Below is a description of all the possible fields that can be selected in Analytics.

It is important to note that all Analytics and fields utilize values from the Completions tab.

## Aggregation Functions

Aggregation Functions denoted with * can also be utilized in Expressions for more complex Analytics.

### Expression

Separate from the overall expression editor, this expression editor can be used for aggregation functions, and is typically useful in the Y axis.

Example: Finding the total percentage of defective parts from the number of completions.

### Count of Completions*

A summation of all the completions that have successfully run in a given app or apps with the selected criteria. If there is no set criteria, this will return the total number of completions.

### Percent of Completions

Percentage of completions that match a given criteria.

### Percent Where True

Frequency that the given criteria is True. Often, this is a good place to check for FPY using a Boolean "Pass/Fail" variable.

**Percent Where False**

Frequency that given criteria is False. The is the inverse Percent Where True. Percent Where False added to Percent Where True on the same data set should equal 100%.

### Count Where True

Returns the total count of completions that match a given criteria.

**Count Where False**

Returns the total count of completions that match a given criteria that is False.

### Average*

Gives the average of a number variable.

### Median

Gives the median value of a number value.

### Sum*

Provides the summation for a variable. For more complex and conditional sum statements, SUM() can be used as a function in the Expression Editor.

### Minimum*

Returns the minimum historical value of a given variable.

### Maximum*

Returns the maximum historical value of a given variable.

### Mode

Returns the most frequent value of a given variable.

### Standard Deviation

Returns the standard deviation of a number variable or expression, categorized by the X axis.

### 95th Percentile

Returns the 95th percentile of a numerical data set. This value means that 95% of the values are below this number.

### 5th Percentile

Returns the 5th percentile of a numerical data set. This value means that only 5% of the values are below this number.

### Ratio

Returns the ratio of one number variable or expression to another. This takes in two number variables (or expressions), where the first acts as the numerator, and the second as the denominator.

### Ratio Complement

Similar to ratio, but takes in two conditions of any type. These conditions may be set when one variable or field is equal to a given condition. It may also be a number variable or an expression. This also takes the first argument as a numerator, and the second as a denominator.

## Fields

Fields are best used as an X axis, or something to Compare By in analytics. This article has some more information on Fields.

### User

The logged in user who completed the app.

### Badge ID

Similar to User, but gives the assigned Badge ID instead of the name.

### Station

Which station the app completed at.

### Start Time

When the app began working towards a completion.

### End Time

When the app completed.

### Week

Groups app completions by the week they completed. Useful for viewing aggregations on a week-by-week basis.

### Month

Groups app completions by month.

### Year

Groups app completions by year.

### Shift Type

Categorizes data by the Shift Type worked. Only applicable to Enterprise Accounts.

### Shift Duration

Displays or categorizes the duration of the Shift Type. Only applicable to Enterprise Accounts.

### Is Working Day

Categorizes completions by the status of being a scheduled workday or not. Only applicable to Enterprise Accounts.

### Day of Week

Categorizes completion data by the day of the week. This will aggregate matching days. For example, if given an All Time range, the analysis will stack all Monday values together.

### Hour of Day

Categorizes completion data by hour of the day. Aggregates similarly to Day of Week.

### Hour

Distills all data to the hour it was completed. This field gives all historical data by hour, and is not aggregated like Hour of Day.

### Process Cycle Time

Displays the overall runtime of the App completion.

### App

Categorizes data by which app the data came from. Applicable only to analyzing data from multiple apps.

### App Version

Groups app data by the Version of the App ran to create the completion.

## Table Records

Here, you can select any table field that is used within the app in the analysis. For more detailed information, check out this article on Table Analyses.

## App Variables

A list of all app variables used within the target app can be found here.

## App Steps

All steps within the app are listed here, in the form of cycle time spent on the steps.

Example 1: find the average time spent on a given step.

Example 2: filter out undesired step times from Process Cycle Time.

## App Forms

Provides a list of all the fields used within application form steps.

This includes the Signature Form. This is named based on the label of the signature widget within the Signature Form Step. You can use this field as the "X Axis" to show all operators that have signed via the Signature Form, for example.

## Functions

Functions vary slightly in each place used. The entries for variables and expressions may differ slightly when used in the X-axis, Y-axis, or Compare By fields. The following will outline what each function does.

### Equals

Checks for equivalency between a variable or expression to a given state or value.

### Not Equal

Checks that a variable or expression is not equivalent to a given state or value.

### Is Blank

Checks for completions where the given variable is blank.

### Is Not Blank

Checks for completions where the given variable has a value.

### Not

Not acts as a modifier to use another Function. It will check for completions where the inverse of the given Function(s) is satisfied.

### And

And is another modifier which allows for multiple Functions to be checked, and returns completions where both statements are satisfied.

### Or

Or is a third modifier which allows for multiple functions, and returns completions where any of them are satisfied.

### Greater Than

Checks that a numeric value is greater than a given value or expression.

### Greater Than Or Equal To

Checks that a numeric value is greater than or equal to a given value or expression.

### Less Than

Checks that a numeric value is less than a given value or expression.

### Less Than or Equal To

Checks that a numeric value is less than or equal to a given value or expression.

### In Last N Working Days

Checks completions that were in the previous N days. Working days are set on a default, unless defined by Schedules (Enterprise Accounts).

### Expression

Expression allows use of the Expression Editor to write custom expressions. For more detail, read about using the Expression Editor in Analytics.

### Bins

Allows binned separation of a numeric value. This allows a histogram to be made in any number of bucketed value ranges with a selected minimum and maximum range.

### Link Fields

Returns the data of the first variable unless it is null. In that case, the second given variable is returned.

### Length Of

On the X-axis, groups completions by the length of a variable in digits. On the Y axis, can be used with an aggregation function to check a given length.

### Starts With

Checks that a variable begins with a given value.

### Ends With

Checks that a variable ends with a given value.

### Contains

Checks a variable contains a given value.

### Is Exactly

Checks that a variable matches completely with a given value.

### Is Not Exactly

Checks that a variable is anything other than a perfect match to a given value.

### Contains All Of

Not yet released. Checks that the contents of a variable matches the given value anywhere.

Example - Variable value 'ABCDE' contains all of 'BCD'

### Does Not Contain All Of

Not yet released. Checks that a variable's contents do not match the given value anywhere.

## Further Reading

To better understand these functions, try playing around with sample data. The Tulip Terminal app in each instance has sample data to use. Make sure to filter the analysis to All Time to test the Analytics data.

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