bio-reporting-rmarkdown-reports
Create reproducible bioinformatics analysis reports with R Markdown including code, results, and visualizations in HTML, PDF, or Word format. Use when generating analysis reports with RMarkdown.
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
npx agent-skills-cli listSkill Instructions
name: bio-reporting-rmarkdown-reports description: Create reproducible bioinformatics analysis reports with R Markdown including code, results, and visualizations in HTML, PDF, or Word format. Use when generating analysis reports with RMarkdown. tool_type: r primary_tool: rmarkdown
Version Compatibility
Reference examples tested with: rmarkdown 2.25+, knitr 1.45+, DESeq2 1.42+, ggplot2 3.5+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
R Markdown Reports
"Create an R Markdown report" → Write reproducible R-based documents combining code chunks, results, and narrative that render to HTML/PDF/Word.
- R:
rmarkdown::render('report.Rmd'), or Knit button in RStudio
Basic Document Structure
---
title: "RNA-seq Analysis Report"
author: "Your Name"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
theme: cosmo
---
Setup Chunk
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
fig.width = 10,
fig.height = 6,
fig.align = 'center'
)
library(tidyverse)
library(DESeq2)
library(pheatmap)
```
Code Chunk Options
```{r analysis, echo=TRUE, results='hide'}
# echo: show code
# results: 'hide', 'asis', 'markup'
# include: FALSE hides chunk entirely
# eval: FALSE shows code but doesn't run
# cache: TRUE caches results
```
Parameterized Reports
---
title: "Sample Report"
params:
sample_id: "sample1"
count_file: "counts.csv"
fdr_threshold: 0.05
---
```{r}
counts <- read.csv(params$count_file)
sample <- params$sample_id
fdr <- params$fdr_threshold
```
# Render with parameters
rmarkdown::render('report.Rmd', params = list(sample_id = 'sample2', fdr_threshold = 0.01))
# Batch render
samples <- c('sample1', 'sample2', 'sample3')
for (s in samples) {
rmarkdown::render('report.Rmd', params = list(sample_id = s),
output_file = paste0(s, '_report.html'))
}
Tables
```{r}
# Basic kable table
knitr::kable(head(results), caption = 'Top DE genes')
# Interactive table with DT
library(DT)
datatable(results, filter = 'top', options = list(pageLength = 10))
# Formatted table with kableExtra
library(kableExtra)
results %>%
head(10) %>%
kable() %>%
kable_styling(bootstrap_options = c('striped', 'hover')) %>%
row_spec(which(results$padj < 0.01), bold = TRUE, color = 'red')
```
Figures
```{r volcano-plot, fig.cap="Volcano plot of differential expression"}
ggplot(results, aes(log2FoldChange, -log10(pvalue))) +
geom_point(aes(color = padj < 0.05)) +
theme_minimal()
```
Inline Code
We identified `r sum(res$padj < 0.05, na.rm=TRUE)` significantly
DE genes (FDR < 0.05) out of `r nrow(res)` tested.
Child Documents
---
title: "Main Report"
---
```{r child='methods.Rmd'}
```
```{r child='results.Rmd'}
```
PDF Output
---
output:
pdf_document:
toc: true
number_sections: true
fig_caption: true
latex_engine: xelatex
---
HTML with Tabs
## Results {.tabset}
### PCA Plot
```{r}
plotPCA(vsd, intgroup = 'condition')
```
### Heatmap
```{r}
pheatmap(assay(vsd)[top_genes, ])
```
Caching Long Computations
```{r deseq-analysis, cache=TRUE, cache.extra=tools::md5sum('counts.csv')}
# Cached unless counts.csv changes
dds <- DESeqDataSetFromMatrix(counts, metadata, ~ condition)
dds <- DESeq(dds)
```
```{r downstream, dependson='deseq-analysis'}
# Re-runs when deseq-analysis cache changes
res <- results(dds)
```
Custom CSS
---
output:
html_document:
css: custom.css
---
/* custom.css */
body { font-family: 'Helvetica', sans-serif; }
h1 { color: #2c3e50; }
.figure { margin: 20px auto; }
Complete Report Template
---
title: "RNA-seq Analysis Report"
author: "Bioinformatics Core"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
params:
count_file: "counts.csv"
metadata_file: "metadata.csv"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(DESeq2)
library(tidyverse)
library(pheatmap)
library(DT)
```
## Data Overview
```{r load-data}
counts <- read.csv(params$count_file, row.names = 1)
metadata <- read.csv(params$metadata_file, row.names = 1)
```
Loaded `r nrow(counts)` genes across `r ncol(counts)` samples.
## Differential Expression
```{r de-analysis, cache=TRUE}
dds <- DESeqDataSetFromMatrix(counts, metadata, ~ condition)
dds <- DESeq(dds)
res <- results(dds) %>% as.data.frame() %>% arrange(padj)
```
## Results
```{r results-table}
datatable(res %>% filter(padj < 0.05), options = list(pageLength = 10))
```
Related Skills
- reporting/quarto-reports - Modern alternative
- data-visualization/ggplot2-fundamentals - Figure creation
- differential-expression/de-visualization - Analysis plots
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