This question is a continuation of the previous question I asked.
Now I have a case where there is also a category column with Prop. So, the dataset becomes like
Hour Category Prop2
00 A 25
00 B 59
00 A 55
00 C 5
00 B 50
...
01 C 56
01 B 45
01 A 56
01 B 35
...
23 D 58
23 A 52
23 B 50
23 B 35
23 B 15
In this case I need to make a stacked area plot in R with the percentages of these different categories for each d开发者_StackOverflow社区ay. So, the result will be like.
A B C D
00 20% 30% 35% 15%
01 25% 10% 40% 25%
02 20% 40% 10% 30%
.
.
.
20
21
22 25% 10% 30% 35%
23 35% 20% 20% 25%
So now I would get the share of each Category in each hour and then plot this is a stacked area plot like this where the x-axis is the hour and y-axis the percentage of Prop2 for each category given by the different colours
You can use the ggplot2
package from Hadley Wickham for that.
R> library(ggplot2)
An example data set :
R> d <- data.frame(t=rep(0:23,each=4),var=rep(LETTERS[1:4],4),val=round(runif(4*24,0,50)))
R> head(d,10)
t var val
1 0 A 1
2 0 B 45
3 0 C 6
4 0 D 14
5 1 A 35
6 1 B 21
7 1 C 13
8 1 D 22
9 2 A 20
10 2 B 44
And then you can use ggplot
with geom_area
:
R> ggplot(d, aes(x=t,y=val,group=var,fill=var)) + geom_area(position="fill")
You can use stackpoly
from the plotrix
package:
library(plotrix)
#create proportions table
pdat <- prop.table(xtabs(Prop2~Hour+Category,Dat),margin=1)
#draw chart
stackpoly(pdat,stack=T,xaxlab=rownames(pdat))
#add legend
legend(1,colnames(pdat),bg="#ffffff55",fill=rainbow(dim(pdat)[2]))
If you want to take the borders away you can use scale_x_discrete and coord_cartesian this way
p <- ggplot(d, aes(x=Date,y=Volume,group=Platform,fill=Platform)) + geom_area(position="fill")
base_size <- 9
p + theme_set(theme_bw(base_size=9)) + scale_x_discrete(expand = c(0, 0)) + coord_cartesian(ylim=c(0,1))
精彩评论