如何使用 roxygen 记录数据集？

发布于 2024-08-22 06:12:47 字数 110 浏览 6 评论 0原文

是否可以在 roxygen 进程中将 .R 文件包含在我的包的数据目录中？

我已将几个 .R 文件放入数据目录中。当它们使用 data() 获取数据时，它们会读取原始数据文件并执行一些转换。

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娇俏 2024-08-29 06:12:47

Roxygen 可以在 R 文件中的任何位置使用（换句话说，它后面不必跟函数）。它还可用于记录 R 文档中的任何 docType。

因此，您可以将数据记录在单独的块中（如下所示）：

#' This is data to be included in my package
#'
#' @name data-name
#' @docType data
#' @author My Name \email{blahblah@@roxygen.org}
#' @references \url{data_blah.com}
#' @keywords data
NULL

Roxygen can be used anywhere within an R file (in other words, it doesn't have to be followed by a function). It can also be used to document any docType in the R documentation.

So you can just document your data in a separate block (something like this):

#' This is data to be included in my package
#'
#' @name data-name
#' @docType data
#' @author My Name \email{blahblah@@roxygen.org}
#' @references \url{data_blah.com}
#' @keywords data
NULL

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﹂绝世的画 2024-08-29 06:12:47

从 roxygen2 >4.0.0 开始，您可以记录定义的数据对象
在其他地方通过记录定义为字符串的对象名称：

#' This is data to be included in my package
#'
#' @author My Name \email{blahblah@@roxygen.org}
#' @references \url{data_blah.com}
"data-name"

As of roxygen2 >4.0.0, you can document the data object defined
elsewhere by documenting the name of the object defined as a string:

#' This is data to be included in my package
#'
#' @author My Name \email{blahblah@@roxygen.org}
#' @references \url{data_blah.com}
"data-name"

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淡淡の花香 2024-08-29 06:12:47

我发现研究 ggplot2 包中的示例很有用。

请参阅 github 上的 ggplot2.r

一些值得注意的事项

：数据集的 Roxygen 代码可以包含在包的 R 目录中的单个 .r 文件中。

例如，请参阅 diamonds 数据集：

#' Prices of 50,000 round cut diamonds
#'
#' A dataset containing the prices and other attributes of almost 54,000
#'  diamonds. The variables are as follows:
#'
#' \itemize{
#'   \item price. price in US dollars (\$326--\$18,823)
#'   \item carat. weight of the diamond (0.2--5.01)
#'   \item cut. quality of the cut (Fair, Good, Very Good, Premium, Ideal)
#'   \item colour. diamond colour, from J (worst) to D (best)
#'   \item clarity. a measurement of how clear the diamond is (I1 (worst), SI1, SI2, VS1, VS2, VVS1, VVS2, IF (best))
#'   \item x. length in mm (0--10.74)
#'   \item y. width in mm (0--58.9)
#'   \item z. depth in mm (0--31.8)
#'   \item depth. total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)
#'   \item table. width of top of diamond relative to widest point (43--95)
#' }
#'
#' @docType data
#' @keywords datasets
#' @name diamonds
#' @usage data(diamonds)
#' @format A data frame with 53940 rows and 10 variables
NULL

这会生成一个如下所示的帮助文件：

roxygen 文档示例

I found it useful to study the examples in the ggplot2 package.

See ggplot2.r on github

A few things of note:

All the Roxygen code for datasets can be included in a single .r file in the R directory of the package.

See for examples, the diamonds dataset:

#' Prices of 50,000 round cut diamonds
#'
#' A dataset containing the prices and other attributes of almost 54,000
#'  diamonds. The variables are as follows:
#'
#' \itemize{
#'   \item price. price in US dollars (\$326--\$18,823)
#'   \item carat. weight of the diamond (0.2--5.01)
#'   \item cut. quality of the cut (Fair, Good, Very Good, Premium, Ideal)
#'   \item colour. diamond colour, from J (worst) to D (best)
#'   \item clarity. a measurement of how clear the diamond is (I1 (worst), SI1, SI2, VS1, VS2, VVS1, VVS2, IF (best))
#'   \item x. length in mm (0--10.74)
#'   \item y. width in mm (0--58.9)
#'   \item z. depth in mm (0--31.8)
#'   \item depth. total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)
#'   \item table. width of top of diamond relative to widest point (43--95)
#' }
#'
#' @docType data
#' @keywords datasets
#' @name diamonds
#' @usage data(diamonds)
#' @format A data frame with 53940 rows and 10 variables
NULL

This results in a help file that looks like this:

roxygen documentation example

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