This tutorial will walk you through how to use Scroll for data analysis and visualization, from basic concepts to advanced techniques.

Let's dive in!

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Scroll comes with several sample datasets. Let's start with the famous iris dataset:

sepal_length	sepal_width	petal_length	petal_width	species
6.1	3	4.9	1.8	virginica
5.6	2.7	4.2	1.3	versicolor
5.6	2.8	4.9	2	virginica
6.2	2.8	4.8	1.8	virginica
7.7	3.8	6.7	2.2	virginica
5.3	3.7	1.5	0.2	setosa
6.2	3.4	5.4	2.3	virginica
4.9	2.5	4.5	1.7	virginica
5.1	3.5	1.4	0.2	setosa
5	3.4	1.5	0.2	setosa

You can also load datasets from Vega's collection:

zip_code	latitude	longitude	city	state	county
501	40.922326	-72.637078	Holtsville	NY	Suffolk
544	40.922326	-72.637078	Holtsville	NY	Suffolk
601	18.165273	-66.722583	Adjuntas	PR	Adjuntas
602	18.393103	-67.180953	Aguada	PR	Aguada
603	18.455913	-67.14578	Aguadilla	PR	Aguadilla

Let's explore some basic operations on the iris dataset:

name	type	incompleteCount	uniqueCount	count	sum	median	mean	min	max	mode
sepal_length	number	0	8	10	57.699999999999996	5.6	5.77	4.9	7.7	5.6
sepal_width	number	0	8	10	31.599999999999998	3.2	3.1599999999999997	2.5	3.8	2.8
petal_length	number	0	8	10	39.8	4.65	3.9799999999999995	1.4	6.7	4.9
petal_width	number	0	7	10	13.699999999999996	1.75	1.3699999999999997	0.2	2.3	0.2
species	string	0	3	10						virginica

This gives us summary statistics for each column.

Let's look at filtering:

sepal_length	sepal_width	petal_length	petal_width	species
5.3	3.7	1.5	0.2	setosa
5.1	3.5	1.4	0.2	setosa
5	3.4	1.5	0.2	setosa

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Let's start with a simple scatterplot of the iris data:

Let's look at some time series data:

Let's create a bar chart showing precipitation:

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Let's look at some more complex transformations:

count	weather	avg_max_temp	avg_min_temp
129	drizzle	18.555813953488368	10.143410852713178
459	rain	15.535294117647041	9.04727668845315
1674	sun	18.064157706093184	8.87275985663083
78	snow	4.528205128205127	-1.4346153846153844
582	fog	15.261855670103111	8.527319587628869

Let's add some computed columns:

sepal_length	sepal_width	petal_length	petal_width	species	ratio
6.1	3	4.9	1.8	virginica	2.033333333333333
5.6	2.7	4.2	1.3	versicolor	2.074074074074074
6.2	2.8	4.8	1.8	virginica	2.2142857142857144
7.7	3.8	6.7	2.2	virginica	2.0263157894736845

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Let's create a heatmap of correlation values:

You can combine multiple visualizations:

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This tutorial covered the basics of data science with Scroll. Some key takeaways:

• Scroll makes it easy to load and manipulate data
• Visualizations are simple to create and customize
• Complex transformations can be done with simple commands
• Everything is readable and version-controllable

For more information, check out:

• The Scroll documentation
• The Scroll data visualization examples
• The complete sample datasets