确定。

您需要比较“已安装的包”和“所需的包”。这非常接近于我用蔓越莓做的事情，因为我需要比较“存储的已知包”和“当前已知的包”，以确定新的和/或更新的包。

就像这样

AP <- available.packages(contrib.url(repos[i,"url"]))   # available t repos[i]

为了获得所有已知的包，模拟调用当前安装的包，并将其与给定的目标包集进行比较。

非常基本的一个。

pkgs = c("pacman","data.table")
if(length(new.pkgs <- setdiff(pkgs, rownames(installed.packages())))) install.packages(new.pkgs)

关于你的主要目标“安装他们还没有的库”。并且不管使用" installed .packages() "”。下面的函数掩码了require的原始函数。它尝试加载和检查命名包“x”，如果它没有安装，直接安装它，包括依赖项;最后正常加载。将函数名从'require'重命名为'library'以保持完整性。唯一的限制是包名应该加引号。

require <- function(x) { 
  if (!base::require(x, character.only = TRUE)) {
  install.packages(x, dep = TRUE) ; 
  base::require(x, character.only = TRUE)
  } 
}

所以你可以加载和安装包的旧时尚的方式R。 要求(“ggplot2”) 要求(“Rcpp”)

站在@MichaelChirico的肩膀上:

stopifnot(3 == length(find.package(c('foo', 'bar', 'baz'))))

 48 lapply_install_and_load <- function (package1, ...)
 49 {
 50     #
 51     # convert arguments to vector
 52     #
 53     packages <- c(package1, ...)
 54     #
 55     # check if loaded and installed
 56     #
 57     loaded        <- packages %in% (.packages())
 58     names(loaded) <- packages
 59     #
 60     installed        <- packages %in% rownames(installed.packages())
 61     names(installed) <- packages
 62     #
 63     # start loop to determine if each package is installed
 64     #
 65     load_it <- function (p, loaded, installed)
 66     {
 67         if (loaded[p])
 68         {
 69             print(paste(p, "loaded"))
 70         }
 71         else
 72         {
 73             print(paste(p, "not loaded"))
 74             if (installed[p])
 75             {
 76                 print(paste(p, "installed"))
 77                 do.call("library", list(p))
 78             }
 79             else
 80             {
 81                 print(paste(p, "not installed"))
 82                 install.packages(p)
 83                 do.call("library", list(p))
 84             }
 85         }
 86     }
 87     #
 88     lapply(packages, load_it, loaded, installed)
 89 }

使用packrat使共享库完全相同，而不会改变其他环境。

就优雅和最佳实践而言，我认为你从根本上走错了方向。打包程序就是为这些问题而设计的。它是由RStudio由Hadley Wickham开发的。packrat使用自己的目录，将您的程序的所有依赖项安装在其中，而不涉及别人的环境，这样他们就不必安装依赖项并可能弄乱别人的环境系统。

Packrat is a dependency management system for R. R package dependencies can be frustrating. Have you ever had to use trial-and-error to figure out what R packages you need to install to make someone else’s code work–and then been left with those packages globally installed forever, because now you’re not sure whether you need them? Have you ever updated a package to get code in one of your projects to work, only to find that the updated package makes code in another project stop working? We built packrat to solve these problems. Use packrat to make your R projects more: Isolated: Installing a new or updated package for one project won’t break your other projects, and vice versa. That’s because packrat gives each project its own private package library. Portable: Easily transport your projects from one computer to another, even across different platforms. Packrat makes it easy to install the packages your project depends on. Reproducible: Packrat records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.

https://rstudio.github.io/packrat/