Greg Adkison wrote:
I would be incredibly grateful to anyone who'll help me translate some
SAS code into R code.
Say for example that I have a dataset named "dat1" that includes five
variables: wshed, site, species, bda, and sla. I can calculate with the
following SAS code the mean, CV, se, and number of observations of
"bda" and "sla" for each combination of "wshed," "species," and "site,"
restricting the species considered to only three of several species in
dat1 (b, c, and p). Moreover, I can output these calculations and
grouping variables to a dataset named "dat2" that will reside in RAM
and include the variables wshed, site, species, mBdA, msla, cBda,
sBdA, ssla, nBda, and nsla.
proc sort data=dat1;
by wshed site species;
proc means data=dat1 noprint mean cv stderr n;
by wshed site species;
where species in ('b', 'c', 'p');
var BdA sla;
output out=dat2
mean=mBdA msla
cv=cBdA csla
stderr=sBdA ssla
n=nBdA nsla;
Thanks,
Greg
The following handles any number of analysis variables, with automatic
naming of all statistics computed from them. It requires the Hmisc package.
# Generate some data. Put one NA in sla.
set.seed(1)
dat1 <- expand.grid(wshed=1:2, site=c('A','B'),
species=c('a','b','c','p'),
reps=1:10)
n <- nrow(dat1)
dat1 <- transform(dat1,
BdA = rnorm(n, 100, 20),
sla = c(rnorm(n-1, 200, 30), NA))
# Can use upData function in Hmisc in place of transform
# Summarization function, per stratum, for a matrix of analysis
# variables
g <- function(y) {
n <- apply(y, 2, function(z) sum(!is.na(z)))
m <- apply(y, 2, mean, na.rm=TRUE)
s <- apply(y, 2, sd, na.rm=TRUE)
cv <- s/m
se <- s/sqrt(n)
w <- c(m, cv, se, n)
names(w) <- t(outer(c('m','c','s','n'), colnames(y), paste, sep=''))
w
}
library(Hmisc)
dat2 <- with(dat1,
summarize(cbind(BdA, sla),
llist(wshed, site, species),
g,
subset=species %in% c('b','c','p'),
stat.name='mBdA')
)
options(digits=3)
dat2 # is a data frame
wshed site species mBdA msla cBdA csla sBdA ssla nBdA nsla
1 1 A b 100.5 195 0.133 0.1813 4.23 11.20 10 10
2 1 A c 99.7 206 0.101 0.1024 3.17 6.68 10 10
3 1 A p 101.4 188 0.239 0.1580 7.65 9.39 10 10
4 1 B b 109.9 203 0.118 0.1433 4.09 9.21 10 10
5 1 B c 98.4 221 0.193 0.1250 6.01 8.72 10 10
6 1 B p 102.9 203 0.216 0.1446 7.03 9.29 10 10
7 2 A b 95.8 195 0.241 0.2011 7.31 12.40 10 10
8 2 A c 98.7 207 0.194 0.1274 6.04 8.33 10 10
9 2 A p 102.2 191 0.217 0.1709 7.01 10.31 10 10
10 2 B b 97.8 191 0.235 0.2079 7.27 12.58 10 10
11 2 B c 100.9 194 0.164 0.0987 5.24 6.07 10 10
12 2 B p 103.0 209 0.144 0.0769 4.69 5.35 10 9
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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