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Running LCZ4r in Posit Cloud

Max Anjos

March 25, 2026

Source: vignettes/posit_cloud.Rmd
posit_cloud.Rmd

Introduction

Welcome to the LCZ4r package demonstration on Posit Cloud! This guide will walk you through the steps to run LCZ4r and explore its powerful tools for Local Climate Zone (LCZ) analysis in a cloud-based environment.

Why Posit Cloud?

Posit Cloud (formerly RStudio Cloud) offers several advantages for LCZ4r users: - No installation required: Run R and LCZ4r directly in your browser - Pre-configured environment: All dependencies are already installed - Access anywhere: Work from any computer with internet access - Collaboration ready: Share projects with colleagues easily - Free tier available: Get started without any cost

Getting Started

Follow these step-by-step instructions to access and run LCZ4r in Posit Cloud:

1 Access the Posit Workspace

Click on the following link to access the LCZ4r workspace:

🔗 LCZ4r Demo Workspace: https://posit.cloud/content/9921467

2 Log in or Create an Account

  • If you don’t already have a Posit Cloud account, click “Sign Up” to create one (free tier available)
  • Log in to your account to access the workspace
  • You can use your Google or GitHub account for quick registration

3 Save a Permanent Copy

Important: Make sure to Save a Permanent Copy of the project:

  1. Click on the project name in the top-left corner
  2. Select “Save a Permanent Copy” from the dropdown menu
  3. Choose a name for your copy (e.g., “My LCZ4r Analysis”)
  4. This ensures you have your own version that won’t be affected by changes to the original

4 Explore the R Scripts

The project contains three main R scripts organized by functionality:

📊 general_functions.R

Demonstrates general functions for LCZ analysis, including:

  • Downloading and visualizing LCZ maps from global datasets
  • Calculating LCZ areas and urban canopy parameters
  • Plotting basic LCZ maps and statistics
🏙️ local_functions.R

Focuses on localized LCZ analysis with advanced capabilities:

  • Time series analysis of air temperature
  • Thermal anomaly calculations
  • Spatial interpolation with kriging
  • Urban Heat Island intensity assessment
🌡️ modelling_temperature.R

Specialized temperature modeling and UHI analysis:

  • Temperature interpolation and mapping
  • Thermal anomaly mapping
  • UHI intensity calculation by LCZ class
  • Model evaluation and validation

5 Run the Scripts

Good news: The LCZ4r package and all dependencies are already installed in the Posit Cloud environment, so you’re ready to go!

To run the scripts: 1. Open each script by clicking on its name in the Files pane 2. Run code line-by-line with Ctrl + Enter (or Cmd + Enter on Mac) 3. Or run entire sections by selecting code and clicking “Run” 4. Explore the results in the Console and Plots panes

Visualizing the Workspace

Below is a screenshot of the Posit Cloud workspace to help you get oriented:

Posit Cloud workspace interface showing the LCZ4r demo project

Posit Cloud workspace interface showing the LCZ4r demo project with the file explorer, code editor, and console panes.

Workspace Overview

Key Panels in Posit Cloud

Panel Location Purpose
Files Bottom-right Browse project files and scripts
Editor Top-left/center Write and edit R code
Console Bottom-left Execute commands and view output
Plots Bottom-right View generated graphics
Environment Top-right See loaded objects and variables

Script Workflow Recommendations

  1. Start with general_functions.R: Get familiar with basic LCZ map operations
  2. Move to local_functions.R: Explore time series and anomaly analysis
  3. Advance to modelling_temperature.R: Dive into spatial interpolation and UHI modeling

Running Your First Analysis

Let’s test that everything is working correctly:

# Load the LCZ4r package
library(LCZ4r)

# Test with a simple LCZ map download
lcz_map <- lcz_get_map(city = "Berlin")

# Visualize the map
lcz_plot_map(lcz_map)

If you see an LCZ map appear in the Plots panel, everything is working correctly!

Troubleshooting Common Issues

Connection Timeouts

If you experience connection timeouts when downloading LCZ maps: - Check your internet connection - Try again during off-peak hours - Use the lcz_get_map_generator() function with a known ID for faster access

Memory Limitations

The free tier of Posit Cloud has limited memory (1 GB): - Start with smaller spatial resolutions (e.g., sp.res = 500 instead of 100) - Process data for shorter time periods first - Use gc() to clear memory when needed - Consider upgrading to a paid plan for larger analyses

Saving Your Work

  • Your project is automatically saved in Posit Cloud
  • Export plots using the “Export” button in the Plots panel
  • Download results using write.csv() for data frames
  • Save maps with writeRaster() for raster outputs

Customizing Your Workspace

Adding Additional Packages

If you need additional packages beyond those pre-installed:

# Install packages as needed
install.packages("package_name")

# For GitHub packages
if (!require("remotes")) install.packages("remotes")
remotes::install_github("username/repository")

Creating New Scripts

  1. Click “New File” → “R Script” in the Files pane
  2. Write your analysis code
  3. Save with an appropriate name (e.g., “my_analysis.R”)

Organizing Outputs

Create folders to keep your work organized:

# Create output directories
dir.create("figures", showWarnings = FALSE)
dir.create("data", showWarnings = FALSE)
dir.create("results", showWarnings = FALSE)

# Save plots to the figures folder
ggsave("figures/my_plot.png", width = 8, height = 6)

# Save data to the data folder
write.csv(my_data, "data/my_results.csv", row.names = FALSE)

Collaboration Features

Posit Cloud makes collaboration easy:

  1. Share your project: Click “Share” button to invite collaborators
  2. Set permissions: Choose “View” or “Edit” access levels
  3. Real-time collaboration: Work simultaneously with team members
  4. Version history: Access previous versions of your work

Learning Resources

To get the most out of LCZ4r on Posit Cloud:

Resource Description
LCZ4r Documentation Complete package documentation and examples
R for Data Science Learn R programming fundamentals
Spatial Data Science Advanced spatial analysis in R
LCZ Generator Access LCZ maps for your study areas

Quick Reference

Essential Keyboard Shortcuts

Action Windows/Linux Mac
Run current line/selection Ctrl + Enter Cmd + Enter
Run entire script Ctrl + Shift + Enter Cmd + Shift + Enter
Clear console Ctrl + L Cmd + L
Comment/uncomment lines Ctrl + Shift + C Cmd + Shift + C

Useful Functions to Get Started 

# Check package version
packageVersion("LCZ4r")

# Get help for any function
?lcz_get_map

# List all LCZ4r functions
ls("package:LCZ4r")

# View available datasets
data(package = "LCZ4r")

Frequently Asked Questions

Q: Can I use my own data in Posit Cloud?

A: Yes! Upload your data files through the Files pane (Upload button) or use read.csv() with a URL to your data.

Q: How long will my Posit Cloud project be available?

A: Free accounts have up to 25 project hours per month. Paid accounts have unlimited hours. Your projects remain indefinitely as long as you maintain your account.

Q: Can I install additional packages?

A: Yes, you can install any CRAN or GitHub package using install.packages() or remotes::install_github().

Q: How do I export my results?

A: Use write.csv() for data frames, writeRaster() for spatial rasters, and the “Export” button in the Plots panel for graphics.

Q: What if the demo workspace is unavailable?

A: You can create a new Posit Cloud project and manually install LCZ4r using remotes::install_github("ByMaxAnjos/LCZ4r").

Have feedback or suggestions?

Do you have an idea for improvement or did you spot a mistake? We’d love to hear from you! Click the button below to create a new issue (GitHub) and share your feedback or suggestions directly with us.

Open GitHub issue