堆叠多个图,垂直使用相同的x轴但在R中使用不同的Y轴

Question

堆叠多个图,垂直使用相同的x轴但在R中使用不同的Y轴

我有一个data.frame,具有针对日期的多个时间序列向量:时间向量.我想绘制所有相关的矢量,垂直堆叠在具有相同X轴但具有唯一Y轴的单独图形上.与此类似的图表: 在此输入图像描述

我的数据看起来像这样:

 dt <- structure(list(DEPTH = c(156, 156.5, 157.4, 158.15, 158.8, 159.2, 
159.75, 160.35, 160.85, 161.1, 161.6, 162.05, 162.5, 162.65, 
163.15, 163.45, 163.55, 163.8, 163.65, 163.75, 163.8, 163.8, 
163.75, 164.45, 164.8, 165.35, 165.65, 165.75, 166.1, 166.75, 
167, 167.2, 167.65, 168, 168.8, 169.3, 169.7, 170.2, 170.65, 
170.9, 171.45, 171.65, 172, 172.1, 172.25, 173, 173.4, 173.9, 
174.2, 174.6, 175, 175.25, 175.45, 175.9, 176.25, 176.7, 177, 
177.15, 177.5, 178, 178.5, 179.05, 179.2, 180.7, 181.05, 181.25, 
181.5, 181.7, 182.1, 182.3, 182.35, 182.75, 183.1, 183.65, 184.3, 
184.6, 185.1, 185.15, 185.3, 185.15, 185.25, 185.3, 185.15), 
    Smooth.Vert.Speed = c(-0.550000000000011, -0.5, -0.900000000000006, 
    -0.75, -0.650000000000006, -0.399999999999977, -0.550000000000011, 
    -0.599999999999994, -0.5, -0.25, -0.5, -0.450000000000017, 
    -0.449999999999989, -0.150000000000006, -0.5, -0.299999999999983, 
    -0.100000000000023, -0.25, 0.150000000000006, -0.0999999999999943, 
    -0.0500000000000114, 0, 0.0500000000000114, -0.699999999999989, 
    -0.350000000000023, -0.549999999999983, -0.300000000000011, 
    -0.0999999999999943, -0.349999999999994, -0.650000000000006, 
    -0.25, -0.199999999999989, -0.450000000000017, -0.349999999999994, 
    -0.800000000000011, -0.5, -0.399999999999977, -0.5, -0.450000000000017, 
    -0.25, -0.549999999999983, -0.200000000000017, -0.349999999999994, 
    -0.0999999999999943, -0.150000000000006, -0.75, -0.400000000000006, 
    -0.5, -0.299999999999983, -0.400000000000006, -0.400000000000006, 
    -0.25, -0.199999999999989, -0.450000000000017, -0.349999999999994, 
    -0.449999999999989, -0.300000000000011, -0.150000000000006, 
    -0.349999999999994, -0.5, -0.5, -0.550000000000011, -0.149999999999977, 
    -1.5, -0.350000000000023, -0.199999999999989, -0.25, -0.199999999999989, 
    -0.400000000000006, -0.200000000000017, -0.049999999999983, 
    -0.400000000000006, -0.349999999999994, -0.550000000000011, 
    -0.650000000000006, -0.299999999999983, -0.5, -0.0500000000000114, 
    -0.150000000000006, 0.150000000000006, -0.0999999999999943, 
    -0.0500000000000114, 0.150000000000006), DIVE_SURF = c("dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21", "dive21", "dive21", 
    "dive21", "dive21", "dive21", "dive21"), X = c(2050L, 2062L, 
    2026L, 2078L, 2058L, 2076L, 2050L, 2068L, 2060L, 2078L, 2058L, 
    2088L, 2080L, 2065L, 2088L, 2076L, 2084L, 2105L, 2084L, 2102L, 
    2123L, 2096L, 2074L, 2054L, 2090L, 2089L, 2080L, 2078L, 2068L, 
    2092L, 2084L, 2082L, 2094L, 2056L, 2062L, 2067L, 2082L, 2084L, 
    2091L, 2058L, 2076L, 2098L, 2104L, 2090L, 2058L, 2050L, 2080L, 
    2074L, 2074L, 2082L, 2070L, 2088L, 2062L, 2062L, 2082L, 2086L, 
    2070L, 2081L, 2092L, 2058L, 2060L, 2076L, 2094L, 2083L, 2072L, 
    2107L, 2104L, 2066L, 2110L, 2104L, 2072L, 2076L, 2065L, 2042L, 
    2066L, 2093L, 2080L, 2083L, 2108L, 2107L, 2086L, 2096L, 2126L
    ), Y = c(2036L, 2000L, 2049L, 1966L, 2042L, 2078L, 2072L, 
    2055L, 2036L, 2128L, 2044L, 2112L, 2066L, 2051L, 2102L, 2060L, 
    2054L, 2043L, 2034L, 2086L, 1980L, 2076L, 2003L, 2033L, 2107L, 
    1992L, 2028L, 2027L, 2024L, 2005L, 2050L, 2010L, 1944L, 2010L, 
    2046L, 2020L, 2088L, 2086L, 2034L, 2066L, 2060L, 2152L, 2044L, 
    2078L, 2040L, 2067L, 2080L, 2072L, 2073L, 2028L, 2066L, 2082L, 
    2030L, 2042L, 1990L, 2076L, 2054L, 2064L, 2016L, 2048L, 2029L, 
    2008L, 2090L, 2038L, 2026L, 2096L, 2002L, 2025L, 2001L, 2098L, 
    2061L, 2022L, 2054L, 2064L, 2043L, 2090L, 2042L, 2086L, 2073L, 
    2066L, 2040L, 2081L, 2087L), Z = c(2488L, 2484L, 2490L, 2486L, 
    2488L, 2492L, 2498L, 2490L, 2492L, 2484L, 2491L, 2494L, 2497L, 
    2493L, 2488L, 2493L, 2494L, 2484L, 2486L, 2487L, 2478L, 2490L, 
    2478L, 2493L, 2490L, 2486L, 2488L, 2486L, 2488L, 2482L, 2488L, 
    2480L, 2480L, 2488L, 2490L, 2490L, 2490L, 2489L, 2492L, 2490L, 
    2486L, 2480L, 2488L, 2491L, 2486L, 2488L, 2488L, 2494L, 2490L, 
    2488L, 2492L, 2498L, 2484L, 2491L, 2480L, 2491L, 2497L, 2487L, 
    2482L, 2490L, 2490L, 2478L, 2488L, 2492L, 2492L, 2482L, 2484L, 
    2489L, 2482L, 2484L, 2485L, 2492L, 2488L, 2493L, 2487L, 2490L, 
    2492L, 2488L, 2490L, 2487L, 2484L, 2486L, 2478L)), .Names = c("DEPTH", 
"Smooth.Vert.Speed", "DIVE_SURF", "X", "Y", "Z"), row.names = 7222:7304, class = "data.frame")

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我希望在具有共同X轴的单独图形上绘制DEPTH,X,Y和Z.

Answer 1

aar*_*len 12

我同意@PaulHiemstra,ggplot2是要走的路.

假设Smooth.Vert.Speed是要针对其绘制常见的X轴变量DEPTH,X,Y和Z...

library(ggplot2)
library(reshape2)

# Add time variable as per @BenBolker's suggestion
dt$time <- seq(nrow(dt))

# Use melt to reshape data so values and variables are in separate columns
dt.df <- melt(dt, measure.vars = c("DEPTH", "X", "Y", "Z"))

ggplot(dt.df, aes(x = time, y = value)) +
  geom_line(aes(color = variable)) +
  facet_grid(variable ~ ., scales = "free_y") +
  # Suppress the legend since color isn't actually providing any information
  opts(legend.position = "none")

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根据常见的x变量绘制多个y变量

更改颜色非常容易(将`color = variable`添加为`aes()`函数的参数;如果要手动分配颜色而不是自动分配颜色,请参阅`？scale_colour_manual`.单个y轴标签会更难,但右侧的条带标签*可能是一个可接受的替代品.出于美观原因,我喜欢`theme_update(theme_bw())`并导入`library(grid)`并将`+ opts(panel.margin = unit(0,"lines"))`添加到ggplot调用中.正如我上面所说,@ DirkEddelbuettel的方法将是最可定制的. (2认同)

Answer 2

Dir*_*tel 8

只是为了与众不同,让我提一个既不涉及格子也不涉及ggplot2的解决方案 - 几年前我将其发布到Romain的R Graph Gallery作为条目65,其中包含代码.它只是叠加图形,使用par()设置来保持它们堆叠.

请注意,垂直尺寸可以选择不同,它们也可以很容易地具有相同的高度.

在此输入图像描述

试试[我的网站上的这个链接](http://dirk.eddelbuettel.com/code/snippets/bollingerBands.R).该代码已有九年了...... (2认同)

Answer 3

Ben*_*ker 6

如果你想要老式,你可以使用lattice.与@aaronwolen不同,我假设time数据集中有一个缺失的变量,所以我做了一个:

dt$time <- seq(nrow(dt))
library(reshape2)
mm <- melt(subset(dt,select=c(time,DEPTH,X,Y,Z)),id.var="time")
library(lattice)
xyplot(value~time|variable,data=mm,type="l",
       scales=list(y=list(relation="free")),
       layout=c(1,4))

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在此输入图像描述

我更新了代码,因此每个变量的颜色都不同.我不知道为每个y轴添加单个标签或仅为DEPTH反转y轴的方法.ggplot2的faceting函数的目的是将数据的子集放在单独的面板中,同时仍然根据相同的x和y变量绘制它们.因此每个轴只有一个标签.考虑到你想做什么,也许最好只生成4个单独的图堆叠在彼此之上,只标记底部图的x轴. (2认同)

Answer 4

Joj*_*Ono 5

我实际上已经想出了另一种有趣的方式来使用动物园图书馆做到这一点：

library(zoo)
z <- with(dt, zoo(cbind(DEPTH, X, Y, Z),as.POSIXct(time))) 
plot.zoo(z,  ylab=c("Depth (m)", "Pitch Angle (degrees)", "Swaying Acceleration (m/s^2)", "Heaving Acceleration (m/s^2)"), col=c("black", "blue", "darkred", "darkgreen"), 
     xlab = c("Time"), lwd=2, ylim=list((rev(range(dt$DEPTH))), c(-90,90), c(-10,10), c(-10,10)))

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因此，在动物园图中，您可以以列表形式创建新的轴标签，并且所有图都可以具有不同的颜色。

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