d1= read.csv ("C:/Users/ADMIN/OneDrive/Statistical courses/Dinh Tien Hoang-Jun2019/Datasets cho thuc hanh/Du lieu theo thoi gian 1.csv")
names (d1)## [1] "id" "Group" "W1" "W2" "W5"d1## id Group W1 W2 W5
## 1 101 A 15 17 29
## 2 102 A 21 26 31
## 3 103 B 5 17 21
## 4 104 B 11 10 8Biến đổi từ cột sang dòng
library (reshape2)
d2= melt(d1, id= c("id", "Group"), measure.vars= c("W1", "W2", "W5"))
d2## id Group variable value
## 1 101 A W1 15
## 2 102 A W1 21
## 3 103 B W1 5
## 4 104 B W1 11
## 5 101 A W2 17
## 6 102 A W2 26
## 7 103 B W2 17
## 8 104 B W2 10
## 9 101 A W5 29
## 10 102 A W5 31
## 11 103 B W5 21
## 12 104 B W5 8Đặt tên lại biến số
names (d2)[names(d2)=='variable']= 'W'
names (d2)[names(d2)=='value']= 'W.value'
d2## id Group W W.value
## 1 101 A W1 15
## 2 102 A W1 21
## 3 103 B W1 5
## 4 104 B W1 11
## 5 101 A W2 17
## 6 102 A W2 26
## 7 103 B W2 17
## 8 104 B W2 10
## 9 101 A W5 29
## 10 102 A W5 31
## 11 103 B W5 21
## 12 104 B W5 8Chuyển trở lại dữ liệu gốc
#cách thứ nhất
d3= dcast(d2, id + Group ~ W)## Using W.value as value column: use value.var to override.d3## id Group W1 W2 W5
## 1 101 A 15 17 29
## 2 102 A 21 26 31
## 3 103 B 5 17 21
## 4 104 B 11 10 8#cách thứ hai
d3= acast(d2, id + Group ~ W)## Using W.value as value column: use value.var to override.d3## W1 W2 W5
## 101_A 15 17 29
## 102_A 21 26 31
## 103_B 5 17 21
## 104_B 11 10 8d4= read.csv ("C:/Users/ADMIN/OneDrive/Statistical courses/Dinh Tien Hoang-Jun2019/Datasets cho thuc hanh/du lieu theo thoi gian 2.csv")
d4## id age sex wc1 wc2 wc3 wc4 wc5 hip1 hip2 hip3 hip4 hip5
## 1 3 20 M 101.0 97.0 95.0 94.5 94.0 95.0 95.0 93.0 90.5 90.5
## 2 37 32 M 88.5 89.0 87.0 85.0 84.5 89.5 89.5 88.0 86.0 85.0
## 3 39 27 M 97.0 93.0 90.0 88.0 87.0 90.5 88.5 88.0 86.0 85.0
## 4 1 29 F 93.0 94.0 90.0 85.0 84.0 89.5 87.5 87.0 80.0 80.0
## 5 2 22 F 86.5 88.5 84.0 82.0 81.0 83.0 80.4 79.0 78.0 76.5
## 6 5 50 F 96.0 102.0 96.0 90.0 92.5 95.0 94.6 90.5 92.0 91.0
## 7 6 20 F 84.5 88.5 82.0 80.0 79.5 79.0 82.0 78.0 76.5 77.0
## 8 7 21 F 88.0 90.0 88.0 82.5 81.0 85.5 86.5 83.0 80.0 79.5
## 9 8 23 F 87.5 85.0 83.5 78.0 75.0 80.5 80.0 75.0 77.0 73.0Chuyển từ cột sang dòng lần 1
d5= melt (d4, id= c("id", "age", "sex"), measure.vars= c("wc1", "wc2", "wc3", "wc4", "wc5"))
names (d5)[names (d5)=='variable']= 'WC'
names (d5)[names (d5)=='value']= 'WC.value'
d5## id age sex WC WC.value
## 1 3 20 M wc1 101.0
## 2 37 32 M wc1 88.5
## 3 39 27 M wc1 97.0
## 4 1 29 F wc1 93.0
## 5 2 22 F wc1 86.5
## 6 5 50 F wc1 96.0
## 7 6 20 F wc1 84.5
## 8 7 21 F wc1 88.0
## 9 8 23 F wc1 87.5
## 10 3 20 M wc2 97.0
## 11 37 32 M wc2 89.0
## 12 39 27 M wc2 93.0
## 13 1 29 F wc2 94.0
## 14 2 22 F wc2 88.5
## 15 5 50 F wc2 102.0
## 16 6 20 F wc2 88.5
## 17 7 21 F wc2 90.0
## 18 8 23 F wc2 85.0
## 19 3 20 M wc3 95.0
## 20 37 32 M wc3 87.0
## 21 39 27 M wc3 90.0
## 22 1 29 F wc3 90.0
## 23 2 22 F wc3 84.0
## 24 5 50 F wc3 96.0
## 25 6 20 F wc3 82.0
## 26 7 21 F wc3 88.0
## 27 8 23 F wc3 83.5
## 28 3 20 M wc4 94.5
## 29 37 32 M wc4 85.0
## 30 39 27 M wc4 88.0
## 31 1 29 F wc4 85.0
## 32 2 22 F wc4 82.0
## 33 5 50 F wc4 90.0
## 34 6 20 F wc4 80.0
## 35 7 21 F wc4 82.5
## 36 8 23 F wc4 78.0
## 37 3 20 M wc5 94.0
## 38 37 32 M wc5 84.5
## 39 39 27 M wc5 87.0
## 40 1 29 F wc5 84.0
## 41 2 22 F wc5 81.0
## 42 5 50 F wc5 92.5
## 43 6 20 F wc5 79.5
## 44 7 21 F wc5 81.0
## 45 8 23 F wc5 75.0Chuyển từ cột sang dòng lần 2
d6= melt (d4, id= c("id", "age", "sex"), measure.vars= c("hip1", "hip2", "hip3", "hip4", "hip5"))
names (d6)[names (d6)== 'variable']= 'hip'
names (d6)[names (d6)== 'value']= 'hip.value'
d6## id age sex hip hip.value
## 1 3 20 M hip1 95.0
## 2 37 32 M hip1 89.5
## 3 39 27 M hip1 90.5
## 4 1 29 F hip1 89.5
## 5 2 22 F hip1 83.0
## 6 5 50 F hip1 95.0
## 7 6 20 F hip1 79.0
## 8 7 21 F hip1 85.5
## 9 8 23 F hip1 80.5
## 10 3 20 M hip2 95.0
## 11 37 32 M hip2 89.5
## 12 39 27 M hip2 88.5
## 13 1 29 F hip2 87.5
## 14 2 22 F hip2 80.4
## 15 5 50 F hip2 94.6
## 16 6 20 F hip2 82.0
## 17 7 21 F hip2 86.5
## 18 8 23 F hip2 80.0
## 19 3 20 M hip3 93.0
## 20 37 32 M hip3 88.0
## 21 39 27 M hip3 88.0
## 22 1 29 F hip3 87.0
## 23 2 22 F hip3 79.0
## 24 5 50 F hip3 90.5
## 25 6 20 F hip3 78.0
## 26 7 21 F hip3 83.0
## 27 8 23 F hip3 75.0
## 28 3 20 M hip4 90.5
## 29 37 32 M hip4 86.0
## 30 39 27 M hip4 86.0
## 31 1 29 F hip4 80.0
## 32 2 22 F hip4 78.0
## 33 5 50 F hip4 92.0
## 34 6 20 F hip4 76.5
## 35 7 21 F hip4 80.0
## 36 8 23 F hip4 77.0
## 37 3 20 M hip5 90.5
## 38 37 32 M hip5 85.0
## 39 39 27 M hip5 85.0
## 40 1 29 F hip5 80.0
## 41 2 22 F hip5 76.5
## 42 5 50 F hip5 91.0
## 43 6 20 F hip5 77.0
## 44 7 21 F hip5 79.5
## 45 8 23 F hip5 73.0Trộn 2 bộ dữ liệu vừa tạo ra để có một data mới
d7= merge (d5, d6, by= c("id", "age", "sex"), all.x= T, all.y= T)
d7## id age sex WC WC.value hip hip.value
## 1 1 29 F wc4 85.0 hip4 80.0
## 2 1 29 F wc4 85.0 hip5 80.0
## 3 1 29 F wc4 85.0 hip3 87.0
## 4 1 29 F wc4 85.0 hip2 87.5
## 5 1 29 F wc4 85.0 hip1 89.5
## 6 1 29 F wc5 84.0 hip4 80.0
## 7 1 29 F wc5 84.0 hip5 80.0
## 8 1 29 F wc5 84.0 hip3 87.0
## 9 1 29 F wc5 84.0 hip2 87.5
## 10 1 29 F wc5 84.0 hip1 89.5
## 11 1 29 F wc3 90.0 hip4 80.0
## 12 1 29 F wc3 90.0 hip5 80.0
## 13 1 29 F wc3 90.0 hip3 87.0
## 14 1 29 F wc3 90.0 hip2 87.5
## 15 1 29 F wc3 90.0 hip1 89.5
## 16 1 29 F wc2 94.0 hip4 80.0
## 17 1 29 F wc2 94.0 hip5 80.0
## 18 1 29 F wc2 94.0 hip3 87.0
## 19 1 29 F wc2 94.0 hip2 87.5
## 20 1 29 F wc2 94.0 hip1 89.5
## 21 1 29 F wc1 93.0 hip4 80.0
## 22 1 29 F wc1 93.0 hip5 80.0
## 23 1 29 F wc1 93.0 hip3 87.0
## 24 1 29 F wc1 93.0 hip2 87.5
## 25 1 29 F wc1 93.0 hip1 89.5
## 26 2 22 F wc4 82.0 hip4 78.0
## 27 2 22 F wc4 82.0 hip1 83.0
## 28 2 22 F wc4 82.0 hip3 79.0
## 29 2 22 F wc4 82.0 hip5 76.5
## 30 2 22 F wc4 82.0 hip2 80.4
## 31 2 22 F wc1 86.5 hip4 78.0
## 32 2 22 F wc1 86.5 hip1 83.0
## 33 2 22 F wc1 86.5 hip3 79.0
## 34 2 22 F wc1 86.5 hip5 76.5
## 35 2 22 F wc1 86.5 hip2 80.4
## 36 2 22 F wc3 84.0 hip4 78.0
## 37 2 22 F wc3 84.0 hip1 83.0
## 38 2 22 F wc3 84.0 hip3 79.0
## 39 2 22 F wc3 84.0 hip5 76.5
## 40 2 22 F wc3 84.0 hip2 80.4
## 41 2 22 F wc5 81.0 hip4 78.0
## 42 2 22 F wc5 81.0 hip1 83.0
## 43 2 22 F wc5 81.0 hip3 79.0
## 44 2 22 F wc5 81.0 hip5 76.5
## 45 2 22 F wc5 81.0 hip2 80.4
## 46 2 22 F wc2 88.5 hip4 78.0
## 47 2 22 F wc2 88.5 hip1 83.0
## 48 2 22 F wc2 88.5 hip3 79.0
## 49 2 22 F wc2 88.5 hip5 76.5
## 50 2 22 F wc2 88.5 hip2 80.4
## 51 3 20 M wc1 101.0 hip1 95.0
## 52 3 20 M wc1 101.0 hip4 90.5
## 53 3 20 M wc1 101.0 hip5 90.5
## 54 3 20 M wc1 101.0 hip3 93.0
## 55 3 20 M wc1 101.0 hip2 95.0
## 56 3 20 M wc4 94.5 hip1 95.0
## 57 3 20 M wc4 94.5 hip4 90.5
## 58 3 20 M wc4 94.5 hip5 90.5
## 59 3 20 M wc4 94.5 hip3 93.0
## 60 3 20 M wc4 94.5 hip2 95.0
## 61 3 20 M wc5 94.0 hip1 95.0
## 62 3 20 M wc5 94.0 hip4 90.5
## 63 3 20 M wc5 94.0 hip5 90.5
## 64 3 20 M wc5 94.0 hip3 93.0
## 65 3 20 M wc5 94.0 hip2 95.0
## 66 3 20 M wc3 95.0 hip1 95.0
## 67 3 20 M wc3 95.0 hip4 90.5
## 68 3 20 M wc3 95.0 hip5 90.5
## 69 3 20 M wc3 95.0 hip3 93.0
## 70 3 20 M wc3 95.0 hip2 95.0
## 71 3 20 M wc2 97.0 hip1 95.0
## 72 3 20 M wc2 97.0 hip4 90.5
## 73 3 20 M wc2 97.0 hip5 90.5
## 74 3 20 M wc2 97.0 hip3 93.0
## 75 3 20 M wc2 97.0 hip2 95.0
## 76 37 32 M wc5 84.5 hip5 85.0
## 77 37 32 M wc5 84.5 hip4 86.0
## 78 37 32 M wc5 84.5 hip2 89.5
## 79 37 32 M wc5 84.5 hip3 88.0
## 80 37 32 M wc5 84.5 hip1 89.5
## 81 37 32 M wc4 85.0 hip5 85.0
## 82 37 32 M wc4 85.0 hip4 86.0
## 83 37 32 M wc4 85.0 hip2 89.5
## 84 37 32 M wc4 85.0 hip3 88.0
## 85 37 32 M wc4 85.0 hip1 89.5
## 86 37 32 M wc2 89.0 hip5 85.0
## 87 37 32 M wc2 89.0 hip4 86.0
## 88 37 32 M wc2 89.0 hip2 89.5
## 89 37 32 M wc2 89.0 hip3 88.0
## 90 37 32 M wc2 89.0 hip1 89.5
## 91 37 32 M wc3 87.0 hip5 85.0
## 92 37 32 M wc3 87.0 hip4 86.0
## 93 37 32 M wc3 87.0 hip2 89.5
## 94 37 32 M wc3 87.0 hip3 88.0
## 95 37 32 M wc3 87.0 hip1 89.5
## 96 37 32 M wc1 88.5 hip5 85.0
## 97 37 32 M wc1 88.5 hip4 86.0
## 98 37 32 M wc1 88.5 hip2 89.5
## 99 37 32 M wc1 88.5 hip3 88.0
## 100 37 32 M wc1 88.5 hip1 89.5
## 101 39 27 M wc5 87.0 hip5 85.0
## 102 39 27 M wc5 87.0 hip2 88.5
## 103 39 27 M wc5 87.0 hip4 86.0
## 104 39 27 M wc5 87.0 hip3 88.0
## 105 39 27 M wc5 87.0 hip1 90.5
## 106 39 27 M wc2 93.0 hip5 85.0
## 107 39 27 M wc2 93.0 hip2 88.5
## 108 39 27 M wc2 93.0 hip4 86.0
## 109 39 27 M wc2 93.0 hip3 88.0
## 110 39 27 M wc2 93.0 hip1 90.5
## 111 39 27 M wc4 88.0 hip5 85.0
## 112 39 27 M wc4 88.0 hip2 88.5
## 113 39 27 M wc4 88.0 hip4 86.0
## 114 39 27 M wc4 88.0 hip3 88.0
## 115 39 27 M wc4 88.0 hip1 90.5
## 116 39 27 M wc3 90.0 hip5 85.0
## 117 39 27 M wc3 90.0 hip2 88.5
## 118 39 27 M wc3 90.0 hip4 86.0
## 119 39 27 M wc3 90.0 hip3 88.0
## 120 39 27 M wc3 90.0 hip1 90.5
## 121 39 27 M wc1 97.0 hip5 85.0
## 122 39 27 M wc1 97.0 hip2 88.5
## 123 39 27 M wc1 97.0 hip4 86.0
## 124 39 27 M wc1 97.0 hip3 88.0
## 125 39 27 M wc1 97.0 hip1 90.5
## 126 5 50 F wc5 92.5 hip5 91.0
## 127 5 50 F wc5 92.5 hip4 92.0
## 128 5 50 F wc5 92.5 hip3 90.5
## 129 5 50 F wc5 92.5 hip1 95.0
## 130 5 50 F wc5 92.5 hip2 94.6
## 131 5 50 F wc4 90.0 hip5 91.0
## 132 5 50 F wc4 90.0 hip4 92.0
## 133 5 50 F wc4 90.0 hip3 90.5
## 134 5 50 F wc4 90.0 hip1 95.0
## 135 5 50 F wc4 90.0 hip2 94.6
## 136 5 50 F wc3 96.0 hip5 91.0
## 137 5 50 F wc3 96.0 hip4 92.0
## 138 5 50 F wc3 96.0 hip3 90.5
## 139 5 50 F wc3 96.0 hip1 95.0
## 140 5 50 F wc3 96.0 hip2 94.6
## 141 5 50 F wc1 96.0 hip5 91.0
## 142 5 50 F wc1 96.0 hip4 92.0
## 143 5 50 F wc1 96.0 hip3 90.5
## 144 5 50 F wc1 96.0 hip1 95.0
## 145 5 50 F wc1 96.0 hip2 94.6
## 146 5 50 F wc2 102.0 hip5 91.0
## 147 5 50 F wc2 102.0 hip4 92.0
## 148 5 50 F wc2 102.0 hip3 90.5
## 149 5 50 F wc2 102.0 hip1 95.0
## 150 5 50 F wc2 102.0 hip2 94.6
## 151 6 20 F wc5 79.5 hip5 77.0
## 152 6 20 F wc5 79.5 hip3 78.0
## 153 6 20 F wc5 79.5 hip2 82.0
## 154 6 20 F wc5 79.5 hip4 76.5
## 155 6 20 F wc5 79.5 hip1 79.0
## 156 6 20 F wc3 82.0 hip5 77.0
## 157 6 20 F wc3 82.0 hip3 78.0
## 158 6 20 F wc3 82.0 hip2 82.0
## 159 6 20 F wc3 82.0 hip4 76.5
## 160 6 20 F wc3 82.0 hip1 79.0
## 161 6 20 F wc2 88.5 hip5 77.0
## 162 6 20 F wc2 88.5 hip3 78.0
## 163 6 20 F wc2 88.5 hip2 82.0
## 164 6 20 F wc2 88.5 hip4 76.5
## 165 6 20 F wc2 88.5 hip1 79.0
## 166 6 20 F wc4 80.0 hip5 77.0
## 167 6 20 F wc4 80.0 hip3 78.0
## 168 6 20 F wc4 80.0 hip2 82.0
## 169 6 20 F wc4 80.0 hip4 76.5
## 170 6 20 F wc4 80.0 hip1 79.0
## 171 6 20 F wc1 84.5 hip5 77.0
## 172 6 20 F wc1 84.5 hip3 78.0
## 173 6 20 F wc1 84.5 hip2 82.0
## 174 6 20 F wc1 84.5 hip4 76.5
## 175 6 20 F wc1 84.5 hip1 79.0
## 176 7 21 F wc5 81.0 hip5 79.5
## 177 7 21 F wc5 81.0 hip3 83.0
## 178 7 21 F wc5 81.0 hip4 80.0
## 179 7 21 F wc5 81.0 hip2 86.5
## 180 7 21 F wc5 81.0 hip1 85.5
## 181 7 21 F wc3 88.0 hip5 79.5
## 182 7 21 F wc3 88.0 hip3 83.0
## 183 7 21 F wc3 88.0 hip4 80.0
## 184 7 21 F wc3 88.0 hip2 86.5
## 185 7 21 F wc3 88.0 hip1 85.5
## 186 7 21 F wc4 82.5 hip5 79.5
## 187 7 21 F wc4 82.5 hip3 83.0
## 188 7 21 F wc4 82.5 hip4 80.0
## 189 7 21 F wc4 82.5 hip2 86.5
## 190 7 21 F wc4 82.5 hip1 85.5
## 191 7 21 F wc2 90.0 hip5 79.5
## 192 7 21 F wc2 90.0 hip3 83.0
## 193 7 21 F wc2 90.0 hip4 80.0
## 194 7 21 F wc2 90.0 hip2 86.5
## 195 7 21 F wc2 90.0 hip1 85.5
## 196 7 21 F wc1 88.0 hip5 79.5
## 197 7 21 F wc1 88.0 hip3 83.0
## 198 7 21 F wc1 88.0 hip4 80.0
## 199 7 21 F wc1 88.0 hip2 86.5
## 200 7 21 F wc1 88.0 hip1 85.5
## 201 8 23 F wc4 78.0 hip4 77.0
## 202 8 23 F wc4 78.0 hip3 75.0
## 203 8 23 F wc4 78.0 hip1 80.5
## 204 8 23 F wc4 78.0 hip2 80.0
## 205 8 23 F wc4 78.0 hip5 73.0
## 206 8 23 F wc3 83.5 hip4 77.0
## 207 8 23 F wc3 83.5 hip3 75.0
## 208 8 23 F wc3 83.5 hip1 80.5
## 209 8 23 F wc3 83.5 hip2 80.0
## 210 8 23 F wc3 83.5 hip5 73.0
## 211 8 23 F wc1 87.5 hip4 77.0
## 212 8 23 F wc1 87.5 hip3 75.0
## 213 8 23 F wc1 87.5 hip1 80.5
## 214 8 23 F wc1 87.5 hip2 80.0
## 215 8 23 F wc1 87.5 hip5 73.0
## 216 8 23 F wc2 85.0 hip4 77.0
## 217 8 23 F wc2 85.0 hip3 75.0
## 218 8 23 F wc2 85.0 hip1 80.5
## 219 8 23 F wc2 85.0 hip2 80.0
## 220 8 23 F wc2 85.0 hip5 73.0
## 221 8 23 F wc5 75.0 hip4 77.0
## 222 8 23 F wc5 75.0 hip3 75.0
## 223 8 23 F wc5 75.0 hip1 80.5
## 224 8 23 F wc5 75.0 hip2 80.0
## 225 8 23 F wc5 75.0 hip5 73.0