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Manuscript_Figures.R
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# Loading packages
library(plotly)
library(ggplot2)
library(cowplot)
library(rgdal)
library(gridExtra)
library(htmlwidgets)
library(data.table)
library(httr)
library(ggtext)
library(tidyverse)
library(extrafont)
library(janitor)
library(ggpubr)
library(glue)
library(fdapace)
library(EpiEstim)
library(BMS)
# Reading and cleaning data
set.seed(04152021)
options(stringsAsFactors = F)
Data.districts = read.csv(url("https://api.covid19india.org/csv/latest/districts.csv"))
Data.states = read.csv(url("https://api.covid19india.org/csv/latest/states.csv"))
Districts = c(sort(unique(Data.districts$District[Data.districts$State ==
"Odisha"]))[-c(26, 30)], "All Districts")
Start.Date = "2020-05-01"
End.Date = "2021-04-15"
Lockdown.Dates = c(
"2020-05-04",
"2020-05-18",
"2020-06-01",
"2020-07-01",
"2020-08-01",
"2020-09-01",
"2020-10-01",
"2020-11-01",
"2020-12-01",
"2021-01-01",
"2021-02-01",
"2021-03-01",
"2021-04-01"
)
Lockdown.Labels = data.frame(
Date = Lockdown.Dates,
Labels = c(
"Lockdown 3.0",
"Lockdown 4.0",
"Unlock 1.0",
"Unlock 2.0",
"Unlock 3.0",
"Unlock 4.0",
"Unlock 5.0",
"Unlock 6.0",
"Unlock 7.0",
"Unlock 8.0",
"Unlock 9.0",
"Unlock 10.0",
"Unlock 11.0"
)
)
Lockdown.Labels = Lockdown.Labels[as.numeric(as.Date(Lockdown.Labels$Date)) < as.numeric(as.Date(End.Date)), ]
# Figure 3
## Panels 3A and 3B
R.Curve = Incid.Data = list()
for (i in 1:length(Districts))
{
District = Districts[i]
if (District == "All Districts")
{
DistrictCum.Table = Data.states[Data.states$State == "Odisha", c("Date", "Confirmed")]
DistrictInc.Table = data.frame(
Date = DistrictCum.Table$Date,
Incidence = c(
DistrictCum.Table$Confirmed[1],
diff(DistrictCum.Table$Confirmed)
)
)
}
else
{
DistrictCum.Table = Data.districts[Data.districts$State == "Odisha" &
Data.districts$District == District, c("Date", "Confirmed")]
DistrictInc.Table = data.frame(
Date = DistrictCum.Table$Date,
Incidence = c(
DistrictCum.Table$Confirmed[1],
diff(DistrictCum.Table$Confirmed)
)
)
}
rownames(DistrictInc.Table) = 1:nrow(DistrictInc.Table)
colnames(DistrictInc.Table) = c("Date", "Incidence")
DistrictInc.Table$Incidence = pmax(0, DistrictInc.Table$Incidence)
DistrictInc.Table$Date = as.Date(DistrictInc.Table$Date)
Length = nrow(DistrictInc.Table)
R_Summary = estimate_R(
DistrictInc.Table$Incidence,
method = "parametric_si",
config = list(
t_start = 2:(Length - 4),
t_end = 5:(Length - 1),
n1 = 500,
mean_si = 3.96,
std_si = 4.75,
n2 = 100,
seed = 1,
mcmc_control = list(
init.pars = NULL,
burnin = 10000,
thin = 1000,
seed = 1
)
)
)
mean.R <- R_Summary$R$`Mean(R)`
std.R <- R_Summary$R$`Std(R)`
lowerCI.R <- R_Summary$R$`Quantile.0.025(R)`
upperCI.R <- R_Summary$R$`Quantile.0.975(R)`
date.R <- as.Date(DistrictInc.Table$Date[5:(Length - 1)])
R.summary <-
data.frame(date.R, mean.R, std.R, lowerCI.R, upperCI.R)
R.summary = R.summary[as.numeric(as.Date(R.summary$date.R)) <= as.numeric(as.Date(End.Date)), ]
R.Curve[[i]] = R.summary
incid.dates.R = DistrictInc.Table$Date
incid.R = DistrictInc.Table$Incidence
cumsum.R = cumsum(incid.R)
incid = data.frame(incid.dates.R, incid.R, cumsum.R)
incid = incid[as.numeric(as.Date(incid$incid.dates.R)) <= as.numeric(as.Date(End.Date)), ]
Incid.Data[[i]] = incid
}
names(R.Curve) = names(Incid.Data) = Districts
### Panel 3A
R.Summary <- R.Curve$`All Districts`
R.Summary = R.Summary[as.numeric(as.Date(R.Summary$date.R)) >= as.numeric(as.Date(Start.Date)), ]
Labels.Height.3A = 3.6
Figure.3A <-
ggplot(data = R.Summary, aes(x = date.R, y = mean.R)) + geom_point() + geom_line() + ylim(0, Labels.Height.3A *
10 / 9) +
geom_ribbon(aes(ymin = lowerCI.R, ymax = upperCI.R),
linetype = 2,
alpha = 0.1) + xlab("Date") + ylab("Reproduction Number") + ggtitle("(A)") +
scale_x_date(date_labels = "%d %b", date_breaks = "7 days") + geom_hline(yintercept = 1,
linetype = "dashed",
color = "#138808") + geom_hline(yintercept = 2,
linetype = "dashed",
color = "#eb4034") +
theme_classic() +
theme(
text = element_text(family = "Helvetica Neue", face = "bold"),
plot.title = ggtext::element_markdown(size = 15),
plot.subtitle = element_text(size = 14, color = "#36454f"),
plot.caption = ggtext::element_markdown(
hjust = 0,
size = 15,
lineheight = 1.1,
face = "bold"
),
axis.text = element_text(size = 10, color = "#36454f"),
axis.title = element_text(size = 12),
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
axis.text.x = element_text(angle = 90)
) +
geom_vline(
mapping = aes(xintercept = as.Date(Date)),
data = Lockdown.Labels,
show.legend = FALSE
) +
geom_label(
data = Lockdown.Labels,
mapping = aes(x = as.Date(Date),
y = Labels.Height.3A,
label = Labels),
fontface = "bold"
)
### Panel 3B
Incid.Summary = Incid.Data$`All Districts`
Incid.Summary = Incid.Summary[as.numeric(as.Date(Incid.Summary$incid.dates.R)) >= as.numeric(as.Date(Start.Date)), ]
Labels.Height.3B = 2100
Figure.3B = ggplot(Incid.Summary, aes(x = incid.dates.R, y = incid.R)) + geom_col() + xlab("Date") + ylab("Incidence of Confirmed Cases") +
scale_x_date(date_labels = "%d %b", date_breaks = "7 days") + ggtitle("(B)") +
theme_classic() +
theme(
text = element_text(family = "Helvetica Neue", face = "bold"),
plot.title = ggtext::element_markdown(size = 15),
plot.subtitle = element_text(size = 14, color = "#36454f"),
plot.caption = ggtext::element_markdown(
hjust = 0,
size = 15,
lineheight = 1.1,
face = "bold"
),
axis.text = element_text(size = 10, color = "#36454f"),
axis.title = element_text(size = 12),
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
axis.text.x = element_text(angle = 90)
) +
geom_vline(
mapping = aes(xintercept = as.Date(Date)),
data = Lockdown.Labels,
show.legend = FALSE
) +
geom_label(
data = Lockdown.Labels,
mapping = aes(x = as.Date(Date),
y = Labels.Height.3B,
label = Labels),
fontface = "bold"
)
## Panels 3C and 3D
R.Curves.All = rbindlist(R.Curve, idcol = TRUE)
colnames(R.Curves.All)[1] = "District"
R.Curves.All = R.Curves.All[as.numeric(as.Date(R.Curves.All$date.R)) >= as.numeric(as.Date(Start.Date)), ]
### Panel 3C
heatmap_colors <- c(
"alarm" = "red",
"eh" = "darkred",
"eh1" = "red4",
"eh2" = "red3",
"eh3" = "red2",
"eh4" = "red1",
"good" = "#138808",
"none" = "white"
)
Decision = function(x)
{
if (x >= 2)
{
return("alarm")
}
if (x >= 1.8 & x < 2)
{
return("eh")
}
if (x >= 1.6 & x < 1.8)
{
return("eh1")
}
if (x >= 1.4 & x < 1.6)
{
return("eh2")
}
if (x >= 1.2 & x < 1.4)
{
return("eh3")
}
if (x >= 1 & x < 1.2)
{
return("eh4")
}
if (x < 1)
{
return("good")
}
}
danger <- 2
safe <- 1
title <- "(C)"
subtitle <- NULL
x_lab <- "Date"
y_lab <- "District"
caption <- glue("**Note:**<br>",
" - Colored red if estimate is above 1 and green if below 1.<br>")
Labels.Height.3C = 33
R.Curves.All$heatmap = unlist(lapply(R.Curves.All$mean.R, Decision))
R.Curves.All$labels = rep(NA, nrow(R.Curves.All))
Lockdown.Labels.3C = data.frame(
rep("", nrow(Lockdown.Labels)),
as.Date(Lockdown.Labels$Date),
rep(NA, nrow(Lockdown.Labels)),
rep(NA, nrow(Lockdown.Labels)),
rep(NA, nrow(Lockdown.Labels)),
rep(NA, nrow(Lockdown.Labels)),
rep("none", nrow(Lockdown.Labels)),
Lockdown.Labels$Labels
)
colnames(Lockdown.Labels.3C) = colnames(R.Curves.All)
R.Curves.All = rbind(Lockdown.Labels.3C, R.Curves.All)
Figure.3C = ggplot(data = R.Curves.All,
mapping = aes(
x = date.R,
y = fct_relevel(fct_rev(District), "All Districts"),
fill = heatmap
)) + geom_tile() + scale_fill_manual(values = heatmap_colors) + expand_limits(y = 36) +
labs(
title = title,
subtitle = subtitle,
x = x_lab,
y = y_lab,
caption = caption
) +
theme_minimal() +
theme(
text = element_text(family = "Helvetica Neue", face = "bold"),
plot.title = ggtext::element_markdown(size = 15),
plot.subtitle = element_text(size = 14, color = "#36454f"),
plot.caption = ggtext::element_markdown(
hjust = 0,
size = 15,
lineheight = 1.1,
face = "bold"
),
axis.text = element_text(size = 10, color = "#36454f"),
axis.title = element_text(size = 12),
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
axis.text.x = element_text(angle = 90)
) +
scale_x_date(date_labels = "%d %b", date_breaks = "7 days") +
geom_vline(
mapping = aes(xintercept = as.Date(Date)),
data = Lockdown.Labels,
show.legend = FALSE
) +
geom_label(
mapping = aes(
x = as.Date(date.R),
y = Labels.Height.3C,
label = labels
),
fontface = "bold"
)
### Panel 3D
Incid.All = rbindlist(Incid.Data, idcol = TRUE)
colnames(Incid.All)[1] = "District"
Last.Date = Incid.All[as.numeric(as.Date(Incid.All$incid.dates.R)) == as.numeric(as.Date(End.Date)), ]
Last.Fortnight = R.Curves.All[as.numeric(as.Date(R.Curves.All$date.R)) >= as.numeric(as.Date(End.Date)) - 13, ]
Last.Fortnight = Last.Fortnight[, -2] %>% group_by(District) %>% summarise_all(list(mean))
Last.Date = Last.Date[order(Last.Date$District), ]
Last.Fortnight = Last.Fortnight[order(Last.Fortnight$District), ]
test_data <- structure(
list(
name = Last.Fortnight$District,
Rvalue = Last.Fortnight$mean.R,
upper = Last.Fortnight$upperCI.R,
lower = Last.Fortnight$lowerCI.R,
cases = Last.Date$cumsum.R
),
class = c("tbl_df", "tbl", "data.frame"),
row.names = c(NA,-nrow(Last.Date))
)
fplot_colors <- heatmap_colors
fplot_shapes <- c("India" = 18,
"Not_India" = 16)
fplot_sizes <- c("India" = 1.35,
"Not_India" = 0.72)
title <-
"(D)"
subtitle <- NULL
x_lab <- "District"
y_lab <- "Instantaneous R"
caption <- glue(
"**Note:**<br>",
" - Numbers in the left column indicate total cases by district on the date mentioned.<br>",
" - Estimates and 95% confidence intervals are provided in each plot by district.<br>",
" - Colored red if estimate is above 1 and green if below 1."
)
Figure.3D = test_data %>%
mutate(
fplot = unlist(lapply(Rvalue, Decision)),
shape = ifelse(name == "All Districts", "India", "Not_India"),
size = shape
) %>%
ggplot(aes(x = fct_relevel(reorder(name, Rvalue), "All Districts"), y = Rvalue)) +
geom_hline(yintercept = safe,
color = "gray40",
linetype = 2) +
geom_hline(yintercept = danger,
color = "gray40",
linetype = 2) +
geom_pointrange(aes(
ymin = lower,
ymax = upper,
color = fplot,
shape = shape,
size = size
)) +
scale_color_manual(values = fplot_colors) + scale_shape_manual(values = fplot_shapes) + scale_size_manual(values = fplot_sizes) +
labs(
title = title,
subtitle = subtitle,
x = x_lab,
y = y_lab,
caption = caption
) +
coord_flip(ylim = c(NA, ceiling(max(
Last.Fortnight$upperCI.R
)))) +
theme_minimal() +
theme(
text = element_text(family = "Helvetica Neue", face = "bold"),
plot.title = ggtext::element_markdown(size = 15),
plot.subtitle = element_text(size = 14, color = "#36454f"),
plot.caption = ggtext::element_markdown(
hjust = 0,
size = 15,
lineheight = 1.1,
face = "bold"
),
axis.text = element_text(size = 10, color = "#36454f"),
axis.title = element_text(size = 12),
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank()
) +
geom_text(mapping = aes(label = cases, y = -0.45))
## Saving Figure 3
cairo_pdf("Figure 3.pdf",
onefile = TRUE,
height = 16.2,
width = 27.0)
do.call("grid.arrange", c(list(
Figure.3A, Figure.3B, Figure.3C, Figure.3D
), nrow = 2))
dev.off()
png(
filename = "Figure 3.png",
height = 16.2,
width = 27.0,
units = "in",
type = "cairo",
res = 144
)
do.call("grid.arrange", c(list(
Figure.3A, Figure.3B, Figure.3C, Figure.3D
), nrow = 2))
dev.off()
# Figure 4
## Panels 4A-4F
percent.rank <- function(x, or)
{
uni = sort(x, decreasing = ifelse(or == 0, FALSE, TRUE))
r = sapply(x, function(x) {
which(uni == x)[1]
})
return((r - 1) / (length(x) - 1))
}
Theme.Vuls = read.csv("Data/Vulnerability_Data/Vulnerability_Final.csv")
Vuls.List = list()
Vuls.List[[1]] = Theme.Vuls[, c(1, 2:5)]
colnames(Vuls.List[[1]])[-1] = c("Population",
"Literacy Rate",
"Work Participation",
"Per Capita NDDP")
Vuls.List[[2]] = Theme.Vuls[, c(1, 6:8)]
colnames(Vuls.List[[2]])[-1] = c(
"Households with Improved Drinking",
"Households Using Improved Sanitation ",
"Households Using Clean Fuel"
)
Vuls.List[[3]] = Theme.Vuls[, c(1, 9:11)]
colnames(Vuls.List[[3]])[-1] = c("Public Hospitals",
"Sub-divisional Hospitals",
"General No. of Beds")
Vuls.List[[4]] = Theme.Vuls[, c(1, 12:16)]
colnames(Vuls.List[[4]])[-1] = c(
"Total Beds Capacity",
"Total ICU Beds Capacity",
"Temporary Medical Camps Rural",
"Temporary Medical Camps Urban",
"COVID Hospital Test Centers"
)
Vuls.List[[5]] = Theme.Vuls[, c(1, 17:26)]
colnames(Vuls.List[[5]])[-1] = c(
"HIV",
"P Vivax",
"P Falciparum",
"Pneumonia",
"TB",
"Hypertension",
"Cervical Cancer",
"Breast Cancer",
"Oral Cancer",
"Obesity"
)
Vuls.List[[6]] = Theme.Vuls[, c(1, 27:31)]
colnames(Vuls.List[[6]])[-1] = c(
"Socioeconomic and Demographic Factors",
"Housing and Hygiene Conditions",
"Availability of Health Care",
"COVID Preparedness",
"Epidemiological Factors"
)
names(Vuls.List) = c(
"Socioeconomic and Demographic Factors",
"Housing and Hygiene Conditions",
"Availability of Health Care",
"COVID Preparedness",
"Epidemiological Factors",
"Overall Vulnerability"
)
Theme.Vuls = Vuls.List$`Overall Vulnerability`
Theme.Vuls$Overall = percent.rank(rowSums(Theme.Vuls[,-1]), 0)
colnames(Theme.Vuls)[7] = "Overall Vulnerability"
states.shp = readOGR("Data/Geographical_Data/IND_adm2.shp")
num.states = which(states.shp$NAME_1 == "Orissa")
get_legend <- function(myggplot) {
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x)
x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}
Summary.R = data.frame(District = Districts, matrix(0, length(Districts), 1))
colnames(Summary.R)[-1] = "Last Fortnight"
for (i in 1:nrow(Summary.R))
{
R.Summary.Full = R.Curve[[i]]$mean.R
R.Summary.Fortnight = R.Summary.Full[(length(R.Summary.Full) - 13):length(R.Summary.Full)]
Summary.R[i, 2] = mean(R.Summary.Fortnight)
}
Summaries = Summary.R[-nrow(Summary.R), ]
Figure.4 = list()
for (i in 2:ncol(Theme.Vuls))
{
mydata = data.frame(NAME_2 = states.shp$NAME_2[num.states], id = states.shp$ID_2[num.states])
mydata$NAME_2[c(2, 3, 5, 18, 23, 29)] = Theme.Vuls$District[c(3, 4, 2, 18, 23, 29)]
mydata = mydata[order(mydata$NAME_2),]
mydata = data.frame(mydata, vulnerability = Theme.Vuls[, i])
states.shp.f = fortify(states.shp[num.states,], region = "ID_2")
merge.shp.coef = merge(states.shp.f, mydata, by = "id", all.x = TRUE)
final.plot = merge.shp.coef[order(merge.shp.coef$order), ]
cnames = aggregate(
cbind(long, lat) ~ NAME_2,
data = final.plot,
FUN = function(x)
mean(range(x))
)
cnames = data.frame(cnames, rvalue = Summaries$`Last Fortnight`)
cnames = cnames %>% mutate(colorscale = unlist(lapply(rvalue, Decision)))
plot = ggplot() +
geom_polygon(
data = final.plot,
aes(
x = long,
y = lat,
group = group,
fill = vulnerability
),
color = "black",
size = 0.25
) +
coord_map() +
scale_fill_gradient(
name = "Vulnerability Index",
limits = c(0, 1),
low = "#FFFFCC",
high = "#990000"
) +
labs(title = paste0("(", LETTERS[ifelse(i == 7, 1, i)], ") ", colnames(Theme.Vuls)[i])) +
xlab(NULL) + ylab(NULL) +
theme(
text = element_text(face = "bold"),
plot.title = element_text(
hjust = 0.5,
face = "bold",
size = 18
),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
# plot.margin = unit(c(-3, 0, 0, 0), "cm"),
legend.position = "bottom",
legend.text = element_text(size = 9),
legend.title = element_text(size = 24)
) +
geom_text(
data = cnames,
aes(long, lat, label = NAME_2),
size = 3.6,
fontface = "bold"
) +
geom_point(
data = cnames,
aes(long, lat - 0.1, # color = colorscale,
size = 1.2 * rvalue),
show.legend = FALSE,
color = "red"
) # +
# scale_color_manual(values = heatmap_colors)
Figure.4[[length(Figure.4) + 1]] = plot
names(Figure.4)[length(Figure.4)] = paste0("Figure.4", LETTERS[ifelse(i == 7, 1, i)])
}
Figure.4A = Figure.4$Figure.4A
Legend.4 = get_legend(Figure.4A)
for (i in 1:length(Figure.4))
{
Figure.4[[i]] = Figure.4[[i]] + theme(legend.position = "none")
}
Figure.4A = Figure.4$Figure.4A
Figure.4[length(Figure.4)] = NULL
Figure.4 = append(list(Legend.4, Figure.4A), Figure.4)
names(Figure.4)[1:2] = c("Legend", "Figure.4A")
blankPlot <- ggplot() + geom_blank(aes(1, 1)) + theme_nothing()
## Saving Figure 4
cairo_pdf("Figure 4.pdf",
onefile = TRUE,
height = 18,
width = 27)
grid.arrange(
Figure.4$Figure.4A,
Figure.4$Figure.4B,
Figure.4$Figure.4C,
blankPlot,
Figure.4$Legend,
blankPlot,
Figure.4$Figure.4D,
Figure.4$Figure.4E,
Figure.4$Figure.4F,
ncol = 3,
nrow = 3,
widths = rep(9, 3),
heights = c(8.4, 1.2, 8.4)
)
dev.off()
png(
filename = "Figure 4.png",
height = 18,
width = 27,
units = "in",
type = "cairo",
res = 144
)
grid.arrange(
Figure.4$Figure.4A,
Figure.4$Figure.4B,
Figure.4$Figure.4C,
blankPlot,
Figure.4$Legend,
blankPlot,
Figure.4$Figure.4D,
Figure.4$Figure.4E,
Figure.4$Figure.4F,
ncol = 3,
nrow = 3,
widths = rep(9, 3),
heights = c(8.4, 1.2, 8.4)
)
dev.off()
# Figure 5
## Panels A-F
Figure.5.Data = list()
for (i in 1:length(Vuls.List))
{
y = Summaries$`Last Fortnight`
x = as.matrix.data.frame(Vuls.List[[i]][-1])
Temp = coef(bms(
X.data = y ~ x,
mprior = "uniform",
user.int = FALSE
))
rownames(Temp) = gsub("x.", "", rownames(Temp))
Temp = Temp[order(rownames(Temp)), ]
Temp = data.frame(Temp,
Covariate = rownames(Temp),
Source = rep(names(Vuls.List)[i], nrow(Temp)))
Figure.5.Data[[i]] = Temp
}
names(Figure.5.Data) = c(
"Socioeconomic and Demographic Factors",
"Housing and Hygiene Conditions",
"Availability of Health Care",
"COVID Preparedness",
"Epidemiological Factors",
"Overall Vulnerability"
)
Figure.5.Data = do.call("rbind", Figure.5.Data)
Figure.5.Data$Sign = ifelse(sign(Figure.5.Data$Post.Mean) == 1, "+", "-")
rownames(Figure.5.Data) = NULL
Figure.5 = list()
Colors = c("darkolivegreen3",
"darkorange2",
"gold1",
"cornflowerblue",
"salmon1",
"black")
Panels = c("A", "B", "B", "C", "C", "A")
for (i in 1:length(Vuls.List))
{
Figure.5[[length(Figure.5) + 1]] = Figure.5.Data[Figure.5.Data$Source == names(Vuls.List)[i], ] %>%
ggplot(aes(x = PIP,
y = reorder(Covariate, desc(PIP)))) + geom_col(position = "dodge", fill = Colors[i]) +
labs(
title = paste0("(", Panels[i], ") ", names(Vuls.List)[i]),
x = "Posterior Inclusion Probability",
y = "Covariates"
) +
theme_minimal() +
theme(
text = element_text(family = "Helvetica Neue", face = "bold"),
plot.title = ggtext::element_markdown(size = 15),
plot.subtitle = element_text(size = 14, color = "#36454f"),
plot.caption = ggtext::element_markdown(
hjust = 0,
size = 8,
lineheight = 1.1
),
axis.text.x = element_text(size = 10),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
legend.position = "right",
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
) +
geom_label(mapping = aes(label = Sign),
fontface = "bold",
size = 9)
names(Figure.5)[length(Figure.5)] = paste0("Figure.5", LETTERS[ifelse(i == 6, 1, i + 1)])
}
Figure.5A = Figure.5$Figure.5A
Figure.5[length(Figure.5)] = NULL
Figure.5 = append(list(Figure.5A), Figure.5)
names(Figure.5)[1] = "Figure.5A"
## Saving Figure 5
cairo_pdf("Figure 5.pdf",
onefile = TRUE,
height = 8,
width = 24)
grid.arrange(
Figure.5$Figure.5A,
Figure.5$Figure.5D,
Figure.5$Figure.5E,
ncol = 3,
nrow = 1,
widths = rep(8, 3),
heights = rep(8, 1)
)
dev.off()
png(
filename = "Figure 5.png",
height = 8,
width = 24,
units = "in",
type = "cairo",
res = 144
)
grid.arrange(
Figure.5$Figure.5A,
Figure.5$Figure.5D,
Figure.5$Figure.5E,
ncol = 3,
nrow = 1,
widths = rep(8, 3),
heights = rep(8, 1)
)
dev.off()
# Supplementary Figure 2
cairo_pdf(
"Supplementary Figure 2.pdf",
onefile = TRUE,
height = 8,
width = 24
)
grid.arrange(
Figure.5$Figure.5B,
Figure.5$Figure.5C,
Figure.5$Figure.5F,
ncol = 3,
nrow = 1,
widths = rep(8, 3),
heights = rep(8, 1)
)
dev.off()
png(
filename = "Supplementary Figure 2.png",
height = 8,
width = 24,
units = "in",
type = "cairo",
res = 144
)
grid.arrange(
Figure.5$Figure.5B,
Figure.5$Figure.5C,
Figure.5$Figure.5F,
ncol = 3,
nrow = 1,
widths = rep(8, 3),
heights = rep(8, 1)
)
dev.off()
# Supplementary Figure 3
## Panels A-F
no_of_districts = length(Districts) - 1
number_of_days = rep(0, no_of_districts)
District <- unique(R.Curves.All$District)
R.Curve = list()
for (i in 1:(no_of_districts))
{
R.Curve[[i]] <-
R.Curves.All[which(R.Curves.All$District == Districts[i])]
}
names(R.Curve) = Districts[1:no_of_districts]
for (i in 1:no_of_districts)
{
number_of_days[i] = length(R.Curve[[i]]$date.R)
}
min_number_of_days = 14
mean_R = time_R = list()
for (i in 1:no_of_districts)
{
n = number_of_days[i]
mean_R[[i]] <- R.Curve[[i]]$mean.R[(n - min_number_of_days + 1):n]
time_R[[i]] <- R.Curve[[i]]$date.R[(n - min_number_of_days + 1):n]
}
opts = list(maxK = 4)
functional_pca = FPCA(mean_R, time_R, optns = opts)
pca = data.frame('pca1' = functional_pca$xiEst[, 1], 'pca2' = functional_pca$xiEst[, 2])
pca$District = names(R.Curve)
SFigure.3.Data = list()
for (i in 1:length(Vuls.List))
{
y = pca$pca1
x = as.matrix.data.frame(Vuls.List[[i]][-1])
Temp = coef(bms(
X.data = y ~ x,
mprior = "uniform",
user.int = FALSE
))
rownames(Temp) = gsub("x.", "", rownames(Temp))
Temp = Temp[order(rownames(Temp)), ]
Temp = data.frame(Temp,