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How Do I Even Start? A Practical Primer for Finding What You Need in R

✨ Preface: Why This Guide Exists

In our team, a lot of people want to get better at R. But most training we’ve seen is too formal, too long, or too disconnected from real work. People often walk away from training sessions feeling overwhelmed or unsure how to apply what they just saw.

This guide is different. It’s designed for when you’re sitting at your desk, trying to solve a real problem, and thinking:

“I don’t know how to do this in R… now what?”

Instead of memorizing everything up front, this guide helps you:

• Figure out what kind of problem you’re dealing with.
• Search effectively.
• Find the right package.
• Use the built-in tools that most R users (even pros!) rely on every day.

The goal is to help you build independence, so that you’re not waiting for formal training to start learning—but instead learning as you work.

🥇 Step 1: Use R in Small, Daily Tasks

→ Why this matters: You can’t learn a language without speaking it—and you can’t learn R without using it.

Even if you’re not writing models or fancy dashboards yet, just using R for basic filtering or renaming columns will build the habit.

✅ Try replacing one small Excel task each day with an R script.
Instead of “I’ll wait until I understand more,” say, “I’ll do one thing in R today.”

🥈 Step 2: Learn to Google Like a Developer

→ Why this matters: The fastest way to improve in R is not to memorize every function—it's to master how to find what you need.

Even experienced coders don’t know every function or fix. What they do know is how to search.

✅ Use Google with intent.
Instead of long, vague phrases, search with focused terms like:

xxxR r package manipulate time data
r dplyr remove NA values site:stackoverflow.com
xxx

This is your #1 skill: lowering the time between “I don’t know” and “I do now.”

🥉 Step 3: Identify the Type of Problem You’re Solving

→ Why this matters: Not all problems are equal. Some are spatial. Some are about time. Some are about strings.

Before looking for an answer, pause and ask:
“What kind of problem is this?”
That leads you to the right tools—and prevents endless Googling of bad questions.

✅ Example: You’re struggling with a date column.
Instead of searching:

xxxR how do I fix this date column
xxx

Try:

xxxR r package working with dates
xxx

That’ll lead you to lubridate, which is a full set of tools for exactly that domain.

🏅 Step 4: Dig Into Package Resources

→ Why this matters: When you find a package that might help—don’t stop at the function names.
Most good packages come with help pages, reference docs, and full tutorials.

✅ Use these in RStudio:

xxxR ?mutate # Quick help
vette("dplyr") # Full walkthroughs
xxx

🚦 Real-World Example: SF Network Routing in a Transportation Project

You're doing a transportation project and working with a spatial network where your data is already in an sf object. Because of that, you likely want a package that works well with the sf data structure.

If you search:

xxxR r package sf network analysis
xxx

You’ll likely discover the sfnetworks package. It allows you to:

• Convert sf LINESTRINGs into graph representations
• Analyze, clean, and manipulate network structures
• Perform routing, centrality, and path analysis

Not only does this package have functions, but also:

• A complete Reference Page
• And full Articles/Vignettes that walk you through practical workflows

📌 Key point:
A good package doesn’t just give you functions—it often includes tutorials, vignettes, and worked examples. Always check for them!

🎓 Step 5: Go Beyond Packages—Look for Books

→ Why this matters: Sometimes you don’t just need a function—you need a framework.
You want someone to walk you through an entire process, like:

• Cleaning messy survey data
• Doing predictive modeling
• Building a dashboard

That’s where books come in—and R has a ton of them, for free.

✅ Use the Big Book of R
Over 400+ online books. Most are tutorials. Many are goldmines.

Example:
You’re doing urban accessibility work with transit networks.
The Geospatial section includes:

• Crash course on Urban Accessibility
• Geocomputation with R

Find a book. Scan for your topic. Follow the code. Adapt it to your work.

✅ Final Checklist

Question	What to do
“I don’t know what this error means.”	Copy-paste into Google, StackOverflow
“What’s the best way to do this in R?”	Identify the domain (time, spatial, etc.)
“Which package should I use?”	Search r package <domain>
“How do I learn this package?”	Use ?, vignette(), read the docs
“Where can I find a tutorial?”	Check Big Book of R

By following these steps, you'll be well on your way to becoming proficient in R and finding what you need to solve real-world problems. Remember to build independence, use R in small daily tasks, learn to Google like a developer, identify the type of problem you're solving, dig into package resources, and go beyond packages to find books. With practice and patience, you'll become a skilled R user and be able tackle even the most complex tasks with confidence.
Frequently Asked Questions: Mastering R for Real-World Problems

Q: I'm new to R and don't know where to start. What's the best way to learn?

A: Start by using R in small, daily tasks. Try replacing one small Excel task each day with an R script. This will help you build the habit of using R and get familiar with its syntax.

Q: I'm struggling with a specific problem in R. Where can I find help?

A: The fastest way to improve in R is to master how to find what you need. Use Google with intent, searching with focused terms like "xxxR" or "r package manipulate time data". You can also try searching on StackOverflow or R forums.

Q: I've found a package that might help, but I don't know how to use it. What's the best way to learn?

A: Don't stop at the function names! Most good packages come with help pages, reference docs, and full tutorials. Use these in RStudio to learn more about the package and its functions.

Q: I'm working on a project and need to perform a specific task in R. How do I know which package to use?

A: Identify the type of problem you're solving. Is it spatial, temporal, or string-based? This will help you narrow down the search and find the right package. Try searching "r package " to find relevant packages.

Q: I've found a package, but I don't know how to learn it. What's the best way to get started?

A: Use the package's help pages, reference docs, and full tutorials. These resources will give you a comprehensive understanding of the package and its functions. You can also try searching for tutorials or examples online.

Q: I'm looking for a tutorial or book to learn R. Where can I find one?

A: The Big Book of R is a great resource, with over 400 online books and tutorials. You can also try searching for specific topics or domains, such as geospatial or data visualization.

Q: I'm struggling with a specific error in R. How do I troubleshoot it?

A: Copy-paste the error into Google or StackOverflow to see if others have encountered the same issue. You can also try searching for specific error messages or codes.

Q: I'm working on a project and need to perform a complex task in R. How do I know which package to use?

A: Identify the type of problem you're solving and search for relevant packages. You can also try searching for specific tasks or functions, such as "r package for data cleaning" or "r function for data visualization".

Q: I'm new to R and don't know how to use it for data analysis. Where can I find resources?

A: The R documentation is a great place to start, with comprehensive guides and tutorials on data analysis and visualization. You can also try searching for specific topics or domains, such as data cleaning or machine learning.

Q: I'm working on a project and need to perform a task that requires multiple packages. How do I know which packages to use?

A: Identify the type of problem you're solving and search for relevant packages. You can also try searching for specific tasks or functions, such as "r package for data" or "r function for data visualization". Don't be afraid to experiment and try different packages until you find the one that works best for you.

By following these tips and resources, you'll be well on your way to mastering R and tackling even the most complex tasks with confidence.