Cowplot 包：在 R 中使用 plot_grid() 对齐图 space 左上角的文本标签
Cowplot Package: Aligning text labels in the top lefthand corner of plot space using plot_grid() in R
概览：
我使用下面的 R-code 制作了一系列地图（见下文），我使用了 plot_grid()在 Cowplot 包 中使用下面的两个数据框 "QuercusRobur 1" 和 "QuercusRobur2".[=57 来排列绘图=]
问题：
情节看起来真不错；但是，文本标签没有很好地对齐。一些标签覆盖在 y-axis 的顶部，名为 Latitude，其中两个文本标签名为“A：城市化指数”和“ B: Urbanization Index”并没有位于他们的地块上方，它们还覆盖了名为 "Observation Period 1" 和 "Observation Period 2" 的主标题。
有谁知道如何整齐地对齐地块标签，使它们位于每个地块的左上角，同时防止它们覆盖 y-axis 或部分地图（请参阅下面的所需输出） ？
如果有人能提供帮助，我将不胜感激
R-Code
##Import Packages
library(ggplot2)
library(maps)
library(mapdata)
library(tidyverse)
##Get a map of the UK from maps:
UK <- map_data(map = "world", region = "UK")
head(UK)
dim(UK)
##Produce point data
MapUK<-ggplot(data = UK, aes(x = long, y = lat, group = group)) +
geom_polygon() +
coord_map()
##head
head(QuercusRobur1)
head(QuercusRobur2)
##Remove weird data points
QuercusRobur1<-QuercusRobur1%>%filter(Longitude<=3)
##Observation 1
p1 <- ggplot(QuercusRobur1,
aes(x = Longitude, y = Latitude)) +
geom_polygon(data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE) +
geom_point() +
coord_map(xlim = c(-10, 5)) + #limits added as there are some points really far away
theme_classic()
Urban1<-p1 +
aes(color = Urbanisation_index) +
scale_color_discrete(name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural"))
Stand1<-p1 +
aes(color = Stand_density_index) +
scale_color_discrete(name = "Stand Density Index",
labels = c("Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"))
Phenology1<-p1 +
aes(color = factor(Phenological_Index)) +
scale_color_discrete(name = "Phenological Index",
labels = c("No indication of autumn timing",
"First autumn tinting",
"Partial autumn tinting (>25% of leaves)",
"Advanced autumn tinting (>75% of leaves)"))
##Observation 2
p2 <- ggplot(QuercusRobur2,
aes(x = Longitude, y = Latitude)) +
geom_polygon(data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE) +
geom_point() +
coord_map(xlim = c(-10, 5)) + #limits added as there are some points really far away
theme_classic()
Urban2<-p2 +
aes(color = Urbanisation_index) +
scale_color_discrete(name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural"))
Stand2<-p2 +
aes(color = Stand_density_.index) +
scale_color_discrete(name = "Stand Density Index",
labels = c("Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"))
Phenology2<-p2 +
aes(color = factor(Phenological_Index)) +
scale_color_discrete(name = "Phenological Index",
labels = c("No indication of autumn timing",
"First autumn tinting",
"Partial autumn tinting (>25% of leaves)",
"Advanced autumn tinting (>75% of leaves)"))
##Arrange the individual plots into one main plot
plot_grid(Urban1 + ggtitle("Observational Period 1"),
Urban2 + ggtitle("Observational Period 2"),
Stand1,
Stand2,
Phenology1,
Phenology2,
labels=c("A: Urbanisation Index", "B: Urbanisation Index",
"C: Stand Density Index","D: Stand Density Index",
"E: Phenological Index","F: Phenological Index"),
align = "v",
label_fontface="bold",
label_fontfamily="Times New Roman",
label_size = 8,
rel_widths = c(1, 1.3),
ncol = 2,
nrow = 3,
hjust = 0,
label_x = 0.01)
根据R-code
制作的情节
期望输出
数据框 - QuercusRobur1
structure(list(Obs_.no = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,
35L, 36L, 37L, 38L, 39L, 44L, 45L, 46L, 47L, 57L, 58L, 59L, 60L,
61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 74L,
75L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 93L,
102L, 103L, 104L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L,
120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L,
131L, 135L, 136L, 137L, 138L, 143L, 144L, 145L, 146L, 147L, 148L,
149L, 150L, 151L, 152L, 153L, 154L, 155L, 158L, 159L, 160L, 161L,
162L, 163L, 164L, 165L, 169L, 170L, 171L, 172L, 177L, 178L, 179L,
180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L,
191L, 192L, 193L, 194L, 195L, 196L, 200L), Date_observed = structure(c(4L,
15L, 6L, 6L, 6L, 6L, 2L, 2L, 8L, 8L, 8L, 8L, 8L, 8L, 6L, 6L,
6L, 6L, 6L, 6L, 11L, 11L, 11L, 11L, 12L, 7L, 7L, 9L, 9L, 9L,
9L, 5L, 5L, 5L, 5L, 14L, 14L, 14L, 14L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 6L, 6L, 5L, 5L, 9L, 9L, 9L, 9L, 3L, 3L, 3L, 3L, 4L, 4L,
1L, 1L, 11L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 3L, 11L,
11L, 11L, 4L, 4L, 4L, 4L, 8L, 8L, 10L, 10L, 10L, 10L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 13L,
13L, 13L, 13L, 8L, 8L, 8L, 8L, 10L, 10L, 10L, 10L, 3L, 3L, 3L,
3L, 13L), .Label = c("10/1/18", "10/19/18", "10/20/18", "10/21/18",
"10/22/18", "10/23/18", "10/24/18", "10/25/18", "10/26/18", "10/27/18",
"10/28/18", "10/28/19", "10/29/18", "12/9/18", "8/20/18"), class = "factor"),
Latitude = c(51.4175, 52.12087, 52.0269, 52.0269, 52.0269,
52.0269, 52.947709, 52.947709, 51.491811, 51.491811, 52.59925,
52.59925, 52.59925, 52.59925, 51.60157, 51.60157, 52.6888,
52.6888, 52.6888, 52.6888, 50.697802, 50.697802, 50.697802,
50.697802, 53.62417, 50.446841, 50.446841, 53.959679, 53.959679,
53.959679, 53.959679, 51.78375, 51.78375, 51.78375, 51.78375,
51.456965, 51.456965, 51.456965, 51.456965, 51.3651, 51.3651,
51.3651, 51.3651, 52.01182, 52.01182, 52.01182, 52.01182,
50.114277, 50.114277, 51.43474, 51.43474, 51.10676, 51.10676,
51.10676, 51.10676, 50.435984, 50.435984, 50.435984, 50.435984,
51.78666, 51.78666, 52.441088, 52.441088, 52.552344, 49.259471,
49.259471, 49.259471, 49.259471, 50.461625, 50.461625, 50.461625,
50.461625, 51.746642, 51.746642, 51.746642, 51.746642, 52.2501,
52.2501, 52.2501, 52.2501, 52.423336, 52.423336, 52.423336,
52.423336, 53.615575, 53.615575, 53.615575, 53.615575, 51.08474,
51.08474, 51.08474, 53.19329, 53.19329, 53.19329, 53.19329,
55.96785, 55.96785, 56.52664, 56.52664, 56.52664, 56.52664,
51.8113, 51.8113, 51.8113, 51.8113, 52.580157, 52.580157,
52.580157, 52.580157, 50.52008, 50.52008, 50.52008, 50.52008,
51.48417, 51.48417, 51.48417, 51.48417, 54.58243, 54.58243,
54.58243, 54.58243, 52.58839, 52.58839, 52.58839, 52.58839,
52.717283, 52.717283, 52.717283, 52.717283, 50.740764, 50.740764,
50.740764, 50.740764, 52.57937), Longitude = c(-0.32118,
-0.29293, -0.7078, -0.7078, -0.7078, -0.7078, -1.435407,
-1.435407, -3.210324, -3.210324, 1.33011, 1.33011, 1.33011,
1.33011, -3.67111, -3.67111, -3.30909, -3.30909, -3.30909,
-3.30909, -2.11692, -2.11692, -2.11692, -2.11692, -2.43155,
-3.706923, -3.706923, -1.061008, -1.061008, -1.061008, -1.061008,
-0.65046, -0.65046, -0.65046, -0.65046, -2.624917, -2.624917,
-2.624917, -2.624917, 0.70706, 0.70706, 0.70706, 0.70706,
-0.70082, -0.70082, -0.70082, -0.70082, -5.541128, -5.541128,
0.45981, 0.45981, -2.32071, -2.32071, -2.32071, -2.32071,
-4.105617, -4.105617, -4.105617, -4.105617, -0.71433, -0.71433,
-0.176158, -0.176158, -1.337177, -123.107788, -123.107788,
-123.107788, -123.107788, 3.560973, 3.560973, 3.560973, 3.560973,
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825,
-0.8825, -0.8825, -1.787563, -1.787563, -1.787563, -1.787563,
-2.432959, -2.432959, -2.432959, -2.432959, -0.73645, -0.73645,
-0.73645, -0.63793, -0.63793, -0.63793, -0.63793, -3.18084,
-3.18084, -3.40313, -3.40313, -3.40313, -3.40313, -0.22894,
-0.22894, -0.22894, -0.22894, -1.948571, -1.948571, -1.948571,
-1.948571, -4.20756, -4.20756, -4.20756, -4.20756, -0.34854,
-0.34854, -0.34854, -0.34854, -5.93229, -5.93229, -5.93229,
-5.93229, -1.96843, -1.96843, -1.96843, -1.96843, -2.410575,
-2.410575, -2.410575, -2.410575, -2.361234, -2.361234, -2.361234,
-2.361234, -1.89325), Altitude = c(5L, 0L, 68L, 68L, 68L,
68L, 104L, 104L, 15L, 15L, 23L, 23L, 23L, 23L, 184L, 184L,
176L, 176L, 176L, 176L, 12L, 12L, 12L, 12L, 178L, 36L, 36L,
11L, 11L, 11L, 11L, 210L, 210L, 210L, 210L, 97L, 97L, 97L,
97L, 23L, 23L, 23L, 23L, 0L, 0L, 0L, 0L, 9L, 9L, 4L, 4L,
200L, 200L, 200L, 200L, 160L, 160L, 160L, 160L, 166L, 166L,
0L, 0L, 0L, 47L, 47L, 47L, 47L, 58L, 58L, 58L, 58L, 43L,
43L, 43L, 43L, 97L, 97L, 97L, 97L, 133L, 133L, 133L, 133L,
123L, 123L, 123L, 123L, 128L, 128L, 128L, 15L, 15L, 15L,
15L, 14L, 14L, 65L, 65L, 65L, 65L, 129L, 129L, 129L, 129L,
140L, 140L, 140L, 140L, 18L, 18L, 18L, 18L, 30L, 30L, 30L,
30L, 19L, 19L, 19L, 19L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
96L, 96L, 96L, 96L, 169L), Species = 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, 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, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Quercus robur", class = "factor"),
Tree_diameter = c(68.8, 10, 98.5, 97, 32.5, 45.1, 847, 817,
62, 71, 140, 111.4, 114.6, 167.1, 29, 40.1, 68, 45, 60, 54,
104, 122, 85, 71, 81, 39.8, 43.6, 20.1, 17.8, 15.6, 12.1,
81.8, 102.5, 75.5, 57.3, 0.3, 0.2, 0.3, 0.3, 70, 36, 53,
44, 31.5, 27.1, 23.3, 22, 69.4, 37.3, 19.9, 14.6, 196, 122,
118, 180, 58.6, 54.1, 58, 61.5, 58.4, 61, 134, 64, 52.2,
170, 114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237,
322, 302, 175, 182, 141, 155, 89, 41, 70, 83, 141, 86.5,
82, 114.5, 129, 127, 143, 125, 92, 68, 90, 24.5, 20.1, 63.7,
39.8, 66.2, 112.4, 124.5, 94.1, 68.6, 74.4, 23.6, 27.7, 22.9,
25.2, 24.2, 54.7, 43, 33.1, 306, 274, 56, 60, 72.5, 128.5,
22, 16, 143, 103, 53, 130, 48.4, 69.8, 6.4, 18.6, 129.2,
41.7, 57.6, 14, 41.7), Urbanisation_index = c(2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L), Stand_density_index = c(3L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L,
4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L), Canopy_Index = c(85L,
85L, 85L, 75L, 45L, 25L, 75L, 65L, 75L, 75L, 95L, 95L, 95L,
95L, 95L, 65L, 85L, 65L, 95L, 85L, 85L, 85L, 75L, 75L, 65L,
85L, 85L, 75L, 75L, 85L, 65L, 95L, 85L, 95L, 95L, 75L, 75L,
85L, 85L, 85L, 85L, 85L, 75L, 85L, 85L, 85L, 85L, 75L, 75L,
85L, 85L, 65L, 75L, 85L, 75L, 95L, 95L, 95L, 95L, 75L, 65L,
95L, 95L, 55L, 75L, 65L, 75L, 65L, 85L, 95L, 95L, 75L, 95L,
75L, 95L, 65L, 75L, 75L, 85L, 85L, 65L, 95L, 65L, 65L, 65L,
65L, 65L, 65L, 85L, 85L, 75L, 95L, 85L, 85L, 75L, 45L, 55L,
35L, 35L, 25L, 25L, 95L, 85L, 75L, 85L, 85L, 75L, 75L, 65L,
75L, 85L, 65L, 45L, 95L, 95L, 95L, 95L, 65L, 75L, 45L, 35L,
75L, 95L, 95L, 85L, 75L, 65L, 85L, 95L, 75L, 85L, 85L, 95L,
65L), Phenological_Index = c(2L, 4L, 2L, 2L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 3L,
2L, 2L, 2L, 1L, 3L, 1L, 4L)), class = "data.frame", row.names = c(NA,
-134L))
数据框 - QuercusRobur2
structure(list(X = c(1L, 2L, 3L, 4L, 13L, 14L, 15L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 35L, 36L,
37L, 38L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L,
59L, 63L, 64L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 80L, 89L, 90L, 91L, 95L, 96L, 97L, 98L, 99L, 100L, 101L,
102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L,
113L, 114L, 118L, 119L, 120L, 121L, 126L, 127L, 128L, 129L, 130L,
131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 141L, 142L, 143L,
144L, 148L, 149L, 150L, 151L, 156L, 157L, 158L, 159L, 160L, 161L,
162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L,
173L, 174L, 175L, 179L, 180L, 181L, 182L, 183L, 185L, 187L, 189L,
190L, 191L, 192L, 193L, 194L, 195L, 196L, 208L, 209L, 210L, 212L,
214L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 242L, 243L, 244L,
245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L,
256L, 257L, 258L, 259L, 260L, 261L), Obs_no = c(1L, 2L, 3L, 4L,
13L, 14L, 15L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 35L, 36L, 37L, 38L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 63L, 64L, 68L, 69L, 70L, 71L, 72L,
73L, 74L, 75L, 76L, 77L, 78L, 80L, 89L, 90L, 91L, 95L, 96L, 97L,
98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L,
109L, 110L, 111L, 112L, 113L, 114L, 118L, 119L, 120L, 121L, 126L,
127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L,
138L, 141L, 142L, 143L, 144L, 148L, 149L, 150L, 151L, 156L, 157L,
158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L,
169L, 170L, 171L, 172L, 173L, 174L, 175L, 179L, 180L, 181L, 182L,
183L, 185L, 187L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L,
208L, 209L, 210L, 212L, 214L, 225L, 226L, 227L, 228L, 229L, 230L,
231L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L,
252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L),
Date_observed = structure(c(9L, 14L, 3L, 3L, 12L, 12L, 10L,
10L, 8L, 8L, 8L, 8L, 11L, 11L, 11L, 11L, 5L, 5L, 9L, 9L,
13L, 13L, 13L, 13L, 8L, 8L, 8L, 8L, 13L, 13L, 13L, 13L, 7L,
7L, 7L, 7L, 6L, 6L, 11L, 11L, 11L, 11L, 11L, 11L, 4L, 4L,
4L, 4L, 12L, 12L, 12L, 12L, 5L, 1L, 1L, 1L, 1L, 5L, 5L, 5L,
5L, 12L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 13L, 13L, 13L, 8L, 8L, 8L, 8L, 13L, 13L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
3L, 3L, 3L, 3L, 13L, 13L, 13L, 13L, 10L, 10L, 10L, 10L, 12L,
12L, 12L, 12L, 3L, 3L, 3L, 3L, 13L, 13L, 5L, 5L, 5L, 11L,
11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
9L, 9L, 12L, 12L, 12L, 12L, 8L, 8L, 8L, 5L, 5L, 5L, 5L, 12L,
12L, 12L, 12L, 11L, 11L, 11L, 11L, 13L, 13L, 13L, 13L, 5L,
5L, 5L, 5L), .Label = c("10/23/18", "11/18/18", "11/30/18",
"12/1/18", "12/10/18", "12/12/18", "12/2/18", "12/3/18",
"12/4/18", "12/6/18", "12/7/18", "12/8/18", "12/9/18", "9/10/18"
), class = "factor"), Latitude = c(51.41752, 52.243806, 52.947709,
52.947709, 51.491811, 51.491811, 51.60157, 51.60157, 52.68959,
52.68959, 52.68959, 52.68959, 50.697802, 50.697802, 50.697802,
50.697802, 53.62417, 53.62417, 50.446841, 50.446841, 53.959679,
53.959679, 53.959679, 53.959679, 51.78375, 51.78375, 51.78375,
51.78375, 51.456965, 51.456965, 51.456965, 51.456965, 52.011812,
52.011812, 52.011812, 52.011812, 50.121978, 50.121978, 51.43474,
51.43474, 51.10708, 51.10708, 51.10708, 51.10708, 50.435984,
50.435984, 50.435984, 50.435984, 51.78666, 51.78666, 52.441088,
52.441088, 52.552344, 49.259471, 49.259471, 49.259471, 49.259471,
50.462, 50.462, 50.462, 50.462, 51.746642, 51.746642, 51.746642,
51.746642, 52.2501, 52.2501, 52.2501, 52.2501, 52.42646,
52.42646, 52.42646, 52.42646, 53.615575, 53.615575, 53.615575,
53.615575, 51.08478, 51.08478, 51.08478, 53.19329, 53.19329,
53.19329, 53.19329, 55.968437, 55.968437, 56.52664, 56.52664,
56.52664, 56.52664, 51.8113, 51.8113, 51.8113, 51.8113, 50.52008,
50.52008, 50.52008, 50.52008, 51.48417, 51.48417, 51.48417,
51.48417, 54.58243, 54.58243, 54.58243, 54.58243, 52.58839,
52.58839, 52.58839, 52.58839, 52.717283, 52.717283, 52.717283,
52.717283, 50.740764, 50.740764, 50.740764, 50.740764, 50.733412,
50.733412, 50.79926, 50.79926, 50.79926, 53.675788, 53.675788,
48.35079, 48.35079, 48.35079, 48.35079, 51.36445, 51.36445,
51.36445, 51.36445, 52.122402, 52.122402, 52.122402, 52.16104,
52.16104, 51.88468, 51.88468, 51.88468, 51.88468, 52.34015,
52.34015, 52.34015, 52.026042, 52.026042, 52.026042, 52.026042,
51.319032, 51.319032, 51.319032, 51.319032, 51.51365, 51.51365,
51.51365, 51.51365, 53.43202, 53.43202, 53.43202, 53.43202,
51.50797, 51.50797, 51.50797, 51.50797), Longitude = c(-0.32116,
1.30786, -1.435407, -1.435407, -3.210324, -3.210324, -3.67111,
-3.67111, -3.3081, -3.3081, -3.3081, -3.3081, -2.11692, -2.11692,
-2.11692, -2.11692, -2.43155, -2.43155, -3.706923, -3.706923,
-1.061008, -1.061008, -1.061008, -1.061008, -0.65046, -0.65046,
-0.65046, -0.65046, -2.624917, -2.624917, -2.624917, -2.624917,
-0.70082, -0.70082, -0.70082, -0.70082, -5.555169, -5.555169,
0.45981, 0.45981, -2.32027, -2.32027, -2.32027, -2.32027,
-4.105617, -4.105617, -4.105617, -4.105617, -0.71433, -0.71433,
-0.176158, -0.176158, -1.337177, -123.107788, -123.107788,
-123.107788, -123.107788, -3.5607, -3.5607, -3.5607, -3.5607,
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825,
-0.8825, -0.8825, -1.78771, -1.78771, -1.78771, -1.78771,
-2.432959, -2.432959, -2.432959, -2.432959, -0.73626, -0.73626,
-0.73626, -0.63793, -0.63793, -0.63793, -0.63793, -3.179732,
-3.179732, -3.40313, -3.40313, -3.40313, -3.40313, -0.22894,
-0.22894, -0.22894, -0.22894, -4.20756, -4.20756, -4.20756,
-4.20756, -0.34854, -0.34854, -0.34854, -0.34854, -5.93229,
-5.93229, -5.93229, -5.93229, -1.96843, -1.96843, -1.96843,
-1.96843, -2.410575, -2.410575, -2.410575, -2.410575, -2.361234,
-2.361234, -2.361234, -2.361234, -2.014029, -2.014029, -3.19446,
-3.19446, -3.19446, -1.272404, -1.272404, 10.91812, 10.91812,
10.91812, 10.91812, -0.23106, -0.23106, -0.23106, -0.23106,
-0.487443, -0.487443, -0.487443, 0.18702, 0.18702, -0.17853,
-0.17853, -0.17853, -0.17853, -1.27795, -1.27795, -1.27795,
-0.503113, -0.503113, -0.503113, -0.503113, -0.472994, -0.472994,
-0.472994, -0.472994, -3.18722, -3.18722, -3.18722, -3.18722,
-2.27968, -2.27968, -2.27968, -2.27968, -0.25931, -0.25931,
-0.25931, -0.25931), Altitude = c(0, 0, 103.9, 103.9, 15,
15, 184, 184, 176, 176, 176, 176, 12, 12, 12, 12, 178, 178,
36, 36, 11, 11, 11, 11, 210, 210, 210, 210, 97, 97, 97, 97,
0, 0, 0, 0, 68, 68, 4, 4, 200, 200, 200, 200, 160, 160, 160,
160, 165.8, 165.8, 0, 0, 0, 47, 47, 47, 47, 0, 0, 0, 0, 43,
43, 43, 43, 97, 97, 97, 97, 133, 133, 133, 133, 123, 123,
123, 123, 127, 127, 127, 15, 15, 15, 15, 14, 14, 65, 65,
65, 65, 129, 129, 129, 129, 18, 18, 18, 18, 30, 30, 30, 30,
19, 19, 19, 19, 0, 0, 0, 0, 0, 0, 0, 0, 96, 96, 96, 96, 0,
0, 0, 0, 0, 49, 49, 0, 0, 0, 0, 48, 48, 48, 48, 43, 43, 43,
75, 75, 94, 94, 94, 94, 112, 112, 112, 103, 103, 103, 103,
0, 0, 0, 0, 37.5, 37.5, 37.5, 37.5, 29, 29, 29, 29, 63, 63,
63, 63), Species = 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, 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, 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), .Label = "Quercus robur", class = "factor"),
Tree_diameter = c(68.8, 300, 847, 817, 62, 71, 29, 40.1,
68, 45, 60, 54, 104, 122, 85, 71, 81, 118, 39.8, 43.6, 19.8,
16.6, 15.1, 11.9, 81.8, 102.5, 75.5, 57.3, 0.3, 0.2, 0.3,
0.3, 99, 85, 74, 68, 82, 51.8, 19.9, 14.6, 196, 122, 118,
180, 58.6, 54.1, 58, 61.5, 58.4, 61, 134, 64, 52.2, 170,
114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237, 322,
302, 173, 186, 144, 155, 89, 41, 68, 83, 141.6, 85.5, 82.8,
114.1, 129, 127, 143, 125, 92, 68, 90, 25, 20, 63.7, 39.8,
66.2, 112.4, 124.5, 94.1, 68.6, 74.4, 24.2, 54.7, 43, 33.1,
306, 274, 56, 60, 72.5, 128.5, 22, 16, 143, 103, 53, 130,
48.4, 69.8, 6.4, 18.6, 129.2, 41.7, 57.6, 14, 320, 352, 120.9,
108.3, 53.2, 274, 85, 52, 43, 38, 37, 219, 215, 216, 175,
85.9, 49.7, 97.1, 40.8, 62.4, 181.5, 149.7, 122, 143.6, 148,
145, 99, 27.5, 32, 54, 54.1, 169, 152, 160, 138, 90.8, 87.9,
77.4, 81.2, 91.7, 62.7, 50, 72.9, 24.8, 61, 88.6, 80.1),
Urbanisation_index = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 3L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 1L, 1L,
1L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("1", "2",
"3", "4"), class = "factor"), Stand_density_.index = structure(c(3L,
4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L,
3L, 3L, 4L, 3L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L), .Label = c("1",
"2", "3", "4"), class = "factor"), Canopy_Index = c(15L,
95L, 45L, 5L, 5L, 5L, 25L, 15L, 25L, 25L, 35L, 35L, 25L,
35L, 15L, 15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L, 35L, 35L,
55L, 35L, 5L, 5L, 5L, 5L, 95L, 95L, 95L, 95L, 25L, 25L, 15L,
5L, 25L, 25L, 25L, 25L, 5L, 5L, 5L, 5L, 5L, 5L, 35L, 25L,
5L, 35L, 35L, 25L, 25L, 5L, 5L, 5L, 5L, 35L, 25L, 25L, 25L,
5L, 5L, 15L, 15L, 35L, 65L, 35L, 35L, 25L, 25L, 25L, 25L,
15L, 15L, 5L, 35L, 35L, 45L, 35L, 5L, 15L, 15L, 25L, 5L,
15L, 15L, 5L, 5L, 15L, 5L, 5L, 5L, 5L, 5L, 85L, 5L, 35L,
15L, 5L, 5L, 5L, 25L, 25L, 15L, 35L, 95L, 95L, 95L, 95L,
15L, 15L, 5L, 25L, 25L, 5L, 15L, 15L, 5L, 15L, 5L, 25L, 25L,
25L, 25L, 5L, 5L, 5L, 5L, 25L, 25L, 55L, 35L, 25L, 15L, 15L,
25L, 15L, 45L, 35L, 35L, 15L, 35L, 15L, 15L, 35L, 15L, 25L,
25L, 15L, 15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L, 15L, 15L
), Phenological_Index = c(4L, 4L, 3L, 4L, 2L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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, 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, 3L, 2L, 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)), row.names = c(NA, -165L), class = "data.frame")
如何使用 subtitle 来标记每个单独的地块？
## Observation 1
p1 <- ggplot(
QuercusRobur1,
aes(x = Longitude, y = Latitude)
) +
geom_polygon(
data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE
) +
coord_map(xlim = c(-10, 5)) + # limits added as there are some points really far away
theme_classic()
Urban1 <- p1 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "A: Urbanisation Index") +
theme(legend.justification = "left")
Stand1 <- p1 +
geom_point(aes(color = factor(Stand_density_index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "C: Stand Density Index") +
theme(legend.justification = "left")
## Observation 2
p2 <- ggplot(
QuercusRobur2,
aes(x = Longitude, y = Latitude)
) +
geom_polygon(
data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE
) +
coord_map(xlim = c(-10, 5)) +
theme_classic()
Urban2 <- p2 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "B: Urbanisation Index") +
theme(legend.justification = "left")
Stand2 <- p2 +
geom_point(aes(color = factor(Stand_density_.index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "D: Stand Density Index") +
theme(legend.justification = "left")
## Arrange the individual plots into one main plot
plot_grid(
Urban1 + ggtitle("Observational Period 1\n") + theme(plot.title = element_text(hjust = 1.0)),
Urban2 + ggtitle("Observational Period 2\n") + theme(plot.title = element_text(hjust = 1.0)),
Stand1,
Stand2,
align = "hv",
axis = 'tblr',
label_fontface = "bold",
label_fontfamily = "Times New Roman",
label_size = 8,
rel_widths = c(1, 1.3),
ncol = 2,
nrow = 2,
hjust = 0,
label_x = 0.01
)
编辑：删除重复的轴标签和图例，然后使用egg::ggarrange组合子图。
## Observation 1
Urban1 <- p1 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "A: Urbanisation Index") +
theme(legend.position = "none")
Stand1 <- p1 +
geom_point(aes(color = factor(Stand_density_index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "C: Stand Density Index") +
theme(legend.position = "none")
## Observation 2
p2 <- ggplot(
QuercusRobur2,
aes(x = Longitude, y = Latitude)
) +
geom_polygon(
data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE
) +
coord_map(xlim = c(-10, 5)) +
theme_classic() +
ylab("")
Urban2 <- p2 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "B: Urbanisation Index") +
theme(legend.justification = "left")
Stand2 <- p2 +
geom_point(aes(color = factor(Stand_density_.index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "D: Stand Density Index") +
theme(legend.justification = "left")
## Use the `egg` package
library(egg)
ggarrange(
Urban1 + ggtitle("Observational Period 1\n") + theme(plot.title = element_text(hjust = 0.5)),
Urban2 + ggtitle("Observational Period 2\n") + theme(plot.title = element_text(hjust = 0.5)),
Stand1,
Stand2,
nrow = 2,
ncol = 2
)
概览：
我使用下面的 R-code 制作了一系列地图（见下文），我使用了 plot_grid()在 Cowplot 包 中使用下面的两个数据框 "QuercusRobur 1" 和 "QuercusRobur2".[=57 来排列绘图=]
问题：
情节看起来真不错；但是，文本标签没有很好地对齐。一些标签覆盖在 y-axis 的顶部，名为 Latitude，其中两个文本标签名为“A：城市化指数”和“ B: Urbanization Index”并没有位于他们的地块上方，它们还覆盖了名为 "Observation Period 1" 和 "Observation Period 2" 的主标题。
有谁知道如何整齐地对齐地块标签，使它们位于每个地块的左上角，同时防止它们覆盖 y-axis 或部分地图（请参阅下面的所需输出） ？
如果有人能提供帮助，我将不胜感激
R-Code
##Import Packages
library(ggplot2)
library(maps)
library(mapdata)
library(tidyverse)
##Get a map of the UK from maps:
UK <- map_data(map = "world", region = "UK")
head(UK)
dim(UK)
##Produce point data
MapUK<-ggplot(data = UK, aes(x = long, y = lat, group = group)) +
geom_polygon() +
coord_map()
##head
head(QuercusRobur1)
head(QuercusRobur2)
##Remove weird data points
QuercusRobur1<-QuercusRobur1%>%filter(Longitude<=3)
##Observation 1
p1 <- ggplot(QuercusRobur1,
aes(x = Longitude, y = Latitude)) +
geom_polygon(data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE) +
geom_point() +
coord_map(xlim = c(-10, 5)) + #limits added as there are some points really far away
theme_classic()
Urban1<-p1 +
aes(color = Urbanisation_index) +
scale_color_discrete(name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural"))
Stand1<-p1 +
aes(color = Stand_density_index) +
scale_color_discrete(name = "Stand Density Index",
labels = c("Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"))
Phenology1<-p1 +
aes(color = factor(Phenological_Index)) +
scale_color_discrete(name = "Phenological Index",
labels = c("No indication of autumn timing",
"First autumn tinting",
"Partial autumn tinting (>25% of leaves)",
"Advanced autumn tinting (>75% of leaves)"))
##Observation 2
p2 <- ggplot(QuercusRobur2,
aes(x = Longitude, y = Latitude)) +
geom_polygon(data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE) +
geom_point() +
coord_map(xlim = c(-10, 5)) + #limits added as there are some points really far away
theme_classic()
Urban2<-p2 +
aes(color = Urbanisation_index) +
scale_color_discrete(name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural"))
Stand2<-p2 +
aes(color = Stand_density_.index) +
scale_color_discrete(name = "Stand Density Index",
labels = c("Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"))
Phenology2<-p2 +
aes(color = factor(Phenological_Index)) +
scale_color_discrete(name = "Phenological Index",
labels = c("No indication of autumn timing",
"First autumn tinting",
"Partial autumn tinting (>25% of leaves)",
"Advanced autumn tinting (>75% of leaves)"))
##Arrange the individual plots into one main plot
plot_grid(Urban1 + ggtitle("Observational Period 1"),
Urban2 + ggtitle("Observational Period 2"),
Stand1,
Stand2,
Phenology1,
Phenology2,
labels=c("A: Urbanisation Index", "B: Urbanisation Index",
"C: Stand Density Index","D: Stand Density Index",
"E: Phenological Index","F: Phenological Index"),
align = "v",
label_fontface="bold",
label_fontfamily="Times New Roman",
label_size = 8,
rel_widths = c(1, 1.3),
ncol = 2,
nrow = 3,
hjust = 0,
label_x = 0.01)
根据R-code
制作的情节期望输出
数据框 - QuercusRobur1
structure(list(Obs_.no = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,
35L, 36L, 37L, 38L, 39L, 44L, 45L, 46L, 47L, 57L, 58L, 59L, 60L,
61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 74L,
75L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 93L,
102L, 103L, 104L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L,
120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L,
131L, 135L, 136L, 137L, 138L, 143L, 144L, 145L, 146L, 147L, 148L,
149L, 150L, 151L, 152L, 153L, 154L, 155L, 158L, 159L, 160L, 161L,
162L, 163L, 164L, 165L, 169L, 170L, 171L, 172L, 177L, 178L, 179L,
180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L,
191L, 192L, 193L, 194L, 195L, 196L, 200L), Date_observed = structure(c(4L,
15L, 6L, 6L, 6L, 6L, 2L, 2L, 8L, 8L, 8L, 8L, 8L, 8L, 6L, 6L,
6L, 6L, 6L, 6L, 11L, 11L, 11L, 11L, 12L, 7L, 7L, 9L, 9L, 9L,
9L, 5L, 5L, 5L, 5L, 14L, 14L, 14L, 14L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 6L, 6L, 5L, 5L, 9L, 9L, 9L, 9L, 3L, 3L, 3L, 3L, 4L, 4L,
1L, 1L, 11L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 3L, 11L,
11L, 11L, 4L, 4L, 4L, 4L, 8L, 8L, 10L, 10L, 10L, 10L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 13L,
13L, 13L, 13L, 8L, 8L, 8L, 8L, 10L, 10L, 10L, 10L, 3L, 3L, 3L,
3L, 13L), .Label = c("10/1/18", "10/19/18", "10/20/18", "10/21/18",
"10/22/18", "10/23/18", "10/24/18", "10/25/18", "10/26/18", "10/27/18",
"10/28/18", "10/28/19", "10/29/18", "12/9/18", "8/20/18"), class = "factor"),
Latitude = c(51.4175, 52.12087, 52.0269, 52.0269, 52.0269,
52.0269, 52.947709, 52.947709, 51.491811, 51.491811, 52.59925,
52.59925, 52.59925, 52.59925, 51.60157, 51.60157, 52.6888,
52.6888, 52.6888, 52.6888, 50.697802, 50.697802, 50.697802,
50.697802, 53.62417, 50.446841, 50.446841, 53.959679, 53.959679,
53.959679, 53.959679, 51.78375, 51.78375, 51.78375, 51.78375,
51.456965, 51.456965, 51.456965, 51.456965, 51.3651, 51.3651,
51.3651, 51.3651, 52.01182, 52.01182, 52.01182, 52.01182,
50.114277, 50.114277, 51.43474, 51.43474, 51.10676, 51.10676,
51.10676, 51.10676, 50.435984, 50.435984, 50.435984, 50.435984,
51.78666, 51.78666, 52.441088, 52.441088, 52.552344, 49.259471,
49.259471, 49.259471, 49.259471, 50.461625, 50.461625, 50.461625,
50.461625, 51.746642, 51.746642, 51.746642, 51.746642, 52.2501,
52.2501, 52.2501, 52.2501, 52.423336, 52.423336, 52.423336,
52.423336, 53.615575, 53.615575, 53.615575, 53.615575, 51.08474,
51.08474, 51.08474, 53.19329, 53.19329, 53.19329, 53.19329,
55.96785, 55.96785, 56.52664, 56.52664, 56.52664, 56.52664,
51.8113, 51.8113, 51.8113, 51.8113, 52.580157, 52.580157,
52.580157, 52.580157, 50.52008, 50.52008, 50.52008, 50.52008,
51.48417, 51.48417, 51.48417, 51.48417, 54.58243, 54.58243,
54.58243, 54.58243, 52.58839, 52.58839, 52.58839, 52.58839,
52.717283, 52.717283, 52.717283, 52.717283, 50.740764, 50.740764,
50.740764, 50.740764, 52.57937), Longitude = c(-0.32118,
-0.29293, -0.7078, -0.7078, -0.7078, -0.7078, -1.435407,
-1.435407, -3.210324, -3.210324, 1.33011, 1.33011, 1.33011,
1.33011, -3.67111, -3.67111, -3.30909, -3.30909, -3.30909,
-3.30909, -2.11692, -2.11692, -2.11692, -2.11692, -2.43155,
-3.706923, -3.706923, -1.061008, -1.061008, -1.061008, -1.061008,
-0.65046, -0.65046, -0.65046, -0.65046, -2.624917, -2.624917,
-2.624917, -2.624917, 0.70706, 0.70706, 0.70706, 0.70706,
-0.70082, -0.70082, -0.70082, -0.70082, -5.541128, -5.541128,
0.45981, 0.45981, -2.32071, -2.32071, -2.32071, -2.32071,
-4.105617, -4.105617, -4.105617, -4.105617, -0.71433, -0.71433,
-0.176158, -0.176158, -1.337177, -123.107788, -123.107788,
-123.107788, -123.107788, 3.560973, 3.560973, 3.560973, 3.560973,
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825,
-0.8825, -0.8825, -1.787563, -1.787563, -1.787563, -1.787563,
-2.432959, -2.432959, -2.432959, -2.432959, -0.73645, -0.73645,
-0.73645, -0.63793, -0.63793, -0.63793, -0.63793, -3.18084,
-3.18084, -3.40313, -3.40313, -3.40313, -3.40313, -0.22894,
-0.22894, -0.22894, -0.22894, -1.948571, -1.948571, -1.948571,
-1.948571, -4.20756, -4.20756, -4.20756, -4.20756, -0.34854,
-0.34854, -0.34854, -0.34854, -5.93229, -5.93229, -5.93229,
-5.93229, -1.96843, -1.96843, -1.96843, -1.96843, -2.410575,
-2.410575, -2.410575, -2.410575, -2.361234, -2.361234, -2.361234,
-2.361234, -1.89325), Altitude = c(5L, 0L, 68L, 68L, 68L,
68L, 104L, 104L, 15L, 15L, 23L, 23L, 23L, 23L, 184L, 184L,
176L, 176L, 176L, 176L, 12L, 12L, 12L, 12L, 178L, 36L, 36L,
11L, 11L, 11L, 11L, 210L, 210L, 210L, 210L, 97L, 97L, 97L,
97L, 23L, 23L, 23L, 23L, 0L, 0L, 0L, 0L, 9L, 9L, 4L, 4L,
200L, 200L, 200L, 200L, 160L, 160L, 160L, 160L, 166L, 166L,
0L, 0L, 0L, 47L, 47L, 47L, 47L, 58L, 58L, 58L, 58L, 43L,
43L, 43L, 43L, 97L, 97L, 97L, 97L, 133L, 133L, 133L, 133L,
123L, 123L, 123L, 123L, 128L, 128L, 128L, 15L, 15L, 15L,
15L, 14L, 14L, 65L, 65L, 65L, 65L, 129L, 129L, 129L, 129L,
140L, 140L, 140L, 140L, 18L, 18L, 18L, 18L, 30L, 30L, 30L,
30L, 19L, 19L, 19L, 19L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
96L, 96L, 96L, 96L, 169L), Species = 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, 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, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Quercus robur", class = "factor"),
Tree_diameter = c(68.8, 10, 98.5, 97, 32.5, 45.1, 847, 817,
62, 71, 140, 111.4, 114.6, 167.1, 29, 40.1, 68, 45, 60, 54,
104, 122, 85, 71, 81, 39.8, 43.6, 20.1, 17.8, 15.6, 12.1,
81.8, 102.5, 75.5, 57.3, 0.3, 0.2, 0.3, 0.3, 70, 36, 53,
44, 31.5, 27.1, 23.3, 22, 69.4, 37.3, 19.9, 14.6, 196, 122,
118, 180, 58.6, 54.1, 58, 61.5, 58.4, 61, 134, 64, 52.2,
170, 114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237,
322, 302, 175, 182, 141, 155, 89, 41, 70, 83, 141, 86.5,
82, 114.5, 129, 127, 143, 125, 92, 68, 90, 24.5, 20.1, 63.7,
39.8, 66.2, 112.4, 124.5, 94.1, 68.6, 74.4, 23.6, 27.7, 22.9,
25.2, 24.2, 54.7, 43, 33.1, 306, 274, 56, 60, 72.5, 128.5,
22, 16, 143, 103, 53, 130, 48.4, 69.8, 6.4, 18.6, 129.2,
41.7, 57.6, 14, 41.7), Urbanisation_index = c(2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L), Stand_density_index = c(3L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L,
4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L), Canopy_Index = c(85L,
85L, 85L, 75L, 45L, 25L, 75L, 65L, 75L, 75L, 95L, 95L, 95L,
95L, 95L, 65L, 85L, 65L, 95L, 85L, 85L, 85L, 75L, 75L, 65L,
85L, 85L, 75L, 75L, 85L, 65L, 95L, 85L, 95L, 95L, 75L, 75L,
85L, 85L, 85L, 85L, 85L, 75L, 85L, 85L, 85L, 85L, 75L, 75L,
85L, 85L, 65L, 75L, 85L, 75L, 95L, 95L, 95L, 95L, 75L, 65L,
95L, 95L, 55L, 75L, 65L, 75L, 65L, 85L, 95L, 95L, 75L, 95L,
75L, 95L, 65L, 75L, 75L, 85L, 85L, 65L, 95L, 65L, 65L, 65L,
65L, 65L, 65L, 85L, 85L, 75L, 95L, 85L, 85L, 75L, 45L, 55L,
35L, 35L, 25L, 25L, 95L, 85L, 75L, 85L, 85L, 75L, 75L, 65L,
75L, 85L, 65L, 45L, 95L, 95L, 95L, 95L, 65L, 75L, 45L, 35L,
75L, 95L, 95L, 85L, 75L, 65L, 85L, 95L, 75L, 85L, 85L, 95L,
65L), Phenological_Index = c(2L, 4L, 2L, 2L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 3L,
2L, 2L, 2L, 1L, 3L, 1L, 4L)), class = "data.frame", row.names = c(NA,
-134L))
数据框 - QuercusRobur2
structure(list(X = c(1L, 2L, 3L, 4L, 13L, 14L, 15L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 35L, 36L,
37L, 38L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L,
59L, 63L, 64L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 80L, 89L, 90L, 91L, 95L, 96L, 97L, 98L, 99L, 100L, 101L,
102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L,
113L, 114L, 118L, 119L, 120L, 121L, 126L, 127L, 128L, 129L, 130L,
131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 141L, 142L, 143L,
144L, 148L, 149L, 150L, 151L, 156L, 157L, 158L, 159L, 160L, 161L,
162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L,
173L, 174L, 175L, 179L, 180L, 181L, 182L, 183L, 185L, 187L, 189L,
190L, 191L, 192L, 193L, 194L, 195L, 196L, 208L, 209L, 210L, 212L,
214L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 242L, 243L, 244L,
245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L,
256L, 257L, 258L, 259L, 260L, 261L), Obs_no = c(1L, 2L, 3L, 4L,
13L, 14L, 15L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 35L, 36L, 37L, 38L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 63L, 64L, 68L, 69L, 70L, 71L, 72L,
73L, 74L, 75L, 76L, 77L, 78L, 80L, 89L, 90L, 91L, 95L, 96L, 97L,
98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L,
109L, 110L, 111L, 112L, 113L, 114L, 118L, 119L, 120L, 121L, 126L,
127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L,
138L, 141L, 142L, 143L, 144L, 148L, 149L, 150L, 151L, 156L, 157L,
158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L,
169L, 170L, 171L, 172L, 173L, 174L, 175L, 179L, 180L, 181L, 182L,
183L, 185L, 187L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L,
208L, 209L, 210L, 212L, 214L, 225L, 226L, 227L, 228L, 229L, 230L,
231L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L,
252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L),
Date_observed = structure(c(9L, 14L, 3L, 3L, 12L, 12L, 10L,
10L, 8L, 8L, 8L, 8L, 11L, 11L, 11L, 11L, 5L, 5L, 9L, 9L,
13L, 13L, 13L, 13L, 8L, 8L, 8L, 8L, 13L, 13L, 13L, 13L, 7L,
7L, 7L, 7L, 6L, 6L, 11L, 11L, 11L, 11L, 11L, 11L, 4L, 4L,
4L, 4L, 12L, 12L, 12L, 12L, 5L, 1L, 1L, 1L, 1L, 5L, 5L, 5L,
5L, 12L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 13L, 13L, 13L, 8L, 8L, 8L, 8L, 13L, 13L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
3L, 3L, 3L, 3L, 13L, 13L, 13L, 13L, 10L, 10L, 10L, 10L, 12L,
12L, 12L, 12L, 3L, 3L, 3L, 3L, 13L, 13L, 5L, 5L, 5L, 11L,
11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
9L, 9L, 12L, 12L, 12L, 12L, 8L, 8L, 8L, 5L, 5L, 5L, 5L, 12L,
12L, 12L, 12L, 11L, 11L, 11L, 11L, 13L, 13L, 13L, 13L, 5L,
5L, 5L, 5L), .Label = c("10/23/18", "11/18/18", "11/30/18",
"12/1/18", "12/10/18", "12/12/18", "12/2/18", "12/3/18",
"12/4/18", "12/6/18", "12/7/18", "12/8/18", "12/9/18", "9/10/18"
), class = "factor"), Latitude = c(51.41752, 52.243806, 52.947709,
52.947709, 51.491811, 51.491811, 51.60157, 51.60157, 52.68959,
52.68959, 52.68959, 52.68959, 50.697802, 50.697802, 50.697802,
50.697802, 53.62417, 53.62417, 50.446841, 50.446841, 53.959679,
53.959679, 53.959679, 53.959679, 51.78375, 51.78375, 51.78375,
51.78375, 51.456965, 51.456965, 51.456965, 51.456965, 52.011812,
52.011812, 52.011812, 52.011812, 50.121978, 50.121978, 51.43474,
51.43474, 51.10708, 51.10708, 51.10708, 51.10708, 50.435984,
50.435984, 50.435984, 50.435984, 51.78666, 51.78666, 52.441088,
52.441088, 52.552344, 49.259471, 49.259471, 49.259471, 49.259471,
50.462, 50.462, 50.462, 50.462, 51.746642, 51.746642, 51.746642,
51.746642, 52.2501, 52.2501, 52.2501, 52.2501, 52.42646,
52.42646, 52.42646, 52.42646, 53.615575, 53.615575, 53.615575,
53.615575, 51.08478, 51.08478, 51.08478, 53.19329, 53.19329,
53.19329, 53.19329, 55.968437, 55.968437, 56.52664, 56.52664,
56.52664, 56.52664, 51.8113, 51.8113, 51.8113, 51.8113, 50.52008,
50.52008, 50.52008, 50.52008, 51.48417, 51.48417, 51.48417,
51.48417, 54.58243, 54.58243, 54.58243, 54.58243, 52.58839,
52.58839, 52.58839, 52.58839, 52.717283, 52.717283, 52.717283,
52.717283, 50.740764, 50.740764, 50.740764, 50.740764, 50.733412,
50.733412, 50.79926, 50.79926, 50.79926, 53.675788, 53.675788,
48.35079, 48.35079, 48.35079, 48.35079, 51.36445, 51.36445,
51.36445, 51.36445, 52.122402, 52.122402, 52.122402, 52.16104,
52.16104, 51.88468, 51.88468, 51.88468, 51.88468, 52.34015,
52.34015, 52.34015, 52.026042, 52.026042, 52.026042, 52.026042,
51.319032, 51.319032, 51.319032, 51.319032, 51.51365, 51.51365,
51.51365, 51.51365, 53.43202, 53.43202, 53.43202, 53.43202,
51.50797, 51.50797, 51.50797, 51.50797), Longitude = c(-0.32116,
1.30786, -1.435407, -1.435407, -3.210324, -3.210324, -3.67111,
-3.67111, -3.3081, -3.3081, -3.3081, -3.3081, -2.11692, -2.11692,
-2.11692, -2.11692, -2.43155, -2.43155, -3.706923, -3.706923,
-1.061008, -1.061008, -1.061008, -1.061008, -0.65046, -0.65046,
-0.65046, -0.65046, -2.624917, -2.624917, -2.624917, -2.624917,
-0.70082, -0.70082, -0.70082, -0.70082, -5.555169, -5.555169,
0.45981, 0.45981, -2.32027, -2.32027, -2.32027, -2.32027,
-4.105617, -4.105617, -4.105617, -4.105617, -0.71433, -0.71433,
-0.176158, -0.176158, -1.337177, -123.107788, -123.107788,
-123.107788, -123.107788, -3.5607, -3.5607, -3.5607, -3.5607,
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825,
-0.8825, -0.8825, -1.78771, -1.78771, -1.78771, -1.78771,
-2.432959, -2.432959, -2.432959, -2.432959, -0.73626, -0.73626,
-0.73626, -0.63793, -0.63793, -0.63793, -0.63793, -3.179732,
-3.179732, -3.40313, -3.40313, -3.40313, -3.40313, -0.22894,
-0.22894, -0.22894, -0.22894, -4.20756, -4.20756, -4.20756,
-4.20756, -0.34854, -0.34854, -0.34854, -0.34854, -5.93229,
-5.93229, -5.93229, -5.93229, -1.96843, -1.96843, -1.96843,
-1.96843, -2.410575, -2.410575, -2.410575, -2.410575, -2.361234,
-2.361234, -2.361234, -2.361234, -2.014029, -2.014029, -3.19446,
-3.19446, -3.19446, -1.272404, -1.272404, 10.91812, 10.91812,
10.91812, 10.91812, -0.23106, -0.23106, -0.23106, -0.23106,
-0.487443, -0.487443, -0.487443, 0.18702, 0.18702, -0.17853,
-0.17853, -0.17853, -0.17853, -1.27795, -1.27795, -1.27795,
-0.503113, -0.503113, -0.503113, -0.503113, -0.472994, -0.472994,
-0.472994, -0.472994, -3.18722, -3.18722, -3.18722, -3.18722,
-2.27968, -2.27968, -2.27968, -2.27968, -0.25931, -0.25931,
-0.25931, -0.25931), Altitude = c(0, 0, 103.9, 103.9, 15,
15, 184, 184, 176, 176, 176, 176, 12, 12, 12, 12, 178, 178,
36, 36, 11, 11, 11, 11, 210, 210, 210, 210, 97, 97, 97, 97,
0, 0, 0, 0, 68, 68, 4, 4, 200, 200, 200, 200, 160, 160, 160,
160, 165.8, 165.8, 0, 0, 0, 47, 47, 47, 47, 0, 0, 0, 0, 43,
43, 43, 43, 97, 97, 97, 97, 133, 133, 133, 133, 123, 123,
123, 123, 127, 127, 127, 15, 15, 15, 15, 14, 14, 65, 65,
65, 65, 129, 129, 129, 129, 18, 18, 18, 18, 30, 30, 30, 30,
19, 19, 19, 19, 0, 0, 0, 0, 0, 0, 0, 0, 96, 96, 96, 96, 0,
0, 0, 0, 0, 49, 49, 0, 0, 0, 0, 48, 48, 48, 48, 43, 43, 43,
75, 75, 94, 94, 94, 94, 112, 112, 112, 103, 103, 103, 103,
0, 0, 0, 0, 37.5, 37.5, 37.5, 37.5, 29, 29, 29, 29, 63, 63,
63, 63), Species = 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, 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, 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), .Label = "Quercus robur", class = "factor"),
Tree_diameter = c(68.8, 300, 847, 817, 62, 71, 29, 40.1,
68, 45, 60, 54, 104, 122, 85, 71, 81, 118, 39.8, 43.6, 19.8,
16.6, 15.1, 11.9, 81.8, 102.5, 75.5, 57.3, 0.3, 0.2, 0.3,
0.3, 99, 85, 74, 68, 82, 51.8, 19.9, 14.6, 196, 122, 118,
180, 58.6, 54.1, 58, 61.5, 58.4, 61, 134, 64, 52.2, 170,
114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237, 322,
302, 173, 186, 144, 155, 89, 41, 68, 83, 141.6, 85.5, 82.8,
114.1, 129, 127, 143, 125, 92, 68, 90, 25, 20, 63.7, 39.8,
66.2, 112.4, 124.5, 94.1, 68.6, 74.4, 24.2, 54.7, 43, 33.1,
306, 274, 56, 60, 72.5, 128.5, 22, 16, 143, 103, 53, 130,
48.4, 69.8, 6.4, 18.6, 129.2, 41.7, 57.6, 14, 320, 352, 120.9,
108.3, 53.2, 274, 85, 52, 43, 38, 37, 219, 215, 216, 175,
85.9, 49.7, 97.1, 40.8, 62.4, 181.5, 149.7, 122, 143.6, 148,
145, 99, 27.5, 32, 54, 54.1, 169, 152, 160, 138, 90.8, 87.9,
77.4, 81.2, 91.7, 62.7, 50, 72.9, 24.8, 61, 88.6, 80.1),
Urbanisation_index = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 3L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 1L, 1L,
1L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("1", "2",
"3", "4"), class = "factor"), Stand_density_.index = structure(c(3L,
4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L,
3L, 3L, 4L, 3L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L), .Label = c("1",
"2", "3", "4"), class = "factor"), Canopy_Index = c(15L,
95L, 45L, 5L, 5L, 5L, 25L, 15L, 25L, 25L, 35L, 35L, 25L,
35L, 15L, 15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L, 35L, 35L,
55L, 35L, 5L, 5L, 5L, 5L, 95L, 95L, 95L, 95L, 25L, 25L, 15L,
5L, 25L, 25L, 25L, 25L, 5L, 5L, 5L, 5L, 5L, 5L, 35L, 25L,
5L, 35L, 35L, 25L, 25L, 5L, 5L, 5L, 5L, 35L, 25L, 25L, 25L,
5L, 5L, 15L, 15L, 35L, 65L, 35L, 35L, 25L, 25L, 25L, 25L,
15L, 15L, 5L, 35L, 35L, 45L, 35L, 5L, 15L, 15L, 25L, 5L,
15L, 15L, 5L, 5L, 15L, 5L, 5L, 5L, 5L, 5L, 85L, 5L, 35L,
15L, 5L, 5L, 5L, 25L, 25L, 15L, 35L, 95L, 95L, 95L, 95L,
15L, 15L, 5L, 25L, 25L, 5L, 15L, 15L, 5L, 15L, 5L, 25L, 25L,
25L, 25L, 5L, 5L, 5L, 5L, 25L, 25L, 55L, 35L, 25L, 15L, 15L,
25L, 15L, 45L, 35L, 35L, 15L, 35L, 15L, 15L, 35L, 15L, 25L,
25L, 15L, 15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L, 15L, 15L
), Phenological_Index = c(4L, 4L, 3L, 4L, 2L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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, 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, 3L, 2L, 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)), row.names = c(NA, -165L), class = "data.frame")
如何使用 subtitle 来标记每个单独的地块？
## Observation 1
p1 <- ggplot(
QuercusRobur1,
aes(x = Longitude, y = Latitude)
) +
geom_polygon(
data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE
) +
coord_map(xlim = c(-10, 5)) + # limits added as there are some points really far away
theme_classic()
Urban1 <- p1 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "A: Urbanisation Index") +
theme(legend.justification = "left")
Stand1 <- p1 +
geom_point(aes(color = factor(Stand_density_index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "C: Stand Density Index") +
theme(legend.justification = "left")
## Observation 2
p2 <- ggplot(
QuercusRobur2,
aes(x = Longitude, y = Latitude)
) +
geom_polygon(
data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE
) +
coord_map(xlim = c(-10, 5)) +
theme_classic()
Urban2 <- p2 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "B: Urbanisation Index") +
theme(legend.justification = "left")
Stand2 <- p2 +
geom_point(aes(color = factor(Stand_density_.index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "D: Stand Density Index") +
theme(legend.justification = "left")
## Arrange the individual plots into one main plot
plot_grid(
Urban1 + ggtitle("Observational Period 1\n") + theme(plot.title = element_text(hjust = 1.0)),
Urban2 + ggtitle("Observational Period 2\n") + theme(plot.title = element_text(hjust = 1.0)),
Stand1,
Stand2,
align = "hv",
axis = 'tblr',
label_fontface = "bold",
label_fontfamily = "Times New Roman",
label_size = 8,
rel_widths = c(1, 1.3),
ncol = 2,
nrow = 2,
hjust = 0,
label_x = 0.01
)
编辑：删除重复的轴标签和图例，然后使用egg::ggarrange组合子图。
## Observation 1
Urban1 <- p1 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "A: Urbanisation Index") +
theme(legend.position = "none")
Stand1 <- p1 +
geom_point(aes(color = factor(Stand_density_index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "C: Stand Density Index") +
theme(legend.position = "none")
## Observation 2
p2 <- ggplot(
QuercusRobur2,
aes(x = Longitude, y = Latitude)
) +
geom_polygon(
data = UK,
aes(x = long, y = lat, group = group),
inherit.aes = FALSE
) +
coord_map(xlim = c(-10, 5)) +
theme_classic() +
ylab("")
Urban2 <- p2 +
geom_point(aes(color = factor(Urbanisation_index))) +
scale_color_discrete(
name = "Urbanisation Index",
labels = c("Urban", "Suburban", "Village", "Rural")
) +
labs(subtitle = "B: Urbanisation Index") +
theme(legend.justification = "left")
Stand2 <- p2 +
geom_point(aes(color = factor(Stand_density_.index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"Standing alone",
"Within a few trees or close proximity to other trees",
"Within a stand of 10-30 trees",
"Large or woodland"
)
) +
labs(subtitle = "D: Stand Density Index") +
theme(legend.justification = "left")
## Use the `egg` package
library(egg)
ggarrange(
Urban1 + ggtitle("Observational Period 1\n") + theme(plot.title = element_text(hjust = 0.5)),
Urban2 + ggtitle("Observational Period 2\n") + theme(plot.title = element_text(hjust = 0.5)),
Stand1,
Stand2,
nrow = 2,
ncol = 2
)