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− | Open Food Facts data is released as Open Data: it can be reused freely by anyone, under the Open Database License (ODBL). | + | [[Category:Reuse]] |
| + | Open Food Facts data is released as Open Data: it can be reused freely by anyone, under the Open Database License (ODBL). While this page is related to practical reuse, you must really be aware of [[ODBL License|rights and duties provided by the Open Database]] License (ODBL). |
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| == Where is the data? == | | == Where is the data? == |
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| Then use the advanced search. The Open Food Facts advanced search feature allows to download selections of the data. See: https://world.openfoodfacts.org/cgi/search.pl | | Then use the advanced search. The Open Food Facts advanced search feature allows to download selections of the data. See: https://world.openfoodfacts.org/cgi/search.pl |
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− | When you search is done, you will be able to download the selection in '''CSV or Excel format''', just give a try! | + | When your search is done, you will be able to download the selection in '''CSV or Excel format''', just give a try! |
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| === Looking for the whole database? === | | === Looking for the whole database? === |
| The whole database can be downloaded at https://world.openfoodfacts.org/data | | The whole database can be downloaded at https://world.openfoodfacts.org/data |
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− | It's very big. Open Food Facts hosts more than 1,200,000 products (as of April 2020). So you will probably need skills to reuse the data. | + | It's very big. Open Food Facts hosts more than 1,400,000 products (as of July 2020). So you will probably need skills to reuse the data. |
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− | You'll be able to find there different kinds of data. | + | You'll be able to find here different kinds of data. |
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| ==== The MongoDB daily export ==== | | ==== The MongoDB daily export ==== |
| It represents the most complete data; it's very big and you have to know how to deal with MongoDB. | | It represents the most complete data; it's very big and you have to know how to deal with MongoDB. |
| + | |
| + | ==== The JSONL daily export ==== |
| + | While still undocumented, there is a daily export of the whole database in jsonl format. It represents the same data as the MongoDB export. It's very big! More than 14GB uncompressed. |
| + | |
| + | You can find it at https://static.openfoodfacts.org/data/openfoodfacts-products.jsonl.gz |
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| ==== The CSV daily export ==== | | ==== The CSV daily export ==== |
− | It represents a subset of the database but it is generally fitted to the majority of usages. It's a 2.3GB file (as of April 2020), so it can't be opened by Libre Office or Excel with an 8GB machine. | + | It contains all the products, but with a subset of the database fields. [https://world.openfoodfacts.org/data/data-fields.txt This subset is very large] and include main characteristics (EAN, name, brand...), many tags (such as categories, origins, labels, packaging...), ingredients and nutrition facts. Thus, it is generally fitted to the majority of usages. It's a 2.3GB file (as of April 2020), so it can't be opened by Libre Office or Excel with an 8GB machine. |
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| == How to reuse? == | | == How to reuse? == |
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| ==== R stat ==== | | ==== R stat ==== |
| For people who have R stat skills, there are [https://www.kaggle.com/openfoodfacts/world-food-facts/kernels?sortBy=hotness&group=everyone&pageSize=20&datasetId=20&language=R more than 50 notebooks from Kaggle community]. | | For people who have R stat skills, there are [https://www.kaggle.com/openfoodfacts/world-food-facts/kernels?sortBy=hotness&group=everyone&pageSize=20&datasetId=20&language=R more than 50 notebooks from Kaggle community]. |
| + | |
| + | === JSONL export === |
| + | JSONL is a huge file! It's not possible to play with it with common editors or common tools. But there is some command line tools that allows interesting things, like [https://stedolan.github.io/jq/manual/v1.6/ jq]. |
| + | |
| + | ==== jq ==== |
| + | * start decompress the file (be carreful => 14GB after decompression): |
| + | $ gunzip openfoodfacts-products.jsonl.gz |
| + | * work on a small subset to test. E.g. for 100 products: |
| + | $ head -n 100 openfoodfacts-products.jsonl > small.jsonl |
| + | |
| + | You can start playing with jq. Here are examples. |
| + | $ cat small.jsonl | jq . # print all file in JSON format |
| + | |
| + | $ cat small.jsonl | jq -r .code # print all products' codes. |
| + | |
| + | $ cat small.jsonl | jq -r '[.code,.product_name] | @csv' # output CSV data containing code,product_name |
| + | |
| + | Then you can try on the whole database: |
| + | $ cat openfoodfacts-products.jsonl | jq -r '[.code,.product_name] | @csv' > names.csv # output CSV file (name.csv) containing all products with code,product_name |
| + | |
| + | If you don't have enough disk place to uncompress the .gz file, you can use zcat directly on the compressed file. Example: |
| + | $ zcat openfoodfacts-products.jsonl.gz | jq -r '[.code,.product_name] | @csv' # output CSV data containing code,product_name |