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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). |
| | | |
− | == Where is the data == | + | == Where is the data? == |
| You'll find different kind of ways to get the data. | | You'll find different kind of ways to get the data. |
| | | |
− | === Searching for a selection of product? === | + | === Looking for a selection of products? === |
| 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 |
| | | |
− | When you search is done, you will be able to download the selection, 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! |
| | | |
− | === Searching 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 |
| | | |
− | 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. |
| | | |
| ==== 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. It's very big! More than 9GB uncompressed. |
| + | |
| + | ==== The JSONL daily export ==== |
| + | While still undocumented, there is a daily export of the whole database in [https://jsonlines.org/ JSONL format] (sometimes called LDJSON or NDJSON) where each line is a JSON object. It represents the same data as the MongoDB export. The file is 2,7GB (2020-09), compressed with gzip. It takes more than 14GB uncompressed. |
| + | |
| + | You can find it at https://static.openfoodfacts.org/data/openfoodfacts-products.jsonl.gz |
| | | |
| ==== 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. |
| | | |
| == How to reuse? == | | == How to reuse? == |
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| </code> | | </code> |
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− | ==== Import CSV in PostGRE SQL ==== | + | ''Selecting products based on a regular expression''. csvkit can search in some specified fields, allowing to make powerful selections. The following command creates a CSV file (selection.csv) containing all products where the barcode (code) is beginning by 325798 (<code>-r "^325798(.*)"</code>). |
| + | |
| + | <code> |
| + | $ csvgrep -t -c code -r "^325798(.*)" en.openfoodfacts.org.products.csv > selection.csv |
| + | </code> |
| + | |
| + | The following command creates a CSV file (calissons.csv) containing all products where the category (categories) is containing "calisson". |
| + | |
| + | <code> |
| + | $ csvgrep -t -c categories -r "calisson" en.openfoodfacts.org.products.csv > calisson.csv |
| + | </code> |
| + | |
| + | ==== Import CSV in PostgreSQL ==== |
| See this article: https://blog-postgresql.verite.pro/2018/12/21/import-openfoodfacts.html (in french, but should be understandable with Google Translator). | | See this article: https://blog-postgresql.verite.pro/2018/12/21/import-openfoodfacts.html (in french, but should be understandable with Google Translator). |
| + | |
| + | Alternative way - feel free to use a project from github: https://github.com/ArchiMageAlex/off_converter |
| + | |
| + | ==== Import CSV to SQLite ==== |
| + | |
| + | The repository [https://github.com/fairdirect/foodrescue-content foodrescue-content] contains Ruby scripts that import Open Food Facts CSV data into a [https://www.sqlite.org/index.html SQLite] database with full table normalization. Only a few fields are imported so far, but this can be extended easily. Data imported so far includes: |
| + | |
| + | * barcode number |
| + | * product name |
| + | * product categories |
| + | * product countries |
| + | * full categories hierarchy imported from the <code>categories.txt</code> taxonomy ([https://github.com/openfoodfacts/openfoodfacts-server/tree/master/taxonomies see]) |
| | | |
| ==== Python ==== | | ==== Python ==== |
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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]. |
| + | |
| + | Moreover, here a link to transform .bson file to a dataframe: https://github.com/gnaweric/openfoodfact_database_queries |
| + | |
| + | With the use of {mongolite}, first connect to the base, then import the .bson file, then get a sample of it to make sure it is ready. Finally save it to a .rdata file for example. |
| + | |
| + | Beware, each line is a product and some variable need to be unnest: tidyverser::unnest_wider() |
| + | |
| + | === 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 space 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 |
| + | |
| + | ==== Filtering JSONL export with jq ==== |
| + | Filtering a specific country: |
| + | $ zcat openfoodfacts-products.jsonl.gz | jq '. | select(.countries_tags[]? == "en:germany")' |
| + | |
| + | The previous command produces a json output containing all the products sold in Germany. If you want a JSONL output, add -c parameter. |
| + | $ zcat openfoodfacts-products.jsonl.gz | jq -c '. | select(.countries_tags[]? == "en:germany")' |
| + | |
| + | You can add multiple filters and export the result to a CSV file. For example, here is a command that 1. selects products having the Nutri-Score computed and belonging to the TOP 90% most scanned products in 2020, and 2. exports barcode (<code>code</code>) and number of scans (<code>scans_n</code>) as a CSV file. |
| + | $ zcat openfoodfacts-products.jsonl.gz | jq -r '. | select(.misc_tags[]? == "en:nutriscore-computed" and .popularity_tags[]? == "top-90-percent-scans-2020") | [.code,.scans_n] | @csv' > displayed.ns.in.top90.2020.world.csv |
| + | |
| + | These operations can be quite long (more than 10 minutes depending on your computer and your selection). |