the pie chart

Bar plot or similar

ggplot(measles,aes(vacc_status,prop))+
            geom_bar(stat="identity")+
             scale_y_continuous(breaks=seq(0,1,.1),
                                labels=percent,    #convert to %
                                limits=c(0,1))+
            labs(x="",y="")+
            theme_minimal()

ggplot(measles,aes(x=vacc_status,prop,ymin=0,ymax=prop))+
            geom_pointrange()+
             scale_y_continuous(breaks=seq(0,1,.1),
                                labels=percent,    #convert to %
                                limits=c(0,1))+
            labs(x="",y="")+
            theme_minimal()

#custom themes definitions
theme_singlebar <- theme_bw()+
  theme(
    legend.position = "bottom",
    axis.title = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.y = element_blank(),
    panel.border = element_blank(),
    panel.grid=element_blank(),
    plot.title=element_text(size=14, face="bold")
  )

#plot
mutate(measles,
       vacc_status=factor(vacc_status,               #we change the order of the categories
                          rev(levels(vacc_status))))%>%
ggplot(aes(1,prop,fill=vacc_status))+  #we put 1 in x to create a single bar
         geom_bar(stat="identity")+
          scale_y_continuous(breaks=seq(0,1,.1),
                             labels=percent,
                             limits=c(0,1),
                             expand=c(.01,.01))+
          scale_x_continuous(expand=c(0,0))+
              scale_fill_brewer("",palette="Set1")+
                coord_flip()+
                  theme_singlebar

#we expand our data with numbers from Italy
measles2 <- mutate(measles,
                  italy=c(0.826,0.081,0.053,0.013,0.027),
                  vacc_status=factor(vacc_status,rev(levels(vacc_status))))%>%
                rename(europe="prop")%>%
                gather(region,prop,europe:italy)

#plot
ggplot(measles2,aes(region,prop,fill=vacc_status))+
            geom_bar(stat="identity",position="stack")+ #stack bar
             scale_y_continuous(breaks=seq(0,1,.1),
                                labels=percent,    #convert to %
                                limits=c(0,1),
                                expand=c(0,0))+
            scale_fill_brewer(palette = "Set1")+
            labs(x="",y="",fill="Vaccination Status")+
            theme_minimal()

Waffle plot

#package
library(waffle)

#the waffle function uses a vector with names
val_measles <- round(measles$prop*100)
names(val_measles) <- measles$vacc_status

#plot
waffle(val_measles, #data
        colors=brewer.pal(5,"Set1"), #colors
        rows=5) #row number 

The Waffle chart seems very interesting to me when we want to show a proportion of an individual category.

#data
medida <- c(41,59) #data from the OECD 2015
names(medida) <- c("Estudios Superiores","Otros estudios")

#plot
waffle(medida,
       colors=c("#377eb8","#bdbdbd"),
       rows=5)

Treemap

#package
library(treemap)

#plot
treemap(measles,
index="vacc_status", #variable with categories
vSize="prop",        #values
type="index",        #style more in ?treemap
title="",            
palette = brewer.pal(5,"Set1") #colors
)

Personally, I think that all types of graphic representations have their advantages and disadvantages. However, we currently have a huge variety of alternatives to avoid using the pie chart. If you still want to make a pie chart, which I would not rule out either, I recommend following certain rules, which you can find very well summarized in a recent post by Lisa Charlotte Rost. For example, you should order from the highest to the lowest unless there is a natural order or use a maximum of five categories. Finally, I leave you a link to a cheat sheet from policyviz with basic rules of data visualization. A good reference on graphics using different programs from Excel to R can be found in the book Creating More Effective Graphs (Robbins 2013).

References