https://github.com/jedick/plotmydata
Use AI agents to access, transform, and plot your data. With a live demo and growing evaluation set.
https://github.com/jedick/plotmydata
adk agents data evals graphics huggingface plotting r
Last synced: about 5 hours ago
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Use AI agents to access, transform, and plot your data. With a live demo and growing evaluation set.
- Host: GitHub
- URL: https://github.com/jedick/plotmydata
- Owner: jedick
- License: mit
- Created: 2025-08-08T03:20:47.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2026-01-26T10:48:38.000Z (5 months ago)
- Last Synced: 2026-01-27T00:36:31.097Z (5 months ago)
- Topics: adk, agents, data, evals, graphics, huggingface, plotting, r
- Language: Python
- Homepage:
- Size: 258 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PlotMyData
[](https://huggingface.co/spaces/jedick/plotmydata)
PlotMyData is an agentic data analysis and visualization system.
It follows your prompts to drive an [R] session.
You can start with example datasets, upload your own data, or download data from a URL.
If you want to ask about the data or transform it before plotting, just say what you want to do.
## Features
- Multiple data sources: Use built-in [R datasets] or user-provided data (currently CSV files are supported)
- Interactive analysis: The system uses an R session so variables persist across invocations
- Instant visualization: Plots are shown in the chat interface and are downloadable as PNG files
### Agents and tools refined through many usage trials
- *Help tools*
- Provide access to help pages for packages and topics
- *Data agent*
- Knows about R datasets and can access uploaded files or URLs
- Data files are automatically summarized for the LLM
- *This lets you describe a plot without knowing the exact variable names*
- *Run agent*
- Runs R code generated by the LLM
- If you want to run specific code, just send it in a message
- LLM chooses invisible or visible results depending on requirements
- *Plot agent*
- Tools are provided for making plots with base [R graphics] (default) and [ggplot2]
- *To use ggplot2, just mention "ggplot" or "ggplot2" in your message*
- *Install agent*
- Installs CRAN packages to add capabilities to the running application
- Can be called by other agents or requested by the user
- User confirmation is required for installing any packages
## Running the application
The application can be run with or without a container.
Containerless
- Install R and run `install.packages(c("ellmer", "mcptools", "readr", "ggplot2", "tidyverse"))`
- Install Python with packages listed in `requirements.txt`
- Put your OpenAI API key in a file named `secret.openai-api-key`
- Execute `run_web.sh` to start an R session and launch the ADK web UI
Containerized
First, build the project.
This creates a `plotmydata` Docker Compose project and a `plotmydata-app` image.
```sh
docker compose build
```
Now run the project.
This uses your OpenAI API key (`sk-proj-...`) from `secret.openai-api-key`.
```sh
docker compose up
```
Changing the model
If you want to change the remote LLM from the default (gpt-4o), change it in the startup script (`run_web.sh` or `entrypoint.sh`).
To use a local LLM, install [Docker Model Runner] then run this command.
```sh
docker compose -f compose.yaml -f model-runner.yaml up
```
See `model-runner.yaml` to change the local LLM used.
## Examples
Plot data
- *Plot radius_worst (y) vs radius_mean (x) from https://github.com/jedick/plotmydata/raw/refs/heads/main/evals/data/breast-cancer.csv. Add a blue 1:1 line and title "Breast Cancer Wisconsin (Diagnostic)".*
Plot functions
- *Plot a Sierpiński Triangle*
Interactive analysis
- *Save 100 random numbers from a normal distribution in x*
- *Run y = x^2*
- *Plot a histogram of y*
## Evaluations
Most recent eval run: **74% accuracy** on 50 cases with GPT-4o.
Evals history
Accuracy = fraction of correct plots.
Plot correctness is judged by a human.
| Eval set | Size | Agent version | Accuracy | Notes |
|-|-|-|-|-|
| 04 | 50 | [1c3f5bd] | 0.74 | More base graphics and add Install agent: corrr, scatterplot3d, nlme, parcoord, kde, and custom plots
| 03 | 40 | [24fb91f] | 0.75 | **Model: gpt-4o**
| 03 | 40 | [b8e5f8c] | 0.38 | Add agent for loading and summarizing data
| 03 | 40 | [30c22a1] | 0.50 | Handle uploaded CSV files
| 02 | 37 | [e9180aa] | 0.49 | More base graphics: hist, image, lines, matplot, mosaicplot, pairs, rug, spineplot, plot.window
| 01 | 27 | [e9180aa] | 0.52 | Add help tools to get R documentation
| 01 | 27 | [bb4eead] | 0.41 | Mainly base graphics: barplot, boxplot, cdplot, coplot, contour, dotchart, filled.contour, grid (**Model: gpt-4o-mini**)
Evals info
The repo tracks both evaluation sets and prompt sets.
For example, the `evals/01` directory contains all results for the first evaluation set using different prompt sets.
The file name uses the short commit hash for the prompt set used for evaluation.
Each eval consists of a query and reference code and image.
Because of their size, reference and generated images are not stored in this repo.
To run evals, copy the latest eval CSV file to `evals/evals.csv`.
Then use e.g. `run_eval.sh 1` to run the first eval.
This script: 1) saves the tool calls, generated code, and current date to the CSV file and 2) saves the generated image to the `evals/generated` directory.
After running evals, change to the `evals` directory and run `streamlit run view.py` to edit the eval CSV file.
This app allows:
- Choosing an eval to edit
- Viewing the reference and generated images side-by-side
- Indicating whether the generated plot is correct (True or False)
- Editing other eval data (e.g. query, file name for data upload, reference code, notes)
- Adding new evals
## Architecture
- An [Agent Development Kit] client is connected to an MCP server from the [mcptools] R package
- The startup scripts launch a persistent R session with some preloaded packages and helper functions
- Data files are saved in a temporary directory using ADK's artifacts and callbacks
- This is how the R session can access the files
Container notes:
- The Docker image is based on [rocker/r-ver] and adds R packages and a Python installation
- [Docker Compose] is used for port mapping, secrets, and watching file changes with [Docker Watch]
## Licenses
- This code in repo is licensed under MIT
- Some examples used in evals are taken from R and are licensed under GPL-2|GPL-3
- `breast-cancer.csv` (from [UCI Machine Learning Repository] via [Kaggle]) is licensed under CC BY 4.0
[R]: https://www.r-project.org/
[R datasets]: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html
[R graphics]: https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/00Index.html
[ggplot2]: https://ggplot2.tidyverse.org/
[Agent Development Kit]: https://google.github.io/adk-docs/
[mcptools]: https://github.com/posit-dev/mcptools
[Docker Model Runner]: https://docs.docker.com/ai/model-runner/
[docker/compose-for-agents]: https://github.com/docker/compose-for-agents
[rocker/r-ver]: https://rocker-project.org/images/versioned/r-ver
[Docker Compose]: https://docs.docker.com/compose/
[Docker Watch]: https://docs.docker.com/compose/how-tos/file-watch/
[UCI Machine Learning Repository]: https://doi.org/10.24432/C5DW2B
[Kaggle]: https://www.kaggle.com/datasets/yasserh/breast-cancer-dataset
[1c3f5bd]: https://github.com/jedick/plotmydata/commit/1c3f5bd6c72c01ecddf2984c0bd1424144e6d82d
[24fb91f]: https://github.com/jedick/plotmydata/commit/24fb91f7d810da7f0078c1b9cb13bf82dde61445
[b8e5f8c]: https://github.com/jedick/plotmydata/commit/b8e5f8ce5e03360b9bde26ff32acb7180d969694
[30c22a1]: https://github.com/jedick/plotmydata/commit/30c22a166a237bfe26413b6c28278a6c467a65a7
[e9180aa]: https://github.com/jedick/plotmydata/commit/e9180aa363195fd2cc011e11e4febc0f544f7878
[bb4eead]: https://github.com/jedick/plotmydata/commit/bb4eead2346d936f9c83108b16f20faf3e3c522c