In [1]:
library(ggplot2)
library(gdata)
In [2]:
D <- read.table("../Data/All_data.txt")
D <- D[order(D$Region,D$Disease),]
In [3]:
names(D)
In [4]:
metr_burden <- "daly"
metr_res <- "Patients"
In [5]:
#We compare RCTs to DALYs
dpl <- D[D$Region!="Non-HI",
c(which(names(D)%in%c("Region","Disease")),
intersect(grep(metr_burden,names(D)),grep("^burden",names(D))),
intersect(grep(metr_res,names(D)),grep("^Nb",names(D)))),]
In [6]:
head(dpl)
In [7]:
#Order diseases: increasing burden
dis <- dpl$Disease[dpl$Region=="All"][order(dpl$burden[dpl$Region=="All"])]
In [8]:
dis
In [9]:
dis <- dis[dis!="All"]
In [10]:
#Number of RCTs per region
regs <- dpl$Region[dpl$Disease=="All"][order(dpl[dpl$Disease=="All",grep("med",names(dpl))],
decreasing=TRUE)]
In [11]:
regs
In [12]:
regs <- regs[regs!="All"]
In [13]:
#Region labels
reg_labs <- c("High-income countries",
"Sub-Saharian\nAfrica",
"Southeast Asia,\nEast Asia and Oceania",
"South Asia",
"Eastern Europe\nand Central Asia",
"Latin America\nand Caribbean",
"North Africa and\nMiddle East"
)
In [14]:
dpl <- dpl[dpl$Region!="All" & dpl$Disease!="All",]
In [15]:
#Normalizing regions: max RCts = max GBD
Norm_fact <- max(dpl[,grep("up",names(dpl))],na.rm=TRUE)/max(dpl$burden)
dpl$gpl <- (dpl$burden/max(dpl$burden))*max(dpl[,grep("up",names(dpl))],na.rm=TRUE)
In [16]:
#Bar size = wdt*2
wdt <- 0.45
#Distance between regions (end to end)
d_reg <- 400*0.5*1e3
#Distance between center of region and start of bars (for disease labels)
esp_dis_nb <- 200*0.5*1e3
#Inner circle
IC <- 8
In [17]:
#Rectangles for a given region and disease
#Rg = central position of region
#d = name of the disease
#rg = name of the region
displt <-
function(d,Rg,rg){
res_pl <- data.frame( xmin = which(d==dis)-wdt,
xmax = which(d==dis)+wdt,
ymin = Rg+esp_dis_nb,
ymax = Rg+esp_dis_nb+dpl[dpl$Dis==d & dpl$Region==rg,grep("med",names(dpl))],
metr="Research",
reg=rg,
ycent=Rg,
dis_nb=which(d==dis),
disease=d)
burd_pl <- data.frame( xmin = which(d==dis)-wdt,
xmax = which(d==dis)+wdt,
ymin = Rg-esp_dis_nb,
ymax = Rg-esp_dis_nb-dpl$gpl[dpl$Dis==d & dpl$Region==rg],
metr="Burden",
reg=rg,
ycent=Rg,
dis_nb=which(d==dis),
disease=d)
rbind(res_pl,burd_pl)
}
In [18]:
displt_err <- function(d,Rg,rg){
data.frame(x = which(d==dis),
ymin = Rg+esp_dis_nb+dpl[dpl$Dis==d & dpl$Region==rg,grep("low",names(dpl))],
ymax = Rg+esp_dis_nb+dpl[dpl$Dis==d & dpl$Region==rg,grep("up",names(dpl))],
metr="Research",
reg=rg,
dis_nb=which(d==dis),
disease=d)
}
In [19]:
#Rectangles pour toutes les maladies, une région donnée
regplt <- function(Rg,rg) do.call('rbind',lapply(dis,function(x){displt(x,Rg,rg)}))
regplt_err <- function(Rg,rg) do.call('rbind',lapply(dis,function(x){displt_err(x,Rg,rg)}))
In [20]:
#Emplacement des régions
RG <- 0
for(i in 2:length(regs)){
RG <- c(RG,
RG[i-1]-(2*esp_dis_nb+
max(dpl$gpl[dpl$Region==regs[i-1]])+
d_reg+max(dpl[dpl$Region==regs[i],grep("up",names(dpl))],na.rm=TRUE)))
}
In [21]:
#DataFrame Plot
DPLOT <- do.call('rbind',lapply(1:length(regs),function(i){regplt(RG[i],regs[i])}))
#Error_bars dataframe
DPLOT_err <- do.call('rbind',lapply(1:length(regs),function(i){regplt_err(RG[i],regs[i])}))
In [22]:
#Inner circle
DPLOT$xmin <- DPLOT$xmin + IC
DPLOT$xmax <- DPLOT$xmax + IC
DPLOT$xcent <- DPLOT$dis_nb + IC
DPLOT_err$x <- DPLOT_err$x + IC
DPLOT_err$xcent <- DPLOT_err$dis_nb + IC
In [23]:
totalLength <- max(DPLOT_err$ymax,na.rm=TRUE)-min(DPLOT$ymax,na.rm=TRUE)+d_reg
In [24]:
#Polar coordinates
alphaStart <- 2*pi*((max(DPLOT_err$ymax[DPLOT_err$reg==regs[1]]+d_reg/2,na.rm=TRUE))/
totalLength)
In [25]:
#REGION LABELS
readableAngle<-function(x){
angle<-x*(360/totalLength)
}
familyLabelsDF<-data.frame(xmin=RG,label=reg_labs)
familyLabelsDF$angle <- readableAngle(familyLabelsDF$xmin)
In [26]:
#Disease labels: size
DPLOT$size_dis_lab = 2.3*(40+DPLOT$dis_nb)/(40+max(DPLOT$dis_nb))
In [27]:
max(dpl[,grep("up",names(dpl))],na.rm=TRUE)/1e3
In [28]:
#Research
rcttks <- c(0,100,500,1000,2000,3000,4000,5000)*1e3
maj_rcts <- function(nb){
x <- nb
k <- 0
while(x>=100){x <- x%/%10
k <- k+1}
(x+1)*10^k
}
In [29]:
#Faire que les ticks aillent jusqu'au max des RCTs arrondi au sup
RCTtcks <- do.call('rbind',lapply(regs,function(x){
data.frame(
breaks = unique(DPLOT$ymin[DPLOT$metr=="Research" & DPLOT$reg==x]) +
c(rcttks[2:findInterval(max(dpl[dpl$Region==x,grep("up",names(dpl))],na.rm=TRUE),rcttks)],
maj_rcts(max(dpl[dpl$Region==x,grep("up",names(dpl))],na.rm=TRUE))),
labels=c(rcttks[2:findInterval(max(dpl[dpl$Region==x,grep("up",names(dpl))],na.rm=TRUE),rcttks)],
maj_rcts(max(dpl[dpl$Region==x,grep("up",names(dpl))],na.rm=TRUE)))/1e3,
region=x)
}))
RCTtcks$col <- "1RCT"
In [30]:
#Pour GBD
gbdtks <- c(0,1e7,2e7,3e7,5e7,7.5e7,1e8,1.5e8,2e8)/1e6
maj_gbd <- function(x) ifelse(trunc(x)==x,x,trunc(x) + 1)
In [31]:
GBDtcks <- do.call('rbind',lapply(regs,function(x){
data.frame(
breaks = unique(DPLOT$ymin[DPLOT$metr=="Burden" & DPLOT$reg==x]) -
c(gbdtks[2:findInterval(max(dpl[dpl$Region==x,grep("^burden",names(dpl))],na.rm=TRUE)/1e6,gbdtks)],
maj_gbd(max(dpl[dpl$Region==x,grep("^burden",names(dpl))],na.rm=TRUE)/1e6))*
1e6*Norm_fact,
labels=c(gbdtks[2:findInterval(max(dpl[dpl$Region==x,grep("^burden",names(dpl))],na.rm=TRUE)/1e6,gbdtks)],
maj_gbd(max(dpl[dpl$Region==x,grep("^burden",names(dpl))],na.rm=TRUE)/1e6)),
region=x)}))
GBDtcks$col <- "2GBD"
In [32]:
#High-income countries, burden from 44 to 45
GBDtcks[GBDtcks$labels==44 & GBDtcks$region=="High-income",1] <-
GBDtcks[GBDtcks$labels==44 & GBDtcks$region=="High-income",1] - (45-44)*1e6*Norm_fact
GBDtcks[GBDtcks$labels==44 & GBDtcks$region=="High-income",2] <- 45
#Sub-Saharia Africa, burden from 126 to 125
GBDtcks[GBDtcks$labels==126 & GBDtcks$region=="Sub-Saharian Africa",1] <-
GBDtcks[GBDtcks$labels==126 & GBDtcks$region=="Sub-Saharian Africa",1] - (125-126)*1e6*Norm_fact
GBDtcks[GBDtcks$labels==126 & GBDtcks$region=="Sub-Saharian Africa",2] <- 125
#Sub-Saharian Africa, suppressing research for 1000
RCTtcks <- RCTtcks[!(RCTtcks$labels==1000 & RCTtcks$region=="Sub-Saharian Africa"),]
#Southeast Asia, burden from 82 to 80
GBDtcks[GBDtcks$labels==82 & GBDtcks$region=="Southeast Asia, East Asia and Oceania",1] <-
GBDtcks[GBDtcks$labels==82 & GBDtcks$region=="Southeast Asia, East Asia and Oceania",1] - (80-82)*1e6*Norm_fact
GBDtcks[GBDtcks$labels==82 & GBDtcks$region=="Southeast Asia, East Asia and Oceania",2] <- 80
#South Asia, burden from 131 to 130
GBDtcks[GBDtcks$labels==131 & GBDtcks$region=="South Asia",1] <-
GBDtcks[GBDtcks$labels==131 & GBDtcks$region=="South Asia",1] - (130-131)*1e6*Norm_fact
GBDtcks[GBDtcks$labels==131 & GBDtcks$region=="South Asia",2] <- 130
#Eastern Europe, burden from 54 to 55
GBDtcks[GBDtcks$labels==54 & GBDtcks$region=="Central Europe, Eastern Europe, and Central Asia",1] <-
GBDtcks[GBDtcks$labels==54 & GBDtcks$region=="Central Europe, Eastern Europe, and Central Asia",1] - (55-54)*1e6*Norm_fact
GBDtcks[GBDtcks$labels==54 & GBDtcks$region=="Central Europe, Eastern Europe, and Central Asia",2] <- 55
#Latin America, burden from 16 to 15
GBDtcks[GBDtcks$labels==16 & GBDtcks$region=="Latin America and Caribbean",1] <-
GBDtcks[GBDtcks$labels==16 & GBDtcks$region=="Latin America and Caribbean",1] - (15-16)*1e6*Norm_fact
GBDtcks[GBDtcks$labels==16 & GBDtcks$region=="Latin America and Caribbean",2] <- 15
In [33]:
RCTtcks$labels <- as.character(RCTtcks$label)
GBDtcks$labels <- as.character(GBDtcks$label)
tcks <- rbind(RCTtcks,GBDtcks)
tcks$col <- as.factor(tcks$col)
In [34]:
p <- ggplot(DPLOT) +
geom_rect(aes(xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax,fill=metr)) +
geom_errorbar(aes(x=x,ymax=ymax,ymin=ymin),size=0.1,width=0.5,data=DPLOT_err) +
#Disease numbers
geom_text(aes( x=xcent,
y=ycent,
label=dis_nb,
hjust=0.5),
size=DPLOT$size_dis_lab,
col="#42442E") +
theme_minimal() +
theme( axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks.x=element_blank()
) +
theme(legend.position = "none") +
scale_x_continuous(breaks = NULL,limits = c(0,max(DPLOT$xmax,na.rm=TRUE)+3)) +
#Region labels
geom_text(
aes( x=length(dis)+IC+3,
y=xmin,
label=label,
angle=angle,
hjust=0.5,vjust=0),
data=familyLabelsDF,
size=4.3) +
#Colors burden and research
scale_fill_manual(values = c("Burden"="orange","Research"="blue"))
In [35]:
#Tickmarks
p <- p+ scale_y_continuous(minor_breaks = tcks$breaks, breaks=tcks$breaks,
labels=rep("",nrow(tcks)),
limits=c(min(DPLOT$ymax,na.rm=TRUE)-d_reg/2,max(DPLOT_err$ymax,na.rm=TRUE)+d_reg/2)) +
theme(panel.grid.minor=element_line(color="#D3D3D3",size=0.1)) +
geom_text(
aes(x=length(dis)+IC+1.5,
y=breaks,
label=labels,
hjust=0.5),
data=tcks,
size=2,
col=as.numeric(tcks$col))
In [36]:
ggsave(filename = "../Figures/polar_props_Patients_DALYs.pdf",
plot = p + coord_polar(theta="y",start=alphaStart,direction=-1),
width=12,height=12)
In [ ]:
In [ ]: