4. Improve the block view

Let's add CSV file parsing.

There are many CSV parsers available for NodeJS. We'll use Papaparse because it also works in the browser.

We'll need to add the dependency to the add-on if you haven't already done so, as instructed in the first chapter. When using yarn workspaces, the workflow is a bit different. For our simple use case, we could probably run yarn add papaparse inside the src/addons/volto-datatable-tutorial, but the correct way is to run this command within the project root.

First, run yarn workspaces info to see the workspaces we have available.

yarn workspaces info

{
  "@plone-collective/volto-datatable-tutorial": {
    "location": "src/addons/volto-datatable-tutorial",
    "workspaceDependencies": [],
    "mismatchedWorkspaceDependencies": []
  }
}

To add a dependency to the package, run:

> yarn workspace @plone-collective/volto-datatable-tutorial add papaparse

And finally, the new block code within src/DataTable/DataTable.jsx:

import React from 'react';
import { useDispatch, useSelector } from 'react-redux';
import { Table } from 'semantic-ui-react';
import csv from 'papaparse';
import { getRawContent } from '@plone-collective/volto-datatable-tutorial/actions';

const DataTableView = ({ data: { file_path } }) => {
  const id = file_path?.[0]?.['@id'];
  const path = id ? `${id}/@@download` : null;

  const dispatch = useDispatch();
  const request = useSelector((state) => state.rawdata?.[path]);

  const content = request?.data;

  React.useEffect(() => {
    if (path && !content) dispatch(getRawContent(path));
  }, [dispatch, path, content]);

  const file_data = React.useMemo(() => {
    if (content) {
      const res = csv.parse(content, { header: true });
      return res;
    }
  }, [content]);

  const fields = file_data?.meta?.fields || [];

  return file_data ? (
    <Table celled>
      <Table.Header>
        <Table.Row>
          {fields.map((f) => (
            <Table.Cell key={f}>{f}</Table.Cell>
          ))}
        </Table.Row>
      </Table.Header>
      <Table.Body>
        {file_data.data.map((o, i) => (
          <Table.Row key={i}>
            {fields.map((f) => (
              <Table.Cell>{o[f]}</Table.Cell>
            ))}
          </Table.Row>
        ))}
      </Table.Body>
    </Table>
  ) : (
    <div>No data</div>
  );
};

export default DataTableView;

Writing components where the useEffect triggers network calls can be pretty tricky. According to the rule of hooks, hooks can't be triggered conditionally. They always have to be executed. For this reason, it's important to add relevant conditions inside the hook code. Be sure to identify and prepare a way to tell, from inside the hook, if the network-fetching action should be dispatched.

4.1. The React HOC Pattern

It is a good idea to split the code into generic "code blocks" so that behavior and look are separated. This has many benefits:

• It makes components easier to write and test.

• It separates business logic into reusable behaviors.

Can we abstract the data-grabbing logic? Let's write a simple Higher-Order Component (HOC) that does the data grabbing. The simplest HOC wrapper looks like this:

const withFileData = (WrappedComponent) => {
  return (props) => <WrappedComponent {...props} />;
};

export default withFileData(DataTableView);

Notice the similarity with Python decorators. In our case, the HOC is a function that, given a component as input, returns a React component.

Now let's move the file download and parsing logic to this HOC. We'll create the src/hocs/withFileData.js file:

import React from 'react';

import { useDispatch, useSelector } from 'react-redux';
import csv from 'papaparse';

import { getRawContent } from '@plone-collective/volto-datatable-tutorial/actions';

const withFileData = (WrappedComponent) => {
  return (props) => {
    const {
      data: { file_path },
    } = props;
    const id = file_path?.[0]?.['@id'];
    const path = id ? `${id}/@@download` : null;

    const dispatch = useDispatch();
    const request = useSelector((state) => state.rawdata?.[path]);

    const content = request?.data;

    React.useEffect(() => {
      if (path && !request?.loading && !request?.loaded && !content)
        dispatch(getRawContent(path));
    }, [dispatch, path, content, request?.loaded, request?.loading]);

    const file_data = React.useMemo(() => {
      if (content) {
        const res = csv.parse(content, { header: true });
        return res;
      }
    }, [content]);
    return <WrappedComponent file_data={file_data} {...props} />;
  };
};

export default withFileData;

This HOC now gets the data from the Redux store using the logic and code we've previously used. It then injects it as a new property onto the original wrapped component.

A HOC is a simple function that gets a component and returns another component. For a Python developer, decorators are a very similar concept. Take note that React component names must be referenced as PascalCase in JSX code.

Now the view component is simple, neat, and focused.

Now write the src/hocs/index.js file where you export the new HOC.

import withFileData from './withFileData';
export { withFileData };

Back to the src/DataTable/DataTable.jsx, it becomes:

import React from 'react';
import { Table } from 'semantic-ui-react';
import { withFileData } from '@plone-collective/volto-datatable-tutorial/hocs';

const DataTableView = ({ file_data }) => {
  const fields = file_data?.meta?.fields || [];

  return file_data ? (
    <Table celled>
      <Table.Header>
        <Table.Row>
          {fields.map((f) => (
            <Table.Cell key={f}>{f}</Table.Cell>
          ))}
        </Table.Row>
      </Table.Header>
      <Table.Body>
        {file_data.data.map((o, i) => (
          <Table.Row key={i}>
            {fields.map((f) => (
              <Table.Cell>{o[f]}</Table.Cell>
            ))}
          </Table.Row>
        ))}
      </Table.Body>
    </Table>
  ) : (
    <div>No data</div>
  );
};

export default withFileData(DataTableView);