S3 method for tabular class

[RomanTsegelskyi]

S3 method for tabular class as part of the test for GSoC 2015

Given possibly complicated structure of tabular (see example(tabular) for better idea), I decided to add table row names as first columns and highlight them based on emphasize.rownames param.
Also colnames are multiline cells, so the table looks nice when converted using pandoc
For tests, I used the idea, that no matter how underlying implementation of pandoc.table changes, resulting rendering should have the same amount of rows based on data. I think other methods for S3 classes can be tested in similar fashion.

> example(pander.tabular)

pndr.t> pander(tabular(as.factor(am) ~ (mpg+hp+qsec) * (mean+median), data = mtcars), split.tables = Inf)

------------------------------------------------------------------
      \          mpg\      \      hp\      \      qsec\      \    
 as.factor(am)   mean    median   mean   median    mean    median 
--------------- ------- -------- ------ -------- -------- --------
      *0*        17.15    17.3   160.3    175     18.18    17.82  

      *1*        24.39    22.8   126.8    109     17.36    17.02  
------------------------------------------------------------------


pndr.t> pander(tabular( (Species + 1) ~ (n=1) + Format(digits=2)*
pndr.t+ (Sepal.Length + Sepal.Width)*(mean + sd), data=iris ), split.tables = Inf)

------------------------------------------------------------
     \        \    Sepal.Length\    \    Sepal.Width\    \  
  Species     n         mean        sd       mean        sd 
------------ ---- ---------------- ---- --------------- ----
  *setosa*    50        5.01       0.35      3.43       0.38

*versicolor*  50        5.94       0.52      2.77       0.31

*virginica*   50        6.59       0.64      2.97       0.32

   *All*     150        5.84       0.83      3.06       0.44
------------------------------------------------------------


pndr.t> Sex <- factor(sample(c("Male", "Female"), 100, rep=TRUE))

pndr.t> Status <- factor(sample(c("low", "medium", "high"), 100, rep=TRUE))

pndr.t> z <- rnorm(100)+5

pndr.t> fmt <- function(x) {
pndr.t+    s <- format(x, digits=2)
pndr.t+    even <- ((1:length(s)) %% 2) == 0
pndr.t+    s[even] <- sprintf("(%s)", s[even])
pndr.t+    s
pndr.t+ }

pndr.t> tab <- tabular( Justify(c)*Heading()*z*Sex*Heading(Statistic)*Format(fmt())*(mean+sd)
pndr.t+                ~ Status )

pndr.t> pander(tab)

-----------------------------------------------
   \         \        Status\     \       \    
  Sex     Statistic     high     low    medium 
-------- ----------- ---------- ------ --------
*Female*   *mean*       4.76     4.99    4.80  

            *sd*       (0.85)   (1.08)  (1.24) 

 *Male*    *mean*       5.18     5.02    4.59  

            *sd*       (0.90)   (0.88)  (1.19) 
-----------------------------------------------

[coveralls]

Coverage increased (+0.33%) to 54.53% when pulling a62c158 on RomanTsegelskyi:tabular into 54a7adf on Rapporter:master.

[daroczig]

Thank you, this looks extremely promising. Please do not forget to register on Google Melange as part of the official GSoC application.