Creating Power BI Visuals with R Packages - When It Is Worth It and When It Is Not

Power BI's built-in charts cover a lot of ground. Bars, lines, maps, scatter plots, the usual suspects, and they handle the vast majority of what most businesses need to show. But every so often someone in a team, usually someone with a stats or data science background, wants something the standard visuals simply do not do. A proper correlation matrix. A survival curve. A forecast with confidence bands drawn the way a statistician expects. And that is where Power BI's support for R visuals comes in.

The idea is straightforward: you can write R code inside a Power BI report to generate a chart, and Power BI renders the output as if it were any other visual. Microsoft's documentation is Create visuals with R packages, and it covers the mechanics and the list of supported packages. This post is about whether you should actually use it, which is a more interesting question than how.

How it works

An R visual in Power BI is a special visual type you drop onto your report canvas. You drag the fields you want into it, which hands that data to R as a data frame, and then you write R script in the editor to plot it. When the report renders, Power BI runs your R code, R produces a plot image, and Power BI displays that image in the visual's spot. To the person reading the report it looks like any other chart. Under the hood it is R doing the drawing.

You get access to R's plotting ecosystem, which is enormous. ggplot2 for elegant statistical graphics, plus a long list of packages for specialised charts. In Power BI Desktop, R runs on your own machine using your local R installation, so you can use whatever packages you have installed. In the Power BI service, where published reports live, R runs in Microsoft's environment, and this is the important catch: only a specific approved list of R packages is supported there. If your visual uses a package that is on the list, it renders in the service. If it uses one that is not, it works fine on your laptop and then breaks the moment you publish, which is a genuinely annoying way to find out.

So the workflow is: build and test in Desktop, but check your packages against the supported list before you publish, so the service does not surprise you.

What R visuals actually give you

The real value is doing things the native visuals cannot. A few examples we have actually reached for.

Statistical charts that Power BI does not ship. A correlation heatmap across dozens of variables. A box-and-whisker plot with the exact quartile behaviour a domain expert expects. Density plots, violin plots, dendrograms. If your audience is technical and wants a specific statistical view, R gives it to you without compromise.

Forecasting and modelling shown inline. You can run an actual forecasting model in R, ARIMA or similar, and plot the forecast with proper confidence intervals directly in the report. The built-in Power BI forecasting is fine for a quick trend, but if you need a real model with a statistician's rigour behind it, R lets you put that in front of business users in a familiar dashboard rather than a separate tool.

Highly customised output. ggplot2 gives you control over every element of a chart down to the pixel. If you have a specific visual standard to hit, or you want something genuinely bespoke that the native visuals cannot be bent into, R is a way to get there.

The through-line is that R visuals are for the cases where the standard toolkit runs out, not for everyday reporting. They are a specialist tool for specialist needs.

The honest downsides

I like R visuals for the right job, but I would be doing you a disservice if I did not lay out where they hurt.

They are not interactive. This is the big one. A native Power BI visual cross-filters: click a bar and the rest of the report responds. An R visual is a static image. You cannot click into it, it does not filter other visuals, and it does not participate in the interactivity that makes Power BI feel alive. It reacts to slicers and filters applied to the report, so it will redraw when you change a filter, but the image itself is not clickable. For a lot of business audiences, losing interactivity is a real cost, and it is worth asking whether a slightly less perfect native chart that you can click beats a perfect static one that you cannot.

They depend on R being present and packages being supported. In Desktop, everyone who opens the file needs R installed to see the visual render. In the service, you are limited to the supported package list. This adds a maintenance burden and a dependency that plain Power BI reports do not have. When something breaks, you are now debugging an R environment as well as a report.

They are slower and heavier. Running R code to generate an image is more work than drawing a native chart, so R visuals can be sluggish, especially with larger data. There are also limits on how much data you can pass into an R visual, so they are not the tool for plotting millions of rows.

And they need someone who can write R. This sounds obvious but it matters for the long-term health of a report. If the person who wrote the R visual leaves and nobody else on the team knows R, that visual becomes a black box nobody dares touch. A native visual anyone on the team can maintain is often the better call even if it is slightly less capable, purely because it does not create a single point of knowledge failure.

Where they actually fit

My rule of thumb is this. If a native Power BI visual can do the job, use it. You keep interactivity, you keep maintainability, and you avoid the dependencies. Reach for an R visual only when there is a genuine analytical need the native visuals cannot meet, and when you have someone who can own it properly.

Good fits: a technical or analytical audience who values statistical rigour over click-through interactivity, a specific chart type Power BI does not offer, or a report where a real model needs to be shown to business users in a familiar place. Poor fits: everyday operational dashboards, anything where interactivity is the point, or a team with no R skills who will be stuck the day it breaks.

The most sensible pattern we have used is a hybrid report. The bulk of the report is native, interactive, and maintainable by anyone. One or two R visuals handle the specific statistical views that genuinely need them. You get the best of both, and you contain the R dependency to the parts that truly warrant it rather than sprinkling it everywhere.

If you are doing this kind of work, it usually means you have moved past basic reporting into real analytics, and that is where our machine learning and data science team tends to get involved. Deciding when to reach for R, when to push the model upstream into your data platform, and when a native visual is genuinely good enough is exactly the sort of judgement call we help teams make. It also connects to the broader Power BI consulting work we do, because getting the reporting layer right is inseparable from getting the analytics behind it right.

The short version

R visuals let you draw charts in Power BI that the built-in visuals cannot, using R's huge plotting ecosystem. They are brilliant for specialised statistical output and genuinely bespoke charts. They also give up interactivity, add dependencies, run slower, and need someone who knows R to maintain them.

Use them deliberately, for the cases that actually need them, inside an otherwise native report. Do not scatter them through everyday dashboards where a clickable native chart would serve people better. If you want help working out where the line sits for your reporting, get in touch and we will give you a straight answer.