I took a shot at getting "assymetric lableling but it's by no means perfect. You really, really ,really, should learn to offer data with dput. See below:

On Feb 13, 2010, at 9:27 AM, Orvalho Augusto wrote:

I am using pyramid.plot() from the plotrix package.

I have something like this

############################################
xy.pop<-dados$masfr
xx.pop<-dados$femfr
#agelabels<-dados$femlab
xycol<-color.gradient(c(0,0,0.5,1),c(0,0,0.5,1),c(1,1,0.5,1),11)
xxcol<-color.gradient(c(1,1,0.5,1),c(0.5,0.5,0.5,1),c(0.5,0.5,0.5,1), 11)
xylab<-dados$maslab
xxlab<-dados$femlab
agelabels<-xylab

png("piramide9808.png")
par (mar = pyramid .plot (xy .pop,xx.pop,labels=agelabels,top.labels=c("Masculino","","Feminino"),
      main="Primeiras 10 cancros mais frequentes por
sexo...",xycol=xycol,xxcol=xxcol,gap=0, labelcex=0))

dev.off()

#################################

the dados a dataframe fabircated by someother program and looks like:

######################
ordem   femlab  femfa   femfr   maslab  masfa   masfr

1       Colo do utero   258     26.76348548     Prostata        613     
43.81701215

2       Mama    110     11.41078838     Figado  84      6.004288778

3       Esofago 62      6.43153527      Pele    70      5.003573981

4       Figado  60      6.22406639      Sarcoma de Kaposi       65      
4.64617584

5       Pele    48      4.979253112     Esofago 63      4.503216583

6       Bexiga  37      3.838174274     Pulmao  46      3.288062902

7       Corpo do utero  34      3.526970954     Bexiga  43      3.073624017

8       "Utero, SOE"  28      2.904564315     Penis   33      2.358827734

9       Sarcoma de Kaposi       28      2.904564315     Laringe 27      
1.929949964

10      Vulva e Vagina  24      2.489626556     Colon   24      1.715511079

11 Outras localizacoes 275 28.52697095 Outras localizacoes 331 23.65975697


In case anyone wants to take a crack at this without the hassle of recreating htis dataset...

dput(dados) can be used to recreate the complete dataframe

dados <- structure(list(ordem = 1:11, femlab = structure(c(2L, 6L, 4L,
5L, 8L, 1L, 3L, 10L, 9L, 11L, 7L), .Label = c("Bexiga", "Colo do utero",
"Corpo do utero", "Esofago", "Figado", "Mama", "Outras localizacoes",
"Pele", "Sarcoma de Kaposi", "Utero SOE", "Vulva e Vagina"), class = "factor"),
    femfa = c(258L, 110L, 62L, 60L, 48L, 37L, 34L, 28L, 28L,
    24L, 275L), femfr = c(26.76348548, 11.41078838, 6.43153527,
    6.22406639, 4.979253112, 3.838174274, 3.526970954, 2.904564315,
    2.904564315, 2.489626556, 28.52697095), maslab = structure(c(9L,
    4L, 7L, 11L, 3L, 10L, 1L, 8L, 5L, 2L, 6L), .Label = c("Bexiga",
    "Colon", "Esofago", "Figado", "Laringe", "Outras localizacoes",
    "Pele", "Penis", "Prostata", "Pulmao", "Sarcoma de Kaposi"
    ), class = "factor"), masfa = c(613L, 84L, 70L, 65L, 63L,
    46L, 43L, 33L, 27L, 24L, 331L), masfr = c(43.81701215, 6.004288778,
    5.003573981, 4.64617584, 4.503216583, 3.288062902, 3.073624017,
2.358827734, 1.929949964, 1.715511079, 23.65975697)), .Names = c("ordem", "femlab", "femfa", "femfr", "maslab", "masfa", "masfr"), class = "data.frame", row.names = c(NA,
-11L))



xy.pop<-dados$masfr
xx.pop<-dados$femfr
#agelabels<-paste(dados$femlab, "\t\t\t", dados$maslab, sep='')
xycol<-color.gradient(c(0,0,0.5,1),c(0,0,0.5,1),c(1,1,0.5,1),11)
xxcol<-color.gradient(c(1,1,0.5,1),c(0.5,0.5,0.5,1),c(0.5,0.5,0.5,1),11)
#xylab<-dados$maslab
#xxlab<-dados$femlab
#agelabels<-xylab


par(mar=pyramid.plot(xy.pop, xx.pop, labels= rep("",length(xy.pop)), top.labels=c("Masculino", "", "Feminino"),
      main="Primeiras 10 cancros mais frequentes por
sexo...", xycol=xycol,xxcol=xxcol, gap=0))
text(-xy.pop-5*nchar(dados$maslab), 1:length(xy.pop), dados $maslab );text(xx.pop+7*nchar(dados$femlab), 1:length(xx.pop), dados $femlab )

# Could not get the right ratio of xx.pop to nchar to get completely correct placement and given the small number you might just want to put in a "hand-crafted" vector,



#####################################

The problem is (1) I do not want plot agelabels on the center and (2)
I want plot different labels for each pair of the bars (one label for
masculine and the other feminine).

The data represent the 10 most frequent cancer in a group of individuals.

Can some one help please?

Caveman



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