RStudio Server Guide

Accessing RStudio Server

Internal Access (Institute Network):

• Server 01: http://r1.srils.cn/
• Server 02: http://r2.srils.cn/

External Access (From Internet):

• Server 01: http://r1.ext.srils.cn/
• Server 02: http://r2.ext.srils.cn/

Getting Started with RStudio

1. Login: Use your server credentials
2. Interface: Familiarize yourself with the 4-panel layout
   • Source: Script editor
   • Console: R command line
   • Environment: Variables and data
   • Files/Plots: File browser and output display

RStudio Features

• Integrated IDE: Complete R development environment
• Package Management: Easy installation and loading of R packages
• Version Control: Built-in Git support
• Publishing: Share analysis and reports
• Project Management: Organize work into projects

Example: R Data Analysis

# Load libraries
library(tidyverse)
library(ggplot2)

# Load and explore data
data <- read.csv("/data/sample_data.csv")
head(data)
summary(data)

# Data visualization
ggplot(data, aes(x = date, y = value)) +
  geom_line() +
  theme_minimal() +
  labs(title = "Data Trends Over Time",
       x = "Date",
       y = "Value")

File Management in RStudio

Accessing Files

Via RStudio Interface:

• Use the Files panel (bottom-right by default)
• Navigate directories using the file browser
• Upload files using the “Upload” button

Via R Console:

# List files
list.files()

# Get current directory
getwd()

# Set working directory
setwd("/data/projects/my_project")

# Create directory
dir.create("new_folder")

Data Storage Locations

See the File Storage Guide for detailed information about data storage locations and file management.

Working with R Projects

Creating Projects

1. New Project: File → New Project
2. Choose Type:
   • New Directory (fresh start)
   • Existing Directory (use existing folder)
   • Version Control (clone from Git)

Project Benefits

• Organization: Keep related files together
• Working Directory: Automatic path management
• Environment: Separate workspace for each project
• Version Control: Easy Git integration

Package Management

Installing Packages

# Install from CRAN
install.packages("package_name")

# Install multiple packages
install.packages(c("dplyr", "ggplot2", "readr"))

# Install from Bioconductor
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("package_name")

Loading Packages

# Load single package
library(dplyr)

# Load multiple packages
library(tidyverse)  # Loads multiple packages

# Check if package is installed
if (!require("package_name")) {
    install.packages("package_name")
    library(package_name)
}

Best Practices for RStudio

Code Organization

1. Script Structure: Start with library loading, then functions, then main code
2. Comments: Use # for comments, #' for function documentation
3. Naming Conventions: Use snake_case for variables and functions
4. Project Structure: Organize files in logical folders

Performance Optimization

1. Memory Management: Use rm() to remove large objects
2. Vectorization: Use vectorized operations instead of loops
3. Data Types: Choose appropriate data types (factors, numerics)
4. Parallel Processing: Use packages like parallel or foreach

Data Security

1. Regular Saves: Save scripts and workspaces frequently
2. Version Control: Use Git for code versioning
3. Data Privacy: Follow institutional data handling guidelines
4. Backup: Regular backup of important projects

Troubleshooting RStudio

Common Issues

RStudio Connection Issues:

# Check RStudio Server status (contact admin)
sudo systemctl status rstudio-server

# Restart RStudio Server (contact admin)
sudo systemctl restart rstudio-server

R Session Problems:

• Session Crashed: Restart R session (Session → Restart R)
• Memory Issues: Clear workspace or restart session
• Package Errors: Check package installation and compatibility

Performance Issues:

• Check system resources with htop or top
• Close unused projects and clear large objects
• Restart R session to free memory
• Contact administrator for resource allocation

Error Diagnosis

# Check R version
R.version.string

# Check installed packages
installed.packages()

# Check memory usage
object.size(your_object)
gc()  # Garbage collection

# Session information
sessionInfo()

Advanced Features

Markdown Reports

• R Markdown: Create dynamic reports mixing R code and text
• Knit: Generate HTML, PDF, or Word documents
• Shiny: Create interactive web applications

Version Control

• Git Integration: Built-in Git support in RStudio
• Commit: Track changes to your code
• Push/Pull: Sync with remote repositories

Publishing

• RPubs: Publish reports to RPubs
• Shiny Server: Deploy Shiny applications
• GitHub: Share code and collaborate

Getting Help

• RStudio Documentation: https://docs.rstudio.com/
• R Documentation: Use ?function_name or help(function_name)
• Community Forums: https://community.rstudio.com/
• Stack Overflow: Tag questions with r and rstudio
• Server Administrator: Contact for technical issues

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Need additional help? Contact the SRILS Server administration team for technical support.