使用ggplot2的方案圖

Question

我想創建一個使用ggplot2的方面圖，代表每月的風向圖。

我最近看了下面的帖子：How to map wind direction and speed (velocity plot) with R。我認爲這對我來說可能是一個好的開始。以下數據集將風值表示爲u和v分量，時間步長爲3小時。我想將它表示爲一個hodogram，這意味着每個矢量都在前一個矢量之後。

   u    v 
[1,] -4.0000000 -6.928203e+00 
[2,] -6.1283555 -5.142301e+00 
[3,] -5.0000000 1.224647e-15 
[4,] -3.7587705 1.368081e+00 
[5,] 4.0000000 -4.898587e-16 
[6,] 4.6984631 -1.710101e+00 
[7,] 5.6381557 2.052121e+00 
[8,] 6.1283555 5.142301e+00 
[9,] -9.1925333 -7.713451e+00 
[10,] -6.5778483 2.394141e+00 
[11,] -5.3623111 4.499513e+00 
[12,] -4.5962667 3.856726e+00 
[13,] -7.0000000 1.714506e-15 
[14,] -6.5778483 -2.394141e+00 
[15,] 6.0000000 -7.347881e-16 
[16,] -6.5778483 -2.394141e+00 
[17,] -6.0000000 1.469576e-15 
[18,] -8.0000000 1.959435e-15 
[19,] -5.6381557 2.052121e+00 
[20,] -6.0000000 1.469576e-15 
[21,] -4.5962667 3.856726e+00 
[22,] 2.0000000 -3.464102e+00 
[23,] 5.6381557 -2.052121e+00 
[24,] 6.0000000 -7.347881e-16 
[25,] 5.6381557 -2.052121e+00 
[26,] -5.3623111 -4.499513e+00 
[27,] -4.5962667 -3.856726e+00 
[28,] -6.1283555 -5.142301e+00 
[29,] -4.6984631 -1.710101e+00 
[30,] 0.8682409 -4.924039e+00 
[31,] 2.5000000 -4.330127e+00 
[32,] -0.8682409 -4.924039e+00 
[33,] -6.0000000 1.469576e-15 
[34,] -5.3623111 -4.499513e+00 
[35,] -3.8302222 -3.213938e+00 
[36,] -4.5962667 -3.856726e+00 
[37,] -3.5000000 -6.062178e+00 
[38,] 1.0418891 -5.908847e+00 
[39,] 5.3623111 -4.499513e+00 
[40,] 4.5962667 -3.856726e+00 
[41,] 3.8302222 -3.213938e+00 
[42,] 3.0000000 -5.196152e+00 
[43,] 5.3623111 -4.499513e+00 
[44,] 5.3623111 -4.499513e+00 
[45,] 4.5962667 -3.856726e+00 
[46,] 3.0000000 -5.196152e+00 
[47,] 4.5962667 -3.856726e+00 
[48,] 3.8302222 -3.213938e+00 
[49,] 1.0418891 -5.908847e+00 
[50,] 3.8302222 -3.213938e+00 

你可以在這裏找到一個矢端曲線例如：（如在左下角的一個）。

有了這些hodograms（每月1個），我想用ggplot2來繪製一個刻面圖，但我想（我希望）我可以管理這個部分。

任何幫助，將不勝感激。

非常感謝您提前！

Answer 1

這裏是我的結果。它需要一些改進，但我已經接近我想要的結果，正如帖子中所述。下面是我使用的代碼：給社區＃1再次

感謝這始終是真正的反應，樂於助人：

library("ggplot2") 
library("plyr") 
mydata <- read.table("C:\\myfile.csv", sep="\t", header=TRUE) 
seasons <- mydata$seasons 
years <- mydata$year 
u <- mydata$u 
v <- mydata$v 
intensity <- mydata$intensity 
wind <- cbind(u,v,intensity,seasons,years) 
wind <- data.frame(wind) 
x <- ddply(wind, .(years, seasons), summarize, x=cumsum(u*0.0108)) 
y <- ddply(wind, .(years, seasons), summarize, y=cumsum(v*0.0108)) 
x <- x$x 
y <- y$y 
wind <- cbind(wind,x,y) 
wind <- data.frame(wind) 
wind$seasons[wind$seasons == 1] <- "winter" 
wind$seasons[wind$seasons == 2] <- "spring" 
wind$seasons[wind$seasons == 3] <- "summer" 
wind$seasons[wind$seasons == 4] <- "autumn" 
p <- ggplot(wind, aes(x, y)) + geom_path(aes(colour = intensity))+ scale_colour_gradientn(colours=c("blue","yellow","red")) 
p + facet_grid(seasons ~ years) 

給我下面的結果！

Answer 2

我得到的東西，我還在做這個工作...

u <- mydata$u 
v <- mydata$v 
x <- cumsum(mydata$u[56297:56704]*10.8) 
y <- cumsum(mydata$v[56297:56704]*10.8) 
wind <- cbind(x,y) 
wind <- data.frame(wind) 
p <- ggplot(wind) + geom_path(aes(x, y, colour = x)) 

未完待續......不要猶豫評論！）

Answer 3

這裏我會怎麼做的一個概念。我會讓你擺弄細節（如刪除標題條）。

library(ggplot2) 
library(gridExtra) 
library(MASS) 

linedata <- data.frame(time = rep(1:200, 3), 
         vals = runif(600), 
         source = rep(letters[1:3], each = 200)) 

normdata <- as.data.frame(mvrnorm(n = 600, mu = c(0, 0), Sigma = matrix(c(0.5, 0, 0, 0.5), ncol = 2))) 
normdata$time <- rep(1:200, times = 3) 
normdata$source = rep(letters[1:3], each = 200) 
names(normdata)[1:2] <- c("x", "y") 

linegraph <- ggplot(linedata, aes(x = time, y = vals)) + 
    theme_bw() + 
    geom_line() + 
    facet_wrap(~ source, ncol = 1) 

normgraph <- ggplot(normdata, aes(x = x, y = y)) + 
    theme_bw() + 
    geom_path() + 
    facet_wrap(~ source, ncol = 3) 

grid.arrange(linegraph, normgraph, ncol = 1)