add docs

Basic-usage.md

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+# Choose SQLite database or CSV file
+
+You can choose a database or a CSV file right on the welcom page (fig. 1). The
+supported file extentions: `.csv`, `.db`,`.sqlite` and `.sqlite3`.
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_welcome.png" width="500"/>
+</p>
+
+<p align="center">
+  <sub>
+    Fig. 1: Welcome page
+  </sub>
+</p>
+
+If you choose a CSV file it will be parsed. Then sqliteviz creates a new
+database with data from the CSV. You can change parsing settings and table name
+in the dialog which is shown automatically if you choose a CSV file (fig. 2).
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_csv.png?0.15.0" width="700"/>
+</p>
+
+<p align="center">
+  <sub>
+    Fig. 2: CSV import dialog
+  </sub>
+</p>
+
+If you don't have a database or CSV press `Create empty database` button and
+then fill an empty database using DDL and DML statements. A tab with an example
+of how to do it will be opened automatically (fig. 3).
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_editor.png?0.15.0" width="700"/>
+</p>
+
+<p align="center">
+  <sub>
+    Fig. 3: Workspace (empty database)
+  </sub>
+</p>
+
+Database tables, columns and their types are shown in the left panel of the
+`Workspace` (fig. 4).
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_editor_with_db.png?0.15.0" width="750"/>
+</p>
+
+<p align="center">
+  <sub>
+    Fig. 4: Workspace (not empty database)
+  </sub>
+</p>
+
+# Create inquiry
+
+## Open new tab
+
+Press `Create` button in the top toolbar or use `Ctrl+b` (`Cmd+b` for MacOS)
+keyboard shortcut to open a new tab for an inquiry. An inquiry consists of three
+parts: SQL query, result set (result of the query execution) and visualisation
+of the result set. A tab consists of two panels. Each of them can show one of
+the three parts of inquiry. By default the top panel shows SQL query editor and
+the bottom panel shows the result set. You can switch any panel to any other
+view with corresponding buttons in the right side toolbar.
+
+* <img src="./img/sql.svg"/> – Switch the panel to an SQL query editor. In that
+  editor you can specify and run not only a `SELECT` statement for getting data
+  but also DDL/DML statements for modifying the database.
+* <img src="./img/table.svg"/> – Switch the panel to the result set. Here you
+  can see the result of the query execution.
+* <img src="./img/visualisation.svg"/> – Switch the panel to visualisation. This
+  panel allows building a pivot table and charts from the result set.
+
+> **Note:** The query editor can show you hints: SQL keywords, table and column
+> names. In a common case column names are shown in the hint if you specify the
+> table name, e.g. `SELECT table_name.`. But if there is only one table in your
+> database column names will be always available in the hint. Press `Ctrl+Space`
+> to show a hint explicitly.
+
+## Run query
+
+Press <img src="./img/run.svg"/> button in the right side toolbar of an SQL
+editor panel or use `Ctrl+r` or `Ctrl+Enter` (`Cmd+r` or `Cmd+Enter` for MacOS)
+keyboard shortcut to execute a query in the current opened tab.
+
+> **Note:**  Running is not available if a query for the current tab is not
+> specified.
+
+The query result will be displayed in the result panel (fig. 4).
+
+## Create visualisation
+
+After running a query switch any panel to the visualisation.  Switch to the
+disired type of visualisation with buttons in the right side toolbar of the
+visualisation panel.
+
+* <img src="./img/chart.svg"/> – Switch to a chart type visualisation.
+* <img src="./img/pivot.svg"/> – Switch to a pivot type visualisation.
+
+> **Note:**  All unsaved changes in the current visualisation will be lost when
+> you switch to the other visualisation type.
+
+A pivot table can be represented as a regular or multi-header table or
+visualised with a chart.
+Read more details of working with pivot in [Pivot tables][2].
+
+All customised charts in sqliteviz are created with a `react-chart-editor`
+component (fig. 5). The same component with some additional features is used in
+Plotly Chart Studio. Explore its [documentation][1] to learn how to build charts
+with `react-chart-editor`.
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_chart.png?0.15.0" width="750"/>
+</p>
+
+<p align="center">
+  <sub>
+    Fig. 5: Visualisation with a chart
+  </sub>
+</p>
+
+You can save any visualisation as an image by clicking <img
+src="./img/camera.svg"/>.
+
+# Save inquiry
+
+Press `Save` button in the top toolbar or use `Ctrl+s` (`Cmd+s` for MacOS)
+keyboard shortcut to save an inquiry in the current opened tab to local storage
+of your browser.
+
+After that, the inquiry will be in the list on `Inquiries` page.
+
+> **Note:**  Only the text of the SQL query and the visualisation settings will
+> be saved. The result of the query execution won't be saved.
+
+[1]: https://plotly.com/chart-studio-help/tutorials/#basic
+[2]: Pivot-table

Diagnostic-information.md

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+SQLite running in sqliteviz is compiled with particular [options][1] that can
+enable or disable some SQLite features. You can get a list of them in `App
+info` dialog (fig. 1) by clicking on <img src="./img/info.svg"/> icon in the top
+toolbar.
+
+<p align="center">
+  <img class="figure" src="./img/Screenshot_app_info.png?0.13.0"/>
+</p>
+
+<p align="center">
+  <sub>
+    Fig. 1: App info dialog
+  </sub>
+</p>
+
+[1]: https://sqlite.org/compile.html

Export-current-database.md

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+Sqliteviz allows running not only `SELECT` statements but DML/DDL statements
+too. You can save database modifications into a `.sqlite` file by clicking on
+icon <img src="./img/file-export.svg"/> next to the database name on `Workspace`
+page.

Home.md

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+Welcome to the sqliteviz wiki!
+
+# For users
+1. [Installation][0]
+2. [Basic usage][1]
+3. [Multiple CSV file import][9]
+4. [Manage inquiries][2]
+5. [Export current database][4]
+6. [Pivot table][3]
+7. [Predefined inquiries][5]
+8. [Diagnostic information][6]
+
+## Examples and tutorials
+1. [How to rename tables and columns after CSV import][8]
+2. [How to build a pivot table in SQL(ite)][10]
+
+# For developers
+1. [Integrate predefined inquiries][7]
+
+[0]: Installation
+[1]: Basic-usage
+[2]: Manage-inquiries
+[3]: Pivot-table
+[4]: Export-current-database
+[5]: Predefined-inquiries
+[6]: Diagnostic-information
+[7]: Integrate-predefined-inquiries
+[8]: How-to-rename-tables-and-columns-after-CSV-import
+[9]: Multiple-CSV-file-import
+[10]: How-to-build-a-pivot-table-in-SQLite

How-to-build-a-pivot-table-in-SQLite.md

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+This how-to explores how to build pivot tables in SQLite, which doesn't have a
+special constructs like `PIVOT` or `CROSSTAB` in its SQL dialect.
+
+# Static-column pivot table
+
+If the columns of a pivot table are known beforehand, it's possible to write a
+standard, say SQL-92, query that would produce a pivot table in its result set.
+This example uses World Bank [country indicators][1]. This query calculates
+average fertility and life expectancy for a few country groups.
+
+```sql
+SELECT
+  "Country Name",
+  AVG(IIF(
+    FLOOR(year / 10) = 196 AND "Indicator Name" LIKE 'Fertility rate%',
+    value,
+    NULL
+  )) AS "FR 196x",
+  AVG(IIF(
+    FLOOR(year / 10) = 196 AND "Indicator Name" LIKE 'Life expectancy%',
+    value,
+    NULL
+  )) AS "LE 196x",
+  AVG(IIF(
+    FLOOR(year / 10) = 197 AND "Indicator Name" LIKE 'Fertility rate%',
+    value,
+    NULL
+  )) AS "FR 197x",
+  AVG(IIF(
+    FLOOR(year / 10) = 197 AND "Indicator Name" LIKE 'Life expectancy%',
+    value,
+    NULL
+  )) AS "LE 197x",
+  AVG(IIF(
+    FLOOR(year / 10) = 198 AND "Indicator Name" LIKE 'Fertility rate%',
+    value,
+    NULL
+  )) AS "FR 198x",
+  AVG(IIF(
+    FLOOR(year / 10) = 198 AND "Indicator Name" LIKE 'Life expectancy%',
+    value,
+    NULL
+  )) AS "LE 198x",
+  AVG(IIF(
+    FLOOR(year / 10) = 199 AND "Indicator Name" LIKE 'Fertility rate%',
+    value,
+    NULL
+  )) AS "FR 199x",
+  AVG(IIF(
+    FLOOR(year / 10) = 199 AND "Indicator Name" LIKE 'Life expectancy%',
+    value,
+    NULL
+  )) AS "LE 199x",
+  AVG(IIF(
+    FLOOR(year / 10) = 200 AND "Indicator Name" LIKE 'Fertility rate%',
+    value,
+    NULL
+  )) AS "FR 200x",
+  AVG(IIF(
+    FLOOR(year / 10) = 200 AND "Indicator Name" LIKE 'Life expectancy%',
+    value,
+    NULL
+  )) AS "LE 200x"
+FROM country_indicators
+WHERE "Country Name" IN(
+  'Arab World',
+  'Central Europe and the Baltics',
+  'East Asia & Pacific',
+  'European Union',
+  'Latin America & Caribbean',
+  'High income',
+  'Middle income',
+  'Low income'
+)
+GROUP BY 1
+ORDER BY
+  CASE "Country Name"
+    WHEN 'High income' THEN 1
+    WHEN 'Middle income' THEN 2
+    WHEN 'Low income' THEN 3
+    WHEN 'European Union' THEN 4
+    WHEN 'Central Europe and the Baltics' THEN 5
+    WHEN 'East Asia & Pacific' THEN 6
+    WHEN 'Latin America & Caribbean' THEN 7
+    WHEN 'Arab World' THEN 8
+    ELSE 99
+  END
+```
+
+# Dynamic-column pivot table
+
+SQLite in sqliteviz is built with [pivot_vtab][2] extension. The same result set
+can be produced with this, arguably simpler and more maintainable, query.
+
+```sql
+CREATE VIRTUAL TABLE temp.pivot USING pivot_vtab(
+  (
+    WITH t(country_name) AS (VALUES
+      ('High income'),
+      ('Middle income'),
+      ('Low income'),
+      ('European Union'),
+      ('Central Europe and the Baltics'),
+      ('East Asia & Pacific'),
+      ('Latin America & Caribbean'),
+      ('Arab World')
+    )
+    SELECT country_name FROM t
+  ),
+  (
+    SELECT
+      FLOOR(year / 10) || '|' || "Indicator Name" column_key,
+      CASE
+        WHEN "Indicator Name" LIKE 'Fertility rate%' THEN 'FR'
+        WHEN "Indicator Name" LIKE 'Life expectancy%' THEN 'LE'
+      END || ' ' || FLOOR(year / 10) || 'x' column_name
+    FROM country_indicators
+    WHERE
+      "Indicator Name" LIKE 'Fertility rate%'
+      OR "Indicator Name" LIKE 'Life expectancy%'
+    GROUP BY 1
+  ),
+  (
+    SELECT AVG(value)
+    FROM country_indicators
+    WHERE
+      "Country Name" = :country_name
+      AND FLOOR(year / 10) || '|' || "Indicator Name" = :column_key
+  )
+);
+SELECT * FROM pivot
+```
+
+[1]: https://github.com/plotly/datasets/blob/master/country_indicators.csv
+[2]: https://github.com/jakethaw/pivot_vtab

How-to-rename-tables-and-columns-after-CSV-import.md

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+# Rename columns
+
+If sqliteviz parses CSV without `Use first row as column headers` option then
+it will name the columns like `col1`, `col2` etc. You can easily rename the
+columns after import with `ALTER TABLE` statements like this:
+
+```sql
+ALTER TABLE your_table_name
+RENAME COLUMN current_column_name TO new_column_name;
+```
+
+## Column rename example
+
+There is a table `dots` with columns `col1`, `col2`, `col3`. Here are the steps
+to rename the columns to `x`, `y` and `z` respectively:
+
+- Click `Create` in the top toolbar
+- In the opened query editor write a script
+
+```sql
+ALTER TABLE dots
+RENAME COLUMN col1 TO x;
+
+ALTER TABLE dots
+RENAME COLUMN col2 TO y;
+
+ALTER TABLE dots
+RENAME COLUMN col3 TO z;
+```
+
+- Click <img src="./img/run.svg"/> to run the script
+
+
+# Rename table
+
+```sql
+ALTER TABLE current_table_name
+RENAME TO new_table_name;
+```
+
+## Table rename example
+
+There is a table `dots`. Here are the steps to rename it to `point`:
+
+- Click `Create` in the top toolbar
+- In the opened query editor write a script
+
+```sql
+ALTER TABLE dots RENAME TO point
+```
+
+- Click <img src="./img/run.svg"/> to run the script

Installation.md

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+The latest release of sqliteviz is running on [Github pages][1].
+
+Basically, you don't need to install sqliteviz. But if you want you can install
+it and use it as a regular desktop application with the following steps:
+
+1. Open sqliteviz with [Chrome browser][2]
+2. At the top right of the address bar, click `Install`
+3. Confirm installation
+
+See more about PWA on [Google Chrome Help][3].
+
+[1]: https://lana-k.github.io/sqliteviz
+[2]: https://www.google.com/chrome
+[3]: https://support.google.com/chrome/answer/9658361?hl=en&ref_topic=7439636

Integrate-predefined-inquiries.md

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+If you run sqliteviz on your own server you can specify predefined inquiries.
+These inquiries will appear in `Inquiries` list for all users working with
+sqliteviz on your server.
+
+To create a list of predefined inquiries choose inquiries in `Inquiries` list
+and export them to `inquiries.json`. Then (re)place this file on the server in
+the same directory as `index.html`.
+
+A user can't edit, rename or delete a predefined inquiry. The rest operations
+are available.

Manage-inquiries.md

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+# Organise
+
+You can find all inquiries that you saved in `Inquiries` (fig. 1).
+
+<p align="center"> <img class="figure" src="./img/Screenshot_my_inquiries.png"
+width="700"/> </p>
+
+<p align="center"> <sub> Fig. 1: Inquiries </sub> </p>
+
+To manipulate one inquiry hover the cursor over the row with the inquiry and
+choose the action:
+
+* <img src="./img/rename.svg"/> – rename an inquiry
+* <img src="./img/copy.svg"/> – duplicate an inquiry
+* <img src="./img/file-export.svg"/> – export an inquiry to JSON file
+* <img src="./img/delete.svg"/> – delete an inquiry
+
+To edit a query or visualisation settings of an inquiry click on the respective
+row. You will be redirected to `Workspace` where the chosen inquiry will be
+opened in a tab.
+
+> **Note:** After opening an inquiry there will be no visualisation for it even
+> if you specified it and saved. That is so because there is no data to build
+> the visualisation. Run the query and all saved chart settings will be applied.
+
+You can also delete or export a group of inquiries to a JSON file. Select
+inquiries with checkboxes and press `Delete`/`Export` button above the grid
+(fig. 2).
+
+<p align="center"> <img class="figure" src="./img/Screenshot_group.png?0.15.0"
+width="600"/>  </p>
+
+
+<p align="center"> <sub> Fig. 2: Inquiries: a group of inquiries is selected
+</sub> </p>
+
+> **Note:** Some operations are not available for predefined inquiries. Read
+> [Predefined inquiries][1] for details.
+
+# Import
+
+Click `Import` button on `Inquiries` page to import inquiries from a JSON file
+generated by export.
+
+[1]: Predefined-inquiries

Multiple-CSV-file-import.md

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+Sometimes it's useful to import several CSV files as tables in one database. For
+example, to be able to `JOIN` them in SQL.
+
+In sqliteviz you can not only create a database from a CSV file, but also add
+another table from CSV to the existing database.
+
+- Click <img src="./img/add-csv.svg"/> icon in the schema panel on `Workspace`
+  page
+- Choose a CSV file
+- Import it with `CSV import` dialog.

Pivot-table.md

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+# Pivot table UI
+
+Sqliteviz allows building pivot tables and visualizing them. To build a pivot
+run a query to get data. Then open visualisation panel by clicking <img
+src="./img/visualisation.svg"/> in any of the two side toolbars and choose a
+pivot mode by clicking <img src="./img/pivot.svg"/>.
+
+A pivot visualisation has the following settings:
+
+- Columns – choose one or more column names from the result set. The values in
+  the chosen columns will be column names of the pivot table.
+- Rows – choose one or more column names from the result set. The values in the
+  chosen columns will be row names of the pivot table.
+- Order of columns and rows.
+- Aggregator and its arguments – a function which will be used for pivot cell
+  calculation. An aggregator can have from zero to two arguments. An aggregator
+  argument is one of the columns of the result set.
+- View – pivot table visualisation. It can be a table, a heatmap, a chart,
+  etc. See some examples of different views of the same pivot table below.
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_pivot_table.png" width="900"/>
+</p>
+<p align="center">
+  <sub>
+    Fig. 1: Table
+  </sub>
+</p>
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_pivot_heatmap.png" width="900"/>
+</p>
+<p align="center">
+  <sub>
+    Fig. 2: Heatmap
+  </sub>
+</p>
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_pivot_barchart.png" width="900"/>
+</p>
+<p align="center">
+  <sub>
+    Fig. 3: Horizontal Stacked Bar Chart
+  </sub>
+</p>
+
+There are several built-in chart views for a pivot. But you can build your own
+with `Custom chart` view (fig. 4).
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_pivot_custom chart.png"
+    width="900"/>
+</p>
+<p align="center">
+  <sub>
+    Fig. 4: Custom Chart
+  </sub>
+</p>
+
+> **Note:**  You can switch to other pivot views and back to `Custom chart` –
+> all your custom chart settings will be remembered. But if you switch the
+> visualisation mode from pivot to any other mode, unsaved changes will be lost.
+
+You can save any visualisation as an image by clicking <img
+src="./img/camera.svg"/>.
+
+# Pivot table SQL
+
+Pivot table (in the form of a result set) can be built on the SQL-level and,
+technically speaking, can be visualised as any other result set. Practically
+though there are a couple of challenges with that:
+
+1. Visualising a dataset of long/tall shape is much more convenient in Plotly
+   (chart editor) rather than one of wide/fat shape.
+2. SQLite doesn't have a special constructs like `PIVOT` or `CROSSTAB` in
+   its SQL dialect.
+
+[How to build a pivot table in SQL(ite)][1] explores two options with static
+(or beforehand-known) and dynamic columns.
+
+[1]: How-to-build-a-pivot-table-in-SQLite

Predefined-inquiries.md

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+Predefined inquiries come out of the box on the sqliteviz instance. In sqliteviz
+deployed on [Github Pages][1] there are no predefined inquiries, but you may
+see them if you use sqliteviz integrated into another app.
+
+These inquiries are shown in `Inqueries` list with a special label on the mouse
+over (fig. 1).
+
+<p align="center">
+   <img class="figure" src="./img/Screenshot_predefined.png?0.15.0"
+    width="700"/>
+</p>
+<p align="center">
+  <sub>
+    Fig. 1: Predefined inquiry
+  </sub>
+</p>
+
+As a user, you can't rename or delete a predefined inquiry. You can't save
+changes in a predefined inquiry, but you can save them as a new inquiry. The
+rest operations, copy and export, are available.
+
+[1]: https://lana-k.github.io/sqliteviz

img/add-csv.svg

@@ -0,0 +1,10 @@
+<svg width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg">
+<g clip-path="url(#clip0)">
+<path fill-rule="evenodd" clip-rule="evenodd" d="M13.6573 1.5H2.59985C1.77485 1.5 1.09985 2.175 1.09985 3V13.6649C1.09985 14.4899 1.77485 15.1649 2.59985 15.1649H9.84V13.6649H8.87866V9.08244H13.6573V9.83777H15.1573V3C15.1573 2.17 14.4873 1.5 13.6573 1.5ZM13.6573 7.58244V3H8.87866V7.58244H13.6573ZM7.37866 3H2.59985V7.58244H7.37866V3ZM2.59985 9.08244V13.6649H7.37866V9.08244H2.59985ZM13.1702 10.8434H15.6702V13.1717H18.0001V15.6717H15.6702V18H13.1702V15.6717H10.8401V13.1717H13.1702V10.8434Z" fill="#A2B1C6"/>
+</g>
+<defs>
+<clipPath id="clip0">
+<rect width="18" height="18" fill="white"/>
+</clipPath>
+</defs>
+</svg>

img/camera.svg

@@ -0,0 +1,5 @@
+<svg width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg">
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