Store and browse aggregated data using Prisma database tool

npm install automizer-data@0.1.2


Parse, tag and query xlsx-data

This package is used to parse structured tabular data from CSV or XLSX. Stored data can be browsed and transformed into a desired two-dimensional result table.

Its primary purpose is to deliver data for pptx-automizer. This project is Work in progress. Nevertheless, you might already use automizer-data to handle table files coming from statistical analytics software. The example-xlsx in __test__/data-folder is based on GESStabs.

Storage and querying is done with Prisma ORM tools.


As a package

If you are working on an existing project, you can add automizer-data to it using npm or yarn. Run

$ yarn add automizer-data


$ npm install automizer-data

in the root folder of your project. This will download and install the most recent version into your existing project.

As a cloned repository

If you want to see how it works and you like to run own tests, you should clone this repository and install the dependencies:

$ git clone my-project
$ cd my-project
$ yarn install
$ yarn prisma generate

Prisma studio

You can open prisma studio and take a look at the data:

$ yarn prisma studio

A lot of good stuff can be found at

Import Data

According to parser's configuration, parsed data will sliced, tagged and separated into two-dimensional tables.

The Database contains:

  • Categories: Generic nouns to describe the basic structure of your project
  • Tags: Values of a certain category
  • Sheets: Two-dimensional tables and their additional info

Each Sheet will contain:

  • a collection of rows
  • a collection of columns
  • the two-dimensional table body
  • a collection of tags
  • a collection of metadata that came along with the sheet

Example usage

import { PrismaClient } from '@prisma/client'
import { Parser, Store } from '../src/index';
import { ParserOptions, Tagger, RawResultInfo } from "../src/types";

const store = new Store(
  new PrismaClient()

const config = <ParserOptions> {
  // This string separates tables if found in Column A
  separator: 'Table Separator',
  // A row that fits to any of the strings below will be
  // separated into "meta"-field if found in Col A
  metaMap: {
    base: ['BASE'],
    topBox: ['Top-2-Box (1-2)'],
    bottomBox: ['Bottom-2-Box (4-5)'],
    mean: ['Mean Value']
  // Rows that equal to one of the labels below will be skipped.
  skipRows: [
    '* Annotation',
    '(Sum of answers)'
  // A callback function to be applied to every body row
  renderRow: (row: string[]): (number|null)[] => {
    return => {
      if(cell === ' ') return null
      else return Number(cell)
  // The info array of each sub-table will be passed to this
  // callback. Tagging can be fine tuned here.
  renderTags: (info: RawResultInfo[], tags: Tagger): void => {
    info.forEach((info, level) => {
      let cat
      // info.key contains the info string's original section
      // could be 'body' or 'info'
      if(info.key === 'body') {
        cat = 'vartitle'
      } else if(info.value.indexOf('- ') === 0) {
        cat = 'measure'
      } else if(level === 0) {
        // We strip the table separator and pass CountryName
        info.value = info.value.replace('Table Separator – ', '')
        cat = 'country'
      } else if(level === 1) {
        cat = 'variable'
      } else if(level === 2) {
        cat = 'questionText'
      if(cat) {
        tags.push(cat, info.value)

const parse = new Gesstabs(config)
const file = `${__dirname}/data/test-data.xlsx`
const datasheets = await parse.fromXlsx(filename)
const summary = await

Intermediate JSON

Xlsx-Parser will tranform tabular data into an intermediate JSON object. The closer your input data comes to this format, the easier it will be to implement a new parser type.

  "tags": [
      "category": "country",
      "value": "Norway"
      "category": "variable",
      "value": "Q12"
      "category": "category",
      "value": "Bar soap"
      "category": "subgroup",
      "value": "Age"
      "category": "measure",
      "value": "nominal"
  "columns": ["Total", "19-29", "30-39", "40-69"],
  "rows": ["answer 1", "answer 2", "answer 3"],
  "data": [
  "meta": {
    "significance": [

Query Data

As all the Sheets are tagged, our queries will use tags to find the desired datasets.

import { getData, Store } from '../src';
import { all } from '../src/filter';
import { value } from '../src/cell';

// A selector is an array of tags.
const selector = [
      category: 'country',
      value: 'Norway'
      category: "variable",
      value: "Q12"

// The grid will define rows, cols and a callback
// to run inside a target cell.
const grid = {
    rows: all('row'),
    columns: all('column'),
    cell: value

const result = await getData(selector, grid)

// automizer-data will convert the result directly into
// a pptx-automizer-object. 
const chartData = result.toSeriesCategories()