[R] Nicely formatted summary table with mean, standard deviation or number and proportion

From: Keith Wong <keithw_at_med.usyd.edu.au>
Date: Mon, 14 May 2007 11:56:45 +1000


Dear all,

The incredibly useful Hmisc package provides a method to generate summary tables that can be typeset in latex. The Alzola and Harrell book   "An introduction to S and the Hmisc and Design libraries" provides an example that generates mean and quartiles for continuous variables, and numbers and percentages for count variables: summary() with method = 'reverse'.

I wonder if there is a way to change it so the mean and standard deviation are reported instead for continuous variables.

I illustrate my question below using an example from the book.

Thank you.

Keith

> ####
> library(Hmisc)
>
> set.seed(173)
> sex = factor(sample(c("m", "f"), 500, rep = T))
> age = rnorm(500, 50, 5)
> treatment = factor(sample(c("Drug", "Placebo"), 500, rep = T))
> summary(sex ~ treatment, fun = table)
sex N=500

+---------+-------+---+---+---+
| | |N |f |m |
+---------+-------+---+---+---+
|treatment|Drug |263|140|123|
| |Placebo|237|133|104|
+---------+-------+---+---+---+
|Overall | |500|273|227|
+---------+-------+---+---+---+
>
>
>
> (x = summary(treatment ~ age + sex, method = "reverse"))
> # generates quartiles for continuous variables

Descriptive Statistics by treatment

+-------+--------------+--------------+

|       |Drug          |Placebo       |
|       |(N=263)       |(N=237)       |

+-------+--------------+--------------+
|age |46.5/49.9/53.2|46.7/50.0/53.4|
+-------+--------------+--------------+
|sex : m| 47% (123) | 44% (104) |
+-------+--------------+--------------+
>
>
> # latex(x) generates a very nicely formatted table
> # but I'd like "mean (standard deviation)" instead of quartiles.

> # this function from

http://tolstoy.newcastle.edu.au/R/e2/help/06/11/4713.html
> g <- function(y) {

+   s <- apply(y, 2,
+              function(z) {
+                z <- z[!is.na(z)]
+                n <- length(z)
+                if(n==0) c(NA,NA,NA,0) else
+                if(n==1) c(z, NA,NA,1) else {
+                  m <- mean(z)
+                  s <- sd(z)
+                  c(N=n, Mean=m, SD=s)
+                }
+              })
+   w <- as.vector(s)
+   names(w) <-  as.vector(outer(rownames(s), colnames(s), paste, sep=''))
+   w
+ }

>
> summary(treatment ~ age + sex, method = "reverse", fun = g)
> # does not work, 'fun' or 'FUN" argument is ignored.

Descriptive Statistics by treatment

+-------+--------------+--------------+

|       |Drug          |Placebo       |
|       |(N=263)       |(N=237)       |

+-------+--------------+--------------+
|age |46.5/49.9/53.2|46.7/50.0/53.4|
+-------+--------------+--------------+
|sex : m| 47% (123) | 44% (104) |
+-------+--------------+--------------+
>
>
> (x1 = summarize(cbind(age), llist(treatment), FUN = g,
stat.name=c("n", "mean", "sd")))

   treatment n mean sd
1 Drug 263 49.9 4.94
2 Placebo 237 50.1 4.97
>
> # this works but table is rotated, and it count data has to be
> # treated separately.

-- 
Keith Wong
PhD candidate
Sleep & Circadian Research Group
Woolcock Institute of Medical Research

email   keithw_at_med.usyd.edu.au
Phone   +61 2 9515 8981
Fax     +61 2 9515 7070
Mail    PO Box M77, Missenden Road NSW 2050, Australia

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Received on Mon 14 May 2007 - 02:04:20 GMT

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