數(shù)據(jù)可視化工具大全
散點(diǎn)圖真是一個(gè)比較神奇的圖形，正如它的姓名相同，成堆紛亂如麻的圓點(diǎn)，看似無跡可尋卻能顯現(xiàn)出數(shù)據(jù)難以顯現(xiàn)的內(nèi)涵邏輯關(guān)系。很多人稱它“萬表之王”，它在數(shù)據(jù)剖析師手里現(xiàn)已演化成了一個(gè)強(qiáng)壯的數(shù)據(jù)剖析東西。
你一般會(huì)選擇哪種工具來做數(shù)據(jù)可視化？Lisa Charlotte Rost從去年五月開始嘗試了24種工具或語(yǔ)言來畫一張氣泡圖，經(jīng)過半年的學(xué)習(xí)實(shí)踐發(fā)現(xiàn)沒有完美的可視化工具，每個(gè)工具都有各自的優(yōu)缺點(diǎn)，但是對(duì)于某些領(lǐng)域目的，還是有比較推薦的可視化工具。
以下紅色的是軟件，藍(lán)色的是語(yǔ)言
越靠左越適合做數(shù)據(jù)分析，越靠右越適合做展示
越靠右越靈活
左側(cè)是靜態(tài)，右側(cè)是互動(dòng)
越往左越容易上手，越往上越靈活
這是一張工具選擇推薦圖，根據(jù)目的分類
左上是簡(jiǎn)單快捷的目的，左下是故事導(dǎo)向，右上是為了分享的分析，右側(cè)是創(chuàng)新型圖表，右下是分析型工具
在看完對(duì)工具的推薦后，有興趣的可以看下這24種工具是如何實(shí)現(xiàn)氣泡圖的。
數(shù)據(jù)源統(tǒng)一如下，4個(gè)字段分別為國(guó)家，人均收入，壽命，人口總數(shù)，想要做的效果是一個(gè)氣泡圖，X軸為人均收入，Y軸為壽命，氣泡大小為人口總數(shù)
工具1：Excel
工具2：Google Sheets
工具3：Adobe Illustrator
工具4：RAW by DensityDesign
工具5：Lyra
工具6：Tableau Public
工具7：Polestar
工具8：Quadrigram
工具9：Highcharts Cloud
工具10：Easychart
工具11：Plotly
工具12：NodeBox
工具13：R – native
#set working directorysetwd("Desktop")#read csvd = read.csv("data.csv", header=TRUE)#pl
工具14：R – ggplot2
#import librarylibrary(ggplot2)#set working directorysetwd("Desktop")#read csvd = read.csv("data.csv", header=TRUE)#plot chartggplot(d) +
geom_point(aes(x=log(income),y=health,size=population)) +
expand_limits(x=0)
工具15：R – ggvis
#import librarylibrary(ggvis)library(dplyr)#set working directorysetwd("Desktop")#read csvd = read.csv("data.csv", header=TRUE)#plot chartd %>%
ggvis(~income, ~health) %>%
layer_points(size= ~population,opacity:=0.6) %>%
scale_numeric("x",trans = "log",expand=0)
工具16：Python – matplotlib
#import librariesimport numpy as npimport pandas as pdimport matplotlib.pyplot as plt#r工具17：Python – Seaborn
#import librariesimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns#rea工具18：Python – Bokeh
#import librariesimport pandas as pdfrom bokeh.plotting import figure, show, output_file#read datadata = pd.read_csv("data.csv")#plot chartp =
figure(x_axis_type="log")p.scatter(data['income'], data['health'], radius=data['population']/100000,
? ? ? ? ?fill_color='black', fill_alpha=0.6, line_color=None)#write as html file工具19：Processing
void setup() {size(1000,500); #sets size of the canvasbackground(255); #sets background colorscale(1, -1);
#inverts y & x axistranslate(0, -height); #inverts y & x axis, step 2Table table = loadTable("data.csv", "header"); #loads csv
?for (TableRow row : table.rows()) { #for each rown in the csv, do:
? ?float health = row.getFloat("health");
? ?float income = row.getFloat("income");
? ?int population = row.getInt("population");
? ?#map the range of the column to the available height:
? ?float health_m = map(health,50,90,0,height);
? ?float income_log = log(income);
? ?float income_m = map(income_log,2.7, 5.13,0,width/4);
? ?float population_m =map(population,0,1376048943,1,140);
? ?ellipse(income_m,health_m,population_m,population_m); //draw the ellipse
?}}工具20：D3.js
<!-- mostly followed this example:
http://bl.ocks.org/weiglemc/6185069 --><!DOCTYPE html><html><head>
?<style>
?circle {
? ?fill: black;
? ?opacity:0.7;
?}
?</style>
?<script type="text/javascript" src="D3.v3.min.js"></script></head><body>
?<script type="text/javascript">
?// load data
?var data = D3.csv("data.csv", function(error, data) {
? ?// change string (from CSV) into number format
? ?data.forEach(function(d) {
? ? ?d.health = +d.health;
? ? ?d.income = Math.log(+d.income);
? ? ?d.population = +d.population;
? ? ?console.log(d.population, Math.sqrt(d.population))
? ?});
?// set scales
?var x = D3.scale.linear()
? ?.domain([0, D3.max(data, function(d) {return d.income;})])
? ?.range([0, 1000]);
?var y = D3.scale.linear()
? ?.domain([D3.min(data, function(d) {return d.health;}),
? ? ?D3.max(data, function(d) {return d.health; })])
? ?.range([500, 0]);
?var size = D3.scale.linear()
? ?.domain([D3.min(data, function(d) {return d.population;}),
? ? ?D3.max(data, function(d) {return d.population; })])
? ?.range([2, 40]);
?// append the chart to the website and set height&width
?var chart = D3.select("body")
? .append("svg:svg")
? .attr("width", 1000)
? .attr("height", 500)
?// draw the bubbles
?var g = chart.append("svg:g");
?g.selectAll("scatter-dots")
? ?.data(data)
? ?.enter().append("svg:circle")
? ? ? ?.attr("cx", function(d,i) {return x(d.income);})
? ? ? ?.attr("cy", function(d) return y(d.health);})
? ? ? ?.attr("r", function(d) {return size(d.population);});
?});
?</script></body></html>工具21：D3.js Templates
...nv.addGraph(function() {
? ?var chart = nv.models.scatter() //define that it's a scatterplot
? ? ? ?.xScale(D3.scale.log()) //log scale
? ? ? ?.pointRange([10, 5000]) //define bubble sizes
? ? ? ?.color(['black']); //set color
? ?D3.select('#chart') //select the div in which the chart should be plotted
? ? ? ?.datum(exampleData)
? ? ? ?.call(chart);
? ?//plot the chart
? ?return chart;});工具22：Highcharts.js
<!DOCTYPE HTML><html>
?<head>
? ?<script src="https://ajax.lug.ustc.edu.cn/ajax/libs/jquery/1.7.1/jquery.min.js" type="text/javascript"></script>
? ?<script src="https://code.highcharts.com/highcharts.js"></script>
? ?<script src="https://code.highcharts.com/modules/data.js"></script>
? ?<script src="https://code.highcharts.com/highcharts-more.js"></script>
?</head>
?<body>
? ?<div id="chart"></div>
? ?<script>
? ?var url = 'data.csv';
? ?$.get(url, function(csv) {
? ?// A hack to see through quoted text in the CSV
? ?csv = csv.replace(/(,)(?=(?:[^"]|"[^"]*")*$)/g, '|');
? ?$('#chart').highcharts({
? ? ?chart: {
? ? ? ?type: 'bubble'
? ? ?},
? ? ?data: {
? ? ? ?csv: csv,
? ? ? ?itemDelimiter: '|',
? ? ? ?seriesMapping: [{
? ? ? ? ?name: 0,
? ? ? ? ?x: 1,
? ? ? ? ?y: 2,
? ? ? ? ?z: 3
? ? ? ? ?}]
? ? ? ?},
? ? ? ?xAxis: {
? ? ? ? ?type: "logarithmic"
? ? ? ?},
? ? ? ?colors: ["#000000"],
? ? ?});
? ?});
? ?</script>
?</body></html>工具23：Vega
{
?"width": 1000,
?"height": 500,
?"data": [
? ?{
? ? ?"name": "data",
? ? ?"url": "data.csv",
? ? ?"format": {
? ? ? ?"type": "csv",
? ? ? ?"parse": {
? ? ? ? ?"income": "number"
? ? ? ?}
? ? ?}
? ?}
?],
?"scales": [
? ?{
? ? ?"name": "xscale",
? ? ?"type": "log",
? ? ?"domain": {
? ? ? ?"data": "data",
? ? ? ?"field": ["income"]
? ? ?},
? ? ?"range": "width",
? ? ?"nice": true,
? ? ?"zero": true
? ?},
? ?{
? ? ?"name": "yscale",
? ? ?"type": "linear",
? ? ?"domain": {
? ? ? ?"data": "data",
? ? ? ?"field": ["health"]
? ? ?},
? ? ?"range": "height",
? ? ?"zero": false
? ?},
? ?{
? ? ?"name": "size",
? ? ?"type": "linear",
? ? ?"domain": {
? ? ? ?"data": "data",
? ? ? ?"field": "population"
? ? ?},
? ? ?"range": [0,700]
? ?}
?],
?"axes": [
? ?{
? ? ?"type": "x",
? ? ?"scale": "xscale",
? ? ?"orient": "bottom"
? ?},
? ?{
? ? ?"type": "y",
? ? ?"scale": "yscale",
? ? ?"orient": "left"
? ?}
?],
?"marks": [
? ?{
? ? ?"type": "symbol",
? ? ?"from": {
? ? ? ?"data": "data"
? ? ?},
? ? ?"properties": {
? ? ? ?"enter": {
? ? ? ? ?"x": {
? ? ? ? ? ?"field": "income",
? ? ? ? ? ?"scale": "xscale"
? ? ? ? ?},
? ? ? ? ?"y": {
? ? ? ? ? ?"field": "health",
? ? ? ? ? ?"scale": "yscale"
? ? ? ? ?},
? ? ? ? ?"size": {
? ? ? ? ? ?"field":"population",
? ? ? ? ? ?"scale":"size",
? ? ? ? ? ?"shape":"cross"
? ? ? ? ?},
? ? ? ? ?"fill": {"value": "#000"},
? ? ? ? ?"opacity": {"value": 0.6}
? ? ? ?}
? ? ?}
? ?}
?]}工具24：Vega Lite
{
?"data": {"url": "data.csv", "formatType": "csv"},
?"mark": "circle",
?"encoding": {
? ?"y": {
? ? ?"field": "health",
? ? ?"type": "quantitative",
? ? ?"scale": {"zero": false}
? ?},
? ?"x": {
? ? ?"field": "income",
? ? ?"type": "quantitative",
? ? ?"scale": {"type": "log"}
? ?},
? ?"size": {
? ? ?"field": "population",
? ? ?"type": "quantitative"
? ?},
? ?"color": {"value": "#000"}
?},
?"config": {"cell": {"width": 1000,"height": 500}}
?}工具25：BIT 超級(jí)數(shù)據(jù)分析平臺(tái)
End.