notebook/jsonld.md

In cboettig/emljson: Ecological Metadata as Linked Data

R Notebook

library(jsonld)
library(jsonlite)
library(tidyverse)
library(printr)

Switching Contexts: Expansion & Compaction

• Good: homonyms (same word has different meanings), synonyms (different word has ~ same meaning)
• Fails: Mapping isn't 1:1

A simple example

ex <- 
 '{ 
      "date": "1993-10-02",
      "site": "N654",
      "scientificName": "Picea rubens",
      "area": 2,
      "count": 26
 }'

No need to alter your own data files/source data files. Simply define a context that maps those terms into the reference vocabulary. Data can use whatever fields it likes.

map <- 
'{ 
  "@context": {
    "@vocab": "http://example.com/",
    "scientificName": "http://example.com/species"
  }
}'
target <- '{"@context": {"@vocab": "http://example.com/"}}'
add_context(ex, map) %>% 
  jsonld_compact(target)

## {
##   "@context": {
##     "@vocab": "http://example.com/"
##   },
##   "area": 2,
##   "count": 26,
##   "date": "1993-10-02",
##   "site": "N654",
##   "species": "Picea rubens"
## }

Using tabular schema

df <- 
tribble(
  ~Date,       ~Site, ~Species, ~Area, ~Count,
  "10/1/1993", "N654", "PIRU",   2.0,     26,
  "10/3/1994", "N654", "PIRU",   2.0,     29,
  "10/1/1993", "N654", "BEPA",   1.0,     3
)

map <- 
'{ 
  "@context": {
    "@vocab": "http://example.com/",
    "Species": "http://example.com/scientificName"
  }
}'
target <- '{"@context": {"@vocab": "http://example.com/"}}'

df %>% 
  toJSON() %>% 
  add_context(map) %>% 
  jsonld_compact(target) %>% 
  drop_context() %>%
  fromJSON()

| Area| Count| Date | Site | scientificName | |--------:|---------:|:-------------|:--------|:--------------------| | 2| 26| 10/1/1993 | N654 | PIRU | | 2| 29| 10/3/1994 | N654 | PIRU | | 1| 3| 10/1/1993 | N654 | BEPA | | Wow doe| s that s| eem like a r | ound-ab | out way to just do: |

df %>% rename(scientificName = Species)

| Date | Site | scientificName | Area| Count| |:----------|:-----|:---------------|-----:|------:| | 10/1/1993 | N654 | PIRU | 2| 26| | 10/3/1994 | N654 | PIRU | 2| 29| | 10/1/1993 | N654 | BEPA | 1| 3|

and it is. But a few things are worth noting:

• Robust to what columns are actually present. Instead of a default vocabulary we can map all terms explicitly.
• Terms not expanded by the map (that is, not part of our context) will be dropped. (Count in the example below)
• Terms not in our target context remain as external keys, and are not compacted.
• Typesafe transforms. Terms (keys/column names) with the wrong type will not compact if the types do not match the target transform. This way they are not actually lost, but cannot be confused.

map <- 
'{ 
  "@context": {
    "ex": "http://example.com/",
    "Species": "ex:scientificName",
    "Site": "ex:Site",
    "Date": "ex:Date",
    "Area": "ex:Area"
  }
}'

target <- '{
"@context": {
  "ex": "http://example.com/",
  "scientificName": "ex:scientificName",
  "Site": "ex:Site",
  "Count": "ex:count",
  "Date": {"@id": "ex:Date", "@type": "ex:Date"}
}}'

df %>% 
  toJSON() %>% 
  add_context(map) %>% 
  jsonld_compact(target) %>% 
  drop_context() %>%
  fromJSON() 

| ex:Area| ex:Date | Site | scientificName | |--------:|:----------|:-----|:---------------| | 2| 10/1/1993 | N654 | PIRU | | 2| 10/3/1994 | N654 | PIRU | | 1| 10/1/1993 | N654 | BEPA |

map <- '{ "@context": { "@vocab": "https://data.berkeley.edu/" }}'

target <- '{
"@context": {
  "ucb": "https://data.berkeley.edu/",
  "hav": "http://data.harvard.edu/",
  "scientificName": "ucb:Species",
  "Site": "ucb:Site",
  "Count": "ucb:Count",
  "Date": "ucb:Date",
  "Area": "ucb:Area",
  "Density": "hav:density"
}}'

df %>% 
  toJSON() %>% 
  add_context(map) %>% 
  jsonld_compact(target) %>% 
  drop_context() %>%
  fromJSON() -> ucsb_df

ucsb_df %>% mutate(density = Count / Area)

| Area| Count| Date | Site | scientificName | density| |-----:|------:|:----------|:-----|:---------------|--------:| | 2| 26| 10/1/1993 | N654 | PIRU | 13.0| | 2| 29| 10/3/1994 | N654 | PIRU | 14.5| | 1| 3| 10/1/1993 | N654 | BEPA | 3.0|

Integration: Flattening shares id

context <- '{   "@context": { "@vocab": "http://example.com/" } }'

ex <- '{
  "@context": { "@vocab": "http://example.com/" },
  "datasets": [
    {
      "@id": "http://global_unique_id",
      "@type": "dataset",
      "image": "http://image_uri",
      "name": "stuff"
    },

    {
      "@type": "dataset",
      "@id": "http://global_unique_id",
      "name": "Species name"
    }
  ]
}'

jsonld_flatten(ex, context)

## {
##   "@context": {
##     "@vocab": "http://example.com/"
##   },
##   "@graph": [
##     {
##       "@id": "_:b0",
##       "datasets": {
##         "@id": "http://global_unique_id"
##       }
##     },
##     {
##       "@id": "http://global_unique_id",
##       "@type": "dataset",
##       "image": "http://image_uri",
##       "name": [
##         "stuff",
##         "Species name"
##       ]
##     }
##   ]
## }

hdf5: column data, with units, metadata

cboettig/emljson documentation built on Oct. 27, 2020, 1:27 a.m.