HyNet overview
1 Packages and functions
Load required packages and functions.
Code
library(dplyr)
library(ggplot2)
library(patchwork)
theme_set(theme_light())
library(maptools)
library(igraph)
source("../code/fun_create_adj.R")2 Data
Code
# Load Ches Bay shape file
shoreline <- maptools::readShapePoly("../CBnet_shoreline/final_shoreline.shp")
shoreline <- maptools::readShapePoly("../CBnet_shoreline/CBnet_Shoreline_simplify.shp")
rca_cells <- data.table::fread("../data_rca/rca_cells_2.csv") %>%
filter(FSM == 1) %>%
mutate(CellID = paste0("x", CellID)) %>%
mutate(Atlantic = (LAT < 37.22 & LON > -75.96) |
(LAT < 37.5 & LON > -75.8) |
(LAT < 38.0 & LON > -75.6) |
(LAT < 36.96 & LON > -75.99)
)
AtlanticCells <- rca_cells %>%
filter(Atlantic) %>%
pull(CellID)
rca_cells <- rca_cells %>%
filter(!Atlantic)
#Use info from the shape file to find projections for the stations
###http://www.prj2epsg.org/search
###26918 - NAD_1983_UTM_Zone_18N
###http://spatialreference.org/ref/epsg/26918/
tmp <- rgdal::project(cbind(360 + rca_cells$LON, rca_cells$LAT),
proj = "+proj=utm +zone=18 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")
rca_cells <- rca_cells %>%
mutate(lon = tmp[, 1],
lat = tmp[, 2])Code
lagmax = 30Load results for the years 2002 and 2011 with maximum lag of 30 days.
Code
# Load model coefficients
# Mcoef2002 <- as.matrix(readRDS(paste0("../dataderived/BigVAR_2002_coef_lagmax",
# lagmax, ".rds")))
# Mcoef2011 <- as.matrix(readRDS(paste0("../dataderived/BigVAR_2011_coef_lagmax",
# lagmax, ".rds")))
# Mcoef2002 <- readRDS("../dataderived/VARpairs_2002.rds")
# Mcoef2011 <- readRDS("../dataderived/VARpairs_2011.rds")
Mcoef2002 <- readRDS("../dataderived/CAUSpairs_2002.rds")
Mcoef2011 <- readRDS("../dataderived/CAUSpairs_2011.rds")3 Network
3.1 Network construction
Process results into adjacency matrices.
Code
proc2002 <- create_adj_pairs(Mcoef2002)
proc2011 <- create_adj_pairs(Mcoef2011)Explore coefficients.
Code
summary(as.vector(proc2002$Acoef[!is.na(proc2002$Acoef)]))#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.000 0.059 0.090 0.141 0.863Code
summary(as.vector(proc2011$Acoef[!is.na(proc2011$Acoef)]))#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.001 0.066 0.085 0.139 0.922Additionally threshold coefficients.
Code
proc2002$Acoef[proc2002$Acoef < 0.01] <- 0
proc2011$Acoef[proc2011$Acoef < 0.01] <- 0Explore lags.
Code
summary(as.vector(proc2002$Alag))#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0 2 18 15 27 30 3064Code
summary(as.vector(proc2011$Alag))#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0 12 26 20 29 30 3064Additionally adjust the Granger test \(p\)-values for the number of tests. Here by the family of tests we understand the number of relationships tested between each \(i\)th spatial cell (\(i = 1, \dots,\) 3064) and a cell \(j\), \(j \neq i\). To control the false discovery rate in each family of tests, we use Benjamini–Hochberg (BH) or Benjamini–Yekutieli (BY) adjustment. The false discovery rate is a less stringent condition than the family-wise error rate, so these methods are more powerful than other methods such as the Bonferroni correction. The BY procedure is more conservative than the BH procedure.
Code
tmp_BH <- apply(proc2002$Gp, 2, p.adjust, method = "BH")
tmp_BY <- apply(proc2002$Gp, 2, p.adjust, method = "BY")
summary(as.vector(tmp_BH[!is.na(tmp_BH)]))#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.011 0.137 0.287 0.512 1.000Code
summary(as.vector(tmp_BY[!is.na(tmp_BY)]))#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.091 1.000 0.626 1.000 1.000Code
proc2002$Gp <- tmp_BY #apply(proc2002$Gp, 2, p.adjust, method = "BY")
proc2011$Gp <- apply(proc2011$Gp, 2, p.adjust, method = "BY")
# Fill in the diagonal for easier assignment of the edge attributes
diag(proc2002$Gp) <- diag(proc2011$Gp) <- 1Create weighted directed networks, where edge weight is the average weighted coefficient.
Code
# If subsample
# set.seed(123)
# ss <- sort(sample(3300, 500))
# If not subsample
ss <- 1:nrow(rca_cells)Calculate network stats.
Closeness centrality captures the notion that a vertex is ‘central’ if it is ‘close’ to many other vertices. \[ C_{clos}(v) = \frac{1}{\sum_{u \in V} dist(v, u)}, \] where \(dist(v, u)\) is the geodesic distance (distance over the network) between the vertices \(u, v \in V\). Often the normalization factor \(N_v - 1\) is used so \(C_{clos}(v) \in [0, 1]\) (Section 4.2.2 in Kolaczyk and Csárdi 2014).
Eigenvector centrality measures a node’s importance by considering the importance of its neighbors. This makes the eigenvector centrality suitable for measuring importance of a node. It is represented by the principal eigenvector of the adjacency matrix, and is similar to the core of Google’s PageRank algorithm, which they use to rank web pages.
Code
# Create networks
# - based on penalized pairwise VAR coefficients
g2002 <- igraph::graph_from_adjacency_matrix(proc2002$Acoef[ss, ss]
,weighted = TRUE
,mode = "directed"
,diag = FALSE)
# - based on Granger p-values
g2002G <- igraph::graph_from_adjacency_matrix(proc2002$Gp[ss, ss] < alpha
,weighted = NULL
,mode = "directed"
,diag = FALSE) %>%
set_edge_attr("Coef", value = proc2002$Acoef[ss, ss][proc2002$Gp[ss, ss] < alpha]) %>%
set_edge_attr("Lag", value = proc2002$Alag[ss, ss][proc2002$Gp[ss, ss] < alpha])
# - based on penalized pairwise VAR coefficients
g2011 <- igraph::graph_from_adjacency_matrix(proc2011$Acoef[ss, ss]
,weighted = TRUE
,mode = "directed"
,diag = FALSE)
# - based on Granger p-values
g2011G <- igraph::graph_from_adjacency_matrix(proc2011$Gp[ss, ss] < alpha
,weighted = NULL
,mode = "directed"
,diag = FALSE) %>%
set_edge_attr("Coef", value = proc2011$Acoef[ss, ss][proc2011$Gp[ss, ss] < alpha]) %>%
set_edge_attr("Lag", value = proc2011$Alag[ss, ss][proc2011$Gp[ss, ss] < alpha])
# Network stats
D <- rca_cells[ss, ] %>%
mutate(deg_in_2002 = degree(g2002, mode = "in"),
deg_in_2011 = degree(g2011, mode = "in"),
deg_in_2002G = degree(g2002G, mode = "in"),
deg_in_2011G = degree(g2011G, mode = "in"),
deg_out_2002 = degree(g2002, mode = "out"),
deg_out_2011 = degree(g2011, mode = "out"),
deg_out_2002G = degree(g2002G, mode = "out"),
deg_out_2011G = degree(g2011G, mode = "out"),
clos_out_2002 = closeness(g2002, mode = "out"),
clos_out_2011 = closeness(g2011, mode = "out"),
clos_out_2002G = closeness(g2002G, mode = "out"),
clos_out_2011G = closeness(g2011G, mode = "out"),
cent_2002 = eigen_centrality(g2002, directed = TRUE)$vector,
cent_2011 = eigen_centrality(g2011, directed = TRUE)$vector,
cent_2002G = eigen_centrality(g2002G, directed = TRUE)$vector,
cent_2011G = eigen_centrality(g2011G, directed = TRUE)$vector
)Calculate correlations between node stats and O2.
Code
# Load O2 data
o2_2002 <- data.table::fread(paste0("../data_rca/rca_ts_2002_2.csv"),
select = c("DOAVEG_avg", "CellID", "Date")) %>%
mutate(CellID = paste0("x", CellID)) %>%
filter(!is.element(CellID, AtlanticCells))
o2_2002_avg <- o2_2002 %>%
group_by(CellID) %>%
summarise(O2_2002g = mean(DOAVEG_avg))
Dtmp <- merge(D, o2_2002_avg, by = "CellID")
# Load deseasonalized O2 data
o2_2002_ds <- readRDS("../dataderived/Dwide_mat_deseas_2002.rds")
o2_2002_ds_avg <- apply(o2_2002_ds, 2, mean)
o2_2002_ds_avg <- tibble(CellID = names(o2_2002_ds_avg), O2_2002g_ds = o2_2002_ds_avg)
Dtmp <- merge(Dtmp, o2_2002_ds_avg, by = "CellID")
Dtmp %>%
select(contains("2002G")) %>%
GGally::ggpairs()
3.2 Compare 2002 and 2011
Numeric summaries.
Code
D %>%
select(!c(CellID, H, DX, DY, LAT, LON, FSM, lat, lon)) %>%
psych::describe(quant = c(0.1, 0.25, 0.5, 0.75, 0.90)) #%>%#> vars n mean sd median trimmed mad min max range
#> Atlantic 1 3064 NaN NA NA NaN NA Inf -Inf -Inf
#> deg_in_2002 2 3064 1631.07 957.23 1629.50 1638.82 1260.95 0 3063 3063
#> deg_in_2011 3 3064 1666.67 954.90 1751.00 1686.21 1269.11 0 3063 3063
#> deg_in_2002G 4 3064 659.80 460.45 584.00 621.00 465.54 0 2189 2189
#> deg_in_2011G 5 3064 501.66 387.54 435.50 467.56 448.49 0 1788 1788
#> deg_out_2002 6 3064 1631.07 789.05 1593.50 1620.06 1010.39 274 3063 2789
#> deg_out_2011 7 3064 1666.67 767.17 1640.00 1658.96 956.28 252 3063 2811
#> deg_out_2002G 8 3064 659.80 334.54 629.00 642.91 363.98 25 1681 1656
#> deg_out_2011G 9 3064 501.66 281.76 473.00 481.95 302.45 17 1588 1571
#> clos_out_2002 10 3064 0.00 0.00 0.00 0.00 0.00 0 0 0
#> clos_out_2011 11 3064 0.00 0.00 0.00 0.00 0.00 0 0 0
#> clos_out_2002G 12 3064 0.00 0.00 0.00 0.00 0.00 0 0 0
#> clos_out_2011G 13 3064 0.00 0.00 0.00 0.00 0.00 0 0 0
#> cent_2002 14 3064 0.09 0.16 0.02 0.05 0.03 0 1 1
#> cent_2011 15 3064 0.12 0.18 0.04 0.08 0.06 0 1 1
#> cent_2002G 16 3064 0.30 0.23 0.25 0.28 0.25 0 1 1
#> cent_2011G 17 3064 0.23 0.21 0.17 0.20 0.22 0 1 1
#> skew kurtosis se Q0.1 Q0.25 Q0.5 Q0.75 Q0.9
#> Atlantic NA NA NA NA NA NA NA NA
#> deg_in_2002 -0.02 -1.30 17.29 314.60 786.75 1629.50 2490.25 2976.70
#> deg_in_2011 -0.13 -1.30 17.25 311.30 804.75 1751.00 2550.00 2939.00
#> deg_in_2002G 0.70 -0.06 8.32 102.30 299.00 584.00 939.25 1316.70
#> deg_in_2011G 0.62 -0.48 7.00 51.00 159.00 435.50 781.00 1053.40
#> deg_out_2002 0.10 -1.17 14.25 579.30 940.75 1593.50 2319.25 2756.70
#> deg_out_2011 0.07 -1.14 13.86 625.50 1014.00 1640.00 2312.25 2756.70
#> deg_out_2002G 0.39 -0.52 6.04 239.00 394.75 629.00 890.25 1124.00
#> deg_out_2011G 0.64 0.16 5.09 167.00 279.00 473.00 685.25 877.00
#> clos_out_2002 0.23 -1.24 0.00 0.00 0.00 0.00 0.00 0.00
#> clos_out_2011 -0.03 -1.02 0.00 0.00 0.00 0.00 0.00 0.00
#> clos_out_2002G 0.31 -0.13 0.00 0.00 0.00 0.00 0.00 0.00
#> clos_out_2011G 0.16 0.87 0.00 0.00 0.00 0.00 0.00 0.00
#> cent_2002 2.90 9.71 0.00 0.00 0.00 0.02 0.11 0.26
#> cent_2011 2.01 4.12 0.00 0.00 0.00 0.04 0.17 0.37
#> cent_2002G 0.72 -0.28 0.00 0.02 0.10 0.25 0.45 0.65
#> cent_2011G 0.94 0.16 0.00 0.01 0.04 0.17 0.35 0.54Code
# t()3.2.1 VAR-based networks
Code
p1 <- ggplot(D, aes(x = deg_in_2002)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
p2 <- ggplot(D, aes(x = deg_in_2011)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
p3 <- ggplot(D, aes(x = deg_out_2002)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
p4 <- ggplot(D, aes(x = deg_out_2011)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
(p1 + p2) / (p3 + p4) +
plot_annotation(tag_levels = 'A') & theme_light()
Code
# Size of points on the map
sz = 1
# Aspect ratio for lat and lon
# https://stackoverflow.com/questions/66018518/aspect-ratio-for-lat-lon-plots
lat_mean <- mean(rca_cells$LAT)
rto <- 1 / cos(lat_mean * pi / 180)
p1 <- ggplot(D, aes(x = lon, y = lat, color = deg_in_2002)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p2 <- ggplot(D, aes(x = lon, y = lat, color = deg_in_2011)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p3 <- ggplot(D, aes(x = lon, y = lat, color = deg_out_2002)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p4 <- ggplot(D, aes(x = lon, y = lat, color = deg_out_2011)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
(p1 + p2) / (p3 + p4) +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = deg_in_2011 - deg_in_2002)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz) +
scale_color_continuous(type = "viridis")
p2 <- ggplot(D, aes(x = lon, y = lat, color = deg_out_2011 - deg_out_2002)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz) +
scale_color_continuous(type = "viridis")
(p1 + p2) +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
Code
p1 <- ggplot(D, aes(x = clos_out_2002)) +
geom_histogram(aes(y = after_stat(density)), bins = 50, fill = "grey50") +
ylab("Density")
p2 <- ggplot(D, aes(x = clos_out_2011)) +
geom_histogram(aes(y = after_stat(density)), bins = 50, fill = "grey50") +
ylab("Density")
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light()
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = clos_out_2002)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p2 <- ggplot(D, aes(x = lon, y = lat, color = clos_out_2011)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
Code
p1 <- ggplot(D, aes(x = cent_2002)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 0.01, fill = "grey50") +
ylab("Density")
p2 <- ggplot(D, aes(x = cent_2011)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 0.01, fill = "grey50") +
ylab("Density")
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light()
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = cent_2002)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p2 <- ggplot(D, aes(x = lon, y = lat, color = cent_2011)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
3.2.2 Granger causality-based networks
Code
p1 <- ggplot(D, aes(x = deg_in_2002G)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
p2 <- ggplot(D, aes(x = deg_in_2011G)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
p3 <- ggplot(D, aes(x = deg_out_2002G)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
p4 <- ggplot(D, aes(x = deg_out_2011G)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density")
(p1 + p2) / (p3 + p4) +
plot_annotation(tag_levels = 'A') & theme_light()
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = deg_in_2002G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p2 <- ggplot(D, aes(x = lon, y = lat, color = deg_in_2011G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p3 <- ggplot(D, aes(x = lon, y = lat, color = deg_out_2002G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p4 <- ggplot(D, aes(x = lon, y = lat, color = deg_out_2011G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
(p1 + p2) / (p3 + p4) +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = deg_in_2011G - deg_in_2002G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz) +
scale_color_continuous(type = "viridis")
p2 <- ggplot(D, aes(x = lon, y = lat, color = deg_out_2011G - deg_out_2002G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz) +
scale_color_continuous(type = "viridis")
(p1 + p2) +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
Code
p1 <- ggplot(D, aes(x = clos_out_2002G)) +
geom_histogram(aes(y = after_stat(density)), bins = 50, fill = "grey50") +
ylab("Density")
p2 <- ggplot(D, aes(x = clos_out_2011G)) +
geom_histogram(aes(y = after_stat(density)), bins = 50, fill = "grey50") +
ylab("Density")
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light()
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = clos_out_2002G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p2 <- ggplot(D, aes(x = lon, y = lat, color = clos_out_2011G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
Code
p1 <- ggplot(D, aes(x = cent_2002G)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 0.01, fill = "grey50") +
ylab("Density")
p2 <- ggplot(D, aes(x = cent_2011G)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 0.01, fill = "grey50") +
ylab("Density")
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light()
Code
p1 <- ggplot(D, aes(x = lon, y = lat, color = cent_2002G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p2 <- ggplot(D, aes(x = lon, y = lat, color = cent_2011G)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p1 + p2 +
plot_annotation(tag_levels = 'A') &
theme_light() &
coord_fixed(ratio = rto)
3.3 Visualize all networks
Code
YEARS = 1986L:2015L
alpha = 0.05
# ndims = 5
# edims <- numeric()
# E_ASE <- E_LSE <- numeric()
# Full loop on weighted net is about 5 minutes.
for (year in YEARS) { # year = 2002
# Load data obtained on cluster
Mcoef <- readRDS(paste0("../dataderived/CAUSpairs_", year, ".rds"))
proc <- create_adj_pairs(Mcoef)
# Thresholding / adjusting
# proc$Acoef[proc$Acoef < 0.01] <- 0
proc$Gp <- apply(proc$Gp, 2, p.adjust, method = "BY")
diag(proc$Gp) <- 1
# Unweighted directed adjacency matrix
AM <- proc$Gp < alpha
# Weighted directed adjacency matrix
AMw <- proc$Acoef
AMw[AM == 0] <- 0
# Create graph, add attributes
graphGranger <- igraph::graph_from_adjacency_matrix(AMw, weighted = TRUE
#AM, weighted = NULL
,mode = "directed"
,diag = FALSE)
# ?cluster_walktrap
# ?cluster_infomap
clusters <- cluster_walktrap(graphGranger)
# print(clusters)
# table(clusters$membership)
Cluster <- as.factor(clusters$membership)
# plot(graphGranger, vertex.color = membership(clusters))
# igraph::is_weighted(graphGranger)
deg_in = degree(graphGranger, mode = "in") %>% as_tibble()
deg_out = degree(graphGranger, mode = "out") %>% as_tibble()
p1 <- ggplot(deg_in, aes(x = value)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density") + xlab("In-degree") +
ggtitle(year)
p2 <- ggplot(deg_out, aes(x = value)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10, fill = "grey50") +
ylab("Density") + xlab("Out-degree")
p3 <- ggplot(D, aes(x = lon, y = lat, color = deg_in$value)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p4 <- ggplot(D, aes(x = lon, y = lat, color = deg_out$value)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz)
p_cl <- ggplot(D, aes(x = lon, y = lat, color = Cluster)) +
geom_polygon(data = shoreline,
aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(size = sz) +
scale_color_viridis_d()
print(
p1 + p2 + p3 + p4 + p_cl +
plot_layout(nrow = 1) +
plot_annotation(tag_levels = 'A') & theme_light())
}
References
Kolaczyk ED, Csárdi G (2014) Statistical analysis of network data with R. Springer, New York