My suggestion would be to use the packages reshape2 and ggplot2. With those you can easily reassemble the data and plot it in a nicer way than the R default.

You just need the following commands:

library(ggplot2)
library(reshape2)

reshaped_data=melt(data,id="sample")

p=ggplot(reshaped_data,aes(x=as.factor(sample),y=value,fill=variable))

p+geom_boxplot()

There are many ways to do this.

For example, with ggplot2:

library(ggplot2)
d = read.table('mydataset.csv', sep='\t', header=T)
ggplot(d) + geom_boxplot(aes(x='NAF', y=NAF)) + geom_boxplot(aes(x='TAF', y=TAF)) + facet_wrap(~sample, ncol=2) + theme_bw() + scale_x_discrete('x axis label') + scale_y_continuous('value')

An alternative is to reshape your dataset in a 'long' format:

library(reshape2)
d.long = melt(d, id.vars='sample')
ggplot(d, aes(x=variable, y=value)) + geom_boxplot()
# you can even use lattice
library(lattice)
bwplot(value~variable|sample, d.long, layout=c(2,2))

You can use the ncol and nrow parameters in facet_wrap (or with the layout argument if you use lattice) to adjust the number of panels displayed in each row/column. If you have a lot of samples, the best thing to do is to add another column classifying the samples into smaller groups, and then plot one page per group.

You can try BoxPlotR which generates side-by-side box plots. This tool can be run online or locally.

I hope this will work:

input <-read.table('mydata.txt', sep='\t', header=T)
mydata_frame <- data.frame(values=c(input[,2],input[,3]),vars = rep(c("NAF","TAF"), times = c(length(input[,2]),length(input[,2]))))
vars = rep(c("NAF","TAF"), times = c(length(input[,2]),length(input[,2]))))
values=c(input[,2],input[,3])
boxplot(values ~ vars, data = mydata_frame)