Usage

render_template(name, data, path = "")

Arguments

name

of the template

data

data for the template

path

location to create file. If "" (the default), prints to standard out.

Examples

rd <- parse_rd("colSums", "base")
html <- to_html(rd, package = "base")
render_template("topic", html)


  
    
    
    
    
    

    
    
    
    
    

    
    
  

  

    

      

        

          
          

            

              
 Index
            
          

        
      
    

    

      
Form Row and Column Sums and Means

      

        

          
Usage
          
colSums(x, na.rm = FALSE, dims = 1)

rowSums(x, na.rm = FALSE, dims = 1)

colMeans(x, na.rm = FALSE, dims = 1)

rowMeans(x, na.rm = FALSE, dims = 1)

.colSums(X, m, n, na.rm = FALSE)

.rowSums(X, m, n, na.rm = FALSE)

.colMeans(X, m, n, na.rm = FALSE)

.rowMeans(X, m, n, na.rm = FALSE)
          
          
Arguments
          

            
x
            
an array of two or more dimensions, containing numeric,
    complex, integer or logical values, or a numeric data frame.
            
na.rm
            
logical.  Should missing values (including NaN)
    be omitted from the calculations?
            
dims
            
integer: Which dimensions are regarded as ‘rows’ or
    ‘columns’ to sum over.  For row*, the sum or mean is
    over dimensions dims+1, ...; for col* it is over
    dimensions 1:dims.
            
X
            
a numeric matrix.
            
m, n
            
the dimensions of X.
          
          
          

            
Description
            
            
Form row and column sums and means for numeric arrays.
        
          

          

            
Details
            
            
These functions are equivalent to use of apply with
  FUN = mean or FUN = sum with appropriate margins, but
  are a lot faster.  As they are written for speed, they blur over some
  of the subtleties of NaN and NA.  If na.rm =
    FALSE and either NaN or NA appears in a sum, the
  result will be one of NaN or NA, but which might be
  platform-dependent.
        
            
Notice that omission of missing values is done on a per-column or
  per-row basis, so column means may not be over the same set of rows,
  and vice versa.  To use only complete rows or columns, first select
  them with na.omit or complete.cases
  (possibly on the transpose of x). 
  
  The versions with an initial dot in the name are ‘bare-bones’
  versions for use in programming: they apply only to numeric matrices
  and do not name the result.
        
          

          

            
Value
            
            
A numeric or complex array of suitable size, or a vector if the result
  is one-dimensional.  For the first four functions the dimnames
  (or names for a vector result) are taken from the original
  array.
        
            
If there are no values in a range to be summed over (after removing
  missing values with na.rm = TRUE), that
  component of the output is set to 0 (*Sums) or NaN
  (*Means), consistent with sum and
  mean.
        
          
      
          
Examples
          
## Compute row and column sums for a matrix:
x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
rowSums(x); colSums(x)
x1 x2 
24 24 

dimnames(x)[[1]] <- letters[1:8]
rowSums(x); colSums(x); rowMeans(x); colMeans(x)
x1 x2 
 3  3 

x[] <- as.integer(x)
rowSums(x); colSums(x)
x1 x2 
24 24 

x[] <- x < 3
rowSums(x); colSums(x)
x1 x2 
 0  3 

x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
x[3, ] <- NA; x[4, 2] <- NA
rowSums(x); colSums(x); rowMeans(x); colMeans(x)
x1 x2 
NA NA 

rowSums(x, na.rm = TRUE); colSums(x, na.rm = TRUE)
x1 x2 
21 21 

rowMeans(x, na.rm = TRUE); colMeans(x, na.rm = TRUE)
 x1  x2 
3.0 3.5 


## an array
dim(UCBAdmissions)
[1] 2 2 6

rowSums(UCBAdmissions); rowSums(UCBAdmissions, dims = 2)
          Gender
Admit      Male Female
  Admitted 1198    557
  Rejected 1493   1278

colSums(UCBAdmissions); colSums(UCBAdmissions, dims = 2)
  A   B   C   D   E   F 
933 585 918 792 584 714 


## complex case
x <- cbind(x1 = 3 + 2i, x2 = c(4:1, 2:5) - 5i)
x[3, ] <- NA; x[4, 2] <- NA
rowSums(x); colSums(x); rowMeans(x); colMeans(x)
x1 x2 
NA NA

rowSums(x, na.rm = TRUE); colSums(x, na.rm = TRUE)
    x1     x2 
21+14i 21-30i

rowMeans(x, na.rm = TRUE); colMeans(x, na.rm = TRUE)
    x1     x2 
3.0+2i 3.5-5i

        
        

          
            
          
See also
          
  apply, rowsum