[Yandex Cloud documentation](../../index.md) > [Tutorials](../index.md) > [Data analysis and visualization](index.md) > DataLens > Service analytics > Yandex Metrica: data export, post-processing, and visualization

# Yandex Metrica: data export, post-processing, and visualization



{% note info %}

For advanced analysis of raw Yandex Metrica data and access to the full range of DataLens analytical functions, we recommend using data export to ClickHouse® available as part of the [Metrica Pro](https://yandex.ru/project/metrica/pro) package.

For the Metrica Pro package, real-time data streaming is available. For more information, see [this guide](https://yandex.ru/support/metrica/en/pro/data-work).

{% endnote %}


In this tutorial, you will learn how to build conversion funnels, run cohort analysis, calculate the Retention rate for the user base in Yandex DataSphere, and visualize the data in Yandex DataLens.

We will use Yandex Metrica data as the source.

1. [Connect ClickHouse® and DataSphere](#ch-datasphere-connection):
    1. [Connect ClickHouse®](#ch-connection).
    1. [Connect DataSphere](#datasphere-connection).
    1. [Clone the repository to DataSphere](#clone-repo-to-datasphere).
1. [Retrieve and upload data to ClickHouse®](#get-download-data-in-ch):
    1. [Yandex Metrica. Create an app and get an access token](#create-metrica-app-token).
    1. [DataSphere. Export data via the Yandex Metrica Logs API](#uploading-data-logs-api).
    1. [DataSphere. Export test tag data via Yandex Disk](#uploading-data-counter-from-disk).
    1. [ClickHouse®. Get the cluster address](#getting-ch-cluster-host).
    1. [DataSphere. Upload the data to ClickHouse®](#uploading-data-counter-to-ch).
1. [Connect DataLens and create charts](#datalens-connection-chart-creation):
    1. [Connect to DataLens](#datalens-connection).
    1. [Create a connection to ClickHouse® in DataLens](#creation-datalens-connection-to-ch).
    1. [Create a dataset based on the connection](#creating-dataset-based-on-connection).
    1. [Create an area chart](#creating-area-chart).
    1. [Create a pivot table chart](#creating-pivot-table).
1. [Create and configure a dashboard in DataLens](#creating-configuring-dashboard):
    1. [Create a dashboard](#creating-dashboard).
    1. [Set up the dashboard](#configuring-dashboard).
1. [Build conversion funnels](#funnels):
    1. [DataSphere. Build funnels](#calculating-funnels-datasphere).
    1. [DataLens. Funnels by browser. Create a dataset](#calculating-browser-funnels-dataset).
    1. [DataLens. Funnels by browser. Create a chart](#calculating-browser-funnels-chart).
    1. [DataLens. Funnels by browser. Add a chart to the dashboard](#add-browser-funnels-chart-on-dashboard}).
    1. [DataLens. Funnels by browser. Set up the dashboard](#setting-browser-funnels-chart-on-dashboard).
1. [Perform cohort analysis](#cohorts):
    1. [DataSphere. Perform cohort analysis](#cohort-analysis).
    1. [DataLens. Create a dataset and a chart with cohort visualization](#creating-dataset-chart-with-cohort).
    1. [DataLens. Set up a chart with cohort visualization](#creating-chart-with-cohort).
    1. [DataLens. Create a chart with retention](#creating-chart-with-retention).
    1. [DataLens. Add charts to a new dashboard tab](#adding-charts-to-dashboard-tab).
    1. [DataLens. Create charts](#creating-chart).
    1. [DataLens. Add charts to the dashboard](#adding-chart-to-dashboard).

If you no longer need the resources you created, [delete them](#clear-out).

## Getting started {#before-you-begin}

Before getting started, register in Yandex Cloud, set up a [community](../../datasphere/concepts/community.md), and link your [billing account](../../billing/concepts/billing-account.md) to it.
1. [On the DataSphere home page](https://datasphere.yandex.cloud), click **Try for free** and select an account to log in with: Yandex ID or your working account with the identity federation (SSO).
1. Select the [Yandex Identity Hub organization](../../organization/index.md) you are going to use in Yandex Cloud.
1. [Create a community](../../datasphere/operations/community/create.md).
1. [Link your billing account](../../datasphere/operations/community/link-ba.md) to the DataSphere community you are going to work in. Make sure you have a linked billing account and its [status](../../billing/concepts/billing-account-statuses.md) is `ACTIVE` or `TRIAL_ACTIVE`. If you do not have a billing account yet, create one in the DataSphere interface.

{% note tip %}

To use Yandex DataLens and Yandex DataSphere within the Yandex Cloud network, create their instances in the same organization.

{% endnote %}

### Required paid resources {#paid-resources}

The cost of the infrastructure deployment includes:

* Fee for cluster computing resources and storage space (see [Managed Service for ClickHouse® pricing](../../managed-clickhouse/pricing.md)).
* Fee for the computation time (see [DataSphere pricing](../../datasphere/pricing.md)).
* Fee for the outbound traffic (see [Virtual Private Cloud pricing](../../vpc/pricing.md)).

## 1. Connect ClickHouse® and DataSphere {#ch-datasphere-connection}

### 1.1. Connect ClickHouse® {#ch-connection}

1. In the [management console](https://console.yandex.cloud), select the folder to create a ClickHouse® cluster in.
1. Navigate to **Managed Service for ClickHouse**.
1. In the window that opens, click **Create ClickHouse cluster**.
1. Configure your ClickHouse® cluster:
   1. Under **Basic parameters**, specify a name for the cluster.
   1. Under **Resources**, select `Intel Cascade Lake` for the platform, `burstable` for the type, and `b2.medium` for the host class.
   
      {% note warning %}
   
      We do not recommend using `burstable` VM configurations in production environments. This tutorial uses them as an example. For production solutions, use `standard` or `memory-optimized` configurations.

      {% endnote %}

   1. Under **Storage size**, keep the `10 GB` value.
   1. Under **Hosts**, click ![pencil](../../_assets/console-icons/pencil.svg). Enable the **Public access** option and click **Save**.
   1. Under **DBMS settings**, disable user management via SQL, enter the username, password, and database name, e.g., `metrica_data`.
 
   1. Under **Service settings**, enable these options:
        * **DataLens access**
        * **Access from the management console**
        * **Access from Metrica and AppMetrica**
   1. Click **Create cluster**.

### 1.2. Connect DataSphere {#datasphere-connection}

1. Open the DataSphere [home page](https://datasphere.yandex.cloud).
1. In the left-hand panel, select ![image](../../_assets/console-icons/circles-concentric.svg) **Communities**.
1. Select the community where you want to create a project.
1. On the community page, click ![image](../../_assets/console-icons/folder-plus.svg) **Create project**.
1. In the window that opens, enter a name for the project. You can also add a description as needed. The naming requirements are as follows:

   * Length: between 3 and 63 characters.
   * It can only contain lowercase Latin letters, numbers, and hyphens.
   * It must start with a letter and cannot end with a hyphen.

1. Click **Create**.
1. Click **Open project in JupyterLab**.

This is the JupyterLab development environment, and you are going to use it to complete the next steps.

### 1.3. Clone the repository to DataSphere {#clone-repo-to-datasphere}

1. In the **Git** menu, select **Clone**.
1. In the window that opens, specify the repository **URI**, `https://github.com/zhdanchik/yandex_metrika_cloud_case.git`, and click **CLONE**.
1. Click **OK**.

## 2. Get and upload data to ClickHouse® {#get-download-data-in-ch}

If you do not have a Yandex Metrica tag or it has not accumulated enough data, or if you want to make sure that you will get a result by completing all the tutorial steps, go to step [2.3](#uploading-data-counter-from-disk) (skip steps [2.1](#create-metrica-app-token) and [2.2](#uploading-data-logs-api)).

If you have a Yandex Metrica tag and can access it, go to step [2.1](#create-metrica-app-token) and [2.2](#uploading-data-logs-api) (skip step [2.3](#uploading-data-counter-from-disk)). We recommend these steps for experienced users as the logic of calculating funnels and cohorts depends on the data itself, and you might need to edit scripts. 

### 2.1. Yandex Metrica. Create an app and get an access token {#create-metrica-app-token}

1. To work with the API, get your [OAuth token](https://tech.yandex.com/oauth/doc/dg/tasks/get-oauth-token-docpage/).
1. Create an application:
     1. Go to [https://oauth.yandex.ru/client/new](https://oauth.yandex.com/client/new).
     1. Enter a name for the service.
     1. Go to **Platforms** → **Web services**. In the **Redirect URI** field, paste `https://oauth.yandex.com/verification_code`.
     1. Under **Data access**, enter `metrika` and select **Get statistics, read data from your own and trusted counters (metrika:read)**.
     1. Click **Create app**.
     1. In the window that opens, you will see a description of your application. Save the ClientID of your app.

1. Go to `https://oauth.yandex.ru/authorize?response_type=token&client_id=<app_ID>`. Paste your app's ClientID as `<app_ID>`.
1. Click **Log in as**.
1. Save the received access token.

### 2.2. DataSphere. Upload data via the Yandex Metrica Logs API {#uploading-data-logs-api}

1. In the DataSphere project, in the root of the working directory, create a text file. To do this, click **Text File** in the workspace.
1. Name the file `.yatoken.txt` and paste the received access token to the file. Save the changes and close the file.
1. Open the **yandex_metrika_cloud_case** folder → **1a. get_data_via_logs_api.ipynb** notebook.
1. Specify your Yandex Metrica tag ID as the `COUNTER_ID` variable value. You can find your Yandex Metrica tag ID on the [My tags](https://metrika.yandex.ru/list?) page.
1. Specify the start date of the analyzed period as the `START_DATE` variable value.
1. Specify the end date of the analyzed period as the `END_DATE` variable value.

   {% note warning %}

   The date range will NOT include the end date. For example, to get data up to December 5, 2022, paste `2022-12-06` into the `END_DATE` variable.

   {% endnote %}

1. Complete all steps (run the cells with code) in the **1a. get_data_via_logs_api.ipynb** notebook.

If you could not get data for the demo tag from the Logs API, you can [download it via Yandex Disk](#uploading-data-counter-from-disk). 

### 2.3. DataSphere. Download the test tag data via Yandex Disk {#uploading-data-counter-from-disk}

{% note info %}

Skip this section if you are using your own tag data.

{% endnote %}

1. Open the **yandex_metrika_cloud_case** folder → **1b. get_data_via_yadisk.ipynb** notebook.
1. Complete all steps (run the cells with code) in the **1b. get_data_via_yadisk.ipynb** notebook.

### 2.4. ClickHouse®. Get the cluster address {#getting-ch-cluster-host}

1. In the [management console](https://console.yandex.cloud), go to the ClickHouse® cluster you created. Wait until the cluster status changes to `Alive`. Then open the cluster by clicking it.
1. Select ![hosts](../../_assets/console-icons/cube.svg) **Hosts** from the list on the left.
1. On the **Overview** tab, copy the host name. 

### 2.5. DataSphere. Upload the data to ClickHouse® {#uploading-data-counter-to-ch}

1. Open the **yandex_metrika_cloud_case** folder → **2. upload_data_to_ClickHouse®.ipynb** notebook:
    1. Paste the copied host name into the `CH_HOST_NAME` variable.
    1. In the `CH_USER` variable, insert the username you specified when [creating your ClickHouse® cluster](#ch-connection).
    1. In the `CH_DB_NAME` variable, insert the database name you specified when [creating your ClickHouse® cluster](#ch-connection).

1. In the root directory, create a new text file named `.chpass.txt`.
1. In the `.chpass.txt` file, insert the user password you specified when [creating your ClickHouse® cluster](#ch-connection). Save and close the file.
1. Complete all the steps, i.e., run the cells with code, in the notebook.

## 3. Connect DataLens and create charts {#datalens-connection-chart-creation}

### 3.1. Connect to DataLens {#datalens-connection}

1. In the [management console](https://console.yandex.cloud), open the page of the new ClickHouse® cluster.
1. On the left side of the window, select ![datalens](../../_assets/console-icons/chart-column.svg) **DataLens**.
1. Click **Create connection**.

### 3.2. Create a connection to ClickHouse® in DataLens {#creation-datalens-connection-to-ch}

1. Set up your connection:

   1. Select the cluster from the **Cluster** drop-down list or create a new one. If the cluster is missing in the list, click **Specify manually**, then specify the [ClickHouse® cluster](#ch-connection) name.
   1. Select the [ClickHouse® host](#ch-connection) from the **Host name** drop-down list.
   1. Select the [username](#ch-connection).
   1. Enter the [password](#ch-connection) and click **Check connection**.

1. After a successful check, click **Create connection**. In the window that opens, enter the connection name and click **Create**.

### 3.3. Create a dataset based on the connection {#creating-dataset-based-on-connection}

1. In the top-right corner, click **Create dataset**.
1. Select the `metrica_data.hits` table as the source by dragging the table from the list on the left to the editing area.
1. Open the **Fields** tab.
1. In the top-right corner, click ![plus](../../_assets/console-icons/plus.svg) **Add field**.
1. To calculate the number of hits, create a calculated field: name it `Hits`, enter `1` in the workspace, and click **Create**. 
1. For the `Hits` field, select the **Amount** value in the **Aggregation** column.
1. Rename the `Browser` field to `Browser`.
1. In the top-right corner, click **Save**.
1. Name the dataset `ch_metrica_data_hits` and click **Create**.

### 3.4. Create an area chart {#creating-area-chart}

1. In the top-right corner, click **Create chart**.
1. In the window that opens, drag the following fields to these chart sections:
    * `EventDate`, to **X**.
    * `Browser`, to **Colors**.
    * `Hits`, to **Y**.
1. Change the chart type from **Column chart** to **Area chart**. 
1. Click **Save**. 
1. In the window that opens, enter `ch_metrica_data_hits_area` as the chart name and click **Save**.

### 3.5. Create a pivot table chart {#creating-pivot-table}

1. In the top-right corner, click ![save-button](../../_assets/console-icons/chevron-down.svg) → **Save as copy**.
1. Enter `ch_metrica_data_hits_table` as the new name for the chart copy and click **Save**.
1. Select **Pivot table** as the new chart type.
1. Add or drag the following fields to the chart area:
    * `Browser`, to the **Rows** section.
    * `Hits`, to the **Sorting** section.
1. Click **Save**.

## 4. Create and configure a dashboard in DataLens {#creating-configuring-dashboard}

### 4.1. Create a dashboard {#creating-dashboard}

1. Select ![dashboards](../../_assets/console-icons/layout-cells-large.svg) **Dashboards** in the left-hand panel and click **Create dashboard**.
1. Add your first chart to the dashboard. To do this, in the top-right corner, click **Add** ![save-button](../../_assets/console-icons/chevron-down.svg) → **Chart**:
    1. From the **Chart** drop-down list, select `ch_metrica_data_hits_area`.
    1. In the **Name** field, enter **Hits by browser** as the chart name and click **Add**.
1. Similarly, add the `ch_metrica_data_hits_table` chart named **Hits by browser over period**.
1. Move the charts and resize them on the dashboard:
    1. Drag the table chart to the right of the diagram chart.
    1. To change the vertical dimensions of the charts, drag them by the bottom-right corner.
1. Save the dashboard:

    1. In the top-right corner, click **Save**.
    1. Enter `ch_metrica_data` for the dashboard name and click **Create**.

### 4.2. Set up the dashboard {#configuring-dashboard}

1. Add filtering to select a specific browser. To do this, in the top-right corner, click **Add** ![save-button](../../_assets/console-icons/chevron-down.svg) → **Chart**.
1. You can link the selector to a field from any dataset. From the **Dataset** list, select the `ch_metrica_data_hits` dataset you created.
1. In the **Field** list, select `Browser`. 
1. Enable **Multiple choice**.
1. In the **Default value** field, select these browsers:
    * `android_browser`
    * `chrome`
    * `chromemobile`
    * `firefox`
    * `opera`
    * `safari`
    * `safari_mobile`
    * `samsung_internet`
    * `yandex_browser`
    * `yandexsearch`
1. In the **Name** field, enter a name for the selector and enable this option.
1. Click **Add**. 
1. Drag the selector to the top of the dashboard and stretch it horizontally.
1. In the top-right corner, click **Save**. 

## 5. Build conversion funnels {#funnels}
 
### 5.1. DataSphere. Build funnels {#calculating-funnels-datasphere}

1. Open the DataSphere [home page](https://datasphere.yandex.cloud).
1. Open the **3. funnels.ipynb** notebook. Specify the host, username, and database name.
1. Run the cells and evaluate the analysis results. 
In ClickHouse®, the `metrica_data.funnels_by_bro` table will be created with funnels counted by browser. 

### 5.2. DataLens. Funnels by browser. Create a dataset {#calculating-browser-funnels-dataset}

Create a new dataset based on the new table and the connection to ClickHouse®:

1. Open the DataLens [home page](https://datalens.ru/?skipPromo=true).
1. In the left-hand panel, click ![image](../../_assets/console-icons/circles-intersection.svg) **Datasets**.
1. Click **Create dataset**.
1. Go to **Connections** and click ![image](../../_assets/console-icons/plus.svg) **Add**.
1. From the list of connections, select the connection name that you created in step [3.2](#creation-datalens-connection-to-ch).
1. Drag the new `metrica_data.funnels_by_bro` table to the editing area.
1. Open the **Fields** tab:
   1. Rename the `step X` fields to `Step X`, where X is the step number.
   1. Select the **Sum** value in the **Aggregation** column for the `Step X` fields and click **Save**.
1. Name the dataset `ch_metrica_data_funnels_by_bro` and click **Create**.

### 5.3. DataLens. Funnels by browser. Create a chart {#calculating-browser-funnels-chart}

Create a chart based on the `ch_metrica_data_funnels_by_bro` dataset:

1. Click **Create chart**.
1. Select **Pivot table** as the chart type.
1. Drag the fields to the chart sections:
    * `Browser`, to the **Rows** section.
    * `Step X`, to the **Measures** section, where X is the step number.
    * `Step 1`, to the **Sorting** section.
1. Click **Save**.
1. Specify the `ch_metrica_data_funnels_by_bro_table` chart name and click **Save**.

### 5.4. DataLens. Funnels by browser. Add the chart to your dashboard {#add-browser-funnels-chart-on-dashboard}

1. Go to your dashboard (from the [dashboards](https://datalens.ru/dashboards) page).
1. Add a new chart. In the top-right corner, click **Edit**:
    1. Click **Add** ![save-button](../../_assets/console-icons/chevron-down.svg) → **Chart**.
    1. From the **Chart** drop-down list, select `ch_metrica_data_funnels_by_bro_table`.
    1. In the **Name** field, enter `Funnels by browser` as the chart name and click **Add**.
1. Place the new chart to the right of the existing two. Stretch the chart so that it matches the others vertically and reaches the right border of the page. 
1. Click **Save**.

### 5.5. DataLens. Funnels by browser. Set up the dashboard {#setting-browser-funnels-chart-on-dashboard}

Configure relationships so that the selector affects the new chart from another dataset: 

1. Click **Edit** → **Links**.
1. In the window that opens, select the `Browser` selector from the list.
1. On the page with the other dashboard elements, scroll down to the `Funnels by browser` chart, and click the list with the link.
1. Select **Outgoing link** as the link type.
1. From each list, select the fields for the `Browser` link. Click **Add**.
1. Click **Save**.
1. In the top-left corner, click ![image](../../_assets/console-icons/ellipsis.svg) → **Rename**.
1. Enter `Supermarket.ru: funnel and cohort analysis` as the name. Click **Done**.

## 6. Perform cohort analysis {#cohorts}

### 6.1. DataSphere. Perform cohort analysis {#cohort-analysis}

1. Open the **4. cohorts.ipynb** notebook. Specify the host, username, and database name.
1. Run the cells and evaluate the analysis results. 
 
In ClickHouse®, the `metrica_data.retention_users` table will be created with all the data needed to render visualization in DataLens. 

### 6.2. DataLens. Create a dataset and a chart with cohort visualization {#creating-dataset-chart-with-cohort}

Create a new dataset based on the new table and the connection to ClickHouse®: 

1. Open the DataLens [home page](https://datalens.ru/?skipPromo=true).
1. In the left-hand panel, click ![image](../../_assets/console-icons/circles-intersection.svg) **Datasets**.
1. Click **Create dataset**.
1. In the **Connections** section, click ![image](../../_assets/console-icons/plus.svg) **Add**.
1. From the list, select the [connection](#creation-datalens-connection-to-ch) you created.
1. Drag the new `metrica_data.retention_users` table to the workspace to connect to it.
1. Open the **Fields** tab and create a new calculated field named `week_num` equal to `([date]-[min_date])/7`.
   This field will indicate the number of weeks from the user's first visit.
1. Click **Create**.
1. For the `visits`, `purchases`, and `revenue` fields, select the **Sum** value in the **Aggregation** column.
1. Rename the fields to `Visits`, `Purchases`, and `Revenue`, respectively. 
1. Save the dataset:
    1. Name the dataset `ch_metrica_data_users_visits`.
    1. Click **Create**.
1. Create a new chart based on the dataset: 
    1. Change the chart type to **Pivot table**.
    1. Drag the `week_num` field to the **Columns** section.
    1. Drag the `min_date` field to the **Rows** section.
    1. Drag the `Visits` field to the **Measures** section.

### 6.3. DataLens. Configure a chart with cohort visualization {#creating-chart-with-cohort}

Filter out incomplete weeks of June 29, 2020 and September 28, 2020:

1. Drag the `min_date` field to the **Filters** section.
   1. In the window that opens, select the start and end dates of the date range for filtering:
      * Start date: `29.06.2020`
      * End date: `27.09.2020`
   1. Click **Apply filter**.
1. Format the numbers in the `week_num` field values by removing the decimal places. To do this, click ![image](../../_assets/console-icons/frame.svg) in the `week_num` field under **Rows**. In the window that opens, set the following configuration:
    1. Set **Decimal places** to `0`. 
    1. Set the **Show delimiter** measure to `Hide`.
    1. Click **Apply**.
1. To color the table, add the `Visits` field to the **Colors** section and click ![gear](../../_assets/console-icons/gear.svg). In the window that opens, configure the colors:
    1. Select **Gradient type**: `3-point`.
    1. Select **Color**: `Orange-Violet-Blue`.
    1. Enable **Set threshold values** and specify `100`, `1000`, and `5000`.
    1. Click **Apply**.
1. Click **Save**.
1. Name the chart `ch_metrica_data_users_visits_cohorts_abs` and click **Save**.

### 6.4. DataLens. Create a chart with retention {#creating-chart-with-retention}

Create a chart with retention based on the `ch_metrica_data_users_visits_cohorts_abs` chart. You can open the chart from the dashboard or find it in the [chart list](https://datalens.ru/widgets).

1. In the top-right corner, click ![save-button](../../_assets/console-icons/chevron-down.svg) → **Save as copy**.
1. Enter `ch_metrica_data_users_visits_cohorts_rel` as the chart name and click **Save**.
1. Create a calculated field to calculate the retention rate relative to the first week:
    1. On the left side of the screen, click ![image](../../_assets/console-icons/plus.svg) above the list of dataset fields and select **Field**.
    1. Name the field `Visits from the first week`.
    1. Paste the following formula: `SUM([Visits])/RMAX(SUM([Visits]) among [week_num])`.
    1. Click **Create**.
1. Drag the `Visits from the first week` field to the **Measures** section.
1. Drag the `Visits from the first week` field to the **Colors** section in place of the `Visits` field.
1. Select the format for `Visits from the first week`. To do this, click ![image](../../_assets/console-icons/frame.svg) under **Measures** in the `Visits from the first week` field. In the window that opens, set the following configuration:
    1. Set **Format** to `Percent`.
    1. Click **Apply**.
1. Edit the threshold values for the measure colors. Under **Colors**, click ![gear](../../_assets/console-icons/gear.svg). In the window that opens, enable **Set threshold values**, specify the threshold values of `0.01`, `0.025`, and `0.1`, and click **Apply**.
1. Click **Save**.

### 6.5. DataLens. Add charts to a new dashboard tab {#adding-charts-to-dashboard-tab}

1. In the left-hand panel, click ![dashboards](../../_assets/console-icons/layout-cells-large.svg) **Dashboards** and open the dashboard.
1. Click **Edit** → **Tabs**.
1. Rename the existing tab to `Overview + Funnels`.
1. Add a new tab and name it `Cohorts`. Click **Save**.
1. Go to the new `Cohort` tab:
    1. Add the `ch_metrica_data_users_visits_cohorts_abs` chart to the dashboard.
    1. In the **Name** field, specify `Visits by cohort (absolute)`.
1. To add a new tab, click **Add** on the left:
    1. In the new tab, add the `ch_metrica_data_users_visits_cohorts_rel` chart.
    1. Enter `Visits by cohort (relative)` as the name.
    1. Click **Add**.
    1. Click **Save**.

Now you have a chart with two switchable tabs.

### 6.6. DataLens. Create charts {#creating-chart}

Create a new chart based on the `ch_metrica_data_users_visits_cohorts_abs` chart. You can open the chart from the dashboard or find it in the [chart list](https://datalens.ru/widgets).

1. In the top-right corner, click ![image](../../_assets/console-icons/chevron-down.svg) → **Save as copy**.
1. Enter `ch_metrica_data_users_revenue_cohorts_abs` as the chart name and click **Save**.
1. Drag the `Revenue` field to the **Measures** and **Colors** sections on top of the `Visits` field.
1. In the `Revenue` section, click ![image](../../_assets/console-icons/frame.svg). Change the field formatting: 
    1. Select `1` decimal place.
    1. Select the `Millions, M` scale.
    1. Change the color thresholds for the new field to `500000`, `1500000`, and `10000000`.
1. Save the chart.

Create another chart based on the `ch_metrica_data_users_visits_cohorts_rel` chart:

1. In the top-right corner, click ![image](../../_assets/console-icons/chevron-down.svg) → **Save as copy**.
1. Enter `ch_metrica_data_users_revenue_cohorts_rel` as the chart name and click **Save**.
1. Change the `Visits from the first week` field:
    1. Rename the field to `Revenue from the first week`.
    1. Change the formula to `SUM([Revenue])/RMAX(SUM([Revenue]) among [week_num])`.
    1. Change the color thresholds for the new field to `0.01`, `0.2`, and `0.3`.
1. Save the chart.

### 6.7. DataLens. Add charts to the dashboard {#adding-chart-to-dashboard}

Add charts with cohort visualization to the dashboard:

1. Click **Edit**.
1. Click **Add** ![save-button](../../_assets/console-icons/chevron-down.svg) → **Chart**.
1. Select `ch_metrica_data_users_revenue_cohorts_abs` from the chart list.
1. Enter `Revenue by cohort (absolute)` as the name.
1. Use the ![plus](../../_assets/console-icons/plus.svg) **Add** button to create a new tab:
    1. In the new tab, select `ch_metrica_data_users_revenue_cohorts_rel` from the chart list.
    1. Enter `Revenue by cohort (relative)` as the name.
    1. In the top-right corner, click **Save**.
1. Arrange the charts side by side.

## How to delete the resources you created {#clear-out}

To stop paying for the resources you created, [delete the cluster](../../managed-clickhouse/operations/cluster-delete.md).

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