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:

iris printTable

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:

sampleData zipcodes.csv limit 0 5 printTable

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:

iris summarize printTable

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:

iris where species = setosa printTable

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:

iris scatterplot x sepal_width y sepal_length title Sepal Length vs Width fill species

Let's look at some time series data:

sampleData seattle-weather.csv parseDate date linechart x date y temp_max title Maximum Temperature in Seattle stroke steelblue

Let's create a bar chart showing precipitation:

sampleData seattle-weather.csv groupBy weather reduce precipitation mean precip_avg barchart x weather y precip_avg fill teal title Average Precipitation by Weather Type

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Let's look at some more complex transformations:

sampleData weather.csv groupBy weather reduce temp_max mean avg_max_temp reduce temp_min mean avg_min_temp orderBy -avg_max_temp printTable

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:

iris compute ratio {sepal_length}/{sepal_width} where ratio > 2 printTable

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 annual precipitation values:

sampleData seattle-weather.csv splitYear groupBy year reduce precipitation mean precipitation_mean select year precipitation_mean transpose heatrix

You can create multiple visualizations:

iris scatterplot x sepal_length y sepal_width fill species barchart x species y sepal_length fill teal title Sepal Length by Species

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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

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