aggregate_by_mean <- function(data, xs) {

  if(dim(data)[2]!=length(xs))stop("length between data and variable 'xs' do not match!")

  library(reshape)

  library(data.table)

  # Convert to data.table.

  dat <- data.table(t(data))

  # Append the vector of group names as an extra column.

  dat$agg_var <- as.character(xs)

  # Melt the data.table so all values are in one column called "value".

  dat <- melt(dat, id.vars = "agg_var")

  # Cast the data.table back into the original shape, and take the mean.

  dat <- dcast.data.table(

    dat, agg_var ~ variable, value.var = "value",

    fun.aggregate = mean, na.rm = TRUE

  )

  namecol=dat$agg_var

  # Delete the extra column.

  dat[ , agg_var := NULL]

  dat=t(dat)

  rownames(dat) <- rownames(data)

  colnames(dat)=namecol

  return(dat)

}

aggregate_by_median <- function(data, xs) {

  if(dim(data)[2]!=length(xs))stop("length between data and variable 'xs' do not match!")

  library(reshape)

  library(data.table)

  # Convert to data.table.

  dat <- data.table(t(data))

  # Append the vector of group names as an extra column.

  dat$agg_var <- as.character(xs)

  # Melt the data.table so all values are in one column called "value".

  dat <- melt(dat, id.vars = "agg_var")

  # Cast the data.table back into the original shape, and take the mean.

  dat <- dcast.data.table(

    dat, agg_var ~ variable, value.var = "value",

    fun.aggregate = median, na.rm = TRUE

  )

  namecol=dat$agg_var

  # Delete the extra column.

  dat[ , agg_var := NULL]

  dat=t(dat)

  rownames(dat) <- rownames(data)

  colnames(dat)=namecol

  return(dat)

}