散点图真是一个比较神奇的图形，正如它的名字一样，一堆纷乱如麻的圆点，看似无迹可寻却能显示出数据难以显示的内在逻辑关系。很多人称它“万表之王”，它在数据分析师手里已经演化成了一个强大的数据分析工具。
你一般会选择哪种工具来做数据可视化？Lisa Charlotte Rost从去年五月开始尝试了24种工具或语言来画一张气泡图，经过半年的学习实践发现没有完美的可视化工具，每个工具都有各自的优缺点，但是对于某些领域目的，还是有比较推荐的可视化工具。
以下红色的是软件，蓝色的是语言
越靠左越适合做数据分析，越靠右越适合做展示
越靠右越灵活
左侧是静态，右侧是互动
越往左越容易上手，越往上越灵活
这是一张工具选择推荐图，根据目的分类
左上是简单快捷的目的，左下是故事导向，右上是为了分享的分析，右侧是创新型图表，右下是分析型工具
在看完对工具的推荐后，有兴趣的可以看下这24种工具是如何实现气泡图的。
数据源统一如下，4个字段分别为国家，人均收入，寿命，人口总数，想要做的效果是一个气泡图，X轴为人均收入，Y轴为寿命，气泡大小为人口总数
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)#plot chart, set range for x-axis between 0 and 11symbols(log(d$income),d$health,circles=d$population,xlim = c(0,11))
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#read datadata = pd.read_csv("data.csv")#plot chartplt.scatter(np.log(data['income']), data['health'], s=data['population']/1000000, c='black')plt.xlim(xmin=0) #set origin for x axis to zeroplt.show()
工具17：Python – Seaborn
#import librariesimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns#read datadata = pd.read_csv("data.csv")#plot chartg = sns.regplot('income', 'health', data=data, color='k',fit_reg=False)g.set_xscale('log')plt.show()
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 and open in browseroutput_file("scatterplot.html")show(p)
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.googleapis.com/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 超级数据分析平台
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来源：数据君微信公众datakong
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原文始发于微信公众号（PPV课数据科学社区）：工具 | 一张图，教你用25种可视化工具如何完成
原创文章，作者：ppvke，如若转载，请注明出处：http://www.ppvke.com/archives/2619
