Positron Assistant and Databot

6.1 Overview

Imagine having a knowledgeable colleague sitting right next to you while you work—someone who never gets tired, never judges when you forget a function name, and genuinely enjoys helping you figure out why your code isn’t behaving. That’s essentially what Positron Assistant brings to your desk.

In this chapter, we’re going to meet two helpful teammates: Positron Assistant and Databot. They live in the same ecosystem, but they do different jobs. Positron Assistant is your default coding copilot for chat, inline help, and code suggestions. Databot is an exploratory analysis agent that can accelerate early-stage EDA workflows.

We’ll treat Positron Assistant as the primary path for most readers and Databot as an optional extension, because Databot is currently in research preview.

Whether you’re just starting out with R or you’ve been writing code for years, these tools can genuinely make your research life easier. Let’s get acquainted!

6.2 Meet Your New Teammate: Positron Assistant

Think of Positron Assistant as a well-read colleague who happens to know R inside and out. It can use context from your project and your interactive workflow to provide more relevant help than generic chatbot responses.

6.2.1 What Can It Do?

Here’s what this helpful teammate brings to the table:

  • Code Suggestions: Not just basic autocomplete, but context-aware snippets that can help you move from intent to working code more quickly.

  • Code Explanation: Ever looked at someone’s code and thought “what on earth is happening here?” Select any block, ask Positron Assistant, and it will translate cryptic code into plain language.

  • Bug Detective: When something breaks (and let’s be honest, it happens to all of us), Positron Assistant can inspect the error and suggest fixes quickly.

  • Code Generation: Tell it what you want to accomplish in plain language—“I need to calculate the average score by group and handle the missing values”—and watch it write the R code for you.

  • Refactoring: Got messy code that works but is hard to maintain? Positron Assistant can propose cleaner versions and explain the tradeoffs.

6.2.2 Why Would an Educational Researcher Want This?

Great question! You might be thinking, “I’ve gotten along fine without AI help so far.” Here’s the thing—it’s not about replacing what you know. It’s about:

  • Saving time on the stuff that isn’t the interesting part of your research—we’ve all spent 20 minutes trying to remember exactly how to pivot a dataframe. The AI Assistant just does it.

  • Learning new tricks—it often suggests approaches you might not have considered, which is a nice way to pick up new R skills without explicitly studying.

  • Reducing frustration—there’s nothing more demoralizing than staring at an error message for an hour. The AI Assistant can often spot the issue in seconds.

  • Building confidence—if you’re newer to R, having a supportive “colleague” to double-check your work makes experimentation less intimidating.

A gentle reminder: Positron Assistant is a tool to work with, not a replacement for your expertise as a researcher. It’s great at generating code, but you’re the expert on your data and research questions. Always review what it generates—treat it as a first draft that needs your expert polish.

6.3 Getting Started with Your New Teammate

6.3.1 What You’ll Need

Before we dive in, make sure you have:

  • Positron installed (use a current release; Positron Assistant documentation currently tracks the 2025.07+ generation of features)
  • At least one configured model provider (for example, Anthropic, GitHub Copilot, OpenAI, Amazon Bedrock, or Snowflake Cortex)
  • R (version 4.0 or later) installed on your machine

You should also know your institution’s guidance on external AI tools before using non-public data.

6.3.2 Setting Things Up

Getting started is straightforward:

  1. Download and install Positron from the website
  2. Enable Positron Assistant by turning on positron.assistant.enable in Settings
  3. Reload Positron (restart or run Developer: Reload Window)
  4. Configure a provider with Positron Assistant: Configure Language Model Providers
  5. Open chat (robot icon or Chat: Open Chat) and start asking questions

6.3.3 Making It Feel Right for You

Positron Assistant can adapt to your style. In settings, you can adjust:

  • How detailed responses should be (brief vs. thorough)
  • Whether it prefers tidyverse-style code or base R
  • Whether generated code should include comments

Play with these settings until it feels comfortable for you. Everyone’s preferences are different!

6.3.4 Choosing a Provider for Research Work

If you’re working in educational research settings, choose providers with your workflow constraints in mind:

  • Compliance first: If your institution restricts providers, follow that policy before convenience.
  • Cost awareness: API-key providers are often usage-based; budget and monitor token usage.
  • Reproducibility: Record provider + model name in your methods notes.
  • Stability: Keep one default model for a project to reduce output variability.

For most readers, a good starting strategy is simple: pick one approved provider, use one primary model for the project, and document those decisions.

6.4 Actually Using Positron Assistant

Enough setup—let’s see this teammate in action!

6.4.1 Getting Code Explained

We’ve all been there: you find some code online (maybe in a tutorial or from a colleague), and it looks like hieroglyphics. Here’s what you do:

  1. Highlight the confusing code
  2. Ask Positron Assistant something like “what does this do?”
  3. Get a friendly, plain-language explanation

This is honestly one of my favorite features. It’s like having a patient friend who can explain anything code-related without making you feel silly for asking.

6.4.2 When Things Break

Oh, the joy of debugging. Here’s a common scenario:

# You run this, and R throws an error
mean(student_data$score)

Instead of spending precious research time hunting through Stack Overflow, show that error to Positron Assistant. It might say something like:

“I see the issue! You’re trying to calculate a mean, but there are missing values in your data. Add na.rm = TRUE to ignore them, or decide whether those NAs should be handled differently. Want me to show you both options?”

Helpful, right?

6.5 Meet Databot (Experimental): Your EDA Accelerator

If Positron Assistant is your coding colleague, Databot is your exploratory analysis accelerator. It is designed for rapid, iterative data exploration and can generate and run short analysis steps.

Databot is currently experimental (research preview), so treat it as an acceleration layer for exploration, not as an unreviewed source of final results.

6.5.1 What Can Databot Handle?

A few examples:

  • Generating import templates — instead of writing “read.csv()” from scratch every time you get a new dataset, let Databot create a reusable function
  • Building data cleaning pipelines — those steps you do every single time (convert text to lowercase, strip whitespace, handle missing values) can be automated
  • Creating report templates — if you generate similar reports quarterly, Databot can build the skeleton for you
  • Setting up quality checks — automated tests that catch data problems before they become headaches

6.5.2 Example: Never Write Import Code Again

Every time you get a new CSV, do you find yourself typing the same import code? Databot can create a smart template that remembers your preferences:

# This template was created once, now you use it forever!
library(readr)
library(here)

import_data <- function(file_path) {
  read_csv(
    file = here("data", file_path),
    col_types = cols(
      id = col_character(),
      score = col_double(),
      grade = col_factor(),
      .default = col_character()
    ),
    na = c("", "NA", "N/A", "null")
  ) |>
  mutate(across(where(is.character), str_squish))
}

# Now importing is one line:
# student_data <- import_data("student_scores.csv")

See what happened? You wrote the function once, and now every future data import is one line. Databot helped you think through what you needed once, and now you’re saved from doing it over and over.

6.5.3 Example: Automatic Quality Checks

Here’s another scenario: you get new survey data and you want to make sure it’s not obviously broken before you start analyzing. Databot can generate checks that run automatically:

# Auto-generated validation checks - run these on every new dataset
validate_student_data <- function(df) {
  checks <- list(
    check_columns_exist = function(d) {
      required <- c("id", "score", "grade")
      missing <- setdiff(required, names(d))
      if (length(missing) > 0) stop("Missing columns: ", paste(missing, collapse = ", "))
      TRUE
    },
    check_no_missing_id = function(d) {
      if (any(is.na(d$id))) stop("Missing IDs found")
      TRUE
    },
    check_score_range = function(d) {
      if (any(d$score < 0 | d$score > 100, na.rm = TRUE)) {
        stop("Scores out of valid range (0-100)")
      }
      TRUE
    }
  )
  
  for (check in checks) {
    check(df)
  }
  message("All validation checks passed!")
  TRUE
}

Now you’ve got a safety net. Every time new data arrives, just run validate_student_data(your_new_data) and you’ll know right away if something’s wrong.

6.6 Real Talk: Using These Tools in Your Research

6.6.1 A Concrete Example

To keep this chapter reproducible, use the demo files from data/ch6_demo/: student_scores.csv and teacher_survey.csv.

In this example workflow, you can:

  • ask Databot to draft an import function for student_scores.csv (Section 6.5.2),
  • ask Databot to generate validation checks for teacher_survey.csv (Section 6.5.3),
  • use Positron Assistant to refine or explain the generated code,
  • run the cleaned analysis pipeline and produce a quick plot for reporting.

The screenshot below should show this end-to-end flow in one workspace view: editor code, Databot/Assistant output, Console results, and a plot. The goal is not to present final publication results, but to document a realistic AI-assisted analysis process from import to quality checks to exploratory output.

6.6.2 What This Means for Your Research

The real value isn’t just saving time (though that’s nice). It’s that these tools let you focus on what matters: the research itself. When you’re not stuck fighting with code, you can spend more mental energy on:

  • Understanding your data deeply
  • Choosing the right analytical approaches
  • Thinking critically about what your findings mean
  • Communicating results effectively to your audience

6.7 Working Responsibly with AI Helpers

Here’s the thing—we’ve talked a lot about how great these tools are, but let’s be real about using them thoughtfully. They’re powerful, but they’re not magic, and they’re definitely not a substitute for your expertise as a researcher.

6.7.1 Always Review What You Get

Treat AI-generated code like a first draft from a well-meaning but imperfect colleague. It might be mostly right, but it’s probably not perfect. Before you use any code it generates in actual research:

  • Test it on a small sample first—run the code on a subset of your data to make sure it does what you expect
  • Verify the results against something you know to be true, or compare with a manual calculation
  • Add your own comments explaining what the code is doing, especially if the logic is complex

6.7.2 Be Transparent

If you use AI tools in your research workflow, it’s good practice to be open about it. This doesn’t mean you have to apologize—just acknowledge it in your methods. Something like:

“We used the Positron AI Assistant to help generate initial data processing code, which was then reviewed and validated by the authors.”

Simple, honest, and it helps others understand your process.

6.7.3 Keep Things Reproducible

A few tips to make sure your work still holds up:

  • Note what version of the AI tools you used
  • Keep your human-written code as the authoritative version—don’t rely solely on what the AI generated
  • Use version control (like Git) to track changes, so you can always see the history of your work

6.7.4 Protect Data and Document AI Use

For AI-assisted workflows, keep a short project log with:

  • Positron version
  • provider name (for example, Anthropic or GitHub Copilot)
  • model name
  • date/time of major AI-assisted runs
  • what was AI-generated vs. researcher-edited

Also remember the privacy boundary: Positron routes requests to your selected provider. Posit documents that it does not store your prompts and AI conversations, but your chosen provider may have its own data retention policy. Review provider terms before using sensitive data.

6.8 Wrapping Up

We’ve covered a lot of ground! Here’s the takeaway:

Positron Assistant and Databot are genuinely useful teammates in your research journey. They won’t replace your expertise—let’s be clear about that. What they will do is handle repetitive coding work, support learning in context, and make computational research less frustrating.

Think of them as tools that amplify what you already bring to the table: your research questions, your domain expertise, your critical thinking. They handle the coding heavy lifting so you can focus on the parts that really need a human mind.

Key Points to Remember

  • Positron Assistant offers real-time code help, explanations, and debugging support
  • Databot can accelerate exploratory analysis and repetitive scaffolding, but requires careful review
  • Always review and validate AI-generated code—it’s a starting point, not the final answer
  • Being transparent about AI use builds trust with your readers
  • These tools free you up to focus on what matters: your research

What’s Next?

In the next chapter, we’re going to explore another fascinating side of AI in research: Local LLMs. You’ll see how researchers can run powerful language models entirely on their own computers—perfect for working with sensitive data where privacy really matters. It’s a different approach from what we’ve covered here, and it opens up some really interesting possibilities for qualitative analysis.

Ready to keep exploring? Let’s go!