How-to

How to chart and pivot a dataset in DataLens

Four ways of looking at the same data — charts for shape, histograms for distribution, correlation for relationships, pivots for numbers.

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What you will do

You will visualise a dataset from four angles: charts, histograms, a correlation matrix and a pivot table.

This is exploration rather than reporting. The goal is to find what is interesting, then explain it.

When this is useful

  • You have a clean dataset and want to know what is in it.
  • A metric moved and you want to see where.
  • You suspect two variables are related and want to check before asserting it.
  • You need a cross-tabulation without exporting to a spreadsheet.

Before you start

  • A clean dataset — Charting a column that loaded as text will not work. Cast types in Prepare first.
  • A question, ideally — Exploration without one produces a lot of charts and few conclusions.

Steps

  1. Open Analyse → Explore & Visualise

    Select Analyse in the navigation, then Explore & Visualise, and choose your dataset.

    Four tabs are available: Charts, Histograms, Correlation and Pivot Table.

    The four tabs, with Charts selected.

  2. Build a chart

    Choose a Chart Type — Bar, Line, Scatter or Grouped Bar — then set the fields it needs and an Aggregation.

    Use Color By to break the chart down by a category, and Max groups to keep a high-cardinality field from producing an unreadable chart.

    The chart rendered from your data.

  3. Look at distributions

    Open Histograms to see how a numeric column is distributed.

    This is where you find that an average is hiding two populations, which no summary statistic will tell you.

    A distribution for the selected numeric column.

  4. Check correlations

    Open Correlation for a Pearson Correlation Matrix across the numeric columns.

    Read it as a place to look, not as a conclusion. Correlation says two columns move together; it does not say one causes the other.

    A matrix of correlation values across numeric columns.

  5. Build a pivot table

    Open Pivot Table and set a Row Field, an optional Column Field, a Value Field and an Aggregation.

    This is the cross-tabulation people usually leave for a spreadsheet — revenue by region by quarter, without the export.

    A pivot table with your chosen rows, columns and aggregated values.

  6. Take the finding somewhere

    Deliver → Publish holds Dashboards & Reports, and export options for getting the numbers out.

What happens next

Where exploration produces something worth watching, Dashboards & Reports under Deliver is where it goes.

Where it produces a question about the future rather than the past, Analyse → Simulate & Decide is the next screen.

Example

An analyst charts revenue by month and sees a dip. A grouped bar with Color By set to region localises it to one region. A pivot of revenue by region by product narrows it to one product line. Three views, five minutes, one answer.

Tips

  • Set Max groups on high-cardinality fields. A bar chart with 300 bars communicates nothing.
  • Look at the histogram before trusting an average — bimodal data makes means meaningless.
  • Treat correlation as a hint. It is a place to investigate, not a finding to report.
  • Use Grouped Bar with Color By when comparing categories across a second dimension.

Limitations

  • Chart types are Bar, Line, Scatter and Grouped Bar. Other chart types are not offered here.
  • The correlation matrix is Pearson and covers numeric columns; non-linear relationships will not show up in it.
  • Charting works on a dataset. Charting across datasets means joining them into a view first.
  • Very high-cardinality categories need a group limit to render usefully.

Related how-to guides

Clean a messy dataset

Drop nulls, fill gaps, remove duplicates and cap outliers — with a live preview before anything is applied.

Run a scenario simulation

Ask what happens if a variable changes — forecasts, shocks, comparisons and impact, on your own data.

Publish and export data

Export as CSV, TSV, JSON, JSONL or Excel, publish an API endpoint, or generate a PDF or Word report.

Join datasets and create views

Join datasets on the keys that relate them and save the result as a reusable view.

Related questions

More of these on the DataLens FAQ page.

What chart types does DataLens offer?

Bar, line, scatter and grouped bar charts, plus histograms for distributions, a Pearson correlation matrix across numeric columns, and a pivot table for cross-tabulation.

Can I build a pivot table in DataLens?

Yes. The Pivot Table tab under Analyse → Explore & Visualise takes a row field, an optional column field, a value field and an aggregation — the cross-tabulation you would otherwise export to a spreadsheet for.

Try this in DataLens

DataLens is in private beta. Request access and work through this guide on your own data.

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