## Here is a plot. The input was parsed with Jim Holtman's code.
## The panel.dumbell is something I devised to show differences.
## Rich
input <- readLines(textConnection("
Private + Shared = RAM used Program
96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
108.0 KiB + 12.5 KiB = 120.5 KiB klogd
124.0 KiB + 17.0 KiB = 141.0 KiB hidd
116.0 KiB + 30.0 KiB = 146.0 KiB acpid
124.0 KiB + 29.5 KiB = 153.5 KiB hald-addon-storage
144.0 KiB + 15.0 KiB = 159.0 KiB gpm
136.0 KiB + 26.5 KiB = 162.5 KiB pam_timestamp_check
---------------------------------------------------------
453.9 MiB
=================================
Private + Shared = RAM used Program
96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
108.0 KiB + 12.5 KiB = 120.5 KiB klogd
124.0 KiB + 17.0 KiB = 141.0 KiB hidd
116.0 KiB + 30.0 KiB = 146.0 KiB acpid
124.0 KiB + 29.5 KiB = 153.5 KiB hald-addon-storage
144.0 KiB + 15.0 KiB = 159.0 KiB gpm
136.0 KiB + 26.5 KiB = 162.5 KiB pam_timestamp_check
----------------------------------------------------------
453.9 MiB
================================="))
# keep only the data
input <- input[grepl('=', input)]
# separate into groups
grps <- split(input, cumsum(grepl("= RAM", input)))
# parse the data (not sure if there is also 'MiB')
parsed <- lapply(grps, function(.grp){
# parse ignoring first and last lines
.data <- sub(".*= ([^ ]+) ([^ ]+)\\s+(.*)", "\\1 \\2 \\3"
, .grp[2:(length(.grp) - 1L)]
)
# return matrix
do.call(rbind, strsplit(.data, ' '))
})
parsed
tmp1 <- do.call(rbind, lapply(parsed, function(x) data.frame(x)))
names(tmp1) <- c("RamUsed", "units", "Program")
tmp1$Time <- factor(rep(1:2, each=7))
tmp1$RamUsed <- as.numeric(tmp1$RamUsed)
library(lattice)
dotplot(Program ~ RamUsed, groups=Time, data=tmp1)
## this is silly. Let me construct a more interesting example with
different values at each time.
tmp1$RamUsed[8:14] <- tmp1$RamUsed[1:7] + 10*(sample(1:7))
tmp1
dotplot(Program ~ RamUsed, groups=Time, data=tmp1,
auto.key=list(title="Time", border=TRUE, columns=2))
panel.dumbell <- function(x, y, ..., lwd=1) {
n <- length(x)/2
panel.segments(x[1:n], as.numeric(y)[n+(1:n)], x[n+(1:n)],
as.numeric(y)[n+(1:n)], lwd=lwd)
panel.dotplot(x, y, ...)
}
dotplot(Program ~ RamUsed, groups=Time, data=tmp1,
auto.key=list(title="Time", border=TRUE, columns=2),
panel=panel.dumbell,
par.settings=list(superpose.symbol=list(pch=19)),
)
On Fri, Aug 30, 2013 at 9:44 AM, jim holtman <jholt...@gmail.com> wrote:
> Here is how to parse the data and put it into groups. Not sure what
> the 'timing' of each group is since not time information was given.
> Also not sure is there is an 'MiB' qualifier on the data, but you have
> the matrix of data which is easy to do with as you want.
>
>
> > input <- readLines(textConnection("
> + Private + Shared = RAM used Program
> +
> + 96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
> + 108.0 KiB + 12.5 KiB = 120.5 KiB klogd
> + 124.0 KiB + 17.0 KiB = 141.0 KiB hidd
> + 116.0 KiB + 30.0 KiB = 146.0 KiB acpid
> + 124.0 KiB + 29.5 KiB = 153.5 KiB hald-addon-storage
> + 144.0 KiB + 15.0 KiB = 159.0 KiB gpm
> + 136.0 KiB + 26.5 KiB = 162.5 KiB pam_timestamp_check
> + ---------------------------------------------------------
> + 453.9 MiB
> +
> + =================================
> + Private + Shared = RAM used Program
> +
> + 96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
> + 108.0 KiB + 12.5 KiB = 120.5 KiB klogd
> + 124.0 KiB + 17.0 KiB = 141.0 KiB hidd
> + 116.0 KiB + 30.0 KiB = 146.0 KiB acpid
> + 124.0 KiB + 29.5 KiB = 153.5 KiB hald-addon-storage
> + 144.0 KiB + 15.0 KiB = 159.0 KiB gpm
> + 136.0 KiB + 26.5 KiB = 162.5 KiB pam_timestamp_check
> + ----------------------------------------------------------
> + 453.9 MiB
> + ================================="))
> >
> > # keep only the data
> > input <- input[grepl('=', input)]
> >
> > # separate into groups
> > grps <- split(input, cumsum(grepl("= RAM", input)))
> >
> > # parse the data (not sure if there is also 'MiB')
> > parsed <- lapply(grps, function(.grp){
> + # parse ignoring first and last lines
> + .data <- sub(".*= ([^ ]+) ([^ ]+)\\s+(.*)", "\\1 \\2 \\3"
> + , .grp[2:(length(.grp) - 1L)]
> + )
> + # return matrix
> + do.call(rbind, strsplit(.data, ' '))
> + })
> >
> >
> >
> > parsed
> $`1`
> [,1] [,2] [,3]
> [1,] "107.5" "KiB" "uuidd"
> [2,] "120.5" "KiB" "klogd"
> [3,] "141.0" "KiB" "hidd"
> [4,] "146.0" "KiB" "acpid"
> [5,] "153.5" "KiB" "hald-addon-storage"
> [6,] "159.0" "KiB" "gpm"
> [7,] "162.5" "KiB" "pam_timestamp_check"
>
> $`2`
> [,1] [,2] [,3]
> [1,] "107.5" "KiB" "uuidd"
> [2,] "120.5" "KiB" "klogd"
> [3,] "141.0" "KiB" "hidd"
> [4,] "146.0" "KiB" "acpid"
> [5,] "153.5" "KiB" "hald-addon-storage"
> [6,] "159.0" "KiB" "gpm"
> [7,] "162.5" "KiB" "pam_timestamp_check"
>
> >
> Jim Holtman
> Data Munger Guru
>
> What is the problem that you are trying to solve?
> Tell me what you want to do, not how you want to do it.
>
>
> On Fri, Aug 30, 2013 at 7:24 AM, <mohan.radhakrish...@polarisft.com>
> wrote:
> > Hi,
> > I haven't tried the code yet. Is there a way to parse this data
> > using R and create bar plots so that each program's 'RAM used' figures
> are
> > grouped together.
> > So 'uuidd' bars will be together. The data will have about 50 sets. So if
> > there are 100 processes each will have about 50 bars.
> >
> > What is the recommended way to graph these big barplots ? I am looking
> for
> > only 'RAM used' figures.
> >
> >
> > Thanks,
> > Mohan
> >
> >
> > Private + Shared = RAM used Program
> >
> > 96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
> > 108.0 KiB + 12.5 KiB = 120.5 KiB klogd
> > 124.0 KiB + 17.0 KiB = 141.0 KiB hidd
> > 116.0 KiB + 30.0 KiB = 146.0 KiB acpid
> > 124.0 KiB + 29.5 KiB = 153.5 KiB hald-addon-storage
> > 144.0 KiB + 15.0 KiB = 159.0 KiB gpm
> > 136.0 KiB + 26.5 KiB = 162.5 KiB pam_timestamp_check
> > ---------------------------------------------------------
> > 453.9 MiB
> >
> > =================================
> > Private + Shared = RAM used Program
> >
> > 96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
> > 108.0 KiB + 12.5 KiB = 120.5 KiB klogd
> > 124.0 KiB + 17.0 KiB = 141.0 KiB hidd
> > 116.0 KiB + 30.0 KiB = 146.0 KiB acpid
> > 124.0 KiB + 29.5 KiB = 153.5 KiB hald-addon-storage
> > 144.0 KiB + 15.0 KiB = 159.0 KiB gpm
> > 136.0 KiB + 26.5 KiB = 162.5 KiB pam_timestamp_check
> > ----------------------------------------------------------
> > 453.9 MiB
> > =================================
> >
> >
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