Any license info on this repo?

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it would be very helpful to have a machine readable licence file like path / spdx tsv

I'm trying to run the examples specs in latest vega release, but am getting some errors with missing datasets, including flare-dependencies.json from https://github.com/vega/vega/blob/master/spec/tree-radial-bundle.vg.json, normal-2.json and flights-200k.json. Are these available somewhere

(apologies if this issue is in the wrong spot)

See discussion in vega/vega-lite#3185

I suggest this top goal-scorers and footballers since 1980:

https://johnburnmurdoch.github.io/projects/goal-lines/all-comps/

I noticed that #388 changed build/vega-datasets.min.js to be an IIFE:

Whereas build/vega-datasets.js is still a UMD:

The problem is that require("vega-datasets") on Observable will use your unpkg/jsdelivr entry point which points to the IIFE, and thus errors:

You can see it breaking here:

https://observablehq.com/@vega/vega-lite-input-binding

If you want to drop UMD support, we could fix that notebook (and presumably others) by using ES import instead of require:

world = (await import('vega-datasets')).default['world-110m.json'].url

But if you are supporting IIFE, maybe it’s worth continuing to support UMD for backwards compatibility?

When trying to load the birdstrikes data, a 404 error is thrown.

vega_datasets: 0.8
Ubuntu 20.04 LTS

Python 3.8.3 (default, May 19 2020, 18:47:26) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.15.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from vega_datasets import data                                                            

In [2]: bird = data.birdstrikes()                                                                 
---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
<ipython-input-2-a4e393dbcf55> in <module>
----> 1 bird = data.birdstrikes()

~/miniconda3/envs/cookbook/lib/python3.8/site-packages/vega_datasets/core.py in __call__(self, use_local, **kwargs)
    222             parsed data
    223         """
--> 224         datasource = BytesIO(self.raw(use_local=use_local))
    225 
    226         kwds = self._pd_read_kwds.copy()

~/miniconda3/envs/cookbook/lib/python3.8/site-packages/vega_datasets/core.py in raw(self, use_local)
    201             return pkgutil.get_data("vega_datasets", self.pkg_filename)
    202         else:
--> 203             return urlopen(self.url).read()
    204 
    205     def __call__(self, use_local=True, **kwargs):

~/miniconda3/envs/cookbook/lib/python3.8/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    220     else:
    221         opener = _opener
--> 222     return opener.open(url, data, timeout)
    223 
    224 def install_opener(opener):

~/miniconda3/envs/cookbook/lib/python3.8/urllib/request.py in open(self, fullurl, data, timeout)
    529         for processor in self.process_response.get(protocol, []):
    530             meth = getattr(processor, meth_name)
--> 531             response = meth(req, response)
    532 
    533         return response

~/miniconda3/envs/cookbook/lib/python3.8/urllib/request.py in http_response(self, request, response)
    638         # request was successfully received, understood, and accepted.
    639         if not (200 <= code < 300):
--> 640             response = self.parent.error(
    641                 'http', request, response, code, msg, hdrs)
    642 

~/miniconda3/envs/cookbook/lib/python3.8/urllib/request.py in error(self, proto, *args)
    567         if http_err:
    568             args = (dict, 'default', 'http_error_default') + orig_args
--> 569             return self._call_chain(*args)
    570 
    571 # XXX probably also want an abstract factory that knows when it makes

~/miniconda3/envs/cookbook/lib/python3.8/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
    500         for handler in handlers:
    501             func = getattr(handler, meth_name)
--> 502             result = func(*args)
    503             if result is not None:
    504                 return result

~/miniconda3/envs/cookbook/lib/python3.8/urllib/request.py in http_error_default(self, req, fp, code, msg, hdrs)
    647 class HTTPDefaultErrorHandler(BaseHandler):
    648     def http_error_default(self, req, fp, code, msg, hdrs):
--> 649         raise HTTPError(req.full_url, code, msg, hdrs, fp)
    650 
    651 class HTTPRedirectHandler(BaseHandler):

HTTPError: HTTP Error 404: Not Found

There are several movies listed in the movies.json dataset (everything after 2011) that are listed as having come out 100 years after they were actually released. It's a pretty quick fix and I'm wondering if a pull request would be welcome to either fix the data or create a new dataset with corrected data.

Can we delete it?

See https://github.com/allisonhorst/penguins

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@domoritz sounds good! I'll do one better for you guys:

I woke up this morning thinking we could use a simple vega datasets preview js notebook :)

https://observablehq.com/@randomfractals/vega-datasets

I'll leave it up to you guys if you want to add this vega datasets preview utility Observable notebook to your editor or this datasets repo readme.md.

This notebook can be used as a supplemental tool for online vega editor and examples that use these data sources since it's much faster in data loading and scrolling than what github and vega editor provides.

I might add something similar to the https://github.com/RandomFractals/vscode-vega-viewer as a split panel in vega chart preview in the next major release.

cc @kanitw @arvind & @jheer

Cheers! 🤗

Originally posted by @RandomFractals in #64 (comment)

I think the addition of an "Open High Low Close" dataset would useful.

The VL example Candlestick Chart hard codes data that is found in an earlier Protovis example found here .

The dataset contains the performance of the Chicago Board Options Exchange Volatility Index (VIX) in the summer of 2009.

I think including this dataset would be especially useful for people in finance.

Possible names: vix.json, vix-ohlc.json or just ohlc.json?

Let me know what your think and I can put together a PR.

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The idea would be that someone can just load vega-datsets and access the datasets (probably with async loading) or their URLs directly.

It appears that some invalid data has crept into cars.json. Presumably, this is to highlight Voyager functionality, but it's had the knock-on effect of producing erroneous output with the Vega examples:

Perhaps cars.json should contain the clean values, and a secondary duplicate data file introduces the error? Note: I believe this error was also introduced to the Vega test cases, so we'll want to update that too.

As I continue to refine and expand the Altair example gallery, the barley dataset has become our standby for stacked bar charts.

It would be nice to fill out its sources entry in the same way we did the wheat dataset. Can someone here verify its origin?

I figured once the Seattle temperatures are updated (#127) we should also update the corresponding San Francisco temperatures. I've already downloaded the data from the San Francisco International Airport weather station.

Making a note of it here so we don't forget.

For the 2.0 release, let's clean up datasets we don't need anymore.

• Remove graticule
• Consolidate weather datasets
• Update the census dataset. #171
• Update the CO2 dataset

I'm finding it useful for creating simple bar chart examples in Altair. I'm interested to learn more about where the data comes from.

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• Thanks for your great work
• Can't find Oglala Lakota - 46102 in us_10m.json

I work in data journalism and I think it would be cool to include some simple but famous datasets from our profession in your examples. If I submitted some would you be open to considering them?

https://github.com/vega/vega-datasets/blob/gh-pages/data/weather.csv the date field cannot be parsed automatically in safari.

Right now, the generated URLs point to github, which may change as we change the content of the files in master. Instead, we should generate URLs to jsdelivr and include the version number. This way we never change data without the user knowing.

I couldn't load the earthquakes dataset, then i tried to manually download the json file and tried to read with pandas, the same error occured.

The CSV loader in data.ts uses the following call:
return d3.csvParseRows(await result.text(), (d3 as any).autoType)

However, this method does not parse the header row as field names. This is the intended documented behavior of csvParseRows (see https://github.com/d3/d3-dsv#dsv_parseRows).

We need to update vega-datasets to return a properly parsed CSV that uses the header row to determine object field names.

The seattle-temps dataset claims that the temperature in Seattle never rose above 76 degrees F in 2010:

Vega Editor Link

According to my own memory, the temperature was much hotter. Other more reliable sources agree; for example, weather underground claims that Seattle hit 96 degrees F in August 2010: https://www.wunderground.com/history/monthly/us/wa/seattle/KSEA/date/2010-8

Perhaps the dataset is mislabeled?

Any plans to update us-10m to represent the latest county FIPS.

https://cdn.jsdelivr.net/npm/us-atlas@2/us/10m.json

Looks like 2.5.1 #390 includes the wrong version 2.5.0 in the published artifacts. Search for “version” here:

https://cdn.jsdelivr.net/npm/[email protected]/build/vega-datasets.module.js

This results in broken URLs that point to 2.5.0, which is missing data. For example, this:

world = (await import('[email protected]')).default['world-110m.json'].url

https://cdn.jsdelivr.net/npm/[email protected]/data/world-110m.json

If you are already aware of this, ignore this.

But when using the data/movies.json for example on line 11 the movie "Duel in the Sun" has a Release Date of "Dec 31 2046". When the movie actually was released in 1946.

There are numerous places where this mixup happens.

There are only 5 such files. I think one should remove these specials characters.

https://github.com/vega/vega/blob/master/docs/data/volcano.json is used by Vega examples and we can only build examples in the editor if the datasets are in vega-datasets.

What's the source if this dataset? Is there a cleaner version that doesn't have the specific format that works well for just Vega?

Description
Hello, Vega team!

I hope you are doing well. I came across an issue while exploring datasets in the Vega dataset repository. Specifically, I found that 7 datasets in the following directory cannot be loaded using the pd.read_json(url) method:

https://github.com/vega/vega-datasets/tree/main/data

I would greatly appreciate it if you could take a look at this issue and provide a possible solution. If you need any additional information from me, please let me know.

Thank you for your time and attention!

Steps to Reproduce
Go to https://github.com/vega/vega-datasets/tree/main/data
Select any of the following 7 datasets:
https://raw.githubusercontent.com/vega/vega-datasets/main/data/annual-precip.json
https://raw.githubusercontent.com/vega/vega-datasets/main/data/earthquakes.json
https://raw.githubusercontent.com/vega/vega-datasets/main/data/londonBoroughs.json
https://raw.githubusercontent.com/vega/vega-datasets/main/data/londonTubeLines.json
https://raw.githubusercontent.com/vega/vega-datasets/main/data/miserables.json
https://raw.githubusercontent.com/vega/vega-datasets/main/data/us-10m.json
https://raw.githubusercontent.com/vega/vega-datasets/main/data/world-110m.json

Load the dataset using pd.read_json(url) method.
Observe that the dataset cannot be loaded.

Expected behavior
The datasets in the aforementioned directory should be able to be loaded using pd.read_json(url) method.

Actual behavior
7 of the datasets in the directory cannot be loaded using pd.read_json(url) method.

Additional Information
Operating System: Windows
Python version: 3.10.9
Pandas version: 1.5.2

list of Erorrs received:
[['ValueError', 'All arrays must be of the same length'],
['ValueError', 'All arrays must be of the same length'],
['ValueError', 'All arrays must be of the same length'],
['ValueError', 'All arrays must be of the same length'],
['ValueError', 'All arrays must be of the same length'],
['ValueError',
'Mixing dicts with non-Series may lead to ambiguous ordering.'],
['ValueError',
'Mixing dicts with non-Series may lead to ambiguous ordering.']]

A general demonstration is outlined here in the google collar file: https://colab.research.google.com/drive/1oKhivD5T9Yi1gMl0_7dUwqVFqiNfD43k?usp=sharing

The 'flights-200k.arrow" is producing an error every time I tried to read in the file using Pandas package.