Best practices for building data visualizations with Go and Plotly

Best practices for building visual data using Go and Plotly

In practical applications, data visualization can display information such as data characteristics and trends, helping people understand the data more intuitively. Both the Go language and Plotly provide powerful tools for data visualization. Through this article, we will introduce the best practices for building data visualizations using Go and Plotly.

1. Install and configure Go and Plotly

First, you need to install the Go language and Plotly's Go library. You can install the Go language and Plotly through the following commands:

$ wget https://golang.org/dl/go1.16.6.linux-amd64.tar.gz
$ tar -C /usr/local -xzf go1.16.6.linux-amd64.tar.gz
$ export PATH=$PATH:/usr/local/go/bin
$ go get github.com/plotly/plotly.go

After the installation is complete, you need to run the following command to verify whether the installation is successful:

$ go version
$ go env

If the output is correct, the Go language and Plotly The library has been installed successfully.

2. Making data visualization

Next, we start making data visualization. First, the data needs to be defined. In this article, we use a structure slice containing dates and numbers to represent data:

type DataPoint struct {
    Date  time.Time
    Value float64
}

var data = []DataPoint{
    {time.Date(2021, 1, 1, 0, 0, 0, 0, time.UTC), 1.0},
    {time.Date(2021, 2, 1, 0, 0, 0, 0, time.UTC), 2.0},
    {time.Date(2021, 3, 1, 0, 0, 0, 0, time.UTC), 3.0},
    {time.Date(2021, 4, 1, 0, 0, 0, 0, time.UTC), 4.0},
}

Next, we use Plotly to draw a basic line chart:

import (
    "github.com/plotly/plotly.go"
    "github.com/plotly/plotly.go/plot"
)

func main() {
    trace := plotly.Trace{
        X: []string{"2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01"},
        Y: []float64{1.0, 2.0, 3.0, 4.0},
        Type: "scatter",
    }

    data := plotly.Data{trace}
    layout := plotly.Layout{Title: "My Plot"}

    fig := plotly.NewFigure(data, layout)

    plotly.Show(fig)
}

This code will Generates a line chart with dates on the x-axis and data values ​​on the y-axis.

3. Add multiple datasets and styles

Next, we try to add another dataset and some styles to optimize our visualization:

trace1 := plotly.Trace{
    X:    []string{"2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01"},
    Y:    []float64{1.0, 2.0, 3.0, 4.0},
    Name: "Data 1",
    Type: "scatter",
    Mode: "lines+markers",
}

trace2 := plotly.Trace{
    X:     []string{"2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01"},
    Y:     []float64{4.0, 3.0, 2.0, 1.0},
    Name:  "Data 2",
    Type:  "scatter",
    Mode:  "lines",
    Line:  plotly.Line{Color: "red"},
    Marker: plotly.Marker{Symbol: "x", Size: 10},
}

data := plotly.Data{trace1, trace2}
layout := plotly.Layout{
    Title: "My Plot",
    Xaxis: plotly.Xaxis{Title: "Date"},
    Yaxis: plotly.Yaxis{Title: "Value"},
}

fig := plotly.NewFigure(data, layout)

plotly.Show(fig)

This code A visualization containing two data sets will be generated, one containing the marker line and the other containing the red line and marker "x".

4. Customized visual layout

Finally, we try to customize the visual layout, such as adding a comment and changing the color of the graph:

annotation := plotly.Annotation{
    Text:     "Some handy info",
    X:        "2021-04-01",
    Y:        2.5,
    ShowArrow: true,
    ArrowColor: "red",
}

data := plotly.Data{trace1, trace2}
layout := plotly.Layout{
    Title: "My Plot",
    Xaxis: plotly.Xaxis{Title: "Date", Range: []string{"2021-01-01", "2021-04-01"}},
    Yaxis: plotly.Yaxis{Title: "Value", Range: []float64{-0.5, 4.5}},
    Annotations: []plotly.Annotation{annotation},
    PlotBgColor: "lightgray",
}

fig := plotly.NewFigure(data, layout)

plotly.Show(fig)

This code will generate a custom A defined visual layout that contains an annotation and a light gray background.

5. Summary

By using Go language and Plotly, we can easily create beautiful visual charts. In this article, we introduced how to install and configure Go and Plotly, make basic line charts, add multiple data sets and styles, and customize visualization layouts. Through these best practices, we can better understand and present data for better decision-making and planning.

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