A simple utility for SQL-like joins with Json or geoJson data in Node, the browser and on the command line. Also creates join reports so you can know how successful a given join is.

Try it in the browser --> https://mhkeller.github.io/join.report/

var data = [
  { "id": "1", "name": "UT" },
  { "id": "4", "name": "NM" }
]

var newData = [
  { "state_name": "NM", "avg_temp": 45 }
]

var joinedData = joiner({
  leftData: data,
  leftDataKey: 'name',
  rightData: newData,
  rightDataKey: 'state_name'
})

console.log(joinedData)
/*
{ data:
  [ { id: '1', name: 'UT', avg_temp: null },
    { id: '4', name: 'NM', avg_temp: 45 }
  ],
  report:
    { diff:
      { a: [ 'NM', 'UT' ],
        b: [ 'NM' ],
        a_and_b: [ 'NM' ],
        a_not_in_b: [ 'UT' ],
        b_not_in_a: []
      },
     prose:
      { summary: '1 row matched in A and B. 1 row in A not in B. All 1 row in B in A.',
        full: 'Matches in A and B: NM. A not in B: UT.' } } }
*/

See the examples folder for different file formats and options. Joiner is useful to verify whether all of your joins were successful and to spot any patterns among fields that didn't join properly. For example, you can see that the county_01 row in dataset A didn't match with the county_1 in dataset B and that you have a zero-padding issue going on.

To install as a Node.js module:

npm install --save joiner

Or to install as a command-line utility:

npm install joiner -g

To use as both, run both commands.

All joins return an object with the following structure:

data: [data object],
report: {
	diff: {
		a: [data in A],
		b: [data in B],
		a_and_b: [data in A and B],
		a_not_in_b: [data in A not in B],
		b_not_in_a: [data in B not in A]
	}:
	prose: {
		summary: [summary description of join result, number of matches in A and B, A not in B, B not in A.]
		full:    [full list of which rows were joined in each of the above categories]
	}
}

Perform a left join on the two array of object json datasets. It performs a deep clone using lodash.clonedeep of the objects you pass in and returns the new object.

Optionally, you can pass in a key name under nestKey in case the left data's attribute dictionary is nested under another key, such as in geoJson when it's under the properties object. More on that below.

If geoJson is true, performs a left join onto the properties object of each feature in a geoJson array.

If you want to join on the "id" property, omit leftDataKey. If you want to join on a value in the properties object, set leftDataKey to 'properties.<desired-key-name>' and set nestKey to 'properties'. See examples for more.

Usage: joiner -a DATASET_A_PATH -k DATASET_A_KEY -b DATASET_B_PATH -j DATASET_B_KEY -o OUT_FILE_PATH [-r (summary|full) -n NEST_KEY --geojson]

Options:
  -h, --help     Display help           [default: false]
  -a, --apath    Dataset A path
  -k, --akey     Dataset A key
  -b, --bpath    Dataset B path
  -j, --bkey     Dataset B key
  -g, --geojson  Is dataset A geojson?  [default: false]
  -n, --nestkey  Nested key name
  -o, --out      Out path
  -r, --report   Report format          [default: "summary"]

In most cases, the first four parameters (--apath, --akey, --bpath and --bkey) are required. --akey is not required if you have set geojson to true by using -g or --geojson since it will join on the "id" field. If you want to join on a property field in geojson, then set that using --akey.

If you specify an output file, it will write the join result to the specified file and the report to the same directory. Intermediate directories will be created if they don't already exist. For example, -o path/to/output.csv will also write -o path/to/output-report.json and create the to/ folder if it isn't already there. If you don't specify an output file, it will print the results to the console.

If you don't specify an output file with -o, Joiner will print the join report to the console. By default, it will just specify the summary report. To print the full report, specify -d full.

Setting -g or --geojson is the equivalent of setting geojson: true above.

It converts the specified input file into json and writes the joined dataset to file using indian ocean, which currently supports the following formats: json, geojson, csv, psv, tsv and dbf. The format is inferred from the file extension of the input and output file paths. For example, -a path/to/input/file.csv will read in a csv and -o path/to/output/file.csv will write a csv.