List of tables rather than an extra dimension in the table or (l)apply(xtabs) - R-help

Mon, Mar 21, 2005 9:34 PM #

I'm not sure how to best explain what I am after but here goes. I have a data frame with 2 geographical factors. One is the major region the other is the component regions.

I am trying to process all the regions at the same time without using "for". So I need (think, I do)  a list of matrices each structured according to the number of subregions within each region.

So is there a way of using lapply with xtabs or is there a better way to achieve my desired output?

Using the Titanic data as an example

t1 <- as.data.frame(Titanic)
t2 <- split(t1,t1$Class)

# I would then drop any unused levels in the factors for the geography creating distinctly different data.frames (see end of message)

, , Class = 1st

       Sex
Age     Male Female
  Child   5    1   
  Adult 175  144   

, , Class = 2nd

       Sex
Age     Male Female
  Child  11   13   
  Adult 168   93   

, , Class = 3rd

       Sex
Age     Male Female
  Child  48   31   
  Adult 462  165   

, , Class = Crew

       Sex
Age     Male Female
  Child   0    0   
  Adult 862   23   

Can I do something with t2 to produce a list which is in effect an Age by Sex crosstab with one item for each value of Class. I would be wanting to drop.unused.levels, so that the last part of the table is just 

       Sex
Age     Male Female
  Adult 862   23   

or in my case each item in the list has the same number of rows as there are subregions for that region.

List of 9
 $ 1:`data.frame':      4009 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ lga      : Factor w/ 23 levels "Carnamah (S)",..: 1 2 3 4 5 6 7 8 9 10 ...   # 23 subregions
  ..$ psn      : num [1:4009] 71 336 26 84 30 133 904 385 99 110 ...
  ..$ year     : num [1:4009] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ RegionNum: num [1:4009] 1 1 1 1 1 1 1 1 1 1 ...
 $ 2:`data.frame':      720 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 2 2 2 2 3 3 ...
  ..$ lga      : Factor w/ 4 levels "Broome (S)","De..",..: 1 2 3 4 1 2 3 4 1 2 ... # 4 subregions etc
  ..$ psn      : num [1:720] 495 445 189 377 415 374 189 330 324 319 ...
  ..$ year     : num [1:720] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 2 2 2 2 3 3 ...
  ..$ RegionNum: num [1:720] 2 2 2 2 2 2 2 2 2 2 ...

So these two items would produce

agecomp
lga                  0-4   5-9   10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65plus
  Carnamah (S)         500   400   300   200   300   300   500   400   400   300   300   200   100   300 
  Carnarvon (S)       2800  3000  2600  2100  2400  2700  2800  2600  2400  2200  2000  1600  1300  2800 
  Chapman Valley (S)   300   400   300   200   200   300   300   300   300   400   400   300   200   300 
  Coorow (S)           700   700   600   200   300   600   700   600   500   500   400   400   300   500 
  Cue (S)              200   200   100   100   200   200   300   200   200   200   200   100   100   100 
  Exmouth (S)          900  1000   800   600   700  1100  1100  1100  1100   800   700   500   400   700 
  Geraldton (C)       7700  7700  8100  8200  7200  7400  7500  7200  6900  6100  5400  4600  4300 12400 
  Greenough (S)       4700  5400  5500  4400  3100  3700  4800  5100  5200  4200  3500  2600  1900  3200 
  Irwin (S)           1000  1100  1000   600   600   900  1000  1200  1000   900   800   900   800  1800 
  Meekatharra (S)      800   700   600   600   900  1000   900   700   600   500   400   300   200   400 
  Mingenew (S)         300   300   200   100   200   200   300   300   200   200   200   200   100   200 
  Morawa (S)           400   500   400   400   200   400   500   400   300   300   300   300   200   500 
  Mount Magnet (S)     500   400   300   200   400   500   400   400   300   300   200   200   100   200 
  Mullewa (S)          600   600   800   400   400   500   500   400   300   300   300   300   200   400 
  Murchison (S)        100   100   100   100     0   100   100     0     0     0   100     0     0     0 
  Northampton (S)     1300  1300  1200   700   700   900  1200  1300  1200  1200  1000  1000   900  2000 
  Perenjori (S)        300   300   300   100   200   200   300   300   300   200   200   200   100   300 
  Sandstone (S)          0     0     0     0   100   100   100   100   100   100   100   100     0   100 
  Shark Bay (S)        300   300   200   200   200   300   400   400   400   300   300   300   200   600 
  Three Springs (S)    300   300   300   100   200   300   400   300   300   200   300   200   200   400 
  Upper Gascoyne (S)   100   200   200   100   100   100   100   100   100   100   100   100   100   100 
  Wiluna (S)           200   200   200   300   600   700   600   400   300   300   300   200   100   100 
  Yalgoo (S)           100   100   100     0   200   200   200   100   200   200   100   100   100   100

agecomp
lga                          0-4  5-9  10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65plus
  Broome (S)                 5600 5400 4500  3900  4900  5800  6100  5500  4500  3700  2800  2000  1500  2200  
  Derby-West Kimberley (S)   4000 3900 3400  3100  3800  4000  3800  3100  2500  1900  1500  1200   900  1800  
  Halls Creek (S)            2100 2100 1700  1600  1800  1600  1400  1100  1000   900   700   600   400   800  
  Wyndham-East Kimberley (S) 3500 3300 2800  2300  2900  3500  3500  3000  2600  2100  1800  1300   800  1200  

 $ 3:`data.frame':      2130 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ lga      : Factor w/ 12 levels "Albany (C)","Br..",..: 1 2 3 4 5 6 7 8 9 10 ...
  ..$ psn      : num [1:2130] 1107   21   63  167  115 ...
  ..$ year     : num [1:2130] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ RegionNum: num [1:2130] 3 3 3 3 3 3 3 3 3 3 ...
 $ 4:`data.frame':      5188 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ lga      : Factor w/ 29 levels "Beverley (S)",..: 1 2 3 4 5 6 7 8 9 10 ...
  ..$ psn      : num [1:5188] 55 58 84 90 105 134 57 132 56 70 ...
  ..$ year     : num [1:5188] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ RegionNum: num [1:5188] 4 4 4 4 4 4 4 4 4 4 ...
 $ 5:`data.frame':      5400 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ lga      : Factor w/ 30 levels "Armadale (C)",..: 1 2 3 4 5 6 7 8 9 10 ...
  ..$ psn      : num [1:5400] 2163  479 1824  865  749 ...
  ..$ year     : num [1:5400] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ RegionNum: num [1:5400] 5 5 5 5 5 5 5 5 5 5 ...
 $ 6:`data.frame':      720 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 2 2 2 2 3 3 ...
  ..$ lga      : Factor w/ 4 levels "Ashburton (S)",..: 1 2 3 4 1 2 3 4 1 2 ...
  ..$ psn      : num [1:720] 532 624 699 930 433 539 689 846 320 379 ...
  ..$ year     : num [1:720] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 2 2 2 2 3 3 ...
  ..$ RegionNum: num [1:720] 6 6 6 6 6 6 6 6 6 6 ...
 $ 7:`data.frame':      1601 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 2 ...
  ..$ lga      : Factor w/ 9 levels "Coolgardie ..",..: 1 2 3 4 5 6 7 8 9 1 ...
  ..$ psn      : num [1:1601]  342  105  534 1352   85 ...
  ..$ year     : num [1:1601] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 2 ...
  ..$ RegionNum: num [1:1601] 7 7 7 7 7 7 7 7 7 7 ...
 $ 8:`data.frame':      2880 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ lga      : Factor w/ 16 levels "Augusta-Mar..",..: 1 2 3 4 5 6 7 8 9 10 ...
  ..$ psn      : num [1:2880]  294   66   85  188 1144 ...
  ..$ year     : num [1:2880] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ RegionNum: num [1:2880] 8 8 8 8 8 8 8 8 8 8 ...
 $ 9:`data.frame':      2694 obs. of  7 variables:
  ..$ sex      : Factor w/ 2 levels "Females","Males": 2 2 2 2 2 2 2 2 2 2 ...
  ..$ age      : Factor w/ 18 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ lga      : Factor w/ 15 levels "Brookton (S)",..: 1 2 3 4 5 6 7 9 8 10 ...
  ..$ psn      : num [1:2694] 49 67 38 46 67 51 104 214 44 69 ...
  ..$ year     : num [1:2694] 1991 1991 1991 1991 1991 ...
  ..$ agecomp  : Factor w/ 14 levels "0-4","5-9","10-14",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..$ RegionNum: num [1:2694] 9 9 9 9 9 9 9 9 9 9 ...

platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    0.1            
year     2004           
month    11             
day      15             
language R

Tom Mulholland
Senior Demographer
Spatial Information and Research
State and Regional Policy
Department for Planning and Infrastructure
Perth, Western Australia
+61 (08) 9264 7936