\n - `summarise()` reduces multiple values down to a single summary.\n - `arrange()` changes the ordering of the rows.\n\nThese all combine naturally with `group_by()` which allows you to\nperform any operation “by group”. You can learn more about them in\n`vignette(\"dplyr\")`. As well as these single-table verbs, dplyr also\nprovides a variety of two-table verbs, which you can learn about in\n`vignette(\"two-table\")`.\n\ndplyr is designed to abstract over how the data is stored. That means as\nwell as working with local data frames, you can also work with remote\ndatabase tables, using exactly the same R code. Install the dbplyr\npackage then read `vignette(\"databases\", package = \"dbplyr\")`.\n\nIf you are new to dplyr, the best place to start is the [data import\nchapter](http://r4ds.had.co.nz/transform.html) in R for data science.\n\n## Installation\n\n``` r\n# The easiest way to get dplyr is to install the whole tidyverse:\ninstall.packages(\"tidyverse\")\n\n# Alternatively, install just dplyr:\ninstall.packages(\"dplyr\")\n\n# Or the development version from GitHub:\n# install.packages(\"devtools\")\ndevtools::install_github(\"tidyverse/dplyr\")\n```\n\nIf you encounter a clear bug, please file a minimal reproducible example\non [github](https://github.com/tidyverse/dplyr/issues). For questions\nand other discussion, please use the [manipulatr mailing\nlist](https://groups.google.com/group/manipulatr).\n\n## Usage\n\n``` r\nlibrary(dplyr)\n\nstarwars %>% \n filter(species == \"Droid\")\n#> # A tibble: 5 x 13\n#> name height mass hair_color skin_color eye_color birth_year gender\n#> \n#> 1 C-3PO 167 75. gold yellow 112. \n#> 2 R2-D2 96 32. white, blue red 33. \n#> 3 R5-D4 97 32. white, red red NA \n#> 4 IG-88 200 140. none metal red 15. none \n#> 5 BB8 NA NA none none black NA none \n#> # ... with 5 more variables: homeworld , species , films ,\n#> # vehicles , starships \n\nstarwars %>% \n select(name, ends_with(\"color\"))\n#> # A tibble: 87 x 4\n#> name hair_color skin_color eye_color\n#> \n#> 1 Luke Skywalker blond fair blue \n#> 2 C-3PO gold yellow \n#> 3 R2-D2 white, blue red \n#> 4 Darth Vader none white yellow \n#> 5 Leia Organa brown light brown \n#> # ... with 82 more rows\n\nstarwars %>% \n mutate(name, bmi = mass / ((height / 100) ^ 2)) %>%\n select(name:mass, bmi)\n#> # A tibble: 87 x 4\n#> name height mass bmi\n#> \n#> 1 Luke Skywalker 172 77. 26.0\n#> 2 C-3PO 167 75. 26.9\n#> 3 R2-D2 96 32. 34.7\n#> 4 Darth Vader 202 136. 33.3\n#> 5 Leia Organa 150 49. 21.8\n#> # ... with 82 more rows\n\nstarwars %>% \n arrange(desc(mass))\n#> # A tibble: 87 x 13\n#> name height mass hair_color skin_color eye_color birth_year gender\n#> \n#> 1 Jabba … 175 1358. green-tan,… orange 600. herma…\n#> 2 Grievo… 216 159. none brown, whi… green, ye… NA male \n#> 3 IG-88 200 140. none metal red 15.0 none \n#> 4 Darth … 202 136. none white yellow 41.9 male \n#> 5 Tarfful 234 136. brown brown blue NA male \n#> # ... with 82 more rows, and 5 more variables: homeworld ,\n#> # species , films , vehicles , starships \n\nstarwars %>%\n group_by(species) %>%\n summarise(\n n = n(),\n mass = mean(mass, na.rm = TRUE)\n ) %>%\n filter(n > 1)\n#> # A tibble: 9 x 3\n#> species n mass\n#> \n#> 1 Droid 5 69.8\n#> 2 Gungan 3 74.0\n#> 3 Human 35 82.8\n#> 4 Kaminoan 2 88.0\n#> 5 Mirialan 2 53.1\n#> # ... with 4 more rows\n```\n\n-----\n\nPlease note that this project is released with a [Contributor Code of\nConduct](CONDUCT.md). By participating in this project you agree to\nabide by its terms.","jobInfo":{"package":"dplyr","version":"0.7.5","parsingStatus":"success","parserVersion":1,"parsedAt":"2018-06-29T23:30:40+0000"}}

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