Question

概述：

我已经使用下面的 R代码制作了一系列地图（见下文），并且我使用了 plot_grid（）使用下面称为**的数据框来布置图。 QuercusRobur1” 和“ QuercusRobur2” 。

我的目标是使用 ggtitle（）将两个称为 Observation 1 Observation 2 的居中对齐主标题放置在已布置地块的两列上方并更改文本的大小以为绘图提供更多的绘图空间。

有人知道如何集中对齐主要标题并更改文本大小吗？

如果有人可以帮助我，我将非常感激

R代码

    ##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
        )

根据R代码生成的图

数据框-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))

数据框-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))

Cowplot：如何在R中使用plot_grid（）在图的排列上使用ggtitle（）将两个主要图标题集中对齐

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