Wednesday, March 4, 2015
Creating Custom Charts

Apps created with Code On Time generator will automatically compose up to nine charts based on the columns present in your tables. If the default charts are insufficient, then the developer may choose to define their own charts for each data view.

Charts are configured using a simple tagging language applied on the data fields of a view. Let’s start configuring charts for the Orders page of the Northwind sample app.

Creating a Chart

Start the Project Designer. In the Project Explorer on the right side of the screen, switch to the Controllers tab and double-click on the Orders / Views / grid1 / CustomerID data field.

Clicking on the CustomerID data field of the Orders controller.

Change the following property:

Property Value
Tag pivot1-row1-column

The tag does the following: “pivot1” will declare that the data field CustomerID will be used for pivot with ID of “1”. The keyword “row1” declares that the data field will be used to define the first row. Finally, the keyboard “column” declares the chart type.

Press OK to save the data field. On the toolbar, press Browse to regenerate the app and open it in the default browser. When the page comes up, navigate to the Orders page. There will be a single chart of type “column” available.

A simple column chart in Orders.

The data behind the chart is shown in the table below:

The data behind a simple column chart in Orders.

The first column shows the values of each row (the customer company name). The second column is the actual value used to render the chart. No value field was specified by the developer, so a count of records has been used.

Sorting

Notice that the order of rows is alphabetical. Let’s sort the rows in descending order by the value in the second column. Replace the tag using the method described above with the following:

Data Field Tag
CustomerID pivot1-row1-column-sortdescbyvalue

The image below shows the new chart, with the rows sorted.

Orders column chart with the columns sorted by value

The data will look like the following:

The data has been sorted by value.

This has given us the correct order, but this chart can still be improved. Notice that there are too many columns displayed, making it hard to read. Let’s reduce that number.

Reducing the Result

Replace the tag with the following:

Data Field Tag
CustomerID pivot1-row1-column-sortdescbyvalue-top10

The “topX” keyword will only take the top X number of rows, and will throw away the rest. The new chart can be seen below.

The new chart only shows 10 columns.

The table can be seen below.

Only 10 rows are available in the table.

There are now ten rows of data available, as expected.

It may be possible that the user does not want the rest of the rows to be hidden, but grouped into one last column.

The “Other” Column

Using the “other” keyword in combination with “topX” will only display the top X number of rows. For rows that do not make the cut, their values will be summed up into an “Other” row. Use the following tag:

Data Field Tag
CustomerID pivot1-row1-column-sortdescbyvalue-top10-other

The new chart will display this “Other” column.

Only the top 10 customers are displayed, and the rest are grouped into an 'Other' column.

The values in this “Other” column will also be present in the chart data.

Values for the "Other" column are displayed as the last row in the table.

Defining Titles

Notice that a title has been automatically generated from the chart definition. The title, as well as the horizontal and vertical axis labels, can also be defined.

Replace the tag with the following. Notice that multiple space-separated tags are defined.

Data Field Tag
CustomerID pivot1-row1-column-sortdescbyvalue-top10 pivot1-title:"My Top 10 Customers"   pivot1-haxistitle:"Customers"        pivot1-vaxistitle:"Orders"

The new chart will use the specified titles:

An Orders chart with custom titles.

The new title will be shown above the table as well:

The chart data also shows a custom title.

This chart looks complete. Let’s add another chart that shows how many orders were received over time.

Working With Dates

Orders table in the Northwind database contains the OrderDate field. Let’s show how many orders were received per quarter. Apply the following tags to the relevant data fields:

Data Field Tag
OrderDate pivot2-row1-quarter-line

The “quarter” keyword will group the values by quarters. The “line” keyword is the chart type.

A chart showing the count of orders by quarter.

In this chart, the orders have now been grouped by the quarter. Notice that there are no years – we need to define another group in order to split the orders by year, and then by quarter.

The chart data shows how the orders have been grouped by quarter.

Make the following changes:

Data Field Tag
OrderDate pivot2-row1-year-line pivot2-row2-quarter

This configuration will define a chart that uses the OrderDate field twice – first time to group by year, and then to group by quarter. The results can be seen below.

The chart groups orders by year, and then by quarter.

The new chart now shows quarters from each year that Northwind has been in business.

The chart table groups orders by year, and then by quarter.

Revealing Gaps

Suppose that the data has some gaps in the time period. The example below was filtered to customer “Frankenversand”, and it is clear that “1997, Q1” is missing. A missing row is easy to spot in the table, but it would be harder to spot in the chart.

The chart data reveals a missing row - '1997, Q1'.

Use the “all” keyword to ensure that gaps are displayed in the result.

Data Field Tag
OrderDate pivot2-row1-year-line pivot2-row2-quarter-all

Empty data periods are now revealed on the chart.

The chart now presents gaps in the data.

Empty gaps are revealed in the chart data.

This chart looks good with the current data set. What happens when the data takes on a different shape?

Automatic Date Sizing

The view has been filtered to only include data for 3 months. The end result is not so useful or attractive when using year/quarter.

When the data has been filtered to three months, grouping by quarter is not that helpful.

The table is equally unhelpful.

When the data has been filtered to three months, grouping by quarter is not that helpful.

This is where the “date” keyword comes to the rescue. Instead of manually specifying the date groupings, we will command the API to compose several result and use the best fit. Replace the tag with the following:

Data Field Tag
OrderDate pivot2-row1-line-date

It appears that the chart using year/month/week has the most useful presentation according to the algorithm.

The auto date algorithm has decided to use year/month/week as the best grouping.

The table now reveals more useful information as well.

The chart data shows values grouped by week.

Specifying a Column Field

It may be helpful to split apart each data point by introducing a column field. Let’s show the number of orders from the top three customers. Change the tags for pivot2 as follows:

Data Field Tag
CustomerID pivot2-col1-top3-sortdescbyvalue
OrderDate pivot2-row1-columnstacked-date

The chart will now show a breakdown of orders from the top three customers at each date.

The chart now displays orders broken down by customer over time.

The table will now have multiple columns.

The data shows the orders broken down into columns by customer over time.

Specifying a Value Field

Instead of simply showing a count of orders, let’s use the sum of Freight as the value. To do this, we will need to define the data field Freight as a value field. Specify the tags below. The “sum” keyword determines the type of value. By default, the chart will use “count”.

Data Field Tag
CustomerID pivot2-col1-top3-sortdescbyvalue
OrderDate pivot2-row1-columnstacked-date
Freight pivot2-val1-sum

The chart will now display values according to the sum of Freight field.

The chart now uses the sum of Freight as the value.

The table will reveal the correct values.

The table shows the use of sum of Freight as the value.