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Multiple filter and CSV export
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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).
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[[Category:Reuse]]
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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). 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? ==
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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!
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When your search is done, you will be able to download the selection in '''CSV or Excel format''', just give a try!
    
=== 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.
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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.
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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.
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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.
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==== The JSONL daily export ====
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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.
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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.
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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 ====
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==== 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).
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Alternative way - feel free to use a project from github: https://github.com/ArchiMageAlex/off_converter
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==== Import CSV to SQLite ====
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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:
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* barcode number
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* product name
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* product categories
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* product countries
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* 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].
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Moreover, here a link to transform .bson file to a dataframe: https://github.com/gnaweric/openfoodfact_database_queries
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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.
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Beware, each line is a product and some variable need to be unnest: tidyverser::unnest_wider()
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=== JSONL export ===
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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].
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==== jq ====
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* start decompress the file (be carreful => 14GB after decompression):
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$ gunzip openfoodfacts-products.jsonl.gz
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* work on a small subset to test. E.g. for 100 products:
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$ head -n 100 openfoodfacts-products.jsonl > small.jsonl
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You can start playing with jq. Here are examples.
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$ cat small.jsonl | jq . # print all file in JSON format
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$ cat small.jsonl | jq -r .code # print all products' codes.
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$ cat small.jsonl | jq -r '[.code,.product_name] | @csv' # output CSV data containing code,product_name
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Then you can try on the whole database:
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$ cat openfoodfacts-products.jsonl | jq -r '[.code,.product_name] | @csv' > names.csv # output CSV file (name.csv) containing all products with code,product_name
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If you don't have enough disk space to uncompress the .gz file, you can use zcat directly on the compressed file. Example:
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$ zcat openfoodfacts-products.jsonl.gz | jq -r '[.code,.product_name] | @csv' # output CSV data containing code,product_name
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==== Filtering JSONL export with jq ====
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Filtering a specific country:
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$ zcat openfoodfacts-products.jsonl.gz | jq '. | select(.countries_tags[]? == "en:germany")'
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The previous command produces a json output containing all the products sold in Germany. If you want a JSONL output, add -c parameter.
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$ zcat openfoodfacts-products.jsonl.gz | jq -c '. | select(.countries_tags[]? == "en:germany")'
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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.
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$ 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
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These operations can be quite long (more than 10 minutes depending on your computer and your selection).

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