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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