Обзор:
Я создал серию карт (см. Ниже), используя R-код ниже , и я использовал plot_grid ()упорядочить графики, используя приведенные ниже кадры данных, называемые ** "QuercusRobur1" и "QuercusRobur2" .
Моя цель - разместить два центрально выровненных главных заголовка под названием Наблюдение 1 Наблюдение 2 над двумя столбцами упорядоченных графиков, используя ggtitle () и измените размер текста, чтобы обеспечить больше места для графиков.
Кто-нибудь знает, как централизованно выровнять основные заголовки и изменить размер текста?
Если кто-нибудь может помочь, я был бы очень признателен
R-код
##Import Packages
library(ggplot2)
library(maps)
library(mapdata)
library(cowplot)
##Get a map of the UK from maps:
UK <- map_data(map = "world", region = "UK")
head(UK)
dim(UK)
##Produce point data
dev.new()
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
) +
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")
Phenology1 <- p1 +
geom_point(aes(color = factor(Phenological_Index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"No indication of autumn timing",
"First autumn tinting",
"Partial autumn tinting (>25% of leaves)",
"Advanced autumn tinting (>75% of leaves)"
)
) +
labs(subtitle = "E: Phenological 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")
Phenology2 <- p2 +
geom_point(aes(color = factor(Phenological_Index))) +
scale_color_discrete(
name = "Stand Density Index",
labels = c(
"No indication of autumn timing",
"First autumn tinting",
"Partial autumn tinting (>25% of leaves)",
"Advanced autumn tinting (>75% of leaves)"
)
) +
labs(subtitle = "F: Phenological Index") +
theme(legend.justification = "left")
## Arrange the individual plots into one main plot
dev.new()
plot_grid(
Urban1 + ggtitle("Observational Period 1\n") + theme(plot.title = element_text(hjust = 1.0)) + theme(legend.justification = c(0,1)),
Urban2 + ggtitle("Observational Period 2\n") + theme(plot.title = element_text(hjust = 1.0)) + theme(legend.justification = c(0,1)),
Stand1 + theme(legend.justification = c(0,1)),
Stand2 + theme(legend.justification = c(0,1)),
Phenology1 + theme(legend.justification = c(0,1)),
Phenology2 + theme(legend.justification = c(0,1)),
align = "hv",
axis = 'tblr',
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
)
Участок, созданный из R-кода 
Фрейм данных - QuercusRobur1
structure(list(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, 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"),
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, 52.57937, 52.57937, 52.57937,
50.736531, 50.736531, 50.79926, 50.79926, 50.79926, 53.675996,
53.675996, 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, 55.91913, 51.6528, 51.6528, 51.6528,
51.6528, 51.88485, 51.88485, 51.88485, 51.88485, 52.34015,
52.34015, 52.34015, 52.026042, 52.026042, 52.026042, 52.026042,
51.319032, 51.319032, 51.319032, 51.319032, 51.51357, 51.51357,
51.51357, 51.51357, 53.43202, 53.43202, 53.43202, 53.43202,
51.50823, 51.50823, 51.50823, 51.50823), 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, -1.89325, -1.89325, -1.89325, -2.011143,
-2.011143, -3.19446, -3.19446, -3.19446, -1.272824, -1.272824,
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, -3.20987, -1.57361, -1.57361, -1.57361, -1.57361,
-0.17844, -0.17844, -0.17844, -0.17844, -1.27795, -1.27795,
-1.27795, -0.503114, -0.503114, -0.503114, -0.503114, -0.472994,
-0.472994, -0.472994, -0.472994, -3.18738, -3.18738, -3.18738,
-3.18738, -2.27968, -2.27968, -2.27968, -2.27968, -0.25847,
-0.25847, -0.25847, -0.25847), 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, 2L, 2L,
2L, 4L, 4L, 2L, 2L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 1L, 3L, 3L, 3L, 3L, 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), 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, 4L, 4L,
4L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 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), 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,
65L, 45L, 65L, 85L, 35L, 95L, 85L, 85L, 85L, 85L, 75L, 65L,
65L, 65L, 65L, 55L, 75L, 85L, 85L, 95L, 85L, 75L, 75L, 85L,
65L, 45L, 75L, 75L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 85L,
65L, 75L, 75L, 75L, 65L, 75L, 35L, 75L, 75L, 75L, 75L, 25L,
45L, 45L, 35L, 85L, 95L, 85L, 95L), 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, 2L, 4L,
3L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L)), class = "data.frame", row.names = c(NA,
-189L))
Фрейм данных - QuercusRobur2
structure(list(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), .Label = "Quercus robur", 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), 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), Urbanisation_index = 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), Stand_density_.index = 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), 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), 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)), class = "data.frame", row.names = c(NA,
-99L))