From f3474b3876db000307fa2461711fe2b4c355b192 Mon Sep 17 00:00:00 2001
From: Sam Firke <samuel.firke@gmail.com>
Date: Sat, 19 Aug 2023 12:54:09 -0400
Subject: [PATCH] change to _PACKAGE, update descriptive text

---
 R/janitor.R            | 30 +++++++++++-------------
 man/janitor-package.Rd | 53 ++++++++++++++++++++++++++++++++++++++++++
 man/janitor.Rd         | 33 --------------------------
 3 files changed, 66 insertions(+), 50 deletions(-)
 create mode 100644 man/janitor-package.Rd
 delete mode 100644 man/janitor.Rd

diff --git a/R/janitor.R b/R/janitor.R
index 38eb4e9a..f7e39c4a 100644
--- a/R/janitor.R
+++ b/R/janitor.R
@@ -3,28 +3,24 @@
 #' janitor has simple little tools for examining and cleaning dirty data.
 #'
 #' @section Main functions:
-#' The main janitor functions can: perfectly format ugly \code{data.frame} column names; isolate
-#' duplicate records for further study; and provide quick one- and two-variable tabulations
-#' (i.e., frequency tables and crosstabs) that improve on the base R function \code{table()}.
-#'
-#'
-#' Other functions in the package can format for reporting the results of these tabulations.
-#' These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel.
+#' The main janitor functions can: perfectly format data.frame
+#' column names; provide quick counts of variable combinations (i.e.,
+#' frequency tables and crosstabs); and explore duplicate records. Other
+#' janitor functions nicely format the tabulation results. These
+#' tabulate-and-report functions approximate popular features of SPSS and
+#' Microsoft Excel.
 #'
 #' @section Package context:
-#' This package follows the principles of the "tidyverse" and in particular works well with
-#' the \code{\%>\%} pipe function.
-#'
+#' This package follows the principles of the "tidyverse" and works
+#' well with the pipe function %>%.
 #'
 #' janitor was built with beginning-to-intermediate R users in mind
-#' and is optimized for user-friendliness.  Advanced users can already do everything
-#' covered here, but they can do it faster with janitor and save their thinking for
-#' more fun tasks.
+#' and is optimized for user-friendliness.  Advanced users can do most
+#' things covered here, but they can do it faster with janitor and save
+#' their thinking for more fun tasks.
 #'
-#' @docType package
-#' @name janitor
-#' @keywords internal
-NULL
+"_PACKAGE"
+
 ## quiets concerns of R CMD check re: the .'s that appear in pipelines
 ## and the "n" that is produced by dplyr::count() in a pipeline
 if (getRversion() >= "2.15.1") utils::globalVariables(c(".", "n"))
diff --git a/man/janitor-package.Rd b/man/janitor-package.Rd
new file mode 100644
index 00000000..506aa187
--- /dev/null
+++ b/man/janitor-package.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/janitor.R
+\docType{package}
+\name{janitor-package}
+\alias{janitor}
+\alias{janitor-package}
+\title{janitor}
+\description{
+janitor has simple little tools for examining and cleaning dirty data.
+}
+\section{Main functions}{
+
+The main janitor functions can: perfectly format data.frame
+column names; provide quick counts of variable combinations (i.e.,
+frequency tables and crosstabs); and explore duplicate records. Other
+janitor functions nicely format the tabulation results. These
+tabulate-and-report functions approximate popular features of SPSS and
+Microsoft Excel.
+}
+
+\section{Package context}{
+
+This package follows the principles of the "tidyverse" and works
+well with the pipe function %>%.
+
+janitor was built with beginning-to-intermediate R users in mind
+and is optimized for user-friendliness.  Advanced users can do most
+things covered here, but they can do it faster with janitor and save
+their thinking for more fun tasks.
+}
+
+\seealso{
+Useful links:
+\itemize{
+  \item \url{https://github.com/sfirke/janitor}
+  \item \url{https://sfirke.github.io/janitor/}
+  \item Report bugs at \url{https://github.com/sfirke/janitor/issues}
+}
+
+}
+\author{
+\strong{Maintainer}: Sam Firke \email{samuel.firke@gmail.com}
+
+Other contributors:
+\itemize{
+  \item Bill Denney \email{wdenney@humanpredictions.com} [contributor]
+  \item Chris Haid \email{chrishaid@gmail.com} [contributor]
+  \item Ryan Knight \email{ryangknight@gmail.com} [contributor]
+  \item Malte Grosser \email{malte.grosser@gmail.com} [contributor]
+  \item Jonathan Zadra \email{jonathan.zadra@sorensonimpact.com} [contributor]
+}
+
+}
diff --git a/man/janitor.Rd b/man/janitor.Rd
deleted file mode 100644
index 986a3109..00000000
--- a/man/janitor.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/janitor.R
-\docType{package}
-\name{janitor}
-\alias{janitor}
-\title{janitor}
-\description{
-janitor has simple little tools for examining and cleaning dirty data.
-}
-\section{Main functions}{
-
-The main janitor functions can: perfectly format ugly \code{data.frame} column names; isolate
-duplicate records for further study; and provide quick one- and two-variable tabulations
-(i.e., frequency tables and crosstabs) that improve on the base R function \code{table()}.
-
-
-Other functions in the package can format for reporting the results of these tabulations.
-These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel.
-}
-
-\section{Package context}{
-
-This package follows the principles of the "tidyverse" and in particular works well with
-the \code{\%>\%} pipe function.
-
-
-janitor was built with beginning-to-intermediate R users in mind
-and is optimized for user-friendliness.  Advanced users can already do everything
-covered here, but they can do it faster with janitor and save their thinking for
-more fun tasks.
-}
-
-\keyword{internal}