[[tutorial-visualizing]]
== Visualizing Your Data

To start visualizing your data, click *Visualize* in the side navigation.

The *Visualize* tools enable you to view your data in several ways. For example,
let's use that venerable visualization, the pie chart, to get some insight
into the account balances in the sample bank account data. To get started, click the big blue
**Create a visualization** button in the center of the screen.

image::images/tutorial-visualize-landing.png[]

There are a number of visualization types to choose from. Let's click the one
called *Pie*.

image::images/tutorial-visualize-wizard-step-1.png[]

You can build visualizations from saved searches, or enter new search criteria. To enter
new search criteria, you first need to select an index pattern to specify
what indices to search. We want to search the account data, so select the `ba*`
index pattern.

image::images/tutorial-visualize-wizard-step-2.png[]

The default search matches all documents. Initially, a single "slice"
encompasses the entire pie:

image::images/tutorial-visualize-pie-1.png[]

To specify what slices to display in the chart, you use an Elasticsearch
{ref}/search-aggregations.html[bucket aggregation]. A bucket aggregation
simply sorts the documents that match your search criteria into different
categories, aka _buckets_. For example, the account data includes the balance
of each account. Using a bucket aggregation, you can establish multiple ranges
of account balances and find out how many accounts fall into each range.

To define a bucket for each range:

. Click the *Split Slices* buckets type.
. Select *Range* from the *Aggregation* list.
. Select the *balance* field from the *Field* list.
. Click *Add Range* four times to bring the
total number of ranges to six.
. Define the following ranges:
+
[source,text]
0             999
1000         2999
3000         6999
7000        14999
15000       30999
31000       50000

. Click *Apply changes* image:images/apply-changes-button.png[] to update the chart.

Now you can see what proportion of the 1000 accounts fall into each balance
range.

image::images/tutorial-visualize-pie-2.png[]

Let's take a look at another dimension of the data: the account holder's
age. By adding another bucket aggregation, you can see the ages of the account
holders in each balance range:

. Click *Add sub-buckets* below the buckets list.
. Click *Split Slices* in the buckets type list.
. Select *Terms* from the aggregation list.
. Select *age* from the field list.
. Click  *Apply changes* image:images/apply-changes-button.png[].

Now you can see the break down of the account holders' ages displayed
in a ring around the balance ranges.

image::images/tutorial-visualize-pie-3.png[]

To save this chart so we can use it later, click *Save* and enter the name _Pie Example_.

Next, we're going to look at data in the Shakespeare data set. Let's find out how the
plays compare when it comes to the number of speaking parts and display the information
in a bar chart:

. Click *New* and select *Vertical bar chart*.
. Select the `shakes*` index pattern. Since you haven't defined any buckets yet,
you'll see a single big bar that shows the total count of documents that match
the default wildcard query.
+
image::images/tutorial-visualize-bar-1.png[]

. To show the number of speaking parts per play along the y-axis, you need to
configure the Y-axis {ref}/search-aggregations.html[metric aggregation]. A metric
aggregation computes metrics based on values extracted from the search results.
To get the number of speaking parts per play, select the *Unique Count*
aggregation and choose *speaker* from the field list. You can also give the
axis a custom label, _Speaking Parts_.

. To show the different plays long the x-axis, select the X-Axis buckets type,
select *Terms* from the aggregation list, and choose *play_name* from the field
list. To list them alphabetically, select *Ascending* order. You can also give
the axis a custom label, _Play Name_.

. Click *Apply changes* image:images/apply-changes-button.png[] to view the
results.

image::images/tutorial-visualize-bar-2.png[]

Notice how the individual play names show up as whole phrases, instead of being broken down into individual words. This
is the result of the mapping we did at the beginning of the tutorial, when we marked the *play_name* field as 'not
analyzed'.

Hovering over each bar shows you the number of speaking parts for each play as a tooltip. To turn tooltips
off and configure other options for your visualizations, select the Visualization builder's *Options* tab.

Now that you have a list of the smallest casts for Shakespeare plays, you might also be curious to see which of these
plays makes the greatest demands on an individual actor by showing the maximum number of speeches for a given part.

. Click *Add metrics* to add a Y-axis aggregation.
. Choose the *Max* aggregation and select the *speech_number* field.
. Click *Options* and change the *Bar Mode* from *stacked* to *normal*.
. Click  *Apply changes* image:images/apply-changes-button.png[]. Your chart should now look like this:

image::images/tutorial-visualize-bar-3.png[]

As you can see, _Love's Labours Lost_ has an unusually high maximum speech number, compared to the other plays, and
might therefore make more demands on an actor's memory.

Note how the *Number of speaking parts* Y-axis starts at zero, but the bars don't begin to differentiate until 18. To
make the differences stand out, starting the Y-axis at a value closer to the minimum, go to Options and select
*Scale Y-Axis to data bounds*.

Save this chart with the name _Bar Example_.

Next, we're going to use a coordinate map chart to visualize geographic information in our log file sample data.

. Click *New*.
. Select  *Coordinate map*.
. Select the `logstash-*` index pattern.
. Set the time window for the events we're exploring:
. Click the time picker in the Kibana toolbar.
. Click *Absolute*.
. Set the start time to May 18, 2015 and the end time to May 20, 2015.
+
image::images/tutorial-timepicker.png[]

. Once you've got the time range set up, click the *Go* button and close the time picker by
clicking the small up arrow in the bottom right corner.

You'll see a map of the world, since we haven't defined any buckets yet:

image::images/tutorial-visualize-map-1.png[]

To map the geo coordinates from the log files select *Geo Coordinates* as
the bucket and click *Apply changes* image:images/apply-changes-button.png[].
Your chart should now look like this:

image::images/tutorial-visualize-map-2.png[]

You can navigate the map by clicking and dragging, zoom with the
image:images/viz-zoom.png[] buttons, or hit the *Fit Data Bounds*
image:images/viz-fit-bounds.png[] button to zoom to the lowest level that
includes all the points. You can also include or exclude a rectangular area
by clicking the *Latitude/Longitude Filter* image:images/viz-lat-long-filter.png[]
button and drawing a bounding box on the map. Applied filters are displayed
below the query bar. Hovering over a filter displays controls to toggle,
pin, invert, or delete the filter.

image::images/tutorial-visualize-map-3.png[]

Save this map with the name _Map Example_.

Finally, create a Markdown widget to display extra information:

. Click *New*.
. Select *Markdown widget*.
. Enter the following text in the field:
+
[source,markdown]
# This is a tutorial dashboard!
The Markdown widget uses **markdown** syntax.
> Blockquotes in Markdown use the > character.

. Click  *Apply changes* image:images/apply-changes-button.png[] render the Markdown in the
preview pane.
+
image::images/tutorial-visualize-md-1.png[]





image::images/tutorial-visualize-md-2.png[]

Save this visualization with the name _Markdown Example_.