Skip to main content
SimilarExport ZIP
100%

Unload R packages that override certain tidyverse functions (scran, scater) if they appear in search()

✓ Published0🌍 Public
FFloWuenne
Last edited Jun 7, 2017
Created on Jun 7, 2017

This example demonstrates how to resolve function conflicts when loading single-cell R packages alongside the tidyverse. It shows a defensive code snippet that checks whether the `scran` or `scater` packages are present in the search path using `search()`, and if so, forcibly unloads them with `detach(..., unload=TRUE)` to prevent their functions from overriding tidyverse verbs like `mutate` or `arrange`. The approach relies on base R functions, specifically `search()`, `%in%`, and `detach()`, to conditionally remove packages at runtime. The visualization highlights a practical workflow safeguard, using the `gistfile1.txt` source as the basis for the code logic.

AI-generated description

Similar vizzes

Loading thumbnail…

Run certain gists in R from GithubGist

This example shows how to run a series of R scripts hosted as GitHub Gists, each generating a clustering module for a Shiny application. It demonstrates the use of the `devtools` package’s `source_gist` function to fetch and execute code directly from specific gist URLs, with filenames specified for each dataset. The code processes specific time-point Rds files, all time points, and full datasets, including renamed and merged clustering variants. The visualization highlights a reproducible workflow for sourcing remote R code, relying on the gist API and local file handling rather than any graphical or interactive rendering.

FFloWuenne
53% match
Loading thumbnail…

Analyze and fix .gtf file for Drop-seq pipeline to remove warnings and skipping for genes with issues

This R script demonstrates a bioinformatics workflow for cleaning a modified GTF genome annotation file by identifying and filtering problematic gene entries before running a Drop-seq pipeline. It shows how duplicated gene names and multiple gene IDs cause warnings and skips during processing. The code uses the `refGenome` package to parse the GTF and extract gene positions, then leverages `dplyr` and `tidyr` for data manipulation to categorize issues. It cross-references gene expression data from two DGE files and queries the Ensembl database via `biomaRt` to retrieve current gene IDs. The visualization ultimately classifies problematic genes by duplication, chromosomal disagreement, or strand disagreement, filtering them out to create a clean annotation set.

FFloWuenne
52% match
Loading thumbnail…

Rprofile.R

This R configuration script customizes a user’s R environment for data analysis, primarily by setting package options, a CRAN mirror, and database connections via RODBC. It defines utility functions like `tdf` for transposing data frames, `propmiss` for summarizing missing values, and `qq` for generating QQ plots of p-values. The code uses base R functions such as `lapply`, `sapply`, `hist`, and `plot`, along with `utils::head` and `base::summary` shortcuts. The script demonstrates practical workflows for data wrangling, statistical visualization, and system-specific setup, emphasizing reproducible coding habits.

DDougManuel
52% match
Loading thumbnail…

Cell cycle assignment for single cell data using scran and cyclone with gene symbols instead of ensembl IDs

This example demonstrates how to assign cell cycle phases to single-cell RNA sequencing data by converting the pretrained marker gene pairs in the scran package from Ensembl IDs to gene symbols. It shows the resulting phase assignments being added as metadata to a Seurat object for downstream analysis. The code uses the scran cyclone function, the org.Hs.eg.db database for ID conversion via bitr, and dplyr for data manipulation. The visualization implicitly relies on the Seurat object’s structure to store the new cell cycle labels, though no plot is generated in the provided script.

FFloWuenne
51% match
Loading thumbnail…

Setup Gist for new Rshiny server VMs.

This example shows a setup script for provisioning a new R Shiny server virtual machine, listing the essential R packages and GitHub-hosted tools required for a typical bioinformatics workflow. It demonstrates how to automate dependency installation using base R functions like `install.packages()` and `install_github()` from the `devtools` package, alongside `install_bitbucket()` for MAGeCKFlute. The code explicitly names libraries such as `tidyverse`, `plotly`, `shinydashboard`, and `manhattanly`, and includes custom forks like `manhattanlyCRISPR`. The rendering approach is a plain-text R script, not a visualization, but the list clarifies the environment’s package landscape for reproducibility.

FFloWuenne
50% match
Loading thumbnail…

A gist in R showing how to plot twitter data from ScraperWiki as a smoothScatter plot

This example shows the relationship between follower and following counts for a subset of Twitter users, revealing a dense clustering pattern near the origin with sparse outliers at higher values. The R code first retrieves JSON data from a ScraperWiki SQL endpoint, converts it into a data frame using the `rjson` package, and then renders a smoothScatter plot—a base R function that uses hexagonal binning and color density to visualize overlapping points. The plot is restricted to a 0–5000 range on both axes to focus on the majority of users while still exposing the distribution’s shape. The approach highlights typical user behavior rather than individual outliers.

IIanHopkinson
48% match
Loading thumbnail…

Fetching Remove Password comments from Github. Using Power Query and R

This example visualizes a GitHub code search for commits that remove passwords, using a Power Query workflow that calls the GitHub Search API to collect matching commits and their file patches. The data flows through Power Query’s table expansion functions before being handed to an R script via `R.Execute`, where a regular expression pattern extracts only the removed lines from each patch. The final table retains the commit metadata and the cleaned removed-line text, omitting the original patch column. The visualization demonstrates a hybrid ETL pipeline combining GitHub’s REST API, Power Query’s M language, and base R string processing.

HHugoberry
48% match
Loading thumbnail…

Extract data for clustering module from all time points (For .Rdata)

This example demonstrates a data-extraction workflow that prepares Seurat clustering results from multiple developmental time points for a Shiny server application. It loads seven Seurat objects (E14.5 through P6) and, for each, extracts normalized expression matrices, t-SNE embeddings, cluster identities, and metadata. The script explicitly uses the `dplyr` and `tidyr` packages for data manipulation, converting sparse expression matrices to dense data frames and merging cell embeddings with identity assignments. The resulting data frames are saved as `.RData` files per time point, preserving the structure needed for interactive clustering visualization. The code relies on Seurat’s slot accessors (`@data`, `@dr$tsne`, `@ident`, `@meta.data`) to gather all relevant components.

FFloWuenne
48% match