kibana/docs/user/dashboard/lens-advanced.asciidoc

[[create-a-dashboard-of-panels-with-ecommerce-data]]
== Analyze time series data

In this tutorial, you'll use the ecommerce sample data to analyze sales trends, but you can use any type of data to complete the tutorial.
Before using this tutorial, review the <<kibana-concepts-analysts>>.

[role="screenshot"]
image::images/final_time_series_analysis_dashboard.png[Final dashboard with ecommerce sample data, width=50%]

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[[add-the-data-and-create-the-dashboard-advanced]]
=== Add the data and create the dashboard

Add the sample ecommerce data that you'll use to create the dashboard panels. 

. Go to the {kib} *Home* page, then click *Try our sample data*.

. On the *Sample eCommerce orders* card, click *Add data*.

Create the dashboard where you'll display the visualization panels.

. Open the main menu, then click *Dashboard*.

. On the *Dashboards* page, click *Create dashboard*.

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[[open-and-set-up-lens-advanced]]
=== Open and set up Lens

Open *Lens*, then make sure the correct fields appear.

. From the dashboard, click *Create visualization*.

. Make sure the *kibana_sample_data_ecommerce* index appears.
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If you are using your own data, select the <<index-patterns, index pattern>> that contains your data.

[discrete]
[[custom-time-interval]]
=== View a date histogram with a custom time interval

It is common to use the automatic date histogram interval, but sometimes you want a larger or smaller
interval. For performance reasonse, *Lens* lets you choose the minimum time interval, not the exact time interval. The performance limit is controlled by the <<histogram-maxbars, `histogram:maxBars`>> setting and the <<set-time-filter,time range>>. 

If you are using your own data, use one of the following options to see hourly sales over the last 30 days:

* View less than 30 days at a time, then use the time filter to select each day separately.

* Increase `histogram:maxBars` to at least 720, which is the number of hours in 30 days. This affects all visualizations and can reduce performance.

If you are using the sample data, use *Normalize unit*, which converts *Average sales per 12 hours*
into *Average sales per 12 hours (per hour)* by dividing the number of hours:

. Set the <<set-time-filter,time filter>> to *Last 30 days*.

. From the *Available fields* list, drag *Records* to the workspace.

. To zoom in on the data you want to view, click and drag your cursor across the bars. 
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[role="screenshot"]
image::images/lens_advanced_1_1.png[Added records to the workspace]

. In the layer pane, click *Count of Records*.

.. In the *Display name* field, enter `Number of orders`.

.. Click *Add advanced options > Normalize by unit*. 

.. From the *Normalize by unit* dropdown, select *per hour*, then click *Close*.

. To hide the *Horizontal axis* label, open the *Bottom Axis* menu, then deselect *Show*.
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You have a bar chart that shows you how many orders were made at your store every hour.
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[role="screenshot"]
image::images/lens_advanced_1_2.png[Orders per day]

. Click *Save and return*.

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[[add-a-data-layer-advanced]]
=== Monitor multiple series

It is often required to monitor multiple series within a time interval. These series can have similar configurations with minor differences.
*Lens* copies a function when you drag it to the *Drop a field or click to add* field within the same group.

To quickly create many copies of a percentile metric that shows distribution of price over time:

. On the dashboard, click *Create visualization*.

. Open the *Chart Type* dropdown, then select *Line*.

. From the *Available fields* list, drag *products.price* to the workspace.

Create the 95th percentile.

. In the layer pane, click *Median of products.price*.

. Click the *Percentile* function.

. In the *Display name* field, enter `95th`, then click *Close*.

To create the 90th percentile, duplicate the `95th` percentile.

. Drag the *95th* field to the *Drop a field or click to add* field in the *Vertical axis* group.
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[role="screenshot"]
image::images/lens_advanced_2_2.gif[Easily duplicate the items with drag and drop]

. Click *95th [1]*, then enter `90` in the *Percentile* field.

. In the *Display name* field enter `90th`, then click *Close*.

. Repeat the duplication steps to create the `50th` and `10th` percentiles.

. Open the *Left Axis* menu, then enter `Percentiles for product prices` in the *Axis name* field.
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You have a line chart that shows you the price distribution of products sold over time.
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[role="screenshot"]
image::images/lens_advanced_2_3.png[Percentiles for product prices chart]

. Click *Save and return*.

[discrete]
[[add-a-data-layer]]
==== Add multiple chart types or index patterns

To overlay visualization types or index patterns, add layers. When you create layered charts, match the data on the horizontal axis so that it uses the same scale. 

. On the dashboard, click *Create visualization*.

. From the *Available fields* list, drag *products.price* to the workspace.

. In the layer pane, click *Median of products.price*.

.. Click the *Average* function.

.. In the *Display name* field, enter `Average of prices`, then click *Close*.

. Open the *Chart Type* dropdown, then select *Area*.

Create a new layer to overlay with custom traffic.

. In the layer pane, click *+*.

. From the *Available fields* list, drag *customer_id* to the *Vertical Axis* field in the second layer.

. In the second layer, click *Unique count of customer_id*.

.. In the *Display name* field, enter `Unique customers`.

.. In the *Series color* field, enter *#D36086*.

.. Click *Right* for the *Axis side*, then click *Close*.

. From the *Available fields* list, drag *order_date* to the *Horizontal Axis* field in the second layer.

. In the second layer pane, open the *Chart type* menu, then click the line chart.
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[role="screenshot"]
image::images/lens_advanced_3_2.png[Change layer type]

. Open the *Legend* menu, then select the arrow that points up.

. Click *Save and return*.

[discrete]
[[percentage-stacked-area]]
=== Compare the change in percentage over time

By default, *Lens* shows *date histograms* using a stacked chart visualization, which helps understand how distinct sets of documents perform over time. Sometimes it is useful to understand how the distributions of these sets change over time.
Combine *filters* and *date histogram* functions to see the change over time in specific
sets of documents. To view this as a percentage, use a *Stacked percentage* bar or area chart.

. On the dashboard, click *Create visualization*.

. From the *Available fields* list, drag *Records* to the workspace.

. Open the *Chart type* dropdown, then select *Area percentage*.

For each category type, create a filter. 

. In the layer pane, click the *Drop a field or click to add* field for *Break down by*.

. Click the *Filters* function.

. Click *All records*, enter the following, then press Return:

* *KQL* &mdash; `category.keyword : *Clothing`

* *Label* &mdash; `Clothing`

. Click *Add a filter*, enter the following, then press Return:

* *KQL* &mdash; `category.keyword : *Shoes`

* *Label* &mdash; `Shoes`

. Click *Add a filter*, enter the following, then press Return:

* *KQL* &mdash; `category.keyword : *Accessories`

* *Label* &mdash; `Accessories`

. Click *Close*.

. Open the *Legend* menu, then select the arrow that points up.
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[role="screenshot"]
image::images/lens_advanced_4_1.png[Prices share by category]

. Click *Save and return*.

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[[view-the-cumulative-number-of-products-sold-on-weekends]]
=== View the cumulative number of products sold on weekends

To determine the number of orders made only on Saturday and Sunday, create an area chart, then add it to the dashboard.

. On the dashboard, click *Create visualization*.

. Open the *Chart Type* dropdown, then select *Area*.

Configure the cumulative sum of the store orders.

. From the *Available fields* list, drag *Records* to the workspace.

. In the layer pane, click *Count of Records*.

. Click the *Cumulative sum* function.

. In the *Display name* field, enter `Cumulative orders during weekend days`, then click *Close*.

Filter the results to display the data for only Saturday and Sunday.

. In the layer pane, click the *Drop a field or click to add* field for *Break down by*. 

. Click the *Filters* function.

. Click *All records*, enter the following, then press Return:

* *KQL* &mdash; `day_of_week : "Saturday" or day_of_week : "Sunday"`

* *Label* &mdash; `Saturday and Sunday`
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The <<kuery-query,KQL filter>> displays all documents where `day_of_week` matches `Saturday` or `Sunday`.

. Open the *Legend* menu, then click *Hide*.
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[role="screenshot"]
image::images/lens_advanced_5_2.png[Line chart with cumulative sum of orders made on the weekend]

. Click *Save and return*.

[discrete]
[[compare-time-ranges]]
=== Compare time ranges

*Lens* allows you to compare the selected time range with historical data using the *Time shift* option. 

If multiple time shifts are used in a single chart, a multiple of the date histogram interval should be chosen, or the data points might not line up and gaps can appear.
For example, if a daily interval is used, shifting one series by *36h*, and another by *1d* is not recommended. You can reduce the interval to *12h*, or create two separate charts.

To compare current sales numbers with sales from a week ago, follow these steps: 

. On the dashboard, click *Create visualization*.

. Open the *Chart Type* dropdown, then select *Line*.

. From the *Available fields* list, drag *Records* to the workspace.

. In the layer pane, drag *Count of Records* to the *Drop a field or click to add* field in the *Vertical axis* group.

To create a week-over-week comparison, shift the second *Count of Records* by one week.

. In the layer pane, click *Count of Records [1]*.

. Open the *Add advanced options* dropdown, then select *Time shift*.

. Click *1 week ago*.
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To define custom time shifts, enter the time value, the time increment, then press Enter. For example, to use a one week time shift, enter *1w*.
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[role="screenshot"]
image::images/lens_time_shift.png[Line chart with week-over-week sales comparison]

. Click *Save and return*.

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[[compare-time-as-percent]]
==== Compare time ranges as a percent change

To view the percent change in sales between the current time and the previous week, create a *Formula*.

. On the dashboard, click *Create visualization*.

. From the *Available fields* list, drag *Records* to the workspace.

. In the layer pane, click *Count of Records*.

.. Click *Formula*, then enter `count() / count(shift='1w') - 1`.

.. Open the *Value format* dropdown, select *Percent*, then enter `0` in the *D*ecimals* field.

.. In the *Display name* field, enter `Percent change`, then click *Close*.
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[role="screenshot"]
image::images/lens_percent_chage.png[Bar chart with percent change in sales between the current time and the previous week]

. Click *Save and return*.

[discrete]
[[view-customers-over-time-by-continents]]
=== Create a table of customers by category over time

Tables are useful when you want to display the actual field values.
You can build a date histogram table, and group the customer count metric by category, such as the continent registered in user accounts.

In *Lens* you can split the metric in a table leveraging the *Columns* field, where each data value from the aggregation is used as column of the table and the relative metric value is shown.

. On the dashboard, click *Create visualization*.

. Open the *Chart Type* dropdown, then click *Table*.

. From the *Available fields* list, drag *customer_id* to the *Metrics* field in the layer pane.

. In the layer pane, click *Unique count of customer_id*.

. In the *Display name* field, enter `Customers`, then click *Close*.

. From the *Available fields* list, drag *order_date* to the *Rows* field in the layer pane.

. In the layer pane, click the *order_date*.

.. Select *Customize time interval*.

.. Change the *Minimum interval* to *1 days*.

.. In the *Display name* field, enter `Sale`, then click *Close*.

Add columns for each continent.

. From the *Available fields* list, drag *geoip.continent_name* to the *Columns* field in the layer pane.
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[role="screenshot"]
image::images/lens_table_over_time.png[Date histogram table with groups for the customer count metric]

. Click *Save and return*.

[discrete]
=== Save the dashboard

Now that you have a complete overview of your ecommerce sales data, save the dashboard.

. In the toolbar, click *Save*.

. On the *Save dashboard* window, enter `Ecommerce sales`, then click *Save*.

. Select *Store time with dashboard*.

. Click *Save*.