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Customizing Dashboard Tables

This article contains information on advanced customization for dashboard tables, including instructions for nesting rows, displaying grids, and adjusting data summary views.

Overview

There are a variety of options for customizing the tables you can create within a research dashboard. This article will guide you through the process of nesting rows, adjusting table display, and enabling stat testing within your dashboard tables.  

1: Nesting Rows

Table rows can be nested to provide multiple levels on which users can drill-down to view data. To specify the top level of an hierarchy, set rowlevel=1. Any filters under that one would be children of that filter, listed with numbers in ascending order for each new level (i.e., rowlevel=2 for children of rowlevel=1, rowlevel=3 for children of rowlevel=2, and so on).

When nested rows are applied to a table, children of a filter are hidden until users click on the nesting parent.

Note: When you are nesting rows, make sure that the dashboard's Compat is set to level 2+.

Example:

If you have a question with response options broken out across states and their respective counties, you could put rowlevel=1 on the state rows, and rowlevel=2 on your county rows so that they would be collapsed under the state rows:

Table TableDrillDown Demo With Row Levels
row rowlevel=1 q1.r15 Oklahoma
row rowlevel=2 q1.r2 Cleveland area
row rowlevel=2 q1.r3 McClain area
row rowlevel=2 q1.r4 Haskell area
row rowlevel=1 q1.r5 Illinois
row rowlevel=2 q1.r6 Lake area
row rowlevel=2 q1.r7 Madison area
row rowlevel=2 q1.r8 Kane area
row rowlevel=1 q1.r9 Texas
row rowlevel=2 q1.r10 Austin area
row rowlevel=2 q1.r11 Dallas area
row rowlevel=2 q1.r12 Houston area
row rowlevel=2 q1.r13 San Antonio area
row rowlevel=2 q1.r14 Other area

The code above creates a nested table like the one displayed below:

DB_tabledrilldown.png

1.1: Adding a Net

The keyword net adds a row that nets the response data for a specified count of the rows listed below it. If the count specified is negative, the net includes the rows listed above it.

net <count> [title]

Example:

If you have a question asking respondents for their favorite color, and want to know which two colors are the most popular and which two are the least popular, you might use the following code to add nets for the table’s “Top 2”/”Bottom 2” responses:

table Favorite Colors
   net 2 Top 2
   rows Q11.r1-r5
   net -2 Bottom 2

1.2: Adding a Sum

The keyword sum adds a row that sums the response data for the next specified count of rows. If the count is negative, then sum includes the previous rows. This option makes the most sense when trying to sum checkboxes, where respondents can select multiple responses in one question.

sum <count> [title]

Example:

If you have a question asking which colors respondents like, and want to see how many people overall are picking black and blue, you might use the following code:

table Likeable Colors
   row Q1.r1 Green
   row Q1.r2 Yellow
   row Q1.r3 Red
   row Q1.r4 Blue
   row Q1.r5 Black
   sum -2 Likes Black & Blue

1.3: Adding a Total

The keyword total adds a row which will count respondents that fulfill the conditions for all other rows in the table. If a title for the total is not specified, "Total" will be used.

total [title]

Example:

If you want to see the total number of respondents answering a gender question (rather than just a split between males and females), you might use the following code:

table Gender of Respondents
   rows Q1.r1-r2
   total

2: Displaying a Grid Question Within a Table

It is possible to display a grid question within a single table. To create this display, you would use the conds attribute on each row you would like to include and define multiple conditions for these rows that correspond to each segment of the table.

Note:  For a chart, each row renders as a stacked bar and the columns renders in the relative color for each stacked bar.

Example:

If you wanted to view the data for a rating question evaluating your service’s “availability”, ”speed”, and ”stability”, you might use the following code, where the conds attribute is used to specify the correct rows for each specific segment:

table Rating
  banner.local
  segment q3.r1.any "Availability"
  segment q3.r2.any "Speed"
  segment q3.r3.any "Stability"
row conds="q3.c1.r1, q3.c1.r2, q3.c1.r3" q3.c1.any Poor
row conds="q3.c2.r1, q3.c2.r2, q3.c2.r3" q3.c2.any Fair
row conds="q3.c3.r1, q3.c3.r2, q3.c3.r3" q3.c3.any Good
row conds="q3.c4.r1, q3.c4.r2, q3.c4.r3" q3.c4.any Excellent

This code should output the table below:

=2015-12-08_0918 (1).png

3: Adding Horizontal Percentages

By default, the vertical percentage in a table is generated by dividing the row count by the base. However, you can add a set of horizontal percentages to a table to view percentages based on a segment instead. The keyword hp adds horizontal percentages based on the first banner segment in a dashboard, with the precision specified on a table or row. By default, this feature is off, but can be enabled simply by setting a precision.

hp <precision or off>

Example:

If you wanted to see the percentage of each gender answering additional questions in a table, you might add the following code:

table Gender of Respondents
   hp 2

4: Setting the Percentage Calculation

It is possible to change the base for the percentage calculation within a table to use survey logic. The keyword base re-bases the percentage calculation using another specified condition. This overrides basing from the total row and banner segments and can be set to use any logic from within the survey.

base <condition>

Example:

If you want to view the top and bottom 2 choices from your favorite color question based on everyone who provided an answer to that question alone, you might use the following code:

table Favorite Color
   row base=Q2.any "Q2.r1 or Q2.r2" Top 2 Choices
   row base=Q2.any "Q2.r3 or Q2.r5" Bot 2 Choices

Note: This option should not be used when horizontal percentages are enabled. The base keyword is used to display vertical percentages only.

5: Enabling Statistical Calculations

The stat rows supplied in a table are calculated using the overall percentages for numeric data. If needed, though, you can change this method for each individual row. The keyword stats enables the calculation of statistical data, rather than percentage, on numeric data.

stats <option>

The following options are available when using stats:

mean

The standard mean

stddev

The sample standard deviation (i.e., sigma-1)

sum

The total sum of all responses

stderr

The standard error

median

The median

count

The number of non-empty (zero/null) responses

pct

The count divided by base (e.g., 40% of all respondents answered this question)

 
Example:

If you want to see the averages for TV and radio ownership based on your statistical data, rather than on a percentage of your overall respondent base, you might use the following code:

table Things Owned
   row stats=mean Q3.r1.val TVs
   row stats=mean,stddev Q3.r2.val Radios

5.1: Setting the Precision of Stat Values

You can also adjust the precision used when calculating the statistical data in your stat rows. The stats.precision keyword is used to specify the decimal digit of precision for statistics.

Example:

If you want to view your TV/radio ownership calculations to the third decimal digit, you might use the following code:

table Things owned
stats.prec 3
row stats=mean q1.r1.val TVs
row stats=mean q1.r2.val Radios

What's Next for Creating a Dashboard?

The following articles describe additional features that you can apply to your dashboard tables:

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