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Supported CSV format

In this guide, you can learn how to format your CSV data so WebDataRocks can process it.

CSV format

WebDataRocks supports the following CSV format:

• The first data record contains field names and optional data type prefixes.
• Each data record is on a separate line.
• Field names and values are separated by the same character: comma ,, semicolon ;, or a custom field separator.

Here is an example of a valid CSV file:

Invoice Date, Quantity, Country, Week Day
2018-05-15, 3, France, Tuesday
2018-05-16, 4, Italy, Wednesday
2018-05-17, 2, Spain, Thursday
2018-05-12, 2, Japan, Saturday

Input value formats

Number field format

Number values can contain digits, -, and + characters. Point . must be used as a decimal separator. Numbers in exponential notation are also supported. Examples of valid values: -20, 2.50, 1.0E+2.

String field format

String values can be enclosed in double quotation marks or specified without them. If a field value contains line breaks or a field separator, it must be enclosed in double quotation marks. If a field is quoted, it must be escaped with double quotation marks. Examples of valid values: Apple, "A-Z" section, "1, 2, 3", "The ""A, B, C"" magazine".

Date field format

Date values must be specified in the ISO 8601 format. Examples: "2018-01-10" (date), "2018-01-10T08:14:00" (date and time), "2018-01-10T06:14:00Z" (date and time in UTC).

Time field format

Time values must be specified as a number of seconds. The component displays values in the "HH:mm:ss" format. Examples of valid values: 5400 (displayed as "01:30:00" in the component).

To ensure the detection of time values, set the field data type prefix to +t. Otherwise, they will be processed as numbers.

Month field format

Month values must start with a capital letter. Full names and 3-letter abbreviations of months are supported. Examples of valid values: "October", "Dec", "May".

To ensure the detection of month values, set the field data type prefix to +m. Otherwise, they will be processed as strings.

Weekday field format

Weekday values must start with a capital letter. Full names and 3-letter abbreviations of the days of the week are supported. Examples of valid values: "Monday", "Sun", "Friday".

To ensure the detection of weekday values, set the field data type prefix to +w. Otherwise, they will be processed as strings.

See also

• Setting data types
• Connecting to CSV
• Data type prefixes

In this guide, you can learn how to format your CSV data so WebDataRocks can process it.

CSV format

WebDataRocks supports the following CSV format:

• The first data record contains field names and optional data type prefixes.
• Each data record is on a separate line.
• Field names and values are separated by the same character: comma ,, semicolon ;, or a custom field separator.

Here is an example of a valid CSV file:

Invoice Date, Quantity, Country, Week Day
2018-05-15, 3, France, Tuesday
2018-05-16, 4, Italy, Wednesday
2018-05-17, 2, Spain, Thursday
2018-05-12, 2, Japan, Saturday

Input value formats

Number field format

Number values can contain digits, -, and + characters. Point . must be used as a decimal separator. Numbers in exponential notation are also supported. Examples of valid values: -20, 2.50, 1.0E+2.

String field format

String values can be enclosed in double quotation marks or specified without them. If a field value contains line breaks or a field separator, it must be enclosed in double quotation marks. If a field is quoted, it must be escaped with double quotation marks. Examples of valid values: Apple, "A-Z" section, "1, 2, 3", "The ""A, B, C"" magazine".

Date field format

Date values must be specified in the ISO 8601 format. Examples: "2018-01-10" (date), "2018-01-10T08:14:00" (date and time), "2018-01-10T06:14:00Z" (date and time in UTC).

Time field format

Time values must be specified as a number of seconds. The component displays values in the "HH:mm:ss" format. Examples of valid values: 5400 (displayed as "01:30:00" in the component).

To ensure the detection of time values, set the field data type prefix to +t. Otherwise, they will be processed as numbers.

Month field format

Month values must start with a capital letter. Full names and 3-letter abbreviations of months are supported. Examples of valid values: "October", "Dec", "May".

To ensure the detection of month values, set the field data type prefix to +m. Otherwise, they will be processed as strings.

Weekday field format

Weekday values must start with a capital letter. Full names and 3-letter abbreviations of the days of the week are supported. Examples of valid values: "Monday", "Sun", "Friday".

To ensure the detection of weekday values, set the field data type prefix to +w. Otherwise, they will be processed as strings.

See also

• Setting data types
• Connecting to CSV
• Data type prefixes

In this guide, you can learn how to format your CSV data so WebDataRocks can process it.

CSV format

WebDataRocks supports the following CSV format:

• The first data record contains field names and optional data type prefixes.
• Each data record is on a separate line.
• Field names and values are separated by the same character: comma ,, semicolon ;, or a custom field separator.

Here is an example of a valid CSV file:

Invoice Date, Quantity, Country, Week Day
2018-05-15, 3, France, Tuesday
2018-05-16, 4, Italy, Wednesday
2018-05-17, 2, Spain, Thursday
2018-05-12, 2, Japan, Saturday

Input value formats

Number field format

Number values can contain digits, -, and + characters. Point . must be used as a decimal separator. Numbers in exponential notation are also supported. Examples of valid values: -20, 2.50, 1.0E+2.

String field format

String values can be enclosed in double quotation marks or specified without them. If a field value contains line breaks or a field separator, it must be enclosed in double quotation marks. If a field is quoted, it must be escaped with double quotation marks. Examples of valid values: Apple, "A-Z" section, "1, 2, 3", "The ""A, B, C"" magazine".

Date field format

Date values must be specified in the ISO 8601 format. Examples: "2018-01-10" (date), "2018-01-10T08:14:00" (date and time), "2018-01-10T06:14:00Z" (date and time in UTC).

Time field format

Time values must be specified as a number of seconds. The component displays values in the "HH:mm:ss" format. Examples of valid values: 5400 (displayed as "01:30:00" in the component).

To ensure the detection of time values, set the field data type prefix to +t. Otherwise, they will be processed as numbers.

Month field format

Month values must start with a capital letter. Full names and 3-letter abbreviations of months are supported. Examples of valid values: "October", "Dec", "May".

To ensure the detection of month values, set the field data type prefix to +m. Otherwise, they will be processed as strings.

Weekday field format

Weekday values must start with a capital letter. Full names and 3-letter abbreviations of the days of the week are supported. Examples of valid values: "Monday", "Sun", "Friday".

To ensure the detection of weekday values, set the field data type prefix to +w. Otherwise, they will be processed as strings.

See also

• Setting data types
• Connecting to CSV
• Data type prefixes

Using the metadata object for JSON, you can create multilevel hierarchies from fields of any type.

In this guide, we’ll create a Food hierarchy with Category, Item, and Serving Size levels based on the data below:

[
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

Step 1. In the metadata object, set the type of Category, Item, and Serving Size fields as "level":

[
  {
    "Category": {
      type: "level"
    },
    "Item": {
      type: "level"
    },
    "Serving Size": {
      type: "level"
    },
    "Calories": {
      type: "number"
    }
  },
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

Step 2. Use the hierarchy, parent, and level properties of the metadata object to create the Food hierarchy:

[
  {
    "Category": {
      type: "level",
      hierarchy: "Food"
    },
    "Item": {
      type: "level",
      hierarchy: "Food",
      level: "Dish",
      parent: "Category"
    },
    "Serving Size": {
      type: "level",
      hierarchy: "Food",
      level: "Size",
      parent: "Dish"
    },
    "Calories": {
      type: "number"
    }
  },
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

See how this dataset will be visualized in WebDataRocks:

Check out a live demo on CodePen.

See also

• Supported JSON formats
• Setting data types
• Connecting to JSON
• Metadata object for JSON

Using the metadata object for JSON, you can create multilevel hierarchies from fields of any type.

In this guide, we’ll create a Food hierarchy with Category, Item, and Serving Size levels based on the data below:

[
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

Step 1. In the metadata object, set the type of Category, Item, and Serving Size fields as "level":

[
  {
    "Category": {
      type: "level"
    },
    "Item": {
      type: "level"
    },
    "Serving Size": {
      type: "level"
    },
    "Calories": {
      type: "number"
    }
  },
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

Step 2. Use the hierarchy, parent, and level properties of the metadata object to create the Food hierarchy:

[
  {
    "Category": {
      type: "level",
      hierarchy: "Food"
    },
    "Item": {
      type: "level",
      hierarchy: "Food",
      level: "Dish",
      parent: "Category"
    },
    "Serving Size": {
      type: "level",
      hierarchy: "Food",
      level: "Size",
      parent: "Dish"
    },
    "Calories": {
      type: "number"
    }
  },
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

See how this dataset will be visualized in WebDataRocks:

Check out a live demo on CodePen.

See also

• Supported JSON formats
• Setting data types
• Connecting to JSON
• Metadata object for JSON

Using the metadata object for JSON, you can create multilevel hierarchies from fields of any type.

In this guide, we’ll create a Food hierarchy with Category, Item, and Serving Size levels based on the data below:

[
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

Step 1. In the metadata object, set the type of Category, Item, and Serving Size fields as "level":

[
  {
    "Category": {
      type: "level"
    },
    "Item": {
      type: "level"
    },
    "Serving Size": {
      type: "level"
    },
    "Calories": {
      type: "number"
    }
  },
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

Step 2. Use the hierarchy, parent, and level properties of the metadata object to create the Food hierarchy:

[
  {
    "Category": {
      type: "level",
      hierarchy: "Food"
    },
    "Item": {
      type: "level",
      hierarchy: "Food",
      level: "Dish",
      parent: "Category"
    },
    "Serving Size": {
      type: "level",
      hierarchy: "Food",
      level: "Size",
      parent: "Dish"
    },
    "Calories": {
      type: "number"
    }
  },
  {
    "Category": "Breakfast",
    "Item": "Frittata",
    "Serving Size": "4.8 oz (136 g)",
    "Calories": 300
  },
  {
    "Category": "Breakfast",
    "Item": "Boiled eggs",
    "Serving Size": "4.8 oz (135 g)",
    "Calories": 250
  }
]

See how this dataset will be visualized in WebDataRocks:

Check out a live demo on CodePen.

See also

• Supported JSON formats
• Setting data types
• Connecting to JSON
• Metadata object for JSON

The metadata object is used for setting data types, creating multilevel hierarchies, and changing captions. This object is specified as the first element of the JSON array.

Properties

Name	Type	Description
type	String	optional The field’s data type. Possible values:  "number" "string" "date" "date string" "year/month/day" "year/quarter/month/day" "datetime" "time" "month" "weekday" "level" "hidden"  Read more about available data types.
caption	String	optional The field’s caption.
hierarchy	String	optional The name of the hierarchy. Used for multilevel hierarchies. Set this property to make the field a level of the specified hierarchy. In this case, the type must be set to "level".
parent	String	optional The caption of the parent hierarchy level. Used for multilevel hierarchies. In this case, the type must be set to "level".
level	String	optional The caption of the hierarchy level. Used for multilevel hierarchies. In this case, the type must be set to "level".

Examples

1) Setting the metadata object in a JSON array of objects:

[
  {
    "Product": {
      "type": "string"
    },
    "Price": {
      "type": "number"
    }
  },
  {
    "Product": "Apple",
    "Price": 2.50
  },
  {
    "Product": "Cherry",
    "Price": 5.25
  }
]

See the full example on CodePen.

2) Setting the metadata object in a JSON array of arrays:

[
  {
    "Product": {
      "type": "string"
    },
    "Price": {
      "type": "number"
    },
  },
  ["Apple", 2.50],
  ["Cherry", 5.25]
]

Try a live demo on CodePen.

See also

• Supported JSON formats
• Setting data types
• Connecting to JSON
• Creating multilevel hierarchies

The metadata object is used for setting data types, creating multilevel hierarchies, and changing captions. This object is specified as the first element of the JSON array.

Properties

Name	Type	Description
type	String	optional The field’s data type. Possible values:  "number" "string" "date" "date string" "year/month/day" "year/quarter/month/day" "datetime" "time" "month" "weekday" "level" "hidden"  Read more about available data types.
caption	String	optional The field’s caption.
hierarchy	String	optional The name of the hierarchy. Used for multilevel hierarchies. Set this property to make the field a level of the specified hierarchy. In this case, the type must be set to "level".
parent	String	optional The caption of the parent hierarchy level. Used for multilevel hierarchies. In this case, the type must be set to "level".
level	String	optional The caption of the hierarchy level. Used for multilevel hierarchies. In this case, the type must be set to "level".

Examples

1) Setting the metadata object in a JSON array of objects:

[
  {
    "Product": {
      "type": "string"
    },
    "Price": {
      "type": "number"
    }
  },
  {
    "Product": "Apple",
    "Price": 2.50
  },
  {
    "Product": "Cherry",
    "Price": 5.25
  }
]

See the full example on CodePen.

2) Setting the metadata object in a JSON array of arrays:

[
  {
    "Product": {
      "type": "string"
    },
    "Price": {
      "type": "number"
    },
  },
  ["Apple", 2.50],
  ["Cherry", 5.25]
]

Try a live demo on CodePen.

See also

• Supported JSON formats
• Setting data types
• Connecting to JSON
• Creating multilevel hierarchies

The metadata object is used for setting data types, creating multilevel hierarchies, and changing captions. This object is specified as the first element of the JSON array.

Properties

Name	Type	Description
type	String	optional The field’s data type. Possible values:  "number" "string" "date" "date string" "year/month/day" "year/quarter/month/day" "datetime" "time" "month" "weekday" "level" "hidden"  Read more about available data types.
caption	String	optional The field’s caption.
hierarchy	String	optional The name of the hierarchy. Used for multilevel hierarchies. Set this property to make the field a level of the specified hierarchy. In this case, the type must be set to "level".
parent	String	optional The caption of the parent hierarchy level. Used for multilevel hierarchies. In this case, the type must be set to "level".
level	String	optional The caption of the hierarchy level. Used for multilevel hierarchies. In this case, the type must be set to "level".

Examples

1) Setting the metadata object in a JSON array of objects:

[
  {
    "Product": {
      "type": "string"
    },
    "Price": {
      "type": "number"
    }
  },
  {
    "Product": "Apple",
    "Price": 2.50
  },
  {
    "Product": "Cherry",
    "Price": 5.25
  }
]

See the full example on CodePen.

2) Setting the metadata object in a JSON array of arrays:

[
  {
    "Product": {
      "type": "string"
    },
    "Price": {
      "type": "number"
    },
  },
  ["Apple", 2.50],
  ["Cherry", 5.25]
]

Try a live demo on CodePen.

See also

• Supported JSON formats
• Setting data types
• Connecting to JSON
• Creating multilevel hierarchies

Use the drill-down feature to drill into the different levels of a hierarchy. This operation helps to reveal more details of your data and get a more specific view of the hierarchies. Drill back up when you need to get a general view of the data.

Example

Starting from a country level, drill down to a city level that can be further drilled down to an address level. Drill up the levels to return to the previous view of the hierarchy.

Note. This feature can be used only with the multilevel hierarchies. Refer to the Data types guide for details on how to define levels in the data types.

Move up