資料格式&&資料之間關系 VS 圖表分類

資料格式可分以下6類：

資料格式 VS圖表分類：

eg，Numeric且categoric類資料下各類圖表，

資料之間關系可分以下7類：

資料之間關系 VS 圖表分類：

一圖*Python語言**、R語言、D3.js三種實作方式*【代碼全部免費提供】

點擊任意圖，出現如下三個按鈕，即是一圖三種實作方法源代碼~

同時介紹圖形的使用范圍、常犯錯誤、更多學習資料，

以圈圖為例：

Python實作

# python實作
import pandas as pd
import holoviews as hv
from holoviews import opts, dim
from bokeh.sampledata.les_mis import data

# data set
nodes = hv.Dataset(pd.DataFrame(data['nodes']), 'index')

# chord diagram
chord = hv.Chord((links, nodes)).select(value=(5, None))
chord.opts(
    opts.Chord(cmap='Category20', edge_cmap='Category20', edge_color=dim('source').str(), 
               labels='name', node_color=dim('index').str()))

R實作

#R實作
library(circlize)
circos.clear()

#Create data
data = data.frame(
    factor = sample(letters[1:8], 1000, replace = TRUE),
    x = rnorm(1000), 
    y = runif(1000)
    )
 
#Initialize the plot.
par(mar = c(1, 1, 1, 1) ) 
circos.initialize(factors = data$factor, x = data$x )
 

# Build the regions of track #1 
circos.trackPlotRegion(factors = data$factor, y=data$y, panel.fun = function(x, y) {
    circos.axis(labels.cex=0.5, labels.font=1, lwd=0.8)
    })
# --> Add a scatterplot on it:
circos.trackPoints(data$factor, data$x, data$y, col = rgb(0.1,0.5,0.8,0.3), pch=20)
 
 
# Build the regions of track #2:
circlize::circos.trackPlotRegion(factors = data$factor, y=data$y, panel.fun = function(x, y) {
    circos.axis(labels=FALSE, major.tick=FALSE)
    })
# --> Add a scatterplot on it
circos.trackPoints(data$factor, data$x, data$y, col = rgb(0.9,0.5,0.8,0.3), pch=20, cex=2)
 
 
# Add track #3 --> don't forget you can custom the height of tracks!
circos.par("track.height" = 0.4)
circos.trackPlotRegion(factors = data$factor, y=data$y, panel.fun = function(x, y) {
    circos.axis(labels=FALSE, major.tick=FALSE)
    })
circos.trackLines(data$factor, data$x, data$y, col = rgb(0.9,0.5,0.1,0.3), pch=20, cex=2, type="h")
# and continue as long as needed!

D3.js實作