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Moon charts, a pie chart alternative for two groups in ggplot2

gggibbous can be installed from CRAN:

install.packages("gggibbous")

The development version can be installed from GitHub with thedevtoolspackage:

devtools::install_github("mnbram/gggibbous")

A pie chart divides a circle into multiple sections where the arclengths (and so also the areas) of the sections represent proportions ofa whole. A moon chart, similarly, divides a circle into sections wherethe areas represent proportions of a whole, but in a moon chart theareas are drawn as crescent or gibbous portions of a circle—like thephases of the moon.

The motivation behind using a moon chart instead of a pie chart isprimarily one of aesthetic choice. Note also that because the sectionsof a moon chart are swept from one or the other side of the circle, theyare generally only appropriate for depicting one or two groups.

Moon charts are similar to Kosara’s (2019)[1] “circular slice” chart.In studying subjects’ perception of percentages in different charttypes, the “circular slice” performed similarly to pie charts. Mooncharts differ from the “circular slice” in that the latter slides asecond disc of the same size over a base circle, more like a lunareclipse than the sphases of the moon. Both depend on area as theperceptual cue, however.

gggibbous extends theggplot2 data visualization package to providesupport for moon charts in R. Unlike the pie charts supported nativelybycoord_polar() in R, moon charts ingggibbous do not require anyspecial coordinate system. They are drawn most similarly to points inggplot2: their position is defined by anx and ay coordinate andtheir size is defined independently of the coordinate system, so theyalways remain circular.

library(gggibbous)#> Loading required package: ggplot2

ggplot(data.frame(x=1:5,y=1,size=2^(0:4)), aes(x,y,size=size))+  geom_moon()+  geom_point(y=2)+  lims(x= c(0.5,5.5),y= c(0.5,2.5))+  scale_size(range= c(5,10))

Two new aesthetics are also important ingeom_moon:ratio andright.

ratio controls the proportion of the moon to be drawn. It must bebetween 0 (a “new moon” where nothing is actually drawn) and 1 (a “fullmoon”, i.e. a circle).

ggplot(data.frame(x=1:5,y=0,ratio=0:4*0.25), aes(x=x,y=y))+  geom_moon(aes(ratio=ratio),size=20,fill="black")+  geom_text(aes(y=y+1,label=ratio))+  lims(x= c(0.5,5.5),y= c(-1,1.4))+  theme_void()

right takes a boolean value that controls whether the moon is “waxing”or “waning”—that is, whether it is “filled” from the right or the left.

One way to make a “complete” moon with two colors is to useright = TRUE for one color andright = FALSE for the other, withcomplementary ratios. (See the examples below for some otherapproaches.)

tidymoons<-data.frame(x= rep(1:3,6),y= rep(rep(3:1,each=3),2),ratio= c(1:9/10,9:1/10),right= rep(c(TRUE,FALSE),each=9))ggplot(tidymoons)+  geom_moon(aes(x,y,ratio=ratio,right=right,fill=right))+  lims(x= c(0.5,3.5),y= c(0.5,3.5))

gggibbous includes three key glyphs for different types of legends:draw_key_moon,draw_key_moon_left, anddraw_key_full_moon

draw_key_moon—the default ingeom_moon—draws a gibbous moon withright = TRUE (see above).

draw_key_moon_left draws a crescent moon from the left that iscomplementary to the gibbous moon indraw_key_moon, which is usefulfor combined legends:

ggplot(tidymoons, aes(x,y,ratio=ratio,right=right,size=2^x))+  geom_moon(data= subset(tidymoons,right),fill="violetred")+  geom_moon(data= subset(tidymoons,!right),fill="turquoise3",key_glyph=draw_key_moon_left  )+  lims(x= c(0.5,3.5),y= c(0.5,3.5))+  scale_size("size",range= c(5,10),breaks=2^(1:3))

draw_key_full_moon draws a circle. It is similar to the “point”key glyph, but the size is calculated slightly differently, so it ismore appropriate if you want the size of legend moons and the size ofthe plot moons to match.

ggplot(tidymoons)+  geom_moon(    aes(x,y,ratio=ratio,right=right,fill=right,size=2^x),key_glyph=draw_key_full_moon  )+  lims(x= c(0.5,3.5),y= c(0.5,3.5))+  scale_size("size",range= c(5,10),breaks=2^(1:3))+  scale_fill_manual(values= c("firebrick1","dodgerblue2"))+  theme(legend.box="horizontal")

One common use of multiple pie charts is to represent proportions atdifferent coordinates on a map. Thex andy dimensions are alreadycommitted to the map coordinates, so proportional visualizations likebar charts are more difficult. This is a perfect opportunity to try outmoon charts!

Pie chart maps are popular in population genetics, so let’s look at anexample from that field. Thedmeladh data[2] contains frequencies oftwo variants of theAdh gene in Australian and Papua New Guinean fruitfly populations. Many of these populations are close together, so wehave to deal with overplotting, which we do manually below.

dmeladh_adj<-dmeladhdmeladh_adj$long<-dmeladh$Longitude+ c(-2,0,-2,2,-3,3,3,2,3,4,-2.5,-2.5,-1,-2,-2.5,-4,2.5,5,6,7,2,-7,-5.5,-3,0,-7,-2,3,5.5,0.5,-1,-1.5,-3,2)dmeladh_adj$lat<-dmeladh$Latitude+ c(-2,2,0,1,0,0,0,2,0.5,-1,1,-1.5,2,4,1.5,0,2,1,-1,-3,-2,1,-1,-2,-3,-2,-4,-3,-1,1.5,2,2,-2,0)moonmap<- ggplot(dmeladh_adj, aes(long,lat))+  geom_polygon(data= map_data("world",region="(Australia)|(Indonesia)|(Papua New Guinea)"),    aes(group=group),fill="gray80"  )+  geom_segment(aes(xend=Longitude,yend=Latitude),color="gray20")+  geom_point(aes(Longitude,Latitude),size=0.75,color="gray20")+  scale_size(range= c(4,10))+  coord_map(xlim= c(110,160),ylim= c(-45,-5))+  theme_void()+  theme(legend.position= c(0.05,0.05),legend.direction="horizontal",legend.justification= c(0,0)  )moonmap+  geom_moon(    aes(ratio=AdhS/100,size=N),right=FALSE,fill="gold",color="gold",key_glyph=draw_key_moon_left  )+  geom_moon(    aes(ratio=AdhF/100,size=N),fill="forestgreen",color="forestgreen"  )

If we want to label the alleles in the legend explicitly, then we needto map them to a group, which requires that we rearrange the data into a“longer” (“tidy”) format.

tidyadh<- reshape(dmeladh_adj,varying= c("AdhF","AdhS"),v.names="percent",timevar="allele",times= c("AdhF","AdhS"),idvar= c("Locality","Latitude","Longitude","long","lat","N"),direction="long")tidyadh$right<- rep(c(TRUE,FALSE),each= nrow(dmeladh_adj))moonmap+  geom_moon(data=tidyadh,key_glyph=draw_key_full_moon,    aes(ratio=percent/100,fill=allele,color=allele,right=right,size=N)  )+  scale_fill_manual(values= c("forestgreen","gold"))+  scale_color_manual(values= c("forestgreen","gold"))

Sometimes you just want to plot literal representations of the moon. Thelunardist data, adapted from NASA, contains the distance from theEarth to the Moon for each day in 2019, as well as the dates (in UTC) ofeach occurrence of the four principal phases of the moon. We can plotthose principal phases using moon charts (which in this case areidentical to pie charts).

moonphase<- subset(lunardist,!is.na(phase))moonphase$percent<- ifelse(moonphase$phase=="new",0, ifelse(moonphase$phase=="full",1,0.5))ggplot(lunardist, aes(date,distance))+  geom_line()+# Plotting the lower layer as a full circle also works in most cases  geom_moon(data=moonphase,ratio=1,size=5,fill="black")+  geom_moon(data=moonphase, aes(ratio=percent),size=5,fill="yellow",right=moonphase$phase=="first quarter"  )

“Harvey balls” are essentially pie charts used for qualitativecomparisons, often in tabular format. We can use moon charts for thesame purpose.

First, let’s make up some data:

rest_names<- c("Anscombe's Luncheonette","Chai Squared","Tukey's Honest Southern Diner","Bagels ANOVA","Spearmint Row")restaurants<-data.frame(Restaurant=factor(rest_names,levels=rest_names),Food= c(5,3,4,4,1),Decor= c(2,5,3,1,5),Service= c(4,2,3,3,5),Price= c(4,5,2,5,2))

As a regular table:

And now as a table with Harvey moons:

# First we reshape the data into "long" format to facilitate plottingrest_cats<- c("Food","Decor","Service","Price")tidyrest<- reshape(restaurants,varying=rest_cats,v.names="Score",timevar="Category",times=factor(rest_cats,levels=rest_cats),idvar="Restaurant",direction="long")ggplot(tidyrest, aes(0,0))+  geom_moon(aes(ratio= (Score-1)/4),fill="black")+  geom_moon(aes(ratio=1- (Score-1)/4),right=FALSE)+  facet_grid(Restaurant~Category,switch="y")+  theme_minimal()+  theme(panel.grid= element_blank(),strip.text.y.left= element_text(angle=0,hjust=1),axis.text= element_blank(),axis.title= element_blank()  )

1. Kosara, R. 2019. Circular Part-to-Whole Charts Using the AreaVisual Cue. EuroVis 2019 - Short Papers.https://doi.org/10.2312/evs.20191163

2. Oakeshott, J.G., et al. 1982. Alcohol dehydrogenase andglycerol-3-phosphate dehydrogenase clines inDrosophilamelanogaster on different continents. Evolution, 36(1): 86-96.