Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/piotr-kalanski/splot

Scala library for data visualization.
https://github.com/piotr-kalanski/splot
chart charts data-visualization plot plots plotting scala scala-library visualization
Last synced: 10 days ago
JSON representation
Scala library for data visualization.
Host: GitHub
URL: https://github.com/piotr-kalanski/splot
Owner: piotr-kalanski
License: apache-2.0
Created: 2017-04-12T17:01:43.000Z (over 7 years ago)
Default Branch: development
Last Pushed: 2017-04-28T23:28:51.000Z (over 7 years ago)
Last Synced: 2024-10-11T04:47:35.355Z (26 days ago)
Topics: chart, charts, data-visualization, plot, plots, plotting, scala, scala-library, visualization
Language: Scala
Size: 1020 KB
Stars: 10
Watchers: 2
Forks: 1
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

        # SPlot

SPlot is Scala library for data visualization.

[![Build Status](https://api.travis-ci.org/piotr-kalanski/SPlot.png?branch=development)](https://api.travis-ci.org/piotr-kalanski/SPlot.png?branch=development)

[![codecov.io](http://codecov.io/github/piotr-kalanski/SPlot/coverage.svg?branch=development)](http://codecov.io/github/piotr-kalanski/SPlot/coverage.svg?branch=development)

[](http://search.maven.org/#search|ga|1|a%3A%22splot%22)

[![License](http://img.shields.io/:license-Apache%202-red.svg)](http://www.apache.org/licenses/LICENSE-2.0.txt)

# Table of contents

- [Goals](#goals)

- [Getting started](#getting-started)

- [API possibilities](#api-possibilities)

- [Supported charts](#supported-charts)

- [Multiple charts](#multiple-charts)

- [Aggregation functions](#aggregation-functions)

- [Saving plot to file](#saving-plot-to-file)

- [Customizations](#customizations)

# Goals

- Provide simple API in Scala for data visualization similar to ggplot (http://ggplot2.org/) and Seaborn (https://seaborn.pydata.org/)

- Support exploratory data analysis

Currently project is **NOT** focused on:

- Performance

- Rich possibilities for customization

# Getting started

Include dependency:

```scala

"com.github.piotr-kalanski" % "splot" % "0.2.0"

```

or

```xml

    com.github.piotr-kalanski

    splot

    0.2.0

```

Import implicits, which adds methods to Scala collection enabling plotting:

```scala

import com.datawizards.splot.api.implicits._

```

## Basic example

To plot bar chart using Scala sequence you need to call one method:

```scala

import com.datawizards.splot.api.implicits._

Seq(1.0, 4.0, 9.0).plotBar()

```

![](images/basic_bar.png)

# API possibilities

SPlot provides two API's for displaying plots:

## Additional methods on collections

First API enrich Scala collections providing additional methods for plotting charts.

Examples:

```scala

val data = Seq((1,1),(2,2))

// Plot bar chart:

data.plotBar()

// Plot line chart:

data.plotLine()

// Plot scatter chart:

data.plotScatter()

// Start building plot:

data.buildPlot()

```

## Providing independently axis values

Second API provides possibility to independently provide x and y values.

Examples:

```scala

// Plot bar chart:

SPlot.plotBar(Seq(1,2), Seq(1,4))

// Plot line chart:

SPlot.plotLine(Seq(1,2), Seq(1,4))

// Plot scatter chart:

SPlot.plotScatter(Seq(1,2), Seq(1,4))

// Start building plot:

SPlot.buildPlot(Seq(1,2), Seq(1,4))

```

# Supported charts

- [Bar](#bar)

- [Scatter](#scatter)

- [Line](#line)

- [Histogram](#histogram)

- [Bubble](#bubble)

- [Pie](#pie)

- [Area](#area)

> Please note that all below examples **require** importing:

```scala

import com.datawizards.splot.api.implicits._

```

> Please also note that most of below examples are based on randomly generated data!

## Bar

### Bar chart for sequence of numbers

```scala

val data = Seq(1.0, 4.0, 9.0)

data.plotBar()

```

### Bar chart for sequence of case class

```scala

case class Person(name: String, age: Int)

val data = Seq(

    Person("p1", 45),

    Person("p2", 85),

    Person("p3", 78)

)

data.plotBar(_.age)

```

![](images/bar_people.png)

### Bar chart for categorical data

```scala

val populationByCountry = Seq(

    ("DE", 81),

    ("TR", 72),

    ("FR", 63),

    ("UK", 62),

    ("IT", 61),

    ("ES", 46),

    ("UA", 45),

    ("PL", 38),

    ("RO", 19),

    ("NL", 17),

    ("GR", 11),

    ("PT", 11),

    ("BE", 10),

    ("CZ", 10),

    ("HU", 10)

  )

populationByCountry

    .buildPlot()

    .bar(_._1, _._2)

    .titles("Population by country [millions]", "Country", "Population")

    .size(1200, 300)

    .legendVisible(false)

    .display()

```

![](images/bar_chart_with_string.png)

## Scatter

### Scatter chart for sequence of numbers

```scala

val data = Seq(

    (1.0, 1.0),

    (2.0, 4.0),

    (3.0, 9.0)

)

data.plotScatter()

```

![](images/scatter_basic.png)

### Scatter chart for sequence of case class

```scala

case class AgeIncome(age: Int, income: Double)

val data = Seq(

    AgeIncome(20, 1000.0),

    AgeIncome(25, 2000.0),

    AgeIncome(30, 2500.0),

    AgeIncome(35, 3000.0),

    AgeIncome(40, 3500.0),

    AgeIncome(45, 3000.0),

    AgeIncome(50, 2500.0)

)

data.plotScatter(_.age, _.income)

```

![](images/scatter_age_income.png)

## Line

### Line chart for sequence of numbers

```scala

val data = Seq(

    (1.0, 1.0),

    (2.0, 4.0),

    (3.0, 9.0)

)

data.plotLine()

```

![](images/line_basic.png)

### Line chart for sequence of case class

```scala

case class AgeIncome(age: Int, income: Double)

val data = Seq(

    AgeIncome(20, 1000.0),

    AgeIncome(25, 2000.0),

    AgeIncome(30, 2500.0),

    AgeIncome(35, 3000.0),

    AgeIncome(40, 3500.0),

    AgeIncome(45, 3000.0),

    AgeIncome(50, 2500.0)

)

data.plotLine(_.age, _.income)

```

![](images/line_age_income.png)

### Line chart for timeseries data

```scala

timeseriesData2017.plotLine()

```

![](images/line_timeseries.png)

## Histogram

### Histogram for sequence of numbers

```scala

val rand = new Random()

val gaussians = for(i <- 1 to 10000) yield rand.nextGaussian()

gaussians.plotHistogram(100)

```

![](images/histogram_for_gaussians.png)

### Histogram for categorical data

```scala

people

    .buildPlot()

    .histogramForCategories(_.education)

    .size(400, 300)

    .titles("People by education", "Education", "Count")

    .legendVisible(false)

    .display()

```

![](images/histogram_for_categories.png)

## Bubble chart

```scala

  Seq(

    (1, 1, 9.0),

    (1, 2, 40.0),

    (3, 2, 60.0),

    (2, 2, 90.0),

    (1, 3, 30.0),

    (2, 3, 40.0)

  )

  .buildPlot()

  .bubble(_._1, _._2, _._3)

  .display()

```

![](images/bubble_chart.png)

## Pie

```scala

Seq(

    ("DE", 81),

    ("TR", 72),

    ("FR", 63),

    ("UK", 62),

    ("IT", 61)

  )

  .plotPie()

```

![](images/pie_chart.png)

## Area

```scala

Seq(

    (1.0, 1.0),

    (2.0, 4.0),

    (3.0, 9.0)

  )

  .plotArea()

```

![](images/area_chart.png)

# Multiple series

```scala

people

    .buildPlot()

    .scatter(_.age, _.income)

    .seriesBy(_.education)

    .size(500, 400)

    .titles("Age and income by education", "age", "income")

    .display()

```

![](images/scatter_chart_with_multiple_series.png)

# Multiple charts

## Grouping by cols

### Scatter plot

```scala

people

    .buildPlot()

    .scatter(_.age, _.income)

    .colsBy(_.country)

    .display()

```

![](images/people_groupby_country.png)

### Histogram

```scala

people

    .buildPlot()

    .colsBy(_.education)

    .histogram(_.age, 50)

    .size(1200, 400)

    .display()

```

![](images/histogram_multiple_columns.png)

## Grouping by rows

```scala

people

    .buildPlot()

    .scatter(_.age, _.income)

    .rowsBy(_.education)

    .display()

```

![](images/people_groupby_education.png)

## Grouping by cols and/or rows

### Scatter plot

```scala

people

    .buildPlot()

    .scatter(_.age, _.income)

    .colsBy(_.country)

    .rowsBy(_.education)

    .display()

```

![](images/people_groupby_country_education.png)

### Bar plot

```scala

val groupedPeopleByCountryEducation = people

    .groupBy(p => (p.country, p.education))

    .mapValues(pv => pv.size)

groupedPeopleByCountryEducation

    .buildPlot()

    .colsBy(_._1._1)

    .bar(x => x._1._2, x => x._2)

    .size(1200, 300)

    .display()

```

![](images/bar_chart_grids_with_string.png)

## Multiple columns and series

```scala

people

    .buildPlot()

    .scatter(_.age, _.income)

    .size(1200, 300)

    .colsBy(_.country)

    .seriesBy(_.education)

    .display()

```

![](images/scatter_chart_with_multiple_columns_and_series.png)

# Aggregation functions

SPlot enables aggregating your data before displaying chart.

To start using functions you need to import:

```scala

import com.datawizards.splot.functions._

```

Currently supported aggregation functions:

- count

- mean

- sum

## Calculating count

```scala

  people

    .buildPlot()

    .barWithAggregations(_.education, count())

```

![](images/bar_chart_with_count_aggregation.png)

## Calculating mean

```scala

  people

    .buildPlot()

    .barWithAggregations(_.country, mean(_.income))

```

![](images/bar_chart_with_mean_aggregation.png)

## Calculating sum

```scala

  people

    .buildPlot()

    .barWithAggregations(_.country, sum(_.income))

```

# Saving plot to file

To save plot to file you need to call method *save()* instead of calling *display()*.

```scala

import com.datawizards.splot.model.ImageFormats

Seq(1.0, 4.0, 9.0)

    .buildPlot()

    .bar()

    .save("chart.png", ImageFormats.PNG)

```

Currently supported image formats:

- BMP

- PNG

- JPG

- GIF

- EPS

- PDF

- SVG

# Customizations

## Change chart title

```scala

val data = Seq(1.0, 4.0, 9.0)

data

    .buildPlot()

    .bar()

    .titles("Example bar chart", "x values", "y values")

    .display()

```

## Change chart size (width, height)

```scala

val data = Seq(1.0, 4.0, 9.0)

data

    .buildPlot()

    .bar()

    .size(1600, 1200)

    .display()

```

## Change series name

```scala

Seq(1.0, 4.0, 9.0)

    .buildPlot()

    .bar()

    .seriesName("custom name")

    .display()

```

![](images/bar_chart_custom_series_name.png)

## Hide legend

```scala

Seq(1.0, 4.0, 9.0)

    .buildPlot()

    .bar()

    .legendVisible(false)

    .display()

```

![](images/bar_chart_hide_legend.png)

## Chart themes

Currently supported themes:

![](images/ggplot_theme.png) ![](images/matplot_theme.png) ![](images/xchart_theme.png) ![](images/splot_theme.png)

To change theme you need to call method *theme()*:

```scala

data

    .buildPlot()

    .bar()

    .theme(PlotTheme.GGPlot2)

    .display()

```

## Annotations

```scala

Seq(1, 4, 9)

    .buildPlot()

    .bar()

    .showAnnotations(true)

    .display()

```

![](images/annotations.png)

# Bugs

Please report any bugs or submit feature requests to [SPlot Github issue tracker](https://github.com/piotr-kalanski/SPlot/issues).

# Credits

| Library | Category | License |

| ------- | -------- | ------- |

| [XChart](https://github.com/timmolter/XChart) | Graphing | [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0) |

# Contact

[email protected]