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removed yaml test
This commit is contained in:
Greg Lindsay
@ -1,16 +0,0 @@
|
||||
- name: Getting started
|
||||
href: gettingstarted.yml#step-0
|
||||
- name: Getting data
|
||||
href: gettingdata.yml#step-0
|
||||
- name: Modeling
|
||||
href: modeling.yml#step-0
|
||||
- name: Visualizations
|
||||
href: visualizations.yml#step-0
|
||||
- name: Exploring data
|
||||
href: exploringdata.yml#step-0
|
||||
- name: Power BI and Excel
|
||||
href: powerbiandexcel.yml#step-0
|
||||
- name: Publishing and sharing
|
||||
href: publishingandsharing.yml#step-0
|
||||
- name: Introduction to DAX
|
||||
href: introductiontodax.yml#step-0
|
||||
@ -1,82 +0,0 @@
|
||||
### YamlMime:Tutorial
|
||||
title: Exploring data
|
||||
metadata:
|
||||
title: Exploring data | Microsoft Power BI
|
||||
description: Learn how to find, connect to, and interact with data in Power BI.
|
||||
audience: Business User
|
||||
level: Beginner
|
||||
nextTutorialHref: powerbiandexcel.yml
|
||||
nextTutorialTitle: Power BI and Excel
|
||||
displayType: one-column
|
||||
author: davidiseminger
|
||||
manager: kfile
|
||||
ms.service: powerbi
|
||||
ms.topic: conceptual
|
||||
ms.date: 02/26/2019
|
||||
ms.author: davidi
|
||||
items:
|
||||
- durationInMinutes: 1
|
||||
content: |
|
||||
Welcome to the **exploring data** tutorial. Here you'll learn how to find, connect to, and interact with data in Power BI.
|
||||
- title: Introduction to the Power BI service
|
||||
durationInMinutes: 2
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/B2vd4MQrz4M?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-0-intro-power-bi-service.md)]
|
||||
- title: Quick insights in Power BI
|
||||
durationInMinutes: 5
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/et_MLSL2sA8?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-1a-quick-insights.md)]
|
||||
- title: Create and configure a dashboard
|
||||
durationInMinutes: 6
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/lJKgWnvl6bQ?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-2-create-configure-dashboards.md)]
|
||||
- title: Ask questions of your data with natural language
|
||||
durationInMinutes: 9
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/qMf7OLJfCz8?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-3-asking-questions-natural-language.md)]
|
||||
- title: Create custom Q&A suggestions
|
||||
durationInMinutes: 4
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/E1mIAyEXuF4?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-3a-suggested-questions.md)]
|
||||
- title: Share dashboards with your organization
|
||||
durationInMinutes: 7
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/0tUwn8DHo3s?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-4-share-dashboards.md)]
|
||||
- title: Display visuals and tiles full-screen
|
||||
durationInMinutes: 4
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/LoApjhwTse0?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-4b-display-visuals-tiles-fullscreen.md)]
|
||||
- title: Edit tile details and add widgets
|
||||
durationInMinutes: 6
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/XhqVbHqd5jQ?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-4d-change-tile-details.md)]
|
||||
- title: Get more space on your dashboard
|
||||
durationInMinutes: 5
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/c31gZkyvC54?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-4e-get-more-dashboard-space.md)]
|
||||
- title: Install and configure a personal gateway
|
||||
durationInMinutes: 11
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/xjcO5tNvjGs?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/4-6-install-configure-personal-gateway.md)]
|
||||
- content: |
|
||||
This **exploring data** section of Guided Learning has prepared you to work with all sorts of different data types. In the next tutorial, you learn how to use a common data source - Excel.
|
||||
@ -1,58 +0,0 @@
|
||||
### YamlMime:Tutorial
|
||||
title: Getting data
|
||||
metadata:
|
||||
title: Getting data | Microsoft Power BI
|
||||
description: Explore and learn the many ways you can get and work with data in Power BI.
|
||||
audience: Business User
|
||||
level: Beginner
|
||||
nextTutorialHref: modeling.yml
|
||||
nextTutorialTitle: Modeling
|
||||
displayType: one-column
|
||||
author: davidiseminger
|
||||
manager: kfile
|
||||
ms.service: powerbi
|
||||
ms.topic: conceptual
|
||||
ms.date: 02/26/2019
|
||||
ms.author: davidi
|
||||
items:
|
||||
- durationInMinutes: 1
|
||||
content: |
|
||||
Data is at the heart of Power BI. In this **Guided Learning** section, you learn how to get and work with data in Power BI.
|
||||
- title: Overview of Power BI Desktop
|
||||
durationInMinutes: 4
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/mdy-eRev6HM?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/1-1-overview-of-power-bi-desktop.md)]
|
||||
- title: Getting started with Power BI Desktop
|
||||
durationInMinutes: 9
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/1Bo1BrpR3AY?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/0-2-get-started-power-bi-desktop.md)]
|
||||
- title: Connect to data sources in Power BI Desktop
|
||||
durationInMinutes: 8
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/S6s0osmRCZ4?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/1-2-connect-to-data-sources-in-power-bi-desktop.md)]
|
||||
- title: Clean and transform your data with the Query Editor
|
||||
durationInMinutes: 8
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/o-sinon5JYY?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/1-3-clean-and-transform-data-with-query-editor.md)]
|
||||
- title: More advanced data sources and transformation
|
||||
durationInMinutes: 8
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/8WsY0R2V_bw?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/1-4-advanced-data-sources-and-transformation.md)]
|
||||
- title: Cleaning irregularly formatted data
|
||||
durationInMinutes: 8
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/74KQmzdvFV8?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/1-5-cleaning-irregular-data.md)]
|
||||
- content: |
|
||||
Way to go! You've completed the **Getting data** section of Microsoft Power BI Guided Learning. The next step is to learn about **Modeling**, which is the subject of the next tutorial.
|
||||
@ -1,46 +0,0 @@
|
||||
### YamlMime:Tutorial
|
||||
title: Getting started
|
||||
metadata:
|
||||
title: Getting started | Microsoft Power BI
|
||||
description: Learn what Power BI is, how its building blocks work together, and how you can use them to gain insights to your data.
|
||||
audience: Business User
|
||||
level: Beginner
|
||||
nextTutorialHref: gettingdata.yml
|
||||
nextTutorialTitle: Getting data
|
||||
displayType: one-column
|
||||
author: davidiseminger
|
||||
manager: kfile
|
||||
ms.service: powerbi
|
||||
ms.topic: conceptual
|
||||
ms.date: 02/26/2019
|
||||
ms.author: davidi
|
||||
items:
|
||||
- durationInMinutes: 1
|
||||
content: |
|
||||
In this initial section of **Guided Learning**, you learn what Power BI is, how its building blocks work together, and how you can use them to gain insights to your data.
|
||||
- title: Introducing Power BI
|
||||
durationInMinutes: 5
|
||||
content: |
|
||||
[!INCLUDE [](includes/0-0-what-is-power-bi.md)]
|
||||
- title: Using Power BI
|
||||
durationInMinutes: 4
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/1c01r_pAZdk?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/0-1-intro-using-power-bi.md)]
|
||||
- title: The building blocks of Power BI
|
||||
durationInMinutes: 6
|
||||
content: |
|
||||
[!INCLUDE [](includes/0-0b-building-blocks-power-bi.md)]
|
||||
- title: A quick look at the Power BI service
|
||||
durationInMinutes: 9
|
||||
content: |
|
||||
> [!Video https://www.youtube.com/embed/5srP3h3RJf8?rel=0]
|
||||
|
||||
[!INCLUDE [](includes/0-3-dashboards-cloud-services.md)]
|
||||
- title: Review and what's next
|
||||
durationInMinutes: 5
|
||||
content: |
|
||||
[!INCLUDE [](includes/0-4-summary-of-intro-to-power-bi.md)]
|
||||
- content: |
|
||||
You've completed the **Getting started** section of Microsoft Power BI Guided learning. Way to go! Now you can move on to the next part of Guided Learning, which is **Getting data**. Select the **Getting data** button below to keep going.
|
||||
@ -1,40 +0,0 @@
|
||||
Welcome to **Guided Learning** for Power BI. This self-paced online course explains Power BI in a sequential way, so you can build your knowledge from the ground up.
|
||||
|
||||
The course is designed to provide guidance in understandable chunks, with logical flow that helps you absorb concepts, details, and examples. It includes lots of visuals and videos to help you learn, too.
|
||||
|
||||
This **Guided Learning** course contains many **sections**, and each section has many **topics**. After the first few topics in this **Getting Started** section, nearly every topic includes a video describing what you’ll learn. The content below the video further explains the concept, letting you review and learn at your own pace.
|
||||
|
||||
If you're a **beginner** with Power BI this course will get you going, and if you're a Power BI **veteran** this course will tie concepts together, and fill in the gaps. We hope you enjoy the course, and look forward to including more content in the future.
|
||||
|
||||
## What is Power BI?
|
||||
**Power BI** is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Whether your data is a simple Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses, **Power BI** lets you easily connect to your data sources, visualize (or discover) what’s important, and share that with anyone or everyone you want.
|
||||
|
||||
|
||||
|
||||
**Power BI** can be simple and fast – capable of creating quick insights from an Excel spreadsheet or a local database. But **Power BI** is also robust and enterprise-grade, ready for extensive modeling and real-time analytics, as well as custom development. So it can be your personal report and visualization tool, and can also serve as the analytics and decision engine behind group projects, divisions, or entire corporations.
|
||||
|
||||
## The parts of Power BI
|
||||
Power BI consists of a Windows desktop application called **Power BI Desktop**, an online SaaS (*Software as a Service*) service called the **Power BI service**, and mobile Power BI **apps** available on Windows phones and tablets, as well as for iOS and Android devices.
|
||||
|
||||
|
||||
|
||||
These three elements – the **Desktop**, the **service**, and **Mobile** – are designed to let people create, share, and consume business insights in the way that serves them, or their role, most effectively.
|
||||
|
||||
## How Power BI matches your role
|
||||
How you use Power BI may depend on your role in a project or on a team. And other people, in other roles, might use Power BI differently, which is just fine.
|
||||
|
||||
For example, you might primarily use the **Power BI service**, but your number-crunching, business-report-creating coworker might make extensive use of **Power BI Desktop** (and publish Desktop reports to the Power BI service, which you then view). And another coworker, in sales, might mainly use her Power BI phone app to monitor progress on her sales quotas, and to drill into new sales lead details.
|
||||
|
||||
You also might use each element of **Power BI** at different times, depending on what you’re trying to achieve or what your role is for a given project or effort.
|
||||
|
||||
Perhaps you view inventory and manufacturing progress in a real-time dashboard in the service, and also use **Power BI Desktop** to create reports for your own team about customer engagement statistics. How you use Power BI can be based on which feature or service of Power BI is the best tool for your situation – but each part of Power BI is available to you, which is why it’s so flexible and compelling.
|
||||
|
||||
We discuss these three elements – the **Desktop**, **service**, and **Mobile** apps – with more detail a little later in this **Guided Learning** course. We’ll also create reports in Power BI Desktop, share them in the service, and drill into them on our Mobile device in upcoming articles, too.
|
||||
|
||||
## The flow of work in Power BI
|
||||
A common flow of work in Power BI begins in **Power BI Desktop**, where a report is created. That report is then published to the Power BI **service**, and then shared so users of **Power BI Mobile** apps can consume the information.
|
||||
|
||||
It doesn’t always happen that way, and that’s okay, but we’ll use that flow to help you learn the various parts of Power BI, and how they complement one another.
|
||||
|
||||
Okay, now that we have an overview of this course, what Power BI is, and its three main elements, let's take a look at what using **Power BI** is like.
|
||||
|
||||
@ -1,77 +0,0 @@
|
||||
Everything you do in Power BI can be broken down into a few basic **building blocks**. Once you understand these building blocks, you can expand on each of them and begin creating elaborate and complex reports. After all, even seemingly complex things are built from basic building blocks – buildings are created with wood, steel, concrete and glass. Cars are made from metal, fabric, and rubber. Of course, buildings and cars can be basic or elaborate as well – depending on how those basic building blocks are arranged.
|
||||
|
||||
Let’s take a look at these basic building blocks, discuss some simple things that can be built with them, then provide a glimpse into how complex things can be created as well.
|
||||
|
||||
The basic building blocks in Power BI are the following:
|
||||
|
||||
* Visualizations
|
||||
* Datasets
|
||||
* Reports
|
||||
* Dashboards
|
||||
* Tiles
|
||||
|
||||
## Visualizations
|
||||
A **visualization** (sometimes also referred to as a **visual**) is a visual representation of data, such as a chart, a graph, a color-coded map, or other interesting things you can create to represent your data visually. Power BI has all sorts of different visualization types, and more coming all the time. The following image shows a collection of different visualizations that were created in the Power BI service.
|
||||
|
||||
|
||||
|
||||
Visualizations can be simple – like a single number that represents something significant – or they can be visually complex – like a gradient-colored map that shows voter sentiment toward a certain social issue or concern. The goal of a visual is to present data in a way that provides context and insights, either of which would likely be difficult to discern from a raw table of numbers or text.
|
||||
|
||||
## Datasets
|
||||
A **dataset** is a collection of data that Power BI uses to create its visualizations.
|
||||
|
||||
You can have a simple dataset based on a single table from Excel workbook, similar to what’s shown in the following image.
|
||||
|
||||
|
||||
|
||||
**Datasets** can also be a combination of many different sources, which you can filter and combine to provide a unique collection data (a dataset) for use in Power BI.
|
||||
|
||||
For example, you could create a dataset from three different database fields, one website table, an Excel table, and online results of an email marketing campaign. That unique combination is still considered a single **dataset**, even though it was pulled together from many different sources.
|
||||
|
||||
Filtering data before bringing it into Power BI lets you focus on the data that matters to you. For example, you could filter your contact database so only customers who received emails from the marketing campaign were included in the dataset. Then you could create visuals based on that subset (that filtered collection) of customers who were included in the campaign. Filtering helps you focus your data, and your efforts.
|
||||
|
||||
An important and enabling part of Power BI is the multitude of data **connectors** that are included. Whether the data you want is in Excel or an SQL database, in Azure or Oracle, or in a service like Facebook, Salesforce, or MailChimp, Power BI has built-in data connectors that let you easily connect to that data, filter it if necessary, and bring it into your dataset.
|
||||
|
||||
Once you have a dataset, you can begin creating visualizations that display different portions of that dataset in different ways, and with what you see, gain insights. That’s where reports come in.
|
||||
|
||||
## Reports
|
||||
In Power BI, a **report** is a collection of visualizations that appear together on one or more pages. Just like any other report you might create for a sales presentation, or a report you would write for a school assignment, in Power BI a **report** is a collection of items that are related to one another. The following image shows a **report** in Power BI Desktop – in this case, it’s the fifth page in a six-page report. You can also create reports in the Power BI service.
|
||||
|
||||
|
||||
|
||||
Reports let you create many visualizations, on multiple different pages if necessary, and lets you arrange them in whatever way best tells your story.
|
||||
|
||||
You might have a report about quarterly sales, a report about product growth in a particular segment, or you might create a report about migration patterns of polar bears. Whatever your subject may be, reports let you gather and organize your visualizations onto one (or more) pages.
|
||||
|
||||
## Dashboards
|
||||
When you’re ready to share a single page from a report, or share a collection of visualizations, you create a **dashboard**. Much like the dashboard in a car, a Power BI **dashboard** is a collection of visuals from a single page that you can share with others. Often, it’s a selected group of visuals that provide quick insight into the data or story you’re trying to present.
|
||||
|
||||
A dashboard has to fit on a single page, often called a canvas (the canvas is the blank backdrop in Power BI Desktop, or the service, where you place visualizations). Think of it like the canvas that an artist or painter uses – a workspace where you create, combine, and rework interesting and compelling visuals.
|
||||
You can share dashboards with other users or groups, who can then interact with your dashboard when they’re in Power BI service, or on their mobile device.
|
||||
|
||||
## Tiles
|
||||
In Power BI, a **tile** is a single visualization found in a report or on a dashboard. It’s the rectangular box that contains each individual visual. In the following image, you see one tile (highlighted by a bright box) which is also surrounded by other tiles.
|
||||
|
||||
|
||||
|
||||
When you’re *creating* a report or a dashboard in Power BI, you can move or arrange tiles however you want to present your information. You can make them bigger, change their height or width, and snuggle them up to other tiles however you want.
|
||||
|
||||
When you’re *viewing*, or *consuming* a dashboard or report – which means you’re not the creator or owner, but it’s been shared with you – you can interact with it, but not change the size of the tiles or change how they’re arranged.
|
||||
|
||||
## All together now
|
||||
Those are the basics of Power BI, and its building blocks. Let’s take a moment to review.
|
||||
|
||||
Power BI is a collection of services, apps, and connectors that enables you to connect to your data – wherever it happens to reside – filter it if necessary, then bring it into Power BI where you can create compelling visualizations you can share with others.
|
||||
|
||||
Now that you understand the handful of basic building blocks of Power BI, it becomes clear that you can create datasets that make sense *to you*, and create visually compelling reports that tell your story. Stories told with Power BI don’t have to be complex, or complicated, to be compelling.
|
||||
|
||||
For some people, using a single Excel table in a dataset, then sharing a dashboard with their team, will be an incredibly valuable way to use Power BI.
|
||||
|
||||
For others, using real-time Azure SQL Data Warehouse tables that combine with other databases and real-time source, which then get filtered in real-time to build a dataset that monitors moment-by-moment manufacturing progress will be the value in Power BI that they seek.
|
||||
|
||||
For both, the process is the same: create datasets, build compelling visuals, and share them with others. And similarly, the result of both (for each) is the same: harness your ever-expanding world of data, and turn it into actionable insights.
|
||||
|
||||
Whether your data insights require straightforward or complex datasets, Power BI helps you get started quickly, and can expand with your needs to be as complex as your world of data requires. And since Power BI is a Microsoft product, you can count on it being robust, extensible, Office-friendly, and enterprise-ready.
|
||||
|
||||
Now, let's see how this works. We'll start by taking a quick look at the Power BI service.
|
||||
|
||||
@ -1,15 +0,0 @@
|
||||
Now that you know the basics of Power BI, let’s jump into some hands-on experiences and a guided tour. Your video tour guide is **Will Thompson**, a Program Manager on the Power BI team at Microsoft. There are a few guest guides too, a little later on in the course.
|
||||
|
||||
As Will explains the many things you can do with Power BI, keep in mind that all of these activities, and all the analysis you can do with Power BI, generally follow a common flow. The **common flow** of activity in Power BI is the following:
|
||||
|
||||
* Bring data into Power BI Desktop, and create a report.
|
||||
* Publish to the Power BI service, where you create new visualizations or build dashboards
|
||||
* Share your dashboards with others, especially people who are on the go
|
||||
* View and interact with shared dashboards and reports in Power BI Mobile apps
|
||||
|
||||
|
||||
|
||||
As mentioned earlier, you might spend all your time in the **Power BI service**, getting data and creating dashboards – that’s just fine. Someone else on your team might spend all of her time in **Power BI Desktop**, which is fine too. In order to help you understand the full continuum of Power BI and what it can do, we’ll show you all of it… then you can decide how to use it to your best advantage.
|
||||
|
||||
So let’s jump in, and let Will guide us through the experience. The first order of business is to understand the basic building blocks of Power BI, which will provide a solid basis for learning how Power BI turns data into cool reports and visuals.
|
||||
|
||||
@ -1,67 +0,0 @@
|
||||
In this topic, we take a closer look at how the first two parts of Power BI fit together:
|
||||
|
||||
* Create a report in **Power BI Desktop**
|
||||
* Publish the report in the **Power BI service**
|
||||
|
||||
We’ll start in Power BI Desktop, and select **Get Data**. The collection of data sources appears, allowing you to choose a data source. The following image shows selecting a Web page as the source, in the video above, Will selected an **Excel** workbook.
|
||||
|
||||
|
||||
|
||||
Regardless of which data source you choose, Power BI connects to that data source, and shows you the data available from that source. The following image is another example, this one is from a Web page that analyzes different states and some interesting retirement statistics.
|
||||
|
||||
|
||||
|
||||
In Power BI Desktop **Report** view, you can begin to build reports.
|
||||
|
||||
The **Report** view has five main areas:
|
||||
|
||||
1. The ribbon, which displays common tasks associated with reports and visualizations
|
||||
2. The **Report** view, or canvas, where visualizations are created and arranged
|
||||
3. The **Pages** tab area along the bottom, which lets you select or add a report page
|
||||
4. The **Visualizations** pane, where you can change visualizations, customize colors or axes, apply filters, drag fields, and more
|
||||
5. The **Fields** pane, where query elements and filters can be dragged onto the **Report** view, or dragged to the **Filters** area of the **Visualizations** pane
|
||||
|
||||
|
||||
|
||||
The **Visualizations** and **Fields** pane can be collapsed by selecting the small arrow along the edge, providing more space in the **Report** view to build cool visualizations. When modifying visualizations, you'll also see these arrows pointing up or down, which means you can expand or collapse that section, accordingly.
|
||||
|
||||
|
||||
|
||||
To create a visualization, just drag a field from the **Fields** list onto the **Report** view. In this case, let’s drag the State field from *RetirementStats*, and see what happens.
|
||||
|
||||
|
||||
|
||||
Look at that... Power BI Desktop automatically created a map-based visualization, because it recognized that the State field contained geolocation data.
|
||||
|
||||
Now let’s fast-forward a bit, and after creating a report with a few visualizations, we’re ready to publish this to the Power BI service. On the **Home** ribbon in Power BI Desktop, select **Publish**.
|
||||
|
||||
|
||||
|
||||
You’ll be prompted to sign in to Power BI.
|
||||
|
||||
|
||||
|
||||
When you've signed in and the publish process is complete, you see the following dialog. You can select the link (below **Success!**) to be taken to the Power BI service, where you can see the report you just published.
|
||||
|
||||
|
||||
|
||||
When you sign in to Power BI, you'll see Power BI Desktop file you just published in the service. In the image below, the report created in Power BI Desktop is shown in the **Reports** section.
|
||||
|
||||
|
||||
|
||||
In that report, I can choose the **Pin** icon to pin that visual to a dashboard. The following image shows the pin icon highlighted with a bright box and arrow.
|
||||
|
||||
|
||||
|
||||
When I select that, the following dialog appears, letting me pin the visual to an existing dashboard, or to create a new dashboard.
|
||||
|
||||
|
||||
|
||||
When we pin a couple of visuals from our report, we can see them in the dashboard.
|
||||
|
||||
|
||||
|
||||
There’s a lot more you can do with Power BI, of course, such as sharing the dashboards you create. We'll discuss sharing later on in this course.
|
||||
|
||||
Next, we look at a feature that can automatically create dashboards for you, just by connecting to a cloud service like Facebook, Salesforce, and many others.
|
||||
|
||||
@ -1,59 +0,0 @@
|
||||
As we learned, the common flow of work in Power BI is to create a report in Power BI Desktop, publish it to the Power BI service, then share it with others so they can view it in the service or on a mobile app.
|
||||
|
||||
Some people begin in the Power BI service, though, so let's take a quick look at the service, and learn about an easy and popular way to quickly create visuals in Power BI: *content packs*.
|
||||
|
||||
A **content pack** is a collection of pre-configured, ready-made visuals and reports based on specific data sources, such as Salesforce. Using a content pack is like microwaving a TV dinner or ordering a fast-food value meal: with just a few clicks and comments, you're quickly served up a collection of entrees designed to go well together, all presented in a tidy, ready-to-consume package.
|
||||
|
||||
So let's take a quick look at content packs, the service, and how it works. We go into more detail about content packs (and the service) in upcoming sections; think of this as a bit of taste-testing to whet your appetite.
|
||||
|
||||
## Create out-of-the-box dashboards with cloud services
|
||||
With Power BI, connecting to data is easy. From the Power BI service, you can simply select the **Get Data** button in the bottom left corner of the home screen.
|
||||
|
||||
|
||||
|
||||
The *canvas* (the area in the center of the Power BI service) shows you the available sources of data in the Power BI service. In addition to common data sources such as Excel files, databases, or Azure data, Power BI can connect to **software services** (also called SaaS providers, or cloud services) such as Salesforce, Facebook, Google Analytics, and a whole assortment of other SaaS services just as easily.
|
||||
|
||||
|
||||
|
||||
For these software services, the **Power BI service** provides a collection of ready-made visuals, pre-arranged in dashboards and reports called **Content Packs**. Content packs get you up and running in Power BI quickly with data from the service that you select. For example, when you use the Salesforce content pack, Power BI connects to your Salesforce account (once you provide your credentials) and then populates a pre-defined collection of visuals and dashboards in Power BI.
|
||||
|
||||
Power BI provides content packs for all sorts of services. The following image shows the first screen of services, in alphabetical order, that is displayed when you select **Get** from the **Services** box (shown in the previous image). As you can see from the image below, there are many to choose from.
|
||||
|
||||
|
||||
|
||||
For our purposes, we’ll choose **GitHub**. GitHub is an application for online source control. Once I enter the information and credentials for the GitHub content pack, it begins importing my data.
|
||||
|
||||
|
||||
|
||||
Once the data is loaded, the pre-defined GitHub content pack dashboard appears.
|
||||
|
||||
|
||||
|
||||
In addition to the **Dashboard**, the **Report** that was generated (as part of the GitHub content pack) to create the dashboard is available too, as is the **Dataset** (the collection of data pulled from GitHub) that was created during the data import, and used to create the GitHub Report.
|
||||
|
||||
|
||||
|
||||
On the Dashboard, you can click on any of the visuals, and automatically be taken to the **Report** page from which that visual was created. So when the **Top 5 users by pull requests** visual is clicked, Power BI opens the **Pull Requests** page in the Report (the Report page from which that visual was created).
|
||||
|
||||
## Asking questions of your data
|
||||
You can also ask questions of your data, and the Power BI service will create visuals based on your question, in real time. In the following image, you can see Power BI creates a number visual showing the Count of Issues closed, based on what is typed in the **Natural Language Query** bar.
|
||||
|
||||
|
||||
|
||||
When you have a visual that you like, you can select the **Pin** icon, to the right of the Natural Language Query bar, to pin that visual to the dashboard. In this case, the visual is pinned to the GitHub dashboard, since that’s the dashboard currently selected.
|
||||
|
||||
|
||||
|
||||
## Refreshing data in the Power BI service
|
||||
You can also choose to **refresh** the dataset for a content pack, or other data you use in Power BI. To set refresh settings, select the ellipses (the three dots) next to a data set, and a menu appears.
|
||||
|
||||
|
||||
|
||||
Select the **Schedule Refresh** option from the bottom of that menu. The Settings dialog appears on the canvas, letting you set the refresh settings that meet your needs.
|
||||
|
||||
|
||||
|
||||
That's enough for our quick look at the Power BI service. There are many more things you can do with the service, which we'll cover later in this course. Remember, too, that there are many different types of data you can connect to, and all sorts of content packs, with more of each coming all the time.
|
||||
|
||||
Okay, let's move to the next topic, where we summarize this **Getting Started** section, and get you ready for what's next.
|
||||
|
||||
@ -1,41 +0,0 @@
|
||||
Let’s do a quick review of what we covered in this first section.
|
||||
|
||||
**Power BI** is a collection of software services, apps, and connectors that work together to turn your data into interactive insights. You can use data from single basic sources, such as an Excel workbook, or pull data in from multiple databases and cloud sources to create complex datasets and reports. Power BI can be as straightforward as you want, or as enterprise-ready as your complex global business requires.
|
||||
|
||||
Power BI consists of three main elements – the **Power BI Desktop**, the **Power BI service**, and **Power BI Mobile** – which all work collectively to let you create, interact with, share, and consume your data how you want it.
|
||||
|
||||
|
||||
|
||||
We discussed the basic building blocks in Power BI too, which are:
|
||||
|
||||
* **Visualizations** – a visual representation of data, sometimes just called visuals
|
||||
* **Datasets** – a collection of data that Power BI uses to create visualizations
|
||||
* **Reports** – a collection of visuals from a dataset, spanning one or more pages
|
||||
* **Dashboards** – a single page collection of visuals, built from a report
|
||||
* **Tiles** – a single visualization found in a report or dashboard
|
||||
|
||||
From there, we took a look at Power BI in a video tour with our Tour Guide, **Will Thompson**. Will gave us a quick overview of how you can analyze and visualize data with Power BI.
|
||||
|
||||
<!---
|
||||
In **Power BI Desktop**, we connected to a basic Excel file, created visualizations, then published those visualizations to the service. Even if you use Power BI only with your Excel workbooks, you can gain amazing visual insights with those Excel workbooks, and both interact and share it in ways never before possible.
|
||||
-->
|
||||
In the **Power BI service**, we created a dashboard with just a few clicks. We continued with our glimpse into the Power BI service and used a **Content Pack** – a ready-made collection of visuals and reports – and connected to a **software service** to populate the content pack and bring that data to life.
|
||||
|
||||
We also used natural language queries, called **Q & A**, to ask questions of our answers, and let Power BI create visuals based on those questions. Lastly, we set up a **refresh schedule** for our data, so we know when we go back to the Power BI service, that data will be fresh.
|
||||
|
||||
## Next steps
|
||||
**Congratulations!** You've completed the first section of the **Guided Learning** course for Power BI. You now have a firm foundation of knowledge to move on to the next section, **Getting Data**, which is the next step in the logical flow of work for Power BI.
|
||||
|
||||
We mentioned this before, but it's worth restating: this course builds your knowledge by following the common flow of work in Power BI:
|
||||
|
||||
* Bring data into **Power BI Desktop**, and create a report.
|
||||
* **Publish** to the Power BI service, where you create new visualizations or build dashboards
|
||||
* **Share** your dashboards with others, especially people who are on the go
|
||||
* View and interact with shared dashboards and reports in **Power BI Mobile** apps
|
||||
|
||||
|
||||
|
||||
You might not do all that work yourself - some people will only view dashboards in the service that were created by someone else. That's fine, but because *you'll* go through all the sections in this course, you'll *understand* how those dashboards were created, and how they connected to the data... and you might even decide to create one of your own.
|
||||
|
||||
See you in the next section!
|
||||
|
||||
@ -1,23 +0,0 @@
|
||||
Welcome to the second section in this **Guided Learning** course for Power BI, called **Getting Data**. This section looks at the many data-centric features and tools of Power BI, focusing on Power BI Desktop. Many of these tools also apply to the Power BI service, so you're doing double duty with your learning in this section.
|
||||
|
||||
When you get data, sometimes it's not quite as well-formed, or *clean*, as you want it to be. So in this section you learn how to get data, how to clean it up (sometimes called *cleaning* or *transforming* data), and also learn some advanced tricks that can make your data-getting life easier.
|
||||
|
||||
As always in this course, your learning journey follows the same path as the flow of work in Power BI. As such, let's check out **Power BI Desktop**, where it often begins.
|
||||
|
||||
## An overview of Power BI Desktop
|
||||
Power BI Desktop is a tool to connect to, clean, and visualize your data. With Power BI Desktop, you can connect to data and then model and visualize it in different ways. Most users who are working on Business Intelligence projects will spend the majority of their time using Power BI Desktop.
|
||||
|
||||
You can download Power BI Desktop [from the web](http://go.microsoft.com/fwlink/?LinkID=521662), you can also install **Power BI Desktop** as an app from the [**Windows Store**](http://aka.ms/pbidesktopstore), or you can download it from the Power BI service. In the service, to get **Power BI Desktop** you just select the down arrow button in the upper right side of Power BI, then select Power BI Desktop.
|
||||
|
||||
|
||||
|
||||
Power BI Desktop installs as an application on your Windows computer.
|
||||
|
||||
|
||||
|
||||
So once you download it, you'll install Power BI Desktop and run it like other applications on Windows. The following image shows the Start Screen of Power BI Desktop, which appears when you start the application.
|
||||
|
||||
|
||||
|
||||
Power BI Desktop connects to a wide variety of data sources, from local on-premises databases to Excel worksheets to cloud services. It helps you clean and format your data to make it more usable, including splitting and renaming columns, changing data types, and working with dates. You can also create relationships between columns so that it's easier to model and analyze your data.
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
**Power BI Desktop** can connect to a whole range of data sources, including on-premises databases, Excel workbooks, and cloud services. Currently, over 59 different cloud services such as GitHub and Marketo have specific connectors, and you can connect to generic sources through XML, CSV, text, and ODBC. Power BI will even scrape tabular data directly from a website URL! But let's start from the beginning, with opening Power BI Desktop and connecting to data.
|
||||
|
||||
When you start **Power BI Desktop** and move past the Start Screen, you can choose Get Data from the ribbon on the **Home** tab.
|
||||
|
||||
|
||||
|
||||
There are monthly updates to Power BI Desktop, and with each update, the **Power BI Desktop What's New** page gets updated with information about the updates, links to the blog, and a download link.
|
||||
|
||||
In Power BI Desktop, there are all sorts of different data sources available. Select a source to establish a connection. Depending on your selection, you will be asked to find the source on your computer or network, or be prompted to sign in to a service to authenticate your request.
|
||||
|
||||
|
||||
|
||||
After connecting, the first window you'll see is the **Navigator**. The Navigator displays the tables or entities of your data source, and clicking on one gives you a preview of its contents. You can then import your selected tables or entities immediately, or select **Edit** to transform and clean your data before importing.
|
||||
|
||||
|
||||
|
||||
Once you've selected the tables you'd like to bring into Power BI Desktop, you can choose to load them into Power BI Desktop by selecting the **Load** button in the bottom right corner of **Navigator**. There are times, however, where you might want to make changes to those tables before you load them into Power BI Desktop. You might want only a subset of customers, or filter that data for sales that occurred only in a specific country. In those cases, you can select the Edit button and filter or transform that data before bringing it all into Power BI Desktop.
|
||||
|
||||
|
||||
|
||||
We'll pick up there, and edit our data, in the next section.
|
||||
|
||||
@ -1,33 +0,0 @@
|
||||
**Power BI Desktop** includes **Query Editor**, a powerful tool for shaping and transforming data so it's ready for your models and visualizations. When you select Edit from Navigator, Query Editor launches and is populated with the tables or other entities you selected from your data source.
|
||||
|
||||
You can also launch **Query Editor** directly from **Power BI Desktop**, using the **Edit Queries** button on the **Home** ribbon.
|
||||
|
||||
|
||||
|
||||
Once Query Editor is loaded with data that's ready for you to shape, you see a handful of sections:
|
||||
|
||||
1. In the ribbon, many buttons are now active to interact with the data in the query
|
||||
2. In the left pane, queries (one for each table, or entity) are listed and available for selection, viewing, and shaping
|
||||
3. In the center pane, data from the selected query is displayed and available for shaping
|
||||
4. The Query Settings window appears, listing the query’s properties and applied steps
|
||||
|
||||
|
||||
|
||||
In the center pane, right-clicking on a column displays a number of different available transformations, such as removing the column from the table, duplicating the column under a new name, and replacing values. From this menu you can also split text columns into multiples by common delimiters.
|
||||
|
||||
|
||||
|
||||
The **Query Editor** ribbon contains additional tools, such as changing the data type of columns, adding scientific notation, or extracting elements from dates, such as day of the week.
|
||||
|
||||
|
||||
|
||||
As you apply transformations, each step appears in the **Applied Steps** list in the **Query Settings** pane on the right side of **Query Editor**. You can use this list to undo or review specific changes, or even change the name of a step. To save your transformations, select **Close & Apply** on the **Home** tab.
|
||||
|
||||
|
||||
|
||||
Once you select **Close & Apply**, Query Editor applies the query changes you made, and applies them to Power BI Desktop.
|
||||
|
||||
|
||||
|
||||
There are all sorts of things you can do when transforming data in **Query Editor**, including advanced transformations. In the next section, we take a look at a few of those advanced transformations, to give you a sense of the almost immeasurable ways you can transform your data with **Query Editor**.
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
In this article, we investigate some advanced data import and cleaning techniques for **Power BI Desktop**. Once you've shaped your data in **Query Editor** and brought it into **Power BI Desktop**, you can look at it in a few different ways. There are three views in Power BI Desktop: **Report** view, **Data** view, and **Relationships** view. You see each view by selecting its icon in the upper left side of the canvas. In the following image, **Report** view is selected. The yellow bar beside the icon indicates which view is active.
|
||||
|
||||
|
||||
|
||||
To change the view, just select either of other two icons. The yellow bar beside the icon indicates which view is active.
|
||||
|
||||
Power BI Desktop can combine data from multiple sources into a single report, at any time during the modelling process. To add additional sources to an existing report, select **Edit Queries** in the **Home** ribbon and then select **New Source** in **Query Editor**.
|
||||
|
||||
There are many different possible data sources you can use in **Power BI Desktop**, including Folders. By connecting to a folder, you can import data from multiple files at once, such as a series of Excel files or CSV files. The files contained within your selected folder appear in **Query Editor** as binary content, and clicking the double-arrow icon at the top of the **Content** column loads their values.
|
||||
|
||||
|
||||
|
||||
One of Power BI's most useful tools is its *Filters*. For example, selecting the drop-down arrow next to a column opens a checklist of text filters that you can use to remove values from your model.
|
||||
|
||||
You can also merge and append queries, and turn multiple tables (or data from various files, in folders) into a single table that contains just the data you want. You can use the **Append Queries** tool to add the data from a new table to an existing query. Power BI Desktop attempt to match up the columns in your queries, which you can then adjust as necessary in **Query Editor**.
|
||||
|
||||
|
||||
|
||||
Finally, the **Add Custom Column** tool gives advanced users the option of writing query expressions from scratch using the powerful M language. You can add a custom column based on M query language statements, and get your data just the way you want it.
|
||||
|
||||
|
||||
|
||||
@ -1,36 +0,0 @@
|
||||
While Power BI can import your data from almost any source, its visualization and modeling tools work best with columnar data. Sometimes your data will not be formatted in simple columns, which is often the case with Excel spreadsheets, where a table layout that looks good to the human eye is not necessarily optimal for automated queries. For example, the following spreadsheet has headers that span multiple columns.
|
||||
|
||||
|
||||
|
||||
Fortunately, Power BI has tools to quickly transform multi-column tables into datasets that you can use.
|
||||
|
||||
## Transpose data
|
||||
For example, using **Transpose** in **Query Editor**, you can flip data (turn columns to rows, and rows into columns) so you can break data down into formats that you can manipulate.
|
||||
|
||||
|
||||
|
||||
Once you do that a few times, as described in the video, your table begins to shape into something that Power BI can more easily work with.
|
||||
|
||||
## Format data
|
||||
You also may need to format data, so Power BI can properly categorize and identify that data once it's imported.
|
||||
|
||||
With a handful of transformations, including *promoting rows into headers* into to break headers, using **Fill** to turn *null* values into the values found above or below in a given column, and **Unpivot Columns**, you can cleanse that data into a dataset that you can use in Power BI.
|
||||
|
||||
|
||||
|
||||
With Power BI, you can experiment with these transformations on your data, and determine which types get your data into the columnar format that lets Power BI work with it. And remember, all actions you take are recorded in the Applied Steps section of Query Editor, so if a transformation doesn't work the way you intended, you can simply click the **x** next to the step, and undo it.
|
||||
|
||||
|
||||
|
||||
## Create visuals
|
||||
Once your data is in a format that Power BI can use, by transforming and cleansing the data, you can begin to create visuals.
|
||||
|
||||
|
||||
|
||||
## Next steps
|
||||
**Congratulations!** You've completed this section of the **Guided Learning** course for Power BI. You now know how to **get data** into Power BI Desktop, and how to *shape* or *transform* that data, so you can create compelling visuals.
|
||||
|
||||
The next step in learning how Power BI works, and how to make it work *for you*, is to understand what goes into **modeling**. As you learned, a **dataset** is a basic building block of Power BI, but some datasets can be complex and based on many different sources of data. And sometimes, you need to add your own special touch (or *field*) to the dataset you create.
|
||||
|
||||
You'll learn about **modeling**, and a whole lot more, in the next section. See you there!
|
||||
|
||||
@ -1,11 +0,0 @@
|
||||
Welcome to the **Modeling** section of the **Guided Learning** course for Power BI. This section shows you how to get your connected data ready for use, using Power BI Desktop. Often, you'll connect to more than one data source to create your reports, and you'll need all of that data to work together. Modeling is how you get it there.
|
||||
|
||||
To create a logical connection between different data sources, you create a **relationship**. A relationship between data sources enables Power BI to know how those tables relate to one another, allowing you to create interesting visuals and reports. This section explains *relationships* (only the data-centric ones, though), and even shows you how to create relationships when none exists.
|
||||
|
||||
As always in this course, your learning journey follows the same path as the flow of work in Power BI. We'll still be in **Power BI Desktop** for most of this section, but the work done here has direct affect on working in the Power BI service.
|
||||
|
||||
## Introduction to modeling your data
|
||||
Now that we've reviewed how to import your data and transform it, it's time to start modeling.
|
||||
|
||||
One of Power BI's strengths is that you don't need to flatten your data into one table. Instead, you can use multiple tables from multiple sources, and define the **relationship** between them. You can also create your own custom calculations and assign new metrics to view specific segments of your data, and use these new measures in visualizations for easy modeling.
|
||||
|
||||
@ -1,24 +0,0 @@
|
||||
Power BI allows you to visually set the relationship between tables or elements. To see a diagrammatic view of your data, use the **Relationship view**, found on the far left side of the screen next to the Report canvas.
|
||||
|
||||
|
||||
|
||||
From the **Relationships** view, you can see a block that represents each table and its columns, and lines between them to represent relationships.
|
||||
|
||||
Adding and removing relationships is simple. To remove a relationship, right-click on it and select **Delete**. To create a relationship, drag and drop the fields that you want to link between tables.
|
||||
|
||||
|
||||
|
||||
To hide a table or individual column from your report, right-click on it in the Relationship view and select **Hide in Report View**.
|
||||
|
||||
|
||||
|
||||
For a more detailed view of your data relationships, select **Manage Relationships** in the **Home** tab. This will open the **Manage Relationships** dialog, which displays your relationships as a list instead of a visual diagram. From here you can select **Autodetect** to find relationships in new or updated data. Select **Edit** in the **Manage Relationships** dialog to manually edit your relationships. This is also where you can find advanced options to set the *Cardinality* and *Cross-filter* direction of your relationships.
|
||||
|
||||
|
||||
|
||||
Your options for Cardinality are *Many to One*, and *One to One*. *Many to One* is the fact to dimension type relationship, for example a sales table with multiple rows per product being matched up with a table listing products in their own unique row. *One to One* is used often for linking single entries in reference tables.
|
||||
|
||||
By default, relationships will be set to cross-filter in both directions. Cross-filtering in just one direction limited some of the modeling capabilities in a relationship.
|
||||
|
||||
Setting accurate relationships between your data allows you to create complex calculations across multiple data elements.
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
Creating calculated columns is a simple way to enrich and enhance your data. A **calculated column** is a new column that you create by defining a calculation that transforms or combines two or more elements of existing data. For example, you can create a new column by combining two columns into one.
|
||||
|
||||
One useful reason for creating a calculated column is to establish a relationship between tables, when no unique fields exist that can be used to establish a relationship. The lack of a relationship becomes apparent when you create a simple table visual in Power BI Desktop, and you get the same value for all entries, yet you know the underlying data is different.
|
||||
|
||||
|
||||
|
||||
To create a relationship with unique fields in data, you can, for example, create a new calculated column for "Full Phone Number" by combining the values from the "Area Code" and "Local Number" columns when those values exist in your data. Calculated columns are a useful tool for quickly creating models and visualizations.
|
||||
|
||||
To create a calculated column, select the **Data view** in Power BI Desktop from the left side of the report canvas.
|
||||
|
||||
|
||||
|
||||
From the Modeling tab, select **New Column**. This will enable the formula bar where you can enter calculations using DAX (Data Analysis Expressions) language. DAX is a powerful formula language, also found in Excel, that lets you build robust calculations. As you type a formula, Power BI Desktop displays matching formulas or data elements to assist and accelerate the creation of your formula.
|
||||
|
||||
The Power BI formula bar will suggest specific DAX functions and related data columns as you enter your expression.
|
||||
|
||||
|
||||
|
||||
Once the calculated columns are created in each table, they can be used as a unique key to establish a relationship between them. Going to **Relationship** view, you can then drag the field from one table to the other to create the relationship.
|
||||
|
||||
|
||||
|
||||
Returning to **Report** view, you now see a different value for each district.
|
||||
|
||||
|
||||
|
||||
There are all sorts of other things you can do by creating calculated columns, too.
|
||||
|
||||
@ -1,24 +0,0 @@
|
||||
Imported data often contains fields that you don't actually need for your reporting and visualization tasks, either because it's extra information, or because that data is already available in another column. Power BI Desktop has tools to optimize your data, and make it more usable for you to create reports and visuals, and for viewing your shared reports.
|
||||
|
||||
## Hiding fields
|
||||
To hide a column in the **Fields** pane of Power BI Desktop, right-click on it and select **Hide**. Note that your hidden columns are not deleted; if you've used that field in existing visualizations, the data is still in that visual, and you can still use that data in other visualizations too, the hidden field just isn't displayed in the **Fields** pane.
|
||||
|
||||
|
||||
|
||||
If you view tables in the **Relationships** view, hidden fields are indicated by being grayed out. Again, their data is still available and is still part of the model, they're just hidden from view. You can always unhide any field that has been hidden by right-clicking the field, and selecting **unhide**.
|
||||
|
||||
## Sorting visualization data by another field
|
||||
The **Sort by Column** tool, available in the **Modeling** tab, is very useful to ensure that your data is displayed in the order you intended.
|
||||
|
||||
|
||||
|
||||
As a common example, data that includes the name of the month is sorted alphabetically by default, so for example, "August" appears before "February".
|
||||
|
||||
|
||||
|
||||
In this case, selecting the field in the Fields list, then selecting **Sort By Column** from the **Modeling** tab and then choosing a field to sort by can remedy the problem. In this case, the "MonthNo" category sort option orders the months as intended.
|
||||
|
||||
|
||||
|
||||
Setting the data type for a field is another way to optimize your information so it's handled correctly. To change a data type from the report canvas, select the column in the **Fields** pane, and then use the **Format** drop-down menu to select one of the formatting options. Any visuals you've created that display that field are updated automatically.
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
A *measure* is a calculation that exists in your Power BI data model. To create a measure, in **Report** view select **New Measure** from the **Modeling** tab.
|
||||
|
||||
|
||||
|
||||
One of the great things about DAX, the Data Analysis Expression language in Power BI, is that it has lots of useful functions, particularly around time-based calculations such as *Year to Date* or *Year Over Year*. With DAX you can define a measure of time once, and then slice it by as many different fields as you want from your data model.
|
||||
|
||||
In Power BI, a defined calculation is called a *measure*. To create a *measure*, select **New Measure** from the **Home** tab. This opens the Formula bar where you can enter the DAX expression that defines your measure. As you type, Power BI suggests relevant DAX functions and data fields as you enter your calculation, and you'll also get a tooltip explaining some of the syntax and function parameters.
|
||||
|
||||
|
||||
|
||||
If your calculation is particularly long, you can add extra line breaks in the Expression Editor by typing **ALT-Enter**.
|
||||
|
||||
|
||||
|
||||
Once you've created a new measure, it will appear in one of the tables on the **Fields** pane, found on the right side of the screen. Power BI inserts the new measure into whichever table you have currently selected, and while it doesn't matter exactly where the measure is in your data, you can easily move it by selecting the measure and using the **Home Table** drop-down menu.
|
||||
|
||||
|
||||
|
||||
You can use a measure like any other table column: just drag and drop it onto the report canvas or visualization fields. Measures also integrate seamlessly with slicers, segmenting your data on the fly, which means you can define a measure once, and use it in many different visualizations.
|
||||
|
||||
The **Calculate** DAX function is a powerful function that enables all sorts of useful calculations, which is especially useful for financial reporting and visuals.
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
Calculated tables are a function within DAX that allows you to express a whole range of new modeling capabilities. For example, if you want to do different types of merge joins or create new tables on the fly based on the results of a functional formula, calculated tables are the way to accomplish that.
|
||||
|
||||
To create a calculated table, go to **Data view** in Power BI Desktop, which you can activate from the left side of the report canvas.
|
||||
|
||||
|
||||
|
||||
Select **New Table** from the Modeling tab to open the formula bar.
|
||||
|
||||
|
||||
|
||||
Type the name of your new table on the left side of the equal sign, and the calculation that you want to use to form that table on the right. When you're finished your calculation, the new table appears in the Fields pane in your model.
|
||||
|
||||
|
||||
|
||||
Once created, you can use your calculated table as you would any other table in relationships, formulas, and reports.
|
||||
|
||||
@ -1,40 +0,0 @@
|
||||
It's easy to analyze time-based data with Power BI. The modeling tools in Power BI Desktop automatically include generated fields that let you drill down through years, quarters, months, and days with a single click.
|
||||
|
||||
When you create a table visualization in your report using a date field, Power BI Desktop automatically includes breakdowns by time period. For example, the single date field in the **Date** table was automatically separated into Year, Quarter, Month and Day by Power BI, as shown in the following image.
|
||||
|
||||
|
||||
|
||||
Visualizations display data at the *year* level by default, but you can change that by turning on **Drill Down** in the top right-hand corner of the visual.
|
||||
|
||||
|
||||
|
||||
Now when you click on the bars or lines in your chart, it drills down to the next level of time hierarchy, for example from *years* to *quarters*. You can continue to drill down until you reach the most granular level of the hierarchy, which in this example is *days*. To move back up through the time hierarchy, click on **Drill Up** in the top left-hand corner of the visual.
|
||||
|
||||
|
||||
|
||||
You can also drill down through all of the data shown on the visual, rather than one selected period, by using the **Drill All** double-arrow icon, also in the top right-hand corner of the visual.
|
||||
|
||||
|
||||
|
||||
As long as your model has a date field, Power BI will automatically generate different views for different time hierarchies.
|
||||
|
||||
To get back to individual dates rather than using the date hierarchy, simply right-click the column name in the **Fields** well (in the following image, the name of the column is *InvoiceDate*), then select the column name from the menu that appears, rather than **Date Hierarchy**. Your visual then shows the data based on that column data, without using the date hierarchy. Need to go back to using the date hierarchy? No problem - just right-click again and select **Date Hierarchy** from the menu.
|
||||
|
||||
|
||||
|
||||
## Next steps
|
||||
**Congratulations!** You've completed this section of the **Guided Learning** course for Power BI. Now that you know about *modeling* data, you're ready to learn about the fun stuff waiting in the next section: **Visualizations**.
|
||||
|
||||
As mentioned before, this course builds your knowledge by following the common flow of work in Power BI:
|
||||
|
||||
* Bring data into **Power BI Desktop**, and create a report.
|
||||
* Publish to the Power BI service, where you create new **visualizations** and build dashboards
|
||||
* **Share** your dashboards with others, especially people who are on the go
|
||||
* View and interact with shared dashboards and reports in **Power BI Mobile** apps
|
||||
|
||||
|
||||
|
||||
While you might not do all that work yourself, you'll *understand* how those dashboards were created, and how they connected to the data... and when you're done with this course, you'll be able to create one of your own.
|
||||
|
||||
See you in the next section!
|
||||
|
||||
@ -1,17 +0,0 @@
|
||||
Welcome to the **Visualizations** section of the **Guided Learning** course for Power BI. Get ready for a fun, interesting, and enlightening tour of the many, many visualizations Power BI has to offer. And this isn't all of them - there are more visualizations coming all the time!
|
||||
|
||||
|
||||
|
||||
Of course, visuals are the end-result of any business intelligence undertaking... we want to take data, present it in a compelling and insightful way, and *show* what stands out. And Power BI has all sorts of compelling visuals - and a nearly limitless way to customize them - so this is an important section!
|
||||
|
||||
It might seem like there are lots of topics in this section, but don't worry: each topic is short, filled with (you guessed it) visuals, and easy to absorb. You'll likely find yourself cruising through this section, and imagining how you'll make these visuals present your own data.
|
||||
|
||||
We'll start with the mainstays of visualizations - the simple visuals we're all familiar with - and make sure you know the ins and outs. Then we'll get more advanced, or at least a little less common, and fill up your report-creating toolbox.
|
||||
|
||||
Have fun - there's a lot to learn here!
|
||||
|
||||
## Introduction to visuals in Power BI
|
||||
Visualizing data is one of the core parts of Power BI - a basic building block as we defined it earlier in this course - and creating visuals is the easiest way to find and share your insights.
|
||||
|
||||
Power BI has a whole range of visualizations available by default, from simple bar charts to pie charts to maps, and even more esoteric offerings like waterfalls, funnels, gauges, and more. Power BI Desktop also offers extensive page formatting tools, such as shapes and images, that help bring your report to life.
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
Along with data-bound visuals, you can also add static elements such as text boxes, images, and shapes to improve the visual design of your reports. To add a visual element, select **Text Box**, **Image**, or **Shapes** from the **Home** tab.
|
||||
|
||||
|
||||
|
||||
**Text boxes** are an ideal way to display large titles, captions, or short paragraphs of information along with your visualizations. Text boxes can include links, either through a written URL or by highlighting an anchor phrase and selecting the link symbol on the text box options bar. You can include URLs in text boxes, and Power BI automatically detects the link, and makes it live.
|
||||
|
||||
Selecting **Image** will open a file browser where you can select the image from your computer or other networked source. By default, resizing an image in your report will maintain its aspect ratio, but this can be disabled in the visual formatting options.
|
||||
|
||||
**Shapes** have five different options for form, including rectangles and arrows. Shapes can be opaque, or transparent with a colored border. (The latter is useful for creating borders around groups of visualizations.)
|
||||
|
||||
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
Power BI Desktop gives you the ability to control the layout and formatting of your report pages, such as size and orientation.
|
||||
|
||||
Use the **Page View** menu from the Home tab to change the way your report pages scale. The available options include **Fit To Page** (default), **Fit To Width**, and **Actual Size**.
|
||||
|
||||
|
||||
|
||||
You can also change the pages size itself. By default, report pages are 16:9. To change the page size, make sure no visuals are selected, then select the paintbrush icon in the Visualizations pane, then select **Page Size** to expand that section.
|
||||
|
||||
|
||||
|
||||
Options for page size include 4x3 (more square aspect ratio), and Dynamic (the page will stretch to fill the available space). There's a standard letter size for reports as well. Keep in mind that you may need to resize your visuals after changing the page size to ensure that they're completely on the canvas.
|
||||
|
||||
You can also specify a custom page size, setting the size by inches or pixels, and change the background color of the entire report.
|
||||
|
||||
Another option is to select Cortana, which sizes the report so that it can be used as a result for searches using Cortana.
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
When you have multiple visualizations on the same report page, selecting a particular segment by clicking or using a slicer will affect all the visuals on that page. In some cases, though, you may want to slice only specific visuals. This is particularly true when using elements such as scatter plots, where limiting the data to a specific segment will remove crucial meaning. Fortunately, Power BI Desktop lets you control how interactions flow between visuals.
|
||||
|
||||
To change the interaction between your visualizations, select **Edit** from the Visuals section of the **Home** ribbon to toggle **Edit Mode** on.
|
||||
|
||||
>[!NOTE]
|
||||
>The **Edit Interactions** icon in Power BI Desktop has changed since the video was recorded.
|
||||
>
|
||||
>
|
||||
|
||||
|
||||
|
||||
Now when you select a visual on your report canvas, you'll see a small opaque *filter* icon in the top right-hand corner of every other visual it will affect. To exclude a visual from the interaction, click the *None* symbol in the upper right corner, near the *filter* icon.
|
||||
|
||||
|
||||
|
||||
In some instances you can adjust the type of filter interaction that happens between visuals. With **Edit Mode** toggled on, select the visual you use to filter. If you can change the type of interaction on another visual, a *pie chart* icon will appear next to the filter icon in the top right-hand corner.
|
||||
|
||||
|
||||
|
||||
Click the *pie chart* icon to highlight the segmented data. Otherwise, the data will be filtered. As before, you can click the *None* icon to remove all interaction.
|
||||
|
||||
A useful design tip is to draw a transparent shape around visuals that interact with each other, so it's clear to the user that they have an interactive relationship.
|
||||
|
||||
|
||||
|
||||
@ -1,10 +0,0 @@
|
||||
Individual pages of a report can be quite complex, with multiple visualizations that interact in specific ways and have precise formatting. Occasionally when building a report, you may want to use the same visuals and layouts for two different page topics. For example, if you've just put together a report page on gross revenue, you may want an almost identical page on net revenue.
|
||||
|
||||
Recreating all of your work would be difficult, but with Power BI Desktop you can just duplicate a report page.
|
||||
|
||||
Right-click on the tab you want to copy, and select **Duplicate Page**.
|
||||
|
||||
|
||||
|
||||
You can then rename the new page tab as appropriate, update the text title if you have one, and then update your visuals with the new field you want to track.
|
||||
|
||||
@ -1,14 +0,0 @@
|
||||
By default, column headings are only displayed in your reports if they contain data. For example, if you were displaying revenue by country and had no sales in Norway, then Norway would not appear anywhere in your visualization.
|
||||
|
||||
To display empty categories, click on the down-arrow in the field you want to change in the **Visualizations** pane, and select **Show items with no data**.
|
||||
|
||||
|
||||
|
||||
Any empty columns now appear in your visual with blank values.
|
||||
|
||||
|
||||
|
||||
Once you select **Show items with no data** for any field in the **Visualizations** pane, it applies to all fields displayed in the Visualizations pane. So if you add another field, any items that have no data will also be shown, without having to revisit the drop-down menu.
|
||||
|
||||
|
||||
|
||||
@ -1,24 +0,0 @@
|
||||
Power BI has properties that you can assign for fields in your model, which can help report and visualize and present data in ways that provide more clarity. For example, sometimes you want to average a list of numbers, but Power BI automatically sums the list. With Power BI, you can adjust how those lists of numbers are summarized.
|
||||
|
||||
## Numeric control over summarization
|
||||
Let's take an example, and demonstrate how to control the way Power BI summarizes numeric data fields.
|
||||
|
||||
Select a visual on your report canvas, and then select a field in the **Fields** pane. The **Modeling** tab appears in the ribbon and displays options for formatting data, and data properties.
|
||||
|
||||
|
||||
|
||||
You can select the type of currency symbol used, by selecting the currency symbol drop down, which is shown in the following image.
|
||||
|
||||
|
||||
|
||||
There are many different options for formatting fields. For example, you can change the format from currency to percentage.
|
||||
|
||||
You can change how Power BI summarizes the data, too. Select the **Default Summarization** icon to change how a field is summarized, including displaying a sum, a count, or an average.
|
||||
|
||||
|
||||
|
||||
## Manage and clarify your location data
|
||||
You can make similar changes when plotting locations on a map. Select your map, and then select the field in the **Fields** pane that you use for the *Location* value. On the **Modeling** tab, select **Data Category** and then choose the category that represents your location data from the drop-down menu. For example, select state, county, or city.
|
||||
|
||||
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
When you have lots of elements on a report, Power BI lets you manage how they overlap with each other. How items are layered, or arranged on top of one another, is often referred to as the z-order.
|
||||
|
||||
To manage the z-order of elements in a report, select an element, and use the **Arrange** button on the **Home** tab of the ribbon to change its z-order.
|
||||
|
||||
|
||||
|
||||
By using the options in the **Arrange** button menu, you can get the ordering of elements on your report just the way you want it. You can move a visual one layer forward or backward, or send it all the way to the front of the back of the order.
|
||||
|
||||
Using the Arrange button is particularly useful when using shapes as decorative backgrounds or borders, or to highlight particular sections of an individual chart or graph. You can also use them to create a background, such as the following light-blue rectangle being used for a report title background.
|
||||
|
||||
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
When you add a *date* field to a visual in the *Axis* field bucket, Power BI automatically adds a time hierarchy that includes *Year*, *Quarter*, *Month* and *Day*. By doing this, Power BI allows your visuals to have time-based interaction with those viewing your reports, by letting users drill-down through those different time levels.
|
||||
|
||||
|
||||
|
||||
With a hierarchy in place, you can begin drilling down through the time hierarchy. For example, clicking a year in the chart drills down to the next level in the hierarchy, in this case *Quarters*, which are then displayed in the visual.
|
||||
|
||||
|
||||
|
||||
In that automatically created hierarchy, you can also manage to which level your shared report allows people to drill. To do this, in the Visualizations pane, simply click the X beside the hierarchy that you want to remove. The deleted level is removed from the report, and drilling no longer displays that level.
|
||||
|
||||
|
||||
|
||||
If you need to get that level of the hierarchy back, just remove the *date* field, and then add it again from the **Fields** pane, and the hierarchy is once again created for you automatically.
|
||||
|
||||
There may be times when you don't want the hierarchy to be used for a visual. You can control that by selecting the down-arrow button beside the *Date* field (once you've added it to a visual), and select **Date** rather than **Date Hierarchy**. That prompts Power BI to show the raw date values in the visual.
|
||||
|
||||
|
||||
|
||||
You can also expand all data elements currently visible at once, rather than selecting a single quarter, or a single year. To do that, select the *Drill all* icon in the top left of the visual, which is a double-down arrow icon.
|
||||
|
||||
|
||||
|
||||
@ -1,40 +0,0 @@
|
||||
With Power BI Desktop, you can perform analytical and statistical analysis and create compelling visuals by integrating with R. You can host those R visualizations within the Power BI Desktop report.
|
||||
|
||||
When you select the **R visual** icon from the **Visualizations** pane, Power BI creates a placeholder on the canvas to host your R visual, and then presents an R script editor for you to use right on the canvas. As you add fields to the R visual, Power BI Desktop adds them to the R script editor pane.
|
||||
|
||||
|
||||
|
||||
Below what Power BI generates in the R script editor, you can begin creating your R script to generate the visual. Once your script is complete, select **Run** and the following occurs:
|
||||
|
||||
1. The data added to the visual (from the **Fields** pane) is sent from Power BI Desktop to the local installation of R
|
||||
2. The script created in the Power BI Desktop R script editor is run on that local installation of R
|
||||
3. Then Power BI Desktop gets a visual back from the R installation, and displays it on the canvas
|
||||
|
||||
It all happens quite quickly, and the result appears in the **R visual** visualization on the canvas.
|
||||
|
||||
|
||||
|
||||
You can change the R visual by adjusting the R script, and then selecting **Run** again. In the following image, we changed the visual to display circles instead of squares.
|
||||
|
||||
|
||||
|
||||
And since the R visual is just like any other visual in Power BI Desktop, you can interact with it and make connections with other visuals on the canvas as well. When you interact with other visuals on the canvas, through filtering or highlighting, the R visual automatically reacts just like any other Power BI visual, without needing to adjust the R script.
|
||||
|
||||
It's a great way to use the power of R, right in Power BI Desktop.
|
||||
|
||||
## Next steps
|
||||
**Congratulations!** You've completed this **Visualizations** section of the **Guided Learning** course for Power BI. You can consider yourself well-versed in the many visualizations offered in Power BI, and also knowledgeable about how to use, modify, and customize them. And good news: visualizations are essentially the same in Power BI Desktop and the Power BI service, so what you learned applies to both.
|
||||
|
||||
You're now ready to head to the cloud and get immersed in the Power BI service, where you can **Explore Data**. As you know, the flow of work looks something like the following:
|
||||
|
||||
* Bring data into **Power BI Desktop**, and create a report.
|
||||
* Publish to the Power BI service, where you create new **visualizations** and build dashboards
|
||||
* **Share** your dashboards with others, especially people who are on the go
|
||||
* View and interact with shared dashboards and reports in **Power BI Mobile** apps
|
||||
|
||||
|
||||
|
||||
Whether you create reports or just view and interact with them, you now know how all those cool visuals are created, and how they connected to the data. Next we get to see those visuals and reports in action.
|
||||
|
||||
See you in the next section!
|
||||
|
||||
@ -1,27 +0,0 @@
|
||||
In this article you learn how to create new bar charts, pie charts, and treemaps, and customize them to suit your reports.
|
||||
|
||||
There are two different ways to create a new visualization in Power BI Desktop:
|
||||
|
||||
* You can drag field names from the **Fields** pane, and drop them on the report canvas. By default your visualization appears as a table of data.
|
||||
|
||||
|
||||
* You can also click the type of visualization you want to create in the **Visualizations** pane. With this method, the default visual is a blank placeholder that resembles the type of visual you selected.
|
||||
|
||||
|
||||
|
||||
Once you create your graph, map, or chart, you can begin dragging data fields onto the bottom portion of the **Visualization** pane to build and organize your visual. The available fields will change based on the type of visualization that you selected. As you drag and drop data fields, your visualization will automatically update to reflect changes.
|
||||
|
||||
|
||||
|
||||
You can resize your visualization by selecting it and then dragging the handles in or out. You can also move your visualization anywhere on the canvas by clicking and then dragging it. If you want to convert between different types of visualizations, select the visual you want to change and simply select a different visual from the icons in the **Visualization** pane. Power BI attempts to convert your selected fields to the new visual type as closely as possible.
|
||||
|
||||
As you hover over parts of your visualizations, you'll get a tooltip that contains details about that segment, such as labels and total value.
|
||||
|
||||
|
||||
|
||||
Select the **paintbrush** icon on the **Visualizations** pane to make cosmetic changes to your visual, such as background alignment, title text, and data colors.
|
||||
|
||||
|
||||
|
||||
The available options for cosmetic changes to your visual vary depending on the type of visual you have selected.
|
||||
|
||||
@ -1,14 +0,0 @@
|
||||
When you want to visualize two measures that have very different scales, such as revenue and units, a **combination chart** that shows a line and a bar with different axis scales is very useful. Power BI supports many different types of combination charts by default, including the popular **Line** and **Stacked Columns** charts.
|
||||
|
||||
|
||||
|
||||
When you create a combination chart, you're presented with a field for **Shared Axis** (the X-axis), and then values for your two fields, in this case a column and a line. The two Y-axis legends appear on either side of the visualization.
|
||||
|
||||
|
||||
|
||||
You can also split each column by category, by dragging a category into the Column Series field in the Visualizations pane. When you do so, each bar is proportionately colored based on the values within each category.
|
||||
|
||||
|
||||
|
||||
Combination charts are an effective way to visualize multiple measures that have very different scales in a single visualization.
|
||||
|
||||
@ -1,14 +0,0 @@
|
||||
Slicers are one of the most powerful types of visualizations, particularly as part of a busy report. A **slicer** is an on-canvas visual filter in **Power BI Desktop** that lets anyone looking at a report segment the data by a particular value, such as by year or by geographical location.
|
||||
|
||||
To add a slicer to your report, select **Slicer** from the **Visualizations** pane.
|
||||
|
||||
|
||||
|
||||
Drag the field by which you want to slice and drop it top of the slicer placeholder. The visualization turns into a list of elements with checkboxes. These elements are your filters - select the box next to one to segment, and all other visualizations on the same report page are filtered, or *sliced*, by your selection.
|
||||
|
||||
|
||||
|
||||
There are a few different options available to format your slicer. You can set it to accept multiple inputs at once, or toggle **Single Select** mode to use one at a time. You can also add a **Select All** option to your slicer elements, which is helpful when you have a particularly long list. Change the orientation of your slicer from the vertical default to horizontal, and it becomes a selection bar rather than a checklist.
|
||||
|
||||
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
Power BI has two different types of map visualizations: a bubble map that places a bubble over a geographic point, and a shape map that actually shows the outline of area you want to visualize.
|
||||
|
||||
|
||||
|
||||
> [!NOTE]
|
||||
> When working with countries or regions, use the three-letter abbreviation to ensure that geocoding works properly in map visualizations. Do *not* use two-letter abbreviations, as some countries or regions may not be properly recognized.
|
||||
> If you only have two-letter abbreviations, check out [this external blog post](https://blog.ailon.org/how-to-display-2-letter-country-data-on-a-power-bi-map-85fc738497d6#.yudauacxp) for steps on how to associate your two-letter country/region abbreviations with three-letter country/region abbreviations.
|
||||
>
|
||||
>
|
||||
|
||||
## Create bubble maps
|
||||
To create a bubble map, select the **Map** option in the **Visualization** pane. You must add a value to the *Location* bucket in the **Visualizations** options to use a map visual.
|
||||
|
||||
|
||||
|
||||
Power BI is flexible about what type of location value it accepts, from more general details like city name or airport code, down to very specific latitude and longitude data. Add a field to the **Size** bucket to change the size of the bubble accordingly for each map location.
|
||||
|
||||
|
||||
|
||||
## Create shape maps
|
||||
To create a shape map, select the **Filled Map** option in the Visualization pane. As with bubble maps, you must add some kind of value to the Location bucket to use this visual. Add a field to the Size bucket to change the intensity of the fill color accordingly.
|
||||
|
||||
|
||||
|
||||
A warning icon in the top left corner of your visual indicates that the map needs more location data to accurately plot values. This is a particularly common problem when the data in your location field is ambiguous, such as using an area name like *Washington* that could indicate a state or a district. One way to resolve this problem is to rename your column to be more specific, such as *State*. Another way to resolve it is to manually reset the data category by selecting **Data Category** in the Modeling tab. From there you can assign a category to your data such as "State" or "City".
|
||||
|
||||
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
In addition to an abundance of charts, Power BI Desktop also supports more tabular visualizations. In fact, when you grab a categorical field or text field and drag it onto the report canvas, you get a table of results by default. You can scroll up and down through the table, and initially it will be sorted alphabetically.
|
||||
|
||||
|
||||
|
||||
If you have numerical information in a table, such as revenue, a total sum will appear at the bottom. You can manually sort by each column by clicking on its header to toggle ascending or descending order. If a column is not wide enough to display all of its contents, click and drag the header sideways to expand it.
|
||||
|
||||
The order of the fields in the *Values* bucket in the **Visualizations** pane determines the order in which they appear in your table.
|
||||
|
||||
|
||||
|
||||
A **matrix** is similar to a table, but it has different category headers on the columns and rows. As with tables, numerical information will be automatically totaled along the bottom and right side of the matrix.
|
||||
|
||||
|
||||
|
||||
There are many cosmetic options available for matrixes, such as auto-sizing columns, toggling row and column totals, setting colors, and more. When creating a matrix, make sure your categorical data (the non-number data) is on the left of the matrix, and the numeric files on the right to ensure the horizontal scroll bar appears, and to make sure that scrolling behavior works properly.
|
||||
|
||||
@ -1,23 +0,0 @@
|
||||
If you want to compare two different measures, such as unit sales verses revenue, a common visualization to use is a scatter chart.
|
||||
|
||||
|
||||
|
||||
To create a blank chart, select **Scatter chart** from the **Visualizations** pane. Drag and drop the two fields you want to compare from the **Fields** pane to the *X Axis* and *Y Axis* options buckets. At this point, your scatter chart probably just has a small bubble in the center of the visual - you need to add a measure to the *Details* bucket to indicate how you would like to segment your data. For example, if are comparing item sales and revenue, perhaps you want to split the data by category, or manufacturer, or month of sale.
|
||||
|
||||
Adding an additional field to the *Legend* bucket color-codes your bubbles according to the field's value. You can also add a field to the *Size* bucket to alter the bubble size according to that value.
|
||||
|
||||
|
||||
|
||||
Scatter charts have many visual formatting options as well, such as turning on an outline for each colored bubble and toggling individual labels. You can change the data colors for other chart types, as well.
|
||||
|
||||
|
||||
|
||||
You can create an animation of your bubble chart's changes over time by adding a time-based field to the *Play Axis* bucket. Click on a bubble during an animation to see a trace of its path.
|
||||
|
||||
|
||||
|
||||
>[!NOTE]
|
||||
>Remember, if you only see one bubble in your scatter chart, it's because Power BI is aggregating your data, which is the default behavior. Add a category to the *Details* bucket, in the **Visualizations** pane, to get more bubbles.
|
||||
>
|
||||
>
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
Waterfall and funnel charts are two of the more interesting (and perhaps uncommon) standard visualizations that are included in Power BI. To create a blank chart of either type, select its icon from the **Visualizations** pane.
|
||||
|
||||
|
||||
|
||||
**Waterfall charts** are typically used to show changes in a particular value over time.
|
||||
|
||||
|
||||
|
||||
Waterfalls only have two bucket options: *Category* and *Y Axis*. Drag a time-based field such as *year* to the *Category* bucket, and the value you want to track to the *Y Axis* bucket. Time periods where there was an increase in value are displayed in green by default, while periods with a decrease in value are displayed in red.
|
||||
|
||||
**Funnel charts** are typically used to show changes over a particular process, such as a sales pipeline or website retention efforts.
|
||||
|
||||
|
||||
|
||||
Both **Waterfall** and **Funnel** charts can be sliced and visually customized.
|
||||
|
||||
@ -1,20 +0,0 @@
|
||||
Generally, visualizations are used to compare two or more different values. However, sometimes when building reports you may want to track a just single Key Performance Indicator (KPI) or metric over time. The way to do this in Power BI Desktop is with a **Gauge** or **single number** card visual. To create a blank chart of either type, select its icon from the **Visualizations** pane.
|
||||
|
||||
|
||||
|
||||
Gauges are particularly useful when you are building dashboards and want to show progress towards a particular target. To create a gauge, select its icon from the **Visualizations** pane, and drag the field you want to track into the *Value* bucket.
|
||||
|
||||
|
||||
|
||||
Gauges appear by default at 50%, or double the *Value*, and there are two ways to adjust this setting. To dynamically set the values, drag fields to the *Minimum*, *Maximum*, and *Target* Value buckets. Alternatively, use the visual formatting options to manually customize the range of your gauge.
|
||||
|
||||
|
||||
|
||||
Card visualizations simply show a numeric representation of a field. By default card visuals use display units to keep the number short, for example displaying "$5bn" instead of "$5,000,000,000". Use the visual formatting options to change the unit being used, or disable it completely.
|
||||
|
||||
|
||||
|
||||
One interesting application of cards is to have them display a custom measure that you've concatenated with text. To use the earlier example, with a custom measure your card could include advanced DAX functions and display something like, "Total revenue this year: $5bn" or "Progress on unit sales this year:" and then add the number that represents the progress.
|
||||
|
||||
|
||||
|
||||
@ -1,32 +0,0 @@
|
||||
There are many times when you might want to modify the colors used in charts or visuals. Power BI gives you lots of control over how colors are displayed. To get started, select a visual then in the **Visualizations** pane, click the **paintbrush** icon.
|
||||
|
||||
|
||||
|
||||
There are many options for changing the colors or formatting of the visual. You can change the color of all bars of a visual by selecting the color picker beside **Default color**, then selecting your color of choice.
|
||||
|
||||
|
||||
|
||||
You can also change the color of each bar (or other element, depending on the type of visual you selected) by toggling the **Show all** slider to on. When you do, a color selector appears for each element.
|
||||
|
||||
|
||||
|
||||
You can also change the color based on a value, or measure. To do so, drag a field into the **Color saturation** bucket in the Visualizations pane (note that this is available in the **field well** section, not the **paintbrush** section).
|
||||
|
||||
|
||||
|
||||
In addition, you can change the scale and the colors that are used when filling data element colors. You can also select a diverging scale by toggling the Diverging slider to on, which lets the color scale between three colors. And you can also set *Minimum*, *Center*, and *Maximum* values displayed on your chart.
|
||||
|
||||
|
||||
|
||||
You can also use those values to create rules, for example, to set values above zero a certain color, and values below to another color.
|
||||
|
||||
Another handy tool for using colors is setting a *constant line*, also sometimes referred to as a *reference line*. You can set the value of the constant line, set its color, and even have the reference line contain a label. To create a constant line (and other interesting lines), select the **Analytics pane** (looks like a magnifying glass) then expand the **Reference Line** section.
|
||||
|
||||
|
||||
|
||||
There are many other lines you can create for a visual, also found in the **Analytics pane**, such as Min, Max, Average, Median, and Percentile lines.
|
||||
|
||||
|
||||
|
||||
Lastly, you can create a border around an individual visualization, and like other controls, you can specify the color of that border as well.
|
||||
|
||||
@ -1,15 +0,0 @@
|
||||
Welcome to the **Exploring Data** section of the **Guided Learning** course for Power BI. Now that you know about Power BI Desktop, and how to get and visualize data, you're ready to explore data in the Power BI service.
|
||||
|
||||
|
||||
|
||||
There are all sorts of things to learn in this section, and the compelling interactivity and sharing of Power BI really shines here. So get ready for an engaging and interesting section.
|
||||
|
||||
## Introduction to the Power BI service
|
||||
*See what the Power BI service can do for your organization*
|
||||
|
||||
The Power BI service is the natural extension of **Power BI Desktop**, and its features include uploading reports, creating dashboards, and asking questions of your data using natural language. You can use the service to set data refresh times, share data with your organization, and create customized service packs.
|
||||
|
||||
In the following topics, we'll explore the Power BI service, and show you how it can turn your business intelligence data into data insights and a collaborative decision-driving environment.
|
||||
|
||||
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
Publishing your reports to the Power BI service is fast and easy.
|
||||
|
||||
Once you've completed authoring your report in **Power BI Desktop**, just select the **Publish** button on the **Home** tab in Power BI Desktop, and the process begins.
|
||||
|
||||
|
||||
|
||||
Your report and data, including visualizations, queries, and custom measures, are packaged together and uploaded to the Power BI service.
|
||||
|
||||
|
||||
|
||||
> [!NOTE]
|
||||
> It's common to refer to Power BI Desktop reports as **.pbix** files, which is the extension they're given in Windows.
|
||||
>
|
||||
|
||||
Once the upload is complete, a dialog box tells you the publishing process succeeded and a link is provided in that dialog to take you directly to your report in the Power BI service, in a web browser.
|
||||
|
||||
|
||||
|
||||
And that's it - it's easy to publish reports from Power BI Desktop to the Power BI service.
|
||||
|
||||
|
||||
|
||||
@ -1,20 +0,0 @@
|
||||
When you're working with a dashboard, report, or dataset in the Power BI service, you can have Power BI look for quick insights into the data. In Power BI, from the **Datasets** section in the left pane, select the *ellipses* (the three dots) beside the dataset you're interested in. A menu of options appears, and on the far right you'll see an option called **Quick Insights**.
|
||||
|
||||
|
||||
|
||||
When you select Quick Insights, Power BI performs some machine learning and searches the data, analyzing it to find quick insights. You'll see a notification in the upper-right side of the service that indicated Power BI is working on finding insights.
|
||||
|
||||
|
||||
|
||||
After fifteen seconds or so, the notification changes to let you know that Power BI found some insights.
|
||||
|
||||
|
||||
|
||||
When you select the **View insights** button on the notification, you're presented with a page of visuals that show the insights that Power BI found, similar to what you see in the following image. There are a bunch of insights, which you can scroll down through the page to view and consider.
|
||||
|
||||
|
||||
|
||||
Like any other visual, you can interact with the visuals in the Quick Insights page, and you can also pin any of them to a dashboard that you might have, or further filter one or more of them (or as many as you like), to search for additional insights that might be waiting on your curiosity to uncover.
|
||||
|
||||
With **Quick Insights**, you can let Power BI do the work to spot outliers and trends in your data, then use those findings in your dashboards, or further refine and filter them to get to the insights that are most important to you.
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
**Dashboards** in Power BI are one-page collections of visualizations that are created from within the Power BI service. You create dashboards by **pinning** visualizations from reports that you authored and published using Power BI Desktop, or visualizations you created within the Power BI service itself. **Pinning** a visual to a dashboard is a lot like pinning a picture to a corkboard on a wall - it sticks the visual there, in a particular spot, for others to see. To pin a visual, open its report on the Power BI service. Hover over the visual you want to pin and select the **pin** icon.
|
||||
|
||||
|
||||
|
||||
A dialog appears, where you select a destination dashboard for the visual from the drop-down menu, or you can create a new dashboard. You also get a preview of what the pinned visual will look like in the dashboard. You can pin visualizations from multiple reports and pages to a single dashboard, allowing you to combine different datasets and sources into a single page of insights.
|
||||
|
||||
|
||||
|
||||
On **Dashboards**, you can add any sort of visualization including graphs, maps, images, and shapes, by "pinning" them. Once a visual is pinned to a dashboard, it's called a **tile**.
|
||||
|
||||
Your dashboards, including any new ones, appear in the Dashboards section on the left side of the Power BI service. Select a dashboard from the list to view it.
|
||||
|
||||
|
||||
|
||||
You can change the layout of visuals on a dashboard however you'd like. To resize a tile, drag its handles in or out. To move a tile, simply click and drag it to a different location on the dashboard. Hover over a tile and click the **pencil** icon to open the **Tile Details**, where you can change the **Title** or **Subtitle**.
|
||||
|
||||
|
||||
|
||||
Click on a dashboard tile to view the report from which it originated. This lets you quickly view the underlying data beneath a visual. You can also change that link by using the **Set custom link** field in **Tile Details**.
|
||||
|
||||
You can pin tiles from one dashboard to another, for example if you have a collection of dashboards and want to create one summary board. The process is the same: hover over the tile and select the **pin icon**. Dashboards are easy to create, and to change. And you can customize them to make your one-page dashboard show exactly what it should.
|
||||
|
||||
@ -1,20 +0,0 @@
|
||||
Power BI comes with a powerful language recognition engine that lets you ask questions of your data using conversational phrases and questions. So along with building reports and visualizations with Power BI, you can create charts and graphs just by asking simple questions.
|
||||
|
||||
To ask a question of your data, open a dashboard in Power BI and the top of the screen you'll see an input box, where you can ask questions about the displayed data. This feature is sometimes referred to as *Questions & Answers*, or more often, this feature is just referred to as **Q&A**.
|
||||
|
||||
|
||||
|
||||
When you click in the box, Power BI displays prompts with some suggested terms based on your data, such as "YTD revenue". You can click on a suggested term to see the result, often displayed as a simple table or card. When you select one of the suggested phrases, Power BI automatically creates a visual based on your selection, in real time.
|
||||
|
||||
|
||||
|
||||
You can also ask questions using natural language, such as "What was our revenue last year?" or "What product had the highest sales in March 2014?". Power BI displays its interpretation of your question, and chooses the best type of visual to represent the answer. And just like any other visual in Power BI, you can **pin** it to the dashboard of your choice by selecting the **pin** icon.
|
||||
|
||||
|
||||
|
||||
At any point along the way, you can edit the visual created by your natural language question or phrase. Just use the **Visualizations** and **Fields** panes on the right side of the screen. Like any other visual in Power BI, you can alter the layout, adjust filters, and change the field inputs.
|
||||
|
||||
|
||||
|
||||
To save the visualization to a dashboard once you've created the perfect visual, just select the **pin** icon next to the question input box.
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
With Power BI, you can add your own suggested questions for other users who use the natural language query box for a dashboard, frequently referred to as the **Q&A** box. These suggested questions are what users will see when they click on the input box at the top of a dashboard.
|
||||
|
||||
|
||||
|
||||
To add your own questions, select the ellipses (the three dots ...) next to the name of the dashboard you want to use, and select **Settings** from the menu.
|
||||
|
||||
|
||||
|
||||
This opens the **Settings** page for the dashboard and underlying datasets or workbooks. You can disable the Q&A search input box completely from the **Dashboards** section of the **Settings** page, but we want to add questions, so we select the **Datasets** section.
|
||||
|
||||
In the **Datasets** section, all datasets associated with the Dashboard are displayed. Select the dataset associated with your dashboard from the list, select **Featured Q&A Questions**, then select the **Add a question** link. Enter your question or prompt into the input box and select **Apply**.
|
||||
|
||||
|
||||
|
||||
Now any time someone clicks on the search input box on the selected dashboard, they'll see your suggested entries at the top of the prompt list, and selecting that question will take them right through to the Q&A answer. This is a valuable way to get dashboard users thinking about the type of data available, and how they can best use it.
|
||||
|
||||
@ -1,21 +0,0 @@
|
||||
We've already seen how Power BI helps you find data, collect it in a data model, and build reports and visualizations on using that data. We've also seen how you can publish those reports to the Power BI service, and create dashboards that help you monitor your information over time. All these features are even more powerful when you share your insights with others in your organization. Fortunately, sharing your dashboards is easy.
|
||||
|
||||
To share a dashboard, open it in the Power BI service and select the **Share** link in the top right-hand corner.
|
||||
|
||||
|
||||
|
||||
The **Share Dashboard** page appears, where you can select the **Invite** section, then fill in the **Email address** input box with people you'd like to grant access to your dashboard. Power BI checks the email addresses as you type them with accounts in your domain and Office 365 domain, and auto-complete when possible. You can also copy and paste email addresses into this box, or use a distribution list, security group, or Office 365 group to reach multiple people at once.
|
||||
|
||||
|
||||
|
||||
If you selected the checkbox (near the bottom) to *send email notification to recipients*, then your recipients will receive an email letting them know that you shared a dashboard with them, including a link with the dashboard. You can add a note to the email they will receive, or send the note that Power BI created for you (it's in the box directly below where you enter their email addresses).
|
||||
|
||||
>[!NOTE]
|
||||
>Recipients without an existing Power BI account will be taken through the sign-up process before viewing your dashboard.
|
||||
>
|
||||
>
|
||||
|
||||
Anyone with whom you share a dashboard can see and interact with it exactly as you do. However, they have *read-only* access to the underlying reports, and they have *no access* to the underlying datasets.
|
||||
|
||||
You can also select the **Shared With** tab on the Share Dashboard page to see the people with whom you have previously shared this dashboard.
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
If you want to keep track of your data while you're on the move, you can use one of Power BI's mobile applications for iOS, Android, and Windows devices.
|
||||
|
||||
When using the Power BI app, first sign in to your account using your Power BI service account information. On a Windows mobile device (in this case a tablet), the sign in looks like the following.
|
||||
|
||||
|
||||
|
||||
The first screen that you'll see displays all of the content to which you have access, including reports, dashboards, and groups. There are also sample dashboards you can use to get inspired, or just to see Power BI's capabilities. The app is designed to be touch-friendly: tap on a dashboard or report to view it full-screen and scroll through them using your finger.
|
||||
|
||||
|
||||
|
||||
You can open any of the dashboards by tapping on them. Within a dashboard, you can tap on a dashboard tile to focus on it in a larger view.
|
||||
|
||||
|
||||
|
||||
You can also annotate any insights that you discover by tapping the **Annotate** button in the top right corner. This allows you to draw on a focused tile to highlight particular areas of interest. The annotation tools are found along the bottom of the screen.
|
||||
|
||||
|
||||
|
||||
Share your annotated tile by tapping the **Share** link in the top right-hand corner.
|
||||
|
||||
Tap the **Report** link in the top right-hand corner to view the underlying report for a tile. This will display the exact same visualizations that you would see in a web browser or in Power BI Desktop, and also include the ability to interact with the report by tapping on bars, drilling down, or using slicers.
|
||||
|
||||
@ -1,18 +0,0 @@
|
||||
When you're looking at Power BI dashboards or reports in the service, it can sometimes be helpful to focus on an individual chart or visualization. You can do that in two different ways.
|
||||
|
||||
When you're on a dashboard, hover over a tile to see a few different icon options in the top right-hand corner. When you select the ellipses (the three dots) you see a collection of icons that represent actions you can take on the tile.
|
||||
|
||||
The left-most icon is labeled **Focus mode**. Select that icon to expand the tile to encompass the full dashboard space.
|
||||
|
||||
|
||||
|
||||
**Focus mode** allows you to see a lot more detail on your visuals and legends. For example, when you resize a tile in Power BI, some of the columns may not be shown because of the space available in the tile.
|
||||
|
||||
|
||||
|
||||
In **Focus mode**, you can see all the data. You can also pin the visual directly from Focus mode to a different dashboard by selecting the **Pin** icon. To exit **Focus mode**, select the **Back to...** icon in the top-left corner of **Focus mode**.
|
||||
|
||||
The process is similar when viewing a report. Hover over a visual to see the three icons in the top-right corner, and select the **Focus mode** icon. Selecting this expands your visualization to encompass the full report canvas. The visual is still interactive in this mode, although you temporarily lose any cross-filter effect between visualizations.
|
||||
|
||||
Hover over an expanded tile or report and select the **Back to...** left-caret icon in the top-left corner to return to the previous view.
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
Once you've built a dashboard, you can edit its **Tiles* in the Power BI service to make a few formatting changes.
|
||||
|
||||
To make changes to a tile, hover over the tile and select the ellipses (three dots) to show a collection of icons that allow you to make changes to the tile.
|
||||
|
||||
|
||||
|
||||
Select the **pen** icon to open the **Tile Details** pane. From here you can change the tile's **Title**, **Subtitle**, include its last refresh time and date, and other details such as creating a custom link.
|
||||
|
||||
|
||||
|
||||
By default, when you click on a dashboard tile you are taken to the report from which it originated. To change this behavior, use the **Set custom link** field in the **Tile Details** pane. One popular use of this feature is to take users to the organization homepage when clicking on a logo image.
|
||||
|
||||
## Add widgets to your dashboard
|
||||
You can also add widgets to your dashboard. A **widget** is a special dashboard tile that, instead of containing a visualization, contains other items such as an image, an online video, a text box, or rich web content.
|
||||
|
||||
When you select the Add Widget link in the upper-right corner of a Dashboard, the **Add a widget** dialog appears.
|
||||
|
||||
|
||||
|
||||
When you add a text box, for example, a **Tile details** pane appears on the right side, where you can edit details similar what is available when editing the details of any tile. But with widgets, there's also a section to define or modify the widget content, such as a rich text editor for a text box.
|
||||
|
||||
|
||||
|
||||
With widgets and the ability to edit tile details, you can customize your dashboard and make it look just how you want it to be.
|
||||
|
||||
@ -1,26 +0,0 @@
|
||||
You may find that occasionally when you create your dashboards, you have more content than what can fit on your canvas. There are a few solutions that help you manage how your dashboard space is displayed, so you can get a full view of your dashboard content.
|
||||
|
||||
The easiest method to display your entire dashboard in one screen is selecting the **Full Screen Mode** button in the top right-hand corner of the dashboard.
|
||||
|
||||
|
||||
|
||||
Selecting the **Full Screen Mode** button puts your browser into full screen mode, removing all chrome elements from around the dashboard, and thereby increasing the amount of viewable space.
|
||||
|
||||
|
||||
|
||||
From within **Full-Screen Mode**, you can select the **Fit to Screen** option to shrink all of your tiles so they fit on a single screen, without the need for scrollbars. This is commonly called *TV mode*, and is useful for giving presentations with dashboards, or displaying a dashboard on hallway monitors.
|
||||
|
||||
|
||||
|
||||
Another way to manage dashboard space is collapsing the Navigation pane on the left-hand side of the page, by selecting the hamburger icon. To expand the navigation pane, click the icon again.
|
||||
|
||||
|
||||
|
||||
You can ensure that a dashboard will always have a collapsed navigation bar by appending the following to the end of the URL:
|
||||
|
||||
> ?collapseNavigation=true
|
||||
>
|
||||
>
|
||||
|
||||
Users who follow that link will open the dashboard with a collapsed navigation bar.
|
||||
|
||||
@ -1,21 +0,0 @@
|
||||
Sometimes you may want to bring a printed report or dashboard to a meeting, or so you can share it with others. With Power BI, there are a few ways you can make printouts of your visuals.
|
||||
|
||||
In the Power BI service, select the ellipses (three dots) in the top-right side of the service, and select **Print dashboard**.
|
||||
|
||||
|
||||
|
||||
A **Print** dialog appears, where you can select the printer to which you want to send the dashboard, as well as standard print options such as *portrait* or *landscape* orientation.
|
||||
|
||||
|
||||
|
||||
## Export data from a visual
|
||||
You can also export the data from any visual in the Power BI service. Just select the ellipses on any visual, and then select the **Export data** button (the middle button). When you do so, a .CSV file is created and downloaded to your local computer, and a message appears on your browser (just like any other browser-initiated download) letting you know the download is complete.
|
||||
|
||||
|
||||
|
||||
You can also print or export directly from a report. When viewing a report in the Power BI service, select **File > Print** to open the print dialog.
|
||||
|
||||
|
||||
|
||||
And just like with a dashboard, you can export data from a visual within a report, too, by selecting the export button on a visual.
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
You can update reports and datasets that you've already published from Power BI Desktop to the Power BI service. To do so, select **Publish** from the **Home** tab in the ribbon.
|
||||
|
||||
|
||||
|
||||
When you publish a report that already exists in the Power BI service, you're prompted to confirm that you want to replace the previous dataset and reports with the edited version you just chose to update.
|
||||
|
||||
|
||||
|
||||
When you select **Replace**, the datasets and reports in the Power BI service are overwritten with the datasets and reports in the most recent Power BI Desktop version of the file.
|
||||
|
||||
And just like any other **Publish** event from Power BI Desktop, you see a dialog that tells you the publish event was successful, and you get a link to the report in the Power BI service.
|
||||
|
||||
|
||||
|
||||
This is a way to manually refresh your data. You can also automatically update datasets and reports; that process is covered in another learning topic.
|
||||
|
||||
@ -1,51 +0,0 @@
|
||||
In previous topics we've looked at how you can use Power BI to connect to data sources, and how to manually refresh your datasets on the Power BI service. However, you're not going to want to manually refresh things every time your data changes, so you can use Power BI to set up a scheduled refresh that will connect to your data sources and publish them into the Power BI Service automatically. This also gives you a way to connect the service with any on-premises data sources, including Excel files, Access databases, SQL databases, and more.
|
||||
|
||||
The system that lets you connect your on-premises data sources to the Power BI service is called the **data gateway**. It's a small application that runs on your computer, and uses a pre-arranged schedule to connect to your data, gather any updates, and push them up to the Power BI service. The **personal gateway** is a version of the **data gateway** that can be used without any administrator configuration.
|
||||
|
||||
>[!NOTE]
|
||||
>The computer that is running the Power BI personal gateway *must* be on and connected to the Internet for **personal gateway** to work properly.
|
||||
>
|
||||
|
||||
To set up your **personal gateway**, first sign in to the Power BI service. Select the **Download** icon in the top right-hand corner of the screen, and then select **Data Gateways** from the menu.
|
||||
|
||||
|
||||
|
||||
From there you'll be taken to a web page where you can select the **Power BI Gateway - Personal**, as shown below.
|
||||
|
||||
|
||||
|
||||
Run the application once it finishes downloading, and complete the installation wizard.
|
||||
|
||||
|
||||
|
||||
You'll then be prompted to launch the configuration wizard to set up your gateway.
|
||||
|
||||
|
||||
|
||||
You'll be asked first to sign in to your Power BI service account, and then to sign in to the machine's Windows account, since the gateway service runs under your account.
|
||||
|
||||
|
||||
|
||||
Return to the Power BI service. Select the ellipsis (three dots) menu next to the dataset you want to refresh, and then select **Schedule Refresh**. This opens the **Refresh Settings** page. Power BI detects that you've installed a **personal gateway**, and lets you know its status.
|
||||
|
||||
Select **Edit credentials** next to each applicable data source and set up authentication.
|
||||
|
||||
|
||||
|
||||
Finally, set the options under **Schedule Refresh** to activate automatic updates and set when and how frequently they occur.
|
||||
|
||||
|
||||
|
||||
And that's it. On the scheduled times, Power BI will go out to those data sources, using the credentials you provided and the connection to the computer that has your **personal gateway** running, and update the reports and datasets according to your schedule. The next time you go to Power BI, those dashboards, reports, and datasets will reflect data as of the most recent scheduled refresh.
|
||||
|
||||
## Next steps
|
||||
**Congratulations!** You've completed this **Exploring Data** section of the **Guided Learning** course for Power BI. The Power BI service is full of interesting ways to explore data, share insights, and interact with visuals. And it's all accessible from a browser, from a service that you can connect to wherever you are.
|
||||
|
||||
One powerful and well-known partner of Power BI is **Excel**. Power BI and Excel are designed to work well together; your workbooks will feel at home in Power BI, and it's easy to get them there.
|
||||
|
||||
|
||||
|
||||
How easy? In the next section, **Power BI and Excel** you learn exactly that.
|
||||
|
||||
See you in the next section!
|
||||
|
||||
@ -1,17 +0,0 @@
|
||||
Welcome to the **Power BI and Excel** section of the **Guided Learning** course for Power BI.
|
||||
|
||||
Chances are good that you've used **Excel** before. Maybe you used Excel to create or view reports, or to build pie charts or other visuals. Or maybe you used **Power Pivot** or **Power Query** to build solutions that were a bit more technical. If so, you'll be right at home in Power BI, and so will your workbooks.
|
||||
|
||||
This section shows you just how easy it is to bring Excel workbooks into Power BI, and demonstrates that Power BI and Excel make a great partnership.
|
||||
|
||||
## Introduction to using Excel data in Power BI
|
||||
*Watch an introduction to the ways Excel and the Power BI service work together*
|
||||
|
||||
With Power BI, getting your Excel data into Power BI is easy.
|
||||
|
||||
|
||||
|
||||
The following topics guide you through uploading an Excel workbook with a simple table into Power BI. Then you see how to upload workbooks created with Excel's more advanced BI data modeling and reporting features, such as Power Pivot and Power View.
|
||||
|
||||
You'll also learn some benefits of saving your Excel workbook files to OneDrive for Business. When you connect to Excel files on OneDrive from Power BI, your reports and dashboards in Power BI are updated and refreshed automatically when you make changes and save your work.
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
In this topic, we'll first take a look at how you can import an Excel workbook file containing a simple **table** from a local drive into Power BI. You'll then learn how you can begin exploring that table's data in Power BI by creating a report.
|
||||
|
||||
## Make sure your data is formatted as a table
|
||||
In order for Power BI to import data from your workbook, that data needs to be **formatted as a table**. It's easy. In Excel, you can highlight a range of cells, then on the **Insert** tab of the Excel ribbon, click **Table**.
|
||||
|
||||
|
||||
|
||||
You'll want to make sure each column has a good name. It will make it easier to find the data you want when creating your reports in Power BI.
|
||||
|
||||
## Import from a local drive
|
||||
Wherever you keep your files, Power BI makes it easy to import them. In Power BI, you can use **Get Data** > **Files** > **Local File**, to find and select the Excel file we want.
|
||||
|
||||
|
||||
|
||||
Once imported into Power BI, you can begin creating reports.
|
||||
|
||||
Your files don't have to be on a local drive, of course. If you save your files on OneDrive or SharePoint Team Site, that's even better. We'll go into more details about that in a later topic.
|
||||
|
||||
## Start creating reports
|
||||
Once your workbook's data has been imported, a dataset is created in Power BI. It appears under **Datasets**. Now you can begin exploring your data by creating reports and dashboards. Just click on the **Open menu** icon next to the dataset and then click **Explore**. A new blank report canvas appears. Over on the right, under **Fields**, you'll see your tables and columns. Just select the fields you want to create a new visualization on the canvas.
|
||||
|
||||
|
||||
|
||||
You can change the type of visualization and apply **filters** and other properties under **Visualizations**.
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
If you use any of Excel's advanced BI features like **Power Query** (called **Get & Transform** in Excel 2016), to query and load data, **Power Pivot** to create powerful data models, and **Power View** to create dynamic reports, you can import those into Power BI, too.
|
||||
|
||||
|
||||
|
||||
If you use **Power Pivot** to create advanced data models, like those with multiple related tables, measures, calculated columns, and hierarchies, Power BI will import all of that as well.
|
||||
|
||||
If your workbook has **Power View sheets**, no problem. Power BI will re-create them as new **Reports** in Power BI. You can start pinning visualizations to dashboards right away.
|
||||
|
||||
|
||||
|
||||
And here's one of the great features of Power BI: If you use Power Query or Power Pivot to connect to, query, and load data from an external data source, once you've imported your workbook into Power BI, you can setup **scheduled refresh**. By using scheduled refresh, Power BI will use the connection information from your workbook to connect directly to the datasource and query and load any data that has changed. Any visualizations in reports will automatically be updated, too.
|
||||
|
||||
@ -1,29 +0,0 @@
|
||||
Get seamless integration between Power BI and Excel when you save your Excel workbooks to OneDrive.
|
||||
|
||||
|
||||
|
||||
Because OneDrive is in the cloud, just like Power BI, a live connection is made between Power BI and OneDrive. If you make changes to your workbook on OneDrive, those changes are *automatically synchronized* with Power BI. Your visualizations in reports and dashboards are kept up-to-date. If your workbook connects to external data sources like a database or an OData feed, you can use Power BI's **Schedule refresh** features to check for updates. Need to ask questions about data in your workbook? No problem. You can use Power BI's **Q & A** features to do just that.
|
||||
|
||||
There are two ways to connect to your Excel files on OneDrive for Business:
|
||||
|
||||
1. Import Excel data into Power BI
|
||||
2. Connect, manage, and view Excel in Power BI
|
||||
|
||||
|
||||
|
||||
### Import Excel data into Power BI
|
||||
When you choose to import Excel data into Power BI, table data from your workbook is loaded into a new dataset in Power BI. If you have any **Power View** sheets in your workbook, those are imported and new reports are automatically created in Power BI, too.
|
||||
|
||||
Power BI will maintain the connection between it and the workbook file on your OneDrive for Business. If you make any changes to your workbook, when you save, those changes will be *automatically synchronized** with Power BI, usually within an hour. If your workbook connects to external data sources, you can setup scheduled refresh so the dataset in Power BI is kept up-to-date. Because visualizations in reports and dashboard in Power BI will use the data from the dataset, as you explore, your queries are lightning fast.
|
||||
|
||||
### Connect, manage and view Excel in Power BI
|
||||
When you choose to connect to the Excel workbook, you'll get a seamless experience of working with your workbook in Excel and Power BI. When you connect this way, the workbook's report has a small Excel icon next to it.
|
||||
|
||||
|
||||
|
||||
In the report, you see your Excel workbook in Power BI just as you would in **Excel Online**. You can explore and edit your worksheets in Excel Online by selecting Edit from the ellipses menu. When you make changes, any visualizations you've pinned to dashboards are updated automatically.
|
||||
|
||||
|
||||
|
||||
No dataset is created in Power BI. All of the data remains in the workbook on OneDrive. One of the many advantages to this approach is that you can setup **scheduled refresh** if your workbook connects to external data sources. You can select elements such as PivotTables and charts and **pin** them right to dashboards in Power BI. If you make any changes, they're automatically reflected in Power BI. And, you can use Power BI's awesome **Q & A** features to ask questions about the data in your workbook.
|
||||
|
||||
@ -1,11 +0,0 @@
|
||||
In this collection of topics, you learned how easy it is to leverage your existing Excel workbook files by uploading and exploring them in Power BI. You can upload Excel workbooks with simple tables or workbooks created with Excel's more advanced BI features like Power Pivot and Power View.
|
||||
|
||||
You also learned how to connect to **Excel** files that reside on **OneDrive for Business**, and use Power BI's **automatic** refresh features to keep your reports and dashboards up-to-date as you make changes in your workbook.
|
||||
|
||||
## Next steps
|
||||
**Congratulations!** You've completed the **Power BI and Excel** section of the **Guided Learning** course for Power BI. That was easy, wasn't it? Power BI and Excel make a great team, and makes it easy for you to use them together.
|
||||
|
||||
With all the knowledge you've gained, and your ability to create compelling visuals in reports and dashboards, you're probably ready to share your masterpieces. The next section, **Publishing and Sharing**, shows you how to do just that.
|
||||
|
||||
See you in the next section!
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
Welcome to the **Publishing and Sharing** section of the **Guided Learning** course for Power BI.
|
||||
|
||||
Now that you've learned how Excel and Power BI work seamlessly together, and you've learned all about the visuals you can create in reports and dashboards, you likely want to share them with your coworkers and friends. With Power BI, **publishing and sharing** are easy to accomplish.
|
||||
|
||||
## Share and collaborate with colleagues in Power BI
|
||||
*Get introduced to the tools that make sharing and collaborating easier with Power BI*
|
||||
|
||||
In this module, we cover the various ways Power BI offers for sharing and collaborating with your colleagues on your dashboards, reports, and data. For example, you can:
|
||||
|
||||
* *Publish your reports* from Power BI Desktop to the Power BI service
|
||||
* Use *Power BI mobile* apps to view and interact with reports and dashboards
|
||||
* Create *Content packs* that package up a dashboard, report, and dataset to send to your colleagues. They can use it as a starting point and further enhance it.
|
||||
* Create *Groups*, which you can use as a security model to identify a subset of users who have access to dashboards, reports, and datasets you create.
|
||||
|
||||
Throughout much of this module we use the scenario of setting up a new finance group. We'll show how to set up the group, share dashboards, reports, and datasets into the group, and add members who'll have access to the items in the group.
|
||||
|
||||
@ -1,26 +0,0 @@
|
||||
In this lesson, we start by creating a *group*. A **group** defines a set of users who have access to specific dashboards, reports, and data.
|
||||
|
||||
Groups in Power BI are based on groups in Office 365, so if you've been using Office 365 groups to manage your group's email, calendar, and documents, you'll see that Power BI offers the same features, and more. When you create a group in Power BI, you're actually creating an Office 365 group.
|
||||
|
||||
This module uses the scenario of setting up a new finance group. We'll show how to set up the group, share dashboards, reports, and datasets into the group, and add members who'll have access to the items in the group.
|
||||
|
||||
I start here in My Workspace. These are the dashboards, reports, and datasets that I've created or that someone shared with me.
|
||||
|
||||
|
||||
|
||||
If I expand My Workspace, I can select **Create a group**.
|
||||
|
||||
|
||||
|
||||
Here I can give it a name. We're using the scenario or a finance group, so I'll call it Finance. Power BI makes sure the name doesn't exist on the domain.
|
||||
|
||||
|
||||
|
||||
I can set the privacy level by deciding whether anyone in my organization can see the contents of the group, or only its members.
|
||||
|
||||
I type email addresses, security groups, and distribution lists here. I select **Add** to make them members of the group, and save the group.
|
||||
|
||||
|
||||
|
||||
On to the next lesson!
|
||||
|
||||
@ -1,26 +0,0 @@
|
||||
In this lesson, we create a *content pack* with existing artifacts we already have in Power BI, and then share those with colleagues.
|
||||
|
||||
In **My Workspace** I have a dashboard, the report underneath, and the dataset. I want to share them as a package with people in my organization, so they can reuse them.
|
||||
|
||||
|
||||
|
||||
When I select the **Settings** icon (the gear in the top-right of the service), I can see how much storage I've used, and I can create a content pack.
|
||||
|
||||
|
||||
|
||||
In the dialog that appears, I can choose whether to distribute it to specific people or groups, and also give it a title. It's also a good idea to provide a detailed description in the **Description** box, to people browsing for a content pack to know what it contains or what it provides.
|
||||
|
||||
|
||||
|
||||
On the bottom of the dialog, I have an opportunity to upload an image for the content pack, and then the most important step: I select the dashboard I want to include in the content pack, and when I do so, Power BI automatically selects the report and dataset that are used in the dashboard. I cannot unselect the report or dataset, because the dashboard I want to include in the content pack requires them.
|
||||
|
||||
|
||||
|
||||
I could also select other dashboards, reports, and datasets, but I won't now.
|
||||
|
||||
When I publish, the content pack is added to the organization's content gallery.
|
||||
|
||||
|
||||
|
||||
On to the next lesson!
|
||||
|
||||
@ -1,36 +0,0 @@
|
||||
In previous lessons, we already created a content pack and a group. In this lesson, we're going to create an instance of the content pack for everyone in the group.
|
||||
|
||||
I start in My Workspace, ...
|
||||
|
||||
|
||||
|
||||
... and switch back to the Finance group I created in the earlier lesson.
|
||||
|
||||
|
||||
|
||||
The group doesn't have any dashboards, reports, or datasets yet. I'm going to use the content pack I created. I browse content packs from my organization rather than those from third-party services.
|
||||
|
||||
|
||||
|
||||
I find the content pack I created a minute ago. I see why it's important to give it a good title, description, and image, so people can find it. I connect to it.
|
||||
|
||||
|
||||
|
||||
Power BI imports the dashboards, reports, and datasets that are in the content pack.
|
||||
|
||||
|
||||
|
||||
When I select the dataset, Power BI asks if I want to personalize the content pack.
|
||||
|
||||
|
||||
|
||||
I'm creating a copy of the content pack that I can make changes to, and disconnecting it from the published version of the content pack. If the content pack creator makes changes to the published version, I won't automatically get those updates.
|
||||
|
||||
But I can edit the dashboard, the report, and even the dataset, if I want.
|
||||
|
||||
|
||||
|
||||
So content packs are a simple way to reuse content created by other people in your organization.
|
||||
|
||||
On to the next lesson!
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
In this lesson, we're going to edit the original content pack we created previously, and see how that updates the other people who've connected to it.
|
||||
|
||||
I'm back in My Workspace, editing my original dashboard.
|
||||
|
||||
|
||||
|
||||
Anytime I make any change to the dashboard, I get this reminder that I've changed something that I've shared with others in a content pack, and prompting me to update the shared version.
|
||||
|
||||
|
||||
|
||||
I go back to the settings icon and select **View content pack** to see the content packs I've already published.
|
||||
|
||||
|
||||
|
||||
I see the content pack I made. This little icon tells me I've changed something in the content pack, and I need to edit the content pack so others can see my changes.
|
||||
|
||||
|
||||
|
||||
When I select **Edit**, I'm back at the screen where I can edit the title and description, but this time it has an **Update** button, so I select that.
|
||||
|
||||
|
||||
|
||||
Power BI takes those changes and publishes the updated content pack to the content pack gallery.
|
||||
|
||||
Anybody who has connected to my content pack gets a message that the content pack has changed, and asks if they want to accept the changes or keep the older version.
|
||||
|
||||
So as the content pack owner, you can manage the versions that your colleagues are using.
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
In an earlier article we reviewed creating groups to help your organization manage and collaborate on content that's in Power BI. You can also use your Power BI / Office365 groups to collaborate and share by using **OneDrive for Business**.
|
||||
|
||||
Using OneDrive for Business as a source for your Power BI content gives you access to a number of helpful tools, such as version history. You can also share your files with an Office365 group from within OneDrive for Business to give access and allow many people to work on the same Power BI or Excel files.
|
||||
|
||||
|
||||
|
||||
To connect to a PBIX (Power BI Desktop) file on OneDrive for Business, sign in to the Power BI service and select **Get Data**. Choose **Files** under Import or Connect to Data, and then select **OneDrive - Business**. Highlight the desired file, and select **Connect**.
|
||||
|
||||
|
||||
|
||||
Your content appears in the left-hand side navigation bar.
|
||||
|
||||
|
||||
|
||||
Now, any changes made to the file on **OneDrive for Business** will be automatically reflected in the Power BI environment as well, and recorded in the version history.
|
||||
|
||||
@ -1,29 +0,0 @@
|
||||
You did it! **Congratulations!** You've completed the **Guided Learning** course for Power BI. You've come full circle from learning the basic elements of Power BI, taken a tour through its software features and elements, and learned how they all work together.
|
||||
|
||||
|
||||
|
||||
This is the last section (and the final topic, unless you want to learn DAX) in the course, so if you went through all of these sections in order, take a moment to pat yourself on the back. Good job! You're now well versed in all these **important concepts**:
|
||||
|
||||
* What [Power BI is](../gettingstarted.yml?tutorial-step=1)
|
||||
* The [building blocks](../gettingstarted.yml?tutorial-step=3) of Power BI
|
||||
* [Getting](../gettingdata.yml?tutorial-step=3) and [modeling](../modeling.yml?tutorial-step=1) data
|
||||
* [Visualizations](../visualizations.yml?tutorial-step=1)
|
||||
* [Exploring data](../exploringdata.yml?tutorial-step=1) in the Power BI service
|
||||
* Using [Excel and Power BI](../powerbiandexcel.yml?tutorial-step=1) together
|
||||
* And [publishing and sharing](../publishingandsharing.yml?tutorial-step=1) your work
|
||||
|
||||
That's a lot of knowledge, and now that you have all this in your head, you're ready to go put it to use! Here are some **links to downloads** or to get your browser **connected to the Power BI service**:
|
||||
|
||||
* You can always [get the latest Power BI Desktop](https://powerbi.microsoft.com/desktop)
|
||||
* Heading to the [Power BI service](https://powerbi.microsoft.com/) is easy
|
||||
* Getting [mobile apps for Power BI](https://powerbi.microsoft.com/mobile/) is a tap away
|
||||
|
||||
There's more **help and reference** content available, too. Just go to the top of this page, and select **Learning > Documentation** to see our extensive set of Power BI reference content.
|
||||
|
||||
|
||||
|
||||
We hope you've enjoyed your journey through this **Power BI Guided Learning** experience. Best of luck to you, and may your Power BI visuals always be immersive and compelling.
|
||||
|
||||
## One more section for DAX users
|
||||
Still want more? Interested in using **DAX (Data Analysis Expressions)** to create, filter, or use custom columns and tables in Power BI? There's one more section targeted at people inclined to do some code work in Power BI, and it's called **Introduction to DAX**. Take a look if you're interested - it uses the same friendly approach as the rest of Guided Learning.
|
||||
|
||||
@ -1,37 +0,0 @@
|
||||
In this lesson, we learn how to share a Power BI report on a web page, or share it through email, in just a few simple steps. This feature of Power BI is often referred to as **Publish to web**, and it's easy to use and to manage.
|
||||
|
||||
In Power BI, select the report you want to share, so it's displayed on the canvas. Then from the menu, select **File > Publish to web**.
|
||||
|
||||
|
||||
|
||||
From there, you see a dialog that explains you'll get an *embed code* that lets you include the report on a website or in mail.
|
||||
|
||||
|
||||
|
||||
When you select **Create embed code**, Power BI presents another dialog, telling you again that you're about to share your data with everyone on the Internet. Make sure that's okay!
|
||||
|
||||
Power BI presents a dialog with two links:
|
||||
|
||||
* A link you can share in email, which show the report as a web page
|
||||
* HTML code (a link plus within an iframe) so you can embed the report directly into a web page
|
||||
|
||||
For the HTML link, you can choose from predefined sizes for the embedded report, or you can modify the iframe code yourself and customize its size.
|
||||
|
||||
|
||||
|
||||
You can simply paste the email link into a browser, and see your report as a web. You can interact with that web page just as you would if you were viewing the report in Power BI. The following images shows a **Publish to web** page, when its link was copied directly from that dialog into a browser:
|
||||
|
||||
|
||||
|
||||
You can also embed that iframe link into a blog post or website, or into Sway, too.
|
||||
|
||||
Want to delete an embed code you created? No problem. In Power BI, select the **gear** icon in the upper right corner, then select **Manage embed codes**.
|
||||
|
||||
|
||||
|
||||
The Power BI workspace shows the embed codes you've created (in the image below, there's just one). When you click the ellipses, you can then select to get the code for the embed code, or delete the embed code entirely.
|
||||
|
||||
|
||||
|
||||
And that's all there is to publishing your Power BI report to the web, and sharing it with the world. It's easy!
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
Welcome to the Power BI **Guided Learning** section designed to introduce you to **DAX**.
|
||||
|
||||
**DAX** stands for **Data Analysis Expressions**, and it is the formula language used throughout Power BI (it is also used by Power BI behind the scenes). DAX is also found in other offerings from Microsoft, such as Power Pivot and SSAS Tabular, but this collection of Guided Learning topics focuses on how DAX is used - and can be used by you - in Power BI.
|
||||
|
||||
## DAX and this Guided Learning video series
|
||||
The goal of this **Guided Learning** section is to teach you DAX basics and fundamentals - how to think about DAX, how it works, and the most useful features as explained (and learned with lots of experience) by a renowned DAX expert, [Alberto Ferrari](http://www.sqlbi.com/learning-dax).
|
||||
|
||||
|
||||
|
||||
The videos in this **Guided Learning** section on **DAX** teaches you DAX basics from the perspective of how the DAX formula language works. This is useful when creating DAX formulas from scratch, but it's also very useful for understanding how Power BI creates those DAX formulas as you create queries in the **Query Editor**.
|
||||
|
||||
## In this video - introduction to DAX
|
||||
DAX concepts are simple and straightforward, but DAX is powerful. DAX uses some unique programming concepts and patterns which can make it difficult to fully use and understand. Traditional ways of learning languages may not be the best approach to DAX, so the goal of this video is to teach you concepts and theory that will help later on in your Power BI work.
|
||||
|
||||
DAX is a *functional language*, which means the full executed code is contained inside a function.
|
||||
|
||||
In DAX, functions can contain other, nested functions, conditional statements, and value references. Execution in DAX starts from the innermost function or parameter, and works outward. In Power BI, DAX formulas are written in a single line, so formatting your functions correctly is important for readability.
|
||||
|
||||
DAX is designed to work with tables, so it has just two primary data types: **Numeric** and **Other**. **Numeric** can include *integers*, *decimals*, and *currency*. **Other** can include *strings* and *binary objects*. This means that if you build your DAX function to work on one type of number, you can be assured that it will work on any other Numeric data.
|
||||
|
||||
DAX uses operator overloading, which means that you can mix data types in your calculations and the results will change based on the type of data used in the inputs. Conversion happens automatically. This means you don't have to know the data types of the columns you're working with in Power BI, but it also means that sometimes conversion can happen in unexpected ways. It's good practice to understand the data you are using to ensure that your operators are behaving as anticipated.
|
||||
|
||||
There is one data type in particular that you'll likely be working with a lot in Power BI: **DateTime**. **DateTime** is stored as a floating point value with both integer and decimal parts. DateTime can be used accurately for calculations of any time period after March 1, 1900.
|
||||
|
||||
> Video content courtesy of [Alberto Ferrari, SQLBI](http://www.sqlbi.com/learning-dax/?utm_source=powerbi&utm_medium=marketing&utm_campaign=after-summit)
|
||||
>
|
||||
>
|
||||
|
||||
@ -1,63 +0,0 @@
|
||||
There are two primary calculations you can create using DAX:
|
||||
|
||||
* **calculated columns**
|
||||
* **calculated measures**
|
||||
|
||||
Before digging into creating either of those, it's good to have a firm grasp on DAX syntax for tables and columns, which you will use when creating either **calculated columns** or **calculated measures**.
|
||||
|
||||
## DAX table and column name syntax
|
||||
Whether you're creating a new column or measure, it's important to know the general format of table names in DAX:
|
||||
|
||||
'Table Name'[ColumnName]
|
||||
|
||||
If there are spaces in the table name (as shown above), the single quotes around the table name are mandatory. If the table name has no spaces, the single quotes can be omitted, so the syntax looks like the following:
|
||||
|
||||
TableName[ColumnName]
|
||||
|
||||
The following image shows a DAX formula being created in Power BI:
|
||||
|
||||
|
||||
|
||||
You can also omit the table name completely and just use the column name, but this is poor practice for writing clear functions (and thus, for clear DAX code). Column names must always include the square brackets.
|
||||
|
||||
It's best practice to *always* do the following:
|
||||
|
||||
* No spaces in table names
|
||||
* Always include the table name in formulas (don't omit it, even though DAX lets you)
|
||||
|
||||
## Creating calculated columns
|
||||
**Calculated columns** are useful when you want to slice or filter on the value, or if you want a calculation for every row in your table.
|
||||
|
||||
You can create calculated columns in Power BI Desktop by selecting **New Column** from the **Modeling** tab. It's best to be in **Data** view (rather than **Report** or **Relationships** view), since you can see the new column created and the **Formula Bar** is populated and ready for your DAX formula.
|
||||
|
||||
|
||||
|
||||
Once you select the **New Column** button, the **Formula Bar** is populated with a basic column name (which you change to suit your formula, of course) and the **=** operator, and the new column appears in the data grid, as shown in the following image.
|
||||
|
||||
|
||||
|
||||
The required elements for a calculated column are the following:
|
||||
|
||||
* a new column name
|
||||
* at least one function or expression
|
||||
|
||||
If you reference a table or column in your calculated column formula, you do not need to specify a row in the table - Power BI calculates the column for the current row for each calculation.
|
||||
|
||||
## Creating calculated measures
|
||||
Use a **calculated measure** when you are calculating percentages or ratios, or you need complex aggregations. To create a measure using a DAX formula, select the **New Measure** button from the **Modeling** tab. Again, it's best to be in the **Data** view of Power BI Desktop since it shows the **Formula Bar** and makes it easy to write your DAX formula.
|
||||
|
||||
|
||||
|
||||
With **measures**, you see a new measure icon appear in the **Fields** pane with the name of the measure. The **Formula Bar** is again populated with the name of your DAX formula (this time, with your measure).
|
||||
|
||||
|
||||
|
||||
The required elements for a calculated measure are the same as they are for a calculated column:
|
||||
|
||||
* a new measure name
|
||||
* at least one function or expression
|
||||
|
||||
> Video content courtesy of [Alberto Ferrari, SQLBI](http://www.sqlbi.com/learning-dax)
|
||||
>
|
||||
>
|
||||
|
||||
@ -1,95 +0,0 @@
|
||||
With DAX, there are many functions available to shape, form, or otherwise analyze your data. These functions can be grouped into a handful of categories:
|
||||
|
||||
* **Aggregation** functions
|
||||
* **Counting** functions
|
||||
* **Logical** functions
|
||||
* **Information** functions
|
||||
* **Text** functions
|
||||
* **Date** functions
|
||||
|
||||
Similar to Excel, when you start typing your formula into the Power BI Desktop **Formula Bar**, a list of available functions appears to help you determine which available function you want to select. And by using the **up** and **down** arrow keys on your keyboard, you can highlight any of the available functions, and a brief description is displayed.
|
||||
|
||||
Power BI displays the functions that match the letters you've typed so far, so if you type *S* only functions that begin with *S* appear in the list. If you type *Su*, only functions that *contain* the letter sequence *Su* in their name appear in the list (they don't have to start with *Su*, they just have to contain that letter sequence).
|
||||
|
||||
|
||||
|
||||
It's easy to experiment with DAX in this way, and to find each of the various DAX functions that are available in Power BI. All you have to do is start typing, and Power BI helps you along.
|
||||
|
||||
Now that we know how to get that DAX formula started, let's take a look at each of these function categories in turn.
|
||||
|
||||
## Aggregation functions
|
||||
DAX has a number of **aggregation** functions, including the following commonly used functions:
|
||||
|
||||
* SUM
|
||||
* AVERAGE
|
||||
* MIN
|
||||
* MAX
|
||||
* SUMX (and other *X* functions)
|
||||
|
||||
These functions work only on numeric columns, and generally can aggregate only one column at a time.
|
||||
|
||||
However, special aggregation functions that end in **X**, such as **SUMX**, can work on multiple columns. These functions iterate through the table, and evaluate the expression for each row.
|
||||
|
||||
## Counting functions
|
||||
Often-used **counting** functions in DAX include the following:
|
||||
|
||||
* COUNT
|
||||
* COUNTA
|
||||
* COUNTBLANK
|
||||
* COUNTROWS
|
||||
* DISTINCTCOUNT
|
||||
|
||||
These functions count different elements, such as distinct values, non-empty values, and table rows.
|
||||
|
||||
## Logical functions
|
||||
The collection of **logical** functions in DAX include:
|
||||
|
||||
* AND
|
||||
* OR
|
||||
* NOT
|
||||
* IF
|
||||
* IFERROR
|
||||
|
||||
These special functions can also be expressed with *operators*. For example, **AND** can be typed as (replaced with) **&&** in your DAX formula.
|
||||
|
||||
You can use operators (such as **&&**) when you need more than two conditions in your formula, but otherwise, it's best practice use the function name itself (such as **AND**) for readability of your DAX code.
|
||||
|
||||
## Information functions
|
||||
**Information** functions in DAX include:
|
||||
|
||||
* ISBLANK
|
||||
* ISNUMBER
|
||||
* ISTEXT
|
||||
* ISNONTEXT
|
||||
* ISERROR
|
||||
|
||||
While these functions can be situationally useful, there is value in knowing the data type of your columns ahead of time, rather than depending on these functions to provide the data type.
|
||||
|
||||
DAX uses the **MAX** and **MIN** functions to both *aggregate* values, and to *compare* values.
|
||||
|
||||
## Text functions
|
||||
The **text** functions in DAX include the following:
|
||||
|
||||
* CONCATENTATE
|
||||
* REPLACE
|
||||
* SEARCH
|
||||
* UPPER
|
||||
* FIXED
|
||||
|
||||
These **text** work very similarly to the Excel functions that have the same name, so if you're familiar with how Excel handles text functions, you're already a step ahead. If not, you can always experiment with these functions in Power BI, and learn more about how they behave.
|
||||
|
||||
## Date functions
|
||||
DAX includes the following **Date** functions:
|
||||
|
||||
* DATE
|
||||
* HOUR
|
||||
* NOW
|
||||
* EOMONTH
|
||||
* WEEKDAY
|
||||
|
||||
While these functions are useful to calculate and extract information from *date* values, they do not apply to time intelligence, which uses a date table.
|
||||
|
||||
> Video content courtesy of [Alberto Ferrari, SQLBI](http://www.sqlbi.com/learning-dax)
|
||||
>
|
||||
>
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
Using **variables** are an extremely powerful part of a DAX expression.
|
||||
|
||||
|
||||
|
||||
You can define a variable anywhere in a DAX expression, using the following syntax:
|
||||
|
||||
VARNAME = RETURNEDVALUE
|
||||
|
||||
Variables can be any data type, including entire tables.
|
||||
|
||||
Keep in mind that each time you reference a variable in your DAX expression, Power BI must recalculate its value according to your definition. For this reason, it's good practice to avoid repeating variables in your function.
|
||||
|
||||
> Video content courtesy of [Alberto Ferrari, SQLBI](http://www.sqlbi.com/learning-dax)
|
||||
>
|
||||
>
|
||||
|
||||
@ -1,21 +0,0 @@
|
||||
Power BI lets you create relationships among multiple tables, including tables that come from completely different data sources. You can see those relationships for any data model in the **Relationships** view of Power BI Desktop.
|
||||
|
||||
|
||||
|
||||
## DAX relational functions
|
||||
DAX has **relational functions** that enable you to interact with tables that have established relationships.
|
||||
|
||||
You can return the value of a column, or you can return all rows in a relationship using DAX functions.
|
||||
|
||||
For example, the **RELATED** function follows relationships and returns the value of a column, while **RELATEDTABLE** follows relationships, and returns an entire table that is filtered to include only related rows.
|
||||
|
||||
|
||||
|
||||
The **RELATED** function works on *many-to-one* relationships, while **RELATEDTABLE** is for *one-to-many* relationships.
|
||||
|
||||
You can use relational functions to build expressions that include values across multiple tables. DAX will return a result with these functions, regardless of the length of the chain of the relationship.
|
||||
|
||||
> Video content courtesy of [Alberto Ferrari, SQLBI](http://www.sqlbi.com/learning-dax)
|
||||
>
|
||||
>
|
||||
|
||||
@ -1,40 +0,0 @@
|
||||
One significant difference between **DAX** and the Excel formula language is that DAX allows you to pass *entire tables* between expressions, rather than being constrained to a single value. One powerful effect is that DAX allows you to filter tables in its expressions, then work with the filtered set of values.
|
||||
|
||||
|
||||
|
||||
With DAX, you can create entirely new calculated tables and then treat them like any other table - including creating relationships between them and other tables in your data model.
|
||||
|
||||
## DAX table functions
|
||||
DAX has a rich set of **table** functions, including the following:
|
||||
|
||||
* FILTER
|
||||
* ALL
|
||||
* VALUES
|
||||
* DISTINCT
|
||||
* RELATEDTABLE
|
||||
|
||||
These functions return a full table rather than a value. Typically you'll use the results of a **table** function in further analysis as part of a greater expression, rather than using that returned table a final value. It's important to note that When you use a table function, the results inherit the relationships of their columns.
|
||||
|
||||
You can mix table functions in your expression, as long as each function uses a table and returns a table. For example, consider the following DAX expression:
|
||||
|
||||
FILTER (ALL (Table), Condition)
|
||||
|
||||
That expression would put a filter over the entirety of *Table*, ignoring any current filter content.
|
||||
|
||||
The DISTINCT function returns the distinct values of a column that are also visible in the current context. So to use the above DAX expression example, using **ALL** in that expression ignores filters, while replacing **ALL** with **DISTINCT** would observe them.
|
||||
|
||||
## Counting values with DAX
|
||||
One common question that Power BI report builders want to answer is the following:
|
||||
|
||||
* How many values do I have for this column?
|
||||
|
||||
That may be a simple question to answer with a table displayed in front of you, but DAX approaches in a different way in a different way, particularly when there's a relationship between tables.
|
||||
|
||||
For example, Power BI and DAX includes values that are not properly cross-indexed. If the incoming relationship is broken, DAX adds a new row to the related table that has blanks in every field, and links that new row to the unindexed row to guarantee referential integrity. If your function includes blank rows, such as is often the case when using **ALL**, those blank rows will then be included in the number of values returned for that column.
|
||||
|
||||
You can also create entire calculated tables using DAX functions. Calculated tables created using DAX require a **NAME** and a **TABLE** function. Calculated tables can be used like any other table, including establishing relationships.
|
||||
|
||||
> Video content courtesy of [Alberto Ferrari, SQLBI](http://www.sqlbi.com/learning-dax)
|
||||
>
|
||||
>
|
||||
|
||||
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Reference in New Issue
Block a user