编程在R（气泡图图表）

Question

我对旅客出行频率的数据集：

CountryOrigin - 拥有40 +国名
（印度，澳大利亚，中国，日本，BATAM，巴厘岛，新加坡）
CountryDestination - 有40多个国名 （印度，澳大利亚，中国，日本，BATAM，巴厘岛，新加坡）

 IND AUS CHI JAP BAT SING 
IND  0  4 10 12 24  89 
AUS 19  0 12  9  7  20 
CHI 34 56  0  2  6  18 
JAP 12 17 56  0  2  2 
SING 56 34  7  3 35  0 

我需要在y轴在x轴和目的地的名称起源位置名称，频率应该表示为泡沫的大小。

Answer 1

我会使用ggplot2这些（或任何种类）的情节。我们首先创建一些测试数据：

countries = c('IND', 'AUS', 'CHI', 'JAP', 'BAT', 'SING') 
frequencies = matrix(sample(1:100, 36), 6, 6, dimnames = list(countries, countries)) 
diag(frequencies) = 0 

并绘制图。首先，我们必须矩阵数据转换为合适的格式：

library(reshape2) 
frequencies_df = melt(frequencies) 
names(frequencies_df) = c('origin', 'destination', 'frequency') 

并使用ggplot2：

library(ggplot2) 
ggplot(frequencies_df, aes(x = origin, y = destination, size = frequencies)) + geom_point() 

Answer 2

我想离开的另一种方式来可视化这些数据。您可以通过在R中使用circlize和migest包来可视化人们移动或旅行的方式。您必须执行大量编码，但如果您遵循migest中的演示，仍可以创建您想要的内容。你需要一个矩阵和一个数据框来绘制这个数字。但是，一旦你有他们的权利，你可以使用演示代码。在这个例子中，你会看到来自6个国家的人如何旅行。例如，澳大利亚人在这个假数据中访问了日本，马来西亚和印度;三条红线到达这些国家。如果线路较宽，这意味着更多人访问这些国家。同样，中国人访问了澳大利亚，日本和马来西亚。我在这里留下我的代码。

library(circlize) 
library(migest) 
library(dplyr) 

m <- data.frame(order = 1:6, 
      country = c("Ausralia", "India", "China", "Japan", "Thailand", "Malaysia"), 
      V3 = c(1, 150000, 90000, 180000, 15000, 10000), 
      V4 = c(35000, 1, 10000, 12000, 25000, 8000), 
      V5 = c(10000, 7000, 1, 40000, 5000, 4000), 
      V6 = c(7000, 8000, 175000, 1, 11000, 18000), 
      V7 = c(70000, 30000, 22000, 120000, 1, 40000), 
      V8 = c(60000, 90000, 110000, 14000, 30000, 1), 
      r = c(255,255,255,153,51,51), 
      g = c(51, 153, 255, 255, 255, 255), 
      b = c(51, 51, 51, 51, 51, 153), 
      stringsAsFactors = FALSE) 

### Create a data frame 
df1 <- m[, c(1,2, 9:11)] 

### Create a matrix 
m <- m[,-(1:2)]/1e04 
m <- as.matrix(m[,c(1:6)]) 
dimnames(m) <- list(orig = df1$country, dest = df1$country) 


### Sort order of data.frame and matrix for plotting in circos 

df1 <- arrange(df1, order) 

df1$country <- factor(df1$country, levels = df1$country) 

m <- m[levels(df1$country),levels(df1$country)] 


### Define ranges of circos sectors and their colors (both of the sectors and the links) 

df1$xmin <- 0 

df1$xmax <- rowSums(m) + colSums(m) 

n <- nrow(df1) 

df1$rcol<-rgb(df1$r, df1$g, df1$b, max = 255) 

df1$lcol<-rgb(df1$r, df1$g, df1$b, alpha=200, max = 255) 


## 
## Plot sectors (outer part) 
## 

par(mar=rep(0,4)) 

circos.clear() 

### Basic circos graphic parameters 
circos.par(cell.padding=c(0,0,0,0), track.margin=c(0,0.15), start.degree = 90, gap.degree =4) 

### Sector details 
circos.initialize(factors = df1$country, xlim = cbind(df1$xmin, df1$xmax)) 

### Plot sectors 

circos.trackPlotRegion(ylim = c(0, 1), factors = df1$country, track.height=0.1, 
         #panel.fun for each sector 
         panel.fun = function(x, y) { 
         #select details of current sector 
         name = get.cell.meta.data("sector.index") 
         i = get.cell.meta.data("sector.numeric.index") 
         xlim = get.cell.meta.data("xlim") 
         ylim = get.cell.meta.data("ylim") 

         #text direction (dd) and adjusmtents (aa) 
         theta = circlize(mean(xlim), 1.3)[1, 1] %% 360 
         dd <- ifelse(theta < 90 || theta > 270, "clockwise", "reverse.clockwise") 
         aa = c(1, 0.5) 
         if(theta < 90 || theta > 270) aa = c(0, 0.5) 

         #plot country labels 
         circos.text(x=mean(xlim), y=1.7, labels=name, facing = dd, cex=0.6, adj = aa) 

         #plot main sector 
         circos.rect(xleft=xlim[1], ybottom=ylim[1], xright=xlim[2], ytop=ylim[2], 
            col = df1$rcol[i], border=df1$rcol[i]) 

         #blank in part of main sector 
         circos.rect(xleft=xlim[1], ybottom=ylim[1], xright=xlim[2]-rowSums(m)[i], ytop=ylim[1]+0.3, 
            col = "white", border = "white") 

         #white line all the way around 
         circos.rect(xleft=xlim[1], ybottom=0.3, xright=xlim[2], ytop=0.32, col = "white", border = "white") 

         #plot axis 
         circos.axis(labels.cex=0.6, direction = "outside", major.at=seq(from=0,to=floor(df1$xmax)[i],by=5), 
            minor.ticks=1, labels.away.percentage = 0.15) 
        }) 



## 
## Plot links (inner part) 
## 

### Add sum values to df1, marking the x-position of the first links 
### out (sum1) and in (sum2). Updated for further links in loop below. 

df1$sum1 <- colSums(m) 
df1$sum2 <- numeric(n) 

### Create a data.frame of the flow matrix sorted by flow size, to allow largest flow plotted first 
df2 <- cbind(as.data.frame(m),orig=rownames(m), stringsAsFactors=FALSE) 

df2 <- reshape(df2, idvar="orig", varying=list(1:n), direction="long", 
      timevar="dest", time=rownames(m), v.names = "m") 

df2 <- arrange(df2,desc(m)) 

### Keep only the largest flows to avoid clutter 
df2 <- subset(df2, m > quantile(m,0.6)) 

### Plot links 

for(k in 1:nrow(df2)){ 
    #i,j reference of flow matrix 
    i<-match(df2$orig[k],df1$country) 
    j<-match(df2$dest[k],df1$country) 

#plot link 
circos.link(sector.index1=df1$country[i], point1=c(df1$sum1[i], df1$sum1[i] + abs(m[i, j])), 
      sector.index2=df1$country[j], point2=c(df1$sum2[j], df1$sum2[j] + abs(m[i, j])), 
      col = df1$lcol[i]) 

#update sum1 and sum2 for use when plotting the next link 
df1$sum1[i] = df1$sum1[i] + abs(m[i, j]) 
df1$sum2[j] = df1$sum2[j] + abs(m[i, j]) 
}