Rapporter/pander — pull request #74 by RomanTsegelskyi (2014-06-02 13:02:16+00:00)
Title: Implementation of S3 methods for describe

Implementation of S3 method for describe from Hmisc.
Renders simple statistics.
**Basic examples**
```
library(Hmisc)
set.seed(1)
x <- describe(runif(200))
pander(x)
pander(x, short = FALSE)
dfr <- data.frame(x=rnorm(400),y=sample(c('male','female'),400,TRUE))
y <- describe(dfr)
pander(y)
```
There is one thing I could not decide for 2nd example. When rendering description, how is the best way to separate information for variables.
For example, printing for y (separates with dashed lines)
```
d> y
dfr

2 Variables 400 Observations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
x
n missing unique Mean .05 .10 .25 .50 .75 .90 .95
400 0 400 0.001083 -1.64182 -1.32308 -0.64280 -0.05831 0.67754 1.35234 1.72182

lowest : -3.008 -2.889 -2.592 -2.403 -2.343, highest: 2.351 2.447 2.498 2.649 3.810
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
y
n missing unique
400 0 2

female (187, 47%), male (213, 53%)
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
```
And with how I implemented it
```
d> pander(y, split.tables = Inf)
dfr

2 Variables	400 Observations

-------------------------------------------------------------------------------------------
n missing unique Mean .05 .10 .25 .50 .75 .90 .95
--- --------- -------- -------- -------- -------- -------- -------- ------- ------- -------
400 0 400 0.001083 -1.64182 -1.32308 -0.64280 -0.05831 0.67754 1.35234 1.72182
-------------------------------------------------------------------------------------------

Table: Variable x

* lowest: -3.008 -2.889 -2.592 -2.403 -2.343
* highest: 2.351 2.447 2.498 2.649 3.810

----------------------
n missing unique
--- --------- --------
400 0 2
----------------------

Table: Variable y

female (187, 47%), male (213, 53%)
```