你可以使用require的返回值:

if(!require(somepackage)){
    install.packages("somepackage")
    library(somepackage)
}

我在安装后使用library，因为如果安装不成功或由于其他原因无法加载包，它将抛出异常。您可以使其更加健壮和可重用:

dynamic_require <- function(package){
  if(eval(parse(text=paste("require(",package,")")))) return(TRUE)
  
  install.packages(package)
  return(eval(parse(text=paste("require(",package,")"))))
}

此方法的缺点是必须以引号传递包名，而对于真正的require则不这样做。

pckg=c("shiny","ggplot2","dplyr","leaflet","lubridate","RColorBrewer","plotly","DT","shinythemes")

for(i in 1:length(pckg)) 
   {
      print(pckg[i])
      if (!is.element(pckg[i], installed.packages()[,1]))
      install.packages(pckg[i], dep = TRUE)
      require(pckg[i], character.only = TRUE)
}

当前版本的RStudio(>=1.2)包含了一个功能，可以检测library()和require()调用中缺失的包，并提示用户安装它们:

检测丢失的R包 许多R脚本打开时调用library()和require()来加载执行所需的包。如果您打开一个R脚本，该脚本引用了您没有安装的包，RStudio现在将提供一次单击即可安装所有所需的包。不再重复输入install.packages()，直到错误消失! https://blog.rstudio.com/2018/11/19/rstudio-1-2-preview-the-little-things/

这似乎很好地解决了OP最初的问题:

他们中的许多人是新手/中级R用户，并没有意识到他们必须安装他们还没有的包。

今天，我偶然发现了rlang包提供的两个方便函数，即is_installed()和check_installed()。

从帮助页面(强调添加):

These functions check that packages are installed with minimal side effects. If installed, the packages will be loaded but not attached. is_installed() doesn't interact with the user. It simply returns TRUE or FALSE depending on whether the packages are installed. In interactive sessions, check_installed() asks the user whether to install missing packages. If the user accepts, the packages are installed [...]. If the session is non interactive or if the user chooses not to install the packages, the current evaluation is aborted.

interactive()
#> [1] FALSE
rlang::is_installed(c("dplyr"))
#> [1] TRUE
rlang::is_installed(c("foobarbaz"))
#> [1] FALSE
rlang::check_installed(c("dplyr"))
rlang::check_installed(c("foobarbaz"))
#> Error:
#> ! The package `foobarbaz` is required.

由reprex包在2022-03-25创建(v2.0.1)

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/

下面是我的代码:

packages <- c("dplyr", "gridBase", "gridExtra")
package_loader <- function(x){
    for (i in 1:length(x)){
        if (!identical((x[i], installed.packages()[x[i],1])){
            install.packages(x[i], dep = TRUE)
        } else {
            require(x[i], character.only = TRUE)
        }
    }
}
package_loader(packages)