RAPPORT_QI_HL

 

3. REPORTING BY PROGRAM AREA

 

datab<-read.csv("DATAB.csv")
library(tidyverse)
library(RColorBrewer)

#b<-names(datab)
#b<-gsub("\\.+","\\.",b)
#d<-'something'
#for (i in 1:5){d[i]<-b[i]}
#for (i in 6:length(b)){
 #f<-strsplit(b[i],"\\.")
  #f<-unlist(f)
  #d[i]<-paste(f[4],f[5],f[length(f)],sep=".")}
 #d[4:5]<-gsub("[A\\.2\\.1\\.]",'',d[4:5])
 
names(datab)[6:8]<-paste0("SP",1:3)
names(datab)[25:26]<-paste0("palu",c("s","g"),sep="_")
names(datab)[20:21]<-paste0("Diarr_sev",1:2)

#datag$periodname<-as.character(datag$periodname)
#datag$periodname[datag$periodname=="Janvier"]<-"1.Janv"
datab$periodname<-paste0(match(datab$periodname,c("Octobre","Novembre","Décembre","Janvier","Février","Mars")),".",datab$periodname)
datag<-datab[datab$periodname %in% c("4.Janvier","5.Février","6.Mars"),]
#datag$periodname[datag$periodname=="Février"]<-"2.Fev"
#datag$periodname[datag$periodname=="Mars"]<-"3.Mars"
names(datab)[31:35]<-paste0("MildCps",1:5)
sink("Namesb.txt")
names(datab)
sink()

datag<-aggregate(datag[,4:ncol(datag)],list(datag$zs,datag$periodname),sum,na.rm=T)
names(datag)[1:2]<-c("zs","periodname")

sink("Namesg.txt")
names(datag)
sink()

tot2 <- function(xd){
  p<-c(as.character(xd[,1]),'Total')
  a<-apply(xd[,2:ncol(xd)],2,sum,na.rm=T)
  a<-rbind(xd[,2:ncol(xd)],a)
  u<-data.frame(p,a)
  names(u)[1]<-"Mois"
  assign("total",u,envir=.GlobalEnv)
}

cibles<-read.csv("C:/Users/MuyungaP/Documents/PROSANI USAID/QUARTER REPORT/Y2 REPORT/CIBLES2.csv")
ciblea<-read.csv2("C:/Users/MuyungaP/Documents/PROSANI USAID/ANALYSIS/Y2/cibles.csv")
#cibleg<-aggregate(cibles[,3:ncol(cibles)],list(cibles$Group.2),sum,na.rm=T)
#names(cibleg)[1]<-'zs'
datac<-read.csv("DATAC.csv")
#names(datac)<-names(datab)
clus<-data.frame(zs=unique(datab$zs))
clus$pop<-ifelse(clus$zs%in%c("Baka","Kayamba","Kinda","Lwamba"),"1Low",ifelse(clus$zs%in%c("Butumba","KabondoD","Kamina","Kinkondja","Mukanga","Songa"),"2Middle","3High"))
clus<-clus[order(clus$zs),]

MALARIA

 

  - Number of children under 5 years of age with confirmed malaria who received treatment for malaria from an appropriate provider in USG-supported areas [15 fee proxy]

 

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("9407"),c("74430"),c("126.4"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

  .
 

a<-datag
a$palu<-rowSums(a[,24:25])
a<-aggregate(a[,c(20,66)],list(a$periodname),sum)
a$cible<-round(cibles$Paludisme[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$palu*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')
b<-datab
b$palu<-rowSums(b[,25:26],na.rm=T)
#datag$Palu<-rowSums(datag[,20:21],na.rm=T)
datag%>%group_by(zs)%>%summarise(Palu=sum(palus_+palug_))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Palu,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Palu),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

b%>%group_by(periodname)%>%summarise(Palu=sum(palu))->p

ggplot(p,aes(x=periodname,y=Palu))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Palu,0)),size=3,vjust=-.5)+labs(x="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(20000,40000))

  
  #geom_bar(stat='identity',fill="lightblue",col="tomato",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Palu),cex=3,vjust=2)

• Percent of pregnant women who received doses of sulfadoxine/ pyrimethamine (S/P) for Intermittent Preventive Treatment (IPT) during ANC visits [2.4]

   

a<-datag
a$SP<-rowSums(a[,5:7])
a<-aggregate(a[,c(3,50)],list(a$periodname),sum)
a$cible<-a[,2]*3
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$SP*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
b$SP<-rowSums(b[,6:8],na.rm=T)
datag%>%group_by(zs)%>%summarise(spa=sum(SP1+SP2+SP3,na.rm=T),cpna=sum(CPN,na.rm=T))%>% mutate(SP=round(spa*100/(cpna*3)))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=SP,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=SP),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

b%>%group_by(periodname)%>%summarise(spa=sum(SP1+SP2+SP3,na.rm=T),cpna=sum(CPN,na.rm=T))%>% mutate(SP=round(spa*100/(cpna*3)))->p

ggplot(p,aes(x=periodname,y=SP))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(SP,0)),size=3,vjust=-.5)+labs(x="",y="% SP")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(50,90))

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("91.0"),c("37518"),c("41245"),c("100"),c('91.0'))

#knitr::kable(a,"pandoc",align='lcccccr',col.names=c("Region","Province","Achievement",'Numerator','Denominator',"Target","Achievment Rate"))

 
 

   

MATERNAL, NEONATAL, AND CHILD HEALTH

    - Number of children under five years of age that received treatment for an acute respiratory infection from an appropriate provider [5 fee proxy]

a<-datag
a$Pneumonie<-rowSums(a[,22:23])
a<-aggregate(a[,c(18,66)],list(a$periodname),sum)
a$cible<-round(cibles$Pneumonie[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$Pneumonie*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
b$pneum<-rowSums(b[,23:24],na.rm=T)

names(datag)[22:23]<-c("Pneumonis","Pneumonig")
datag%>%group_by(zs)%>%summarise(pneum=sum(Pneumonis+Pneumonig,na.rm=T))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=pneum,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=pneum),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[23:24]<-c("Pneumonis","Pneumonig")
b%>%group_by(periodname)%>%summarise(pneum=sum(pneum,na.rm=T))->p

ggplot(p,aes(x=periodname,y=pneum))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(pneum,0)),size=3,vjust=-.5)+labs(x="",y="Pneumonie < 5")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(5000,8500))

  
  #geom_bar(stat='identity',fill="lightblue",col="tomato",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=pneum),cex=3,vjust=2)

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("19872"),c("21581"),c("92.1"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate(%)"))

 
 

   

• Number of insecticide-treated nets (ITN) distributed during antenatal and/or child immunization visits

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("22958"),c("26045"),c("88.1"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate(%)"))

b<-datab
b$Mild<-rowSums(b[,30:36],na.rm=T)
names(datag)[29:35]<-paste0("mild",1:7)
datag%>%group_by(zs)%>%summarise(Mild=sum(mild1+mild2+mild3+mild4+mild5+mild6+mild7))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Mild,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Mild),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

b%>%group_by(periodname)%>%summarise(Mild=sum(Mild))->p

ggplot(p,aes(x=periodname,y=Mild))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Mild,0)),size=3,vjust=-.5)+labs(x="",title="MIILD", y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(0,18000))

   

 

• Number of cases of child diarrhea treated in USG-supported programs [7 fee proxy]  

a<-datag
a$Diarrhee<-rowSums(a[,21:23])
a<-aggregate(a[,c(15,66)],list(a$periodname),sum)
a$cible<-round(cibles$Diarrhee[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$Diarrhee*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
b$Diarrh<-rowSums(b[,20:22],na.rm=T)
names(b)[22]<-"dias"
names(datag)[21]<-"dias"
datag%>%group_by(zs)%>%summarise(Diarrh=sum(Diarr_sev1+Diarr_sev2+dias,na.rm=T))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Diarrh,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Diarrh),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

b%>%group_by(periodname)%>%summarise(Diarrh=sum(Diarrh))->p

ggplot(p,aes(x=periodname,y=Diarrh))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Diarrh,0)),size=3,vjust=-.5)+labs(x="",y="",title="Diarrhee < 5 ans")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(6000,12000))

NA
NA

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("25543"),c("25486"),c("100.2"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

 

   

• Number of children less than 12 months of age who received three doses of pentavalent vaccine [9 fee proxy]  

a<-datag
a$Penta<-rowSums(a[,41:43])
a<-aggregate(a[,c(41,66)],list(a$periodname),sum,na.rm=T)
a$cible<-round(cibles$Penta[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$Penta*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
b$Penta<-rowSums(b[,27:29],na.rm=T)
names(datag)[26:28]<-paste0("Penta",1:3)
datag%>%group_by(zs)%>%summarise(Penta=sum(Penta1+Penta2+Penta3))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Penta,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Penta),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

b%>%group_by(periodname)%>%summarise(Penta=sum(Penta))->p

ggplot(p,aes(x=periodname,y=Penta))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Penta,0)),size=3,vjust=-.5)+labs(x="",y="",title="VACCIN PENTAVALENT")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(6000,8000))

NA

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("30652"),c("31353"),c("97.8"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

 
 

• Percent of children less than 12 months of age who received measles vaccine from USG-supported programs [10]  

a<-datag
a$VAR<-rowSums(a[,31:33])
a<-aggregate(a[,c(31,50)],list(a$periodname),sum)
a$cible<-round(cibles$VAR[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$VAR*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
b$Var<-rowSums(b[,42:44],na.rm=T)
names(datag)[41:43]<-paste0("Var",1:3)
names(datag)[47]<-"pop"
datag%>%group_by(zs)%>%summarise(Vara=sum(Var1+Var2+Var3,na.rm=T),popa=sum(pop,na.rm=T))%>% mutate(Var=round(Vara*120000/(popa*4)))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Var,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Var),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[42:44]<-paste0("Var",1:3)
names(b)[48]<-"pop"
b%>%group_by(periodname)%>%summarise(Vara=sum(Var,na.rm=T),popa=sum(pop,na.rm=T))%>% mutate(Var=round(Vara*120000/(popa*4)))->p

ggplot(p,aes(x=periodname,y=Var))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Var,0)),size=3,vjust=-.5)+labs(x="",y="",title="% VAR")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(40,90))

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("31408"),c("30168"),c("96.1"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

 

   

• Percentage of pregnant women attending at least one antenatal care (ANC) visit with a skilled provider from USG- supported health facilities  

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("91"),c("37518"),c("41245"),c("100"),c("91"))

#knitr::kable(a,"pandoc",align='lcccccr',col.names=c("Region","Province","Achievement","Numerator","Denominator","Target","Achievment Rate(%)"))

b<-datab
#b$Var<-rowSums(b[,42:44],na.rm=T)
#names(datag)[41:43]<-paste0("Var",1:3)
#names(datag)[47]<-"pop"
datag%>%group_by(zs)%>%summarise(cpna=sum(CPN,na.rm=T),popa=sum(pop,na.rm=T))%>% mutate(CPN=round(cpna*120000/(popa*4)))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=CPN,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=CPN),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

#names(b)[42:44]<-paste0("Var",1:3)
names(b)[48]<-"pop"
b%>%group_by(periodname)%>%summarise(cpna=sum(CPN,na.rm=T),popa=sum(pop,na.rm=T))%>% mutate(CPN=round(cpna*120000/(popa*4)))->p

ggplot(p,aes(x=periodname,y=CPN))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(CPN,0)),size=3,vjust=-.5)+labs(x="",y="",title="% CPN1")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(80,140))

 
 

 

• Percentage of pregnant women attending at least one and at least 4 antenatal care (ANC) with a skilled provider from USG-supported health facilities [12 fee]  

a<-datag
a$CPN_4<-a[,4]
a<-aggregate(a[,c(4,50)],list(a$periodname),sum)
a$cible<-round(cibles$CPN4[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$CPN_4*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
#b$Var<-rowSums(b[,42:44],na.rm=T)
#names(datag)[41:43]<-paste0("Var",1:3)
#names(datag)[47]<-"pop"
datag%>%group_by(zs)%>%summarise(cpna=sum(CPN.4,na.rm=T),popa=sum(pop,na.rm=T))%>% mutate(CPN4=round(cpna*120000/(popa*4)))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=CPN4,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=CPN4),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

#names(b)[42:44]<-paste0("Var",1:3)
names(b)[48]<-"pop"
b%>%group_by(periodname)%>%summarise(cpna=sum(CPN.4,na.rm=T),popa=sum(pop,na.rm=T))%>% mutate(CPN4=round(cpna*120000/(popa*4)))->p

ggplot(p,aes(x=periodname,y=CPN4))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(CPN4,0)),size=3,vjust=-.5)+labs(x="",y="",title="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(40,60))

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("19860"),c("17897"),c("111"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

 

• Percentage of deliveries with an SBA in USG-supported facilities

a<-datag
a$Acc<-a[,8]
a<-aggregate(a[,c(8,37,50)],list(a$periodname),sum)
tot2(a)
total$Acc_P<-round(total$Acc*1200/(total[,3]*.04),0)
total$cible4<-round(total[,3]*.04/12)
total[,2:3]<-NULL
total$cible=90
total<-total[,c(1,2,4,5,3)]
#knitr::kable(total,"pandoc",align='lcccr',col.names=c("Mois","AccAssist","Cible4","Cible90","%"))

b<-datab
#b$Var<-rowSums(b[,42:44],na.rm=T)
names(datag)[8]<-"Accouch_ass"
names(datag)[62]<-"Accouch"
datag%>%group_by(zs)%>%summarise(Accoucha=sum(Accouch_ass,na.rm=T),acca=sum(Accouch,na.rm=T))%>% mutate(Accouch_ass=round(Accoucha*100/acca))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Accouch_ass,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Accouch_ass),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[9]<-"Accouch_ass"
names(b)[63]<-"Accouch"
b%>%group_by(periodname)%>%summarise(Accoucha=sum(Accouch_ass,na.rm=T),acca=sum(Accouch,na.rm=T))%>% mutate(Accouch_ass=round(Accoucha*100/acca))->p

ggplot(p,aes(x=periodname,y=Accouch_ass))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Accouch_ass,0)),size=3,vjust=-.5)+labs(x="",y="",title="% Accouchements assistés")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(40,140))

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("76"),c("31364"),c("41245"),c("90"),c("84.5"))

#knitr::kable(a,"pandoc",align='lcccccr',col.names=c("Region","Province","Q1 Achievement","Numerator","Denominator","Target","Achievment Rate"))

 

 

• Number of women giving birth who received uterotonics in the third stage of labor (OR immediately after birth) through USG-supported programs [2.1.4]  

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("1584"),c("2178"),c("72.7"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

b<-datab
datag%>%group_by(zs)%>%summarise(GATPA=sum(GATPA))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=GATPA,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=GATPA),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

b%>%group_by(periodname)%>%summarise(GATPA=sum(GATPA,na.rm=T))->p

ggplot(p,aes(x=periodname,y=GATPA))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(GATPA,0)),size=3,vjust=-.5)+labs(x="", y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(100,900))

   

• Number and percentage of newborns receiving essential newborn care through USG-supported programs

a<-datag
a$Soins_ess<-a[,29]
a<-aggregate(a[,c(9,29,50)],list(a$periodname),sum)
tot2(a)
total$cible<-100
#total$Soess_P<-round(total$Soins_ess*100/total[,2])
total[,3]<-NULL
total$cible=100
#knitr::kable(total,"pandoc",align='lcccr',col.names=c("Mois","NaissViv","SoinsEss","Cible","%"))

b<-datab
#b$Var<-rowSums(b[,42:44],na.rm=T)
names(datag)[38]<-"Soess"
names(datag)[11]<-"Naissv"
datag%>%group_by(zs)%>%summarise(Soessa=sum(Soess,na.rm=T),naissva=sum(Naissv,na.rm=T))%>% mutate(Soess=round(Soessa*100/naissva))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Soess,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Soess),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[39]<-"Soess"
names(b)[12]<-"Naissv"
b%>%group_by(periodname)%>%summarise(Soessa=sum(Soess,na.rm=T),naissva=sum(Naissv,na.rm=T))%>% mutate(Soess=round(Soessa*100/naissva))->p

ggplot(p,aes(x=periodname,y=Soess))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Soess,0)),size=3,vjust=-.5)+labs(x="",y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(40,100))

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("90"),c("100"),c("89.9"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate"))

 

   

• Number of postpartum/newborn visits within three days of birth in USG-supported programs [2.1.6]

a<-datag
a$CPON_1<-a[,28]
a<-aggregate(a[,c(28,50)],list(a$periodname),sum)
a$cible<-round(as.numeric(as.character(cibles$CPoN1[3]))/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$CPON_1*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
names(datag)[37]<-"Cpon1"
datag%>%group_by(zs)%>%summarise(Cpon1=sum(Cpon1))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Cpon1,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Cpon1),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[38]<-"Cpon1"
b%>%group_by(periodname)%>%summarise(Cpon1=sum(Cpon1,na.rm=T))->p

ggplot(p,aes(x=periodname,y=Cpon1))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Cpon1,0)),size=3,vjust=-.5)+labs(x="", y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(8000,13000))

 

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("28028"),c("23559"),c("119"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate(%)"))

 

 

• Drop-out rate in DTP-HepB-Hib3 among children less than 12 months of age [2.1.9]

 

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("2.3"),c("4"),c("142.8"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate(%)"))

b<-datab
#b$Var<-rowSums(b[,42:44],na.rm=T)
names(datag)[75:77]<-paste0("Penta_1",1:3)
names(datag)[26:28]<-paste0("Penta_3",1:3)
datag%>%group_by(zs)%>%summarise(Penta1=sum(Penta_11+Penta_12+Penta_13,na.rm=T),Penta3=sum(Penta_31+Penta_32+Penta_33,na.rm=T))%>% mutate(Drop=round((Penta1-Penta3)/Penta1,2))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Drop,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Drop),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[76:78]<-paste0("Penta_1",1:3)
names(b)[27:29]<-paste0("Penta_3",1:3)
b%>%group_by(periodname)%>%summarise(Penta1=sum(Penta_11+Penta_12+Penta_13,na.rm=T),Penta3=sum(Penta_31+Penta_32+Penta_33,na.rm=T))%>% mutate(Drop=round((Penta1-Penta3)/Penta1,2))->p

ggplot(p,aes(x=periodname,y=Drop))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Drop,2)),size=3,vjust=-.5)+labs(x="",y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(0,1.5))

 

     

NUTRITION

• Number of individuals receiving nutrition-related professional training through USG supported nutrion programs [2.1.10]

• Number of children under-five (0-59 months) reached by USG-supported nutrition programs [2.1.11]

 

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("353091"),c("158456"),c("44.9"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate(%)"))

b<-datab
names(datag)[40]<-"CPS"
datag%>%group_by(zs)%>%summarise(CPS=sum(CPS))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=CPS,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=CPS),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[41]<-"CPS"
b%>%group_by(periodname)%>%summarise(CPS=sum(CPS,na.rm=T))->p

ggplot(p,aes(x=periodname,y=CPS))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(CPS,0)),size=3,vjust=-.5)+labs(x="", y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(50000,70000))

 

     

REPRODUCTIVE HEALTH/FAMILY PLANNING

  - Number of new acceptors using modern contraceptive methods in USG-supported facilities [3 fee proxy]

a<-datag
a$Accept<-(a[,10])
a<-aggregate(a[,c(10,50)],list(a$periodname),sum)
a$cible<-round(cibles$Nouvelle.acceptante[3]/12)
tot2(a)
total[,2]<-NULL
#total$percent<-round(total$Accept*100/total$cible,0)
#knitr::kable(total,"pandoc",align='lccr')

b<-datab
names(datag)[12]<-"Accept"
datag%>%group_by(zs)%>%summarise(Accept=sum(Accept))->p
p$pop<-clus$pop
ggplot(p,aes(x=zs,y=Accept,fill=pop))+geom_bar(stat='identity',col="grey",alpha=.7)+theme_classic()+labs(x="")+theme(axis.text.x=element_text(angle=90))+geom_text(aes(label=Accept),cex=3,vjust=2)+scale_fill_brewer(palette="YlOrBr")

names(b)[13]<-"Accept"
b%>%group_by(periodname)%>%summarise(Accept=sum(Accept,na.rm=T))->p

ggplot(p,aes(x=periodname,y=Accept))+geom_line(aes(group=1),size=2,color="lightblue")+geom_point(color="red")+theme_classic()+theme(axis.text.x=element_text(angle=0,size=8))+geom_text(aes(label=round(Accept,0)),size=3,vjust=-.5)+labs(x="", y="")+theme(axis.text.x=element_text(angle=90))+coord_cartesian(y=c(10000,23000))

a <- data.frame(c("Katanga"),c("Haut Lomami"),c("33372"),c("20405"),c("163.5"))

#knitr::kable(a,"pandoc",align='lcccr',col.names=c("Region","Province","Achievement","Target","Achievment Rate(%)"))

 

 

• Number of counseling visits for FP/ RH as result of USG support [2.3]

TUBERCULOSIS

• TB notification rate through USG- supported programs [2.1.17]

• Number of multi-drug resistant TB (MDR-TB) cases detected [2.1.20]

WATER, SANITATION, AND HYGIENE

• Number of people gaining access to basic drinking water services as a result of USG assistance [2.6.2]

• Number of people gaining access to a basic sanitation service as a result of USG assistance [2.6.3]

4.REPORTING BY PROGRAM ACTIVITIES

cfr rapport programmatique