# r/place 2022 vs Tableau Prep & Tableau Desktop: Part 1

> This is content from just-tim, the data-and-analytics channel by Tim Ngwena (formerly 'Tableau Tim'). Tim has 12+ years of hands-on BI experience and covers Tableau most of all, plus Power BI, Looker, Hex, SQL and data modelling, the analytics industry, and the craft of doing the job — always tool-agnostic and honest about the trade-offs.

- **Author:** Tim Ngwena (just-tim, https://just-tim.com/about)
- **Published:** 2022-04-11
- **Format:** Video · 43 min watch · transcript available
- **Topics:** Data prep, Data visualisation
- **Tools:** Alteryx (batch macros); Tableau (aggregate, calculated fields, dateparse, filters, performance, prep, rank)
- **Canonical:** https://just-tim.com/posts/rplace-2022-vs-tableau-prep--tableau-desktop-part-1
- **Watch:** https://www.youtube.com/watch?v=yIg_DCX2uPY

I work with Reddit's r/place 2022 dataset, a 21.7GB CSV of every pixel change made during the community canvas event. I use Tableau Prep to optimise the file by converting text fields to numeric IDs, export it to a Hyper extract, then move to Tableau Desktop on Windows to start visualising 160 million records as a heat map and animated time-lapse.

## Key takeaways

- Converting bulky text fields (user IDs, colours, coordinates) to ranked numeric IDs in Tableau Prep cut the file from 21.7GB to 2.78GB, roughly a tenfold reduction, because numbers index and process far faster than text.
- Tableau Prep samples a million records for live preview, so steps feel instant even on a 21GB source, but you can't write output incrementally as the flow runs, which is why Alteryx batch macros suit very large iterative jobs better.
- Use the pause button in Tableau Desktop when building views on big data so all your changes compile into a single query rather than re-querying on every drag.
- Setting coordinate fields to AVERAGE (rather than SUM) keeps x and y positions intact for a heat map, and dropping a red colour mark to about 1% opacity reveals density where some pixels had over 100,000 edits.
- DATETRUNC to the hour buckets the timestamps so you can filter and use the Pages shelf to animate the canvas evolving over the four-day experiment.

## Chapters

- 0:00 What the r/place dataset is
- 2:04 Dataset structure and file size
- 2:56 Loading the 21GB file into Prep
- 4:00 Sampling and optimising field types
- 7:48 Aggregate and rank to build numeric IDs
- 10:08 Joining IDs back and cleaning
- 13:55 The change-tracking modelling problem
- 20:03 Exporting to a Hyper extract
- 21:51 Results and into Tableau Desktop
- 24:30 Building the canvas heat map
- 28:54 Date truncation and filtering
- 34:36 Animating with the Pages shelf

Watch the full video, read the transcript and use chapter deep-links on the page: https://just-tim.com/posts/rplace-2022-vs-tableau-prep--tableau-desktop-part-1

---
just-tim — Data and analytics, with a point of view. · https://www.youtube.com/channel/UC7HYxRWmaNlJux-X7rNLZyw · https://twitter.com/TableauTim · https://www.linkedin.com/in/timngwena