Table of Percentages with Percentage Base
percent.Rdpercent returns a table of percentages along with
the percentage base. It will be useful
in conjunction with Aggregate or genTable.
Usage
percent(x,...)
# Default S3 method
percent(x,weights=NULL,total=!(se || ci),
se=FALSE,ci=FALSE,ci.level=.95,
total.name="N",perc.label="Percentage",...)
# S3 method for class 'logical'
percent(x,weights=NULL,total=!(se || ci),
se=FALSE,ci=FALSE,ci.level=.95,
total.name="N",perc.label="Percentage",...)Arguments
- x
a numeric vector or factor.
- weights
a optional numeric vector of weights of the same length as
x.- total
logical; should the total sum of counts from which the percentages are computed be included into the output?
- se
logical; should standard errors of the percentages be included?
- ci
logical; should confidence intervals of the percentages be included?
- ci.level
numeric; nominal coverage of confidence intervals
- total.name
character; name given for the total sum of counts
- perc.label
character; label given for the percentages if the table has more than one dimensions, e.g. if
seorciis TRUE.- ...
for
percent.mresp: one or several 1-0 vectors or matrices otherwise, further arguments, currently ignored.
Examples
x <- rnorm(100)
y <- rnorm(100)
z <- rnorm(100)
f <- sample(1:3,100,replace=TRUE)
f <- factor(f,labels=c("a","b","c"))
percent(x>0)
#> Percentage N
#> 44 100
percent(f)
#> a b c N
#> 39 36 25 100
genTable(
cbind(percent(x>0),
percent(y>0),
percent(z>0)) ~ f
)
#> , , f = a
#>
#>
#> percent(x > 0) percent(y > 0) percent(z > 0)
#> Percentage 33.33333 38.46154 51.28205
#> N 39.00000 39.00000 39.00000
#>
#> , , f = b
#>
#>
#> percent(x > 0) percent(y > 0) percent(z > 0)
#> Percentage 55.55556 47.22222 55.55556
#> N 36.00000 36.00000 36.00000
#>
#> , , f = c
#>
#>
#> percent(x > 0) percent(y > 0) percent(z > 0)
#> Percentage 44 60 44
#> N 25 25 25
#>
gt <- genTable(
cbind("x > 0" = percent(x>0,ci=TRUE),
"y > 0" = percent(y>0,ci=TRUE),
"z > 0" = percent(z>0,ci=TRUE)) ~ f
)
ftable(gt,row.vars=3:2,col.vars=1)
#> Percentage lower upper
#> f
#> a x > 0 33.33333 19.08810 50.21723
#> y > 0 38.46154 23.36393 55.38094
#> z > 0 51.28205 34.78022 67.58192
#> b x > 0 55.55556 38.09768 72.06458
#> y > 0 47.22222 30.40506 64.51362
#> z > 0 55.55556 38.09768 72.06458
#> c x > 0 44.00000 24.40237 65.07184
#> y > 0 60.00000 38.66535 78.87452
#> z > 0 44.00000 24.40237 65.07184
ex.data <- expand.grid(mean=c(0,25,50),sd=c(1,10,100))[rep(1:9,rep(250,9)),]
ex.data <- within(ex.data,x <- rnorm(n=nrow(ex.data),mean=ex.data$mean,sd=ex.data$sd))
ex.data <- within(ex.data,x.grp <- cases( x < 0,
x >= 0 & x < 50,
x >= 50 & x < 100,
x >= 100
))
genTable(percent(x.grp)~mean+sd,data=ex.data)
#> , , sd = 1
#>
#> mean
#> 0 25 50
#> x < 0 48.8 0 0.0
#> x >= 0 & x < 50 51.2 100 55.2
#> x >= 50 & x < 100 0.0 0 44.8
#> x >= 100 0.0 0 0.0
#> N 250.0 250 250.0
#>
#> , , sd = 10
#>
#> mean
#> 0 25 50
#> x < 0 44 0.4 0.0
#> x >= 0 & x < 50 56 98.4 46.8
#> x >= 50 & x < 100 0 1.2 53.2
#> x >= 100 0 0.0 0.0
#> N 250 250.0 250.0
#>
#> , , sd = 100
#>
#> mean
#> 0 25 50
#> x < 0 51.2 40.8 35.6
#> x >= 0 & x < 50 18.8 22.4 16.0
#> x >= 50 & x < 100 11.6 14.0 21.6
#> x >= 100 18.4 22.8 26.8
#> N 250.0 250.0 250.0
#>
Aggregate(percent(Admit,weight=Freq)~Gender+Dept,data=UCBAdmissions)
#> Gender Dept Admitted Rejected N
#> 1 Male A 62.060606 37.93939 825
#> 2 Female A 82.407407 17.59259 108
#> 3 Male B 63.035714 36.96429 560
#> 4 Female B 68.000000 32.00000 25
#> 5 Male C 36.923077 63.07692 325
#> 6 Female C 34.064081 65.93592 593
#> 7 Male D 33.093525 66.90647 417
#> 8 Female D 34.933333 65.06667 375
#> 9 Male E 27.748691 72.25131 191
#> 10 Female E 23.918575 76.08142 393
#> 11 Male F 5.898123 94.10188 373
#> 12 Female F 7.038123 92.96188 341