Use help to examine the coding scheme for the mother’s race variable in the birthwt{MASS} dataset. The MASS comes with the base R installation but is not automatically loaded when R is invoked.
c(“White”, “Black”, “Other”)[birthwt$race]
library(MASS)
MASS::birthwt
#> low age lwt race smoke ptl ht ui ftv bwt
#> 85 0 19 182 2 0 0 0 1 0 2523
#> 86 0 33 155 3 0 0 0 0 3 2551
#> 87 0 20 105 1 1 0 0 0 1 2557
#> 88 0 21 108 1 1 0 0 1 2 2594
#> 89 0 18 107 1 1 0 0 1 0 2600
#> 91 0 21 124 3 0 0 0 0 0 2622
#> 92 0 22 118 1 0 0 0 0 1 2637
#> 93 0 17 103 3 0 0 0 0 1 2637
#> 94 0 29 123 1 1 0 0 0 1 2663
#> 95 0 26 113 1 1 0 0 0 0 2665
#> 96 0 19 95 3 0 0 0 0 0 2722
#> 97 0 19 150 3 0 0 0 0 1 2733
#> 98 0 22 95 3 0 0 1 0 0 2751
#> 99 0 30 107 3 0 1 0 1 2 2750
#> 100 0 18 100 1 1 0 0 0 0 2769
#> 101 0 18 100 1 1 0 0 0 0 2769
#> 102 0 15 98 2 0 0 0 0 0 2778
#> 103 0 25 118 1 1 0 0 0 3 2782
#> 104 0 20 120 3 0 0 0 1 0 2807
#> 105 0 28 120 1 1 0 0 0 1 2821
#> 106 0 32 121 3 0 0 0 0 2 2835
#> 107 0 31 100 1 0 0 0 1 3 2835
#> 108 0 36 202 1 0 0 0 0 1 2836
#> 109 0 28 120 3 0 0 0 0 0 2863
#> 111 0 25 120 3 0 0 0 1 2 2877
#> 112 0 28 167 1 0 0 0 0 0 2877
#> 113 0 17 122 1 1 0 0 0 0 2906
#> 114 0 29 150 1 0 0 0 0 2 2920
#> 115 0 26 168 2 1 0 0 0 0 2920
#> 116 0 17 113 2 0 0 0 0 1 2920
#> 117 0 17 113 2 0 0 0 0 1 2920
#> 118 0 24 90 1 1 1 0 0 1 2948
#> 119 0 35 121 2 1 1 0 0 1 2948
#> 120 0 25 155 1 0 0 0 0 1 2977
#> 121 0 25 125 2 0 0 0 0 0 2977
#> 123 0 29 140 1 1 0 0 0 2 2977
#> 124 0 19 138 1 1 0 0 0 2 2977
#> 125 0 27 124 1 1 0 0 0 0 2922
#> 126 0 31 215 1 1 0 0 0 2 3005
#> 127 0 33 109 1 1 0 0 0 1 3033
#> 128 0 21 185 2 1 0 0 0 2 3042
#> 129 0 19 189 1 0 0 0 0 2 3062
#> 130 0 23 130 2 0 0 0 0 1 3062
#> 131 0 21 160 1 0 0 0 0 0 3062
#> 132 0 18 90 1 1 0 0 1 0 3062
#> 133 0 18 90 1 1 0 0 1 0 3062
#> 134 0 32 132 1 0 0 0 0 4 3080
#> 135 0 19 132 3 0 0 0 0 0 3090
#> 136 0 24 115 1 0 0 0 0 2 3090
#> 137 0 22 85 3 1 0 0 0 0 3090
#> 138 0 22 120 1 0 0 1 0 1 3100
#> 139 0 23 128 3 0 0 0 0 0 3104
#> 140 0 22 130 1 1 0 0 0 0 3132
#> 141 0 30 95 1 1 0 0 0 2 3147
#> 142 0 19 115 3 0 0 0 0 0 3175
#> 143 0 16 110 3 0 0 0 0 0 3175
#> 144 0 21 110 3 1 0 0 1 0 3203
#> 145 0 30 153 3 0 0 0 0 0 3203
#> 146 0 20 103 3 0 0 0 0 0 3203
#> 147 0 17 119 3 0 0 0 0 0 3225
#> 148 0 17 119 3 0 0 0 0 0 3225
#> 149 0 23 119 3 0 0 0 0 2 3232
#> 150 0 24 110 3 0 0 0 0 0 3232
#> 151 0 28 140 1 0 0 0 0 0 3234
#> 154 0 26 133 3 1 2 0 0 0 3260
#> 155 0 20 169 3 0 1 0 1 1 3274
#> 156 0 24 115 3 0 0 0 0 2 3274
#> 159 0 28 250 3 1 0 0 0 6 3303
#> 160 0 20 141 1 0 2 0 1 1 3317
#> 161 0 22 158 2 0 1 0 0 2 3317
#> 162 0 22 112 1 1 2 0 0 0 3317
#> 163 0 31 150 3 1 0 0 0 2 3321
#> 164 0 23 115 3 1 0 0 0 1 3331
#> 166 0 16 112 2 0 0 0 0 0 3374
#> 167 0 16 135 1 1 0 0 0 0 3374
#> 168 0 18 229 2 0 0 0 0 0 3402
#> 169 0 25 140 1 0 0 0 0 1 3416
#> 170 0 32 134 1 1 1 0 0 4 3430
#> 172 0 20 121 2 1 0 0 0 0 3444
#> 173 0 23 190 1 0 0 0 0 0 3459
#> 174 0 22 131 1 0 0 0 0 1 3460
#> 175 0 32 170 1 0 0 0 0 0 3473
#> 176 0 30 110 3 0 0 0 0 0 3544
#> 177 0 20 127 3 0 0 0 0 0 3487
#> 179 0 23 123 3 0 0 0 0 0 3544
#> 180 0 17 120 3 1 0 0 0 0 3572
#> 181 0 19 105 3 0 0 0 0 0 3572
#> 182 0 23 130 1 0 0 0 0 0 3586
#> 183 0 36 175 1 0 0 0 0 0 3600
#> 184 0 22 125 1 0 0 0 0 1 3614
#> 185 0 24 133 1 0 0 0 0 0 3614
#> 186 0 21 134 3 0 0 0 0 2 3629
#> 187 0 19 235 1 1 0 1 0 0 3629
#> 188 0 25 95 1 1 3 0 1 0 3637
#> 189 0 16 135 1 1 0 0 0 0 3643
#> 190 0 29 135 1 0 0 0 0 1 3651
#> 191 0 29 154 1 0 0 0 0 1 3651
#> 192 0 19 147 1 1 0 0 0 0 3651
#> 193 0 19 147 1 1 0 0 0 0 3651
#> 195 0 30 137 1 0 0 0 0 1 3699
#> 196 0 24 110 1 0 0 0 0 1 3728
#> 197 0 19 184 1 1 0 1 0 0 3756
#> 199 0 24 110 3 0 1 0 0 0 3770
#> 200 0 23 110 1 0 0 0 0 1 3770
#> 201 0 20 120 3 0 0 0 0 0 3770
#> 202 0 25 241 2 0 0 1 0 0 3790
#> 203 0 30 112 1 0 0 0 0 1 3799
#> 204 0 22 169 1 0 0 0 0 0 3827
#> 205 0 18 120 1 1 0 0 0 2 3856
#> 206 0 16 170 2 0 0 0 0 4 3860
#> 207 0 32 186 1 0 0 0 0 2 3860
#> 208 0 18 120 3 0 0 0 0 1 3884
#> 209 0 29 130 1 1 0 0 0 2 3884
#> 210 0 33 117 1 0 0 0 1 1 3912
#> 211 0 20 170 1 1 0 0 0 0 3940
#> 212 0 28 134 3 0 0 0 0 1 3941
#> 213 0 14 135 1 0 0 0 0 0 3941
#> 214 0 28 130 3 0 0 0 0 0 3969
#> 215 0 25 120 1 0 0 0 0 2 3983
#> 216 0 16 95 3 0 0 0 0 1 3997
#> 217 0 20 158 1 0 0 0 0 1 3997
#> 218 0 26 160 3 0 0 0 0 0 4054
#> 219 0 21 115 1 0 0 0 0 1 4054
#> 220 0 22 129 1 0 0 0 0 0 4111
#> 221 0 25 130 1 0 0 0 0 2 4153
#> 222 0 31 120 1 0 0 0 0 2 4167
#> 223 0 35 170 1 0 1 0 0 1 4174
#> 224 0 19 120 1 1 0 0 0 0 4238
#> 225 0 24 116 1 0 0 0 0 1 4593
#> 226 0 45 123 1 0 0 0 0 1 4990
#> 4 1 28 120 3 1 1 0 1 0 709
#> 10 1 29 130 1 0 0 0 1 2 1021
#> 11 1 34 187 2 1 0 1 0 0 1135
#> 13 1 25 105 3 0 1 1 0 0 1330
#> 15 1 25 85 3 0 0 0 1 0 1474
#> 16 1 27 150 3 0 0 0 0 0 1588
#> 17 1 23 97 3 0 0 0 1 1 1588
#> 18 1 24 128 2 0 1 0 0 1 1701
#> 19 1 24 132 3 0 0 1 0 0 1729
#> 20 1 21 165 1 1 0 1 0 1 1790
#> 22 1 32 105 1 1 0 0 0 0 1818
#> 23 1 19 91 1 1 2 0 1 0 1885
#> 24 1 25 115 3 0 0 0 0 0 1893
#> 25 1 16 130 3 0 0 0 0 1 1899
#> 26 1 25 92 1 1 0 0 0 0 1928
#> 27 1 20 150 1 1 0 0 0 2 1928
#> 28 1 21 200 2 0 0 0 1 2 1928
#> 29 1 24 155 1 1 1 0 0 0 1936
#> 30 1 21 103 3 0 0 0 0 0 1970
#> 31 1 20 125 3 0 0 0 1 0 2055
#> 32 1 25 89 3 0 2 0 0 1 2055
#> 33 1 19 102 1 0 0 0 0 2 2082
#> 34 1 19 112 1 1 0 0 1 0 2084
#> 35 1 26 117 1 1 1 0 0 0 2084
#> 36 1 24 138 1 0 0 0 0 0 2100
#> 37 1 17 130 3 1 1 0 1 0 2125
#> 40 1 20 120 2 1 0 0 0 3 2126
#> 42 1 22 130 1 1 1 0 1 1 2187
#> 43 1 27 130 2 0 0 0 1 0 2187
#> 44 1 20 80 3 1 0 0 1 0 2211
#> 45 1 17 110 1 1 0 0 0 0 2225
#> 46 1 25 105 3 0 1 0 0 1 2240
#> 47 1 20 109 3 0 0 0 0 0 2240
#> 49 1 18 148 3 0 0 0 0 0 2282
#> 50 1 18 110 2 1 1 0 0 0 2296
#> 51 1 20 121 1 1 1 0 1 0 2296
#> 52 1 21 100 3 0 1 0 0 4 2301
#> 54 1 26 96 3 0 0 0 0 0 2325
#> 56 1 31 102 1 1 1 0 0 1 2353
#> 57 1 15 110 1 0 0 0 0 0 2353
#> 59 1 23 187 2 1 0 0 0 1 2367
#> 60 1 20 122 2 1 0 0 0 0 2381
#> 61 1 24 105 2 1 0 0 0 0 2381
#> 62 1 15 115 3 0 0 0 1 0 2381
#> 63 1 23 120 3 0 0 0 0 0 2410
#> 65 1 30 142 1 1 1 0 0 0 2410
#> 67 1 22 130 1 1 0 0 0 1 2410
#> 68 1 17 120 1 1 0 0 0 3 2414
#> 69 1 23 110 1 1 1 0 0 0 2424
#> 71 1 17 120 2 0 0 0 0 2 2438
#> 75 1 26 154 3 0 1 1 0 1 2442
#> 76 1 20 105 3 0 0 0 0 3 2450
#> 77 1 26 190 1 1 0 0 0 0 2466
#> 78 1 14 101 3 1 1 0 0 0 2466
#> 79 1 28 95 1 1 0 0 0 2 2466
#> 81 1 14 100 3 0 0 0 0 2 2495
#> 82 1 23 94 3 1 0 0 0 0 2495
#> 83 1 17 142 2 0 0 1 0 0 2495
#> 84 1 21 130 1 1 0 1 0 3 2495Use help to the dataset
It shows that:
birthwt{MASS}
This data frame contains the following columns:
low indicator of birth weight less than 2.5 kg.
age mother’s age in years.
lwt mother’s weight in pounds at last menstrual period.
race mother’s race (1 = white, 2 = black, 3 = other).
smoke smoking status during pregnancy.
ptl number of previous premature labours.
ht history of hypertension.
ui presence of uterine irritability.
ftv number of physician visits during the first trimester.
bwt birth weight in grams.
str(birthwt)
#> 'data.frame': 189 obs. of 10 variables:
#> $ low : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ age : int 19 33 20 21 18 21 22 17 29 26 ...
#> $ lwt : int 182 155 105 108 107 124 118 103 123 113 ...
#> $ race : int 2 3 1 1 1 3 1 3 1 1 ...
#> $ smoke: int 0 0 1 1 1 0 0 0 1 1 ...
#> $ ptl : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ ht : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ ui : int 1 0 0 1 1 0 0 0 0 0 ...
#> $ ftv : int 0 3 1 2 0 0 1 1 1 0 ...
#> $ bwt : int 2523 2551 2557 2594 2600 2622 2637 2637 2663 2665 ...black <- birthwt[birthwt$race == 2, ]
black
#> low age lwt race smoke ptl ht ui ftv bwt
#> 85 0 19 182 2 0 0 0 1 0 2523
#> 102 0 15 98 2 0 0 0 0 0 2778
#> 115 0 26 168 2 1 0 0 0 0 2920
#> 116 0 17 113 2 0 0 0 0 1 2920
#> 117 0 17 113 2 0 0 0 0 1 2920
#> 119 0 35 121 2 1 1 0 0 1 2948
#> 121 0 25 125 2 0 0 0 0 0 2977
#> 128 0 21 185 2 1 0 0 0 2 3042
#> 130 0 23 130 2 0 0 0 0 1 3062
#> 161 0 22 158 2 0 1 0 0 2 3317
#> 166 0 16 112 2 0 0 0 0 0 3374
#> 168 0 18 229 2 0 0 0 0 0 3402
#> 172 0 20 121 2 1 0 0 0 0 3444
#> 202 0 25 241 2 0 0 1 0 0 3790
#> 206 0 16 170 2 0 0 0 0 4 3860
#> 11 1 34 187 2 1 0 1 0 0 1135
#> 18 1 24 128 2 0 1 0 0 1 1701
#> 28 1 21 200 2 0 0 0 1 2 1928
#> 40 1 20 120 2 1 0 0 0 3 2126
#> 43 1 27 130 2 0 0 0 1 0 2187
#> 50 1 18 110 2 1 1 0 0 0 2296
#> 59 1 23 187 2 1 0 0 0 1 2367
#> 60 1 20 122 2 1 0 0 0 0 2381
#> 61 1 24 105 2 1 0 0 0 0 2381
#> 71 1 17 120 2 0 0 0 0 2 2438
#> 83 1 17 142 2 0 0 1 0 0 2495There are 10 black mothers in this data frame.
c(“White”, “Black”, “Other”)[birthwt$race]
c("White", "Black", "Other")[birthwt$race]
#> [1] "Black" "Other" "White" "White" "White" "Other" "White" "Other" "White"
#> [10] "White" "Other" "Other" "Other" "Other" "White" "White" "Black" "White"
#> [19] "Other" "White" "Other" "White" "White" "Other" "Other" "White" "White"
#> [28] "White" "Black" "Black" "Black" "White" "Black" "White" "Black" "White"
#> [37] "White" "White" "White" "White" "Black" "White" "Black" "White" "White"
#> [46] "White" "White" "Other" "White" "Other" "White" "Other" "White" "White"
#> [55] "Other" "Other" "Other" "Other" "Other" "Other" "Other" "Other" "Other"
#> [64] "White" "Other" "Other" "Other" "Other" "White" "Black" "White" "Other"
#> [73] "Other" "Black" "White" "Black" "White" "White" "Black" "White" "White"
#> [82] "White" "Other" "Other" "Other" "Other" "Other" "White" "White" "White"
#> [91] "White" "Other" "White" "White" "White" "White" "White" "White" "White"
#> [100] "White" "White" "White" "Other" "White" "Other" "Black" "White" "White"
#> [109] "White" "Black" "White" "Other" "White" "White" "White" "Other" "White"
#> [118] "Other" "White" "Other" "White" "Other" "White" "White" "White" "White"
#> [127] "White" "White" "White" "White" "Other" "White" "Black" "Other" "Other"
#> [136] "Other" "Other" "Black" "Other" "White" "White" "White" "Other" "Other"
#> [145] "White" "White" "Black" "White" "Other" "Other" "Other" "White" "White"
#> [154] "White" "White" "Other" "Black" "White" "Black" "Other" "White" "Other"
#> [163] "Other" "Other" "Black" "White" "Other" "Other" "White" "White" "Black"
#> [172] "Black" "Black" "Other" "Other" "White" "White" "White" "White" "Black"
#> [181] "Other" "Other" "White" "Other" "White" "Other" "Other" "Black" "White"This R command is to rename the modifiers of “race” variable. 1 = White 2 = Black 3 = Other