Here is one way of doing it: (no exactly sure if 'mode' makes sense
with your data)
> x <- read.table(textConnection("RM mgl
+ 1 215 0.9285714
+ 2 215 0.7352941
+ 3 215 1.6455696
+ 4 215 0.6000000
+ 5 sc 1.8333333
+ 6 sc 0.8333333
+ 7 sc 2.5438596
+ 8 sc 0.2500000
+ 9 202 NA
+ 10 202 0.5500000
+ 11 202 0.8148148
+ 12 202 1.6666667
+ 13 198 0.5038760
+ 14 198 0.3823529
+ 15 198 0.7600000
+ 16 198 0.4800000
+ 17 hc 3.1818182
+ 18 hc 3.7254902
+ 19 hc 4.3750000
+ 20 hc 2.6415094
+ 21 190 0.3500000
+ 22 190 0.4400000
+ 23 190 0.6500000
+ 24 190 0.5000000
+ 25 bc 9.0000000
+ 26 bc 5.0000000
+ 27 bc 4.0000000
+ 28 bc 3.2000000
+ 29 185 0.7386364
+ 30 185 0.5000000
+ 31 185 1.1538462
+ 32 185 0.6000000
+ 33 179 1.8181818
+ 34 179 1.1980000
+ 35 179 2.5000000
+ 36 179 2.0000000
+ 37 148 2.0833333
+ 38 148 2.3333333
+ 39 148 3.1000000
+ 40 148 2.2142857
+ 41 119 2.4444444
+ 42 119 2.3275862
+ 43 119 4.7142857
+ 44 119 1.7692308
+ 45 61 2.8888889
+ 46 61 3.2500000
+ 47 61 4.7500000
+ 48 61 2.6337449"), header=TRUE)
> # compute the stats
> x.stats <- by(x, x$RM, function(.rm){
+ c(mean=mean(.rm$mgl, na.rm=TRUE), median=median(.rm$mgl, na.rm=TRUE))
+ })
> do.call(rbind, x.stats)
mean median
119 2.8138868 2.3860153
148 2.4327381 2.2738095
179 1.8790455 1.9090909
185 0.7481206 0.6693182
190 0.4850000 0.4700000
198 0.5315572 0.4919380
202 1.0104938 0.8148148
215 0.9773588 0.8319327
61 3.3806584 3.0694444
bc 5.3000000 4.5000000
hc 3.4809545 3.4536542
sc 1.3651316 1.3333333
>
>
On Feb 12, 2008 11:57 AM, stephen sefick <[EMAIL PROTECTED]> wrote:
> below is my data frame. I would like to compute summary statistics
> for mgl for each river mile (mean, median, mode). My apologies in
> advance- I would like to get something like the SAS print out of PROC
> Univariate. I have performed an ANOVA and a tukey LSD and I would
> just like the summary statistics.
> thanks
>
> stephen
>
> RM mgl
> 1 215 0.9285714
> 2 215 0.7352941
> 3 215 1.6455696
> 4 215 0.6000000
> 5 sc 1.8333333
> 6 sc 0.8333333
> 7 sc 2.5438596
> 8 sc 0.2500000
> 9 202 NA
> 10 202 0.5500000
> 11 202 0.8148148
> 12 202 1.6666667
> 13 198 0.5038760
> 14 198 0.3823529
> 15 198 0.7600000
> 16 198 0.4800000
> 17 hc 3.1818182
> 18 hc 3.7254902
> 19 hc 4.3750000
> 20 hc 2.6415094
> 21 190 0.3500000
> 22 190 0.4400000
> 23 190 0.6500000
> 24 190 0.5000000
> 25 bc 9.0000000
> 26 bc 5.0000000
> 27 bc 4.0000000
> 28 bc 3.2000000
> 29 185 0.7386364
> 30 185 0.5000000
> 31 185 1.1538462
> 32 185 0.6000000
> 33 179 1.8181818
> 34 179 1.1980000
> 35 179 2.5000000
> 36 179 2.0000000
> 37 148 2.0833333
> 38 148 2.3333333
> 39 148 3.1000000
> 40 148 2.2142857
> 41 119 2.4444444
> 42 119 2.3275862
> 43 119 4.7142857
> 44 119 1.7692308
> 45 61 2.8888889
> 46 61 3.2500000
> 47 61 4.7500000
> 48 61 2.6337449
>
>
> --
> Let's not spend our time and resources thinking about things that are
> so little or so large that all they really do for us is puff us up and
> make us feel like gods. We are mammals, and have not exhausted the
> annoying little problems of being mammals.
>
> -K. Mullis
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
What is the problem you are trying to solve?
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