Changes to datascience

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1 month ago
updated index.scroll
index.scroll
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- buildHtml
- theme roboto
+ title Data Science with Scroll
+ description A comprehensive tutorial on using Scroll for data analysis and visualization
+ theme gazette
- Hello World my name is
+ container 800px
+ # Data Science with Scroll
+  style font-size: 48px;
+ 
+ ## A Comprehensive Tutorial
+  style font-size: 32px;
+ 
+ This tutorial will walk you through how to use Scroll for data analysis and visualization, from basic concepts to advanced techniques.
+ 
+ ? What makes Scroll great for data science?
+ Scroll combines the simplicity of markdown-style syntax with powerful data transformation and visualization capabilities. You can:
+ - Load data from various sources (CSV, JSON, etc.)
+ - Transform and analyze data with simple commands
+ - Create beautiful visualizations
+ - All in a simple, readable format
+ 
+ Let's dive in!
+ 
+ ---
+ 
+ # Part 1: Getting Started with Data
+ 
+ ## Loading Sample Datasets
+ 
+ Scroll comes with several sample datasets. Let's start with the famous iris dataset:
+ 
+ iris
+  printTable
+ 
+ You can also load datasets from Vega's collection:
+ 
+ sampleData zipcodes.csv
+  limit 0 5
+   printTable
+ 
+ ## Basic Data Operations
+ 
+ Let's explore some basic operations on the iris dataset:
+ 
+ iris
+  summarize
+   printTable
+ 
+ This gives us summary statistics for each column.
+ 
+ Let's look at filtering:
+ 
+ iris
+  where species = setosa
+   printTable
+ 
+ ---
+ 
+ # Part 2: Data Visualization
+ 
+ ## Basic Plots
+ 
+ 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
+ 
+ ## Line Charts
+ 
+ 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
+ 
+ ## Bar Charts
+ 
+ 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
+    title Average Precipitation by Weather Type
+ 
+ ---
+ 
+ # Part 3: Advanced Data Transformations
+ 
+ ## Grouping and Aggregation
+ 
+ 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
+ 
+ ## Creating New Columns
+ 
+ Let's add some computed columns:
+ 
+ iris
+  compute ratio {sepal_length}/{sepal_width}
+   where ratio > 2
+    printTable
+ 
+ ---
+ 
+ # Part 4: Advanced Visualizations
+ 
+ ## Heatmaps
+ 
+ Let's create a heatmap of correlation values:
+ 
+ sampleData seattle-weather.csv
+  splitYear
+   groupBy year
+    reduce precipitation mean
+    select year precipitation_mean
+     transpose
+      heatrix
+ 
+ 
+ ## Multiple Views
+ 
+ You can combine multiple visualizations:
+ 
+ iris
+  scatterplot
+   x sepal_length
+   y sepal_width
+   fill Species
+  barchart
+   x species
+   y sepal_length
+   title Sepal Length by Species
+ 
+ ---
+ 
+ # Conclusion
+ 
+ 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
+  https://scroll.pub/tutorial.html
+ - The Scroll data visualization examples
+  https://scroll.pub/examples.html
+ - The complete sample datasets
+  https://github.com/vega/vega-datasets