R Package Installation Verification

R package installation verification

Summary

Verify that required R packages (ggplot2, RColorBrewer, grid) are correctly installed and importable in the active R environment. This skill ensures that downstream R-based analysis steps in bioinformatics pipelines (such as HiC-Pro's visualization and normalization workflows) will succeed.

When to use

After installing R or modifying an R environment via conda, package managers, or container images; before running any pipeline step that depends on R packages for statistical analysis, visualization, or data normalization. Particularly critical when setting up HiC-Pro or similar pipelines that require specific R package versions (e.g., ggplot2 >2.2.1) for Hi-C contact map plotting and quality-control reporting.

When NOT to use

• R packages are already confirmed to be installed and functional in the current session (re-verification is redundant).
• The pipeline does not use R (e.g., pure Python workflows without R dependencies).
• Installing new R packages — this skill verifies existing installations, not installs them; use install.packages() or conda install for installation.

Inputs

• R environment (active shell or conda environment)
• R installation path or conda environment specification
• List of required package names and minimum version constraints (e.g., ggplot2 >2.2.1)

Outputs

• Verification report listing each package name, load status (success/failure), and installed version
• Error messages or warnings if any package failed to load
• Summary of R version and library paths used

How to apply

Launch an R session (interactive or via Rscript) and attempt to load each required package using the library() function. For HiC-Pro, verify ggplot2 (>2.2.1), RColorBrewer, and grid packages are available and callable. If a package fails to load, check the R version (must match pipeline requirements), verify the package was installed to the correct library path, and re-install if necessary via install.packages() or conda. Document the successful load of each package and its version number. The rationale is that R packages installed but not in the R_LIBPATHS or with incompatible versions will cause silent or cryptic runtime failures downstream; early verification prevents wasted compute.

Related tools

• R (Runtime environment for executing library() verification and running downstream statistical/visualization tasks) — http://www.r-project.org/
• ggplot2 (Required R package for plotting and visualizing Hi-C contact maps and QC metrics; version constraint ggplot2 >2.2.1)
• RColorBrewer (Required R package for color palette generation in HiC-Pro visualization outputs)
• grid (Base R graphics package required as a dependency for ggplot2 and multi-panel plot layouts)
• conda (Package manager used to install R and R packages into isolated environments; enables reproducible R environment setup) — https://docs.conda.io/en/latest/miniconda.html

Examples

Rscript -e 'library(ggplot2); library(RColorBrewer); library(grid); cat("R packages loaded successfully\n"); sessionInfo()'

Evaluation signals

• Each required package (ggplot2, RColorBrewer, grid) returns TRUE when passed to require() or library(), indicating successful import.
• Package version strings printed by packageVersion() or sessionInfo() meet or exceed the documented minimum (e.g., ggplot2 version >2.2.1).
• R_LIBPATHS environment variable points to the correct conda or system library directory containing the installed packages.
• No warnings or errors appear in the R console output when loading packages (except non-critical deprecation notices).
• Downstream R scripts that depend on these packages execute without 'package not found' errors or missing function errors.

Limitations

• Verification confirms presence but does not detect functional bugs or missing sub-dependencies within a package; a successful library() load does not guarantee correct behavior at runtime.
• Version constraints (e.g., ggplot2 >2.2.1) are specified in the HiC-Pro documentation but may not be automatically enforced; the user must manually check packageVersion() output.
• R package installation from source (rather than pre-compiled binaries via conda) may fail silently if system compilers or headers are missing; library() success does not verify the build succeeded correctly.
• Platform-specific issues (e.g., macOS missing GNU core utilities, Windows library path separators) may cause packages to install but fail to load correctly; verification should be repeated on the target platform.
• No changelog or version history is provided in HiC-Pro's source for detecting which package versions were last validated; users must cross-reference with the original article or conda environment.yml lock files.

Evidence

• [other] Verify that R is available and that required packages (ggplot2 >2.2.1, RColorBrewer, grid) are installed by testing library() calls in R.: "Verify that R is available and that required packages (ggplot2 >2.2.1, RColorBrewer, grid) are installed by testing library() calls in R."
• [methods] R with the following packages: RColorBrewer and ggplot2 (>2.2.1): "R with the RColorBrewer and ggplot2 (>2.2.1) packages"
• [methods] All required Python libraries and R packages must be installed and importable before pipeline execution.: "verify that all required Python libraries (bx-python >=0.8.8, numpy >=1.18.1, scipy >=1.4.1, pysam >=0.15.4, argparse) are installed and importable"
• [readme] conda env create enables environment creation with all dependencies; activation and verification are essential steps.: "conda env create -f MY_INSTALL_PATH/HiC-Pro/environment.yml -p WHERE_TO_INSTALL_MY_ENV conda activate WHERE_TO_INSTALL_MY_ENV"
• [methods] R package dependencies and version constraints are documented for HiC-Pro visualization pipelines.: "Python (>3.7) with pysam (>=0.15.4), bx-python(>=0.8.8), numpy(>=1.18.1), and scipy(>=1.4.1) libraries. Note that the current version no longer supports python 2
• R with the RColorBrewer"