[R] Error when using the cat function

Sat Jul 28 14:20:05 CEST 2007

Your problem is with ifelse, not with cat.

First clue is that 

ifelse(TRUE,{print("yes")},{print("no")})  # results in "yes" being printed
TWICE. Try this:

tmp <- ifelse(TRUE,{print("yes")},{print("no")}) # one "yes"

tmp  # another "yes"

Try:

print(print("yes")) # prints "yes" and returns "yes" invisibly. This
returned value is passed on to/by ifelse.

Now try:

print(cat("yes\n")) # "yes" appears, but cat("yes") returns NULL, which
ifelse can't handle:

ifelse(TRUE, NULL, "whatever") # Gives the error you saw.

What you need is if { } else { } :

if(!inherits(dat[[n]], "factor")) {cat("yes\n")} else {cat("no\n")}

HTH, Mike.

Stan Hopkins wrote:
> 
> Is the following developed in my console output a recognized bug or am I
> using the cat function incorrectly?
> 
> Thanks,
> 
> Stan
> 
> 
> 
>> ifelse(class(data[[n]])!="factor",{print("yes")},{print("no")})
> [1] "yes"
> [1] "yes"
>> ifelse(class(data[[n]])!="factor",{cat("yes")},{cat("no")})
> yesError in ans[test & !nas] <- rep(yes, length.out = length(ans))[test &
> : 
> incompatible types (from NULL to logical) in subassignment type fix
> 
> 
>>cat("yes")
> yes 
>> class(data[[n]])!="factor"
> [1] TRUE
>> class(data[[n]])
> [1] "numeric"
>> n
> [1] 28
>> length(data[[n]])
> [1] 955
>> class(data)
> [1] "data.frame"
>> dim(data)
> [1] 955 182
>> data[[n]]
> [1] 2.5 4.9 2.6 3.0 4.7 5.0 3.9 1.5 4.8 3.2 3.6 5.2 6.3
> [14] 6.3 5.0 4.6 6.0 4.5 3.9 3.6 5.7 8.5 4.0 5.0 11.8 4.7
> [27] 7.9 2.8 4.8 5.1 4.1 4.2 3.7 2.0 2.1 1.1 14.6 7.0 3.4
> [40] 3.4 10.1 4.7 4.9 5.2 4.3 2.9 2.8 2.3 1.2 2.0 2.0 3.0
> [53] 2.0 1.1 2.0 1.0 2.0 2.0 2.7 1.0 2.0 2.0 2.0 2.0 1.1
> [66] 2.0 2.0 1.0 1.1 2.4 2.0 2.0 5.0 0.8 2.0 3.3 2.7 2.2
> [79] 2.9 1.4 2.0 1.9 1.0 1.9 2.1 2.2 2.0 2.0 1.3 3.0 1.4
> [92] 2.0 1.5 2.1 1.2 1.7 2.1 2.0 2.0 2.3 2.0 1.6 1.5 2.3
> [105] 1.1 2.0 2.0 5.0 2.4 2.0 0.8 4.0 0.0 1.7 8.3 2.0 2.0
> [118] 2.0 6.1 14.4 8.2 5.2 2.5 1.0 1.0 1.8 1.1 4.9 0.9 2.1
> [131] 1.4 1.0 1.0 3.0 2.6 2.0 1.7 1.2 3.3 2.0 1.1 1.7 1.2
> [144] 2.7 0.9 2.0 3.2 1.8 1.8 1.1 1.3 2.3 1.1 1.7 1.9 1.0
> [157] 2.3 1.1 1.0 1.2 1.5 3.2 2.2 1.6 1.0 1.7 2.5 2.0 2.0
> [170] 2.3 1.1 1.5 2.0 1.7 5.1 3.6 2.0 2.0 1.2 1.2 3.1 1.3
> [183] 1.3 2.0 1.7 1.1 2.8 2.0 2.0 1.9 2.0 2.8 4.0 8.8 4.0
> [196] 3.2 5.0 2.1 3.0 7.4 2.5 3.2 3.0 2.8 1.9 3.0 3.2 3.6
> [209] 2.8 3.2 2.1 2.5 2.2 3.0 3.7 3.2 2.3 2.7 3.1 2.5 3.0
> [222] 2.4 2.6 0.9 5.4 2.8 3.9 4.7 2.5 2.9 4.4 4.1 4.0 4.0
> [235] 2.0 4.5 3.2 3.0 4.5 6.5 7.3 1.1 9.3 5.1 4.0 4.5 4.8
> [248] 7.6 6.7 3.0 3.0 6.0 6.0 4.0 5.0 3.0 5.0 1.0 5.0 4.0
> [261] 5.0 4.0 3.8 3.0 7.0 3.0 5.0 120.0 4.0 8.0 4.0 6.0 5.0
> [274] 4.0 6.0 2.0 2.6 3.2 4.0 4.0 3.0 6.0 3.0 3.0 2.0 2.5
> [287] 5.0 5.0 3.0 3.0 4.0 7.3 2.1 6.3 6.6 15.9 3.6 2.0 9.1
> [300] 6.9 4.2 7.8 5.7 7.7 5.6 5.8 16.3 4.0 3.0 3.4 0.0 1.0
> [313] 1.0 2.7 1.6 1.6 1.0 3.0 2.0 1.0 2.0 1.3 2.0 1.4 1.0
> [326] 0.9 1.0 0.8 0.0 0.0 3.1 2.6 1.4 2.0 6.6 2.0 1.2 2.0
> [339] 1.0 1.8 1.7 2.3 1.7 0.0 1.3 2.0 3.5 1.1 0.0 1.2 1.2
> [352] 1.0 2.0 1.2 NA 1.2 2.2 2.0 2.2 1.5 1.0 2.8 1.0 1.0
> [365] 2.1 2.0 1.3 0.0 1.5 1.8 1.4 1.2 1.2 1.1 1.0 1.1 2.0
> [378] 2.0 2.4 2.0 2.8 3.1 1.1 1.8 1.3 1.4 0.7 4.0 4.7 1.0
> [391] 0.6 3.0 1.0 0.9 2.0 1.7 2.1 2.0 1.0 2.0 16.0 3.0 10.0
> [404] 5.0 1.2 0.7 1.2 1.9 1.3 1.7 1.3 2.0 1.6 4.2 3.8 1.4
> [417] 1.2 1.3 2.0 2.1 5.8 5.9 1.2 2.8 1.8 3.6 1.8 1.9 1.1
> [430] 1.3 0.9 2.0 3.2 1.7 1.7 2.9 1.6 5.0 4.0 1.9 2.2 2.0
> [443] 2.7 2.5 1.1 2.0 1.7 1.5 1.9 1.1 1.6 5.2 1.5 1.4 1.0
> [456] 1.9 1.4 1.9 2.2 2.3 3.9 1.7 0.8 0.9 1.5 1.7 2.9 1.2
> [469] 1.9 1.8 2.6 1.4 2.1 1.6 1.7 1.6 1.4 2.0 2.1 1.0 5.0
> [482] 2.3 2.5 1.0 1.0 1.3 2.3 1.1 1.8 0.9 1.5 1.3 1.0 0.8
> [495] 1.0 0.7 0.9 0.9 2.0 2.9 2.6 0.6 1.6 2.0 0.9 1.0 1.1
> [508] 2.0 0.9 1.1 2.0 4.0 3.0 1.0 2.0 2.0 1.4 3.0 3.0 1.3
> [521] 1.0 1.2 0.8 2.0 0.0 0.0 0.7 1.4 1.0 0.8 1.2 1.4 2.1
> [534] 1.0 1.0 1.4 1.2 1.1 4.0 1.3 3.0 1.7 2.0 1.0 1.6 2.0
> [547] 0.9 6.0 1.7 1.7 1.7 1.0 0.8 0.6 2.0 2.0 1.0 2.0 1.4
> [560] 1.0 1.3 1.0 1.0 1.1 1.0 1.1 5.0 4.0 2.0 1.6 3.0 2.1
> [573] 1.2 2.0 0.9 1.2 1.0 1.1 1.9 2.1 2.2 1.0 1.5 1.3 3.0
> [586] 2.0 3.6 2.0 2.0 1.5 11.4 5.2 4.5 3.4 1.6 2.1 1.2 2.4
> [599] 2.1 2.3 1.7 2.0 1.4 0.5 1.6 1.9 2.6 0.4 1.3 1.4 1.2
> [612] 1.1 1.4 2.3 1.0 1.7 1.1 3.4 1.4 2.4 1.2 1.0 1.3 1.0
> [625] 1.2 0.8 2.1 1.7 2.1 0.9 1.4 1.2 1.9 1.1 2.3 1.5 3.0
> [638] 3.0 4.9 5.8 3.0 3.0 4.2 1.1 2.5 4.9 2.0 1.9 1.8 1.2
> [651] 2.0 2.2 1.4 1.8 2.0 1.2 3.2 1.5 2.0 3.5 2.0 0.8 1.8
> [664] 1.1 2.0 2.2 1.4 1.1 2.0 1.7 1.4 3.8 4.0 1.7 1.5 1.2
> [677] 1.1 2.0 3.0 21.0 6.0 20.0 5.0 20.0 13.0 4.0 2.6 2.8 6.1
> [690] 2.1 1.8 2.2 1.9 1.5 4.0 2.9 2.6 2.3 2.2 3.3 3.5 1.2
> [703] 1.5 3.7 2.3 3.0 1.9 2.5 1.5 1.7 2.5 3.0 2.6 1.8 2.5
> [716] 0.9 3.1 1.5 2.1 2.5 0.6 1.9 1.7 3.7 7.4 2.4 3.3 3.2
> [729] 1.2 1.3 2.0 1.4 3.4 1.7 3.5 1.7 2.0 1.3 0.8 3.0 1.9
> [742] 1.9 20.6 3.8 3.8 1.2 1.5 3.2 6.1 5.8 6.6 4.0 5.7 4.0
> [755] 3.0 4.7 6.8 6.9 4.1 1.9 4.5 3.8 2.7 2.3 2.5 2.3 2.6
> [768] 3.8 1.8 2.4 1.8 1.9 6.1 5.1 4.0 3.8 2.8 3.4 3.1 2.3
> [781] 7.5 3.0 3.0 3.1 2.4 6.0 2.3 5.0 2.8 2.7 2.2 5.0 5.0
> [794] 3.0 9.0 7.0 7.0 7.0 9.0 8.0 9.0 2.0 4.0 4.0 3.0 3.0
> [807] 2.0 2.0 3.0 4.0 3.0 3.0 7.9 11.0 16.0 4.0 7.7 5.0 6.6
> [820] 16.0 9.0 19.0 4.0 4.0 7.5 6.6 22.0 20.0 7.2 7.1 7.6 6.6
> [833] 6.2 7.8 6.9 10.9 11.2 6.0 5.0 8.0 7.0 4.0 3.0 6.0 4.0
> [846] 2.6 6.0 7.0 4.0 1.4 2.0 6.0 6.0 6.0 6.0 24.0 27.6 15.8
> [859] 8.0 7.8 7.3 8.2 5.3 3.4 18.1 31.6 5.8 5.5 4.0 4.1 4.7
> [872] 4.9 3.9 0.5 3.9 6.2 3.0 3.0 4.0 2.0 3.0 3.0 2.0 2.0
> [885] 2.0 0.0 3.0 2.0 2.0 4.0 1.6 1.7 2.0 2.0 2.0 2.0 26.0
> [898] 2.0 2.0 2.0 3.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0
> [911] 2.0 0.0 2.0 2.0 2.0 2.0 5.0 2.0 11.7 10.4 8.0 4.0 11.1
> [924] 13.2 14.6 11.7 13.4 14.3 15.8 25.6 10.0 6.0 9.1 9.7 4.0 10.7
> [937] 6.0 5.0 10.9 10.0 10.6 12.9 12.3 11.6 11.8 13.3 15.1 10.7 11.0
> [950] 13.5 32.9 12.9 8.4 8.1 12.5
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