mongodb/charts-demo

For the .local events in general, and for .local SF specifically

MongoDB Charts

Setup the Datasets

Download the airbnb dataset, unzip it, and import it into your MongoDB Atlas cluster. Have a look at the command line tools for directions on how to use mongoimport with MongoDB Atlas. The included dataset is sized to be able to fit into a free M0 cluster on MongoDB Atlas.

Analysing Airbnb Data

Add a Data Source

1. With a dataset in place, you'll want to enable Charts for your project.
2. Select the Charts link on the left-hand side, then select the Activate MongoDB Charts button.
3. Add a new data source to your project, choose the Cluster, database and collection. For this example, I'll use the sanFranciscoListingsAndReviews collection. Then I'll accept the default permissions.

Create a Dashboard

1. Go to the Dashboards tab
2. Click New Dashboard
3. Enter a name and description

Add Some Charts

Inside the MongoDB Charts dashboard, you'll now add a chart to the dashboard.

Multi-Series Stacked Bar Chart

Let's locate the neighborhoods in San Francisco that have the most Airbnb properties and split them out by property type.

1. Select the San Francisco Airbnb (airbnb.sanFranciscoListingsAndReviews) dataset as the datasource.
2. For the Chart Type select Bar/Stacked.
3. Use the following in the Encoding sections for the chart data:
   • X Axis: _id, Count aggregation
   • Y Axis: address.suburb
     • Sort By: Aggregated Value, Descending
     • Limit: 30
   • Series: property_type
4. Add a name to the chart, like Properties by Location

Your chart should look something like:

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Show the areas with the most expensive properties, and correlate with the review scores.

Data Source: Select the San Francisco Airbnb (airbnb.sanFranciscoListingsAndReviews) dataset as the datasource.

Chart Title: Price by Location, Colored by Location Review Score

Chart Type: Bar / Colored

Encodings:

• X Axis: price, Mean aggregation
• Y Axis: address.suburb
  • Sort By: Aggregated Value, Descending
  • Limit: 30
• Color: review_scores.review_scores_location, Mean aggregation

Your chart should look something like:

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Show the number of properties with various review scores

Data Source: Select the San Francisco Airbnb (airbnb.sanFranciscoListingsAndReviews) dataset as the datasource.

Chart Title: Review Score Histogram

Chart Type: Column / Grouped

Encodings:

• X Axis: review_scores.review_scores_rating
  • Binning: On, bin size 5
• Y Axis: _id, Count aggregation
• Filter (to hide properties with no review score):
  • { 'review_scores.review_score_rating': { $gte: 0 }}

Your chart should look something like:

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Show the types of properties that contain pools.

Data Source: Select the San Francisco Airbnb (airbnb.sanFranciscoListingsAndReviews) dataset as the datasource.

Chart Title: Properties with Pools

Chart Type: Circular / Donut

Encodings:

• Label: property_type
• Arc: _id, Count aggregation
• Filter (to show only properties with pools):
  • { amenities: 'Pool' }

Your chart should look something like:

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Show the number of reviews of any property, month by month

Data Source: Select the San Francisco Airbnb (airbnb.sanFranciscoListingsAndReviews) dataset as the datasource.

Chart Title: Reviews Over Time

Chart Type: Area / Discrete

Encodings:

• X Axis: reviews.date
  • Array Reduction: Unwind Array
  • Binning: On, bin by month
• Y Axis: _id, Count aggregation

Your chart should look something like:

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Show the number of properties with and without pools by area

Data Source: Select the San Francisco Airbnb (airbnb.sanFranciscoListingsAndReviews) dataset as the datasource.

Chart Title: Pool or No Pool

Chart Type: Column / Stacked

Encodings:

• X Axis: address.suburb
• Y Axis: _id, Count aggregation
• Series: amenities
  • Array Reduction: Existence of value: "Pool"

Your chart should look something like: