如何在Windows机器上安装归档R包？求可行解决方法

Hey there, sorry to hear you’ve been stuck on this for two hours—let’s break down the most reliable fixes to get these archived packages installed smoothly.

Step 1: Sort Out System Dependencies First #

The Rstem package depends on the Snowball stemmer library, a system-level tool that’s required for compiling the source code. If this is missing, your installation will fail no matter what. Here’s how to install it for your operating system:

• Ubuntu/Debian: Open your terminal and run sudo apt-get install libstemmer-dev
• CentOS/RHEL: Use sudo yum install stemmer-devel (or sudo dnf install stemmer-devel for newer releases)
• macOS: Use Homebrew with brew install libstemmer

Step 2: Install Rstem First (It’s a Hard Dependency for sentiment) #

These packages have a strict order—sentiment won’t install unless Rstem is already on your system. Try these methods in your R console:

Option A: Install Directly from the CRAN Archive URL #

Run these lines one at a time:

# Install Rstem first
install.packages("https://cran.r-project.org/src/contrib/Archive/Rstem/Rstem_0.4-1.tar.gz", repos = NULL, type = "source")

# Then install sentiment
install.packages("https://cran.r-project.org/src/contrib/Archive/sentiment/sentiment_0.2-1.tar.gz", repos = NULL, type = "source")

Option B: Manual Local Installation (If URL Install Fails) #

1. Download both tar.gz files from the archive pages to your computer:
   • Rstem_0.4-1.tar.gz
   • sentiment_0.2-1.tar.gz
2. In R, set your working directory to where you saved the files (use setwd("/path/to/your/saved/files")), then run:

install.packages("./Rstem_0.4-1.tar.gz", repos = NULL, type = "source")
install.packages("./sentiment_0.2-1.tar.gz", repos = NULL, type = "source")

Step 3: Fix R Version Compatibility (If You Still Hit Errors) #

These packages are extremely old (last updated in 2014), so they might not work with modern R versions (4.0+). If you get compilation errors related to R API changes, try these workarounds:

• Use renv to create a project-specific environment with an older R version (e.g., 3.1.3, which was current when these packages were released).
• Use Docker to spin up a container with an old R image—this keeps your main R setup untouched:

  docker run -it rocker/r-ver:3.1.3
  

  Then run the installation commands inside the container.

Alternative: Switch to Maintained Sentiment Analysis Packages #

If you can’t get these old packages working, consider using modern, actively maintained alternatives that do the same job better:

• sentimentr: A robust, up-to-date package for fine-grained sentiment analysis
• tidytext: Integrates seamlessly with the tidyverse for text analysis tasks
• textdata: Provides access to pre-trained sentiment lexicons

内容的提问来源于stack exchange，提问作者Lucinho91