Create summary table of categorical variables of different lengths

Question

In SPSS it is fairly easy to create a summary table of categorical variables using \"Custom Tables\":

Answer 1

Here is a solution using the freq function of the questionr package (shameless autopromotion, sorry) :

R> lapply(df, freq)
$vs
    n    %
0  18 56.2
1  14 43.8
NA  0  0.0

$am
    n    %
0  19 59.4
1  13 40.6
NA  0  0.0

$gear
    n    %
3  15 46.9
4  12 37.5
5   5 15.6
NA  0  0.0

$carb
    n    %
1   7 21.9
2  10 31.2
3   3  9.4
4  10 31.2
6   1  3.1
8   1  3.1
NA  0  0.0

Answer 2

Here's my solution. It ain't pretty, which is why I put a bag over its head (wrap it in a function). I also add another variable to demonstrate that it's general (I hope).

prettyTable <- function(x) {

  tbl <- apply(x, 2, function(m) {
    marc <- sort(unique(m))
    cnt <- matrix(table(m), ncol = 1)
    out <- cbind(marc, cnt)
    out <- out[order(marc), ] # do sorting
    out <- cbind(out, round(prop.table(out, 2)[, 2] * 100, 2))
  })

  x2 <- do.call("rbind", tbl)

  spaces <- unlist(lapply(apply(x, 2, unique), length))
  space.names <- names(spaces)
  spc <- rep("", sum(spaces))
  ind <- cumsum(spaces)
  ind <- abs(spaces - ind)+1
  spc[ind] <- space.names

  out <- cbind(spc, x2)
  out <- as.data.frame(out)

  names(out) <- c("Variable", "Levels", "Count", "Column N %")
  out
}

prettyTable(x = mtcars[, c(2, 8:11)])

   Variable Levels Count Column N %
1       cyl      4    11      34.38
2                6     7      21.88
3                8    14      43.75
4        vs      0    18      56.25
5                1    14      43.75
6        am      0    19      59.38
7                1    13      40.62
8      gear      3    15      46.88
9                4    12       37.5
10               5     5      15.62
11     carb      1     7      21.88
12               2    10      31.25
13               3     3       9.38
14               4    10      31.25
15               6     1       3.12
16               8     1       3.12

Using googleVis package, you can make a handy html table.

plot(gvisTable(prettyTable(x = mtcars[, c(2, 8:11)])))

Answer 3

Unfortunately there seems to be no R package yet that can generate a nice output like SPSS. Most functions for generating tables seem to define their own special formats what gets you into trouble if you want to export or work on it in another way.
But I'm sure R is capable of that and so I started writing my own functions. I'm happy to share the result (work in progress-status, but gets the job done) with you:

The following function returns for all factor variables in a data.frame the frequency or the percentage (calc="perc") for each level of the factor variable "variable".
The most important thing may be that the output is a simple & user friendly data.frame. So, compared to many other functions, it's no problem to export the results an work with it in any way you want.

I realize that there is much potential for further improvements, i.e. add a possibility for selecting row vs. column percentage calculation, etc.

contitable <- function( survey_data, variable, calc="freq" ){    

  # Check which variables are not given as factor    
  # and exlude them from the given data.frame    
 survey_data_factor_test <- as.logical( sapply( Survey, FUN=is.factor) )    
  survey_data <- subset( survey_data, select=which( survey_data_factor_test ) )    

  # Inform the user about deleted variables    
  # is that proper use of printing to console during a function call??    
  # for now it worksjust fine...    
  flush.console()        
  writeLines( paste( "\n ", sum( !survey_data_factor_test, na.rm=TRUE),
            "non-factor variable(s) were excluded\n" ) )

  variable_levels <- levels(survey_data[ , variable ])    
  variable_levels_length <- length( variable_levels )    

  # Initializing the data.frame which will gather the results    
  result <- data.frame( "Variable", "Levels", t(rep( 1, each=variable_levels_length ) ) )    
  result_column_names <- paste( variable, variable_levels, sep="." )    
  names(result) <- c("Variable", "Levels", result_column_names )    

  for(column in 1:length( names(survey_data) ) ){       

      column_levels_length <- length( levels( survey_data[ , column ] ) )
      result_block <- as.data.frame( rep( names(survey_data)[column], each=column_levels_length ) )
      result_block <- cbind( result_block, as.data.frame( levels( survey_data[,column] ) ) )
      names(result_block) <- c( "Variable", "Levels" )

      results <- table( survey_data[ , column ], survey_data[ , variable ] )

      if( calc=="perc" ){ 
        results <- apply( results, MARGIN=2, FUN=function(x){ x/sum(x) }) 
        results <- round( results*100, 1 )
      }

      results <- unclass(results)
      results <- as.data.frame( results )
      names( results ) <- result_column_names
      rownames(results) <- NULL

      result_block <- cbind( result_block, results) 
      result <- rbind( result, result_block ) 
}    
result <- result[-1,]        
return( result )    
}

Answer 4

You may find the following code snippet useful. It utilizes the base package functions table, margin.table, and prop.table and does not require any other packages. It does collect the results to a list with named dimensions however (these could be collected to a single matrix with rbind):

dat <- table(mtcars[,8:11])
result <- list()
for(m in 1:length(dim(dat))){
    martab <- margin.table(dat, margin=m)
    result[[m]] <- cbind(Freq=martab, Prop=prop.table(martab))
}
names(result) <- names(dimnames(dat))

> result
$vs
  Freq   Prop
0   18 0.5625
1   14 0.4375

$am
  Freq    Prop
0   19 0.59375
1   13 0.40625

$gear
  Freq    Prop
3   15 0.46875
4   12 0.37500
5    5 0.15625

$carb
  Freq    Prop
1    7 0.21875
2   10 0.31250
3    3 0.09375
4   10 0.31250
6    1 0.03125
8    1 0.03125

Answer 5

One way to get the output, but not the formatting:

library(plyr)
ldply(mtcars[,8:11],function(x) t(rbind(names(table(x)),table(x),paste0(prop.table(table(x))*100,"%"))))
    .id 1  2       3
1    vs 0 18  56.25%
2    vs 1 14  43.75%
3    am 0 19 59.375%
4    am 1 13 40.625%
5  gear 3 15 46.875%
6  gear 4 12   37.5%
7  gear 5  5 15.625%
8  carb 1  7 21.875%
9  carb 2 10  31.25%
10 carb 3  3  9.375%
11 carb 4 10  31.25%
12 carb 6  1  3.125%
13 carb 8  1  3.125%

Answer 6

A base R solution using lapply() and do.call() with rbind() to stitch together the pieces:

x <- lapply(mtcars[, c("vs", "am", "gear", "carb")], table)

neat.table <- function(x, name){
  xx <- data.frame(x)
  names(xx) <- c("Value", "Count")
  xx$Fraction <- with(xx, Count/sum(Count))
  data.frame(Variable = name, xx)
}

do.call(rbind, lapply(seq_along(x), function(i)neat.table(x[i], names(x[i]))))

Results in:

   Variable Value Count Fraction
1        vs     0    18  0.56250
2        vs     1    14  0.43750
3        am     0    19  0.59375
4        am     1    13  0.40625
5      gear     3    15  0.46875
6      gear     4    12  0.37500
7      gear     5     5  0.15625
8      carb     1     7  0.21875
9      carb     2    10  0.31250
10     carb     3     3  0.09375
11     carb     4    10  0.31250
12     carb     6     1  0.03125
13     carb     8     1  0.03125

The rest is formatting.