Question

概述：

我正在研究纬度如何影响落叶橡树的叶片衰老（叶片损失）。罗伯（Robur）。

我对在R中生成地图完全陌生，并且尝试了几天没有获得成功的预期结果。

如果有人可以提供帮助，我将非常感激。

问题：

我使用 my_map（）制作了英国地图（参见图1），并且有一个名为 lonlat_df 的数据框，其中包含所有橡树的经度和纬度坐标记录树木。

我正在尝试使用 geom_point（）将树数据点合并到英国地图上。但是，我不确定如何将地图，树种的GPS点以及关键参数对象整合在一起。

我的目标

要制作3张单独的英国地图，显示研究中记录的每种橡树树种的GPS点（请参见下面的期望输出），但我希望这些点为4种不同的颜色，以便与每个关键参数类别相关联（请参见下文），并为每个参数类别添加图例。

关键参数：

城市化指数：1 =城市，2 =郊区，3 =乡村，4 =农村
站密度指数：：1 =站立，2 =几棵树以内或与其他树近距离，3 =站在10-30棵树之内，4 =大还是林地
物候指数：：1 =不指示秋季时间，2 =第一次秋季着色，3 =部分秋季着色（> 25％的叶子），4 =高级秋季着色（ > 75％的树叶）

R代码

    ##Import Packages
    library(ggplot2)
    library(maps)
    library(mapdata)
    library(tidyverse)

    ##Create objects for the key parameters from the data frame below called QuercusRobur1 to use as point data

    latitude<-QuercusRobur1$Latitude
    longitude<-QuercusRobur1$Longitude
    PhenologyIndex<-QuercusRobur1$Phenological_Index
    StandDensityIndex<-QuercusRobur1$Stand_density_index
    UrbanisationIndex<-QuercusRobur1$Urbanisation_index
    Species<-QuercusRobur1$Species

   ##Produce new data frame

   lonlat_df<-as.data.frame(cbind(longitude, latitude, PhenologyIndex))
      head(lonlat_df)

  ##Produce a map of the UK from maps:
        UK <- map_data(map = "world", region = "UK")
        head(UK)
        dim(UK)

  ##Visualise the map of the UK using ggplot()
        dev.new()

        UK.Map<-ggplot(data = UK, aes(x = long, y = lat, group = group)) + 
                       geom_point(colour="red", size=3, alpha=0.2)+
                       geom_polygon() +
                       coord_map()

##Produce Point Data
        MapPoints<- MapUK + geom_point(data=lonlat_df, aes(x=long, y=lat, group=PhenologyIndex), colour="red", shape=21, fill="red", size=0.5)

图1

所需的输出：

我想在上面的R代码生成的英国地图上覆盖以下所需输出中显示的点的类型。

数据框

   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, 201L, 202L, 203L, 204L, 
    205L, 206L, 207L, 208L, 210L, 212L, 214L, 215L, 216L, 217L, 218L, 
    219L, 220L, 221L, 233L, 234L, 235L, 237L, 239L, 246L, 255L, 256L, 
    257L, 258L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 277L, 278L, 
    279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 
    290L, 291L, 292L, 293L, 294L, 295L, 296L), 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, 13L, 13L, 13L, 9L, 9L, 10L, 10L, 10L, 2L, 2L, 3L, 3L, 
    3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 5L, 5L, 11L, 9L, 9L, 9L, 
    9L, 10L, 10L, 10L, 10L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 11L, 11L, 11L, 11L, 6L, 6L, 6L, 6L, 11L, 11L, 11L, 11L), .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, 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), 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, 169L, 169L, 169L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 43L, 43L, 43L, 75L, 75L, 
    109L, 110L, 110L, 110L, 110L, 95L, 95L, 95L, 95L, 112L, 112L, 
    112L, 0L, 0L, 0L, 0L, 24L, 24L, 24L, 24L, 38L, 38L, 38L, 38L, 
    29L, 29L, 29L, 29L, 20L, 20L, 20L, 20L), 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"), 
        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, 30.2, 39.5, 24.2, 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, 80.3, 43, 50.3, 28.7, 31.9, 
        181.5, 149.7, 122, 143.6, 148, 145, 99, 28, 32, 54, 54, 169, 
        152, 160, 138, 90.8, 87.9, 77.4, 81.2, 91.7, 62.7, 50, 72.9, 
        23.7, 58, 80.7, 73.7), Urbanisation_index = structure(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), .Label = c("1", "2", "3", 
        "4"), class = "factor"), Stand_density_index = structure(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), .Label = c("1", "2", "3", 
        "4"), class = "factor"), 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)), row.names = c(NA, -189L
    ), class = "data.frame")

Answer 1

不需要创建额外的数据框，尤其是在其中不包含关键参数变量的情况下。

您可以尝试以下方法：

p <- 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()

p + 
  aes(color = Urbanisation_index) + 
  scale_color_discrete(name = "Urbanisation Index",
                       labels = c("Urban", "Suburban", "Village", "Rural"))

p + 
  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"))

p + 
  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)"))

如何使用R

1 个答案: