{"Package":"tidycode","Type":"Package","Title":"Analyze Lines of R Code the Tidy Way","Version":"0.1.0","Authors@R":"c(\nperson(\"Lucy\", \"D'Agostino McGowan\", email = \"lucydagostino@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-7297-9359\")),\nperson(\"Jim\", \"Hester\", email = \"james.f.hester@gmail.com\", role = \"ctb\"),\nperson(\"Jeff\", \"Leek\", email = \"jtleek@gmail.com\", role = c(\"ldr\"))\n)","Description":"Analyze lines of R code using tidy principles. This allows you to\ninput lines of R code and output a data frame with one row per function\nincluded. Additionally, it facilitates code classification via included lexicons.","License":"MIT + file LICENSE","Encoding":"UTF-8","LazyData":"true","RoxygenNote":"6.0.1","Imports":"purrr, pryr, tibble, rlang, glue, matahari","URL":"https://github.com/LucyMcGowan/tidycode","BugReports":"https://github.com/LucyMcGowan/tidycode/issues","Suggests":"testthat, covr, knitr, rmarkdown, dplyr","VignetteBuilder":"knitr","NeedsCompilation":"no","Packaged":"2019-05-17 23:52:37 UTC; lucymcgowan","Author":"Lucy D'Agostino McGowan [aut, cre]\n(),\nJim Hester [ctb],\nJeff Leek [ldr]","Maintainer":"Lucy D'Agostino McGowan ","Repository":"CRAN","Date/Publication":"2019-05-20 09:10:03 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/tidycode_0.1.0.tar.gz","jsonAuthors":[{"name":"Lucy D'Agostino McGowan","email":"lucydagostino@gmail.com","maintainer":true},{"name":"Jim Hester","email":"james.f.hester@gmail.com","maintainer":false},{"name":"Jeff Leek","email":"jtleek@gmail.com","maintainer":false}],"readme":"\n\n\n\n\n[![Travis build\nstatus](https://travis-ci.org/LucyMcGowan/tidycode.svg?branch=master)](https://travis-ci.org/LucyMcGowan/tidycode)\n[![Codecov test\ncoverage](https://codecov.io/gh/LucyMcGowan/tidycode/branch/master/graph/badge.svg)](https://codecov.io/gh/LucyMcGowan/tidycode?branch=master)\n\n\n# tidycode\n\nThe goal of tidycode is to allow users to analyze R expressions in a\ntidy way.\n\n## Installation\n\nYou can install tidycode from github with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"LucyMcGowan/tidycode\")\n```\n\n## Example\n\n### Read in exisiting code\n\nUsing the matahari package, we can read in existing code, either as a\nstring or a file, and turn it into a matahari tibble using\n`matahari::dance_recital()`.\n\n``` r\ncode <- \"\nlibrary(broom)\nlibrary(glue)\nm <- lm(mpg ~ am, data = mtcars)\nt <- tidy(m)\nglue_data(t, 'The point estimate for term {term} is {estimate}.')\n\"\n\nm <- matahari::dance_recital(code)\n```\n\nAlternatively, you may already have a matahari tibble that was recorded\nduring an R session.\n\nLoad the tidycode library.\n\n``` r\nlibrary(tidycode)\n```\n\nWe can use the expressions from this matahari tibble to extract the\nnames of the packages included.\n\n``` r\n(pkg_names <- ls_packages(m$expr))\n#> [1] \"broom\" \"glue\"\n```\n\nCreate a data frame of your expressions, splitting each into individual\nfunctions.\n\n``` r\nu <- unnest_calls(m, expr)\n```\n\nAdd in the function classifications\\!\n\n``` r\nu %>%\n dplyr::inner_join(\n get_classifications(\"crowdsource\", include_duplicates = FALSE)\n )\n#> Joining, by = \"func\"\n#> # A tibble: 8 x 8\n#> result error output warnings messages func args classification\n#> \n#> 1 2 3 4 5 6 7 8 %` and `+`.\n\n``` r\nu %>%\n dplyr::inner_join(\n get_classifications(\"crowdsource\", include_duplicates = FALSE)\n ) %>%\n dplyr::anti_join(get_stopfuncs()) %>%\n dplyr::select(func, classification)\n#> Joining, by = \"func\"\n#> Joining, by = \"func\"\n#> # A tibble: 5 x 2\n#> func classification\n#> \n#> 1 library setup \n#> 2 library setup \n#> 3 lm modeling \n#> 4 tidy modeling \n#> 5 glue_data communication\n```","jobInfo":{"package":"tidycode","version":"0.1.0","parsingStatus":"success","parserVersion":1,"parsedAt":"2019-12-10T21:29:57+0000"}}

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