R/addWGSR.R
wgs.RdThe first function, getWGSR(), is usually called by the addWGSR()
function but could be used as a stand-alone calculator for getting z-score
for a given anthropometric measurement.
getWGSR(sex, firstPart, secondPart, thirdPart = NA, index = NA, standing = NA) addWGSR( data, sex, firstPart, secondPart, thirdPart = NA, index = NA, standing = NULL, output = paste(index, "z", sep = ""), digits = 2 )
| sex | Name of variable specifying the sex of the subject. This must be
coded as |
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| firstPart | Name of variable specifying:
Give a quoted variable name as in (e.g.) |
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| secondPart | Name of variable specifying:
Give a quoted variable name as in (e.g.) |
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| thirdPart | Name of variable specifying age (in days) for BMI/A. Give a
quoted variable name as in (e.g.) |
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| index | The index to be calculated and added to
Give a quoted index name as in (e.g.) |
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| standing | Variable specifying how stature was measured. If NULL then
age (for |
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| data | A survey dataset as a data.frame object |
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| output | The name of the column containing the specified index to be
added to the dataset. This is an optional parameter. If you do not specify
a value for output then the added column will take the name of the
specified index with a |
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| digits | The number of decimal places for |
A data.frame of the survey dataset with the calculated z-scores added.
addWGSR() adds the WHO Growth Reference z-scores to a data frame of
anthropometric data for weight, height or length, MUAC, head circumference,
sub-scapular skinfold, triceps skinfold, and body mass index (BMI).
# Given a male child 10 months old with a weight of 5.7 kgs, height of 64.2 # cms, and MUAC of 125 mm: # # Calculate weight-for-height getWGSR(sex = 1, firstPart = 5.7, secondPart = 64.2, index = "wfh", standing = 3)#> [1] -2.725778# calculate weight-for-age getWGSR(sex = 1, firstPart = 5.7, secondPart = 10, index = "wfa", standing = 3)#> [1] 3.452888# calculate height-for-age getWGSR(sex = 1, firstPart = 64.2, secondPart = 10, index = "hfa", standing = 3)#> [1] 6.584276# Calculate MUAC-for-age z-score for a girl getWGSR(sex = 1, firstPart = 20, secondPart = 62 * (365.25 / 12), index = "mfa")#> [1] 1.992728# Calculate weight-for-height (wfh) for the anthro3 dataset addWGSR(data = anthro3, sex = "sex", firstPart = "weight", secondPart = "height", index = "wfh")#> ================================================================================#> psu age sex weight height muac oedema wfhz #> 1 1 10 1 5.7 64.2 125 2 -2.73 #> 2 1 10 2 5.8 64.4 121 2 -2.04 #> 3 1 9 2 6.5 62.2 139 2 0.13 #> 4 1 11 9 6.5 64.9 129 2 NA #> 5 1 24 2 6.5 72.9 120 2 -3.44 #> 6 1 12 2 6.6 69.4 126 2 -2.26 #> 7 1 9 2 7.0 66.7 136 2 -0.71 #> 8 1 7 1 7.1 63.5 139 2 0.34 #> 9 1 9 2 7.1 66.2 144 2 -0.39 #> 10 1 16 2 7.2 69.0 131 2 -1.12 #> 11 1 13 2 7.4 68.2 137 2 -0.57 #> 12 1 11 2 7.4 70.0 132 2 -1.10 #> 13 1 14 2 7.5 64.8 125 2 0.69 #> 14 2 8 2 7.5 65.7 151 2 0.38 #> 15 2 8 1 7.5 69.1 140 2 -1.13 #> 16 2 13 1 7.6 69.2 131 2 -1.00 #> 17 2 13 2 7.6 70.0 125 2 -0.80 #> 18 2 22 2 7.6 76.2 121 2 -2.43 #> 19 2 16 1 7.7 69.6 136 2 -0.97 #> 20 2 8 2 7.8 63.3 144 2 1.64 #> 21 2 11 2 7.8 71.5 134 2 -0.92 #> 22 2 14 2 7.8 76.6 124 2 -2.21 #> 23 2 7 1 8.0 66.8 139 2 0.48 #> 24 2 8 1 8.0 69.3 147 2 -0.40 #> 25 2 24 2 8.2 70.7 125 2 -0.14 #> 26 2 10 1 8.2 71.1 145 2 -0.68 #> 27 3 15 1 8.3 70.4 133 2 -0.31 #> 28 3 14 2 8.3 71.5 137 2 -0.22 #> 29 3 14 2 8.3 72.9 141 2 -0.59 #> 30 3 17 2 8.3 73.5 136 2 -0.74 #> 31 3 15 1 8.3 76.1 133 2 -1.96 #> 32 3 13 2 8.4 71.8 143 2 -0.17 #> 33 3 20 1 8.4 77.8 132 2 -2.22 #> 34 3 29 2 8.5 75.6 127 2 -0.97 #> 35 3 21 2 8.5 75.6 133 2 -0.97 #> 36 3 21 1 8.5 76.7 123 2 -1.81 #> 37 3 17 1 8.5 77.6 137 2 -2.02 #> 38 3 15 2 8.6 74.9 134 2 -0.67 #> 39 3 24 1 8.6 77.8 137 2 -1.92 #> 40 3 12 1 8.7 72.1 156 2 -0.26 #> 41 4 27 2 8.7 75.8 127 2 -0.75 #> 42 4 25 2 8.7 80.8 129 2 -1.90 #> 43 4 25 1 8.8 81.4 125 2 -2.46 #> 44 4 24 2 8.9 74.3 142 2 -0.15 #> 45 4 30 1 9.0 77.6 132 2 -1.30 #> 46 4 22 1 9.0 82.2 135 2 -2.36 #> 47 4 16 1 9.1 76.4 145 2 -0.88 #> 48 4 24 1 9.1 80.0 126 2 -1.70 #> 49 4 17 2 9.2 74.8 144 2 0.11 #> 50 4 15 2 9.2 75.3 145 2 -0.01 #> 51 4 33 1 9.2 80.1 136 2 -1.59 #> 52 4 25 2 9.2 80.9 142 2 -1.26 #> 53 5 16 1 9.4 75.0 148 2 -0.13 #> 54 5 29 2 9.4 81.9 138 2 -1.26 #> 55 5 16 1 9.5 73.2 151 2 0.48 #> 56 5 17 1 9.6 73.5 146 2 0.52 #> 57 5 29 2 9.6 84.6 134 2 -1.70 #> 58 5 16 1 9.7 74.2 148 2 0.45 #> 59 5 20 1 9.7 80.4 142 2 -0.99 #> 60 5 34 1 9.8 80.2 138 2 -0.82 #> 61 5 21 2 9.8 80.9 145 2 -0.54 #> 62 5 20 1 9.8 82.2 139 2 -1.28 #> 63 5 29 1 9.9 78.6 142 2 -0.35 #> 64 5 25 2 9.9 82.9 138 2 -0.90 #> 65 5 15 1 10.0 75.3 157 2 0.53 #> 66 6 39 1 10.0 81.4 133 2 -0.84 #> 67 6 9 1 10.1 72.6 155 2 1.37 #> 68 6 21 1 10.1 76.7 153 2 0.31 #> 69 6 17 2 10.1 79.1 143 2 0.21 #> 70 6 25 1 10.1 79.5 140 2 -0.30 #> 71 6 13 2 10.2 74.6 157 2 1.27 #> 72 6 25 2 10.3 81.3 141 2 -0.07 #> 73 6 31 2 10.3 85.1 130 2 -1.00 #> 74 6 25 2 10.4 81.3 138 2 0.04 #> 75 6 37 1 10.6 76.2 153 2 1.00 #> 76 6 25 2 10.6 80.7 138 2 0.39 #> 77 6 26 1 10.6 83.1 142 2 -0.52 #> 78 6 36 2 10.6 84.2 135 2 -0.44 #> 79 6 37 2 10.7 84.2 150 2 -0.34 #> 80 7 34 2 10.7 85.7 146 2 -0.71 #> 81 7 47 1 10.7 89.8 131 2 -2.34 #> 82 7 12 1 10.8 82.0 147 2 -0.04 #> 83 7 25 2 10.8 83.6 141 2 -0.08 #> 84 7 31 1 10.8 83.8 158 2 -0.47 #> 85 7 34 1 10.8 84.4 144 2 -0.62 #> 86 7 27 2 10.9 79.0 153 2 1.06 #> 87 7 23 2 10.9 85.9 145 2 -0.55 #> 88 7 19 1 11.0 75.4 168 2 1.62 #> 89 7 26 2 11.0 83.2 145 2 0.22 #> 90 7 54 1 11.0 85.9 139 2 -0.79 #> 91 7 20 1 11.1 79.9 156 2 0.74 #> 92 7 23 1 11.1 84.7 146 2 -0.36 #> 93 7 43 2 11.1 86.0 142 2 -0.37 #> 94 8 31 1 11.1 87.1 143 2 -1.18 #> 95 8 40 2 11.2 95.2 137 2 -2.54 #> 96 8 18 2 11.3 75.5 161 2 2.15 #> 97 8 41 1 11.3 89.4 142 2 -1.53 #> 98 8 40 1 11.3 90.7 148 2 -1.84 #> 99 8 33 2 11.3 91.2 139 2 -1.56 #> 100 8 11 1 11.4 78.4 171 2 1.36 #> 101 8 25 1 11.4 82.2 163 2 0.57 #> 102 8 27 2 11.4 86.8 155 2 -0.26 #> 103 8 38 1 11.4 89.4 141 2 -1.41 #> 104 8 17 2 11.5 80.6 158 2 1.30 #> 105 8 31 2 11.5 85.4 153 2 0.18 #> 106 9 45 1 11.5 86.2 143 2 -0.31 #> 107 9 53 1 11.6 81.4 153 2 0.95 #> 108 9 23 2 11.6 86.3 143 2 0.05 #> 109 9 37 1 11.6 86.3 149 2 -0.23 #> 110 9 38 1 11.6 89.9 146 2 -1.31 #> 111 9 40 1 11.7 93.0 140 2 -1.91 #> 112 9 36 1 11.8 87.1 146 2 -0.40 #> 113 9 29 1 11.9 84.3 166 2 0.59 #> 114 9 26 2 12.0 81.2 164 2 1.62 #> 115 9 33 1 12.0 86.0 154 2 0.26 #> 116 9 36 1 12.0 87.5 150 2 -0.29 #> 117 9 55 2 12.0 96.2 144 2 -1.93 #> 118 10 16 1 12.1 82.3 157 2 1.25 #> 119 10 35 2 12.1 86.6 147 2 0.45 #> 120 10 43 1 12.1 91.7 151 2 -1.18 #> 121 10 38 2 12.2 80.9 175 2 1.86 #> 122 10 37 1 12.2 85.5 162 2 0.59 #> 123 10 41 2 12.2 87.6 155 2 0.14 #> 124 10 45 2 12.2 90.1 149 2 -0.43 #> 125 10 46 2 12.2 99.6 146 2 -2.46 #> 126 10 31 1 12.3 88.0 167 2 -0.11 #> 127 10 44 1 12.3 88.1 151 2 -0.13 #> 128 10 47 2 12.3 94.9 133 2 -1.37 #> 129 10 29 1 12.4 91.8 141 2 -0.89 #> 130 10 46 2 12.4 99.6 140 2 -2.26 #> 131 10 34 1 12.6 87.0 156 2 0.43 #> 132 10 38 2 12.7 85.7 152 2 1.20 #> 133 11 32 1 12.8 87.6 161 2 0.48 #> 134 11 36 1 12.8 90.4 150 2 -0.17 #> 135 11 39 1 12.8 92.7 152 2 -0.69 #> 136 11 42 1 12.8 94.0 146 2 -0.97 #> 137 11 38 2 12.9 83.4 174 2 1.90 #> 138 11 26 2 12.9 86.2 159 2 1.25 #> 139 11 35 1 12.9 90.9 154 2 -0.19 #> 140 11 41 1 12.9 91.2 160 2 -0.26 #> 141 11 32 1 12.9 91.4 148 2 -0.30 #> 142 11 49 1 13.0 93.1 152 2 -0.58 #> 143 11 30 1 13.1 84.3 161 2 1.72 #> 144 11 32 1 13.1 85.1 136 2 1.53 #> 145 11 35 1 13.1 87.5 158 2 0.78 #> 146 11 34 1 13.1 88.6 162 2 0.52 #> 147 11 45 2 13.1 91.6 150 2 0.05 #> 148 12 29 1 13.2 90.8 158 2 0.12 #> 149 12 47 2 13.2 91.4 146 2 0.18 #> 150 12 37 2 13.2 93.8 155 2 -0.33 #> 151 12 35 1 13.3 96.5 142 2 -1.02 #> 152 12 15 2 13.4 82.4 157 2 2.51 #> 153 12 38 2 13.4 93.0 152 2 0.01 #> 154 12 37 2 13.4 94.1 149 2 -0.22 #> 155 12 53 2 13.4 100.3 145 2 -1.50 #> 156 12 37 2 13.5 84.4 168 2 2.14 #> 157 12 43 1 13.5 96.1 151 2 -0.74 #> 158 12 52 1 13.5 97.0 144 2 -0.94 #> 159 12 45 2 13.5 98.0 148 2 -0.94 #> 160 12 39 2 13.5 99.1 145 2 -1.16 #> 161 12 47 2 13.6 97.0 146 2 -0.65 #> 162 13 26 2 13.7 85.7 171 2 2.00 #> 163 13 24 1 13.8 80.9 172 2 3.02 #> 164 13 38 2 13.8 93.3 153 2 0.27 #> 165 13 57 1 13.8 94.0 149 2 -0.02 #> 166 13 42 2 13.8 103.4 144 2 -1.84 #> 167 13 42 2 13.9 92.3 175 2 0.56 #> 168 13 52 1 13.9 95.5 151 2 -0.25 #> 169 13 52 1 13.9 95.7 156 2 -0.29 #> 170 13 57 2 13.9 97.2 153 2 -0.44 #> 171 13 52 2 13.9 97.8 141 2 -0.56 #> 172 13 52 2 13.9 97.9 155 2 -0.58 #> 173 13 49 2 13.9 98.2 150 2 -0.64 #> 174 13 58 2 14.0 96.6 154 2 -0.24 #> 175 13 50 1 14.0 97.9 158 2 -0.68 #> 176 13 39 2 14.1 93.7 155 2 0.43 #> 177 14 49 2 14.1 96.4 140 2 -0.12 #> 178 14 50 2 14.1 97.1 148 2 -0.26 #> 179 14 59 2 14.1 97.3 152 2 -0.30 #> 180 14 52 2 14.1 102.4 156 2 -1.37 #> 181 14 41 1 14.2 96.7 146 2 -0.24 #> 182 14 53 1 14.2 97.7 151 2 -0.46 #> 183 14 49 2 14.2 98.1 156 2 -0.38 #> 184 14 35 2 14.3 89.8 165 2 1.39 #> 185 14 50 1 14.3 93.7 152 2 0.48 #> 186 14 41 1 14.4 94.8 158 2 0.34 #> 187 14 42 1 14.5 95.1 161 2 0.36 #> 188 14 58 1 14.5 104.9 130 2 -1.79 #> 189 14 49 1 14.7 96.6 152 2 0.21 #> 190 14 52 2 14.7 96.7 154 2 0.28 #> 191 14 51 2 14.7 99.2 155 2 -0.22 #> 192 14 49 1 14.7 99.6 164 2 -0.45 #> 193 15 42 2 15.0 93.5 172 2 1.14 #> 194 15 28 2 15.1 91.5 164 2 1.62 #> 195 15 41 2 15.2 95.9 165 2 0.81 #> 196 15 53 1 15.3 95.0 166 2 1.03 #> 197 15 58 1 15.3 100.7 155 2 -0.20 #> 198 15 48 1 15.3 103.5 143 2 -0.82 #> 199 15 54 1 15.5 97.9 159 2 0.57 #> 200 15 58 1 15.5 101.3 166 2 -0.18 #> 201 15 52 2 15.6 100.0 164 2 0.27 #> 202 15 50 1 15.7 99.6 153 2 0.36 #> 203 15 53 2 15.7 103.1 158 2 -0.32 #> 204 15 43 1 15.8 91.1 179 2 2.22 #> 205 15 57 1 15.9 103.8 161 2 -0.42 #> 206 15 55 2 16.3 95.4 169 2 1.65 #> 207 16 54 2 15.3 102.0 156 2 -0.37 #> 208 16 44 1 16.3 96.2 173 2 1.54 #> 209 16 47 1 16.3 102.0 166 2 0.27 #> 210 16 57 1 16.3 108.4 138 2 -1.13 #> 211 16 52 1 16.4 103.9 152 2 -0.07 #> 212 16 52 2 16.6 97.8 144 2 1.37 #> 213 16 56 1 16.6 103.9 148 2 0.07 #> 214 16 55 1 16.7 106.3 154 2 -0.39 #> 215 16 52 1 17.0 101.3 163 2 0.93 #> 216 16 50 1 17.3 101.8 168 2 1.02 #> 217 16 53 1 17.5 102.2 168 2 1.06 #> 218 16 42 1 17.7 100.9 145 2 1.48 #> 219 16 48 2 17.8 111.3 176 2 -0.77 #> 220 16 53 1 17.9 98.7 171 2 2.10 #> 221 16 50 1 18.1 106.4 166 2 0.53# Calculate weight-for-age (wfa) for the anthro3 dataset addWGSR(data = anthro3, sex = "sex", firstPart = "weight", secondPart = "age", index = "wfa")#> ================================================================================#> psu age sex weight height muac oedema wfaz #> 1 1 10 1 5.7 64.2 125 2 3.45 #> 2 1 10 2 5.8 64.4 121 2 3.95 #> 3 1 9 2 6.5 62.2 139 2 5.12 #> 4 1 11 9 6.5 64.9 129 2 NA #> 5 1 24 2 6.5 72.9 120 2 3.82 #> 6 1 12 2 6.6 69.4 126 2 5.01 #> 7 1 9 2 7.0 66.7 136 2 5.91 #> 8 1 7 1 7.1 63.5 139 2 5.84 #> 9 1 9 2 7.1 66.2 144 2 6.06 #> 10 1 16 2 7.2 69.0 131 2 5.53 #> 11 1 13 2 7.4 68.2 137 2 6.13 #> 12 1 11 2 7.4 70.0 132 2 6.33 #> 13 1 14 2 7.5 64.8 125 2 6.18 #> 14 2 8 2 7.5 65.7 151 2 6.79 #> 15 2 8 1 7.5 69.1 140 2 6.36 #> 16 2 13 1 7.6 69.2 131 2 6.02 #> 17 2 13 2 7.6 70.0 125 2 6.43 #> 18 2 22 2 7.6 76.2 121 2 5.52 #> 19 2 16 1 7.7 69.6 136 2 5.84 #> 20 2 8 2 7.8 63.3 144 2 7.26 #> 21 2 11 2 7.8 71.5 134 2 6.95 #> 22 2 14 2 7.8 76.6 124 2 6.63 #> 23 2 7 1 8.0 66.8 139 2 7.23 #> 24 2 8 1 8.0 69.3 147 2 7.13 #> 25 2 24 2 8.2 70.7 125 2 6.17 #> 26 2 10 1 8.2 71.1 145 2 7.23 #> 27 3 15 1 8.3 70.4 133 2 6.82 #> 28 3 14 2 8.3 71.5 137 2 7.38 #> 29 3 14 2 8.3 72.9 141 2 7.38 #> 30 3 17 2 8.3 73.5 136 2 7.04 #> 31 3 15 1 8.3 76.1 133 2 6.82 #> 32 3 13 2 8.4 71.8 143 2 7.64 #> 33 3 20 1 8.4 77.8 132 2 6.41 #> 34 3 29 2 8.5 75.6 127 2 6.12 #> 35 3 21 2 8.5 75.6 133 2 6.89 #> 36 3 21 1 8.5 76.7 123 2 6.44 #> 37 3 17 1 8.5 77.6 137 2 6.88 #> 38 3 15 2 8.6 74.9 134 2 7.71 #> 39 3 24 1 8.6 77.8 137 2 6.26 #> 40 3 12 1 8.7 72.1 156 2 7.76 #> 41 4 27 2 8.7 75.8 127 2 6.57 #> 42 4 25 2 8.7 80.8 129 2 6.76 #> 43 4 25 1 8.8 81.4 125 2 6.43 #> 44 4 24 2 8.9 74.3 142 2 7.14 #> 45 4 30 1 9.0 77.6 132 2 6.21 #> 46 4 22 1 9.0 82.2 135 2 7.02 #> 47 4 16 1 9.1 76.4 145 2 7.86 #> 48 4 24 1 9.1 80.0 126 2 6.94 #> 49 4 17 2 9.2 74.8 144 2 8.35 #> 50 4 15 2 9.2 75.3 145 2 8.60 #> 51 4 33 1 9.2 80.1 136 2 6.20 #> 52 4 25 2 9.2 80.9 142 2 7.45 #> 53 5 16 1 9.4 75.0 148 2 8.30 #> 54 5 29 2 9.4 81.9 138 2 7.32 #> 55 5 16 1 9.5 73.2 151 2 8.44 #> 56 5 17 1 9.6 73.5 146 2 8.46 #> 57 5 29 2 9.6 84.6 134 2 7.58 #> 58 5 16 1 9.7 74.2 148 2 8.73 #> 59 5 20 1 9.7 80.4 142 2 8.22 #> 60 5 34 1 9.8 80.2 138 2 6.88 #> 61 5 21 2 9.8 80.9 145 2 8.73 #> 62 5 20 1 9.8 82.2 139 2 8.36 #> 63 5 29 1 9.9 78.6 142 2 7.48 #> 64 5 25 2 9.9 82.9 138 2 8.41 #> 65 5 15 1 10.0 75.3 157 2 9.30 #> 66 6 39 1 10.0 81.4 133 2 6.71 #> 67 6 9 1 10.1 72.6 155 2 10.22 #> 68 6 21 1 10.1 76.7 153 2 8.66 #> 69 6 17 2 10.1 79.1 143 2 9.67 #> 70 6 25 1 10.1 79.5 140 2 8.18 #> 71 6 13 2 10.2 74.6 157 2 10.36 #> 72 6 25 2 10.3 81.3 141 2 8.95 #> 73 6 31 2 10.3 85.1 130 2 8.31 #> 74 6 25 2 10.4 81.3 138 2 9.09 #> 75 6 37 1 10.6 76.2 153 2 7.62 #> 76 6 25 2 10.6 80.7 138 2 9.37 #> 77 6 26 1 10.6 83.1 142 2 8.74 #> 78 6 36 2 10.6 84.2 135 2 8.22 #> 79 6 37 2 10.7 84.2 150 2 8.26 #> 80 7 34 2 10.7 85.7 146 2 8.54 #> 81 7 47 1 10.7 89.8 131 2 6.96 #> 82 7 12 1 10.8 82.0 147 2 10.88 #> 83 7 25 2 10.8 83.6 141 2 9.64 #> 84 7 31 1 10.8 83.8 158 2 8.45 #> 85 7 34 1 10.8 84.4 144 2 8.15 #> 86 7 27 2 10.9 79.0 153 2 9.54 #> 87 7 23 2 10.9 85.9 145 2 10.02 #> 88 7 19 1 11.0 75.4 168 2 10.18 #> 89 7 26 2 11.0 83.2 145 2 9.79 #> 90 7 54 1 11.0 85.9 139 2 6.85 #> 91 7 20 1 11.1 79.9 156 2 10.18 #> 92 7 23 1 11.1 84.7 146 2 9.78 #> 93 7 43 2 11.1 86.0 142 2 8.24 #> 94 8 31 1 11.1 87.1 143 2 8.84 #> 95 8 40 2 11.2 95.2 137 2 8.62 #> 96 8 18 2 11.3 75.5 161 2 11.27 #> 97 8 41 1 11.3 89.4 142 2 8.15 #> 98 8 40 1 11.3 90.7 148 2 8.23 #> 99 8 33 2 11.3 91.2 139 2 9.41 #> 100 8 11 1 11.4 78.4 171 2 11.92 #> 101 8 25 1 11.4 82.2 163 2 9.94 #> 102 8 27 2 11.4 86.8 155 2 10.22 #> 103 8 38 1 11.4 89.4 141 2 8.53 #> 104 8 17 2 11.5 80.6 158 2 11.71 #> 105 8 31 2 11.5 85.4 153 2 9.89 #> 106 9 45 1 11.5 86.2 143 2 8.07 #> 107 9 53 1 11.6 81.4 153 2 7.61 #> 108 9 23 2 11.6 86.3 143 2 11.00 #> 109 9 37 1 11.6 86.3 149 2 8.88 #> 110 9 38 1 11.6 89.9 146 2 8.78 #> 111 9 40 1 11.7 93.0 140 2 8.73 #> 112 9 36 1 11.8 87.1 146 2 9.22 #> 113 9 29 1 11.9 84.3 166 2 10.11 #> 114 9 26 2 12.0 81.2 164 2 11.15 #> 115 9 33 1 12.0 86.0 154 2 9.79 #> 116 9 36 1 12.0 87.5 150 2 9.48 #> 117 9 55 2 12.0 96.2 144 2 8.41 #> 118 10 16 1 12.1 82.3 157 2 12.19 #> 119 10 35 2 12.1 86.6 147 2 10.24 #> 120 10 43 1 12.1 91.7 151 2 8.96 #> 121 10 38 2 12.2 80.9 175 2 10.06 #> 122 10 37 1 12.2 85.5 162 2 9.63 #> 123 10 41 2 12.2 87.6 155 2 9.77 #> 124 10 45 2 12.2 90.1 149 2 9.42 #> 125 10 46 2 12.2 99.6 146 2 9.33 #> 126 10 31 1 12.3 88.0 167 2 10.40 #> 127 10 44 1 12.3 88.1 151 2 9.12 #> 128 10 47 2 12.3 94.9 133 2 9.37 #> 129 10 29 1 12.4 91.8 141 2 10.77 #> 130 10 46 2 12.4 99.6 140 2 9.57 #> 131 10 34 1 12.6 87.0 156 2 10.45 #> 132 10 38 2 12.7 85.7 152 2 10.69 #> 133 11 32 1 12.8 87.6 161 2 10.93 #> 134 11 36 1 12.8 90.4 150 2 10.49 #> 135 11 39 1 12.8 92.7 152 2 10.18 #> 136 11 42 1 12.8 94.0 146 2 9.90 #> 137 11 38 2 12.9 83.4 174 2 10.94 #> 138 11 26 2 12.9 86.2 159 2 12.38 #> 139 11 35 1 12.9 90.9 154 2 10.72 #> 140 11 41 1 12.9 91.2 160 2 10.12 #> 141 11 32 1 12.9 91.4 148 2 11.06 #> 142 11 49 1 13.0 93.1 152 2 9.55 #> 143 11 30 1 13.1 84.3 161 2 11.56 #> 144 11 32 1 13.1 85.1 136 2 11.32 #> 145 11 35 1 13.1 87.5 158 2 10.97 #> 146 11 34 1 13.1 88.6 162 2 11.08 #> 147 11 45 2 13.1 91.6 150 2 10.51 #> 148 12 29 1 13.2 90.8 158 2 11.82 #> 149 12 47 2 13.2 91.4 146 2 10.45 #> 150 12 37 2 13.2 93.8 155 2 11.42 #> 151 12 35 1 13.3 96.5 142 2 11.23 #> 152 12 15 2 13.4 82.4 157 2 14.84 #> 153 12 38 2 13.4 93.0 152 2 11.57 #> 154 12 37 2 13.4 94.1 149 2 11.68 #> 155 12 53 2 13.4 100.3 145 2 10.18 #> 156 12 37 2 13.5 84.4 168 2 11.80 #> 157 12 43 1 13.5 96.1 151 2 10.66 #> 158 12 52 1 13.5 97.0 144 2 9.91 #> 159 12 45 2 13.5 98.0 148 2 10.99 #> 160 12 39 2 13.5 99.1 145 2 11.59 #> 161 12 47 2 13.6 97.0 146 2 10.93 #> 162 13 26 2 13.7 85.7 171 2 13.47 #> 163 13 24 1 13.8 80.9 172 2 13.32 #> 164 13 38 2 13.8 93.3 153 2 12.07 #> 165 13 57 1 13.8 94.0 149 2 9.90 #> 166 13 42 2 13.8 103.4 144 2 11.65 #> 167 13 42 2 13.9 92.3 175 2 11.77 #> 168 13 52 1 13.9 95.5 151 2 10.38 #> 169 13 52 1 13.9 95.7 156 2 10.38 #> 170 13 57 2 13.9 97.2 153 2 10.45 #> 171 13 52 2 13.9 97.8 141 2 10.85 #> 172 13 52 2 13.9 97.9 155 2 10.85 #> 173 13 49 2 13.9 98.2 150 2 11.10 #> 174 13 58 2 14.0 96.6 154 2 10.49 #> 175 13 50 1 14.0 97.9 158 2 10.66 #> 176 13 39 2 14.1 93.7 155 2 12.34 #> 177 14 49 2 14.1 96.4 140 2 11.34 #> 178 14 50 2 14.1 97.1 148 2 11.25 #> 179 14 59 2 14.1 97.3 152 2 10.52 #> 180 14 52 2 14.1 102.4 156 2 11.08 #> 181 14 41 1 14.2 96.7 146 2 11.71 #> 182 14 53 1 14.2 97.7 151 2 10.66 #> 183 14 49 2 14.2 98.1 156 2 11.46 #> 184 14 35 2 14.3 89.8 165 2 13.05 #> 185 14 50 1 14.3 93.7 152 2 11.01 #> 186 14 41 1 14.4 94.8 158 2 11.96 #> 187 14 42 1 14.5 95.1 161 2 11.98 #> 188 14 58 1 14.5 104.9 130 2 10.64 #> 189 14 49 1 14.7 96.6 152 2 11.57 #> 190 14 52 2 14.7 96.7 154 2 11.78 #> 191 14 51 2 14.7 99.2 155 2 11.87 #> 192 14 49 1 14.7 99.6 164 2 11.57 #> 193 15 42 2 15.0 93.5 172 2 13.12 #> 194 15 28 2 15.1 91.5 164 2 15.06 #> 195 15 41 2 15.2 95.9 165 2 13.48 #> 196 15 53 1 15.3 95.0 166 2 11.94 #> 197 15 58 1 15.3 100.7 155 2 11.56 #> 198 15 48 1 15.3 103.5 143 2 12.37 #> 199 15 54 1 15.5 97.9 159 2 12.10 #> 200 15 58 1 15.5 101.3 166 2 11.79 #> 201 15 52 2 15.6 100.0 164 2 12.83 #> 202 15 50 1 15.7 99.6 153 2 12.67 #> 203 15 53 2 15.7 103.1 158 2 12.86 #> 204 15 43 1 15.8 91.1 179 2 13.47 #> 205 15 57 1 15.9 103.8 161 2 12.32 #> 206 15 55 2 16.3 95.4 169 2 13.37 #> 207 16 54 2 15.3 102.0 156 2 12.30 #> 208 16 44 1 16.3 96.2 173 2 13.97 #> 209 16 47 1 16.3 102.0 166 2 13.66 #> 210 16 57 1 16.3 108.4 138 2 12.78 #> 211 16 52 1 16.4 103.9 152 2 13.32 #> 212 16 52 2 16.6 97.8 144 2 14.00 #> 213 16 56 1 16.6 103.9 148 2 13.21 #> 214 16 55 1 16.7 106.3 154 2 13.41 #> 215 16 52 1 17.0 101.3 163 2 14.02 #> 216 16 50 1 17.3 101.8 168 2 14.56 #> 217 16 53 1 17.5 102.2 168 2 14.52 #> 218 16 42 1 17.7 100.9 145 2 15.90 #> 219 16 48 2 17.8 111.3 176 2 15.84 #> 220 16 53 1 17.9 98.7 171 2 14.98 #> 221 16 50 1 18.1 106.4 166 2 15.51# Calculate height-for-age (hfa) for the anthro3 dataset addWGSR(data = anthro3, sex = "sex", firstPart = "height", secondPart = "age", index = "hfa")#> ================================================================================#> psu age sex weight height muac oedema hfaz #> 1 1 10 1 5.7 64.2 125 2 6.58 #> 2 1 10 2 5.8 64.4 121 2 7.17 #> 3 1 9 2 6.5 62.2 139 2 6.11 #> 4 1 11 9 6.5 64.9 129 2 NA #> 5 1 24 2 6.5 72.9 120 2 10.35 #> 6 1 12 2 6.6 69.4 126 2 9.60 #> 7 1 9 2 7.0 66.7 136 2 8.49 #> 8 1 7 1 7.1 63.5 139 2 6.51 #> 9 1 9 2 7.1 66.2 144 2 8.22 #> 10 1 16 2 7.2 69.0 131 2 9.01 #> 11 1 13 2 7.4 68.2 137 2 8.87 #> 12 1 11 2 7.4 70.0 132 2 10.02 #> 13 1 14 2 7.5 64.8 125 2 6.98 #> 14 2 8 2 7.5 65.7 151 2 8.06 #> 15 2 8 1 7.5 69.1 140 2 9.35 #> 16 2 13 1 7.6 69.2 131 2 8.91 #> 17 2 13 2 7.6 70.0 125 2 9.82 #> 18 2 22 2 7.6 76.2 121 2 12.24 #> 19 2 16 1 7.7 69.6 136 2 8.85 #> 20 2 8 2 7.8 63.3 144 2 6.79 #> 21 2 11 2 7.8 71.5 134 2 10.82 #> 22 2 14 2 7.8 76.6 124 2 13.18 #> 23 2 7 1 8.0 66.8 139 2 8.24 #> 24 2 8 1 8.0 69.3 147 2 9.46 #> 25 2 24 2 8.2 70.7 125 2 9.22 #> 26 2 10 1 8.2 71.1 145 2 10.20 #> 27 3 15 1 8.3 70.4 133 2 9.36 #> 28 3 14 2 8.3 71.5 137 2 10.50 #> 29 3 14 2 8.3 72.9 141 2 11.24 #> 30 3 17 2 8.3 73.5 136 2 11.28 #> 31 3 15 1 8.3 76.1 133 2 12.33 #> 32 3 13 2 8.4 71.8 143 2 10.77 #> 33 3 20 1 8.4 77.8 132 2 12.76 #> 34 3 29 2 8.5 75.6 127 2 11.33 #> 35 3 21 2 8.5 75.6 133 2 12.02 #> 36 3 21 1 8.5 76.7 123 2 12.10 #> 37 3 17 1 8.5 77.6 137 2 12.93 #> 38 3 15 2 8.6 74.9 134 2 12.20 #> 39 3 24 1 8.6 77.8 137 2 12.40 #> 40 3 12 1 8.7 72.1 156 2 10.53 #> 41 4 27 2 8.7 75.8 127 2 11.60 #> 42 4 25 2 8.7 80.8 129 2 14.35 #> 43 4 25 1 8.8 81.4 125 2 14.18 #> 44 4 24 2 8.9 74.3 142 2 11.08 #> 45 4 30 1 9.0 77.6 132 2 11.79 #> 46 4 22 1 9.0 82.2 135 2 14.86 #> 47 4 16 1 9.1 76.4 145 2 12.40 #> 48 4 24 1 9.1 80.0 126 2 13.54 #> 49 4 17 2 9.2 74.8 144 2 11.96 #> 50 4 15 2 9.2 75.3 145 2 12.41 #> 51 4 33 1 9.2 80.1 136 2 12.82 #> 52 4 25 2 9.2 80.9 142 2 14.40 #> 53 5 16 1 9.4 75.0 148 2 11.67 #> 54 5 29 2 9.4 81.9 138 2 14.56 #> 55 5 16 1 9.5 73.2 151 2 10.73 #> 56 5 17 1 9.6 73.5 146 2 10.79 #> 57 5 29 2 9.6 84.6 134 2 15.95 #> 58 5 16 1 9.7 74.2 148 2 11.25 #> 59 5 20 1 9.7 80.4 142 2 14.11 #> 60 5 34 1 9.8 80.2 138 2 12.79 #> 61 5 21 2 9.8 80.9 145 2 14.77 #> 62 5 20 1 9.8 82.2 139 2 15.04 #> 63 5 29 1 9.9 78.6 142 2 12.38 #> 64 5 25 2 9.9 82.9 138 2 15.43 #> 65 5 15 1 10.0 75.3 157 2 11.92 #> 66 6 39 1 10.0 81.4 133 2 13.01 #> 67 6 9 1 10.1 72.6 155 2 11.09 #> 68 6 21 1 10.1 76.7 153 2 12.10 #> 69 6 17 2 10.1 79.1 143 2 14.21 #> 70 6 25 1 10.1 79.5 140 2 13.19 #> 71 6 13 2 10.2 74.6 157 2 12.24 #> 72 6 25 2 10.3 81.3 141 2 14.61 #> 73 6 31 2 10.3 85.1 130 2 16.03 #> 74 6 25 2 10.4 81.3 138 2 14.61 #> 75 6 37 1 10.6 76.2 153 2 10.51 #> 76 6 25 2 10.6 80.7 138 2 14.30 #> 77 6 26 1 10.6 83.1 142 2 14.96 #> 78 6 36 2 10.6 84.2 135 2 15.14 #> 79 6 37 2 10.7 84.2 150 2 15.06 #> 80 7 34 2 10.7 85.7 146 2 16.07 #> 81 7 47 1 10.7 89.8 131 2 16.66 #> 82 7 12 1 10.8 82.0 147 2 15.72 #> 83 7 25 2 10.8 83.6 141 2 15.80 #> 84 7 31 1 10.8 83.8 158 2 14.89 #> 85 7 34 1 10.8 84.4 144 2 14.94 #> 86 7 27 2 10.9 79.0 153 2 13.25 #> 87 7 23 2 10.9 85.9 145 2 17.17 #> 88 7 19 1 11.0 75.4 168 2 11.60 #> 89 7 26 2 11.0 83.2 145 2 15.50 #> 90 7 54 1 11.0 85.9 139 2 14.19 #> 91 7 20 1 11.1 79.9 156 2 13.85 #> 92 7 23 1 11.1 84.7 146 2 16.06 #> 93 7 43 2 11.1 86.0 142 2 15.49 #> 94 8 31 1 11.1 87.1 143 2 16.58 #> 95 8 40 2 11.2 95.2 137 2 20.37 #> 96 8 18 2 11.3 75.5 161 2 12.23 #> 97 8 41 1 11.3 89.4 142 2 16.92 #> 98 8 40 1 11.3 90.7 148 2 17.67 #> 99 8 33 2 11.3 91.2 139 2 18.96 #> 100 8 11 1 11.4 78.4 171 2 13.93 #> 101 8 25 1 11.4 82.2 163 2 14.59 #> 102 8 27 2 11.4 86.8 155 2 17.26 #> 103 8 38 1 11.4 89.4 141 2 17.16 #> 104 8 17 2 11.5 80.6 158 2 14.99 #> 105 8 31 2 11.5 85.4 153 2 16.18 #> 106 9 45 1 11.5 86.2 143 2 14.99 #> 107 9 53 1 11.6 81.4 153 2 12.00 #> 108 9 23 2 11.6 86.3 143 2 17.38 #> 109 9 37 1 11.6 86.3 149 2 15.67 #> 110 9 38 1 11.6 89.9 146 2 17.42 #> 111 9 40 1 11.7 93.0 140 2 18.84 #> 112 9 36 1 11.8 87.1 146 2 16.16 #> 113 9 29 1 11.9 84.3 166 2 15.32 #> 114 9 26 2 12.0 81.2 164 2 14.47 #> 115 9 33 1 12.0 86.0 154 2 15.85 #> 116 9 36 1 12.0 87.5 150 2 16.36 #> 117 9 55 2 12.0 96.2 144 2 19.65 #> 118 10 16 1 12.1 82.3 157 2 15.48 #> 119 10 35 2 12.1 86.6 147 2 16.44 #> 120 10 43 1 12.1 91.7 151 2 17.94 #> 121 10 38 2 12.2 80.9 175 2 13.31 #> 122 10 37 1 12.2 85.5 162 2 15.26 #> 123 10 41 2 12.2 87.6 155 2 16.45 #> 124 10 45 2 12.2 90.1 149 2 17.39 #> 125 10 46 2 12.2 99.6 146 2 22.06 #> 126 10 31 1 12.3 88.0 167 2 17.05 #> 127 10 44 1 12.3 88.1 151 2 16.03 #> 128 10 47 2 12.3 94.9 133 2 19.63 #> 129 10 29 1 12.4 91.8 141 2 19.17 #> 130 10 46 2 12.4 99.6 140 2 22.06 #> 131 10 34 1 12.6 87.0 156 2 16.27 #> 132 10 38 2 12.7 85.7 152 2 15.74 #> 133 11 32 1 12.8 87.6 161 2 16.75 #> 134 11 36 1 12.8 90.4 150 2 17.84 #> 135 11 39 1 12.8 92.7 152 2 18.77 #> 136 11 42 1 12.8 94.0 146 2 19.18 #> 137 11 38 2 12.9 83.4 174 2 14.57 #> 138 11 26 2 12.9 86.2 159 2 17.04 #> 139 11 35 1 12.9 90.9 154 2 18.18 #> 140 11 41 1 12.9 91.2 160 2 17.84 #> 141 11 32 1 12.9 91.4 148 2 18.70 #> 142 11 49 1 13.0 93.1 152 2 18.18 #> 143 11 30 1 13.1 84.3 161 2 15.23 #> 144 11 32 1 13.1 85.1 136 2 15.47 #> 145 11 35 1 13.1 87.5 158 2 16.45 #> 146 11 34 1 13.1 88.6 162 2 17.09 #> 147 11 45 2 13.1 91.6 150 2 18.14 #> 148 12 29 1 13.2 90.8 158 2 18.66 #> 149 12 47 2 13.2 91.4 146 2 17.88 #> 150 12 37 2 13.2 93.8 155 2 19.92 #> 151 12 35 1 13.3 96.5 142 2 21.05 #> 152 12 15 2 13.4 82.4 157 2 16.13 #> 153 12 38 2 13.4 93.0 152 2 19.43 #> 154 12 37 2 13.4 94.1 149 2 20.07 #> 155 12 53 2 13.4 100.3 145 2 21.84 #> 156 12 37 2 13.5 84.4 168 2 15.16 #> 157 12 43 1 13.5 96.1 151 2 20.17 #> 158 12 52 1 13.5 97.0 144 2 19.92 #> 159 12 45 2 13.5 98.0 148 2 21.35 #> 160 12 39 2 13.5 99.1 145 2 22.42 #> 161 12 47 2 13.6 97.0 146 2 20.68 #> 162 13 26 2 13.7 85.7 171 2 16.79 #> 163 13 24 1 13.8 80.9 172 2 14.00 #> 164 13 38 2 13.8 93.3 153 2 19.58 #> 165 13 57 1 13.8 94.0 149 2 18.05 #> 166 13 42 2 13.8 103.4 144 2 24.32 #> 167 13 42 2 13.9 92.3 175 2 18.74 #> 168 13 52 1 13.9 95.5 151 2 19.17 #> 169 13 52 1 13.9 95.7 156 2 19.27 #> 170 13 57 2 13.9 97.2 153 2 19.99 #> 171 13 52 2 13.9 97.8 141 2 20.68 #> 172 13 52 2 13.9 97.9 155 2 20.73 #> 173 13 49 2 13.9 98.2 150 2 21.11 #> 174 13 58 2 14.0 96.6 154 2 19.62 #> 175 13 50 1 14.0 97.9 158 2 20.53 #> 176 13 39 2 14.1 93.7 155 2 19.70 #> 177 14 49 2 14.1 96.4 140 2 20.22 #> 178 14 50 2 14.1 97.1 148 2 20.48 #> 179 14 59 2 14.1 97.3 152 2 19.89 #> 180 14 52 2 14.1 102.4 156 2 22.96 #> 181 14 41 1 14.2 96.7 146 2 20.63 #> 182 14 53 1 14.2 97.7 151 2 20.20 #> 183 14 49 2 14.2 98.1 156 2 21.06 #> 184 14 35 2 14.3 89.8 165 2 18.07 #> 185 14 50 1 14.3 93.7 152 2 18.41 #> 186 14 41 1 14.4 94.8 158 2 19.67 #> 187 14 42 1 14.5 95.1 161 2 19.74 #> 188 14 58 1 14.5 104.9 130 2 23.45 #> 189 14 49 1 14.7 96.6 152 2 19.95 #> 190 14 52 2 14.7 96.7 154 2 20.13 #> 191 14 51 2 14.7 99.2 155 2 21.45 #> 192 14 49 1 14.7 99.6 164 2 21.46 #> 193 15 42 2 15.0 93.5 172 2 19.34 #> 194 15 28 2 15.1 91.5 164 2 19.58 #> 195 15 41 2 15.2 95.9 165 2 20.63 #> 196 15 53 1 15.3 95.0 166 2 18.84 #> 197 15 58 1 15.3 100.7 155 2 21.34 #> 198 15 48 1 15.3 103.5 143 2 23.51 #> 199 15 54 1 15.5 97.9 159 2 20.23 #> 200 15 58 1 15.5 101.3 166 2 21.64 #> 201 15 52 2 15.6 100.0 164 2 21.77 #> 202 15 50 1 15.7 99.6 153 2 21.38 #> 203 15 53 2 15.7 103.1 158 2 23.22 #> 204 15 43 1 15.8 91.1 179 2 17.63 #> 205 15 57 1 15.9 103.8 161 2 22.97 #> 206 15 55 2 16.3 95.4 169 2 19.25 #> 207 16 54 2 15.3 102.0 156 2 22.60 #> 208 16 44 1 16.3 96.2 173 2 20.14 #> 209 16 47 1 16.3 102.0 166 2 22.83 #> 210 16 57 1 16.3 108.4 138 2 25.28 #> 211 16 52 1 16.4 103.9 152 2 23.40 #> 212 16 52 2 16.6 97.8 144 2 20.68 #> 213 16 56 1 16.6 103.9 148 2 23.09 #> 214 16 55 1 16.7 106.3 154 2 24.37 #> 215 16 52 1 17.0 101.3 163 2 22.09 #> 216 16 50 1 17.3 101.8 168 2 22.49 #> 217 16 53 1 17.5 102.2 168 2 22.46 #> 218 16 42 1 17.7 100.9 145 2 22.69 #> 219 16 48 2 17.8 111.3 176 2 27.73 #> 220 16 53 1 17.9 98.7 171 2 20.70 #> 221 16 50 1 18.1 106.4 166 2 24.81# Calculate MUAC-for-age (mfa) for the anthro4 dataset ## Convert age in anthro4 from months to days testData <- anthro4 testData$age <- testData$agemons * (365.25 / 12) addWGSR(data = testData, sex = "sex", firstPart = "muac", secondPart = "age", index = "mfa")#> ================================================================================#> pk_serial muac agemons sex age mfaz #> 1 76220 16.0 73.23203 2 2229 -0.88 #> 2 76290 18.4 93.63450 1 2850 0.07 #> 3 76310 14.4 75.69610 1 2304 -2.20 #> 4 76460 15.4 87.16222 2 2653 -1.63 #> 5 76570 18.0 139.69611 1 4252 -1.76 #> 6 76580 18.0 119.81931 2 3647 -1.04 #> 7 76620 14.6 126.52156 2 3851 -3.39 #> 8 76840 12.6 91.95893 1 2799 -4.72 #> 9 76860 14.5 74.54620 1 2269 -2.06 #> 10 77060 16.0 115.84394 1 3526 -2.28 #> 11 77250 16.5 82.89117 1 2523 -0.74 #> 12 77350 13.4 62.12731 2 1891 -2.63 #> 13 77510 13.5 64.82136 2 1973 -2.60 #> 14 77570 14.9 82.82546 2 2521 -1.88 #> 15 77760 16.0 118.14374 1 3596 -2.36 #> 16 77860 11.5 103.55647 2 3152 -4.94 #> 17 77870 13.6 76.68173 1 2334 -3.06 #> 18 77990 14.0 70.63655 2 2150 -2.30 #> 19 78040 19.0 136.24641 2 4147 -1.10 #> 20 78210 17.2 135.75359 1 4132 -2.12 #> 21 78240 17.0 72.34497 2 2202 -0.23 #> 22 78340 14.7 84.17248 2 2562 -2.06 #> 23 78370 17.0 96.19713 1 2928 -0.86 #> 24 78410 17.0 155.95894 1 4747 -3.21 #> 25 78570 17.5 115.41684 1 3513 -1.15 #> 26 78780 15.3 122.48049 1 3728 -3.14 #> 27 78800 16.0 72.31212 2 2201 -0.86 #> 28 79090 17.6 137.95482 2 4199 -1.89 #> 29 79290 19.0 139.00616 1 4231 -1.15 #> 30 79300 17.0 124.64887 1 3794 -1.82 #> 31 79500 15.0 133.22382 1 4055 -3.73 #> 32 79540 13.7 60.35318 1 1837 -2.26 #> 33 79570 16.7 73.10062 1 2225 -0.25 #> 34 79610 16.0 63.57290 1 1935 -0.46 #> 35 79760 13.5 77.04312 1 2345 -3.18 #> 36 79770 14.5 84.04107 1 2558 -2.48 #> 37 79840 17.6 91.99178 1 2800 -0.34 #> 38 79850 13.0 78.88296 2 2401 -3.33 #> 39 79860 17.0 70.86653 2 2157 -0.19 #> 40 79910 17.0 71.03080 1 2162 0.02 #> 41 79970 15.6 69.61807 1 2119 -0.94 #> 42 80010 16.0 78.39014 1 2386 -0.94 #> 43 80200 16.0 86.37372 1 2629 -1.25 #> 44 80290 16.0 134.37372 1 4090 -3.00 #> 45 80380 17.0 87.55647 1 2665 -0.57 #> 46 80490 15.0 66.03696 2 2010 -1.41 #> 47 80610 17.1 103.29363 2 3144 -1.01 #> 48 80630 14.0 93.33881 2 2841 -2.83 #> 49 80640 15.4 96.65708 2 2942 -1.87 #> 50 80660 16.0 70.43942 2 2144 -0.81 #> 51 80680 15.2 109.73306 2 3340 -2.39 #> 52 80940 17.5 66.62833 2 2028 0.23 #> 53 81060 16.0 126.45585 1 3849 -2.67 #> 54 81080 14.0 71.45791 1 2175 -2.40 #> 55 81100 15.3 87.35934 1 2659 -1.87 #> 56 81260 18.1 142.75154 1 4345 -1.83 #> 57 81540 15.5 89.59343 2 2727 -1.62 #> 58 81590 15.6 77.73306 1 2366 -1.23 #> 59 81630 17.9 126.68583 1 3856 -1.29 #> 60 81880 15.5 116.89528 1 3558 -2.76 #> 61 81890 17.0 117.65092 1 3581 -1.57 #> 62 81900 15.5 69.58521 1 2118 -1.01 #> 63 82010 13.0 64.13142 1 1952 -3.14 #> 64 82080 15.0 132.13963 2 4022 -3.33 #> 65 82190 14.0 82.46407 2 2510 -2.57 #> 66 82320 16.0 93.27310 2 2839 -1.39 #> 67 82370 15.2 99.05544 2 3015 -2.07 #> 68 82560 16.3 154.54620 2 4704 -3.44 #> 69 82670 15.2 77.40452 2 2356 -1.53 #> 70 82690 15.9 68.53388 1 2086 -0.67 #> 71 83720 13.6 60.15606 2 1831 -2.34 #> 72 84080 15.3 65.77412 2 2002 -1.18 #> 73 84220 12.6 100.23819 1 3051 -4.98 #> 74 84430 16.8 63.47433 1 1932 0.11 #> 75 84750 15.0 72.08214 1 2194 -1.51 #> 76 84990 14.0 61.79877 1 1881 -2.06 #> 77 85130 16.0 82.92403 1 2524 -1.12 #> 78 85170 16.0 80.65708 2 2455 -1.06 #> 79 86090 16.6 124.55031 1 3791 -2.11 #> 80 86300 16.3 70.37372 1 2142 -0.44 #> 81 86400 16.4 120.08214 1 3655 -2.10 #> 82 86460 15.0 95.44147 2 2905 -2.12 #> 83 86720 15.0 80.68994 1 2456 -1.86 #> 84 86970 14.6 73.75770 1 2245 -1.93 #> 85 87050 15.1 109.93018 2 3346 -2.46 #> 86 87140 15.0 110.81725 1 3373 -3.03 #> 87 87260 17.0 92.28748 1 2809 -0.73 #> 88 87450 17.2 90.67762 2 2760 -0.60 #> 89 87460 12.8 62.22587 1 1894 -3.28 #> 90 88360 17.5 91.43327 1 2783 -0.38 #> 91 88520 15.2 101.58521 1 3092 -2.50 #> 92 88690 14.3 61.53593 1 1873 -1.77 #> 93 88990 15.5 83.54826 1 2543 -1.54 #> 94 89090 17.0 112.95277 2 3438 -1.35 #> 95 89410 12.3 100.27105 1 3052 -5.27 #> 96 89710 15.0 74.97330 2 2282 -1.62 #> 97 89900 19.0 96.45996 1 2936 0.29 #> 98 90730 16.0 65.67557 1 1999 -0.51 #> 99 90740 13.0 68.36961 2 2081 -3.12 #> 100 90850 16.0 118.20944 2 3598 -2.12 #> 101 91280 16.7 115.64682 1 3520 -1.71 #> 102 91360 15.5 110.12731 1 3352 -2.52 #> 103 91610 14.7 109.73306 1 3340 -3.27 #> 104 91740 16.0 124.61602 1 3793 -2.60 #> 105 91830 12.3 63.01437 2 1918 -3.62 #> 106 91980 15.0 94.35729 2 2872 -2.09 #> 107 92360 16.6 105.75770 1 3219 -1.46 #> 108 92730 17.0 121.46201 1 3697 -1.70 #> 109 94170 16.0 83.15401 2 2531 -1.13 #> 110 94280 12.0 82.46407 1 2510 -4.93 #> 111 94760 14.0 83.87679 2 2553 -2.60 #> 112 95390 17.0 110.06160 1 3350 -1.31 #> 113 95440 16.5 67.18686 1 2045 -0.20 #> 114 95490 16.0 73.23203 2 2229 -0.88 #> 115 95530 15.7 64.52567 2 1964 -0.87 #> 116 95910 15.0 75.79466 1 2307 -1.66 #> 117 95970 18.5 128.91992 1 3924 -1.02 #> 118 96150 16.0 104.47639 2 3180 -1.70 #> 119 96400 15.0 87.95072 1 2677 -2.16 #> 120 96490 16.0 64.59138 2 1966 -0.66 #> 121 96730 19.6 149.81520 2 4560 -1.31 #> 122 96870 15.5 98.03696 1 2984 -2.10 #> 123 96970 14.2 68.27105 1 2078 -2.09 #> 124 97070 16.0 60.68173 1 1847 -0.39 #> 125 97080 17.0 108.68172 1 3308 -1.26 #> 126 97190 15.0 65.54415 1 1995 -1.29 #> 127 97450 16.5 117.88091 1 3588 -1.94 #> 128 97740 14.0 68.89528 1 2097 -2.30 #> 129 97760 18.5 83.81109 2 2551 0.28 #> 130 98160 17.8 141.37166 2 4303 -1.92 #> 131 98190 14.0 71.88501 2 2188 -2.33 #> 132 98360 14.5 72.47639 1 2206 -1.97 #> 133 98420 14.2 75.17043 1 2288 -2.37 #> 134 98640 18.0 66.52978 1 2025 0.78 #> 135 98720 13.7 82.82546 2 2521 -2.83 #> 136 98830 14.7 80.49281 2 2450 -1.97 #> 137 98900 16.0 61.79877 1 1881 -0.41 #> 138 98980 14.8 63.67146 1 1938 -1.41 #> 139 99410 15.0 69.09240 2 2103 -1.48 #> 140 99440 16.6 97.93840 2 2981 -1.14 #> 141 99610 15.8 98.46407 1 2997 -1.85 #> 142 99670 13.2 73.33060 1 2232 -3.31 #> 143 99780 17.0 93.17454 2 2836 -0.78 #> 144 99800 15.5 63.70431 1 1939 -0.84 #> 145 99920 17.5 147.31827 2 4484 -2.35 #> 146 100050 17.5 86.30801 1 2627 -0.20 #> 147 100060 14.0 70.63655 2 2150 -2.30 #> 148 100100 16.2 105.52772 1 3212 -1.76 #> 149 100120 15.4 101.71663 2 3096 -2.01 #> 150 100440 16.0 109.20739 2 3324 -1.84 #> 151 100460 15.0 110.91581 2 3376 -2.57 #> 152 100610 15.0 94.32443 1 2871 -2.42 #> 153 100820 19.0 104.80493 1 3190 0.04 #> 154 101430 15.0 78.62013 2 2393 -1.71 #> 155 101460 13.2 131.48254 2 4002 -4.49 #> 156 101710 16.7 129.77412 1 3950 -2.24 #> 157 101780 17.2 133.35524 2 4059 -1.94 #> 158 101790 15.0 98.36550 2 2994 -2.20 #> 159 101850 16.6 108.05750 2 3289 -1.43 #> 160 101920 16.7 107.63039 1 3276 -1.44 #> 161 101970 15.2 64.39425 1 1960 -1.10 #> 162 102220 19.6 143.14580 1 4357 -1.01 #> 163 102380 16.2 123.89323 1 3771 -2.41 #> 164 102390 15.2 113.21561 1 3446 -2.92 #> 165 102550 14.0 86.43942 1 2631 -3.11 #> 166 102630 13.0 67.38398 1 2051 -3.25 #> 167 102930 17.5 135.62218 1 4128 -1.91 #> 168 103060 19.0 149.02669 1 4536 -1.59 #> 169 103090 14.5 65.41273 2 1991 -1.78 #> 170 103300 16.0 113.41273 1 3452 -2.19 #> 171 103580 13.8 61.17454 2 1862 -2.21 #> 172 103590 16.0 101.94661 2 3103 -1.62 #> 173 103610 16.4 152.47638 2 4641 -3.29 #> 174 104070 14.0 66.69405 2 2030 -2.21 #> 175 104190 17.5 133.55237 2 4065 -1.78 #> 176 104360 18.0 107.23614 2 3264 -0.65 #> 177 104600 15.7 61.47023 2 1871 -0.80 #> 178 104710 16.0 62.32444 1 1897 -0.43 #> 179 104980 16.0 123.66325 2 3764 -2.31 #> 180 105360 16.5 113.24846 1 3447 -1.78 #> 181 105470 13.5 108.41889 1 3300 -4.34 #> 182 105870 15.0 69.81519 1 2125 -1.43 #> 183 105880 17.0 104.31212 2 3175 -1.09 #> 184 106400 14.0 67.84394 1 2065 -2.26 #> 185 106530 16.0 129.08418 2 3929 -2.52 #> 186 106640 13.0 61.83162 1 1882 -3.05 #> 187 106700 17.0 65.37987 2 1990 -0.04 #> 188 106950 14.5 101.25668 2 3082 -2.65 #> 189 107000 16.8 83.31827 1 2536 -0.55 #> 190 107610 14.5 65.21561 1 1985 -1.71 #> 191 107780 14.0 71.12936 2 2165 -2.31 #> 192 107810 18.3 133.61807 1 4067 -1.32 #> 193 107890 15.0 97.08419 1 2955 -2.53 #> 194 108100 15.8 85.12526 2 2591 -1.31 #> 195 108220 14.5 88.27926 2 2687 -2.31 #> 196 108310 13.0 83.05544 2 2528 -3.41 #> 197 108680 16.3 118.24230 1 3599 -2.12 #> 198 108810 17.0 123.17043 2 3749 -1.68 #> 199 109160 12.9 66.26694 1 2017 -3.31 #> 200 109180 22.0 119.06365 1 3624 0.83 #> 201 109190 13.4 61.96304 1 1886 -2.66 #> 202 109230 16.0 73.33060 2 2232 -0.88 #> 203 109280 17.0 150.14374 2 4570 -2.80 #> 204 109440 17.0 64.19713 1 1954 0.22 #> 205 109460 14.1 94.39014 2 2873 -2.78 #> 206 109480 16.2 91.53183 2 2786 -1.21 #> 207 109510 14.5 94.02875 1 2862 -2.92 #> 208 110100 13.8 101.55236 2 3091 -3.20 #> 209 110170 16.0 61.14169 1 1861 -0.40 #> 210 110190 15.5 82.72690 1 2518 -1.51 #> 211 110210 14.0 66.75975 2 2032 -2.22 #> 212 110270 17.5 125.99590 2 3835 -1.50 #> 213 110350 15.4 115.81109 2 3525 -2.44 #> 214 110370 14.0 70.37372 1 2142 -2.36 #> 215 110450 16.0 94.29158 1 2870 -1.54 #> 216 110460 15.4 66.36550 1 2020 -0.99 #> 217 110500 11.5 80.36140 2 2446 -4.59 #> 218 110610 16.0 60.61602 1 1845 -0.39 #> 219 110660 15.0 90.18481 1 2745 -2.25 #> 220 110680 14.0 79.90144 2 2432 -2.51 #> 221 110790 15.0 79.93429 1 2433 -1.83 #> 222 110840 16.5 94.25873 2 2869 -1.10 #> 223 110860 15.3 76.02464 2 2314 -1.43 #> 224 110920 17.1 112.06571 1 3411 -1.31 #> 225 110950 15.5 90.84189 1 2765 -1.83 #> 226 111120 16.0 83.41684 1 2539 -1.13 #> 227 111210 15.0 64.00000 1 1948 -1.25 #> 228 111500 18.0 135.12936 1 4113 -1.56 #> 229 111530 15.0 70.04517 2 2132 -1.51 #> 230 111790 17.9 98.85832 1 3009 -0.38 #> 231 112610 16.0 129.31416 2 3936 -2.53 #> 232 112820 16.5 106.71047 1 3248 -1.56 #> 233 112830 15.5 97.74127 2 2975 -1.83 #> 234 112950 16.0 64.85421 1 1974 -0.49 #> 235 112960 16.0 60.05750 1 1828 -0.37 #> 236 113170 12.7 64.68994 2 1969 -3.31 #> 237 113300 14.5 69.45380 1 2114 -1.85 #> 238 113360 16.5 91.00616 1 2770 -1.04 #> 239 113860 16.0 71.72074 1 2183 -0.70 #> 240 113920 14.0 83.84394 1 2552 -2.99 #> 241 114000 18.0 121.95483 1 3712 -1.06 #> 242 114210 16.4 66.29980 2 2018 -0.44 #> 243 114220 16.0 80.22998 1 2442 -1.01 #> 244 114730 17.2 104.60780 1 3184 -1.00 #> 245 114770 14.0 74.54620 1 2269 -2.54 #> 246 114880 16.0 70.76797 2 2154 -0.82 #> 247 115110 12.0 89.06776 2 2711 -4.32 #> 248 115220 14.5 98.20123 2 2989 -2.56 #> 249 115340 16.2 140.45175 2 4275 -2.88 #> 250 115600 19.0 97.64271 2 2972 0.11 #> 251 115690 14.5 66.82546 1 2034 -1.76 #> 252 115830 13.5 73.49487 1 2237 -3.01 #> 253 115920 14.0 66.23408 1 2016 -2.20 #> 254 115930 15.0 105.06776 2 3198 -2.39 #> 255 115990 20.5 130.33264 1 3967 -0.09 #> 256 116310 14.6 89.16632 2 2714 -2.25 #> 257 116320 18.0 120.57495 2 3670 -1.06