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我正在尝试为多个地点的观测数据和建模数据创建一个显示每月土壤水分垂直剖面的图。
到目前为止,我只能绘制一组值,观察值或建模值,如下例所示:
library(ggplot2)
library(RColorBrewer)
# Create customized color palette
mypal <- colorRampPalette(brewer.pal(6,"PuBu"))
ggplot(df1, aes(x=value, y=depth, colour=as.factor(month))) +
geom_path() +
facet_wrap(~ site) +
scale_y_reverse() +
scale_colour_manual(values=mypal(12)) +
theme_bw(base_size=18) +
ylab("Depth") + xlab(bquote('Soil moisture (' ~m^3~m^-3*')')) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
df1 <- structure(list(site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L), .Label = c("IL_Shabbona_5_NNE", "ME_Limestone_4_NNW",
"ME_Old_Town_2_W", "MI_Chatham_1_SE", "MI_Gaylord_9_SSW", "MN_Goodridge_12_NNW",
"MN_Sandstone_6_W", "NH_Durham_2_N", "NH_Durham_2_SSW", "NY_Ithaca_13_E",
"NY_Millbrook_3_W", "WI_Necedah_5_WNW"), class = "factor"), month = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L), depth = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100), clm = c(0.324,
0.321, 0.334, 0.33, 0.285, 0.277, 0.264, 0.261, 0.261, 0.273,
0.303, 0.339, 0.348, 0.349, 0.356, 0.333, 0.289, 0.281, 0.269,
0.267, 0.266, 0.277, 0.303, 0.352, 0.345, 0.349, 0.355, 0.337,
0.295, 0.288, 0.277, 0.275, 0.274, 0.284, 0.299, 0.344, 0.299,
0.306, 0.314, 0.331, 0.312, 0.305, 0.295, 0.293, 0.292, 0.3,
0.299, 0.295, 0.274, 0.278, 0.284, 0.308, 0.302, 0.298, 0.289,
0.287, 0.286, 0.292, 0.291, 0.281, 0.477, 0.475, 0.475, 0.518,
0.379, 0.353, 0.352, 0.341, 0.347, 0.373, 0.46, 0.499, 0.399,
0.401, 0.399, 0.435, 0.346, 0.326, 0.325, 0.316, 0.32, 0.339,
0.37, 0.409, 0.339, 0.34, 0.337, 0.362, 0.313, 0.294, 0.293,
0.286, 0.289, 0.306, 0.317, 0.345, 0.256, 0.259, 0.26, 0.274,
0.281, 0.262, 0.26, 0.254, 0.255, 0.271, 0.263, 0.256, 0.243,
0.242, 0.242, 0.252, 0.281, 0.267, 0.266, 0.261, 0.26, 0.274,
0.27, 0.25, 0.497, 0.467, 0.461, 0.434, 0.377, 0.361, 0.341,
0.314, 0.334, 0.372, 0.394, 0.514, 0.363, 0.35, 0.344, 0.366,
0.34, 0.327, 0.311, 0.289, 0.304, 0.336, 0.344, 0.367, 0.308,
0.294, 0.286, 0.318, 0.304, 0.293, 0.279, 0.26, 0.272, 0.3, 0.309,
0.308, 0.232, 0.228, 0.237, 0.281, 0.271, 0.263, 0.25, 0.235,
0.239, 0.265, 0.277, 0.25, 0.235, 0.233, 0.24, 0.28, 0.273, 0.265,
0.254, 0.242, 0.241, 0.264, 0.277, 0.258, 0.448, 0.446, 0.454,
0.49, 0.341, 0.31, 0.297, 0.298, 0.325, 0.359, 0.427, 0.478,
0.351, 0.352, 0.359, 0.403, 0.312, 0.287, 0.276, 0.276, 0.298,
0.325, 0.333, 0.355, 0.305, 0.309, 0.312, 0.338, 0.281, 0.26,
0.25, 0.249, 0.266, 0.29, 0.288, 0.3, 0.233, 0.233, 0.237, 0.265,
0.256, 0.238, 0.229, 0.227, 0.236, 0.256, 0.252, 0.239, 0.231,
0.229, 0.228, 0.253, 0.262, 0.247, 0.237, 0.235, 0.24, 0.255,
0.257, 0.24, 0.326, 0.323, 0.341, 0.343, 0.268, 0.236, 0.209,
0.213, 0.228, 0.27, 0.301, 0.34, 0.311, 0.31, 0.321, 0.322, 0.254,
0.228, 0.206, 0.208, 0.22, 0.254, 0.278, 0.317, 0.285, 0.285,
0.295, 0.302, 0.243, 0.22, 0.2, 0.2, 0.211, 0.241, 0.253, 0.283,
0.218, 0.22, 0.222, 0.259, 0.236, 0.215, 0.197, 0.193, 0.198,
0.225, 0.232, 0.22, 0.19, 0.188, 0.187, 0.214, 0.225, 0.205,
0.189, 0.182, 0.183, 0.201, 0.213, 0.2, 0.437, 0.435, 0.437,
0.456, 0.391, 0.367, 0.358, 0.339, 0.347, 0.374, 0.468, 0.468,
0.402, 0.401, 0.406, 0.442, 0.39, 0.369, 0.362, 0.345, 0.351,
0.364, 0.41, 0.425, 0.386, 0.386, 0.389, 0.415, 0.375, 0.355,
0.349, 0.336, 0.34, 0.349, 0.377, 0.401, 0.318, 0.319, 0.32,
0.343, 0.341, 0.32, 0.316, 0.307, 0.307, 0.313, 0.31, 0.321,
0.302, 0.308, 0.312, 0.323, 0.335, 0.313, 0.309, 0.304, 0.303,
0.307, 0.303, 0.299, 0.39, 0.387, 0.391, 0.405, 0.347, 0.335,
0.328, 0.317, 0.327, 0.339, 0.387, 0.411, 0.387, 0.386, 0.39,
0.392, 0.336, 0.325, 0.319, 0.31, 0.318, 0.326, 0.367, 0.399,
0.367, 0.368, 0.373, 0.38, 0.332, 0.322, 0.317, 0.308, 0.315,
0.321, 0.342, 0.374, 0.299, 0.302, 0.305, 0.337, 0.318, 0.307,
0.304, 0.297, 0.3, 0.306, 0.298, 0.3, 0.265, 0.27, 0.274, 0.293,
0.293, 0.281, 0.28, 0.275, 0.276, 0.281, 0.275, 0.265, 0.588,
0.591, 0.555, 0.362, 0.367, 0.355, 0.317, 0.228, 0.161, 0.28,
0.381, 0.412, 0.38, 0.389, 0.399, 0.301, 0.304, 0.294, 0.265,
0.199, 0.149, 0.234, 0.315, 0.303, 0.302, 0.296, 0.323, 0.277,
0.279, 0.27, 0.245, 0.19, 0.148, 0.211, 0.288, 0.262, 0.173,
0.168, 0.188, 0.202, 0.196, 0.184, 0.171, 0.134, 0.113, 0.116,
0.192, 0.184, 0.151, 0.136, 0.142, 0.18, 0.176, 0.164, 0.155,
0.131, 0.119, 0.115, 0.165, 0.166, 0.588, 0.591, 0.555, 0.362,
0.367, 0.355, 0.317, 0.228, 0.161, 0.28, 0.381, 0.412, 0.38,
0.389, 0.399, 0.301, 0.304, 0.294, 0.265, 0.199, 0.149, 0.234,
0.315, 0.303, 0.302, 0.296, 0.323, 0.277, 0.279, 0.27, 0.245,
0.19, 0.148, 0.211, 0.288, 0.262, 0.173, 0.168, 0.188, 0.202,
0.196, 0.184, 0.171, 0.134, 0.113, 0.116, 0.192, 0.184, 0.151,
0.136, 0.142, 0.18, 0.176, 0.164, 0.155, 0.131, 0.119, 0.115,
0.165, 0.166, 0.472, 0.46, 0.457, 0.442, 0.387, 0.382, 0.371,
0.37, 0.377, 0.394, 0.432, 0.497, 0.452, 0.45, 0.45, 0.416, 0.367,
0.362, 0.353, 0.352, 0.358, 0.372, 0.39, 0.453, 0.414, 0.418,
0.42, 0.4, 0.354, 0.35, 0.342, 0.34, 0.346, 0.359, 0.363, 0.408,
0.331, 0.341, 0.345, 0.362, 0.336, 0.332, 0.325, 0.322, 0.327,
0.341, 0.337, 0.327, 0.296, 0.301, 0.306, 0.329, 0.326, 0.321,
0.314, 0.311, 0.314, 0.328, 0.327, 0.309, 0.451, 0.44, 0.458,
0.441, 0.344, 0.315, 0.303, 0.314, 0.324, 0.349, 0.396, 0.47,
0.334, 0.327, 0.349, 0.372, 0.302, 0.279, 0.269, 0.278, 0.285,
0.305, 0.31, 0.34, 0.287, 0.284, 0.293, 0.33, 0.279, 0.259, 0.248,
0.256, 0.263, 0.281, 0.281, 0.288, 0.181, 0.182, 0.181, 0.226,
0.212, 0.194, 0.184, 0.185, 0.192, 0.208, 0.209, 0.188, 0.174,
0.175, 0.177, 0.208, 0.209, 0.193, 0.185, 0.184, 0.189, 0.201,
0.203, 0.186, 0.365, 0.364, 0.367, 0.372, 0.309, 0.303, 0.288,
0.292, 0.299, 0.308, 0.351, 0.382, 0.371, 0.372, 0.379, 0.376,
0.311, 0.305, 0.291, 0.295, 0.301, 0.309, 0.343, 0.383, 0.365,
0.366, 0.372, 0.376, 0.318, 0.313, 0.3, 0.303, 0.309, 0.316,
0.33, 0.371, 0.33, 0.338, 0.342, 0.359, 0.33, 0.325, 0.313, 0.315,
0.32, 0.327, 0.317, 0.324, 0.291, 0.296, 0.3, 0.319, 0.317, 0.312,
0.303, 0.304, 0.307, 0.314, 0.304, 0.293)), row.names = c(NA,
-720L), class = "data.frame", .Names = c("site", "month", "depth",
"value"))
# Create some random data based on the first dataset
df1 <- cbind(df1, type='observed')
df2 <- df1[1:4]
df2$value <- df2$value * 1.5
df2 <- cbind(df2, type='modeled')
df3 <- rbind(df1,df2)
# Try to plot it
ggplot(df3, aes(x=value, y=depth, group=type, colour=as.factor(month))) +
geom_path() +
facet_wrap(~ site) +
scale_y_reverse() +
theme_bw(base_size=18) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
最佳答案
您可以使用 interaction
合并 type
和 month
:color = interaction(as.factor(month), type))
.
而不是:group=type, colour=as.factor(month)
.
并创建红色和蓝色托盘使用两个 mypal
职能:
mypal <- colorRampPalette(brewer.pal(6, "PuBu"))
mypal2 <- colorRampPalette(brewer.pal(6, "YlOrRd"))
library(ggplot2)
library(RColorBrewer)
mypal <- colorRampPalette(brewer.pal(6, "PuBu"))
mypal2 <- colorRampPalette(brewer.pal(6, "YlOrRd"))
ggplot(df3,
aes(value, depth, color = interaction(as.factor(month), type))) +
geom_path() +
facet_wrap(~ site) +
labs(title = "Soil moisture by depth and site",
subtitle = "Observed and expected data",
x = bquote('Soil moisture (' ~m^3~m^-3*')'),
y = "Depth") +
scale_y_reverse() +
scale_colour_manual(values = c(mypal(12), mypal2(12))) +
theme_classic() +
theme(legend.position = "none")
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