R River-Plot（Sankey Diagram）无法正确显示

Question

我正在通过R＆R的Riverplot套餐调查Sankey Diagrams。然而，我的输出根本不清晰，我确实做错了但是对R来说还是新手。基本上，数据（https://raw.githubusercontent.com/kilimba/data/master/infection_flows.csv）表示从特定年龄组到特定年龄组的感染流程（按性别分类）。我的R程序看起来像这样（通过curl获取的数据）：

library(riverplot)
library(curl)

edges <- read.csv(curl("https://raw.githubusercontent.com/kilimba/data/master/infection_flows.csv"))

names(edges) <- c("N1","N2","Value")
edges$N1 <- paste(edges$N1,"-source")
edges$N2 <- paste(edges$N2,"-target")

nodes = data.frame(ID = unique(c(edges$N1, edges$N2)), stringsAsFactors = FALSE)

nodes$x <- ifelse(grepl("Male -target",nodes$ID),3,
                  ifelse(grepl("Male -source",nodes$ID),1,
                         ifelse(grepl("Female",nodes$ID),2,"")
                  )
)
nodes$x <- as.numeric(nodes$x)

#males who are infection sources
msource <- subset(nodes,nodes$x =='1')
msource$y = 0

for(i in 1:nrow(msource)){
    msource["y"][i,] <- i 
}

#females (both sources and targets). Wish is that this will form the central tier of the chart
# as both a target of infection and a source
female <- subset(nodes,nodes$x == '2')
female$y = 0

for(i in 1:nrow(female)){
  female["y"][i,] <- i
}

# males who are infection targets
mtarget <- subset(nodes,nodes$x == '3')
mtarget$y = 0

for(i in 1:nrow(mtarget)){
  mtarget["y"][i,] <- i
}

nodes <- rbind(mtarget,msource,female)

rownames(nodes) = nodes$ID

palette = paste0(brewer.pal(4, "Set1"), "60")

styles = lapply(nodes$y, function(n) {
    list(col = palette[n+1], lty = 0, textcol = "black")
  })

names(styles) = nodes$ID

rp <- list(nodes = nodes, edges = edges, styles = styles)
class(rp) <- c(class(rp), "riverplot")
plot(rp, plot_area = 0.95, yscale=0.06)


msource <- subset(nodes,nodes$x =='1')

for(i in 1:nrow(msource)){
  msource[i,]$y <- i 
}

female <- subset(nodes,nodes$x == '2')

for(i in 1:nrow(female)){
  female[i,]$y <- i 
}

mtarget <- subset(nodes,nodes$x == '3')
for(i in 1:nrow(mtarget)){
  mtarget[i,]$y <- i 
}

nodes <- rbind(mtarget,msource,female)

rp <- list(nodes = nodes, edges = edges)
class(rp) <- c(class(rp), "riverplot")
plot(rp, plot_area = 0.95, yscale=0.06)

正如你所看到的，当你运行它时，输出是难以理解的，而我正在寻找以下内容：

||              ||                                ||
==============>>  ==============================>>||
||              ||==============================>>||
15-19 Male   15-19 Female                     15-19 Male

节点数据帧的输入是：

structure(list(ID = c("15-24 Male -target", "20-24 Male -target", 
"25-29 Male -target", "30-34 Male -target", "35-39 Male -target", 
"40-44 Male -target", "45-49 Male -target", "50-54 Male -target", 
"55-59 Male -target", "60+ Male -target", "15-19 Male -source", 
"20-24 Male -source", "25-29 Male -source", "30-34 Male -source", 
"35-39 Male -source", "40-44 Male -source", "45-49 Male -source", 
"50-54 Male -source", "55-59 Male -source", "60+ Male -source", 
"15-19 Female -source", "20-24 Female -source", "25-29 Female -source", 
"30-34 Female -source", "35-39 Female -source", "40-44 Female -source", 
"45-49 Female -source", "50-54 Female -source", "55-59 Female -source", 
"15-19 Female -target", "20-24 Female -target", "25-29 Female -target", 
"30-34 Female -target", "35-39 Female -target", "40-44 Female -target", 
"45-49 Female -target", "50-54 Female -target", "55-59 Female -target", 
"60+ Female -target"), x = c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2), y = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 
2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 
12, 13, 14, 15, 16, 17, 18, 19)), .Names = c("ID", "x", "y"), row.names = c("15-24 Male -target", 
"20-24 Male -target", "25-29 Male -target", "30-34 Male -target", 
"35-39 Male -target", "40-44 Male -target", "45-49 Male -target", 
"50-54 Male -target", "55-59 Male -target", "60+ Male -target", 
"15-19 Male -source", "20-24 Male -source", "25-29 Male -source", 
"30-34 Male -source", "35-39 Male -source", "40-44 Male -source", 
"45-49 Male -source", "50-54 Male -source", "55-59 Male -source", 
"60+ Male -source", "15-19 Female -source", "20-24 Female -source", 
"25-29 Female -source", "30-34 Female -source", "35-39 Female -source", 
"40-44 Female -source", "45-49 Female -source", "50-54 Female -source", 
"55-59 Female -source", "15-19 Female -target", "20-24 Female -target", 
"25-29 Female -target", "30-34 Female -target", "35-39 Female -target", 
"40-44 Female -target", "45-49 Female -target", "50-54 Female -target", 
"55-59 Female -target", "60+ Female -target"), class = "data.frame")

边缘数据帧的dput（）是：

structure(list(N1 = c("15-19 Male -source", "15-19 Male -source", 
"15-19 Male -source", "20-24 Male -source", "20-24 Male -source", 
"20-24 Male -source", "20-24 Male -source", "20-24 Male -source", 
"25-29 Male -source", "25-29 Male -source", "25-29 Male -source", 
"25-29 Male -source", "25-29 Male -source", "25-29 Male -source", 
"25-29 Male -source", "30-34 Male -source", "30-34 Male -source", 
"30-34 Male -source", "30-34 Male -source", "30-34 Male -source", 
"30-34 Male -source", "30-34 Male -source", "35-39 Male -source", 
"35-39 Male -source", "35-39 Male -source", "35-39 Male -source", 
"35-39 Male -source", "35-39 Male -source", "35-39 Male -source", 
"35-39 Male -source", "35-39 Male -source", "40-44 Male -source", 
"40-44 Male -source", "40-44 Male -source", "40-44 Male -source", 
"40-44 Male -source", "40-44 Male -source", "40-44 Male -source", 
"40-44 Male -source", "40-44 Male -source", "45-49 Male -source", 
"45-49 Male -source", "45-49 Male -source", "45-49 Male -source", 
"45-49 Male -source", "45-49 Male -source", "45-49 Male -source", 
"45-49 Male -source", "45-49 Male -source", "45-49 Male -source", 
"50-54 Male -source", "50-54 Male -source", "50-54 Male -source", 
"50-54 Male -source", "50-54 Male -source", "50-54 Male -source", 
"50-54 Male -source", "50-54 Male -source", "50-54 Male -source", 
"50-54 Male -source", "55-59 Male -source", "55-59 Male -source", 
"55-59 Male -source", "55-59 Male -source", "55-59 Male -source", 
"55-59 Male -source", "55-59 Male -source", "55-59 Male -source", 
"55-59 Male -source", "60+ Male -source", "60+ Male -source", 
"60+ Male -source", "60+ Male -source", "60+ Male -source", "60+ Male -source", 
"60+ Male -source", "60+ Male -source", "60+ Male -source", "60+ Male -source", 
"15-19 Female -source", "15-19 Female -source", "15-19 Female -source", 
"15-19 Female -source", "15-19 Female -source", "15-19 Female -source", 
"20-24 Female -source", "20-24 Female -source", "20-24 Female -source", 
"20-24 Female -source", "20-24 Female -source", "20-24 Female -source", 
"20-24 Female -source", "20-24 Female -source", "20-24 Female -source", 
"20-24 Female -source", "25-29 Female -source", "25-29 Female -source", 
"25-29 Female -source", "25-29 Female -source", "25-29 Female -source", 
"25-29 Female -source", "25-29 Female -source", "25-29 Female -source", 
"25-29 Female -source", "25-29 Female -source", "30-34 Female -source", 
"30-34 Female -source", "30-34 Female -source", "30-34 Female -source", 
"30-34 Female -source", "30-34 Female -source", "30-34 Female -source", 
"30-34 Female -source", "30-34 Female -source", "35-39 Female -source", 
"35-39 Female -source", "35-39 Female -source", "35-39 Female -source", 
"35-39 Female -source", "35-39 Female -source", "35-39 Female -source", 
"35-39 Female -source", "35-39 Female -source", "40-44 Female -source", 
"40-44 Female -source", "40-44 Female -source", "40-44 Female -source", 
"40-44 Female -source", "40-44 Female -source", "40-44 Female -source", 
"40-44 Female -source", "45-49 Female -source", "45-49 Female -source", 
"45-49 Female -source", "45-49 Female -source", "45-49 Female -source", 
"45-49 Female -source", "45-49 Female -source", "50-54 Female -source", 
"50-54 Female -source", "50-54 Female -source", "50-54 Female -source", 
"50-54 Female -source", "50-54 Female -source", "55-59 Female -source", 
"55-59 Female -source"), N2 = c("15-19 Female -target", "20-24 Female -target", 
"25-29 Female -target", "15-19 Female -target", "20-24 Female -target", 
"25-29 Female -target", "30-34 Female -target", "35-39 Female -target", 
"15-19 Female -target", "20-24 Female -target", "25-29 Female -target", 
"30-34 Female -target", "35-39 Female -target", "40-44 Female -target", 
"45-49 Female -target", "15-19 Female -target", "20-24 Female -target", 
"25-29 Female -target", "30-34 Female -target", "35-39 Female -target", 
"40-44 Female -target", "45-49 Female -target", "15-19 Female -target", 
"20-24 Female -target", "25-29 Female -target", "30-34 Female -target", 
"35-39 Female -target", "40-44 Female -target", "45-49 Female -target", 
"50-54 Female -target", "55-59 Female -target", "15-19 Female -target", 
"20-24 Female -target", "25-29 Female -target", "30-34 Female -target", 
"35-39 Female -target", "40-44 Female -target", "45-49 Female -target", 
"50-54 Female -target", "55-59 Female -target", "15-19 Female -target", 
"20-24 Female -target", "25-29 Female -target", "30-34 Female -target", 
"35-39 Female -target", "40-44 Female -target", "45-49 Female -target", 
"50-54 Female -target", "55-59 Female -target", "60+ Female -target", 
"15-19 Female -target", "20-24 Female -target", "25-29 Female -target", 
"30-34 Female -target", "35-39 Female -target", "40-44 Female -target", 
"45-49 Female -target", "50-54 Female -target", "55-59 Female -target", 
"60+ Female -target", "20-24 Female -target", "25-29 Female -target", 
"30-34 Female -target", "35-39 Female -target", "40-44 Female -target", 
"45-49 Female -target", "50-54 Female -target", "55-59 Female -target", 
"60+ Female -target", "15-19 Female -target", "20-24 Female -target", 
"25-29 Female -target", "30-34 Female -target", "35-39 Female -target", 
"40-44 Female -target", "45-49 Female -target", "50-54 Female -target", 
"55-59 Female -target", "60+ Female -target", "15-24 Male -target", 
"20-24 Male -target", "25-29 Male -target", "30-34 Male -target", 
"35-39 Male -target", "40-44 Male -target", "15-24 Male -target", 
"20-24 Male -target", "25-29 Male -target", "30-34 Male -target", 
"35-39 Male -target", "40-44 Male -target", "45-49 Male -target", 
"50-54 Male -target", "55-59 Male -target", "60+ Male -target", 
"15-24 Male -target", "20-24 Male -target", "25-29 Male -target", 
"30-34 Male -target", "35-39 Male -target", "40-44 Male -target", 
"45-49 Male -target", "50-54 Male -target", "55-59 Male -target", 
"60+ Male -target", "20-24 Male -target", "25-29 Male -target", 
"30-34 Male -target", "35-39 Male -target", "40-44 Male -target", 
"45-49 Male -target", "50-54 Male -target", "55-59 Male -target", 
"60+ Male -target", "20-24 Male -target", "25-29 Male -target", 
"30-34 Male -target", "35-39 Male -target", "40-44 Male -target", 
"45-49 Male -target", "50-54 Male -target", "55-59 Male -target", 
"60+ Male -target", "25-29 Male -target", "30-34 Male -target", 
"35-39 Male -target", "40-44 Male -target", "45-49 Male -target", 
"50-54 Male -target", "55-59 Male -target", "60+ Male -target", 
"30-34 Male -target", "35-39 Male -target", "40-44 Male -target", 
"45-49 Male -target", "50-54 Male -target", "55-59 Male -target", 
"60+ Male -target", "35-39 Male -target", "40-44 Male -target", 
"45-49 Male -target", "50-54 Male -target", "55-59 Male -target", 
"60+ Male -target", "45-49 Male -target", "60+ Male -target"), 
    Value = c(0.653, 0.05, 0.005, 4.752, 4.29, 0.171, 0.065, 
    0.076, 2.772, 12.659, 3.636, 0.242, 0.019, 0.019, 0.001, 
    0.523, 3.943, 6.486, 1.655, 0.192, 0.074, 0.059, 0.286, 1.424, 
    3.276, 3.686, 1.349, 0.397, 0.065, 0.455, 0.008, 0.208, 0.763, 
    0.938, 1.567, 2.14, 1.054, 0.102, 0.083, 0.01, 0.023, 0.066, 
    0.1, 0.207, 0.535, 0.338, 0.262, 0.033, 0.292, 0.076, 0.003, 
    0.012, 0.017, 0.116, 0.082, 0.241, 0.358, 0.093, 0.017, 0.05, 
    0.015, 0.078, 0.039, 0.176, 0.119, 0.181, 0.098, 0.029, 0.3, 
    0.002, 0.009, 0.005, 0.008, 0.035, 0.034, 0.03, 0.024, 0.05, 
    0.526, 0.988, 1.894, 0.431, 0.061, 0.024, 0.02, 0.212, 4.804, 
    5.526, 1.29, 0.341, 0.206, 0.039, 0.009, 0.012, 0.02, 0.032, 
    0.28, 2.32, 3.103, 1.146, 0.37, 0.087, 0.018, 0.09, 0.017, 
    0.108, 0.156, 0.803, 1.307, 0.628, 0.183, 0.12, 0.045, 0.026, 
    0.12, 0.011, 0.088, 0.453, 0.812, 0.448, 0.08, 0.195, 0.105, 
    0.008, 0.023, 0.088, 0.264, 0.187, 0.157, 0.087, 0.068, 0.009, 
    0.007, 0.012, 0.07, 0.112, 0.064, 0.029, 0.037, 0.008, 0.007, 
    0.023, 0.027, 0.018, 0.019, 0.012)), .Names = c("N1", "N2", 
"Value"), row.names = c(NA, -146L), class = "data.frame")

任何帮助都会有很大帮助。

谢谢， Tumaini