rggplot2mapscowplot
Cowplot Package: Aligning text labels in the top lefthand corner of plot space using plot_grid() in R
Overview:
I have produced a series of maps (see below) using the R-code below, and I used plot_grid() in the Cowplot package to arrange the plots using the two data frames below called "QuercusRobur 1" and "QuercusRobur2".
Problem:
The plots look really good; however, the text labels are not well aligned. A few labels overlay the top of y-axis called Latitude, and two of the text labels called "A: Urbanisation Index" and "B: Urbanisation Index" are not positioned above their plots, and they also overlay the main titles called "Observation Period 1" and "Observation Period 2".
Does anyone know how to neatly align the plot labels so they are positioned in the top lefthand corner of each plot but also prevent them from overlaying the y-axis or parts of the map (see desired output below)?
If anyone can help, I would be deeply appreciative
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)
Plot produced from R-code
Desired Output
Data frame - 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))
Data frame - 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")
Solution
How about using subtitle for labeling each individual plot?
## 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
)
Edit: remove duplicate axis labels and legends then use egg::ggarrange to combine subplots.
## 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
)
