[R] confusion on levels() function, and how to assign a wanted order to factor levels, intentionally?
baptiste auguie
baptiste.auguie at gmail.com
Tue Jun 16 11:33:01 CEST 2009
Hi,
I tend to use a slightly modified version of stats::relevel, (from an
old thread on this list),
relevel =
function (x, ref, ...)
{
lev <- levels(x)
if (is.character(ref))
ref <- match(ref, lev)
if (any(is.na(ref)))
stop("'ref' must be an existing level")
nlev <- length(lev)
if (any(ref < 1 | ref > nlev))
stop(gettextf("ref = %d must be in 1:%d", ref, nlev),
domain = NA)
factor(x, levels = lev[c(ref, seq_along(lev)[-ref])])
}
levels(d$population)
my.order <- c("YXPy01", "KMPy01", "YLPy01", "GSPy02",
"BCPy01", "LJPy01", "GYPt01", "YLPd01", "CYPd01", "CYPd02", "CYPd03",
"BXPd01", "NSPt01")
d$population <- relevel(d$population, my.order)
levels(d$population)
HTH,
baptiste
PS: I'm guessing many ggplot2 users also subscribe to r-help, it'd be
nice not to post questions to both lists.
Mao Jianfeng wrote:
> Dear R-helpers,
>
> I want to make a series of boxplots on several numeric univariates with two
> group variables (species and population, population nested in species, and
> with population as the X-axis). In order to get a proper order of the
> individual populations in X-axis, I need to assign a wanted order to the
> factor (population). I used the levels() function to do this assignment, but
> it seemed levels() function not only changed the levels of the factor, but
> also the correlations of the factor and the numeric variables.
>
> I am confused. And, I want to know how to assign a wanted order to factor
> levels, intentionally? I think assignment is also indispensible for others
> who are do data analysing using R. Can you help me?
>
> Thank you a lot in advance.
>
> Best regards,
> Mao J-F
>
> data, code, and results I used and got are as followed:
> (You can find that the correlations of the factor and the numeric variables
> changed, before and after the levels() was performed.)
>
>
>
>> d<-read.delim("All.txt",header=T)
>> d
>>
> species population conlen tscale fscale tseen w100s nfsee
> 1 Py YXPy01 8.60 153 69 111 1.680851 94
> 2 Py YXPy01 8.10 173 74 139 1.848485 133
> 3 Py YLPy01 6.50 138 58 99 1.520833 48
> 4 Py YLPy01 5.90 153 67 118 1.355140 107
> 5 Py KMPy01 6.10 113 48 75 1.470588 51
> 6 Py KMPy01 5.10 129 54 100 1.176471 68
> 7 Py KMPy01 3.90 109 37 30 1.500000 22
> 8 Py KMPy01 5.00 128 55 71 1.468750 64
> 9 Py KMPy01 4.70 132 54 32 1.500000 28
> 10 Py KMPy01 5.80 113 52 65 1.136364 45
> 11 Py KMPy01 4.70 114 42 71 1.131148 61
> 12 Py KMPy01 5.00 120 77 131 1.403361 119
> 13 Py GSPy02 6.20 152 59 102 1.348837 43
> 14 Py GSPy02 6.20 111 41 64 2.805556 36
> 15 Py GSPy02 6.70 130 56 67 1.757576 33
> 16 Py GSPy02 6.60 115 47 78 1.603175 63
> 17 Py GSPy02 8.90 137 61 102 1.767677 99
> 18 Py GSPy02 6.20 157 68 115 1.459016 61
> 19 Py BCPy01 5.30 91 39 24 1.263158 19
> 20 Py BCPy01 6.10 100 46 53 1.117647 17
> 21 Py BCPy01 4.50 81 32 46 1.320000 25
> 22 Py LJPy01 6.60 170 65 72 2.035714 56
> 23 Py LJPy01 6.90 104 46 58 1.800000 55
> 24 Py LJPy01 8.60 161 66 38 1.794118 34
> 25 Py LJPy01 5.40 123 40 22 2.428571 21
> 26 Py LJPy01 6.80 123 54 57 2.044444 46
> 27 Py LJPy01 8.60 166 77 77 1.847458 59
> 28 Py LJPy01 6.00 132 51 91 1.119048 84
> 29 Py LJPy01 6.80 108 45 27 1.814815 27
> 30 Py LJPy01 6.20 115 48 70 1.765957 47
> 31 Py LJPy01 8.00 168 80 132 2.036364 111
> 32 Pd CYPd01 6.70 138 57 23 1.555556 9
> 33 Pd CYPd01 6.80 121 46 53 1.973684 38
> 34 Pd CYPd01 5.90 114 52 60 1.250000 12
> 35 Pd CYPd01 5.20 119 53 53 1.432432 37
> 36 Pd CYPd01 7.60 118 46 63 2.000000 23
> 37 Pd CYPd01 6.10 144 61 24 1.428571 14
> 38 Pd CYPd01 5.50 130 46 62 1.320000 54
> 39 Pd CYPd01 6.60 153 57 83 1.558442 77
> 40 Pd CYPd02 5.90 111 32 51 1.300000 10
> 41 Pd CYPd02 7.10 121 51 80 1.451613 31
> 42 Pd CYPd02 7.30 150 68 127 1.681416 113
> 43 Pd CYPd02 5.60 121 38 64 1.228571 36
> 44 Pd CYPd02 7.20 140 62 88 1.585366 41
> 45 Pd CYPd02 6.10 113 54 91 1.256757 74
> 46 Pd CYPd03 4.60 109 45 57 1.093750 32
> 47 Pd CYPd03 4.90 115 44 45 1.235294 17
> 48 Pd CYPd03 6.40 134 44 64 1.209302 45
> 49 Pd CYPd03 4.60 96 42 41 1.150000 21
> 50 Pd CYPd03 5.60 131 43 45 1.771429 35
> 51 Pd CYPd03 6.10 124 48 59 1.578947 38
> 52 Pd CYPd03 5.20 110 57 71 1.340426 47
> 53 Pd CYPd03 5.50 118 57 83 1.625000 48
> 54 Pd CYPd03 6.10 106 61 95 1.559322 60
> 55 Pd CYPd03 6.20 121 64 100 1.707692 65
> 56 Pd CYPd03 5.10 99 38 28 1.430000 20
> 57 Pd CYPd03 5.10 132 45 47 1.791667 24
> 58 Pd YLPd01 6.15 120 43 46 1.446000 21
> 59 Pt BXPd01 4.60 64 18 23 2.166667 18
> 60 Pt BXPd01 5.10 87 26 38 2.250000 32
> 61 Pt BXPd01 4.80 89 27 50 2.130435 46
> 62 Pt BXPd01 6.00 97 29 31 2.684211 19
> 63 Pt BXPd01 5.20 98 32 54 2.292683 41
> 64 Pt GYPt01 4.30 98 27 8 4.000000 5
> 65 Pt GYPt01 4.00 82 27 51 2.781250 32
> 66 Pt GYPt01 5.00 106 35 8 4.333333 6
> 67 Pt GYPt01 5.10 86 24 25 3.375000 16
> 68 Pt GYPt01 4.60 79 25 21 2.631579 19
> 69 Pt GYPt01 5.00 80 30 23 2.823529 17
> 70 Pt NSPt01 5.30 107 27 37 2.850000 33
> 71 Pt NSPt01 5.40 85 26 38 2.270000 32
> 72 Pt NSPt01 5.40 102 31 50 5.320000 40
> 73 Pt NSPt01 5.10 84 23 29 5.320000 23
> 74 Pt NSPt01 NA NA NA NA NA NA
> 75 Pt NSPt01 4.10 57 17 24 2.700000 18
>
>> levels(d$population)
>>
> [1] "BCPy01" "BXPd01" "CYPd01" "CYPd02" "CYPd03" "GSPy02" "GYPt01" "KMPy01"
> "LJPy01" "NSPt01"
> [11] "YLPd01" "YLPy01" "YXPy01"
>
>> levels(d$population)<-c("YXPy01", "KMPy01", "YLPy01", "GSPy02", "BCPy01",
>>
> "LJPy01", "GYPt01", "YLPd01", "CYPd01", "CYPd02", "CYPd03", "BXPd01",
> "NSPt01")
>
>> levels(d$population)
>>
> [1] "YXPy01" "KMPy01" "YLPy01" "GSPy02" "BCPy01" "LJPy01" "GYPt01" "YLPd01"
> "CYPd01" "CYPd02"
> [11] "CYPd03" "BXPd01" "NSPt01"
>
>> d
>>
> species population conlen tscale fscale tseen w100s nfsee
> 1 Pt NSPt01 8.60 153 69 111 1.680851 94
> 2 Pt NSPt01 8.10 173 74 139 1.848485 133
> 3 Pt BXPd01 6.50 138 58 99 1.520833 48
> 4 Pt BXPd01 5.90 153 67 118 1.355140 107
> 5 Pt YLPd01 6.10 113 48 75 1.470588 51
> 6 Pt YLPd01 5.10 129 54 100 1.176471 68
> 7 Pt YLPd01 3.90 109 37 30 1.500000 22
> 8 Pt YLPd01 5.00 128 55 71 1.468750 64
> 9 Pt YLPd01 4.70 132 54 32 1.500000 28
> 10 Pt YLPd01 5.80 113 52 65 1.136364 45
> 11 Pt YLPd01 4.70 114 42 71 1.131148 61
> 12 Pt YLPd01 5.00 120 77 131 1.403361 119
> 13 Pt LJPy01 6.20 152 59 102 1.348837 43
> 14 Pt LJPy01 6.20 111 41 64 2.805556 36
> 15 Pt LJPy01 6.70 130 56 67 1.757576 33
> 16 Pt LJPy01 6.60 115 47 78 1.603175 63
> 17 Pt LJPy01 8.90 137 61 102 1.767677 99
> 18 Pt LJPy01 6.20 157 68 115 1.459016 61
> 19 Pt YXPy01 5.30 91 39 24 1.263158 19
> 20 Pt YXPy01 6.10 100 46 53 1.117647 17
> 21 Pt YXPy01 4.50 81 32 46 1.320000 25
> 22 Pt CYPd01 6.60 170 65 72 2.035714 56
> 23 Pt CYPd01 6.90 104 46 58 1.800000 55
> 24 Pt CYPd01 8.60 161 66 38 1.794118 34
> 25 Pt CYPd01 5.40 123 40 22 2.428571 21
> 26 Pt CYPd01 6.80 123 54 57 2.044444 46
> 27 Pt CYPd01 8.60 166 77 77 1.847458 59
> 28 Pt CYPd01 6.00 132 51 91 1.119048 84
> 29 Pt CYPd01 6.80 108 45 27 1.814815 27
> 30 Pt CYPd01 6.20 115 48 70 1.765957 47
> 31 Pt CYPd01 8.00 168 80 132 2.036364 111
> 32 Py YLPy01 6.70 138 57 23 1.555556 9
> 33 Py YLPy01 6.80 121 46 53 1.973684 38
> 34 Py YLPy01 5.90 114 52 60 1.250000 12
> 35 Py YLPy01 5.20 119 53 53 1.432432 37
> 36 Py YLPy01 7.60 118 46 63 2.000000 23
> 37 Py YLPy01 6.10 144 61 24 1.428571 14
> 38 Py YLPy01 5.50 130 46 62 1.320000 54
> 39 Py YLPy01 6.60 153 57 83 1.558442 77
> 40 Py GSPy02 5.90 111 32 51 1.300000 10
> 41 Py GSPy02 7.10 121 51 80 1.451613 31
> 42 Py GSPy02 7.30 150 68 127 1.681416 113
> 43 Py GSPy02 5.60 121 38 64 1.228571 36
> 44 Py GSPy02 7.20 140 62 88 1.585366 41
> 45 Py GSPy02 6.10 113 54 91 1.256757 74
> 46 Py BCPy01 4.60 109 45 57 1.093750 32
> 47 Py BCPy01 4.90 115 44 45 1.235294 17
> 48 Py BCPy01 6.40 134 44 64 1.209302 45
> 49 Py BCPy01 4.60 96 42 41 1.150000 21
> 50 Py BCPy01 5.60 131 43 45 1.771429 35
> 51 Py BCPy01 6.10 124 48 59 1.578947 38
> 52 Py BCPy01 5.20 110 57 71 1.340426 47
> 53 Py BCPy01 5.50 118 57 83 1.625000 48
> 54 Py BCPy01 6.10 106 61 95 1.559322 60
> 55 Py BCPy01 6.20 121 64 100 1.707692 65
> 56 Py BCPy01 5.10 99 38 28 1.430000 20
> 57 Py BCPy01 5.10 132 45 47 1.791667 24
> 58 Py CYPd03 6.15 120 43 46 1.446000 21
> 59 Pd KMPy01 4.60 64 18 23 2.166667 18
> 60 Pd KMPy01 5.10 87 26 38 2.250000 32
> 61 Pd KMPy01 4.80 89 27 50 2.130435 46
> 62 Pd KMPy01 6.00 97 29 31 2.684211 19
> 63 Pd KMPy01 5.20 98 32 54 2.292683 41
> 64 Pd GYPt01 4.30 98 27 8 4.000000 5
> 65 Pd GYPt01 4.00 82 27 51 2.781250 32
> 66 Pd GYPt01 5.00 106 35 8 4.333333 6
> 67 Pd GYPt01 5.10 86 24 25 3.375000 16
> 68 Pd GYPt01 4.60 79 25 21 2.631579 19
> 69 Pd GYPt01 5.00 80 30 23 2.823529 17
> 70 Pd CYPd02 5.30 107 27 37 2.850000 33
> 71 Pd CYPd02 5.40 85 26 38 2.270000 32
> 72 Pd CYPd02 5.40 102 31 50 5.320000 40
> 73 Pd CYPd02 5.10 84 23 29 5.320000 23
> 74 Pd CYPd02 NA NA NA NA NA NA
> 75 Pd CYPd02 4.10 57 17 24 2.700000 18
>
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--
_____________________________
Baptiste Auguié
School of Physics
University of Exeter
Stocker Road,
Exeter, Devon,
EX4 4QL, UK
Phone: +44 1392 264187
http://newton.ex.ac.uk/research/emag
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