Small R Tricks That Make AI-Generated Code Better

Turning verbose scripts into cleaner R workflows

R-Hacks N.10

AI tools can now generate R code in seconds.

Describe a task and a full script appears. For many analysts, this is quickly becoming part of the normal workflow.

Note

The first version of AI-generated code usually works, but it is often verbose, repetitive, or not very idiomatic.

Just small improvements can turn that code into something clearer, shorter, and easier to maintain.

This issue of R-Hacks highlights a few compact patterns that experienced R users apply almost automatically. They are not advanced tricks, just small habits that help refine AI-generated scripts quickly.

1️⃣ Replace Loops with Vectorised Operations

AI tools sometimes produce loops for simple transformations.

for(i in 1:nrow(df)) { 
  df$new_value[i] <- df$value\[i\] \* 2 
  }

In R, this can usually be written in a vectorised way:

df$new_value <- df$value \* 2

Vectorised operations are:

• faster
• easier to read
• less error-prone

They also reflect how R was designed to work with data.

2️⃣ Replace Repetitive Summaries with across()

AI code often repeats the same operation across multiple variables.

mean(df$var1, na.rm = TRUE)
mean(df$var2, na.rm = TRUE) 
mean(df$var3, na.rm = TRUE)

A more compact approach:

df |> summarise(
  across(starts_with("var"), 
  mean, na.rm = TRUE)
  )

This pattern:

• reduces repetition
• keeps scripts shorter
• scales easily to many variables

3️⃣ Check Missing Values Instantly

AI rarely checks data quality unless explicitly asked.

A quick diagnostic:

colSums(is.na(df))

This immediately reveals:

• which columns contain missing values
• how many observations are affected
• where cleaning may be needed

It is a simple check that prevents many downstream issues.

4️⃣ Verify Transformations with Quick Size Checks

After filtering or joins, it is good practice to confirm how the dataset changed.

dim(df)

or

nrow(df)

Tip

Small checks like these help detect unexpected row losses or accidental duplications.

Adding them after major transformations can prevent silent mistakes.

5️⃣ Use Sampling for Faster Testing

When experimenting with code, running everything on a large dataset can slow iteration.

Instead, work on a small sample first:

df_small <- df |> slice_sample(n = 1000)

This allows you to:

• test transformations quickly
• debug pipelines faster
• iterate more efficiently

Once the logic works, apply it to the full dataset.

Why These Tricks Matter

AI tools are very good at producing working code.

But experienced users recognise patterns that make scripts cleaner and more idiomatic.

These small improvements help you:

• simplify generated code
• reduce repetition
• keep workflows easier to maintain

They also reinforce a useful principle:

Note

AI can generate code quickly.

Understanding your tools helps you improve that code immediately.

In Short

• AI-generated code often works but can be verbose
• Vectorised operations simplify many tasks
• across() reduces repetitive summaries
• Quick checks reveal missing data or unexpected changes
• Sampling helps test transformations faster

Small habits like these can dramatically improve everyday R workflows.

Tip

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