library(xlsx)
nv2 <- read.xlsx("C:/Users/Data/bwt_co_dieu_chinh.xlsx", sheetIndex = 1, header = T)
nv2
## id year bwt gestation parity age height weight smoke
## 1 1 1925 120 284 0 27 62 100 0
## 2 2 1925 112 267 1 22 62 138 0
## 3 3 1925 119 286 0 26 64 123 1
## 4 4 1925 124 287 0 27 62 105 1
## 5 5 1925 105 276 0 22 67 130 0
## 6 6 1925 120 289 1 31 59 102 0
## 7 7 1925 82 274 0 31 64 101 1
## 8 8 1925 111 278 0 29 65 145 1
## 9 1 1926 113 282 0 33 64 135 0
## 10 2 1926 134 297 0 27 67 170 1
## 11 3 1926 97 279 0 29 68 178 1
## 12 4 1926 125 292 0 22 65 122 0
## 13 5 1926 93 246 0 37 65 130 0
## 14 6 1926 146 280 0 23 61 145 0
## 15 7 1926 100 274 0 24 63 113 0
## 16 8 1926 103 250 0 40 59 140 0
## 17 1 1927 128 279 0 28 64 115 1
## 18 2 1927 145 308 0 35 64 110 1
## 19 3 1927 99 252 0 21 64 120 0
## 20 4 1927 110 262 0 25 66 140 0
## 21 5 1927 122 281 0 42 63 103 1
## 22 6 1927 112 283 1 21 62 102 1
## 23 7 1927 114 271 0 32 61 130 0
## 24 8 1927 114 276 0 26 62 127 0
## 25 1 1928 108 282 0 23 67 125 1
## 26 2 1928 116 295 0 32 65 120 0
## 27 3 1928 115 264 1 23 67 134 1
## 28 4 1928 125 279 0 23 63 104 1
## 29 5 1928 133 293 0 23 64 110 1
## 30 6 1928 132 278 0 20 64 150 1
## 31 7 1928 97 269 0 20 65 137 1
## 32 8 1928 75 247 0 36 64 120 1
## 33 1 1929 136 286 0 25 62 93 0
## 34 2 1929 126 278 0 26 64 150 1
## 35 3 1929 139 284 0 37 61 121 0
## 36 4 1929 138 294 0 40 64 125 0
## 37 5 1929 130 296 1 22 66 117 1
## 38 6 1929 146 263 0 39 53 110 1
## 39 7 1929 126 298 0 24 61 112 0
## 40 8 1929 169 296 0 33 67 185 0
## 41 1 1930 138 244 0 33 62 178 0
## 42 2 1930 111 285 0 29 65 130 0
## 43 3 1930 144 304 1 27 58 102 1
## 44 4 1930 142 284 0 39 66 132 0
## 45 5 1930 104 307 0 24 59 122 0
## 46 6 1930 122 275 0 30 68 140 0
## 47 7 1930 122 275 1 20 65 127 0
## 48 8 1930 94 271 0 36 61 130 1
## 49 1 1931 132 245 0 23 65 140 0
## 50 2 1931 126 282 0 33 62 117 0
## 51 3 1931 99 270 0 22 63 115 1
## 52 4 1931 115 278 0 23 60 102 1
## 53 5 1931 106 278 0 31 65 110 1
## 54 6 1931 128 292 0 32 66 130 0
## 55 7 1931 152 295 0 39 62 140 0
## 56 8 1931 150 287 0 36 62 135 0
## 57 1 1932 120 289 0 25 62 125 0
## 58 2 1932 109 291 0 39 64 107 0
## 59 3 1932 105 280 1 22 63 116 0
## 60 4 1932 102 280 0 38 67 140 0
## 61 5 1932 120 281 0 33 63 113 0
## 62 6 1932 119 277 0 24 63 120 1
## 63 7 1932 116 274 0 21 62 110 1
## 64 8 1932 144 248 0 30 70 145 0
## 65 1 1933 143 299 0 30 66 136 1
## 66 2 1933 136 291 0 41 66 191 0
## 67 3 1933 89 275 0 34 66 170 0
## 68 4 1933 140 294 0 25 61 103 0
## 69 5 1933 118 276 1 18 63 128 0
## 70 6 1933 135 278 0 27 66 148 0
## 71 7 1933 132 302 0 36 63 145 1
## 72 8 1933 144 291 0 28 67 130 0
## 73 1 1934 140 351 0 27 68 120 0
## 74 2 1934 119 286 0 22 63 185 1
## 75 3 1934 129 270 0 43 67 160 0
## 76 4 1934 133 276 1 22 63 119 0
## 77 5 1934 140 290 1 19 67 132 1
## 78 6 1934 129 235 0 24 66 135 0
## 79 7 1934 84 260 1 37 66 140 0
## 80 8 1934 143 313 0 20 68 150 0
## 81 1 1935 144 282 0 32 64 124 1
## 82 2 1935 103 267 1 21 66 150 1
## 83 3 1935 119 270 1 20 64 109 0
## 84 4 1935 127 290 0 35 66 165 0
## 85 5 1935 114 268 0 22 64 104 0
## 86 6 1935 116 293 1 28 62 108 0
## 87 7 1935 119 277 1 18 61 89 1
## 88 8 1935 145 304 1 25 63 109 1
## 89 1 1936 141 279 0 23 63 128 1
## 90 2 1936 124 284 1 17 62 112 0
## 91 3 1936 114 291 0 35 60 112 0
## 92 4 1936 104 274 1 20 62 115 1
## 93 5 1936 116 280 0 40 62 159 0
## 94 6 1936 100 275 0 27 64 111 1
## 95 7 1936 106 312 0 24 62 135 1
## 96 8 1936 121 285 0 34 64 110 0
## 97 1 1937 110 281 0 36 61 99 1
## 98 2 1937 155 286 0 31 66 127 0
## 99 3 1937 106 289 0 28 67 120 1
## 100 4 1937 119 275 0 42 67 156 1
## 101 5 1937 129 284 0 24 64 115 0
## 102 6 1937 138 257 0 38 67 138 0
## 103 7 1937 139 291 0 24 65 160 0
## 104 8 1937 105 256 0 31 66 142 0
## 105 1 1938 114 273 0 30 63 154 0
## 106 2 1938 122 282 1 21 66 110 0
## 107 3 1938 122 292 1 34 65 133 0
## 108 4 1938 152 301 0 29 65 150 0
## 109 5 1938 120 286 0 22 62 115 1
## 110 6 1938 123 282 0 22 65 130 0
## 111 7 1938 103 273 0 36 65 158 1
## 112 8 1938 134 286 0 25 64 125 0
## 113 1 1939 115 285 0 38 63 130 0
## 114 2 1939 113 285 0 26 66 140 0
## 115 3 1939 136 261 0 24 65 110 0
## 116 4 1939 123 284 1 20 65 120 1
## 117 5 1939 127 281 0 24 63 112 1
## 118 6 1939 113 288 1 21 61 120 0
## 119 7 1939 112 299 0 24 67 145 1
## 120 8 1939 129 294 1 21 65 132 0
## 121 1 1940 92 255 0 25 65 125 1
## 122 2 1940 122 273 0 26 66 210 0
## 123 3 1940 121 286 1 22 69 130 1
## 124 4 1940 143 273 0 19 66 135 0
## 125 5 1940 71 234 0 32 64 110 1
## 126 6 1940 129 280 1 24 65 140 1
## 127 7 1940 96 276 0 33 64 127 1
## 128 8 1940 114 276 0 24 63 110 0
## 129 1 1941 115 261 0 33 60 125 1
## 130 2 1941 126 293 1 27 62 111 0
## 131 3 1941 112 282 0 26 65 122 0
## 132 4 1941 131 308 0 40 65 160 0
## 133 5 1941 88 274 0 30 66 130 0
## 134 6 1941 122 280 0 24 67 127 1
## 135 7 1941 102 281 1 19 67 135 1
## 136 8 1941 97 265 0 30 61 110 0
## 137 1 1942 144 261 0 33 68 170 0
## 138 2 1942 116 277 0 41 64 124 1
## 139 3 1942 112 266 0 26 64 122 0
## 140 4 1942 141 319 1 20 67 140 1
## 141 5 1942 122 286 0 23 64 145 0
## 142 6 1942 132 281 1 21 67 140 0
## 143 7 1942 120 300 0 34 63 150 1
## 144 8 1942 160 292 0 28 64 120 0
## 145 1 1943 119 288 0 43 66 142 1
## 146 2 1943 102 294 0 21 65 130 1
## 147 3 1943 123 314 0 22 61 121 1
## 148 4 1943 129 277 0 30 66 142 1
## 149 5 1943 106 302 1 19 66 147 0
## 150 6 1943 120 269 1 40 63 130 0
## 151 7 1943 102 338 0 19 64 170 0
## 152 8 1943 65 237 0 31 67 130 0
## 153 1 1944 105 270 0 22 56 93 0
## 154 2 1944 110 181 0 27 64 133 0
## 155 3 1944 139 286 0 33 65 125 1
## 156 4 1944 113 282 1 36 59 140 0
## 157 5 1944 135 285 0 30 66 130 0
## 158 6 1944 114 283 1 20 65 115 0
## 159 7 1944 97 255 1 22 63 107 1
## 160 8 1944 145 288 0 28 64 116 0
## 161 1 1945 115 274 0 27 67 175 1
## 162 2 1945 133 285 1 30 64 160 0
## 163 3 1945 125 290 0 36 59 105 0
## 164 4 1945 119 292 0 33 62 118 1
## 165 5 1945 107 290 0 26 63 112 0
## 166 6 1945 130 280 0 29 66 135 0
## 167 7 1945 113 285 0 22 70 145 0
## 168 8 1945 95 273 0 23 60 90 0
## 169 1 1946 137 287 0 25 66 145 0
## 170 2 1946 125 283 0 29 65 125 0
## 171 3 1946 105 295 1 20 64 112 1
## 172 4 1946 109 295 1 23 63 103 1
## 173 5 1946 129 294 0 32 62 170 1
## 174 6 1946 117 286 0 32 66 127 1
## 175 7 1946 130 297 0 32 58 130 0
## 176 8 1946 139 293 1 21 69 130 0
## 177 1 1947 122 276 0 30 68 182 0
## 178 2 1947 164 286 1 32 66 143 0
## 179 3 1947 130 276 0 41 68 130 0
## 180 4 1947 104 280 1 27 68 146 1
## 181 5 1947 126 274 0 39 62 122 0
## 182 6 1947 142 285 0 33 63 124 0
## 183 7 1947 97 260 1 25 63 115 1
## 184 8 1947 123 288 0 27 63 125 0
## 185 1 1948 131 294 0 23 65 122 0
## 186 2 1948 133 297 0 36 61 125 0
## 187 3 1948 146 294 0 22 66 145 1
## 188 4 1948 131 282 1 21 66 126 0
## 189 5 1948 116 293 1 26 64 125 0
## 190 6 1948 144 273 0 27 62 118 1
## 191 7 1948 116 273 0 31 61 120 0
## 192 8 1948 109 283 0 23 65 112 1
## 193 1 1949 103 261 0 27 65 112 1
## 194 2 1949 124 293 1 19 65 150 0
## 195 3 1949 133 290 0 21 64 145 0
## 196 4 1949 110 293 1 28 64 135 1
## 197 5 1949 124 294 0 26 62 122 0
## 198 6 1949 127 262 1 32 64 125 0
## 199 7 1949 114 266 0 29 64 113 0
## 200 8 1949 110 268 0 34 64 127 0
## 201 1 1950 146 280 0 26 58 106 0
## 202 2 1950 122 306 1 22 62 100 0
## 203 3 1950 147 296 1 19 67 124 0
## 204 4 1950 148 279 0 27 71 189 0
## 205 5 1950 123 281 0 23 68 136 0
## 206 6 1950 115 270 0 25 67 165 1
## 207 7 1950 127 242 0 17 61 135 1
## 208 8 1950 122 296 1 24 65 132 0
## 209 1 1951 114 266 0 20 65 175 1
## 210 2 1951 121 271 1 34 63 129 1
## 211 3 1951 109 269 0 23 63 113 0
## 212 4 1951 137 283 1 20 65 157 0
## 213 5 1951 145 315 0 39 67 143 1
## 214 6 1951 85 258 0 41 67 137 0
## 215 7 1951 87 247 1 18 66 125 1
## 216 8 1951 115 307 0 34 65 128 1
## 217 1 1952 125 292 0 32 65 125 0
## 218 2 1952 100 272 0 30 64 150 1
## 219 3 1952 122 286 0 23 64 120 1
## 220 4 1952 117 283 0 27 63 108 0
## 221 5 1952 102 278 0 27 67 135 1
## 222 6 1952 99 274 0 28 66 118 1
## 223 7 1952 141 281 0 29 54 156 1
## 224 8 1952 108 279 1 19 64 115 0
## 225 1 1953 114 274 0 28 66 132 1
## 226 2 1953 90 266 1 26 67 135 0
## 227 3 1953 135 260 0 43 65 135 0
## 228 4 1953 115 302 1 22 67 135 0
## 229 5 1953 129 293 0 30 65 130 1
## 230 6 1953 123 323 1 17 64 140 0
## 231 7 1953 144 283 1 25 66 140 0
## 232 8 1953 120 287 0 23 67 116 1
## 233 1 1954 122 270 0 26 61 105 0
## 234 2 1954 128 272 1 18 67 109 0
## 235 3 1954 117 272 0 32 66 118 0
## 236 4 1954 98 280 0 35 64 122 1
## 237 5 1954 98 276 1 22 61 121 0
## 238 6 1954 112 281 1 23 61 150 0
## 239 7 1954 116 273 0 33 66 130 1
## 240 8 1954 131 269 0 36 68 145 0
## 241 1 1955 93 278 0 34 61 146 0
## 242 2 1955 86 276 1 23 65 125 1
## 243 3 1955 138 284 0 30 66 133 1
## 244 4 1955 136 303 1 20 68 148 1
## 245 5 1955 110 272 0 28 60 108 0
## 246 6 1955 68 223 0 32 66 149 1
## 247 7 1955 75 265 0 21 65 103 1
## 248 8 1955 136 283 1 24 63 119 0
## 249 1 1956 130 268 0 30 66 123 0
## 250 2 1956 123 282 0 30 63 118 0
## 251 3 1956 120 283 0 28 64 122 1
## 252 4 1956 121 276 1 23 71 152 1
## 253 5 1956 135 282 0 24 67 128 1
## 254 6 1956 102 283 1 19 65 127 1
## 255 7 1956 138 286 1 28 68 120 0
## 256 8 1956 125 290 0 32 63 135 0
## 257 1 1957 119 275 0 23 60 105 0
## 258 2 1957 87 275 0 28 63 110 1
## 259 3 1957 119 273 0 35 65 125 1
## 260 4 1957 132 285 1 25 63 140 0
## 261 5 1957 101 278 1 20 62 105 0
## 262 6 1957 109 273 0 37 65 138 1
## 263 7 1957 99 271 0 39 69 151 0
## 264 8 1957 96 285 1 20 66 117 1
## 265 1 1958 113 281 0 24 65 120 0
## 266 2 1958 128 291 1 27 63 132 0
## 267 3 1958 118 278 1 19 62 126 0
## 268 4 1958 91 264 0 36 60 100 1
## 269 5 1958 96 266 0 26 65 125 0
## 270 6 1958 102 267 1 25 60 93 1
## 271 7 1958 118 293 0 21 63 103 0
## 272 8 1958 102 282 1 29 65 125 1
## 273 1 1959 134 283 0 22 67 130 0
## 274 2 1959 120 288 0 28 63 125 0
## 275 3 1959 105 330 0 23 64 112 1
## 276 4 1959 119 294 0 34 59 105 0
## 277 5 1959 104 276 1 18 60 109 1
## 278 6 1959 99 275 0 23 61 125 1
## 279 7 1959 97 266 0 24 62 109 0
## 280 8 1959 102 288 1 18 65 117 0
## 281 1 1960 107 279 0 24 63 115 0
## 282 2 1960 125 301 1 35 68 181 0
## 283 3 1960 113 306 1 21 65 137 0
## 284 4 1960 85 273 0 26 60 105 1
## 285 5 1960 100 249 0 24 67 100 0
## 286 6 1960 78 256 1 29 65 123 0
## 287 7 1960 146 319 0 28 66 145 0
## 288 8 1960 112 277 1 22 67 120 0
## 289 1 1961 134 288 0 23 63 92 1
## 290 2 1961 118 265 0 27 61 123 0
## 291 3 1961 148 291 1 21 63 115 0
## 292 4 1961 106 271 1 26 61 110 1
## 293 5 1961 154 292 0 40 66 145 0
## 294 6 1961 128 284 1 19 66 111 1
## 295 7 1961 81 285 0 19 63 150 1
## 296 8 1961 135 272 0 30 65 130 0
## 297 1 1962 122 267 0 27 65 101 1
## 298 2 1962 116 284 1 24 66 117 0
## 299 3 1962 140 281 1 22 69 135 0
## 300 4 1962 132 284 0 29 64 122 0
## 301 5 1962 127 293 0 31 67 137 0
## 302 6 1962 107 303 1 25 67 133 0
## 303 7 1962 110 321 0 28 66 180 0
## 304 8 1962 91 266 0 23 60 120 1
## 305 1 1963 129 293 0 30 61 160 0
## 306 2 1963 131 262 0 22 67 135 0
## 307 3 1963 134 287 1 33 67 131 0
## 308 4 1963 80 266 1 25 62 125 0
## 309 5 1963 126 288 0 31 62 150 0
## 310 6 1963 136 295 0 23 64 147 0
## 311 7 1963 135 284 1 19 60 95 0
## 312 8 1963 129 276 0 31 63 125 0
## 313 1 1964 110 278 0 23 63 177 0
## 314 2 1964 151 286 1 22 66 130 0
## 315 3 1964 120 280 0 31 61 111 0
## 316 4 1964 109 286 0 24 64 125 1
## 317 5 1964 126 282 1 23 66 115 1
## 318 6 1964 101 278 0 27 61 99 1
## 319 7 1964 114 290 1 21 65 120 1
## 320 8 1964 155 290 0 26 66 129 1
## 321 1 1965 111 270 0 27 61 119 0
## 322 2 1965 88 273 0 20 66 110 1
## 323 3 1965 123 296 1 26 64 110 1
## 324 4 1965 111 306 0 27 61 102 0
## 325 5 1965 127 279 0 26 67 155 1
## 326 6 1965 100 275 1 25 64 125 0
## 327 7 1965 124 288 1 21 64 116 1
## 328 8 1965 109 274 0 33 69 144 1
## 329 1 1966 87 248 0 37 65 130 1
## 330 2 1966 137 284 0 30 67 110 0
## 331 3 1966 102 275 0 43 64 160 0
## 332 4 1966 143 292 1 21 65 125 0
## 333 5 1966 98 275 0 25 65 112 1
## 334 6 1966 109 272 0 41 66 154 1
## 335 7 1966 115 262 1 23 64 136 1
## 336 8 1966 80 262 1 31 61 100 1
## 337 1 1967 143 274 0 27 63 110 1
## 338 2 1967 127 289 0 23 67 140 0
## 339 3 1967 55 204 0 35 65 140 0
## 340 4 1967 136 290 0 26 66 135 0
## 341 5 1967 127 288 1 21 66 130 0
## 342 6 1967 117 281 1 21 70 141 1
## 343 7 1967 143 281 0 28 65 135 1
## 344 8 1967 125 273 0 30 64 145 0
## 345 1 1968 155 294 0 32 66 150 0
## 346 2 1968 96 278 1 18 60 120 1
## 347 3 1968 103 276 1 19 63 149 1
## 348 4 1968 110 285 1 19 64 130 0
## 349 5 1968 129 299 0 22 68 145 0
## 350 6 1968 88 252 1 21 60 115 1
## 351 7 1968 113 287 1 29 70 145 1
## 352 8 1968 94 284 0 24 63 104 1
## 353 1 1969 110 272 0 25 60 90 0
## 354 2 1969 129 281 0 31 67 155 0
## 355 3 1969 123 283 0 21 65 110 0
## 356 4 1969 98 257 0 29 66 130 1
## 357 5 1969 131 292 1 22 64 124 1
## 358 6 1969 95 270 0 35 65 135 1
## 359 7 1969 109 244 1 21 63 102 1
## 360 8 1969 148 281 0 27 63 110 1
## 361 1 1970 122 275 0 26 66 147 0
## 362 2 1970 128 288 1 26 65 114 0
## 363 3 1970 105 270 1 27 65 134 1
## 364 4 1970 108 305 1 24 65 112 0
## 365 5 1970 132 289 1 19 66 145 0
## 366 6 1970 127 291 1 24 66 135 1
## 367 7 1970 103 278 0 30 60 87 1
## 368 8 1970 73 277 0 29 65 145 0
## 369 1 1971 145 291 0 26 63 119 1
## 370 2 1971 85 255 0 24 68 159 0
## 371 3 1971 138 289 0 33 65 155 0
## 372 4 1971 101 295 0 18 62 145 1
## 373 5 1971 127 280 0 27 62 118 0
## 374 6 1971 107 293 0 20 65 155 1
## 375 7 1971 118 276 0 34 64 116 0
## 376 8 1971 123 267 1 19 66 132 1
## 377 1 1972 115 258 0 26 62 130 0
## 378 2 1972 111 281 1 27 64 112 0
## 379 3 1972 128 281 0 28 63 150 0
## 380 4 1972 71 281 0 32 60 117 1
## 381 5 1972 99 313 1 34 59 100 1
## 382 6 1972 126 262 0 37 66 135 1
## 383 7 1972 127 290 0 27 65 121 0
## 384 8 1972 65 232 0 24 66 125 1
## 385 1 1973 108 283 0 31 65 148 1
## 386 2 1973 124 275 0 28 61 116 0
## 387 3 1973 139 285 0 30 65 129 1
## 388 4 1973 124 292 0 29 68 176 1
## 389 5 1973 115 290 0 30 64 140 1
## 390 6 1973 98 278 0 27 63 110 1
## 391 7 1973 132 270 0 27 65 126 0
## 392 8 1973 118 279 1 21 64 108 0
## 393 1 1974 102 282 0 28 61 110 0
## 394 2 1974 112 292 1 28 62 110 1
## 395 3 1974 104 288 1 27 61 122 1
## 396 4 1974 106 276 0 30 66 130 0
## 397 5 1974 145 290 1 24 67 125 0
## 398 6 1974 96 241 0 23 64 130 1
## 399 7 1974 113 275 1 27 60 100 0
## 400 8 1974 102 283 0 39 60 119 0
## 401 1 1975 143 286 0 31 64 126 0
## 402 2 1975 115 281 0 28 61 128 1
## 403 3 1975 159 296 1 27 64 112 0
## 404 4 1975 101 278 0 25 62 112 1
## 405 5 1975 102 249 1 23 67 134 1
## 406 6 1975 104 282 0 24 63 144 0
## 407 7 1975 128 265 0 24 67 120 0
## 408 8 1975 120 280 0 24 61 118 0
## 409 1 1976 146 267 0 30 67 132 0
## 410 2 1976 72 271 0 39 61 136 0
## 411 3 1976 118 276 0 29 62 130 1
## 412 4 1976 100 277 0 31 62 100 1
## 413 5 1976 136 299 0 29 64 115 0
## 414 6 1976 133 273 1 33 63 135 0
## 415 7 1976 130 291 0 30 65 150 1
## 416 8 1976 108 270 1 21 65 130 1
## 417 1 1977 124 275 0 22 60 130 0
## 418 2 1977 122 281 1 24 65 137 1
## 419 3 1977 99 285 0 25 69 128 1
## 420 4 1977 104 269 0 35 63 110 1
## 421 5 1977 121 282 0 22 66 133 0
## 422 6 1977 93 267 0 25 63 135 1
## 423 7 1977 125 281 1 21 65 110 0
## 424 8 1977 122 280 1 45 62 128 0
## 425 1 1978 124 278 0 26 70 145 1
## 426 2 1978 116 291 0 26 66 153 0
## 427 3 1978 144 281 0 20 63 120 0
## 428 4 1978 117 270 0 24 67 135 1
## 429 5 1978 120 286 0 25 62 105 0
## 430 6 1978 101 280 1 24 65 123 1
## 431 7 1978 117 297 0 38 65 129 0
## 432 8 1978 103 268 0 32 62 97 1
## 433 1 1979 145 257 0 33 65 140 0
## 434 2 1979 127 272 0 20 64 130 1
## 435 3 1979 121 270 0 25 62 108 1
## 436 4 1979 117 267 0 29 65 120 1
## 437 5 1979 118 261 0 26 60 104 0
## 438 6 1979 118 277 0 21 64 155 0
## 439 7 1979 141 277 0 38 66 162 0
## 440 8 1979 105 312 0 41 61 115 1
## 441 1 1980 106 273 0 28 60 116 0
## 442 2 1980 90 266 0 23 61 99 1
## 443 3 1980 117 265 1 24 66 98 0
## 444 4 1980 149 279 0 25 67 135 0
## 445 5 1980 127 304 1 26 62 105 0
## 446 6 1980 130 289 0 21 61 130 1
## 447 7 1980 130 270 1 19 66 130 0
## 448 8 1980 126 273 1 25 68 135 0
## 449 1 1981 75 232 0 33 61 110 0
## 450 2 1981 99 273 1 27 59 115 0
## 451 3 1981 119 293 1 23 65 127 0
## 452 4 1981 135 284 0 25 66 123 0
## 453 5 1981 132 281 1 24 63 117 0
## 454 6 1981 125 288 0 22 63 128 1
## 455 7 1981 139 299 1 20 67 112 0
## 456 8 1981 145 316 0 22 67 142 0
## 457 1 1982 107 273 0 24 61 96 0
## 458 2 1982 144 307 1 26 66 125 0
## 459 3 1982 105 281 1 19 61 130 0
## 460 4 1982 110 283 1 21 66 129 0
## 461 5 1982 102 258 1 22 65 135 0
## 462 6 1982 140 291 1 19 65 122 0
## 463 7 1982 130 283 0 32 65 118 0
## 464 8 1982 139 293 0 34 66 131 0
## 465 1 1983 124 288 0 22 67 118 0
## 466 2 1983 138 280 1 30 65 175 0
## 467 3 1983 125 283 0 37 63 145 1
## 468 4 1983 121 276 0 31 67 130 0
## 469 5 1983 143 279 0 39 65 129 1
## 470 6 1983 115 290 1 19 65 118 0
## 471 7 1983 113 289 1 26 59 91 0
## 472 8 1983 124 290 0 26 65 165 0
## 473 1 1984 122 280 0 23 65 125 1
## 474 2 1984 58 245 0 34 64 156 1
## 475 3 1984 119 259 0 37 62 130 0
## 476 4 1984 142 285 1 24 66 136 0
## 477 5 1984 118 277 0 25 62 120 0
## 478 6 1984 130 293 0 26 63 123 0
## 479 7 1984 77 238 1 23 63 103 1
## 480 8 1984 121 282 0 30 65 122 0
## 481 1 1985 101 245 0 23 63 130 1
## 482 2 1985 109 265 1 24 63 107 1
## 483 3 1985 101 273 0 39 60 113 0
## 484 4 1985 104 260 0 33 64 145 0
## 485 5 1985 102 286 1 22 64 140 0
## 486 6 1985 114 277 1 31 64 125 0
## 487 7 1985 62 228 0 24 61 107 0
## 488 8 1985 126 299 1 21 60 114 0
## 489 1 1986 128 283 0 28 63 125 1
## 490 2 1986 110 277 1 19 62 160 0
## 491 3 1986 105 277 1 25 64 156 0
## 492 4 1986 138 296 0 34 66 120 0
## 493 5 1986 163 280 0 35 69 139 0
## 494 6 1986 105 278 0 21 64 120 0
## 495 7 1986 93 245 0 33 61 100 1
## 496 8 1986 119 286 1 33 67 137 0
## 497 1 1987 104 282 0 36 65 115 1
## 498 2 1987 129 278 0 27 63 128 0
## 499 3 1987 110 281 0 27 60 110 0
## 500 4 1987 112 278 1 21 63 120 0
## 501 5 1987 132 294 0 32 64 116 0
## 502 6 1987 101 289 1 31 60 125 0
## 503 7 1987 109 275 1 37 63 112 1
## 504 8 1987 114 277 1 19 63 107 0
## 505 1 1988 97 246 0 37 63 150 0
## 506 2 1988 150 284 0 40 67 130 0
## 507 3 1988 100 270 1 21 65 132 1
## 508 4 1988 117 293 0 39 60 120 1
## 509 5 1988 116 276 0 33 61 180 0
## 510 6 1988 132 286 0 26 67 122 1
## 511 7 1988 145 283 0 27 65 125 1
## 512 8 1988 118 272 0 23 64 113 0
## 513 1 1989 137 274 0 26 69 137 1
## 514 2 1989 128 279 0 27 66 135 0
## 515 3 1989 98 284 0 29 68 140 0
## 516 4 1989 109 282 0 25 62 106 1
## 517 5 1989 138 288 1 19 66 124 0
## 518 6 1989 112 252 0 37 64 162 0
## 519 7 1989 92 224 0 19 63 134 1
## 520 8 1989 127 295 0 36 65 145 0
## 521 1 1990 103 273 0 31 63 170 1
## 522 2 1990 142 284 1 31 66 137 1
## 523 3 1990 127 276 0 37 64 159 0
## 524 4 1990 131 266 1 28 67 135 0
## 525 5 1990 139 279 0 20 64 143 0
## 526 6 1990 69 232 0 31 59 103 1
## 527 7 1990 120 281 0 26 61 115 0
## 528 8 1990 117 290 1 22 67 110 0
## 529 1 1991 142 276 0 38 63 170 0
## 530 2 1991 115 268 1 31 64 125 0
## 531 3 1991 117 324 0 22 62 164 1
## 532 4 1991 120 273 0 29 64 130 1
## 533 5 1991 132 298 1 23 61 137 0
## 534 6 1991 114 264 0 26 63 110 1
## 535 7 1991 135 284 0 39 67 141 0
## 536 8 1991 137 277 0 41 65 126 0
## 537 1 1992 130 289 0 27 66 130 0
## 538 2 1992 108 283 0 35 62 108 0
## 539 3 1992 122 278 0 37 68 114 0
## 540 4 1992 116 270 0 29 63 132 0
## 541 5 1992 87 282 0 27 63 104 1
## 542 6 1992 123 267 0 29 63 111 1
## 543 7 1992 113 287 0 36 63 118 0
## 544 8 1992 133 292 0 29 65 135 0
## 545 1 1993 156 292 0 26 63 118 0
## 546 2 1993 139 281 0 27 63 137 0
## 547 3 1993 122 273 1 23 64 130 1
## 548 4 1993 140 290 0 23 65 110 0
## 549 5 1993 131 297 0 30 67 132 0
## 550 6 1993 129 284 1 20 66 130 1
## 551 7 1993 126 251 1 28 64 123 0
## 552 8 1993 100 264 0 28 60 111 1
## 553 1 1994 133 284 0 25 66 125 1
## 554 2 1994 115 275 0 25 61 155 1
## 555 3 1994 118 281 1 36 66 140 1
## 556 4 1994 103 273 1 22 64 110 1
## 557 5 1994 130 282 0 26 67 147 1
## 558 6 1994 114 283 1 15 64 117 1
## 559 7 1994 143 270 1 27 70 148 0
## 560 8 1994 107 273 1 26 65 135 0
## 561 1 1995 120 274 0 24 62 120 0
## 562 2 1995 136 288 0 23 62 217 0
## 563 3 1995 137 303 1 23 66 127 1
## 564 4 1995 120 279 1 23 67 135 0
## 565 5 1995 123 290 0 28 66 107 1
## 566 6 1995 115 290 0 31 62 95 0
## 567 7 1995 128 282 1 25 64 125 0
## 568 8 1995 115 276 1 20 62 105 1
## 569 1 1996 91 270 0 24 60 149 1
## 570 2 1996 163 289 1 25 64 126 1
## 571 3 1996 120 275 0 32 63 115 1
## 572 4 1996 139 260 1 32 64 127 0
## 573 5 1996 115 276 1 18 63 110 0
## 574 6 1996 98 272 1 35 64 129 0
## 575 7 1996 98 262 0 22 67 120 0
## 576 8 1996 91 292 1 26 61 113 1
## 577 1 1997 127 274 0 21 62 110 0
## 578 2 1997 131 285 0 26 64 130 0
## 579 3 1997 143 285 0 27 68 185 0
## 580 4 1997 123 254 0 26 62 130 1
## 581 5 1997 116 272 0 27 64 130 1
## 582 6 1997 128 283 0 27 67 126 0
## 583 7 1997 110 306 1 32 61 122 0
## 584 8 1997 112 287 0 27 64 110 1
## 585 1 1998 153 286 0 26 63 107 1
## 586 2 1998 77 238 0 38 67 135 1
## 587 3 1998 108 270 0 29 67 124 1
## 588 4 1998 104 280 1 23 64 107 1
## 589 5 1998 119 286 1 20 67 130 0
## 590 6 1998 119 271 0 28 64 175 1
## 591 7 1998 162 284 0 27 64 126 0
## 592 8 1998 125 289 1 31 61 120 0
## 593 1 1999 121 276 0 39 63 130 0
## 594 2 1999 124 283 1 33 67 156 1
## 595 3 1999 131 284 1 19 61 114 1
## 596 4 1999 111 270 0 22 59 103 0
## 597 5 1999 125 279 1 19 67 135 0
## 598 6 1999 154 288 0 25 65 147 0
## 599 7 1999 116 292 1 20 65 118 0
## 600 8 1999 157 291 0 33 65 121 0
## 601 1 2000 120 277 0 27 63 126 0
## 602 2 2000 104 270 1 26 62 115 0
## 603 3 2000 110 277 0 36 61 116 0
## 604 4 2000 122 277 0 32 63 157 1
## 605 5 2000 144 282 0 33 66 155 1
## 606 6 2000 127 247 1 21 63 140 0
## 607 7 2000 128 284 0 23 62 110 0
## 608 8 2000 108 256 1 26 67 130 0
## 609 1 2001 99 272 0 27 62 103 1
## 610 2 2001 102 267 1 24 61 109 1
## 611 3 2001 105 276 0 20 62 112 1
## 612 4 2001 116 271 1 30 67 144 1
## 613 5 2001 123 269 0 26 67 132 0
## 614 6 2001 131 263 0 29 64 180 1
## 615 7 2001 111 275 1 18 61 108 1
## 616 8 2001 130 279 0 31 62 122 0
## 617 1 2002 149 293 0 35 65 116 0
## 618 2 2002 94 268 0 30 62 105 1
## 619 3 2002 125 255 0 23 63 133 0
## 620 4 2002 129 277 0 27 68 130 1
## 621 5 2002 120 276 0 23 66 114 0
## 622 6 2002 129 288 0 28 59 102 0
## 623 7 2002 137 280 0 34 60 107 0
## 624 8 2002 135 289 0 25 64 127 0
## 625 1 2003 129 280 0 23 64 104 0
## 626 2 2003 158 295 1 37 70 137 0
## 627 3 2003 78 258 1 24 66 115 1
## 628 4 2003 133 292 0 30 65 112 1
## 629 5 2003 140 251 0 28 63 210 0
## 630 6 2003 114 286 1 22 64 116 1
## 631 7 2003 100 264 0 29 64 120 1
## 632 8 2003 123 277 0 24 66 122 0
## 633 1 2004 139 292 0 25 68 135 0
## 634 2 2004 112 275 1 21 68 143 1
## 635 3 2004 114 289 0 36 60 115 0
## 636 4 2004 110 277 0 25 61 130 0
## 637 5 2004 120 271 1 17 64 142 1
## 638 6 2004 110 280 0 29 62 110 1
## 639 7 2004 160 271 0 32 67 215 0
## 640 8 2004 100 281 0 24 61 115 0
is.data.frame(nv2)
## [1] TRUE
length(nv2)
## [1] 9
names(nv2)
## [1] "id" "year" "bwt" "gestation" "parity" "age"
## [7] "height" "weight" "smoke"
dim(nv2)
## [1] 640 9
library(skimr)
skim(nv2)
| Name | nv2 |
| Number of rows | 640 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1 | 4.50 | 2.29 | 1 | 2.75 | 4.5 | 6.25 | 8 | ▇▃▇▃▇ |
| year | 0 | 1 | 1964.50 | 23.11 | 1925 | 1944.75 | 1964.5 | 1984.25 | 2004 | ▇▇▇▇▇ |
| bwt | 0 | 1 | 118.89 | 18.14 | 55 | 107.75 | 120.0 | 131.00 | 169 | ▁▂▇▆▁ |
| gestation | 0 | 1 | 279.33 | 15.80 | 181 | 273.00 | 280.0 | 288.00 | 351 | ▁▁▇▅▁ |
| parity | 0 | 1 | 0.31 | 0.46 | 0 | 0.00 | 0.0 | 1.00 | 1 | ▇▁▁▁▃ |
| age | 0 | 1 | 27.28 | 5.86 | 15 | 23.00 | 26.0 | 31.00 | 45 | ▃▇▅▂▁ |
| height | 0 | 1 | 64.10 | 2.50 | 53 | 62.00 | 64.0 | 66.00 | 71 | ▁▁▅▇▁ |
| weight | 0 | 1 | 128.22 | 19.49 | 87 | 115.00 | 126.0 | 137.00 | 217 | ▃▇▃▁▁ |
| smoke | 0 | 1 | 0.40 | 0.49 | 0 | 0.00 | 0.0 | 1.00 | 1 | ▇▁▁▁▆ |
head(nv2,10)
## id year bwt gestation parity age height weight smoke
## 1 1 1925 120 284 0 27 62 100 0
## 2 2 1925 112 267 1 22 62 138 0
## 3 3 1925 119 286 0 26 64 123 1
## 4 4 1925 124 287 0 27 62 105 1
## 5 5 1925 105 276 0 22 67 130 0
## 6 6 1925 120 289 1 31 59 102 0
## 7 7 1925 82 274 0 31 64 101 1
## 8 8 1925 111 278 0 29 65 145 1
## 9 1 1926 113 282 0 33 64 135 0
## 10 2 1926 134 297 0 27 67 170 1
str(nv2)
## 'data.frame': 640 obs. of 9 variables:
## $ id : num 1 2 3 4 5 6 7 8 1 2 ...
## $ year : num 1925 1925 1925 1925 1925 ...
## $ bwt : num 120 112 119 124 105 120 82 111 113 134 ...
## $ gestation: num 284 267 286 287 276 289 274 278 282 297 ...
## $ parity : num 0 1 0 0 0 1 0 0 0 0 ...
## $ age : num 27 22 26 27 22 31 31 29 33 27 ...
## $ height : num 62 62 64 62 67 59 64 65 64 67 ...
## $ weight : num 100 138 123 105 130 102 101 145 135 170 ...
## $ smoke : num 0 0 1 1 0 0 1 1 0 1 ...
tail(nv2,10)
## id year bwt gestation parity age height weight smoke
## 631 7 2003 100 264 0 29 64 120 1
## 632 8 2003 123 277 0 24 66 122 0
## 633 1 2004 139 292 0 25 68 135 0
## 634 2 2004 112 275 1 21 68 143 1
## 635 3 2004 114 289 0 36 60 115 0
## 636 4 2004 110 277 0 25 61 130 0
## 637 5 2004 120 271 1 17 64 142 1
## 638 6 2004 110 280 0 29 62 110 1
## 639 7 2004 160 271 0 32 67 215 0
## 640 8 2004 100 281 0 24 61 115 0
is.na(nv2)
## id year bwt gestation parity age height weight smoke
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [638,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [639,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [640,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
sum(is.na(nv2))
## [1] 0
which(is.na(nv2))
## integer(0)
names(nv2) <- c('I','Y','B','G','P','A','H','W','S')
nv2
## I Y B G P A H W S
## 1 1 1925 120 284 0 27 62 100 0
## 2 2 1925 112 267 1 22 62 138 0
## 3 3 1925 119 286 0 26 64 123 1
## 4 4 1925 124 287 0 27 62 105 1
## 5 5 1925 105 276 0 22 67 130 0
## 6 6 1925 120 289 1 31 59 102 0
## 7 7 1925 82 274 0 31 64 101 1
## 8 8 1925 111 278 0 29 65 145 1
## 9 1 1926 113 282 0 33 64 135 0
## 10 2 1926 134 297 0 27 67 170 1
## 11 3 1926 97 279 0 29 68 178 1
## 12 4 1926 125 292 0 22 65 122 0
## 13 5 1926 93 246 0 37 65 130 0
## 14 6 1926 146 280 0 23 61 145 0
## 15 7 1926 100 274 0 24 63 113 0
## 16 8 1926 103 250 0 40 59 140 0
## 17 1 1927 128 279 0 28 64 115 1
## 18 2 1927 145 308 0 35 64 110 1
## 19 3 1927 99 252 0 21 64 120 0
## 20 4 1927 110 262 0 25 66 140 0
## 21 5 1927 122 281 0 42 63 103 1
## 22 6 1927 112 283 1 21 62 102 1
## 23 7 1927 114 271 0 32 61 130 0
## 24 8 1927 114 276 0 26 62 127 0
## 25 1 1928 108 282 0 23 67 125 1
## 26 2 1928 116 295 0 32 65 120 0
## 27 3 1928 115 264 1 23 67 134 1
## 28 4 1928 125 279 0 23 63 104 1
## 29 5 1928 133 293 0 23 64 110 1
## 30 6 1928 132 278 0 20 64 150 1
## 31 7 1928 97 269 0 20 65 137 1
## 32 8 1928 75 247 0 36 64 120 1
## 33 1 1929 136 286 0 25 62 93 0
## 34 2 1929 126 278 0 26 64 150 1
## 35 3 1929 139 284 0 37 61 121 0
## 36 4 1929 138 294 0 40 64 125 0
## 37 5 1929 130 296 1 22 66 117 1
## 38 6 1929 146 263 0 39 53 110 1
## 39 7 1929 126 298 0 24 61 112 0
## 40 8 1929 169 296 0 33 67 185 0
## 41 1 1930 138 244 0 33 62 178 0
## 42 2 1930 111 285 0 29 65 130 0
## 43 3 1930 144 304 1 27 58 102 1
## 44 4 1930 142 284 0 39 66 132 0
## 45 5 1930 104 307 0 24 59 122 0
## 46 6 1930 122 275 0 30 68 140 0
## 47 7 1930 122 275 1 20 65 127 0
## 48 8 1930 94 271 0 36 61 130 1
## 49 1 1931 132 245 0 23 65 140 0
## 50 2 1931 126 282 0 33 62 117 0
## 51 3 1931 99 270 0 22 63 115 1
## 52 4 1931 115 278 0 23 60 102 1
## 53 5 1931 106 278 0 31 65 110 1
## 54 6 1931 128 292 0 32 66 130 0
## 55 7 1931 152 295 0 39 62 140 0
## 56 8 1931 150 287 0 36 62 135 0
## 57 1 1932 120 289 0 25 62 125 0
## 58 2 1932 109 291 0 39 64 107 0
## 59 3 1932 105 280 1 22 63 116 0
## 60 4 1932 102 280 0 38 67 140 0
## 61 5 1932 120 281 0 33 63 113 0
## 62 6 1932 119 277 0 24 63 120 1
## 63 7 1932 116 274 0 21 62 110 1
## 64 8 1932 144 248 0 30 70 145 0
## 65 1 1933 143 299 0 30 66 136 1
## 66 2 1933 136 291 0 41 66 191 0
## 67 3 1933 89 275 0 34 66 170 0
## 68 4 1933 140 294 0 25 61 103 0
## 69 5 1933 118 276 1 18 63 128 0
## 70 6 1933 135 278 0 27 66 148 0
## 71 7 1933 132 302 0 36 63 145 1
## 72 8 1933 144 291 0 28 67 130 0
## 73 1 1934 140 351 0 27 68 120 0
## 74 2 1934 119 286 0 22 63 185 1
## 75 3 1934 129 270 0 43 67 160 0
## 76 4 1934 133 276 1 22 63 119 0
## 77 5 1934 140 290 1 19 67 132 1
## 78 6 1934 129 235 0 24 66 135 0
## 79 7 1934 84 260 1 37 66 140 0
## 80 8 1934 143 313 0 20 68 150 0
## 81 1 1935 144 282 0 32 64 124 1
## 82 2 1935 103 267 1 21 66 150 1
## 83 3 1935 119 270 1 20 64 109 0
## 84 4 1935 127 290 0 35 66 165 0
## 85 5 1935 114 268 0 22 64 104 0
## 86 6 1935 116 293 1 28 62 108 0
## 87 7 1935 119 277 1 18 61 89 1
## 88 8 1935 145 304 1 25 63 109 1
## 89 1 1936 141 279 0 23 63 128 1
## 90 2 1936 124 284 1 17 62 112 0
## 91 3 1936 114 291 0 35 60 112 0
## 92 4 1936 104 274 1 20 62 115 1
## 93 5 1936 116 280 0 40 62 159 0
## 94 6 1936 100 275 0 27 64 111 1
## 95 7 1936 106 312 0 24 62 135 1
## 96 8 1936 121 285 0 34 64 110 0
## 97 1 1937 110 281 0 36 61 99 1
## 98 2 1937 155 286 0 31 66 127 0
## 99 3 1937 106 289 0 28 67 120 1
## 100 4 1937 119 275 0 42 67 156 1
## 101 5 1937 129 284 0 24 64 115 0
## 102 6 1937 138 257 0 38 67 138 0
## 103 7 1937 139 291 0 24 65 160 0
## 104 8 1937 105 256 0 31 66 142 0
## 105 1 1938 114 273 0 30 63 154 0
## 106 2 1938 122 282 1 21 66 110 0
## 107 3 1938 122 292 1 34 65 133 0
## 108 4 1938 152 301 0 29 65 150 0
## 109 5 1938 120 286 0 22 62 115 1
## 110 6 1938 123 282 0 22 65 130 0
## 111 7 1938 103 273 0 36 65 158 1
## 112 8 1938 134 286 0 25 64 125 0
## 113 1 1939 115 285 0 38 63 130 0
## 114 2 1939 113 285 0 26 66 140 0
## 115 3 1939 136 261 0 24 65 110 0
## 116 4 1939 123 284 1 20 65 120 1
## 117 5 1939 127 281 0 24 63 112 1
## 118 6 1939 113 288 1 21 61 120 0
## 119 7 1939 112 299 0 24 67 145 1
## 120 8 1939 129 294 1 21 65 132 0
## 121 1 1940 92 255 0 25 65 125 1
## 122 2 1940 122 273 0 26 66 210 0
## 123 3 1940 121 286 1 22 69 130 1
## 124 4 1940 143 273 0 19 66 135 0
## 125 5 1940 71 234 0 32 64 110 1
## 126 6 1940 129 280 1 24 65 140 1
## 127 7 1940 96 276 0 33 64 127 1
## 128 8 1940 114 276 0 24 63 110 0
## 129 1 1941 115 261 0 33 60 125 1
## 130 2 1941 126 293 1 27 62 111 0
## 131 3 1941 112 282 0 26 65 122 0
## 132 4 1941 131 308 0 40 65 160 0
## 133 5 1941 88 274 0 30 66 130 0
## 134 6 1941 122 280 0 24 67 127 1
## 135 7 1941 102 281 1 19 67 135 1
## 136 8 1941 97 265 0 30 61 110 0
## 137 1 1942 144 261 0 33 68 170 0
## 138 2 1942 116 277 0 41 64 124 1
## 139 3 1942 112 266 0 26 64 122 0
## 140 4 1942 141 319 1 20 67 140 1
## 141 5 1942 122 286 0 23 64 145 0
## 142 6 1942 132 281 1 21 67 140 0
## 143 7 1942 120 300 0 34 63 150 1
## 144 8 1942 160 292 0 28 64 120 0
## 145 1 1943 119 288 0 43 66 142 1
## 146 2 1943 102 294 0 21 65 130 1
## 147 3 1943 123 314 0 22 61 121 1
## 148 4 1943 129 277 0 30 66 142 1
## 149 5 1943 106 302 1 19 66 147 0
## 150 6 1943 120 269 1 40 63 130 0
## 151 7 1943 102 338 0 19 64 170 0
## 152 8 1943 65 237 0 31 67 130 0
## 153 1 1944 105 270 0 22 56 93 0
## 154 2 1944 110 181 0 27 64 133 0
## 155 3 1944 139 286 0 33 65 125 1
## 156 4 1944 113 282 1 36 59 140 0
## 157 5 1944 135 285 0 30 66 130 0
## 158 6 1944 114 283 1 20 65 115 0
## 159 7 1944 97 255 1 22 63 107 1
## 160 8 1944 145 288 0 28 64 116 0
## 161 1 1945 115 274 0 27 67 175 1
## 162 2 1945 133 285 1 30 64 160 0
## 163 3 1945 125 290 0 36 59 105 0
## 164 4 1945 119 292 0 33 62 118 1
## 165 5 1945 107 290 0 26 63 112 0
## 166 6 1945 130 280 0 29 66 135 0
## 167 7 1945 113 285 0 22 70 145 0
## 168 8 1945 95 273 0 23 60 90 0
## 169 1 1946 137 287 0 25 66 145 0
## 170 2 1946 125 283 0 29 65 125 0
## 171 3 1946 105 295 1 20 64 112 1
## 172 4 1946 109 295 1 23 63 103 1
## 173 5 1946 129 294 0 32 62 170 1
## 174 6 1946 117 286 0 32 66 127 1
## 175 7 1946 130 297 0 32 58 130 0
## 176 8 1946 139 293 1 21 69 130 0
## 177 1 1947 122 276 0 30 68 182 0
## 178 2 1947 164 286 1 32 66 143 0
## 179 3 1947 130 276 0 41 68 130 0
## 180 4 1947 104 280 1 27 68 146 1
## 181 5 1947 126 274 0 39 62 122 0
## 182 6 1947 142 285 0 33 63 124 0
## 183 7 1947 97 260 1 25 63 115 1
## 184 8 1947 123 288 0 27 63 125 0
## 185 1 1948 131 294 0 23 65 122 0
## 186 2 1948 133 297 0 36 61 125 0
## 187 3 1948 146 294 0 22 66 145 1
## 188 4 1948 131 282 1 21 66 126 0
## 189 5 1948 116 293 1 26 64 125 0
## 190 6 1948 144 273 0 27 62 118 1
## 191 7 1948 116 273 0 31 61 120 0
## 192 8 1948 109 283 0 23 65 112 1
## 193 1 1949 103 261 0 27 65 112 1
## 194 2 1949 124 293 1 19 65 150 0
## 195 3 1949 133 290 0 21 64 145 0
## 196 4 1949 110 293 1 28 64 135 1
## 197 5 1949 124 294 0 26 62 122 0
## 198 6 1949 127 262 1 32 64 125 0
## 199 7 1949 114 266 0 29 64 113 0
## 200 8 1949 110 268 0 34 64 127 0
## 201 1 1950 146 280 0 26 58 106 0
## 202 2 1950 122 306 1 22 62 100 0
## 203 3 1950 147 296 1 19 67 124 0
## 204 4 1950 148 279 0 27 71 189 0
## 205 5 1950 123 281 0 23 68 136 0
## 206 6 1950 115 270 0 25 67 165 1
## 207 7 1950 127 242 0 17 61 135 1
## 208 8 1950 122 296 1 24 65 132 0
## 209 1 1951 114 266 0 20 65 175 1
## 210 2 1951 121 271 1 34 63 129 1
## 211 3 1951 109 269 0 23 63 113 0
## 212 4 1951 137 283 1 20 65 157 0
## 213 5 1951 145 315 0 39 67 143 1
## 214 6 1951 85 258 0 41 67 137 0
## 215 7 1951 87 247 1 18 66 125 1
## 216 8 1951 115 307 0 34 65 128 1
## 217 1 1952 125 292 0 32 65 125 0
## 218 2 1952 100 272 0 30 64 150 1
## 219 3 1952 122 286 0 23 64 120 1
## 220 4 1952 117 283 0 27 63 108 0
## 221 5 1952 102 278 0 27 67 135 1
## 222 6 1952 99 274 0 28 66 118 1
## 223 7 1952 141 281 0 29 54 156 1
## 224 8 1952 108 279 1 19 64 115 0
## 225 1 1953 114 274 0 28 66 132 1
## 226 2 1953 90 266 1 26 67 135 0
## 227 3 1953 135 260 0 43 65 135 0
## 228 4 1953 115 302 1 22 67 135 0
## 229 5 1953 129 293 0 30 65 130 1
## 230 6 1953 123 323 1 17 64 140 0
## 231 7 1953 144 283 1 25 66 140 0
## 232 8 1953 120 287 0 23 67 116 1
## 233 1 1954 122 270 0 26 61 105 0
## 234 2 1954 128 272 1 18 67 109 0
## 235 3 1954 117 272 0 32 66 118 0
## 236 4 1954 98 280 0 35 64 122 1
## 237 5 1954 98 276 1 22 61 121 0
## 238 6 1954 112 281 1 23 61 150 0
## 239 7 1954 116 273 0 33 66 130 1
## 240 8 1954 131 269 0 36 68 145 0
## 241 1 1955 93 278 0 34 61 146 0
## 242 2 1955 86 276 1 23 65 125 1
## 243 3 1955 138 284 0 30 66 133 1
## 244 4 1955 136 303 1 20 68 148 1
## 245 5 1955 110 272 0 28 60 108 0
## 246 6 1955 68 223 0 32 66 149 1
## 247 7 1955 75 265 0 21 65 103 1
## 248 8 1955 136 283 1 24 63 119 0
## 249 1 1956 130 268 0 30 66 123 0
## 250 2 1956 123 282 0 30 63 118 0
## 251 3 1956 120 283 0 28 64 122 1
## 252 4 1956 121 276 1 23 71 152 1
## 253 5 1956 135 282 0 24 67 128 1
## 254 6 1956 102 283 1 19 65 127 1
## 255 7 1956 138 286 1 28 68 120 0
## 256 8 1956 125 290 0 32 63 135 0
## 257 1 1957 119 275 0 23 60 105 0
## 258 2 1957 87 275 0 28 63 110 1
## 259 3 1957 119 273 0 35 65 125 1
## 260 4 1957 132 285 1 25 63 140 0
## 261 5 1957 101 278 1 20 62 105 0
## 262 6 1957 109 273 0 37 65 138 1
## 263 7 1957 99 271 0 39 69 151 0
## 264 8 1957 96 285 1 20 66 117 1
## 265 1 1958 113 281 0 24 65 120 0
## 266 2 1958 128 291 1 27 63 132 0
## 267 3 1958 118 278 1 19 62 126 0
## 268 4 1958 91 264 0 36 60 100 1
## 269 5 1958 96 266 0 26 65 125 0
## 270 6 1958 102 267 1 25 60 93 1
## 271 7 1958 118 293 0 21 63 103 0
## 272 8 1958 102 282 1 29 65 125 1
## 273 1 1959 134 283 0 22 67 130 0
## 274 2 1959 120 288 0 28 63 125 0
## 275 3 1959 105 330 0 23 64 112 1
## 276 4 1959 119 294 0 34 59 105 0
## 277 5 1959 104 276 1 18 60 109 1
## 278 6 1959 99 275 0 23 61 125 1
## 279 7 1959 97 266 0 24 62 109 0
## 280 8 1959 102 288 1 18 65 117 0
## 281 1 1960 107 279 0 24 63 115 0
## 282 2 1960 125 301 1 35 68 181 0
## 283 3 1960 113 306 1 21 65 137 0
## 284 4 1960 85 273 0 26 60 105 1
## 285 5 1960 100 249 0 24 67 100 0
## 286 6 1960 78 256 1 29 65 123 0
## 287 7 1960 146 319 0 28 66 145 0
## 288 8 1960 112 277 1 22 67 120 0
## 289 1 1961 134 288 0 23 63 92 1
## 290 2 1961 118 265 0 27 61 123 0
## 291 3 1961 148 291 1 21 63 115 0
## 292 4 1961 106 271 1 26 61 110 1
## 293 5 1961 154 292 0 40 66 145 0
## 294 6 1961 128 284 1 19 66 111 1
## 295 7 1961 81 285 0 19 63 150 1
## 296 8 1961 135 272 0 30 65 130 0
## 297 1 1962 122 267 0 27 65 101 1
## 298 2 1962 116 284 1 24 66 117 0
## 299 3 1962 140 281 1 22 69 135 0
## 300 4 1962 132 284 0 29 64 122 0
## 301 5 1962 127 293 0 31 67 137 0
## 302 6 1962 107 303 1 25 67 133 0
## 303 7 1962 110 321 0 28 66 180 0
## 304 8 1962 91 266 0 23 60 120 1
## 305 1 1963 129 293 0 30 61 160 0
## 306 2 1963 131 262 0 22 67 135 0
## 307 3 1963 134 287 1 33 67 131 0
## 308 4 1963 80 266 1 25 62 125 0
## 309 5 1963 126 288 0 31 62 150 0
## 310 6 1963 136 295 0 23 64 147 0
## 311 7 1963 135 284 1 19 60 95 0
## 312 8 1963 129 276 0 31 63 125 0
## 313 1 1964 110 278 0 23 63 177 0
## 314 2 1964 151 286 1 22 66 130 0
## 315 3 1964 120 280 0 31 61 111 0
## 316 4 1964 109 286 0 24 64 125 1
## 317 5 1964 126 282 1 23 66 115 1
## 318 6 1964 101 278 0 27 61 99 1
## 319 7 1964 114 290 1 21 65 120 1
## 320 8 1964 155 290 0 26 66 129 1
## 321 1 1965 111 270 0 27 61 119 0
## 322 2 1965 88 273 0 20 66 110 1
## 323 3 1965 123 296 1 26 64 110 1
## 324 4 1965 111 306 0 27 61 102 0
## 325 5 1965 127 279 0 26 67 155 1
## 326 6 1965 100 275 1 25 64 125 0
## 327 7 1965 124 288 1 21 64 116 1
## 328 8 1965 109 274 0 33 69 144 1
## 329 1 1966 87 248 0 37 65 130 1
## 330 2 1966 137 284 0 30 67 110 0
## 331 3 1966 102 275 0 43 64 160 0
## 332 4 1966 143 292 1 21 65 125 0
## 333 5 1966 98 275 0 25 65 112 1
## 334 6 1966 109 272 0 41 66 154 1
## 335 7 1966 115 262 1 23 64 136 1
## 336 8 1966 80 262 1 31 61 100 1
## 337 1 1967 143 274 0 27 63 110 1
## 338 2 1967 127 289 0 23 67 140 0
## 339 3 1967 55 204 0 35 65 140 0
## 340 4 1967 136 290 0 26 66 135 0
## 341 5 1967 127 288 1 21 66 130 0
## 342 6 1967 117 281 1 21 70 141 1
## 343 7 1967 143 281 0 28 65 135 1
## 344 8 1967 125 273 0 30 64 145 0
## 345 1 1968 155 294 0 32 66 150 0
## 346 2 1968 96 278 1 18 60 120 1
## 347 3 1968 103 276 1 19 63 149 1
## 348 4 1968 110 285 1 19 64 130 0
## 349 5 1968 129 299 0 22 68 145 0
## 350 6 1968 88 252 1 21 60 115 1
## 351 7 1968 113 287 1 29 70 145 1
## 352 8 1968 94 284 0 24 63 104 1
## 353 1 1969 110 272 0 25 60 90 0
## 354 2 1969 129 281 0 31 67 155 0
## 355 3 1969 123 283 0 21 65 110 0
## 356 4 1969 98 257 0 29 66 130 1
## 357 5 1969 131 292 1 22 64 124 1
## 358 6 1969 95 270 0 35 65 135 1
## 359 7 1969 109 244 1 21 63 102 1
## 360 8 1969 148 281 0 27 63 110 1
## 361 1 1970 122 275 0 26 66 147 0
## 362 2 1970 128 288 1 26 65 114 0
## 363 3 1970 105 270 1 27 65 134 1
## 364 4 1970 108 305 1 24 65 112 0
## 365 5 1970 132 289 1 19 66 145 0
## 366 6 1970 127 291 1 24 66 135 1
## 367 7 1970 103 278 0 30 60 87 1
## 368 8 1970 73 277 0 29 65 145 0
## 369 1 1971 145 291 0 26 63 119 1
## 370 2 1971 85 255 0 24 68 159 0
## 371 3 1971 138 289 0 33 65 155 0
## 372 4 1971 101 295 0 18 62 145 1
## 373 5 1971 127 280 0 27 62 118 0
## 374 6 1971 107 293 0 20 65 155 1
## 375 7 1971 118 276 0 34 64 116 0
## 376 8 1971 123 267 1 19 66 132 1
## 377 1 1972 115 258 0 26 62 130 0
## 378 2 1972 111 281 1 27 64 112 0
## 379 3 1972 128 281 0 28 63 150 0
## 380 4 1972 71 281 0 32 60 117 1
## 381 5 1972 99 313 1 34 59 100 1
## 382 6 1972 126 262 0 37 66 135 1
## 383 7 1972 127 290 0 27 65 121 0
## 384 8 1972 65 232 0 24 66 125 1
## 385 1 1973 108 283 0 31 65 148 1
## 386 2 1973 124 275 0 28 61 116 0
## 387 3 1973 139 285 0 30 65 129 1
## 388 4 1973 124 292 0 29 68 176 1
## 389 5 1973 115 290 0 30 64 140 1
## 390 6 1973 98 278 0 27 63 110 1
## 391 7 1973 132 270 0 27 65 126 0
## 392 8 1973 118 279 1 21 64 108 0
## 393 1 1974 102 282 0 28 61 110 0
## 394 2 1974 112 292 1 28 62 110 1
## 395 3 1974 104 288 1 27 61 122 1
## 396 4 1974 106 276 0 30 66 130 0
## 397 5 1974 145 290 1 24 67 125 0
## 398 6 1974 96 241 0 23 64 130 1
## 399 7 1974 113 275 1 27 60 100 0
## 400 8 1974 102 283 0 39 60 119 0
## 401 1 1975 143 286 0 31 64 126 0
## 402 2 1975 115 281 0 28 61 128 1
## 403 3 1975 159 296 1 27 64 112 0
## 404 4 1975 101 278 0 25 62 112 1
## 405 5 1975 102 249 1 23 67 134 1
## 406 6 1975 104 282 0 24 63 144 0
## 407 7 1975 128 265 0 24 67 120 0
## 408 8 1975 120 280 0 24 61 118 0
## 409 1 1976 146 267 0 30 67 132 0
## 410 2 1976 72 271 0 39 61 136 0
## 411 3 1976 118 276 0 29 62 130 1
## 412 4 1976 100 277 0 31 62 100 1
## 413 5 1976 136 299 0 29 64 115 0
## 414 6 1976 133 273 1 33 63 135 0
## 415 7 1976 130 291 0 30 65 150 1
## 416 8 1976 108 270 1 21 65 130 1
## 417 1 1977 124 275 0 22 60 130 0
## 418 2 1977 122 281 1 24 65 137 1
## 419 3 1977 99 285 0 25 69 128 1
## 420 4 1977 104 269 0 35 63 110 1
## 421 5 1977 121 282 0 22 66 133 0
## 422 6 1977 93 267 0 25 63 135 1
## 423 7 1977 125 281 1 21 65 110 0
## 424 8 1977 122 280 1 45 62 128 0
## 425 1 1978 124 278 0 26 70 145 1
## 426 2 1978 116 291 0 26 66 153 0
## 427 3 1978 144 281 0 20 63 120 0
## 428 4 1978 117 270 0 24 67 135 1
## 429 5 1978 120 286 0 25 62 105 0
## 430 6 1978 101 280 1 24 65 123 1
## 431 7 1978 117 297 0 38 65 129 0
## 432 8 1978 103 268 0 32 62 97 1
## 433 1 1979 145 257 0 33 65 140 0
## 434 2 1979 127 272 0 20 64 130 1
## 435 3 1979 121 270 0 25 62 108 1
## 436 4 1979 117 267 0 29 65 120 1
## 437 5 1979 118 261 0 26 60 104 0
## 438 6 1979 118 277 0 21 64 155 0
## 439 7 1979 141 277 0 38 66 162 0
## 440 8 1979 105 312 0 41 61 115 1
## 441 1 1980 106 273 0 28 60 116 0
## 442 2 1980 90 266 0 23 61 99 1
## 443 3 1980 117 265 1 24 66 98 0
## 444 4 1980 149 279 0 25 67 135 0
## 445 5 1980 127 304 1 26 62 105 0
## 446 6 1980 130 289 0 21 61 130 1
## 447 7 1980 130 270 1 19 66 130 0
## 448 8 1980 126 273 1 25 68 135 0
## 449 1 1981 75 232 0 33 61 110 0
## 450 2 1981 99 273 1 27 59 115 0
## 451 3 1981 119 293 1 23 65 127 0
## 452 4 1981 135 284 0 25 66 123 0
## 453 5 1981 132 281 1 24 63 117 0
## 454 6 1981 125 288 0 22 63 128 1
## 455 7 1981 139 299 1 20 67 112 0
## 456 8 1981 145 316 0 22 67 142 0
## 457 1 1982 107 273 0 24 61 96 0
## 458 2 1982 144 307 1 26 66 125 0
## 459 3 1982 105 281 1 19 61 130 0
## 460 4 1982 110 283 1 21 66 129 0
## 461 5 1982 102 258 1 22 65 135 0
## 462 6 1982 140 291 1 19 65 122 0
## 463 7 1982 130 283 0 32 65 118 0
## 464 8 1982 139 293 0 34 66 131 0
## 465 1 1983 124 288 0 22 67 118 0
## 466 2 1983 138 280 1 30 65 175 0
## 467 3 1983 125 283 0 37 63 145 1
## 468 4 1983 121 276 0 31 67 130 0
## 469 5 1983 143 279 0 39 65 129 1
## 470 6 1983 115 290 1 19 65 118 0
## 471 7 1983 113 289 1 26 59 91 0
## 472 8 1983 124 290 0 26 65 165 0
## 473 1 1984 122 280 0 23 65 125 1
## 474 2 1984 58 245 0 34 64 156 1
## 475 3 1984 119 259 0 37 62 130 0
## 476 4 1984 142 285 1 24 66 136 0
## 477 5 1984 118 277 0 25 62 120 0
## 478 6 1984 130 293 0 26 63 123 0
## 479 7 1984 77 238 1 23 63 103 1
## 480 8 1984 121 282 0 30 65 122 0
## 481 1 1985 101 245 0 23 63 130 1
## 482 2 1985 109 265 1 24 63 107 1
## 483 3 1985 101 273 0 39 60 113 0
## 484 4 1985 104 260 0 33 64 145 0
## 485 5 1985 102 286 1 22 64 140 0
## 486 6 1985 114 277 1 31 64 125 0
## 487 7 1985 62 228 0 24 61 107 0
## 488 8 1985 126 299 1 21 60 114 0
## 489 1 1986 128 283 0 28 63 125 1
## 490 2 1986 110 277 1 19 62 160 0
## 491 3 1986 105 277 1 25 64 156 0
## 492 4 1986 138 296 0 34 66 120 0
## 493 5 1986 163 280 0 35 69 139 0
## 494 6 1986 105 278 0 21 64 120 0
## 495 7 1986 93 245 0 33 61 100 1
## 496 8 1986 119 286 1 33 67 137 0
## 497 1 1987 104 282 0 36 65 115 1
## 498 2 1987 129 278 0 27 63 128 0
## 499 3 1987 110 281 0 27 60 110 0
## 500 4 1987 112 278 1 21 63 120 0
## 501 5 1987 132 294 0 32 64 116 0
## 502 6 1987 101 289 1 31 60 125 0
## 503 7 1987 109 275 1 37 63 112 1
## 504 8 1987 114 277 1 19 63 107 0
## 505 1 1988 97 246 0 37 63 150 0
## 506 2 1988 150 284 0 40 67 130 0
## 507 3 1988 100 270 1 21 65 132 1
## 508 4 1988 117 293 0 39 60 120 1
## 509 5 1988 116 276 0 33 61 180 0
## 510 6 1988 132 286 0 26 67 122 1
## 511 7 1988 145 283 0 27 65 125 1
## 512 8 1988 118 272 0 23 64 113 0
## 513 1 1989 137 274 0 26 69 137 1
## 514 2 1989 128 279 0 27 66 135 0
## 515 3 1989 98 284 0 29 68 140 0
## 516 4 1989 109 282 0 25 62 106 1
## 517 5 1989 138 288 1 19 66 124 0
## 518 6 1989 112 252 0 37 64 162 0
## 519 7 1989 92 224 0 19 63 134 1
## 520 8 1989 127 295 0 36 65 145 0
## 521 1 1990 103 273 0 31 63 170 1
## 522 2 1990 142 284 1 31 66 137 1
## 523 3 1990 127 276 0 37 64 159 0
## 524 4 1990 131 266 1 28 67 135 0
## 525 5 1990 139 279 0 20 64 143 0
## 526 6 1990 69 232 0 31 59 103 1
## 527 7 1990 120 281 0 26 61 115 0
## 528 8 1990 117 290 1 22 67 110 0
## 529 1 1991 142 276 0 38 63 170 0
## 530 2 1991 115 268 1 31 64 125 0
## 531 3 1991 117 324 0 22 62 164 1
## 532 4 1991 120 273 0 29 64 130 1
## 533 5 1991 132 298 1 23 61 137 0
## 534 6 1991 114 264 0 26 63 110 1
## 535 7 1991 135 284 0 39 67 141 0
## 536 8 1991 137 277 0 41 65 126 0
## 537 1 1992 130 289 0 27 66 130 0
## 538 2 1992 108 283 0 35 62 108 0
## 539 3 1992 122 278 0 37 68 114 0
## 540 4 1992 116 270 0 29 63 132 0
## 541 5 1992 87 282 0 27 63 104 1
## 542 6 1992 123 267 0 29 63 111 1
## 543 7 1992 113 287 0 36 63 118 0
## 544 8 1992 133 292 0 29 65 135 0
## 545 1 1993 156 292 0 26 63 118 0
## 546 2 1993 139 281 0 27 63 137 0
## 547 3 1993 122 273 1 23 64 130 1
## 548 4 1993 140 290 0 23 65 110 0
## 549 5 1993 131 297 0 30 67 132 0
## 550 6 1993 129 284 1 20 66 130 1
## 551 7 1993 126 251 1 28 64 123 0
## 552 8 1993 100 264 0 28 60 111 1
## 553 1 1994 133 284 0 25 66 125 1
## 554 2 1994 115 275 0 25 61 155 1
## 555 3 1994 118 281 1 36 66 140 1
## 556 4 1994 103 273 1 22 64 110 1
## 557 5 1994 130 282 0 26 67 147 1
## 558 6 1994 114 283 1 15 64 117 1
## 559 7 1994 143 270 1 27 70 148 0
## 560 8 1994 107 273 1 26 65 135 0
## 561 1 1995 120 274 0 24 62 120 0
## 562 2 1995 136 288 0 23 62 217 0
## 563 3 1995 137 303 1 23 66 127 1
## 564 4 1995 120 279 1 23 67 135 0
## 565 5 1995 123 290 0 28 66 107 1
## 566 6 1995 115 290 0 31 62 95 0
## 567 7 1995 128 282 1 25 64 125 0
## 568 8 1995 115 276 1 20 62 105 1
## 569 1 1996 91 270 0 24 60 149 1
## 570 2 1996 163 289 1 25 64 126 1
## 571 3 1996 120 275 0 32 63 115 1
## 572 4 1996 139 260 1 32 64 127 0
## 573 5 1996 115 276 1 18 63 110 0
## 574 6 1996 98 272 1 35 64 129 0
## 575 7 1996 98 262 0 22 67 120 0
## 576 8 1996 91 292 1 26 61 113 1
## 577 1 1997 127 274 0 21 62 110 0
## 578 2 1997 131 285 0 26 64 130 0
## 579 3 1997 143 285 0 27 68 185 0
## 580 4 1997 123 254 0 26 62 130 1
## 581 5 1997 116 272 0 27 64 130 1
## 582 6 1997 128 283 0 27 67 126 0
## 583 7 1997 110 306 1 32 61 122 0
## 584 8 1997 112 287 0 27 64 110 1
## 585 1 1998 153 286 0 26 63 107 1
## 586 2 1998 77 238 0 38 67 135 1
## 587 3 1998 108 270 0 29 67 124 1
## 588 4 1998 104 280 1 23 64 107 1
## 589 5 1998 119 286 1 20 67 130 0
## 590 6 1998 119 271 0 28 64 175 1
## 591 7 1998 162 284 0 27 64 126 0
## 592 8 1998 125 289 1 31 61 120 0
## 593 1 1999 121 276 0 39 63 130 0
## 594 2 1999 124 283 1 33 67 156 1
## 595 3 1999 131 284 1 19 61 114 1
## 596 4 1999 111 270 0 22 59 103 0
## 597 5 1999 125 279 1 19 67 135 0
## 598 6 1999 154 288 0 25 65 147 0
## 599 7 1999 116 292 1 20 65 118 0
## 600 8 1999 157 291 0 33 65 121 0
## 601 1 2000 120 277 0 27 63 126 0
## 602 2 2000 104 270 1 26 62 115 0
## 603 3 2000 110 277 0 36 61 116 0
## 604 4 2000 122 277 0 32 63 157 1
## 605 5 2000 144 282 0 33 66 155 1
## 606 6 2000 127 247 1 21 63 140 0
## 607 7 2000 128 284 0 23 62 110 0
## 608 8 2000 108 256 1 26 67 130 0
## 609 1 2001 99 272 0 27 62 103 1
## 610 2 2001 102 267 1 24 61 109 1
## 611 3 2001 105 276 0 20 62 112 1
## 612 4 2001 116 271 1 30 67 144 1
## 613 5 2001 123 269 0 26 67 132 0
## 614 6 2001 131 263 0 29 64 180 1
## 615 7 2001 111 275 1 18 61 108 1
## 616 8 2001 130 279 0 31 62 122 0
## 617 1 2002 149 293 0 35 65 116 0
## 618 2 2002 94 268 0 30 62 105 1
## 619 3 2002 125 255 0 23 63 133 0
## 620 4 2002 129 277 0 27 68 130 1
## 621 5 2002 120 276 0 23 66 114 0
## 622 6 2002 129 288 0 28 59 102 0
## 623 7 2002 137 280 0 34 60 107 0
## 624 8 2002 135 289 0 25 64 127 0
## 625 1 2003 129 280 0 23 64 104 0
## 626 2 2003 158 295 1 37 70 137 0
## 627 3 2003 78 258 1 24 66 115 1
## 628 4 2003 133 292 0 30 65 112 1
## 629 5 2003 140 251 0 28 63 210 0
## 630 6 2003 114 286 1 22 64 116 1
## 631 7 2003 100 264 0 29 64 120 1
## 632 8 2003 123 277 0 24 66 122 0
## 633 1 2004 139 292 0 25 68 135 0
## 634 2 2004 112 275 1 21 68 143 1
## 635 3 2004 114 289 0 36 60 115 0
## 636 4 2004 110 277 0 25 61 130 0
## 637 5 2004 120 271 1 17 64 142 1
## 638 6 2004 110 280 0 29 62 110 1
## 639 7 2004 160 271 0 32 67 215 0
## 640 8 2004 100 281 0 24 61 115 0
a <- nv2[2,3]
a
## [1] 112
B <- nv2$B
B
## [1] 120 112 119 124 105 120 82 111 113 134 97 125 93 146 100 103 128 145
## [19] 99 110 122 112 114 114 108 116 115 125 133 132 97 75 136 126 139 138
## [37] 130 146 126 169 138 111 144 142 104 122 122 94 132 126 99 115 106 128
## [55] 152 150 120 109 105 102 120 119 116 144 143 136 89 140 118 135 132 144
## [73] 140 119 129 133 140 129 84 143 144 103 119 127 114 116 119 145 141 124
## [91] 114 104 116 100 106 121 110 155 106 119 129 138 139 105 114 122 122 152
## [109] 120 123 103 134 115 113 136 123 127 113 112 129 92 122 121 143 71 129
## [127] 96 114 115 126 112 131 88 122 102 97 144 116 112 141 122 132 120 160
## [145] 119 102 123 129 106 120 102 65 105 110 139 113 135 114 97 145 115 133
## [163] 125 119 107 130 113 95 137 125 105 109 129 117 130 139 122 164 130 104
## [181] 126 142 97 123 131 133 146 131 116 144 116 109 103 124 133 110 124 127
## [199] 114 110 146 122 147 148 123 115 127 122 114 121 109 137 145 85 87 115
## [217] 125 100 122 117 102 99 141 108 114 90 135 115 129 123 144 120 122 128
## [235] 117 98 98 112 116 131 93 86 138 136 110 68 75 136 130 123 120 121
## [253] 135 102 138 125 119 87 119 132 101 109 99 96 113 128 118 91 96 102
## [271] 118 102 134 120 105 119 104 99 97 102 107 125 113 85 100 78 146 112
## [289] 134 118 148 106 154 128 81 135 122 116 140 132 127 107 110 91 129 131
## [307] 134 80 126 136 135 129 110 151 120 109 126 101 114 155 111 88 123 111
## [325] 127 100 124 109 87 137 102 143 98 109 115 80 143 127 55 136 127 117
## [343] 143 125 155 96 103 110 129 88 113 94 110 129 123 98 131 95 109 148
## [361] 122 128 105 108 132 127 103 73 145 85 138 101 127 107 118 123 115 111
## [379] 128 71 99 126 127 65 108 124 139 124 115 98 132 118 102 112 104 106
## [397] 145 96 113 102 143 115 159 101 102 104 128 120 146 72 118 100 136 133
## [415] 130 108 124 122 99 104 121 93 125 122 124 116 144 117 120 101 117 103
## [433] 145 127 121 117 118 118 141 105 106 90 117 149 127 130 130 126 75 99
## [451] 119 135 132 125 139 145 107 144 105 110 102 140 130 139 124 138 125 121
## [469] 143 115 113 124 122 58 119 142 118 130 77 121 101 109 101 104 102 114
## [487] 62 126 128 110 105 138 163 105 93 119 104 129 110 112 132 101 109 114
## [505] 97 150 100 117 116 132 145 118 137 128 98 109 138 112 92 127 103 142
## [523] 127 131 139 69 120 117 142 115 117 120 132 114 135 137 130 108 122 116
## [541] 87 123 113 133 156 139 122 140 131 129 126 100 133 115 118 103 130 114
## [559] 143 107 120 136 137 120 123 115 128 115 91 163 120 139 115 98 98 91
## [577] 127 131 143 123 116 128 110 112 153 77 108 104 119 119 162 125 121 124
## [595] 131 111 125 154 116 157 120 104 110 122 144 127 128 108 99 102 105 116
## [613] 123 131 111 130 149 94 125 129 120 129 137 135 129 158 78 133 140 114
## [631] 100 123 139 112 114 110 120 110 160 100
b <- nv2[,2]
b
## [1] 1925 1925 1925 1925 1925 1925 1925 1925 1926 1926 1926 1926 1926 1926 1926
## [16] 1926 1927 1927 1927 1927 1927 1927 1927 1927 1928 1928 1928 1928 1928 1928
## [31] 1928 1928 1929 1929 1929 1929 1929 1929 1929 1929 1930 1930 1930 1930 1930
## [46] 1930 1930 1930 1931 1931 1931 1931 1931 1931 1931 1931 1932 1932 1932 1932
## [61] 1932 1932 1932 1932 1933 1933 1933 1933 1933 1933 1933 1933 1934 1934 1934
## [76] 1934 1934 1934 1934 1934 1935 1935 1935 1935 1935 1935 1935 1935 1936 1936
## [91] 1936 1936 1936 1936 1936 1936 1937 1937 1937 1937 1937 1937 1937 1937 1938
## [106] 1938 1938 1938 1938 1938 1938 1938 1939 1939 1939 1939 1939 1939 1939 1939
## [121] 1940 1940 1940 1940 1940 1940 1940 1940 1941 1941 1941 1941 1941 1941 1941
## [136] 1941 1942 1942 1942 1942 1942 1942 1942 1942 1943 1943 1943 1943 1943 1943
## [151] 1943 1943 1944 1944 1944 1944 1944 1944 1944 1944 1945 1945 1945 1945 1945
## [166] 1945 1945 1945 1946 1946 1946 1946 1946 1946 1946 1946 1947 1947 1947 1947
## [181] 1947 1947 1947 1947 1948 1948 1948 1948 1948 1948 1948 1948 1949 1949 1949
## [196] 1949 1949 1949 1949 1949 1950 1950 1950 1950 1950 1950 1950 1950 1951 1951
## [211] 1951 1951 1951 1951 1951 1951 1952 1952 1952 1952 1952 1952 1952 1952 1953
## [226] 1953 1953 1953 1953 1953 1953 1953 1954 1954 1954 1954 1954 1954 1954 1954
## [241] 1955 1955 1955 1955 1955 1955 1955 1955 1956 1956 1956 1956 1956 1956 1956
## [256] 1956 1957 1957 1957 1957 1957 1957 1957 1957 1958 1958 1958 1958 1958 1958
## [271] 1958 1958 1959 1959 1959 1959 1959 1959 1959 1959 1960 1960 1960 1960 1960
## [286] 1960 1960 1960 1961 1961 1961 1961 1961 1961 1961 1961 1962 1962 1962 1962
## [301] 1962 1962 1962 1962 1963 1963 1963 1963 1963 1963 1963 1963 1964 1964 1964
## [316] 1964 1964 1964 1964 1964 1965 1965 1965 1965 1965 1965 1965 1965 1966 1966
## [331] 1966 1966 1966 1966 1966 1966 1967 1967 1967 1967 1967 1967 1967 1967 1968
## [346] 1968 1968 1968 1968 1968 1968 1968 1969 1969 1969 1969 1969 1969 1969 1969
## [361] 1970 1970 1970 1970 1970 1970 1970 1970 1971 1971 1971 1971 1971 1971 1971
## [376] 1971 1972 1972 1972 1972 1972 1972 1972 1972 1973 1973 1973 1973 1973 1973
## [391] 1973 1973 1974 1974 1974 1974 1974 1974 1974 1974 1975 1975 1975 1975 1975
## [406] 1975 1975 1975 1976 1976 1976 1976 1976 1976 1976 1976 1977 1977 1977 1977
## [421] 1977 1977 1977 1977 1978 1978 1978 1978 1978 1978 1978 1978 1979 1979 1979
## [436] 1979 1979 1979 1979 1979 1980 1980 1980 1980 1980 1980 1980 1980 1981 1981
## [451] 1981 1981 1981 1981 1981 1981 1982 1982 1982 1982 1982 1982 1982 1982 1983
## [466] 1983 1983 1983 1983 1983 1983 1983 1984 1984 1984 1984 1984 1984 1984 1984
## [481] 1985 1985 1985 1985 1985 1985 1985 1985 1986 1986 1986 1986 1986 1986 1986
## [496] 1986 1987 1987 1987 1987 1987 1987 1987 1987 1988 1988 1988 1988 1988 1988
## [511] 1988 1988 1989 1989 1989 1989 1989 1989 1989 1989 1990 1990 1990 1990 1990
## [526] 1990 1990 1990 1991 1991 1991 1991 1991 1991 1991 1991 1992 1992 1992 1992
## [541] 1992 1992 1992 1992 1993 1993 1993 1993 1993 1993 1993 1993 1994 1994 1994
## [556] 1994 1994 1994 1994 1994 1995 1995 1995 1995 1995 1995 1995 1995 1996 1996
## [571] 1996 1996 1996 1996 1996 1996 1997 1997 1997 1997 1997 1997 1997 1997 1998
## [586] 1998 1998 1998 1998 1998 1998 1998 1999 1999 1999 1999 1999 1999 1999 1999
## [601] 2000 2000 2000 2000 2000 2000 2000 2000 2001 2001 2001 2001 2001 2001 2001
## [616] 2001 2002 2002 2002 2002 2002 2002 2002 2002 2003 2003 2003 2003 2003 2003
## [631] 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004
c <- nv2[4,]
c
## I Y B G P A H W S
## 4 4 1925 124 287 0 27 62 105 1
thien1 <- nv2[,c(3,4)]
thien1
## B G
## 1 120 284
## 2 112 267
## 3 119 286
## 4 124 287
## 5 105 276
## 6 120 289
## 7 82 274
## 8 111 278
## 9 113 282
## 10 134 297
## 11 97 279
## 12 125 292
## 13 93 246
## 14 146 280
## 15 100 274
## 16 103 250
## 17 128 279
## 18 145 308
## 19 99 252
## 20 110 262
## 21 122 281
## 22 112 283
## 23 114 271
## 24 114 276
## 25 108 282
## 26 116 295
## 27 115 264
## 28 125 279
## 29 133 293
## 30 132 278
## 31 97 269
## 32 75 247
## 33 136 286
## 34 126 278
## 35 139 284
## 36 138 294
## 37 130 296
## 38 146 263
## 39 126 298
## 40 169 296
## 41 138 244
## 42 111 285
## 43 144 304
## 44 142 284
## 45 104 307
## 46 122 275
## 47 122 275
## 48 94 271
## 49 132 245
## 50 126 282
## 51 99 270
## 52 115 278
## 53 106 278
## 54 128 292
## 55 152 295
## 56 150 287
## 57 120 289
## 58 109 291
## 59 105 280
## 60 102 280
## 61 120 281
## 62 119 277
## 63 116 274
## 64 144 248
## 65 143 299
## 66 136 291
## 67 89 275
## 68 140 294
## 69 118 276
## 70 135 278
## 71 132 302
## 72 144 291
## 73 140 351
## 74 119 286
## 75 129 270
## 76 133 276
## 77 140 290
## 78 129 235
## 79 84 260
## 80 143 313
## 81 144 282
## 82 103 267
## 83 119 270
## 84 127 290
## 85 114 268
## 86 116 293
## 87 119 277
## 88 145 304
## 89 141 279
## 90 124 284
## 91 114 291
## 92 104 274
## 93 116 280
## 94 100 275
## 95 106 312
## 96 121 285
## 97 110 281
## 98 155 286
## 99 106 289
## 100 119 275
## 101 129 284
## 102 138 257
## 103 139 291
## 104 105 256
## 105 114 273
## 106 122 282
## 107 122 292
## 108 152 301
## 109 120 286
## 110 123 282
## 111 103 273
## 112 134 286
## 113 115 285
## 114 113 285
## 115 136 261
## 116 123 284
## 117 127 281
## 118 113 288
## 119 112 299
## 120 129 294
## 121 92 255
## 122 122 273
## 123 121 286
## 124 143 273
## 125 71 234
## 126 129 280
## 127 96 276
## 128 114 276
## 129 115 261
## 130 126 293
## 131 112 282
## 132 131 308
## 133 88 274
## 134 122 280
## 135 102 281
## 136 97 265
## 137 144 261
## 138 116 277
## 139 112 266
## 140 141 319
## 141 122 286
## 142 132 281
## 143 120 300
## 144 160 292
## 145 119 288
## 146 102 294
## 147 123 314
## 148 129 277
## 149 106 302
## 150 120 269
## 151 102 338
## 152 65 237
## 153 105 270
## 154 110 181
## 155 139 286
## 156 113 282
## 157 135 285
## 158 114 283
## 159 97 255
## 160 145 288
## 161 115 274
## 162 133 285
## 163 125 290
## 164 119 292
## 165 107 290
## 166 130 280
## 167 113 285
## 168 95 273
## 169 137 287
## 170 125 283
## 171 105 295
## 172 109 295
## 173 129 294
## 174 117 286
## 175 130 297
## 176 139 293
## 177 122 276
## 178 164 286
## 179 130 276
## 180 104 280
## 181 126 274
## 182 142 285
## 183 97 260
## 184 123 288
## 185 131 294
## 186 133 297
## 187 146 294
## 188 131 282
## 189 116 293
## 190 144 273
## 191 116 273
## 192 109 283
## 193 103 261
## 194 124 293
## 195 133 290
## 196 110 293
## 197 124 294
## 198 127 262
## 199 114 266
## 200 110 268
## 201 146 280
## 202 122 306
## 203 147 296
## 204 148 279
## 205 123 281
## 206 115 270
## 207 127 242
## 208 122 296
## 209 114 266
## 210 121 271
## 211 109 269
## 212 137 283
## 213 145 315
## 214 85 258
## 215 87 247
## 216 115 307
## 217 125 292
## 218 100 272
## 219 122 286
## 220 117 283
## 221 102 278
## 222 99 274
## 223 141 281
## 224 108 279
## 225 114 274
## 226 90 266
## 227 135 260
## 228 115 302
## 229 129 293
## 230 123 323
## 231 144 283
## 232 120 287
## 233 122 270
## 234 128 272
## 235 117 272
## 236 98 280
## 237 98 276
## 238 112 281
## 239 116 273
## 240 131 269
## 241 93 278
## 242 86 276
## 243 138 284
## 244 136 303
## 245 110 272
## 246 68 223
## 247 75 265
## 248 136 283
## 249 130 268
## 250 123 282
## 251 120 283
## 252 121 276
## 253 135 282
## 254 102 283
## 255 138 286
## 256 125 290
## 257 119 275
## 258 87 275
## 259 119 273
## 260 132 285
## 261 101 278
## 262 109 273
## 263 99 271
## 264 96 285
## 265 113 281
## 266 128 291
## 267 118 278
## 268 91 264
## 269 96 266
## 270 102 267
## 271 118 293
## 272 102 282
## 273 134 283
## 274 120 288
## 275 105 330
## 276 119 294
## 277 104 276
## 278 99 275
## 279 97 266
## 280 102 288
## 281 107 279
## 282 125 301
## 283 113 306
## 284 85 273
## 285 100 249
## 286 78 256
## 287 146 319
## 288 112 277
## 289 134 288
## 290 118 265
## 291 148 291
## 292 106 271
## 293 154 292
## 294 128 284
## 295 81 285
## 296 135 272
## 297 122 267
## 298 116 284
## 299 140 281
## 300 132 284
## 301 127 293
## 302 107 303
## 303 110 321
## 304 91 266
## 305 129 293
## 306 131 262
## 307 134 287
## 308 80 266
## 309 126 288
## 310 136 295
## 311 135 284
## 312 129 276
## 313 110 278
## 314 151 286
## 315 120 280
## 316 109 286
## 317 126 282
## 318 101 278
## 319 114 290
## 320 155 290
## 321 111 270
## 322 88 273
## 323 123 296
## 324 111 306
## 325 127 279
## 326 100 275
## 327 124 288
## 328 109 274
## 329 87 248
## 330 137 284
## 331 102 275
## 332 143 292
## 333 98 275
## 334 109 272
## 335 115 262
## 336 80 262
## 337 143 274
## 338 127 289
## 339 55 204
## 340 136 290
## 341 127 288
## 342 117 281
## 343 143 281
## 344 125 273
## 345 155 294
## 346 96 278
## 347 103 276
## 348 110 285
## 349 129 299
## 350 88 252
## 351 113 287
## 352 94 284
## 353 110 272
## 354 129 281
## 355 123 283
## 356 98 257
## 357 131 292
## 358 95 270
## 359 109 244
## 360 148 281
## 361 122 275
## 362 128 288
## 363 105 270
## 364 108 305
## 365 132 289
## 366 127 291
## 367 103 278
## 368 73 277
## 369 145 291
## 370 85 255
## 371 138 289
## 372 101 295
## 373 127 280
## 374 107 293
## 375 118 276
## 376 123 267
## 377 115 258
## 378 111 281
## 379 128 281
## 380 71 281
## 381 99 313
## 382 126 262
## 383 127 290
## 384 65 232
## 385 108 283
## 386 124 275
## 387 139 285
## 388 124 292
## 389 115 290
## 390 98 278
## 391 132 270
## 392 118 279
## 393 102 282
## 394 112 292
## 395 104 288
## 396 106 276
## 397 145 290
## 398 96 241
## 399 113 275
## 400 102 283
## 401 143 286
## 402 115 281
## 403 159 296
## 404 101 278
## 405 102 249
## 406 104 282
## 407 128 265
## 408 120 280
## 409 146 267
## 410 72 271
## 411 118 276
## 412 100 277
## 413 136 299
## 414 133 273
## 415 130 291
## 416 108 270
## 417 124 275
## 418 122 281
## 419 99 285
## 420 104 269
## 421 121 282
## 422 93 267
## 423 125 281
## 424 122 280
## 425 124 278
## 426 116 291
## 427 144 281
## 428 117 270
## 429 120 286
## 430 101 280
## 431 117 297
## 432 103 268
## 433 145 257
## 434 127 272
## 435 121 270
## 436 117 267
## 437 118 261
## 438 118 277
## 439 141 277
## 440 105 312
## 441 106 273
## 442 90 266
## 443 117 265
## 444 149 279
## 445 127 304
## 446 130 289
## 447 130 270
## 448 126 273
## 449 75 232
## 450 99 273
## 451 119 293
## 452 135 284
## 453 132 281
## 454 125 288
## 455 139 299
## 456 145 316
## 457 107 273
## 458 144 307
## 459 105 281
## 460 110 283
## 461 102 258
## 462 140 291
## 463 130 283
## 464 139 293
## 465 124 288
## 466 138 280
## 467 125 283
## 468 121 276
## 469 143 279
## 470 115 290
## 471 113 289
## 472 124 290
## 473 122 280
## 474 58 245
## 475 119 259
## 476 142 285
## 477 118 277
## 478 130 293
## 479 77 238
## 480 121 282
## 481 101 245
## 482 109 265
## 483 101 273
## 484 104 260
## 485 102 286
## 486 114 277
## 487 62 228
## 488 126 299
## 489 128 283
## 490 110 277
## 491 105 277
## 492 138 296
## 493 163 280
## 494 105 278
## 495 93 245
## 496 119 286
## 497 104 282
## 498 129 278
## 499 110 281
## 500 112 278
## 501 132 294
## 502 101 289
## 503 109 275
## 504 114 277
## 505 97 246
## 506 150 284
## 507 100 270
## 508 117 293
## 509 116 276
## 510 132 286
## 511 145 283
## 512 118 272
## 513 137 274
## 514 128 279
## 515 98 284
## 516 109 282
## 517 138 288
## 518 112 252
## 519 92 224
## 520 127 295
## 521 103 273
## 522 142 284
## 523 127 276
## 524 131 266
## 525 139 279
## 526 69 232
## 527 120 281
## 528 117 290
## 529 142 276
## 530 115 268
## 531 117 324
## 532 120 273
## 533 132 298
## 534 114 264
## 535 135 284
## 536 137 277
## 537 130 289
## 538 108 283
## 539 122 278
## 540 116 270
## 541 87 282
## 542 123 267
## 543 113 287
## 544 133 292
## 545 156 292
## 546 139 281
## 547 122 273
## 548 140 290
## 549 131 297
## 550 129 284
## 551 126 251
## 552 100 264
## 553 133 284
## 554 115 275
## 555 118 281
## 556 103 273
## 557 130 282
## 558 114 283
## 559 143 270
## 560 107 273
## 561 120 274
## 562 136 288
## 563 137 303
## 564 120 279
## 565 123 290
## 566 115 290
## 567 128 282
## 568 115 276
## 569 91 270
## 570 163 289
## 571 120 275
## 572 139 260
## 573 115 276
## 574 98 272
## 575 98 262
## 576 91 292
## 577 127 274
## 578 131 285
## 579 143 285
## 580 123 254
## 581 116 272
## 582 128 283
## 583 110 306
## 584 112 287
## 585 153 286
## 586 77 238
## 587 108 270
## 588 104 280
## 589 119 286
## 590 119 271
## 591 162 284
## 592 125 289
## 593 121 276
## 594 124 283
## 595 131 284
## 596 111 270
## 597 125 279
## 598 154 288
## 599 116 292
## 600 157 291
## 601 120 277
## 602 104 270
## 603 110 277
## 604 122 277
## 605 144 282
## 606 127 247
## 607 128 284
## 608 108 256
## 609 99 272
## 610 102 267
## 611 105 276
## 612 116 271
## 613 123 269
## 614 131 263
## 615 111 275
## 616 130 279
## 617 149 293
## 618 94 268
## 619 125 255
## 620 129 277
## 621 120 276
## 622 129 288
## 623 137 280
## 624 135 289
## 625 129 280
## 626 158 295
## 627 78 258
## 628 133 292
## 629 140 251
## 630 114 286
## 631 100 264
## 632 123 277
## 633 139 292
## 634 112 275
## 635 114 289
## 636 110 277
## 637 120 271
## 638 110 280
## 639 160 271
## 640 100 281
thien1 <- nv2[3:5,]
thien1
## I Y B G P A H W S
## 3 3 1925 119 286 0 26 64 123 1
## 4 4 1925 124 287 0 27 62 105 1
## 5 5 1925 105 276 0 22 67 130 0
thien2 <- nv2[c(2,4,6,8),]
thien2
## I Y B G P A H W S
## 2 2 1925 112 267 1 22 62 138 0
## 4 4 1925 124 287 0 27 62 105 1
## 6 6 1925 120 289 1 31 59 102 0
## 8 8 1925 111 278 0 29 65 145 1
thien3 <- nv2[c(4,5,6,7),c(1,2)]
thien3
## I Y
## 4 4 1925
## 5 5 1925
## 6 6 1925
## 7 7 1925
thien4 <- nv2[nv2$A > 25,]
thien4
## I Y B G P A H W S
## 1 1 1925 120 284 0 27 62 100 0
## 3 3 1925 119 286 0 26 64 123 1
## 4 4 1925 124 287 0 27 62 105 1
## 6 6 1925 120 289 1 31 59 102 0
## 7 7 1925 82 274 0 31 64 101 1
## 8 8 1925 111 278 0 29 65 145 1
## 9 1 1926 113 282 0 33 64 135 0
## 10 2 1926 134 297 0 27 67 170 1
## 11 3 1926 97 279 0 29 68 178 1
## 13 5 1926 93 246 0 37 65 130 0
## 16 8 1926 103 250 0 40 59 140 0
## 17 1 1927 128 279 0 28 64 115 1
## 18 2 1927 145 308 0 35 64 110 1
## 21 5 1927 122 281 0 42 63 103 1
## 23 7 1927 114 271 0 32 61 130 0
## 24 8 1927 114 276 0 26 62 127 0
## 26 2 1928 116 295 0 32 65 120 0
## 32 8 1928 75 247 0 36 64 120 1
## 34 2 1929 126 278 0 26 64 150 1
## 35 3 1929 139 284 0 37 61 121 0
## 36 4 1929 138 294 0 40 64 125 0
## 38 6 1929 146 263 0 39 53 110 1
## 40 8 1929 169 296 0 33 67 185 0
## 41 1 1930 138 244 0 33 62 178 0
## 42 2 1930 111 285 0 29 65 130 0
## 43 3 1930 144 304 1 27 58 102 1
## 44 4 1930 142 284 0 39 66 132 0
## 46 6 1930 122 275 0 30 68 140 0
## 48 8 1930 94 271 0 36 61 130 1
## 50 2 1931 126 282 0 33 62 117 0
## 53 5 1931 106 278 0 31 65 110 1
## 54 6 1931 128 292 0 32 66 130 0
## 55 7 1931 152 295 0 39 62 140 0
## 56 8 1931 150 287 0 36 62 135 0
## 58 2 1932 109 291 0 39 64 107 0
## 60 4 1932 102 280 0 38 67 140 0
## 61 5 1932 120 281 0 33 63 113 0
## 64 8 1932 144 248 0 30 70 145 0
## 65 1 1933 143 299 0 30 66 136 1
## 66 2 1933 136 291 0 41 66 191 0
## 67 3 1933 89 275 0 34 66 170 0
## 70 6 1933 135 278 0 27 66 148 0
## 71 7 1933 132 302 0 36 63 145 1
## 72 8 1933 144 291 0 28 67 130 0
## 73 1 1934 140 351 0 27 68 120 0
## 75 3 1934 129 270 0 43 67 160 0
## 79 7 1934 84 260 1 37 66 140 0
## 81 1 1935 144 282 0 32 64 124 1
## 84 4 1935 127 290 0 35 66 165 0
## 86 6 1935 116 293 1 28 62 108 0
## 91 3 1936 114 291 0 35 60 112 0
## 93 5 1936 116 280 0 40 62 159 0
## 94 6 1936 100 275 0 27 64 111 1
## 96 8 1936 121 285 0 34 64 110 0
## 97 1 1937 110 281 0 36 61 99 1
## 98 2 1937 155 286 0 31 66 127 0
## 99 3 1937 106 289 0 28 67 120 1
## 100 4 1937 119 275 0 42 67 156 1
## 102 6 1937 138 257 0 38 67 138 0
## 104 8 1937 105 256 0 31 66 142 0
## 105 1 1938 114 273 0 30 63 154 0
## 107 3 1938 122 292 1 34 65 133 0
## 108 4 1938 152 301 0 29 65 150 0
## 111 7 1938 103 273 0 36 65 158 1
## 113 1 1939 115 285 0 38 63 130 0
## 114 2 1939 113 285 0 26 66 140 0
## 122 2 1940 122 273 0 26 66 210 0
## 125 5 1940 71 234 0 32 64 110 1
## 127 7 1940 96 276 0 33 64 127 1
## 129 1 1941 115 261 0 33 60 125 1
## 130 2 1941 126 293 1 27 62 111 0
## 131 3 1941 112 282 0 26 65 122 0
## 132 4 1941 131 308 0 40 65 160 0
## 133 5 1941 88 274 0 30 66 130 0
## 136 8 1941 97 265 0 30 61 110 0
## 137 1 1942 144 261 0 33 68 170 0
## 138 2 1942 116 277 0 41 64 124 1
## 139 3 1942 112 266 0 26 64 122 0
## 143 7 1942 120 300 0 34 63 150 1
## 144 8 1942 160 292 0 28 64 120 0
## 145 1 1943 119 288 0 43 66 142 1
## 148 4 1943 129 277 0 30 66 142 1
## 150 6 1943 120 269 1 40 63 130 0
## 152 8 1943 65 237 0 31 67 130 0
## 154 2 1944 110 181 0 27 64 133 0
## 155 3 1944 139 286 0 33 65 125 1
## 156 4 1944 113 282 1 36 59 140 0
## 157 5 1944 135 285 0 30 66 130 0
## 160 8 1944 145 288 0 28 64 116 0
## 161 1 1945 115 274 0 27 67 175 1
## 162 2 1945 133 285 1 30 64 160 0
## 163 3 1945 125 290 0 36 59 105 0
## 164 4 1945 119 292 0 33 62 118 1
## 165 5 1945 107 290 0 26 63 112 0
## 166 6 1945 130 280 0 29 66 135 0
## 170 2 1946 125 283 0 29 65 125 0
## 173 5 1946 129 294 0 32 62 170 1
## 174 6 1946 117 286 0 32 66 127 1
## 175 7 1946 130 297 0 32 58 130 0
## 177 1 1947 122 276 0 30 68 182 0
## 178 2 1947 164 286 1 32 66 143 0
## 179 3 1947 130 276 0 41 68 130 0
## 180 4 1947 104 280 1 27 68 146 1
## 181 5 1947 126 274 0 39 62 122 0
## 182 6 1947 142 285 0 33 63 124 0
## 184 8 1947 123 288 0 27 63 125 0
## 186 2 1948 133 297 0 36 61 125 0
## 189 5 1948 116 293 1 26 64 125 0
## 190 6 1948 144 273 0 27 62 118 1
## 191 7 1948 116 273 0 31 61 120 0
## 193 1 1949 103 261 0 27 65 112 1
## 196 4 1949 110 293 1 28 64 135 1
## 197 5 1949 124 294 0 26 62 122 0
## 198 6 1949 127 262 1 32 64 125 0
## 199 7 1949 114 266 0 29 64 113 0
## 200 8 1949 110 268 0 34 64 127 0
## 201 1 1950 146 280 0 26 58 106 0
## 204 4 1950 148 279 0 27 71 189 0
## 210 2 1951 121 271 1 34 63 129 1
## 213 5 1951 145 315 0 39 67 143 1
## 214 6 1951 85 258 0 41 67 137 0
## 216 8 1951 115 307 0 34 65 128 1
## 217 1 1952 125 292 0 32 65 125 0
## 218 2 1952 100 272 0 30 64 150 1
## 220 4 1952 117 283 0 27 63 108 0
## 221 5 1952 102 278 0 27 67 135 1
## 222 6 1952 99 274 0 28 66 118 1
## 223 7 1952 141 281 0 29 54 156 1
## 225 1 1953 114 274 0 28 66 132 1
## 226 2 1953 90 266 1 26 67 135 0
## 227 3 1953 135 260 0 43 65 135 0
## 229 5 1953 129 293 0 30 65 130 1
## 233 1 1954 122 270 0 26 61 105 0
## 235 3 1954 117 272 0 32 66 118 0
## 236 4 1954 98 280 0 35 64 122 1
## 239 7 1954 116 273 0 33 66 130 1
## 240 8 1954 131 269 0 36 68 145 0
## 241 1 1955 93 278 0 34 61 146 0
## 243 3 1955 138 284 0 30 66 133 1
## 245 5 1955 110 272 0 28 60 108 0
## 246 6 1955 68 223 0 32 66 149 1
## 249 1 1956 130 268 0 30 66 123 0
## 250 2 1956 123 282 0 30 63 118 0
## 251 3 1956 120 283 0 28 64 122 1
## 255 7 1956 138 286 1 28 68 120 0
## 256 8 1956 125 290 0 32 63 135 0
## 258 2 1957 87 275 0 28 63 110 1
## 259 3 1957 119 273 0 35 65 125 1
## 262 6 1957 109 273 0 37 65 138 1
## 263 7 1957 99 271 0 39 69 151 0
## 266 2 1958 128 291 1 27 63 132 0
## 268 4 1958 91 264 0 36 60 100 1
## 269 5 1958 96 266 0 26 65 125 0
## 272 8 1958 102 282 1 29 65 125 1
## 274 2 1959 120 288 0 28 63 125 0
## 276 4 1959 119 294 0 34 59 105 0
## 282 2 1960 125 301 1 35 68 181 0
## 284 4 1960 85 273 0 26 60 105 1
## 286 6 1960 78 256 1 29 65 123 0
## 287 7 1960 146 319 0 28 66 145 0
## 290 2 1961 118 265 0 27 61 123 0
## 292 4 1961 106 271 1 26 61 110 1
## 293 5 1961 154 292 0 40 66 145 0
## 296 8 1961 135 272 0 30 65 130 0
## 297 1 1962 122 267 0 27 65 101 1
## 300 4 1962 132 284 0 29 64 122 0
## 301 5 1962 127 293 0 31 67 137 0
## 303 7 1962 110 321 0 28 66 180 0
## 305 1 1963 129 293 0 30 61 160 0
## 307 3 1963 134 287 1 33 67 131 0
## 309 5 1963 126 288 0 31 62 150 0
## 312 8 1963 129 276 0 31 63 125 0
## 315 3 1964 120 280 0 31 61 111 0
## 318 6 1964 101 278 0 27 61 99 1
## 320 8 1964 155 290 0 26 66 129 1
## 321 1 1965 111 270 0 27 61 119 0
## 323 3 1965 123 296 1 26 64 110 1
## 324 4 1965 111 306 0 27 61 102 0
## 325 5 1965 127 279 0 26 67 155 1
## 328 8 1965 109 274 0 33 69 144 1
## 329 1 1966 87 248 0 37 65 130 1
## 330 2 1966 137 284 0 30 67 110 0
## 331 3 1966 102 275 0 43 64 160 0
## 334 6 1966 109 272 0 41 66 154 1
## 336 8 1966 80 262 1 31 61 100 1
## 337 1 1967 143 274 0 27 63 110 1
## 339 3 1967 55 204 0 35 65 140 0
## 340 4 1967 136 290 0 26 66 135 0
## 343 7 1967 143 281 0 28 65 135 1
## 344 8 1967 125 273 0 30 64 145 0
## 345 1 1968 155 294 0 32 66 150 0
## 351 7 1968 113 287 1 29 70 145 1
## 354 2 1969 129 281 0 31 67 155 0
## 356 4 1969 98 257 0 29 66 130 1
## 358 6 1969 95 270 0 35 65 135 1
## 360 8 1969 148 281 0 27 63 110 1
## 361 1 1970 122 275 0 26 66 147 0
## 362 2 1970 128 288 1 26 65 114 0
## 363 3 1970 105 270 1 27 65 134 1
## 367 7 1970 103 278 0 30 60 87 1
## 368 8 1970 73 277 0 29 65 145 0
## 369 1 1971 145 291 0 26 63 119 1
## 371 3 1971 138 289 0 33 65 155 0
## 373 5 1971 127 280 0 27 62 118 0
## 375 7 1971 118 276 0 34 64 116 0
## 377 1 1972 115 258 0 26 62 130 0
## 378 2 1972 111 281 1 27 64 112 0
## 379 3 1972 128 281 0 28 63 150 0
## 380 4 1972 71 281 0 32 60 117 1
## 381 5 1972 99 313 1 34 59 100 1
## 382 6 1972 126 262 0 37 66 135 1
## 383 7 1972 127 290 0 27 65 121 0
## 385 1 1973 108 283 0 31 65 148 1
## 386 2 1973 124 275 0 28 61 116 0
## 387 3 1973 139 285 0 30 65 129 1
## 388 4 1973 124 292 0 29 68 176 1
## 389 5 1973 115 290 0 30 64 140 1
## 390 6 1973 98 278 0 27 63 110 1
## 391 7 1973 132 270 0 27 65 126 0
## 393 1 1974 102 282 0 28 61 110 0
## 394 2 1974 112 292 1 28 62 110 1
## 395 3 1974 104 288 1 27 61 122 1
## 396 4 1974 106 276 0 30 66 130 0
## 399 7 1974 113 275 1 27 60 100 0
## 400 8 1974 102 283 0 39 60 119 0
## 401 1 1975 143 286 0 31 64 126 0
## 402 2 1975 115 281 0 28 61 128 1
## 403 3 1975 159 296 1 27 64 112 0
## 409 1 1976 146 267 0 30 67 132 0
## 410 2 1976 72 271 0 39 61 136 0
## 411 3 1976 118 276 0 29 62 130 1
## 412 4 1976 100 277 0 31 62 100 1
## 413 5 1976 136 299 0 29 64 115 0
## 414 6 1976 133 273 1 33 63 135 0
## 415 7 1976 130 291 0 30 65 150 1
## 420 4 1977 104 269 0 35 63 110 1
## 424 8 1977 122 280 1 45 62 128 0
## 425 1 1978 124 278 0 26 70 145 1
## 426 2 1978 116 291 0 26 66 153 0
## 431 7 1978 117 297 0 38 65 129 0
## 432 8 1978 103 268 0 32 62 97 1
## 433 1 1979 145 257 0 33 65 140 0
## 436 4 1979 117 267 0 29 65 120 1
## 437 5 1979 118 261 0 26 60 104 0
## 439 7 1979 141 277 0 38 66 162 0
## 440 8 1979 105 312 0 41 61 115 1
## 441 1 1980 106 273 0 28 60 116 0
## 445 5 1980 127 304 1 26 62 105 0
## 449 1 1981 75 232 0 33 61 110 0
## 450 2 1981 99 273 1 27 59 115 0
## 458 2 1982 144 307 1 26 66 125 0
## 463 7 1982 130 283 0 32 65 118 0
## 464 8 1982 139 293 0 34 66 131 0
## 466 2 1983 138 280 1 30 65 175 0
## 467 3 1983 125 283 0 37 63 145 1
## 468 4 1983 121 276 0 31 67 130 0
## 469 5 1983 143 279 0 39 65 129 1
## 471 7 1983 113 289 1 26 59 91 0
## 472 8 1983 124 290 0 26 65 165 0
## 474 2 1984 58 245 0 34 64 156 1
## 475 3 1984 119 259 0 37 62 130 0
## 478 6 1984 130 293 0 26 63 123 0
## 480 8 1984 121 282 0 30 65 122 0
## 483 3 1985 101 273 0 39 60 113 0
## 484 4 1985 104 260 0 33 64 145 0
## 486 6 1985 114 277 1 31 64 125 0
## 489 1 1986 128 283 0 28 63 125 1
## 492 4 1986 138 296 0 34 66 120 0
## 493 5 1986 163 280 0 35 69 139 0
## 495 7 1986 93 245 0 33 61 100 1
## 496 8 1986 119 286 1 33 67 137 0
## 497 1 1987 104 282 0 36 65 115 1
## 498 2 1987 129 278 0 27 63 128 0
## 499 3 1987 110 281 0 27 60 110 0
## 501 5 1987 132 294 0 32 64 116 0
## 502 6 1987 101 289 1 31 60 125 0
## 503 7 1987 109 275 1 37 63 112 1
## 505 1 1988 97 246 0 37 63 150 0
## 506 2 1988 150 284 0 40 67 130 0
## 508 4 1988 117 293 0 39 60 120 1
## 509 5 1988 116 276 0 33 61 180 0
## 510 6 1988 132 286 0 26 67 122 1
## 511 7 1988 145 283 0 27 65 125 1
## 513 1 1989 137 274 0 26 69 137 1
## 514 2 1989 128 279 0 27 66 135 0
## 515 3 1989 98 284 0 29 68 140 0
## 518 6 1989 112 252 0 37 64 162 0
## 520 8 1989 127 295 0 36 65 145 0
## 521 1 1990 103 273 0 31 63 170 1
## 522 2 1990 142 284 1 31 66 137 1
## 523 3 1990 127 276 0 37 64 159 0
## 524 4 1990 131 266 1 28 67 135 0
## 526 6 1990 69 232 0 31 59 103 1
## 527 7 1990 120 281 0 26 61 115 0
## 529 1 1991 142 276 0 38 63 170 0
## 530 2 1991 115 268 1 31 64 125 0
## 532 4 1991 120 273 0 29 64 130 1
## 534 6 1991 114 264 0 26 63 110 1
## 535 7 1991 135 284 0 39 67 141 0
## 536 8 1991 137 277 0 41 65 126 0
## 537 1 1992 130 289 0 27 66 130 0
## 538 2 1992 108 283 0 35 62 108 0
## 539 3 1992 122 278 0 37 68 114 0
## 540 4 1992 116 270 0 29 63 132 0
## 541 5 1992 87 282 0 27 63 104 1
## 542 6 1992 123 267 0 29 63 111 1
## 543 7 1992 113 287 0 36 63 118 0
## 544 8 1992 133 292 0 29 65 135 0
## 545 1 1993 156 292 0 26 63 118 0
## 546 2 1993 139 281 0 27 63 137 0
## 549 5 1993 131 297 0 30 67 132 0
## 551 7 1993 126 251 1 28 64 123 0
## 552 8 1993 100 264 0 28 60 111 1
## 555 3 1994 118 281 1 36 66 140 1
## 557 5 1994 130 282 0 26 67 147 1
## 559 7 1994 143 270 1 27 70 148 0
## 560 8 1994 107 273 1 26 65 135 0
## 565 5 1995 123 290 0 28 66 107 1
## 566 6 1995 115 290 0 31 62 95 0
## 571 3 1996 120 275 0 32 63 115 1
## 572 4 1996 139 260 1 32 64 127 0
## 574 6 1996 98 272 1 35 64 129 0
## 576 8 1996 91 292 1 26 61 113 1
## 578 2 1997 131 285 0 26 64 130 0
## 579 3 1997 143 285 0 27 68 185 0
## 580 4 1997 123 254 0 26 62 130 1
## 581 5 1997 116 272 0 27 64 130 1
## 582 6 1997 128 283 0 27 67 126 0
## 583 7 1997 110 306 1 32 61 122 0
## 584 8 1997 112 287 0 27 64 110 1
## 585 1 1998 153 286 0 26 63 107 1
## 586 2 1998 77 238 0 38 67 135 1
## 587 3 1998 108 270 0 29 67 124 1
## 590 6 1998 119 271 0 28 64 175 1
## 591 7 1998 162 284 0 27 64 126 0
## 592 8 1998 125 289 1 31 61 120 0
## 593 1 1999 121 276 0 39 63 130 0
## 594 2 1999 124 283 1 33 67 156 1
## 600 8 1999 157 291 0 33 65 121 0
## 601 1 2000 120 277 0 27 63 126 0
## 602 2 2000 104 270 1 26 62 115 0
## 603 3 2000 110 277 0 36 61 116 0
## 604 4 2000 122 277 0 32 63 157 1
## 605 5 2000 144 282 0 33 66 155 1
## 608 8 2000 108 256 1 26 67 130 0
## 609 1 2001 99 272 0 27 62 103 1
## 612 4 2001 116 271 1 30 67 144 1
## 613 5 2001 123 269 0 26 67 132 0
## 614 6 2001 131 263 0 29 64 180 1
## 616 8 2001 130 279 0 31 62 122 0
## 617 1 2002 149 293 0 35 65 116 0
## 618 2 2002 94 268 0 30 62 105 1
## 620 4 2002 129 277 0 27 68 130 1
## 622 6 2002 129 288 0 28 59 102 0
## 623 7 2002 137 280 0 34 60 107 0
## 626 2 2003 158 295 1 37 70 137 0
## 628 4 2003 133 292 0 30 65 112 1
## 629 5 2003 140 251 0 28 63 210 0
## 631 7 2003 100 264 0 29 64 120 1
## 635 3 2004 114 289 0 36 60 115 0
## 638 6 2004 110 280 0 29 62 110 1
## 639 7 2004 160 271 0 32 67 215 0
thien5 <- nv2[nv2$A >25 & nv2$A< 29,]
thien5
## I Y B G P A H W S
## 1 1 1925 120 284 0 27 62 100 0
## 3 3 1925 119 286 0 26 64 123 1
## 4 4 1925 124 287 0 27 62 105 1
## 10 2 1926 134 297 0 27 67 170 1
## 17 1 1927 128 279 0 28 64 115 1
## 24 8 1927 114 276 0 26 62 127 0
## 34 2 1929 126 278 0 26 64 150 1
## 43 3 1930 144 304 1 27 58 102 1
## 70 6 1933 135 278 0 27 66 148 0
## 72 8 1933 144 291 0 28 67 130 0
## 73 1 1934 140 351 0 27 68 120 0
## 86 6 1935 116 293 1 28 62 108 0
## 94 6 1936 100 275 0 27 64 111 1
## 99 3 1937 106 289 0 28 67 120 1
## 114 2 1939 113 285 0 26 66 140 0
## 122 2 1940 122 273 0 26 66 210 0
## 130 2 1941 126 293 1 27 62 111 0
## 131 3 1941 112 282 0 26 65 122 0
## 139 3 1942 112 266 0 26 64 122 0
## 144 8 1942 160 292 0 28 64 120 0
## 154 2 1944 110 181 0 27 64 133 0
## 160 8 1944 145 288 0 28 64 116 0
## 161 1 1945 115 274 0 27 67 175 1
## 165 5 1945 107 290 0 26 63 112 0
## 180 4 1947 104 280 1 27 68 146 1
## 184 8 1947 123 288 0 27 63 125 0
## 189 5 1948 116 293 1 26 64 125 0
## 190 6 1948 144 273 0 27 62 118 1
## 193 1 1949 103 261 0 27 65 112 1
## 196 4 1949 110 293 1 28 64 135 1
## 197 5 1949 124 294 0 26 62 122 0
## 201 1 1950 146 280 0 26 58 106 0
## 204 4 1950 148 279 0 27 71 189 0
## 220 4 1952 117 283 0 27 63 108 0
## 221 5 1952 102 278 0 27 67 135 1
## 222 6 1952 99 274 0 28 66 118 1
## 225 1 1953 114 274 0 28 66 132 1
## 226 2 1953 90 266 1 26 67 135 0
## 233 1 1954 122 270 0 26 61 105 0
## 245 5 1955 110 272 0 28 60 108 0
## 251 3 1956 120 283 0 28 64 122 1
## 255 7 1956 138 286 1 28 68 120 0
## 258 2 1957 87 275 0 28 63 110 1
## 266 2 1958 128 291 1 27 63 132 0
## 269 5 1958 96 266 0 26 65 125 0
## 274 2 1959 120 288 0 28 63 125 0
## 284 4 1960 85 273 0 26 60 105 1
## 287 7 1960 146 319 0 28 66 145 0
## 290 2 1961 118 265 0 27 61 123 0
## 292 4 1961 106 271 1 26 61 110 1
## 297 1 1962 122 267 0 27 65 101 1
## 303 7 1962 110 321 0 28 66 180 0
## 318 6 1964 101 278 0 27 61 99 1
## 320 8 1964 155 290 0 26 66 129 1
## 321 1 1965 111 270 0 27 61 119 0
## 323 3 1965 123 296 1 26 64 110 1
## 324 4 1965 111 306 0 27 61 102 0
## 325 5 1965 127 279 0 26 67 155 1
## 337 1 1967 143 274 0 27 63 110 1
## 340 4 1967 136 290 0 26 66 135 0
## 343 7 1967 143 281 0 28 65 135 1
## 360 8 1969 148 281 0 27 63 110 1
## 361 1 1970 122 275 0 26 66 147 0
## 362 2 1970 128 288 1 26 65 114 0
## 363 3 1970 105 270 1 27 65 134 1
## 369 1 1971 145 291 0 26 63 119 1
## 373 5 1971 127 280 0 27 62 118 0
## 377 1 1972 115 258 0 26 62 130 0
## 378 2 1972 111 281 1 27 64 112 0
## 379 3 1972 128 281 0 28 63 150 0
## 383 7 1972 127 290 0 27 65 121 0
## 386 2 1973 124 275 0 28 61 116 0
## 390 6 1973 98 278 0 27 63 110 1
## 391 7 1973 132 270 0 27 65 126 0
## 393 1 1974 102 282 0 28 61 110 0
## 394 2 1974 112 292 1 28 62 110 1
## 395 3 1974 104 288 1 27 61 122 1
## 399 7 1974 113 275 1 27 60 100 0
## 402 2 1975 115 281 0 28 61 128 1
## 403 3 1975 159 296 1 27 64 112 0
## 425 1 1978 124 278 0 26 70 145 1
## 426 2 1978 116 291 0 26 66 153 0
## 437 5 1979 118 261 0 26 60 104 0
## 441 1 1980 106 273 0 28 60 116 0
## 445 5 1980 127 304 1 26 62 105 0
## 450 2 1981 99 273 1 27 59 115 0
## 458 2 1982 144 307 1 26 66 125 0
## 471 7 1983 113 289 1 26 59 91 0
## 472 8 1983 124 290 0 26 65 165 0
## 478 6 1984 130 293 0 26 63 123 0
## 489 1 1986 128 283 0 28 63 125 1
## 498 2 1987 129 278 0 27 63 128 0
## 499 3 1987 110 281 0 27 60 110 0
## 510 6 1988 132 286 0 26 67 122 1
## 511 7 1988 145 283 0 27 65 125 1
## 513 1 1989 137 274 0 26 69 137 1
## 514 2 1989 128 279 0 27 66 135 0
## 524 4 1990 131 266 1 28 67 135 0
## 527 7 1990 120 281 0 26 61 115 0
## 534 6 1991 114 264 0 26 63 110 1
## 537 1 1992 130 289 0 27 66 130 0
## 541 5 1992 87 282 0 27 63 104 1
## 545 1 1993 156 292 0 26 63 118 0
## 546 2 1993 139 281 0 27 63 137 0
## 551 7 1993 126 251 1 28 64 123 0
## 552 8 1993 100 264 0 28 60 111 1
## 557 5 1994 130 282 0 26 67 147 1
## 559 7 1994 143 270 1 27 70 148 0
## 560 8 1994 107 273 1 26 65 135 0
## 565 5 1995 123 290 0 28 66 107 1
## 576 8 1996 91 292 1 26 61 113 1
## 578 2 1997 131 285 0 26 64 130 0
## 579 3 1997 143 285 0 27 68 185 0
## 580 4 1997 123 254 0 26 62 130 1
## 581 5 1997 116 272 0 27 64 130 1
## 582 6 1997 128 283 0 27 67 126 0
## 584 8 1997 112 287 0 27 64 110 1
## 585 1 1998 153 286 0 26 63 107 1
## 590 6 1998 119 271 0 28 64 175 1
## 591 7 1998 162 284 0 27 64 126 0
## 601 1 2000 120 277 0 27 63 126 0
## 602 2 2000 104 270 1 26 62 115 0
## 608 8 2000 108 256 1 26 67 130 0
## 609 1 2001 99 272 0 27 62 103 1
## 613 5 2001 123 269 0 26 67 132 0
## 620 4 2002 129 277 0 27 68 130 1
## 622 6 2002 129 288 0 28 59 102 0
## 629 5 2003 140 251 0 28 63 210 0
thien6 <- nv2[nv2$A== 25 | nv2$A== 29,]
thien6
## I Y B G P A H W S
## 8 8 1925 111 278 0 29 65 145 1
## 11 3 1926 97 279 0 29 68 178 1
## 20 4 1927 110 262 0 25 66 140 0
## 33 1 1929 136 286 0 25 62 93 0
## 42 2 1930 111 285 0 29 65 130 0
## 57 1 1932 120 289 0 25 62 125 0
## 68 4 1933 140 294 0 25 61 103 0
## 88 8 1935 145 304 1 25 63 109 1
## 108 4 1938 152 301 0 29 65 150 0
## 112 8 1938 134 286 0 25 64 125 0
## 121 1 1940 92 255 0 25 65 125 1
## 166 6 1945 130 280 0 29 66 135 0
## 169 1 1946 137 287 0 25 66 145 0
## 170 2 1946 125 283 0 29 65 125 0
## 183 7 1947 97 260 1 25 63 115 1
## 199 7 1949 114 266 0 29 64 113 0
## 206 6 1950 115 270 0 25 67 165 1
## 223 7 1952 141 281 0 29 54 156 1
## 231 7 1953 144 283 1 25 66 140 0
## 260 4 1957 132 285 1 25 63 140 0
## 270 6 1958 102 267 1 25 60 93 1
## 272 8 1958 102 282 1 29 65 125 1
## 286 6 1960 78 256 1 29 65 123 0
## 300 4 1962 132 284 0 29 64 122 0
## 302 6 1962 107 303 1 25 67 133 0
## 308 4 1963 80 266 1 25 62 125 0
## 326 6 1965 100 275 1 25 64 125 0
## 333 5 1966 98 275 0 25 65 112 1
## 351 7 1968 113 287 1 29 70 145 1
## 353 1 1969 110 272 0 25 60 90 0
## 356 4 1969 98 257 0 29 66 130 1
## 368 8 1970 73 277 0 29 65 145 0
## 388 4 1973 124 292 0 29 68 176 1
## 404 4 1975 101 278 0 25 62 112 1
## 411 3 1976 118 276 0 29 62 130 1
## 413 5 1976 136 299 0 29 64 115 0
## 419 3 1977 99 285 0 25 69 128 1
## 422 6 1977 93 267 0 25 63 135 1
## 429 5 1978 120 286 0 25 62 105 0
## 435 3 1979 121 270 0 25 62 108 1
## 436 4 1979 117 267 0 29 65 120 1
## 444 4 1980 149 279 0 25 67 135 0
## 448 8 1980 126 273 1 25 68 135 0
## 452 4 1981 135 284 0 25 66 123 0
## 477 5 1984 118 277 0 25 62 120 0
## 491 3 1986 105 277 1 25 64 156 0
## 515 3 1989 98 284 0 29 68 140 0
## 516 4 1989 109 282 0 25 62 106 1
## 532 4 1991 120 273 0 29 64 130 1
## 540 4 1992 116 270 0 29 63 132 0
## 542 6 1992 123 267 0 29 63 111 1
## 544 8 1992 133 292 0 29 65 135 0
## 553 1 1994 133 284 0 25 66 125 1
## 554 2 1994 115 275 0 25 61 155 1
## 567 7 1995 128 282 1 25 64 125 0
## 570 2 1996 163 289 1 25 64 126 1
## 587 3 1998 108 270 0 29 67 124 1
## 598 6 1999 154 288 0 25 65 147 0
## 614 6 2001 131 263 0 29 64 180 1
## 624 8 2002 135 289 0 25 64 127 0
## 631 7 2003 100 264 0 29 64 120 1
## 633 1 2004 139 292 0 25 68 135 0
## 636 4 2004 110 277 0 25 61 130 0
## 638 6 2004 110 280 0 29 62 110 1
d <- iris
d
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
str(d)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
head(d,5)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
tail(d,4)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
d1 <- d[d$Species=='setosa',]
d1
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
str(d1)
## 'data.frame': 50 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
d2 <- d[d$Species=='setosa'|d$Species=='versicolor',]
d2
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
d3 <- d1[d1$Sepal.Length < 5,]
d3
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 48 4.6 3.2 1.4 0.2 setosa
d4 <- d[d$Species != 'setosa',]
d4
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
d <- diamonds
D1 <- filter(d,color=='D'|carat > 1)
D1
## # A tibble: 22,956 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Very Good D VS2 60.5 61 357 3.96 3.97 2.4
## 2 0.23 Very Good D VS1 61.9 58 402 3.92 3.96 2.44
## 3 0.26 Very Good D VS2 60.8 59 403 4.13 4.16 2.52
## 4 0.26 Good D VS2 65.2 56 403 3.99 4.02 2.61
## 5 0.26 Good D VS1 58.4 63 403 4.19 4.24 2.46
## 6 0.22 Premium D VS2 59.3 62 404 3.91 3.88 2.31
## 7 0.3 Premium D SI1 62.6 59 552 4.23 4.27 2.66
## 8 0.3 Ideal D SI1 62.5 57 552 4.29 4.32 2.69
## 9 0.3 Ideal D SI1 62.1 56 552 4.3 4.33 2.68
## 10 0.24 Very Good D VVS1 61.5 60 553 3.97 4 2.45
## # ℹ 22,946 more rows
D2 <- select(D1,color,carat,x,y,z)
D2
## # A tibble: 22,956 × 5
## color carat x y z
## <ord> <dbl> <dbl> <dbl> <dbl>
## 1 D 0.23 3.96 3.97 2.4
## 2 D 0.23 3.92 3.96 2.44
## 3 D 0.26 4.13 4.16 2.52
## 4 D 0.26 3.99 4.02 2.61
## 5 D 0.26 4.19 4.24 2.46
## 6 D 0.22 3.91 3.88 2.31
## 7 D 0.3 4.23 4.27 2.66
## 8 D 0.3 4.29 4.32 2.69
## 9 D 0.3 4.3 4.33 2.68
## 10 D 0.24 3.97 4 2.45
## # ℹ 22,946 more rows
D1 <- d %>% filter(color=='D'|carat > 1)
D1
## # A tibble: 22,956 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Very Good D VS2 60.5 61 357 3.96 3.97 2.4
## 2 0.23 Very Good D VS1 61.9 58 402 3.92 3.96 2.44
## 3 0.26 Very Good D VS2 60.8 59 403 4.13 4.16 2.52
## 4 0.26 Good D VS2 65.2 56 403 3.99 4.02 2.61
## 5 0.26 Good D VS1 58.4 63 403 4.19 4.24 2.46
## 6 0.22 Premium D VS2 59.3 62 404 3.91 3.88 2.31
## 7 0.3 Premium D SI1 62.6 59 552 4.23 4.27 2.66
## 8 0.3 Ideal D SI1 62.5 57 552 4.29 4.32 2.69
## 9 0.3 Ideal D SI1 62.1 56 552 4.3 4.33 2.68
## 10 0.24 Very Good D VVS1 61.5 60 553 3.97 4 2.45
## # ℹ 22,946 more rows
D2 <- D1 %>% select(color,carat,x,y,z)
D2
## # A tibble: 22,956 × 5
## color carat x y z
## <ord> <dbl> <dbl> <dbl> <dbl>
## 1 D 0.23 3.96 3.97 2.4
## 2 D 0.23 3.92 3.96 2.44
## 3 D 0.26 4.13 4.16 2.52
## 4 D 0.26 3.99 4.02 2.61
## 5 D 0.26 4.19 4.24 2.46
## 6 D 0.22 3.91 3.88 2.31
## 7 D 0.3 4.23 4.27 2.66
## 8 D 0.3 4.29 4.32 2.69
## 9 D 0.3 4.3 4.33 2.68
## 10 D 0.24 3.97 4 2.45
## # ℹ 22,946 more rows
D22 <- d %>% filter(color=='D'|carat > 1) %>% select(color,carat,x,y,z)
D22
## # A tibble: 22,956 × 5
## color carat x y z
## <ord> <dbl> <dbl> <dbl> <dbl>
## 1 D 0.23 3.96 3.97 2.4
## 2 D 0.23 3.92 3.96 2.44
## 3 D 0.26 4.13 4.16 2.52
## 4 D 0.26 3.99 4.02 2.61
## 5 D 0.26 4.19 4.24 2.46
## 6 D 0.22 3.91 3.88 2.31
## 7 D 0.3 4.23 4.27 2.66
## 8 D 0.3 4.29 4.32 2.69
## 9 D 0.3 4.3 4.33 2.68
## 10 D 0.24 3.97 4 2.45
## # ℹ 22,946 more rows
P <- trees
P$tich <- P$Girth*P$Height*P$Volume
P1 <- P %>% mutate(l = log(tich))
P1
## Girth Height Volume tich l
## 1 8.3 70 10.3 5984.30 8.696895
## 2 8.6 65 10.3 5757.70 8.658293
## 3 8.8 63 10.2 5654.88 8.640274
## 4 10.5 72 16.4 12398.40 9.425323
## 5 10.7 81 18.8 16293.96 9.698550
## 6 10.8 83 19.7 17659.08 9.779005
## 7 11.0 66 15.6 11325.60 9.334821
## 8 11.0 75 18.2 15015.00 9.616805
## 9 11.1 80 22.6 20068.80 9.906922
## 10 11.2 75 19.9 16716.00 9.724122
## 11 11.3 79 24.2 21603.34 9.980603
## 12 11.4 76 21.0 18194.40 9.808869
## 13 11.4 76 21.4 18540.96 9.827738
## 14 11.7 69 21.3 17195.49 9.752402
## 15 12.0 75 19.1 17190.00 9.752083
## 16 12.9 74 22.2 21192.12 9.961385
## 17 12.9 85 33.8 37061.70 10.520339
## 18 13.3 86 27.4 31340.12 10.352654
## 19 13.7 71 25.7 24998.39 10.126567
## 20 13.8 64 24.9 21991.68 9.998419
## 21 14.0 78 34.5 37674.00 10.536725
## 22 14.2 80 31.7 36011.20 10.491585
## 23 14.5 74 36.3 38949.90 10.570031
## 24 16.0 72 38.3 44121.60 10.694705
## 25 16.3 77 42.6 53467.26 10.886825
## 26 17.3 81 55.4 77632.02 11.259735
## 27 17.5 82 55.7 79929.50 11.288900
## 28 17.9 80 58.3 83485.60 11.332429
## 29 18.0 80 51.5 74160.00 11.213980
## 30 18.0 80 51.0 73440.00 11.204224
## 31 20.6 87 77.0 137999.40 11.835005
P2 <- P %>% mutate(sq = sqrt(tich))
P2
## Girth Height Volume tich sq
## 1 8.3 70 10.3 5984.30 77.35826
## 2 8.6 65 10.3 5757.70 75.87951
## 3 8.8 63 10.2 5654.88 75.19894
## 4 10.5 72 16.4 12398.40 111.34810
## 5 10.7 81 18.8 16293.96 127.64780
## 6 10.8 83 19.7 17659.08 132.88747
## 7 11.0 66 15.6 11325.60 106.42180
## 8 11.0 75 18.2 15015.00 122.53571
## 9 11.1 80 22.6 20068.80 141.66439
## 10 11.2 75 19.9 16716.00 129.29037
## 11 11.3 79 24.2 21603.34 146.98075
## 12 11.4 76 21.0 18194.40 134.88662
## 13 11.4 76 21.4 18540.96 136.16519
## 14 11.7 69 21.3 17195.49 131.13158
## 15 12.0 75 19.1 17190.00 131.11064
## 16 12.9 74 22.2 21192.12 145.57514
## 17 12.9 85 33.8 37061.70 192.51416
## 18 13.3 86 27.4 31340.12 177.03141
## 19 13.7 71 25.7 24998.39 158.10879
## 20 13.8 64 24.9 21991.68 148.29592
## 21 14.0 78 34.5 37674.00 194.09791
## 22 14.2 80 31.7 36011.20 189.76617
## 23 14.5 74 36.3 38949.90 197.35729
## 24 16.0 72 38.3 44121.60 210.05142
## 25 16.3 77 42.6 53467.26 231.22989
## 26 17.3 81 55.4 77632.02 278.62523
## 27 17.5 82 55.7 79929.50 282.71806
## 28 17.9 80 58.3 83485.60 288.93875
## 29 18.0 80 51.5 74160.00 272.32334
## 30 18.0 80 51.0 73440.00 270.99815
## 31 20.6 87 77.0 137999.40 371.48270
tnt <- iris
names(tnt) <- c('SL','SW','PL','PW','S')
str(tnt)
## 'data.frame': 150 obs. of 5 variables:
## $ SL: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ SW: num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ PL: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ PW: num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ S : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
tnt$S.Coded <- ifelse(tnt$S == 'setosa','setosa','Not setosa')
tnt$S.Coded1 <- recode(tnt$S,setosa = 'Loại 1', versicolor = 'Loại 2')
tnt$SL.Coded <- ifelse(tnt$SL >= 5,'Đạt', 'Không đạt')
tnt$SL.Coded1 <- ifelse(tnt$SL >= 5 & tnt$SL <= 6, 'Nhận', 'Loại')
tnt$SL.Coded2 <- case_when(tnt$SL < 5 ~ 'Quá nhỏ', tnt$SL >= 5 & tnt$SL <= 6.5 ~ 'OK', tnt$SL >6.5 ~ 'Quá lớn')
tnt$SL.Coded2 <- cut(tnt$SL,3,labels = c('Loại 1','Loại 2','Loại 3'))
L <- iris
table(L$Species)
##
## setosa versicolor virginica
## 50 50 50
cut(L$Sepal.Length,3)
## [1] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
## [8] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
## [15] (5.5,6.7] (5.5,6.7] (4.3,5.5] (4.3,5.5] (5.5,6.7] (4.3,5.5] (4.3,5.5]
## [22] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
## [29] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
## [36] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
## [43] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
## [50] (4.3,5.5] (6.7,7.9] (5.5,6.7] (6.7,7.9] (4.3,5.5] (5.5,6.7] (5.5,6.7]
## [57] (5.5,6.7] (4.3,5.5] (5.5,6.7] (4.3,5.5] (4.3,5.5] (5.5,6.7] (5.5,6.7]
## [64] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7]
## [71] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9]
## [78] (5.5,6.7] (5.5,6.7] (5.5,6.7] (4.3,5.5] (4.3,5.5] (5.5,6.7] (5.5,6.7]
## [85] (4.3,5.5] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (4.3,5.5] (4.3,5.5]
## [92] (5.5,6.7] (5.5,6.7] (4.3,5.5] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7]
## [99] (4.3,5.5] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7]
## [106] (6.7,7.9] (4.3,5.5] (6.7,7.9] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7]
## [113] (6.7,7.9] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9] (6.7,7.9]
## [120] (5.5,6.7] (6.7,7.9] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7] (6.7,7.9]
## [127] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9] (6.7,7.9] (6.7,7.9] (5.5,6.7]
## [134] (5.5,6.7] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9]
## [141] (5.5,6.7] (6.7,7.9] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7] (5.5,6.7]
## [148] (5.5,6.7] (5.5,6.7] (5.5,6.7]
## Levels: (4.3,5.5] (5.5,6.7] (6.7,7.9]
table(cut(L$Sepal.Length,3))
##
## (4.3,5.5] (5.5,6.7] (6.7,7.9]
## 59 71 20
M <- iris
M$sl.c <- cut(M$Sepal.Length,3, labels = c('ngắn','tb','dài'))
tnt1 <- table(M$Species,M$sl.c)
tnt1
##
## ngắn tb dài
## setosa 47 3 0
## versicolor 11 36 3
## virginica 1 32 17
tnt2 <- M %>% group_by(Species,sl.c) %>% summarise(n= n())
## `summarise()` has grouped output by 'Species'. You can override using the
## `.groups` argument.
tnt2
## # A tibble: 8 × 3
## # Groups: Species [3]
## Species sl.c n
## <fct> <fct> <int>
## 1 setosa ngắn 47
## 2 setosa tb 3
## 3 versicolor ngắn 11
## 4 versicolor tb 36
## 5 versicolor dài 3
## 6 virginica ngắn 1
## 7 virginica tb 32
## 8 virginica dài 17
stem(M$Petal.Length)
##
## The decimal point is at the |
##
## 1 | 012233333334444444444444
## 1 | 55555555555556666666777799
## 2 |
## 2 |
## 3 | 033
## 3 | 55678999
## 4 | 000001112222334444
## 4 | 5555555566677777888899999
## 5 | 000011111111223344
## 5 | 55566666677788899
## 6 | 0011134
## 6 | 6779
stem(M$Sepal.Length,scale = .5)
##
## The decimal point is at the |
##
## 4 | 3444
## 4 | 566667788888999999
## 5 | 000000000011111111122223444444
## 5 | 5555555666666777777778888888999
## 6 | 00000011111122223333333334444444
## 6 | 5555566777777778889999
## 7 | 0122234
## 7 | 677779
tnt2 <- diamonds
summary(tnt2$carat)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2000 0.4000 0.7000 0.7979 1.0400 5.0100
sum(tnt2$carat)
## [1] 43040.87
mean(tnt2$carat,na.rm = T)
## [1] 0.7979397
length(tnt2$carat)
## [1] 53940
var(tnt2$carat)
## [1] 0.2246867
sd(tnt2$carat)
## [1] 0.4740112
median(tnt2$carat)
## [1] 0.7
quantile(tnt2$carat, probs = c(.25,.5,.75))
## 25% 50% 75%
## 0.40 0.70 1.04
tnt3 <- diamonds
nt <- tnt3 %>% group_by(color) %>% summarise(mean_of_carat = mean(carat))
nt
## # A tibble: 7 × 2
## color mean_of_carat
## <ord> <dbl>
## 1 D 0.658
## 2 E 0.658
## 3 F 0.737
## 4 G 0.771
## 5 H 0.912
## 6 I 1.03
## 7 J 1.16
nt <- tnt3 %>% group_by(color) %>% summarise(n = n(),mean_of_carat = mean(carat))
nt
## # A tibble: 7 × 3
## color n mean_of_carat
## <ord> <int> <dbl>
## 1 D 6775 0.658
## 2 E 9797 0.658
## 3 F 9542 0.737
## 4 G 11292 0.771
## 5 H 8304 0.912
## 6 I 5422 1.03
## 7 J 2808 1.16
nt1 <- tnt3 %>% group_by(color) %>% summarise(med_of_carat = median(carat))
nt1
## # A tibble: 7 × 2
## color med_of_carat
## <ord> <dbl>
## 1 D 0.53
## 2 E 0.53
## 3 F 0.7
## 4 G 0.7
## 5 H 0.9
## 6 I 1
## 7 J 1.11
nt2 <- tnt3 %>% group_by(cut) %>% summarise(mean_of_carat = mean(carat))
nt2
## # A tibble: 5 × 2
## cut mean_of_carat
## <ord> <dbl>
## 1 Fair 1.05
## 2 Good 0.849
## 3 Very Good 0.806
## 4 Premium 0.892
## 5 Ideal 0.703
nt2 <- tnt3 %>% group_by(color,cut) %>% summarise(n = n(),mean_of_carat = mean(carat),.groups = 'drop')
nt2
## # A tibble: 35 × 4
## color cut n mean_of_carat
## <ord> <ord> <int> <dbl>
## 1 D Fair 163 0.920
## 2 D Good 662 0.745
## 3 D Very Good 1513 0.696
## 4 D Premium 1603 0.722
## 5 D Ideal 2834 0.566
## 6 E Fair 224 0.857
## 7 E Good 933 0.745
## 8 E Very Good 2400 0.676
## 9 E Premium 2337 0.718
## 10 E Ideal 3903 0.578
## # ℹ 25 more rows
Thao tác rút trích trên file excel “social media_Tiktok”, file này phân tích độ ảnh hưởng của các cá nhân trên nền tảng mạng xã hội Tiktok
nv22 <- read.csv("C:/Users/Data/social media influencers-TIKTOK - ---DEC 2022.csv", header = T)
nv22
## Rank Tiktoker.name Tiktok.name followers
## 1 1 mrbeast MrBeast 60.3M
## 2 2 karolg Karol G 42.4M
## 3 3 yzn47 يزن الأسمر 8.9M
## 4 4 centralcee CentralCee 4.4M
## 5 5 adinross adin 6.1M
## 6 6 thebrandonrobert Brandon Robert 11.6M
## 7 7 mishayoung Міша Городецький 🇺🇦 4.5M
## 8 8 yailinlamasviraloficial_ Yailin La Más Viral 6.1M
## 9 9 daniel.labelle Daniel LaBelle 28.4M
## 10 10 amauryguichon Amaury Guichon 18.4M
## 11 11 urbantheory_ Urban Theory 19.8M
## 12 12 enhypen enhypen 13.3M
## 13 13 conangray conangray 7M
## 14 14 elissadeheart Elissa DeHeart 2.5M
## 15 15 im.camber Camber 94K
## 16 16 matt_rife Matt Rife 6.1M
## 17 17 khaby.lame Khabane lame 153.1M
## 18 18 fadiljaidi Fadil Jaidi 9.2M
## 19 19 rosalia La Rosalia 28.1M
## 20 20 juandamc JuanDa. 19.7M
## 21 21 twitchtok7 tWitch 5.6M
## 22 22 anaraquelhz Raquel 5.7M
## 23 23 karadenizli.maceraci KARADENİZLİ MACERACI 1.8M
## 24 24 lexibrookerivera Lexi Rivera 24.8M
## 25 25 hotspanishmx HotSpanish 11.9M
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## 282 282 datvilla94 🔥Đạt Villa🔥 7.3M
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## 304 304 unnecessaryinventions Unnecessary Inventions 6M
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## 311 311 divabry ⚜️DIVABRY⚜️ 4.3M
## 312 312 jeffwittek jeff 2M
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## 342 342 kekepalmer Keke Palmer 7M
## 343 343 mrcaqui Facundo Izquierdo 7.1M
## 344 344 madelineargy madz 3.3M
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## 346 346 kaelimaee kaeli mae 12.4M
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## 348 348 anas_elshayib انس الشايب 4.6M
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## 351 351 simonservidamusic simonservidamusic 126.4K
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## 358 358 islasvlogs_ Islas Vlogs 2.8M
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## 360 360 pkllipe Pk 20.1M
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## 362 362 adrianbliss Adrian Bliss 7.6M
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## 875 875 claragnds Maria Clara Garcia 19.7M
## 876 876 thekatehudson Kate Hudson 1.3M
## 877 877 _raichouuofficial SHRX • Raichouu 🇵🇭 1.4M
## 878 878 thetiktokdrummer Austin Ware 11.1M
## 879 879 nadina_ioana Nadina Ioana 12.9M
## 880 880 ...anddy_ ANDDY 🐺🌙 1.3M
## 881 881 fabstarden Fabrício :) 66K
## 882 882 ogabrielfalcone NIKITA 3.5M
## 883 883 _catben_ Catherine Benson 11M
## 884 884 camilacabello Camila Cabello 17.2M
## 885 885 wisdm8 Wisdom Kaye 8.7M
## 886 886 eyeout4selen3r Shawn Mac 1.2M
## 887 887 choukripirate CHOUKRI PIRATE 🏴☠️ 1.6M
## 888 888 captincroook Alex Consani 792.5K
## 889 889 delaneysayshello Delaney Rowe 1.1M
## 890 890 anxietycouple Anxiety couple 12.7M
## 891 891 avani avani 42.8M
## 892 892 ugolord ⚖️ The TikTok Attorney ⚖️ 6.4M
## 893 893 thedarcymichael Darcy & Jer 3.1M
## 894 894 msquynhthie Quỳnh Thi 1.3M
## 895 895 armando_netto Armando Netto 5.1M
## 896 896 juliapuzzuoli Julia Puzzuoli Souza 13.5M
## 897 897 zzri9a ZZRi9A 1M
## 898 898 erabii Mohanaderabi 2M
## 899 899 rifirdus RIFIRDUS 2.4M
## 900 900 mult..fand0m Antonella 👍 154K
## 901 901 lilhuhofficial 🔥 Lil Huh 🔥 4.5M
## 902 902 nancyajram Nancy Ajram 2.3M
## 903 903 angelramirezv Angel Ramirez 1.2M
## 904 904 una_arana Una Araña 77K
## 905 905 korean.comic 송은제 🇰🇷 1.4M
## 906 906 ladyyasmina1 Yasmine Sahid 1.6M
## 907 907 nicktrawick13 Nick Trawick 1.2M
## 908 908 la.marymary marymary👑 561.3K
## 909 909 oafonsopadilha Afonso Padilha 3.2M
## 910 910 maurobarrionuevo Mauro Barrionuevo 2.5M
## 911 911 samsmith Sam Smith 7.1M
## 912 912 mohamedmekawy Mohamedmekawy 1.4M
## 913 913 cirowhites1.0 CiroWhites 2.5M
## 914 914 notbehzinga Ethan Payne 1.8M
## 915 915 sjbleau SJ 13.9M
## 916 916 issavegas.fit Issavegas.fit 6M
## 917 917 moontellthat MOONTELLTHAT 14.3M
## 918 918 joerauth_ Joe rauth 6.2M
## 919 919 mrstiventc1977 MrStivenTc 6.8M
## 920 920 ytietofficial Ytiet Official 3.7M
## 921 921 deni.elenaa Deni Elena 327.9K
## 922 922 mimiermakeup Mirta Miler 16.6M
## 923 923 nicolassturniolo Nicolas Sturniolo 4.1M
## 924 924 julesleblanc jules 18.9M
## 925 925 baldybrobryzxz celloszxz🧑🏻🦲 11M
## 926 926 artemmodelka artemmodelka 1M
## 927 927 deiveleonardooficial Deive Leonardo 6.3M
## 928 928 azelmusic Azel 1.3M
## 929 929 1h__aa علي غانم 196.6K
## 930 930 christian_shay Christian Shay 3.1M
## 931 931 shani_ameliaa Shani_amelia 2.9M
## 932 932 tanboykun_asli Tanboy Kun 2.6M
## 933 933 krysandkareem Krys & Kareem 6.3M
## 934 934 alejandrosago Alejandro Sago 2.5M
## 935 935 creatingwonders Jesse J. Pedigo 12.2M
## 936 936 f0urbr0thers F0urBr0thers 1.6M
## 937 937 eldavidgodoy David Godoy 2.9M
## 938 938 alfyfatmasaga Alfy Saga 4.3M
## 939 939 nabela nabela 7.6M
## 940 940 realaa9skillz aa9skillz 295.7K
## 941 941 valennaihomi naihomi 401.7K
## 942 942 rowverytrinidad29 Rowvery Trinidad 3.4M
## 943 943 frnzlynfby Feby 4.4M
## 944 944 lexihensler Lexi Hensler 9.6M
## 945 945 colinjay_ colin eckardt 1.6M
## 946 946 eravfx ERAVFX 1.8M
## 947 947 jailyneojeda Jailyne Ojeda 17.3M
## 948 948 ashraf_alalmei 🇪🇬⚡"ALALMEI"⚡🇵🇸 3.5M
## 949 949 youngji_02 이영지 2.2M
## 950 950 joelbergs JoelBergs 11.6M
## 951 951 axxdermusic Axder 5.3M
## 952 952 chevy2funnyy Chevy2funnyy 2.1M
## 953 953 nilewilsonator Wilsonator 1.2M
## 954 954 farrukoofficialpr FARRU 3.7M
## 955 955 keirariff Keira 5.6M
## 956 956 jimmywells96 Jimmy Wells 2.2M
## 957 957 knzymyln__ Kienzy키엔지미엔✧🇰🇷 8.4M
## 958 958 flickerspark_ FlickerSpark 1.8M
## 959 959 healthy_mandy healthy_mandy 587.8K
## 960 960 robertfrank615 Robert Frank 1.2M
## 961 961 maxthemeatguy Max The Meat Guy 5.3M
## 962 962 ailaloures aila loures 3.9M
## 963 963 ogikdp Zona Ogik | YT MELLO PROJECT 5M
## 964 964 leahh leah 6.3M
## 965 965 karlwolfs Karl Wolf 414.8K
## 966 966 natalieamayaa Natalie Amaya 96.7K
## 967 967 banhbaoxinchao Bánh Bao Xin Chào 2.6M
## 968 968 pocaeve EVE 138.7K
## 969 969 thruhikers Renee-and-Tim 2.1M
## 970 970 thenursery_nurse Charlotte 1.8M
## 971 971 i_am_young22 냥뇽녕냥👻 3.1M
## 972 972 itsmenicksmithy2 Nick Smithyman 😎 13.6M
## 973 973 slamminsammyswank Sam Swank 1.6M
## 974 974 mistermainer mistermainer 16.4M
## 975 975 nelysa_norazlan Nelysa Norazlan 3M
## 976 976 hunterprosper Hunter Prosper 5.5M
## 977 977 mohaimen.alaa مهيمن علاء 4.7M
## 978 978 mei_asami_ 浅見めい 1.5M
## 979 979 rreygrande rey 617.8K
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## 981 981 leesiyoung38 이시영 17.6M
## 982 982 jmartineze_ josi 23.7M
## 983 983 los_escachaitos Los escachaitos 5.3M
## 984 984 pedrinhuol pedrinhuol 6.3M
## 985 985 nouraridaofficial Nour Arida 3.3M
## 986 986 karolsevillaokay karolsevillaofc 26.2M
## 987 987 coldcutz20 CHRIS COLDITZ 1.4M
## 988 988 mclya_ Lya 3.9M
## 989 989 ddlovato Demi Lovato 4.5M
## 990 990 jlo JLO 15.4M
## 991 991 esta.pramanita Esta Pramanita 2.4M
## 992 992 ladydianka LADY BUNNY🐰 9.2M
## 993 993 ediqueixinho Edivando Junior 4.3M
## 994 994 liamcarps Liam Carpenter 1.5M
## 995 995 giovannibonaccinii_ G I O V A N N I🦋 1M
## 996 996 jiembasands Jiemba Sands 4.9M
## 997 997 crissa_ace Crissa Jackson 14.9M
## 998 998 ichadude Alyssa & Dude 468.8K
## 999 999 kanebrown Kane Brown 5.2M
## 1000 1000 nnennab_ Nnenna B | NYC Creator & Actor 149.1K
## views.avg. likes.avg.. comments.avg.. shares.avg..
## 1 29.2M 3.5M 30.8K 7.2K
## 2 23.7M 3.4M 21.7K 25.7K
## 3 48.9M 998.4K 16.3K 60.9K
## 4 19.8M 3.6M 23.3K 24.2K
## 5 21.1M 3.3M 17.5K 25.3K
## 6 13.2M 2.9M 5.7K 50.5K
## 7 21.5M 2.4M 16.3K 24.5K
## 8 25M 1.7M 16.6K 5.3K
## 9 18.6M 1.8M 7.5K 19K
## 10 16.2M 1.6M 7.8K 21.4K
## 11 18.5M 1.9M 5.4K 16.3K
## 12 5.7M 1.7M 35K 25.3K
## 13 8M 2.2M 12.7K 19.3K
## 14 7.5M 1.9M 20.8K 15.5K
## 15 6.4M 1.2M 4.2K 37.5K
## 16 8.5M 1.6M 5.6K 16.1K
## 17 10.6M 1.2M 20.8K 2.3K
## 18 12.7M 1.7M 7.6K 4.4K
## 19 10.6M 1.2M 6.1K 8.5K
## 20 8.1M 2M 5.2K 5.2K
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## 22 6.5M 576.1K 4.3K 34.3K
## 23 18.7M 914.1K 2.7K 5.7K
## 24 11.3M 1.5M 5.6K 2.4K
## 25 11.2M 1M 6.1K 5.9K
## 26 11.5M 1.3M 6.3K 1.4K
## 27 13M 1.6M 4.2K 1.3K
## 28 12.8M 547.3K 4.7K 11.2K
## 29 13M 888.6K 5K 3.8K
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## 31 7.7M 883.3K 8.8K 3.9K
## 32 13.6M 914.6K 728 6.1K
## 33 10.8M 1.3M 1.9K 3.9K
## 34 10.9M 1.1M 4K 2.5K
## 35 8.4M 490.1K 3.8K 13.3K
## 36 5.4M 383.5K 10.5K 22.7K
## 37 9.3M 797.6K 2.2K 6.7K
## 38 7.8M 789.9K 6.9K 3K
## 39 6M 812.3K 6.7K 6K
## 40 3M 847.2K 13.9K 9.8K
## 41 11.5M 570.2K 6.7K 2.1K
## 42 2.5M 336.7K 64.8K 192
## 43 2.9M 876.9K 12.1K 9.4K
## 44 9.3M 924.3K 2.3K 3.8K
## 45 16.5M 668.4K 2.1K 2.7K
## 46 4.7M 920.4K 10.4K 1.4K
## 47 7.2M 1.4M 3.6K 1.3K
## 48 11.6M 920.7K 3.6K 944
## 49 10.6M 1M 2.4K 2.1K
## 50 9.2M 789.2K 2.7K 3.8K
## 51 9.2M 789.2K 2.7K 3.8K
## 52 4.8M 772.6K 5.8K 6.9K
## 53 9.2M 1M 1.6K 3.1K
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## 55 6.7M 680.1K 2.3K 6.9K
## 56 11.4M 888.1K 3K 1.2K
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## 58 8.2M 320K 3K 11.8K
## 59 4.2M 864.8K 6.8K 3.2K
## 60 3.6M 626K 8K 7.8K
## 61 7.2M 783.6K 3.1K 3.2K
## 62 10.3M 909.6K 2.5K 1.3K
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## 64 6.1M 415.6K 10.3K 3.3K
## 65 4.6M 676.6K 3.1K 6.8K
## 66 3M 560.7K 3.8K 14.2K
## 67 5.5M 813.5K 3K 3.4K
## 68 3M 653.9K 3.2K 11.4K
## 69 2.2M 701K 9.2K 9.3K
## 70 11.1M 695.5K 506 3.1K
## 71 4.2M 488.6K 2.1K 11.4K
## 72 4.6M 685.4K 2K 6.3K
## 73 5.7M 790.2K 4.1K 1.7K
## 74 7.5M 793.1K 2.8K 1.6K
## 75 5.7M 648.5K 1.9K 5.1K
## 76 3.6M 715.7K 2.9K 6.6K
## 77 4.7M 827.1K 4.8K 1.4K
## 78 7M 1.2M 2K 1K
## 79 4.7M 440.5K 9.3K 2.4K
## 80 5.1M 813.8K 2K 3.7K
## 81 14M 1.2M 678 705
## 82 6.7M 1M 2.2K 1.2K
## 83 7.5M 513.3K 4.4K 1.3K
## 84 7.5M 898.8K 1.8K 1.6K
## 85 7.9M 725.8K 2.1K 1.8K
## 86 2.8M 533.6K 4K 10.4K
## 87 8.4M 582.8K 2.1K 2.2K
## 88 4M 386.7K 7.4K 5.9K
## 89 4.4M 390.3K 3.6K 7.8K
## 90 10.1M 846K 0 2.3K
## 91 3.4M 732.4K 5.8K 1.7K
## 92 6M 482.2K 4.8K 1.5K
## 93 2.8M 284.6K 5.1K 18.2K
## 94 4.9M 550.8K 4.6K 2.1K
## 95 3.7M 522.6K 4.8K 4.4K
## 96 2.5M 486.4K 3.2K 11.4K
## 97 3.1M 347.2K 6K 10.5K
## 98 5.3M 749K 2.2K 2.1K
## 99 3.8M 466.8K 4.3K 5.2K
## 100 11.5M 466.4K 1.6K 1.5K
## 101 4.7M 759.1K 2.2K 2.3K
## 102 4.9M 834.2K 2.7K 1.2K
## 103 9.4M 565.4K 1.7K 1.2K
## 104 3.6M 715.5K 3.8K 2.1K
## 105 5.4M 469.8K 2.3K 3.6K
## 106 3.7M 464.6K 3.9K 4.7K
## 107 5.9M 708.8K 2.2K 1.2K
## 108 3.1M 515.7K 3.1K 5.7K
## 109 6.4M 421.5K 1.7K 3.4K
## 110 4.3M 740.3K 1.1K 3K
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## 112 4.2M 602.9K 1.8K 3.3K
## 113 5.2M 655.3K 3.5K 258
## 114 3.1M 190.4K 10K 11.6K
## 115 4.5M 545.6K 3.7K 1.3K
## 116 2.3M 207K 4.6K 20.8K
## 117 4.2M 497.6K 3.7K 2.1K
## 118 3.4M 325.9K 4.3K 6.3K
## 119 4.3M 812.8K 556 2.8K
## 120 2.9M 560.1K 2.7K 4.2K
## 121 5.9M 171.6K 4K 6.9K
## 122 4.2M 420K 1.8K 4.3K
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## 124 6.1M 395.6K 2.1K 2.3K
## 125 4.8M 270.4K 1.6K 6.4K
## 126 3.6M 301.7K 3.3K 5.9K
## 127 4.7M 514.6K 2.2K 2K
## 128 2.4M 411.2K 3K 7.3K
## 129 2.3M 451.3K 1.9K 7.7K
## 130 3.3M 598.9K 3.5K 1.5K
## 131 4.3M 607.7K 2.9K 894
## 132 4.8M 527.4K 3K 788
## 133 2.7M 372K 3.7K 5.7K
## 134 3.1M 383K 2.4K 5.6K
## 135 7.3M 645.7K 1.4K 485
## 136 3.1M 425K 3.1K 3.9K
## 137 1.3M 85.2K 4.3K 80.4K
## 138 4.6M 557.4K 2.3K 1.2K
## 139 5.8M 709K 972 1.2K
## 140 2.7M 403.7K 6.5K 1.6K
## 141 2.2M 172.1K 1.3K 22.4K
## 142 3.2M 515K 3.3K 2K
## 143 3.4M 357.5K 5.1K 2K
## 144 5.2M 854.6K 1K 902
## 145 3.2M 497.7K 2.2K 3.1K
## 146 4.7M 825.8K 1K 1.2K
## 147 4.6M 660.7K 2.1K 571
## 148 6.4M 227.3K 1.3K 4.4K
## 149 4.1M 217.2K 3.9K 5.3K
## 150 5.2M 448K 1.9K 1.5K
## 151 2.8M 253K 4.6K 6.7K
## 152 6.1M 334.6K 2.7K 1.2K
## 153 3.3M 433.4K 4.6K 876
## 154 7M 555.6K 648 1.4K
## 155 2.7M 269.1K 1.7K 9.1K
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## 157 5.6M 648.9K 1.6K 554
## 158 4.8M 634.8K 2K 547
## 159 4.1M 322K 2K 3.8K
## 160 2.7M 351.3K 2.9K 5.2K
## 161 2.8M 550.5K 1.9K 3K
## 162 3M 567.4K 2.1K 2.2K
## 163 3.7M 373.9K 4.7K 652
## 164 4.3M 558.1K 1.9K 1.2K
## 165 6.6M 464.4K 1.5K 889
## 166 2M 408.8K 5.8K 3.1K
## 167 2.2M 398.1K 2.6K 5.5K
## 168 2.6M 467.2K 1.1K 4.6K
## 169 5.3M 304.9K 2.4K 2K
## 170 2.4M 353.8K 3.6K 4.6K
## 171 3.9M 454.1K 1.6K 2.3K
## 172 2.3M 274.6K 4.1K 6.9K
## 173 4.9M 510.4K 1.9K 806
## 174 3.1M 321.1K 6.3K 584
## 175 2.3M 572.5K 3.3K 1.9K
## 176 2.7M 213.1K 6K 5.6K
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## 182 1.9M 255.5K 11.4K 1.4K
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## 187 4.8M 665.7K 1.4K 385
## 188 3.5M 470.5K 2.3K 1.2K
## 189 2.6M 406K 2.7K 2.7K
## 190 4M 564.8K 1.7K 833
## 191 5.7M 472.2K 1.1K 1K
## 192 3.6M 418.7K 2.4K 1.4K
## 193 1.9M 381.1K 2.3K 5.4K
## 194 3.9M 227.2K 4.9K 1.4K
## 195 2.5M 476.2K 3.5K 1.1K
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## 197 4.5M 449.9K 1K 1.7K
## 198 3.8M 475.2K 1.8K 1.2K
## 199 2.7M 452.5K 2.7K 1.8K
## 200 3.3M 607.7K 1K 1.7K
## 201 2.8M 458.3K 1.3K 2.9K
## 202 3.4M 352.9K 2.8K 1.7K
## 203 5.2M 526.1K 1.3K 709
## 204 4.6M 389K 2.2K 729
## 205 3.2M 446.1K 2.6K 1.1K
## 206 2.4M 219.9K 684 9.3K
## 207 2M 315.9K 2.8K 5.4K
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## 209 2.1M 238.4K 3.8K 6.9K
## 210 4.8M 476.6K 1.2K 1.1K
## 211 2.3M 431.9K 1.5K 3.7K
## 212 6.8M 284.8K 1.7K 958
## 213 2.2M 336.4K 3.9K 3K
## 214 2.8M 474K 1.9K 2K
## 215 4.6M 388.2K 2.3K 613
## 216 4.7M 356.6K 2.1K 923
## 217 3.2M 440.8K 2K 1.6K
## 218 1.5M 132K 412 25K
## 219 2.3M 275.3K 3.4K 4.6K
## 220 2.5M 194.1K 4.4K 5.9K
## 221 2.2M 174.6K 632 12.3K
## 222 5.1M 397K 901 1.5K
## 223 2.4M 290.7K 1.6K 5.6K
## 224 2.4M 504.9K 2.1K 1.9K
## 225 2.3M 422.5K 2.5K 2.4K
## 226 3.3M 379.5K 1.5K 2.3K
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## 229 3.8M 288.3K 2.2K 2.1K
## 230 2M 129K 1.9K 16.5K
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## 232 4.3M 479.7K 1.4K 857
## 233 4.1M 393.6K 1.8K 1.2K
## 234 3.3M 349.3K 733 3.3K
## 235 1.3M 298.7K 9.1K 2.8K
## 236 3M 377.1K 2.4K 1.6K
## 237 3.1M 339.9K 2.6K 1.8K
## 238 6.8M 227.9K 1.2K 1.8K
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## 242 2.4M 493.1K 2.1K 1.7K
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## 248 3.1M 244.6K 2.2K 3.5K
## 249 2.3M 476K 2.7K 1.1K
## 250 3.6M 220.7K 3.7K 1.6K
## 251 1.1M 245.8K 2K 13.4K
## 252 1.9M 250K 4.7K 3.9K
## 253 1.6M 273.9K 7K 2.2K
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## 255 1.7M 388.2K 2.7K 3.4K
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## 282 1.7M 200.7K 6.6K 3.8K
## 283 1.3M 155.5K 1.4K 15.3K
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## 294 3.3M 398.5K 1.5K 972
## 295 4.4M 694.3K 700 369
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## 324 3.3M 159.9K 1.5K 4K
## 325 1.9M 237.6K 1.1K 5.5K
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## 345 2.3M 235.4K 1.5K 3.4K
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## 740 796.9K 219.5K 1.6K 2.3K
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## 744 1.8M 254.6K 1.2K 343
## 745 1.3M 131.7K 1.3K 2.9K
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## 748 1.1M 163.9K 1.4K 2.4K
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## 753 1.3M 88.7K 1.9K 3.8K
## 754 1.6M 114.7K 3.4K 326
## 755 1.8M 316.8K 810 354
## 756 1.5M 207.4K 1.5K 730
## 757 1.3M 40.8K 519 11.9K
## 758 1.9M 337.7K 698 323
## 759 1.3M 250.8K 769 1.2K
## 760 2.1M 324.6K 718 237
## 761 1.9M 49.6K 515 6.4K
## 762 1.7M 316.7K 570 635
## 763 1.4M 173.3K 2K 806
## 764 3.5M 134.6K 1.2K 193
## 765 1.3M 212.1K 1.6K 739
## 766 991.4K 157.4K 1.9K 2.2K
## 767 2.1M 331.5K 635 293
## 768 2.2M 107.2K 486 2.3K
## 769 1.8M 310.8K 729 422
## 770 1.6M 239.9K 1.2K 461
## 771 2.1M 136.8K 404 1.9K
## 772 1.5M 288.7K 1.2K 320
## 773 2M 146.3K 1.6K 557
## 774 1.5M 200.2K 1.6K 502
## 775 1.6M 195.7K 947 1.1K
## 776 2.7M 363.3K 405 256
## 777 925K 118.3K 1.6K 4.2K
## 778 2.3M 131.1K 407 1.7K
## 779 996.7K 200.8K 880 2.3K
## 780 1.4M 72.2K 327 5.9K
## 781 1.8M 271.5K 503 801
## 782 2M 275.6K 933 198
## 783 1.3M 236.4K 1K 1.1K
## 784 1.8M 270.6K 587 724
## 785 1.5M 225.1K 778 1.1K
## 786 1.7M 166.2K 1.8K 415
## 787 2.3M 131.7K 1.1K 926
## 788 2.3M 157.9K 1.2K 499
## 789 1.2M 112.1K 2.3K 2.3K
## 790 2.7M 202.7K 915 207
## 791 2M 166K 1.2K 722
## 792 1M 238.4K 2.1K 485
## 793 1.3M 196K 1.6K 760
## 794 2.6M 145.6K 1.1K 515
## 795 2.8M 266.2K 554 253
## 796 2.1M 277.2K 507 502
## 797 2.4M 224.3K 801 287
## 798 3.1M 283.2K 400 267
## 799 1.4M 69.1K 3.2K 2.8K
## 800 757.8K 90.4K 1.5K 7.1K
## 801 880.1K 50.4K 971 12.2K
## 802 1.1M 209.4K 2K 579
## 803 2.7M 166K 874 425
## 804 5.8M 113.9K 709 169
## 805 1.3M 130.7K 330 3.1K
## 806 1.4M 203.6K 534 1.5K
## 807 1.9M 180.7K 1.6K 104
## 808 1.5M 262.9K 1K 485
## 809 1M 172.5K 1.5K 1.9K
## 810 1.8M 206.8K 1.3K 238
## 811 2.4M 162.8K 1.2K 269
## 812 2M 203.7K 528 864
## 813 1.5M 225.1K 977 748
## 814 564.4K 67.6K 2.3K 12.5K
## 815 1.4M 125.7K 350 2.9K
## 816 2.5M 201.9K 702 415
## 817 1.6M 212.6K 684 971
## 818 1.8M 262.6K 817 371
## 819 2.3M 81.1K 646 2.4K
## 820 948.1K 158.9K 1.7K 2K
## 821 1.5M 203.8K 1.6K 308
## 822 1.6M 230.3K 1.4K 155
## 823 903.3K 79.3K 486 7.3K
## 824 1.5M 231.9K 614 978
## 825 1.9M 205.7K 1K 438
## 826 1.2M 176.8K 2.1K 528
## 827 2.1M 254.1K 876 155
## 828 2.3M 150.9K 247 1.3K
## 829 1.8M 236.3K 1K 263
## 830 1M 128K 1.8K 2.5K
## 831 1.6M 146.8K 1.6K 675
## 832 935.1K 62.7K 1.3K 8K
## 833 1M 145.8K 1.7K 2K
## 834 2.3M 254.7K 751 172
## 835 1.5M 210.2K 1.5K 171
## 836 1.7M 121.5K 1.6K 922
## 837 1.5M 230.1K 1.1K 495
## 838 1.2M 170.3K 1.3K 1.3K
## 839 3M 146.6K 884 347
## 840 1.7M 280K 792 321
## 841 2.2M 188.2K 916 356
## 842 1.5M 253.7K 1.2K 139
## 843 2.3M 270.8K 456 406
## 844 2.5M 107.5K 1.8K 220
## 845 1.8M 144.2K 1.3K 720
## 846 832.5K 149.4K 1.1K 3.1K
## 847 1M 138.6K 864 2.9K
## 848 2.8M 171.7K 431 651
## 849 2M 151.6K 1.5K 247
## 850 1.3M 131.3K 696 2.3K
## 851 581.6K 99.5K 2.8K 6.2K
## 852 1.2M 192.4K 1.4K 869
## 853 1.9M 168.7K 1.2K 445
## 854 1.9M 255.6K 454 607
## 855 844K 120.7K 994 4K
## 856 1.8M 156.8K 793 1K
## 857 2.5M 217.9K 470 462
## 858 3.1M 106.9K 806 726
## 859 1.7M 187.6K 1.2K 384
## 860 1.3M 62.3K 2K 4.3K
## 861 1.4M 119.1K 2.6K 434
## 862 1.9M 178.4K 1.2K 290
## 863 981K 139.1K 187 3.5K
## 864 1.7M 155.7K 461 1.4K
## 865 1.7M 165.8K 1.2K 557
## 866 2M 252.2K 439 562
## 867 3.3M 267.3K 324 246
## 868 1.3M 273K 648 768
## 869 882.4K 164.8K 656 2.8K
## 870 992.3K 115.2K 3.1K 1.3K
## 871 2.1M 259.7K 586 330
## 872 912K 116.2K 1.6K 3.1K
## 873 2.2M 145.4K 623 930
## 874 1.4M 229.1K 923 622
## 875 1.6M 171.7K 1.2K 578
## 876 2M 178.9K 1.1K 246
## 877 2.2M 145.2K 1.3K 282
## 878 4.1M 107.4K 793 326
## 879 3M 210.9K 473 318
## 880 827.9K 123.7K 3.5K 1.3K
## 881 692.8K 111.3K 2.9K 3.5K
## 882 1.1M 252.8K 1K 728
## 883 1.4M 212.1K 784 864
## 884 1.3M 182.5K 1.2K 905
## 885 1.4M 199.7K 1.3K 415
## 886 736.2K 185.5K 1K 2.5K
## 887 2.3M 186.3K 630 507
## 888 1.1M 257.4K 1.2K 516
## 889 1.4M 178.5K 824 1.2K
## 890 2.1M 203.7K 665 494
## 891 2.1M 209.8K 797 322
## 892 1.7M 142.7K 1.8K 223
## 893 1.1M 130.7K 1.5K 1.8K
## 894 3.7M 154.2K 592 249
## 895 1.6M 249.6K 969 231
## 896 2M 259.1K 367 581
## 897 1.7M 106.6K 1.4K 1.2K
## 898 1.3M 155.1K 1.4K 1K
## 899 1.9M 148.6K 837 899
## 900 543.8K 103.8K 7.1K 1.3K
## 901 2.3M 201.1K 565 452
## 902 1.9M 129.2K 1.1K 769
## 903 1.7M 150.1K 1.1K 760
## 904 2.4M 262.3K 527 217
## 905 972.1K 198.5K 999 1.4K
## 906 1.1M 161.2K 1.1K 1.5K
## 907 756.4K 117.2K 1.7K 3.5K
## 908 1.1M 142.6K 886 2K
## 909 923.1K 119.9K 638 3.5K
## 910 849.5K 58.7K 278 9K
## 911 1.9M 77.7K 1.4K 1.7K
## 912 2.7M 41.7K 953 3.1K
## 913 1.2M 242.6K 529 1.1K
## 914 2.2M 331.4K 468 164
## 915 2.2M 195.3K 664 408
## 916 2M 148.8K 483 1.1K
## 917 1.8M 132.1K 1.2K 717
## 918 1.4M 217.1K 610 947
## 919 1.9M 218K 850 279
## 920 1M 54.3K 1.2K 7.1K
## 921 842.8K 147.2K 736 2.9K
## 922 1.9M 273.3K 731 166
## 923 938.3K 202K 1.9K 467
## 924 1.5M 214.9K 1.1K 259
## 925 2.6M 199.1K 646 221
## 926 2.5M 129.6K 1K 370
## 927 534.7K 77.6K 976 9.8K
## 928 1.3M 129.1K 833 1.9K
## 929 2M 75.4K 1.9K 1.1K
## 930 1.4M 188.6K 1.4K 268
## 931 983.9K 160.1K 1.4K 1.4K
## 932 2.3M 154.4K 511 713
## 933 1.9M 114.8K 362 1.7K
## 934 1.6M 191.9K 409 1K
## 935 554.8K 86.7K 1.2K 7.9K
## 936 1.5M 157.2K 411 1.5K
## 937 1.7M 175.8K 449 1K
## 938 1.7M 105.5K 804 1.6K
## 939 2.1M 251.5K 502 330
## 940 930.5K 135K 2.1K 1.4K
## 941 2.7M 140.3K 955 219
## 942 2.7M 325.6K 295 204
## 943 1.2M 222.5K 887 825
## 944 1.7M 230.6K 399 717
## 945 952.8K 182.9K 1.5K 1K
## 946 2.4M 181.2K 805 199
## 947 1.7M 227.7K 740 387
## 948 1.9M 136.5K 528 1.2K
## 949 1.4M 293.3K 820 240
## 950 1.7M 242.6K 499 554
## 951 531.9K 68.5K 1.8K 10.1K
## 952 917.7K 181K 796 1.8K
## 953 2.4M 194.1K 448 464
## 954 1.4M 96.8K 346 2.8K
## 955 1.4M 250.3K 502 764
## 956 1.1M 133.8K 832 2.1K
## 957 2.8M 110.9K 488 885
## 958 1.1M 245.1K 830 750
## 959 2.2M 150.2K 861 424
## 960 778.4K 78.7K 440 6.5K
## 961 1.2M 178.7K 504 1.5K
## 962 845.5K 165.1K 1.4K 1.6K
## 963 1.6M 227.7K 755 400
## 964 1.7M 167.6K 785 737
## 965 953.6K 75.4K 2.5K 3.3K
## 966 1M 116.2K 837 2.8K
## 967 2.6M 183.6K 623 256
## 968 2.6M 50.9K 1.8K 1.3K
## 969 2.3M 250.5K 289 452
## 970 1.5M 288.7K 768 195
## 971 1.1M 147.2K 739 1.8K
## 972 1.5M 179.1K 757 789
## 973 834.2K 165.2K 482 2.5K
## 974 1.2M 157.6K 1K 1.1K
## 975 1.5M 144.4K 760 1.2K
## 976 1.6M 172.6K 437 1.1K
## 977 1.4M 143.8K 1.3K 801
## 978 1.3M 176.8K 1.1K 768
## 979 700.2K 147.7K 1.9K 2.1K
## 980 6.4M 39.3K 538 1.1K
## 981 2M 143.3K 952 472
## 982 1.8M 235K 651 314
## 983 2.3M 120.9K 692 772
## 984 1.9M 178.2K 448 750
## 985 1.9M 108.2K 733 1.2K
## 986 1.5M 223.2K 913 298
## 987 4.4M 100.3K 494 424
## 988 2.2M 175.5K 713 336
## 989 1.4M 130.8K 1.7K 573
## 990 1.5M 118.9K 1.8K 417
## 991 2.3M 222.7K 390 392
## 992 1.2M 144.7K 1.3K 899
## 993 1.7M 185.2K 754 488
## 994 1.5M 136.6K 697 1.3K
## 995 1M 171.6K 983 1.3K
## 996 3.4M 247.4K 207 264
## 997 1.6M 141.7K 1.2K 580
## 998 2.3M 85.5K 997 1K
## 999 1.7M 96.7K 1.3K 1.2K
## 1000 606.2K 79.5K 2.1K 6.1K
is.data.frame(nv22)
## [1] TRUE
length(nv22)
## [1] 8
names(nv22)
## [1] "Rank" "Tiktoker.name" "Tiktok.name" "followers"
## [5] "views.avg." "likes.avg.." "comments.avg.." "shares.avg.."
dim(nv22)
## [1] 1000 8
head(nv22,5)
## Rank Tiktoker.name Tiktok.name followers views.avg. likes.avg..
## 1 1 mrbeast MrBeast 60.3M 29.2M 3.5M
## 2 2 karolg Karol G 42.4M 23.7M 3.4M
## 3 3 yzn47 يزن الأسمر 8.9M 48.9M 998.4K
## 4 4 centralcee CentralCee 4.4M 19.8M 3.6M
## 5 5 adinross adin 6.1M 21.1M 3.3M
## comments.avg.. shares.avg..
## 1 30.8K 7.2K
## 2 21.7K 25.7K
## 3 16.3K 60.9K
## 4 23.3K 24.2K
## 5 17.5K 25.3K
tail(nv22,5)
## Rank Tiktoker.name Tiktok.name followers views.avg.
## 996 996 jiembasands Jiemba Sands 4.9M 3.4M
## 997 997 crissa_ace Crissa Jackson 14.9M 1.6M
## 998 998 ichadude Alyssa & Dude 468.8K 2.3M
## 999 999 kanebrown Kane Brown 5.2M 1.7M
## 1000 1000 nnennab_ Nnenna B | NYC Creator & Actor 149.1K 606.2K
## likes.avg.. comments.avg.. shares.avg..
## 996 247.4K 207 264
## 997 141.7K 1.2K 580
## 998 85.5K 997 1K
## 999 96.7K 1.3K 1.2K
## 1000 79.5K 2.1K 6.1K
str(nv22)
## 'data.frame': 1000 obs. of 8 variables:
## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Tiktoker.name : chr "mrbeast" "karolg" "yzn47" "centralcee" ...
## $ Tiktok.name : chr "MrBeast" "Karol G" "يزن الأسمر" "CentralCee" ...
## $ followers : chr "60.3M" "42.4M" "8.9M" "4.4M" ...
## $ views.avg. : chr "29.2M" "23.7M" "48.9M" "19.8M" ...
## $ likes.avg.. : chr "3.5M" "3.4M" "998.4K" "3.6M" ...
## $ comments.avg..: chr "30.8K" "21.7K" "16.3K" "23.3K" ...
## $ shares.avg.. : chr "7.2K" "25.7K" "60.9K" "24.2K" ...
is.na(nv22)
## Rank Tiktoker.name Tiktok.name followers views.avg. likes.avg..
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE FALSE
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sum(is.na(nv22))
## [1] 0
which(is.na(nv22))
## integer(0)
library(skimr)
skim(nv22)
| Name | nv22 |
| Number of rows | 1000 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Tiktoker.name | 0 | 1 | 3 | 24 | 0 | 999 | 0 |
| Tiktok.name | 0 | 1 | 1 | 32 | 0 | 999 | 0 |
| followers | 0 | 1 | 2 | 6 | 0 | 372 | 0 |
| views.avg. | 0 | 1 | 2 | 6 | 0 | 162 | 0 |
| likes.avg.. | 0 | 1 | 2 | 6 | 0 | 892 | 0 |
| comments.avg.. | 0 | 1 | 1 | 5 | 0 | 357 | 0 |
| shares.avg.. | 0 | 1 | 2 | 5 | 0 | 432 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Rank | 0 | 1 | 500.5 | 288.82 | 1 | 250.75 | 500.5 | 750.25 | 1000 | ▇▇▇▇▇ |
library(tidyverse)
NaTi <- unique(nv22)
str(NaTi)
## 'data.frame': 1000 obs. of 8 variables:
## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Tiktoker.name : chr "mrbeast" "karolg" "yzn47" "centralcee" ...
## $ Tiktok.name : chr "MrBeast" "Karol G" "يزن الأسمر" "CentralCee" ...
## $ followers : chr "60.3M" "42.4M" "8.9M" "4.4M" ...
## $ views.avg. : chr "29.2M" "23.7M" "48.9M" "19.8M" ...
## $ likes.avg.. : chr "3.5M" "3.4M" "998.4K" "3.6M" ...
## $ comments.avg..: chr "30.8K" "21.7K" "16.3K" "23.3K" ...
## $ shares.avg.. : chr "7.2K" "25.7K" "60.9K" "24.2K" ...
NaTi1 <- distinct(nv22)
NaTi1
## Rank Tiktoker.name Tiktok.name followers
## 1 1 mrbeast MrBeast 60.3M
## 2 2 karolg Karol G 42.4M
## 3 3 yzn47 يزن الأسمر 8.9M
## 4 4 centralcee CentralCee 4.4M
## 5 5 adinross adin 6.1M
## 6 6 thebrandonrobert Brandon Robert 11.6M
## 7 7 mishayoung Міша Городецький 🇺🇦 4.5M
## 8 8 yailinlamasviraloficial_ Yailin La Más Viral 6.1M
## 9 9 daniel.labelle Daniel LaBelle 28.4M
## 10 10 amauryguichon Amaury Guichon 18.4M
## 11 11 urbantheory_ Urban Theory 19.8M
## 12 12 enhypen enhypen 13.3M
## 13 13 conangray conangray 7M
## 14 14 elissadeheart Elissa DeHeart 2.5M
## 15 15 im.camber Camber 94K
## 16 16 matt_rife Matt Rife 6.1M
## 17 17 khaby.lame Khabane lame 153.1M
## 18 18 fadiljaidi Fadil Jaidi 9.2M
## 19 19 rosalia La Rosalia 28.1M
## 20 20 juandamc JuanDa. 19.7M
## 21 21 twitchtok7 tWitch 5.6M
## 22 22 anaraquelhz Raquel 5.7M
## 23 23 karadenizli.maceraci KARADENİZLİ MACERACI 1.8M
## 24 24 lexibrookerivera Lexi Rivera 24.8M
## 25 25 hotspanishmx HotSpanish 11.9M
## 26 26 surthycooks Surthycooks 19.9M
## 27 27 nicollefigueroaa Nicolle Figueroa 15.5M
## 28 28 nicocaponecomedy nicocapone.comedy 24.9M
## 29 29 williesalim WILLIE SALIM 20.9M
## 30 30 luvadepedreiro Iran Ferreira (Lai) 22.1M
## 31 31 kimberly.loaiza Kimberly Loaiza 70.2M
## 32 32 texasgirl_nadia N A D I A 125.7K
## 33 33 slaterkodish slaterkodish 1.6M
## 34 34 bayashi.tiktok バヤシ🥑Bayashi 38.8M
## 35 35 amroqarawe Amro Qarawe 1.2M
## 36 36 mohm.nabeel mohammad.nabeel 2.1M
## 37 37 cedricgrolet Cedric Grolet 3.2M
## 38 38 charlidamelio charli d’amelio 149M
## 39 39 dannero Dannero 8.5M
## 40 40 txt.bighitent TOMORROW X TOGETHER 19M
## 41 41 emillyvickof Emilly Vick 13.9M
## 42 42 xelitobelek xelito 1.2M
## 43 43 seventeen17_official SEVENTEEN 6.6M
## 44 44 amielgarciami Ami Garcia Amiel 1.5M
## 45 45 noelgoescrazy noelgoescrazy 19.5M
## 46 46 dylanmulvaney Dylan Mulvaney 9.7M
## 47 47 ramonvitor ramonvitor 11M
## 48 48 miladmirg Milad 6.4M
## 49 49 mmmjoemele Joe Mele 24.1M
## 50 50 thezachchoi Zach Choi 8M
## 51 51 thezachchoi Zach Choi 8M
## 52 52 morimura MoriMura🍕 9.5M
## 53 53 yano4kaa.aaa yano4kaa 4.7M
## 54 54 salzabilll_ s a l z a b i l l x 🔮 8.1M
## 55 55 elpugaa Puga 10.3M
## 56 56 kukombo H 4.8M
## 57 57 brookemonk_ Brooke Monk 26.3M
## 58 58 yvesbissonsturgeonco yves 1.2M
## 59 59 mewsuppasit21 mewsuppasit 1.5M
## 60 60 therock The Rock 64.3M
## 61 61 elina_karimovaa 🐚Elina_리나대장님🤍 12.7M
## 62 62 bigchungus.tik BigChungus 9.2M
## 63 63 domelipa domelipa 62M
## 64 64 mikaylahau Mikaylah 5.8M
## 65 65 mrnigelng Nigel Ng (Uncle Roger) 7.3M
## 66 66 jakefresca Fresca Fresh 387.5K
## 67 67 bellaamtz Bella 1M
## 68 68 guiedits_ guieditss 149.6K
## 69 69 niallhoran Niall Horan 4.8M
## 70 70 shellyclouds Shelly Clouds 1.5M
## 71 71 jayandsharon Jay & Sharon 2.4M
## 72 72 kane kane 3.2M
## 73 73 future_millionaires Future Millionaires 754.8K
## 74 74 kyliejenner Kylie Jenner 50.4M
## 75 75 rubentuestaok Ruben Tuesta 25.4M
## 76 76 ferxxo444 Feid 5.5M
## 77 77 onwardwanna Wanna🥊 8.4M
## 78 78 montpantoja Montpantoja 36.9M
## 79 79 nekoglai Николай 10M
## 80 80 esnyrrr Esnyr 6M
## 81 81 skythedogtrainer skythedogtrainer 31.4K
## 82 82 mclomaofficiall mclomaofficial 6M
## 83 83 lechilinh88 Lê Chí Linh 4.1M
## 84 84 pongamoslo_a_prueba Pongámoslo a Prueba 38.2M
## 85 85 nianaguerrero Niana Guerrero 35.1M
## 86 86 druskitv DRUSKI 3.8M
## 87 87 jaykindafunny8 Jaykindafunny 25M
## 88 88 zodiac.boyfriend Zodiac Boyfriend🪐🔮 2.7M
## 89 89 andrewlepage23 Andrew Le Page 209K
## 90 90 pandkourt Kourtney-Penelope 5M
## 91 91 pinkpantheress 😘🙈☺️ 1.7M
## 92 92 bellapoarch Bella Poarch 92.6M
## 93 93 thethinktok The Think Tok 782.4K
## 94 94 livvy Olivia Dunne 6.5M
## 95 95 falcopunch Falco 13.2M
## 96 96 vibin.wit.tay Tay 5.4M
## 97 97 swagboygorringe Daniel Gorringe 7M
## 98 98 sadiafza Sadia 2.7M
## 99 99 ryanbakery 𝙍𝙮𝙖𝙣𝘽𝙖𝙠𝙚𝙧𝙮 3.2M
## 100 100 zachking Zach King 72.1M
## 101 101 docdami Doc Dami 4.1M
## 102 102 mdmotivator Zachery Dereniowski 11.3M
## 103 103 cool__bad Kuan - Aisha 🔎 2.8M
## 104 104 kallmekris Kris HC 47.6M
## 105 105 angelaaguilar_ Angela Aguilar :) 10.7M
## 106 106 joshtgodfrey Josh Godfrey 1.1M
## 107 107 kieram.litchfield Kieram Litchfield 2.3M
## 108 108 kervo.dolo Kervo.dolo 10.5M
## 109 109 winnermaxyt WinnerMax 7.1M
## 110 110 olisboa LISBOA 3.9M
## 111 111 imeyhou Meyden 5M
## 112 112 lilireinhart Lili Reinhart 5.6M
## 113 113 therealemilylin Emily Lin 238.9K
## 114 114 nourmar5 nourmar5 14.7M
## 115 115 selenagomez Selena Gomez 44.9M
## 116 116 kumulator Kumulátor zábavy 123K
## 117 117 spencer_serafica Xspencer 14.1M
## 118 118 jesusnalgas JESUSNALGAS 3.9M
## 119 119 makeup_rhk makeup_rhk 860.3K
## 120 120 duncanyounot Duncan Joseph 4.4M
## 121 121 sijad_qasim سجاد قاسم 2.3M
## 122 122 lance210 Lance Stewart 22.7M
## 123 123 ivanaalawi Ivana Alawi 8.6M
## 124 124 muluerror Nama gue Rian, tp bkn babayo 672.8K
## 125 125 millennialmonroe Mandy 112.1K
## 126 126 whatslucasup2 Lucas Peterson BMX art 467.7K
## 127 127 willsmith Will Smith 73M
## 128 128 marisol.viola marisol viola 1.5M
## 129 129 nicolebloomgarden Nicole Bloomgarden 675.6K
## 130 130 thelondoncharles London Charles 6.9M
## 131 131 jimmydarts Jimmy Darts 9.8M
## 132 132 antourny Antony Santos 2.6M
## 133 133 thallyssonsb Thallysson Borges 11.6M
## 134 134 itsblasphemus 🗣📱itsblasphemus 994.5K
## 135 135 yerimuaa Yeri MUA 5.3M
## 136 136 raredoodle rare doodle 2.1M
## 137 137 hf37777 ༊෴نجٍمـ الُثـرٍيَاء〄࿐ 801.5K
## 138 138 brentrivera Brent Rivera 45.8M
## 139 139 tifannylm ⚡️TEF 9.3M
## 140 140 alexisomman Alexis Omman 11.5M
## 141 141 theofficecurrently The Office Currently 308K
## 142 142 keniaos KeniaOs 16.6M
## 143 143 ali.klose11 علي كلوزه 2.4M
## 144 144 alexwaarren Alex warren 15.6M
## 145 145 bilalahy Bilal 4.2M
## 146 146 ferchugimenez Fernanda 8.2M
## 147 147 the.sign.guy Austin Mollno 7.2M
## 148 148 therealhammytv TheRealHammyTV 16.1M
## 149 149 bu3qeel abu aqeel 🙆🏻♀️ 1.2M
## 150 150 attahalilintar Atta Halilintar 13.3M
## 151 151 thekapelariz The Kapelari Family 2.5M
## 152 152 zusjeofficial ZUSJE 8.3M
## 153 153 jaxwritessongs Jax 12.9M
## 154 154 luismariz Luis Mariz 12.7M
## 155 155 pablitocastilloo pablitocastilloo 6.4M
## 156 156 jvke JVKE 9.9M
## 157 157 jombospice JOMBOSPICE 9.8M
## 158 158 julieevlorentzen julie 4.2M
## 159 159 adamw Adam W 17.7M
## 160 160 garett__nolan Garett Nolan 13.4M
## 161 161 ayesebastien Sebastien 8.1M
## 162 162 la_lerma LERMITA 13.4M
## 163 163 soysuco Soysuco 14M
## 164 164 lyodrabeneran Lyodra Ginting 2.5M
## 165 165 pietro_morello Pietro Morello 3.1M
## 166 166 _andrewcurtiss Andrew Curtis 2.9M
## 167 167 good.boy.ollie Good Boy Ollie 5.2M
## 168 168 shxtsngigs ShxtsNGigs Podcast 5.9M
## 169 169 addisonre Addison 88.7M
## 170 170 chriscoquyt Chris Coquyt 136.2K
## 171 171 hisoyvaleria valeria 🖤 9.9M
## 172 172 joaoferdnan João Ferdnan 8.8M
## 173 173 graciajessicajane Jessica Jane🌙 11.6M
## 174 174 sabrenorris Sabre Norris 3.8M
## 175 175 official_nct NCT Official 9.2M
## 176 176 ustazebitlew Ebit Lew 4.6M
## 177 177 sromero29 Samuel29 599.1K
## 178 178 augustogimenez AugustoGimenez 15M
## 179 179 grannybibbins Granny 944.8K
## 180 180 jairsanchezzz Jair Sanchez 8M
## 181 181 k0uvr Kouvr 14M
## 182 182 katjakrasavice KatjaKrasavice 2.8M
## 183 183 janneksplace Janneksplace 2M
## 184 184 allanpendong4 Allan Pendong 130.2K
## 185 185 aureliehermansyahatta Aurelie Hermansyah 9.8M
## 186 186 kulinaria.recetas AGUS LIPORACE 4.5M
## 187 187 areyoukiddingtv AreYouKiddingTV 5.7M
## 188 188 faryanggaa Ryann 6.1M
## 189 189 _lilg4 LIL G⭐️ 3M
## 190 190 _angelomarasigan Angelo 4M
## 191 191 adamalhidayat ADAMALHDYT 3.8M
## 192 192 larissagloor 𝗟𝗮𝗿𝗶𝘀𝘀𝗮 𝗚𝗹𝗼𝗼𝗿 4.2M
## 193 193 bomanizer Bomanizer 1.9M
## 194 194 miakhalifa Mia K. 36.6M
## 195 195 jacobday Jacob Day 6.5M
## 196 196 matheuskriwat Matheuskriwat 11.2M
## 197 197 calebwsimpson CALEB SIMPSON 6.5M
## 198 198 cheekyboyos the cheeky boyos 11M
## 199 199 oliviergiroud Olivier Giroud 1.3M
## 200 200 vincentgiganteee Vincent Gigante 2.6M
## 201 201 thebenjishow benji 6.2M
## 202 202 joaodoce Joao Doce 3.3M
## 203 203 andrea.dvrs Dapaney🦋 2.6M
## 204 204 officialbhafc Brighton & Hove Albion FC 600.3K
## 205 205 claudiaraia Claudia Raia 2.8M
## 206 206 elsaucepapii SaucePapii22 1.5M
## 207 207 werenotreallystrangers WNRS 4.8M
## 208 208 marianagrimaldii La Niña Fresa 🍓 3.8M
## 209 209 qamar_altaey 𝑄𝐴𝑀𝐴𝑅 4.4M
## 210 210 melmaia Mel Maia 🍯 14M
## 211 211 outtpig OuttPig 2.4M
## 212 212 edgar3104 Edgarmendez 5.7M
## 213 213 christianchavezreal Christian Chávez 1.7M
## 214 214 championsleague Champions League 20.3M
## 215 215 gordonramsayofficial Gordon Ramsay 35.6M
## 216 216 dixiedamelio dixie 57.5M
## 217 217 maxtaylorlifts Max Taylor 9.4M
## 218 218 oironyoliveira Kbuloso 😈 7.1M
## 219 219 marko marko 11.2M
## 220 220 dinosonso_ofc DinoSonso 8.8M
## 221 221 firmanskd 𝙆𝙊𝘾𝘼𝙉 3.1M
## 222 222 oficialrc3 Roberto Carlos 3.6M
## 223 223 danythegaggio Daniele Cabras 3.5M
## 224 224 tina_042 𝒯𝒾𝓃𝒶 1.3M
## 225 225 pedroallann Pedroca 2.9M
## 226 226 piffpeterson piff peterson 3.9M
## 227 227 jongmineyo 종민오빠 Jongmin oppa 15.2M
## 228 228 jumpersjump Jumpers Jump Podcast 8.4M
## 229 229 piitien1603 pi tiên ™ 1.7M
## 230 230 gehu.gl Gerson Garcia 573.3K
## 231 231 mesitaaa Mesita 486.9K
## 232 232 szivsz Ziva Magnolya 2.1M
## 233 233 dilanjaniyar Emang namanya Dilan 2.2M
## 234 234 fernandamotafarhat Fe 7.3M
## 235 235 stickyboy69 stickyboy69 9.2M
## 236 236 juliagisella juliagisella 13.5M
## 237 237 mieayamthebstt Unaa🍜 17M
## 238 238 frankielapenna Frankie LaPenna 5.7M
## 239 239 jdpantoja JD Pantoja 31.9M
## 240 240 davidcstt_ David Costa 6.2M
## 241 241 badboyrema Rema 2.7M
## 242 242 simxmargo Seamoan {1M+} 1M
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## 728 728 marquis.culton Marquis Culton 712.3K
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## 735 735 djadoni Djadoni 1.5M
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## 740 740 yonumo ni ☆ 270.8K
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## 767 767 tabithaswatosh Tabs 13.1M
## 768 768 roportajadam Mahsun Karaca 1.3M
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## 786 786 jannatmirza J M 🥀 21.3M
## 787 787 jennychallita Jennychallita 4.6M
## 788 788 caprariu_valentino V A L E N T I N O 4M
## 789 789 section_pull_up Section Pull Up Off 1.1M
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## 799 799 q7cg احمد الحربي 461.6K
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## 803 803 yesimresmi1 ~YEŞİM 17.5M
## 804 804 itsaburob Ahmad Aburob 2.4M
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## 806 806 _desyortega_ ˚₊✩‧₊ d e s ‧₊˚✧ 31.8K
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## 809 809 raonyp Rao 282.1K
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## 811 811 botakteras Mike 4M
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## 813 813 calleypoche Calle y Poché 5.9M
## 814 814 ahmed_hosni1 ahmed Hosni 🌍❤️ 4.1M
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## 832 832 daniaristizabal29 Daniela Aristizabal N 55K
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## 841 841 virginiafonseca Virginia 35.3M
## 842 842 katclark86 Kat Clark 3.4M
## 843 843 brycehall Bryce Hall 23M
## 844 844 vinhlion77777 💎Vinh Lion 5.8M
## 845 845 mumeixxx mumei✖ 2.8M
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## 847 847 riumbau Hector Riumbau 2.8M
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## 850 850 decorsnippets Rue 976.9K
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## 852 852 blakegray Blake Gray 9.8M
## 853 853 iitsace PETER ACE 💜 2.5M
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## 861 861 1spdr دارك 1.7M
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## 865 865 homm9k ХОМЯК 46.4M
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## 870 870 jolly_good_ginger El Chismoso 3.2M
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## 877 877 _raichouuofficial SHRX • Raichouu 🇵🇭 1.4M
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## 879 879 nadina_ioana Nadina Ioana 12.9M
## 880 880 ...anddy_ ANDDY 🐺🌙 1.3M
## 881 881 fabstarden Fabrício :) 66K
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## 885 885 wisdm8 Wisdom Kaye 8.7M
## 886 886 eyeout4selen3r Shawn Mac 1.2M
## 887 887 choukripirate CHOUKRI PIRATE 🏴☠️ 1.6M
## 888 888 captincroook Alex Consani 792.5K
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## 891 891 avani avani 42.8M
## 892 892 ugolord ⚖️ The TikTok Attorney ⚖️ 6.4M
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## 894 894 msquynhthie Quỳnh Thi 1.3M
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## 897 897 zzri9a ZZRi9A 1M
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## 899 899 rifirdus RIFIRDUS 2.4M
## 900 900 mult..fand0m Antonella 👍 154K
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## 904 904 una_arana Una Araña 77K
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## 915 915 sjbleau SJ 13.9M
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## 919 919 mrstiventc1977 MrStivenTc 6.8M
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## 923 923 nicolassturniolo Nicolas Sturniolo 4.1M
## 924 924 julesleblanc jules 18.9M
## 925 925 baldybrobryzxz celloszxz🧑🏻🦲 11M
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## 928 928 azelmusic Azel 1.3M
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## 946 946 eravfx ERAVFX 1.8M
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## 948 948 ashraf_alalmei 🇪🇬⚡"ALALMEI"⚡🇵🇸 3.5M
## 949 949 youngji_02 이영지 2.2M
## 950 950 joelbergs JoelBergs 11.6M
## 951 951 axxdermusic Axder 5.3M
## 952 952 chevy2funnyy Chevy2funnyy 2.1M
## 953 953 nilewilsonator Wilsonator 1.2M
## 954 954 farrukoofficialpr FARRU 3.7M
## 955 955 keirariff Keira 5.6M
## 956 956 jimmywells96 Jimmy Wells 2.2M
## 957 957 knzymyln__ Kienzy키엔지미엔✧🇰🇷 8.4M
## 958 958 flickerspark_ FlickerSpark 1.8M
## 959 959 healthy_mandy healthy_mandy 587.8K
## 960 960 robertfrank615 Robert Frank 1.2M
## 961 961 maxthemeatguy Max The Meat Guy 5.3M
## 962 962 ailaloures aila loures 3.9M
## 963 963 ogikdp Zona Ogik | YT MELLO PROJECT 5M
## 964 964 leahh leah 6.3M
## 965 965 karlwolfs Karl Wolf 414.8K
## 966 966 natalieamayaa Natalie Amaya 96.7K
## 967 967 banhbaoxinchao Bánh Bao Xin Chào 2.6M
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## 969 969 thruhikers Renee-and-Tim 2.1M
## 970 970 thenursery_nurse Charlotte 1.8M
## 971 971 i_am_young22 냥뇽녕냥👻 3.1M
## 972 972 itsmenicksmithy2 Nick Smithyman 😎 13.6M
## 973 973 slamminsammyswank Sam Swank 1.6M
## 974 974 mistermainer mistermainer 16.4M
## 975 975 nelysa_norazlan Nelysa Norazlan 3M
## 976 976 hunterprosper Hunter Prosper 5.5M
## 977 977 mohaimen.alaa مهيمن علاء 4.7M
## 978 978 mei_asami_ 浅見めい 1.5M
## 979 979 rreygrande rey 617.8K
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## 981 981 leesiyoung38 이시영 17.6M
## 982 982 jmartineze_ josi 23.7M
## 983 983 los_escachaitos Los escachaitos 5.3M
## 984 984 pedrinhuol pedrinhuol 6.3M
## 985 985 nouraridaofficial Nour Arida 3.3M
## 986 986 karolsevillaokay karolsevillaofc 26.2M
## 987 987 coldcutz20 CHRIS COLDITZ 1.4M
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## 989 989 ddlovato Demi Lovato 4.5M
## 990 990 jlo JLO 15.4M
## 991 991 esta.pramanita Esta Pramanita 2.4M
## 992 992 ladydianka LADY BUNNY🐰 9.2M
## 993 993 ediqueixinho Edivando Junior 4.3M
## 994 994 liamcarps Liam Carpenter 1.5M
## 995 995 giovannibonaccinii_ G I O V A N N I🦋 1M
## 996 996 jiembasands Jiemba Sands 4.9M
## 997 997 crissa_ace Crissa Jackson 14.9M
## 998 998 ichadude Alyssa & Dude 468.8K
## 999 999 kanebrown Kane Brown 5.2M
## 1000 1000 nnennab_ Nnenna B | NYC Creator & Actor 149.1K
## views.avg. likes.avg.. comments.avg.. shares.avg..
## 1 29.2M 3.5M 30.8K 7.2K
## 2 23.7M 3.4M 21.7K 25.7K
## 3 48.9M 998.4K 16.3K 60.9K
## 4 19.8M 3.6M 23.3K 24.2K
## 5 21.1M 3.3M 17.5K 25.3K
## 6 13.2M 2.9M 5.7K 50.5K
## 7 21.5M 2.4M 16.3K 24.5K
## 8 25M 1.7M 16.6K 5.3K
## 9 18.6M 1.8M 7.5K 19K
## 10 16.2M 1.6M 7.8K 21.4K
## 11 18.5M 1.9M 5.4K 16.3K
## 12 5.7M 1.7M 35K 25.3K
## 13 8M 2.2M 12.7K 19.3K
## 14 7.5M 1.9M 20.8K 15.5K
## 15 6.4M 1.2M 4.2K 37.5K
## 16 8.5M 1.6M 5.6K 16.1K
## 17 10.6M 1.2M 20.8K 2.3K
## 18 12.7M 1.7M 7.6K 4.4K
## 19 10.6M 1.2M 6.1K 8.5K
## 20 8.1M 2M 5.2K 5.2K
## 21 12.1M 678.2K 11.7K 6.5K
## 22 6.5M 576.1K 4.3K 34.3K
## 23 18.7M 914.1K 2.7K 5.7K
## 24 11.3M 1.5M 5.6K 2.4K
## 25 11.2M 1M 6.1K 5.9K
## 26 11.5M 1.3M 6.3K 1.4K
## 27 13M 1.6M 4.2K 1.3K
## 28 12.8M 547.3K 4.7K 11.2K
## 29 13M 888.6K 5K 3.8K
## 30 14.4M 1.1M 4.4K 1.9K
## 31 7.7M 883.3K 8.8K 3.9K
## 32 13.6M 914.6K 728 6.1K
## 33 10.8M 1.3M 1.9K 3.9K
## 34 10.9M 1.1M 4K 2.5K
## 35 8.4M 490.1K 3.8K 13.3K
## 36 5.4M 383.5K 10.5K 22.7K
## 37 9.3M 797.6K 2.2K 6.7K
## 38 7.8M 789.9K 6.9K 3K
## 39 6M 812.3K 6.7K 6K
## 40 3M 847.2K 13.9K 9.8K
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## 46 4.7M 920.4K 10.4K 1.4K
## 47 7.2M 1.4M 3.6K 1.3K
## 48 11.6M 920.7K 3.6K 944
## 49 10.6M 1M 2.4K 2.1K
## 50 9.2M 789.2K 2.7K 3.8K
## 51 9.2M 789.2K 2.7K 3.8K
## 52 4.8M 772.6K 5.8K 6.9K
## 53 9.2M 1M 1.6K 3.1K
## 54 8.5M 709K 4.6K 2.4K
## 55 6.7M 680.1K 2.3K 6.9K
## 56 11.4M 888.1K 3K 1.2K
## 57 8.2M 852K 3.5K 2.2K
## 58 8.2M 320K 3K 11.8K
## 59 4.2M 864.8K 6.8K 3.2K
## 60 3.6M 626K 8K 7.8K
## 61 7.2M 783.6K 3.1K 3.2K
## 62 10.3M 909.6K 2.5K 1.3K
## 63 5.8M 779.9K 5.3K 2.5K
## 64 6.1M 415.6K 10.3K 3.3K
## 65 4.6M 676.6K 3.1K 6.8K
## 66 3M 560.7K 3.8K 14.2K
## 67 5.5M 813.5K 3K 3.4K
## 68 3M 653.9K 3.2K 11.4K
## 69 2.2M 701K 9.2K 9.3K
## 70 11.1M 695.5K 506 3.1K
## 71 4.2M 488.6K 2.1K 11.4K
## 72 4.6M 685.4K 2K 6.3K
## 73 5.7M 790.2K 4.1K 1.7K
## 74 7.5M 793.1K 2.8K 1.6K
## 75 5.7M 648.5K 1.9K 5.1K
## 76 3.6M 715.7K 2.9K 6.6K
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## 503 2.5M 186.5K 1.5K 1.4K
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## 505 2.4M 315.1K 775 1.1K
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## 522 1.4M 207.4K 2.1K 2.4K
## 523 2.7M 485.7K 700 301
## 524 2.4M 385.7K 779 634
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## 527 2.9M 253.6K 1.2K 568
## 528 1.3M 262.6K 2.3K 1.6K
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## 530 1.7M 287.9K 1.4K 1.3K
## 531 1.4M 263.7K 1.1K 2.3K
## 532 1.7M 246.3K 967 2K
## 533 1.7M 379K 1.1K 938
## 534 5.5M 143.8K 969 628
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## 538 3.4M 452K 654 154
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## 541 2M 138K 2.1K 2.5K
## 542 2.4M 254.6K 671 1.3K
## 543 2.2M 300.2K 1K 824
## 544 2.7M 171.8K 1.8K 847
## 545 2.5M 228.7K 1.6K 537
## 546 2.5M 164.5K 1.1K 1.9K
## 547 1.9M 345K 1.3K 574
## 548 1.5M 218.2K 1.4K 2.3K
## 549 1.5M 175.7K 4K 597
## 550 1.2M 162.9K 5.8K 622
## 551 1.1M 190.9K 1.4K 4.2K
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## 553 2.5M 172.6K 1.7K 1.1K
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## 562 1.9M 297.7K 1.8K 155
## 563 1.9M 181.7K 2.5K 677
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## 566 1.8M 266.6K 827 1.5K
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## 580 3.2M 165.7K 1.8K 209
## 581 1.2M 214.2K 3.3K 775
## 582 2.6M 258K 1.1K 499
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## 588 1.3M 242.1K 709 2.6K
## 589 1.9M 219.4K 1.1K 1.4K
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## 592 1.8M 240.6K 1.3K 1.1K
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## 599 1.5M 209.7K 684 2.5K
## 600 1.3M 158.7K 3.6K 1.3K
## 601 1.4M 200.1K 510 3.1K
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## 604 2.4M 114.1K 899 2.8K
## 605 2.4M 222.7K 990 856
## 606 2.3M 137.8K 1.9K 1.2K
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## 617 1.4M 194.4K 1.2K 2.3K
## 618 1.4M 109.7K 4.1K 2.1K
## 619 1.3M 233K 2.2K 945
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## 621 1.1M 260.7K 365 2.9K
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## 643 1.7M 199.3K 1.1K 1.5K
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## 658 1.8M 214.8K 748 1.4K
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## 662 1.4M 176.1K 1.5K 1.9K
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## 745 1.3M 131.7K 1.3K 2.9K
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## 921 842.8K 147.2K 736 2.9K
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## 929 2M 75.4K 1.9K 1.1K
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## 945 952.8K 182.9K 1.5K 1K
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## 962 845.5K 165.1K 1.4K 1.6K
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## 972 1.5M 179.1K 757 789
## 973 834.2K 165.2K 482 2.5K
## 974 1.2M 157.6K 1K 1.1K
## 975 1.5M 144.4K 760 1.2K
## 976 1.6M 172.6K 437 1.1K
## 977 1.4M 143.8K 1.3K 801
## 978 1.3M 176.8K 1.1K 768
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## 984 1.9M 178.2K 448 750
## 985 1.9M 108.2K 733 1.2K
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## 987 4.4M 100.3K 494 424
## 988 2.2M 175.5K 713 336
## 989 1.4M 130.8K 1.7K 573
## 990 1.5M 118.9K 1.8K 417
## 991 2.3M 222.7K 390 392
## 992 1.2M 144.7K 1.3K 899
## 993 1.7M 185.2K 754 488
## 994 1.5M 136.6K 697 1.3K
## 995 1M 171.6K 983 1.3K
## 996 3.4M 247.4K 207 264
## 997 1.6M 141.7K 1.2K 580
## 998 2.3M 85.5K 997 1K
## 999 1.7M 96.7K 1.3K 1.2K
## 1000 606.2K 79.5K 2.1K 6.1K
names(nv22) <- c('R','RN','TN','F','V','L','C','S')
names(nv22)
## [1] "R" "RN" "TN" "F" "V" "L" "C" "S"
nati <- nv22$F
nati
## [1] "60.3M" "42.4M" "8.9M" "4.4M" "6.1M" "11.6M" "4.5M" "6.1M"
## [9] "28.4M" "18.4M" "19.8M" "13.3M" "7M" "2.5M" "94K" "6.1M"
## [17] "153.1M" "9.2M" "28.1M" "19.7M" "5.6M" "5.7M" "1.8M" "24.8M"
## [25] "11.9M" "19.9M" "15.5M" "24.9M" "20.9M" "22.1M" "70.2M" "125.7K"
## [33] "1.6M" "38.8M" "1.2M" "2.1M" "3.2M" "149M" "8.5M" "19M"
## [41] "13.9M" "1.2M" "6.6M" "1.5M" "19.5M" "9.7M" "11M" "6.4M"
## [49] "24.1M" "8M" "8M" "9.5M" "4.7M" "8.1M" "10.3M" "4.8M"
## [57] "26.3M" "1.2M" "1.5M" "64.3M" "12.7M" "9.2M" "62M" "5.8M"
## [65] "7.3M" "387.5K" "1M" "149.6K" "4.8M" "1.5M" "2.4M" "3.2M"
## [73] "754.8K" "50.4M" "25.4M" "5.5M" "8.4M" "36.9M" "10M" "6M"
## [81] "31.4K" "6M" "4.1M" "38.2M" "35.1M" "3.8M" "25M" "2.7M"
## [89] "209K" "5M" "1.7M" "92.6M" "782.4K" "6.5M" "13.2M" "5.4M"
## [97] "7M" "2.7M" "3.2M" "72.1M" "4.1M" "11.3M" "2.8M" "47.6M"
## [105] "10.7M" "1.1M" "2.3M" "10.5M" "7.1M" "3.9M" "5M" "5.6M"
## [113] "238.9K" "14.7M" "44.9M" "123K" "14.1M" "3.9M" "860.3K" "4.4M"
## [121] "2.3M" "22.7M" "8.6M" "672.8K" "112.1K" "467.7K" "73M" "1.5M"
## [129] "675.6K" "6.9M" "9.8M" "2.6M" "11.6M" "994.5K" "5.3M" "2.1M"
## [137] "801.5K" "45.8M" "9.3M" "11.5M" "308K" "16.6M" "2.4M" "15.6M"
## [145] "4.2M" "8.2M" "7.2M" "16.1M" "1.2M" "13.3M" "2.5M" "8.3M"
## [153] "12.9M" "12.7M" "6.4M" "9.9M" "9.8M" "4.2M" "17.7M" "13.4M"
## [161] "8.1M" "13.4M" "14M" "2.5M" "3.1M" "2.9M" "5.2M" "5.9M"
## [169] "88.7M" "136.2K" "9.9M" "8.8M" "11.6M" "3.8M" "9.2M" "4.6M"
## [177] "599.1K" "15M" "944.8K" "8M" "14M" "2.8M" "2M" "130.2K"
## [185] "9.8M" "4.5M" "5.7M" "6.1M" "3M" "4M" "3.8M" "4.2M"
## [193] "1.9M" "36.6M" "6.5M" "11.2M" "6.5M" "11M" "1.3M" "2.6M"
## [201] "6.2M" "3.3M" "2.6M" "600.3K" "2.8M" "1.5M" "4.8M" "3.8M"
## [209] "4.4M" "14M" "2.4M" "5.7M" "1.7M" "20.3M" "35.6M" "57.5M"
## [217] "9.4M" "7.1M" "11.2M" "8.8M" "3.1M" "3.6M" "3.5M" "1.3M"
## [225] "2.9M" "3.9M" "15.2M" "8.4M" "1.7M" "573.3K" "486.9K" "2.1M"
## [233] "2.2M" "7.3M" "9.2M" "13.5M" "17M" "5.7M" "31.9M" "6.2M"
## [241] "2.7M" "1M" "142.8K" "20.7M" "13M" "3.5M" "6.9M" "3.3M"
## [249] "5M" "9M" "2.8M" "6M" "3.6M" "18.9M" "5.3M" "499.7K"
## [257] "2M" "2.1M" "12.9M" "5.9M" "5.6M" "5.3M" "14.5M" "15.1M"
## [265] "3.8M" "2.2M" "1M" "4.6M" "3.7M" "4.9M" "3.3M" "29.1M"
## [273] "2M" "2M" "2M" "14.9M" "39M" "7.2M" "14.3M" "26.4M"
## [281] "8.7M" "7.3M" "740.6K" "13.1M" "12M" "1.5M" "786.4K" "5.9M"
## [289] "387.3K" "1.6M" "1.3M" "1.4M" "1.1M" "10.5M" "22.4M" "7.7M"
## [297] "18.3M" "6M" "598K" "2.3M" "2.8M" "3.6M" "1.6M" "6M"
## [305] "5M" "18.3M" "3.4M" "2.7M" "6.5M" "21.4M" "4.3M" "2M"
## [313] "11.2M" "4.9M" "69K" "6.4M" "2.6M" "23.6M" "6.8M" "29.6M"
## [321] "27.3M" "4.4M" "1.9M" "5M" "4.7M" "15.4M" "3.2M" "5M"
## [329] "18.8M" "1.2M" "303.1K" "14.5M" "1.6M" "2.1M" "2.5M" "4.8M"
## [337] "4.1M" "1.8M" "26.9M" "8.8M" "14.4M" "7M" "7.1M" "3.3M"
## [345] "5.2M" "12.4M" "4.4M" "4.6M" "4.3M" "2.7M" "126.4K" "20M"
## [353] "19.4M" "1.3M" "3.2M" "296.6K" "1.1M" "2.8M" "4.9M" "20.1M"
## [361] "5M" "7.6M" "3.6M" "6.1M" "15.8M" "20M" "674.5K" "1.3M"
## [369] "1.2M" "3.6M" "2.8M" "8.8M" "249.7K" "795.5K" "4.1M" "4.1M"
## [377] "3.2M" "7.5M" "3.3M" "13.4M" "4.2M" "5.8M" "2.3M" "4.2M"
## [385] "11.7M" "3.8M" "234K" "6M" "3.8M" "1.6M" "12.7M" "4.2M"
## [393] "4.2M" "6M" "376.4K" "2.5M" "2M" "11.4M" "2.8M" "911K"
## [401] "5.9M" "723.3K" "132.5K" "1.7M" "5.5M" "4.4M" "1.8M" "896.9K"
## [409] "1.2M" "15.2M" "1.3M" "2.7M" "1.6M" "24.8M" "8.2M" "1.8M"
## [417] "14.2M" "6.9M" "8M" "9.1M" "5.4M" "15.4M" "6M" "9M"
## [425] "6.2M" "4.3M" "13.3M" "3.9M" "15M" "4.3M" "2.5M" "293.1K"
## [433] "2.1M" "7.7M" "4.3M" "11K" "6.8M" "622.9K" "857.4K" "16.1M"
## [441] "1.7M" "121.5K" "27.6K" "1.8M" "2.5M" "5M" "7.6M" "27.7M"
## [449] "2.5M" "3.3M" "768.6K" "4.9M" "2.7M" "608.8K" "6.5M" "62.2K"
## [457] "2.3M" "4.7M" "14M" "19.8M" "602.1K" "13.5M" "13.8M" "9.6M"
## [465] "5.3M" "611.4K" "7.7M" "15.1M" "13.2M" "66.3M" "8.1M" "4M"
## [473] "25.7M" "5.3M" "2.3M" "8.3M" "1.1M" "1.3M" "43.5K" "1.8M"
## [481] "2.8M" "7.8M" "9M" "7.7M" "593.3K" "1M" "1.8M" "6.2M"
## [489] "5.2M" "14.4M" "2M" "2.3M" "646.8K" "3.9M" "4.4M" "1.4M"
## [497] "2.1M" "9M" "5.8M" "2.9M" "1.5M" "7M" "9.7M" "1.3M"
## [505] "2.5M" "2.9M" "7.1M" "3.1M" "5.4M" "3.5M" "3.6M" "31.8M"
## [513] "88.9K" "8.6M" "16.7M" "76.9K" "2M" "3.1M" "21M" "4.1M"
## [521] "6M" "6.7M" "8.8M" "9.2M" "5.3M" "15.2M" "14.5M" "1.8M"
## [529] "466.7K" "2.7M" "62.2K" "15.7M" "10.1M" "19.4M" "1.1M" "1.1M"
## [537] "9.7M" "407.1K" "1.8M" "1.6M" "6.2M" "701.5K" "1.7M" "9.2M"
## [545] "2.2M" "2.4M" "1.8M" "6M" "3.3M" "1.7M" "2.1M" "9.8M"
## [553] "3.3M" "11.4M" "15.2M" "1.5M" "6.6M" "2.1M" "1.3M" "3.9M"
## [561] "7M" "9.7M" "7.3M" "27.5M" "5.4M" "3.9M" "3.4M" "4.2M"
## [569] "16M" "8.1M" "90K" "34.6M" "1M" "2.7M" "5M" "8.9M"
## [577] "2.3M" "4M" "1.1M" "30.4M" "3.7M" "8.5M" "3.2M" "7.8M"
## [585] "2.9M" "9.5M" "10.5M" "9.3M" "308.4K" "2.5M" "2M" "5.6M"
## [593] "4.1M" "3M" "37.1M" "6.3M" "2.1M" "3.1M" "4.8M" "1.9M"
## [601] "3.1M" "2.8M" "17.1M" "2.1M" "29.8K" "12.1M" "35M" "22.9M"
## [609] "2.2M" "15.2M" "3.7M" "9.3M" "4.4M" "6.8M" "5.1M" "11.2M"
## [617] "5.6M" "2M" "3.1M" "381.8K" "161.3K" "3.6M" "638.2K" "3.5M"
## [625] "12.8M" "7.6M" "1.4M" "685.9K" "11.2M" "2.8M" "1.2M" "1.3M"
## [633] "1.8M" "1M" "1.1M" "19.4M" "2.3M" "42.2M" "806K" "1.9M"
## [641] "1.5M" "226.2K" "4.2M" "30.3M" "5.1M" "1.8M" "12.4M" "2.9M"
## [649] "4M" "3.1M" "5M" "387.7K" "21.3M" "9.6M" "3.4M" "512.1K"
## [657] "10.1M" "825.5K" "16.9M" "46.6K" "11.8M" "3.3M" "5.1M" "2.9M"
## [665] "676.7K" "156.3K" "2.6M" "4M" "8.6M" "30.2M" "339.6K" "2.5M"
## [673] "32.9M" "404.3K" "1.5M" "2.7M" "606.6K" "2.5M" "2.7M" "5.6M"
## [681] "6.7M" "7.2M" "4M" "740.5K" "1.9M" "9.7M" "916.7K" "9.6M"
## [689] "6M" "2.3M" "742.4K" "408.2K" "2.1M" "2.8M" "2M" "3.5M"
## [697] "6.5M" "6.2M" "555.6K" "14.5K" "7.5M" "13.2M" "23.2M" "6M"
## [705] "8.6M" "1.8M" "26.5M" "1.2M" "6M" "4.2M" "3.3M" "228.7K"
## [713] "1.6M" "8.7M" "6.1M" "4.4M" "13.4M" "17.4M" "4.9M" "1.7M"
## [721] "2.2M" "13.4M" "13.8M" "2.7M" "2.5M" "13.6M" "7.6M" "712.3K"
## [729] "9.5M" "14.5M" "7.1M" "6.3M" "9M" "1.9M" "1.5M" "32.7M"
## [737] "16.8M" "4.9M" "10.7M" "270.8K" "3.2M" "860.7K" "19.2M" "7.2M"
## [745] "5M" "729.8K" "11.1M" "2.3M" "8.7M" "6.4M" "1.1M" "1.6M"
## [753] "1.7M" "10M" "5.1M" "2.2M" "4.5M" "7.5M" "1.6M" "14.2M"
## [761] "1.4M" "10.6M" "37.2M" "1.8M" "873.2K" "6.5M" "13.1M" "1.3M"
## [769] "26.8M" "1.4M" "956.9K" "3.2M" "4M" "12.4M" "4.9M" "15.3M"
## [777] "246.4K" "11.7M" "2M" "1.6M" "402.8K" "33.7M" "15.6M" "11.6M"
## [785] "614.6K" "21.3M" "4.6M" "4M" "1.1M" "44.2M" "3.7M" "10M"
## [793] "1.7M" "5.3M" "14.2M" "4.4M" "12.2M" "1.5M" "461.6K" "1.5M"
## [801] "361.6K" "4.9M" "17.5M" "2.4M" "5.5M" "31.8K" "20M" "7.1M"
## [809] "282.1K" "499.5K" "4M" "7.8M" "5.9M" "4.1M" "1M" "10.2M"
## [817] "3.6M" "6.9M" "43.9K" "6.4M" "1.6M" "5.7M" "100.9K" "1.4M"
## [825] "20.9M" "18.5M" "8M" "825.3K" "6.8M" "6M" "5.1M" "55K"
## [833] "24.8M" "13.8M" "7.8M" "7.3M" "1M" "2.3M" "3.8M" "1.2M"
## [841] "35.3M" "3.4M" "23M" "5.8M" "2.8M" "10.3K" "2.8M" "6.2M"
## [849] "4.1M" "976.9K" "8.5M" "9.8M" "2.5M" "3.7M" "4.8M" "10.4M"
## [857] "6.1M" "3.3M" "33.3M" "2.4M" "1.7M" "37.6M" "189.3K" "266.5K"
## [865] "46.4M" "3.9M" "6M" "2.6M" "5.2M" "3.2M" "15.6M" "335.7K"
## [873] "2.4M" "5.9M" "19.7M" "1.3M" "1.4M" "11.1M" "12.9M" "1.3M"
## [881] "66K" "3.5M" "11M" "17.2M" "8.7M" "1.2M" "1.6M" "792.5K"
## [889] "1.1M" "12.7M" "42.8M" "6.4M" "3.1M" "1.3M" "5.1M" "13.5M"
## [897] "1M" "2M" "2.4M" "154K" "4.5M" "2.3M" "1.2M" "77K"
## [905] "1.4M" "1.6M" "1.2M" "561.3K" "3.2M" "2.5M" "7.1M" "1.4M"
## [913] "2.5M" "1.8M" "13.9M" "6M" "14.3M" "6.2M" "6.8M" "3.7M"
## [921] "327.9K" "16.6M" "4.1M" "18.9M" "11M" "1M" "6.3M" "1.3M"
## [929] "196.6K" "3.1M" "2.9M" "2.6M" "6.3M" "2.5M" "12.2M" "1.6M"
## [937] "2.9M" "4.3M" "7.6M" "295.7K" "401.7K" "3.4M" "4.4M" "9.6M"
## [945] "1.6M" "1.8M" "17.3M" "3.5M" "2.2M" "11.6M" "5.3M" "2.1M"
## [953] "1.2M" "3.7M" "5.6M" "2.2M" "8.4M" "1.8M" "587.8K" "1.2M"
## [961] "5.3M" "3.9M" "5M" "6.3M" "414.8K" "96.7K" "2.6M" "138.7K"
## [969] "2.1M" "1.8M" "3.1M" "13.6M" "1.6M" "16.4M" "3M" "5.5M"
## [977] "4.7M" "1.5M" "617.8K" "505.5K" "17.6M" "23.7M" "5.3M" "6.3M"
## [985] "3.3M" "26.2M" "1.4M" "3.9M" "4.5M" "15.4M" "2.4M" "9.2M"
## [993] "4.3M" "1.5M" "1M" "4.9M" "14.9M" "468.8K" "5.2M" "149.1K"
nati1 <- nv22[5,3]
nati1
## [1] "adin"
nati2 <- nv22[20:120,]
nati2
## R RN TN F V L C
## 20 20 juandamc JuanDa. 19.7M 8.1M 2M 5.2K
## 21 21 twitchtok7 tWitch 5.6M 12.1M 678.2K 11.7K
## 22 22 anaraquelhz Raquel 5.7M 6.5M 576.1K 4.3K
## 23 23 karadenizli.maceraci KARADENİZLİ MACERACI 1.8M 18.7M 914.1K 2.7K
## 24 24 lexibrookerivera Lexi Rivera 24.8M 11.3M 1.5M 5.6K
## 25 25 hotspanishmx HotSpanish 11.9M 11.2M 1M 6.1K
## 26 26 surthycooks Surthycooks 19.9M 11.5M 1.3M 6.3K
## 27 27 nicollefigueroaa Nicolle Figueroa 15.5M 13M 1.6M 4.2K
## 28 28 nicocaponecomedy nicocapone.comedy 24.9M 12.8M 547.3K 4.7K
## 29 29 williesalim WILLIE SALIM 20.9M 13M 888.6K 5K
## 30 30 luvadepedreiro Iran Ferreira (Lai) 22.1M 14.4M 1.1M 4.4K
## 31 31 kimberly.loaiza Kimberly Loaiza 70.2M 7.7M 883.3K 8.8K
## 32 32 texasgirl_nadia N A D I A 125.7K 13.6M 914.6K 728
## 33 33 slaterkodish slaterkodish 1.6M 10.8M 1.3M 1.9K
## 34 34 bayashi.tiktok バヤシ🥑Bayashi 38.8M 10.9M 1.1M 4K
## 35 35 amroqarawe Amro Qarawe 1.2M 8.4M 490.1K 3.8K
## 36 36 mohm.nabeel mohammad.nabeel 2.1M 5.4M 383.5K 10.5K
## 37 37 cedricgrolet Cedric Grolet 3.2M 9.3M 797.6K 2.2K
## 38 38 charlidamelio charli d’amelio 149M 7.8M 789.9K 6.9K
## 39 39 dannero Dannero 8.5M 6M 812.3K 6.7K
## 40 40 txt.bighitent TOMORROW X TOGETHER 19M 3M 847.2K 13.9K
## 41 41 emillyvickof Emilly Vick 13.9M 11.5M 570.2K 6.7K
## 42 42 xelitobelek xelito 1.2M 2.5M 336.7K 64.8K
## 43 43 seventeen17_official SEVENTEEN 6.6M 2.9M 876.9K 12.1K
## 44 44 amielgarciami Ami Garcia Amiel 1.5M 9.3M 924.3K 2.3K
## 45 45 noelgoescrazy noelgoescrazy 19.5M 16.5M 668.4K 2.1K
## 46 46 dylanmulvaney Dylan Mulvaney 9.7M 4.7M 920.4K 10.4K
## 47 47 ramonvitor ramonvitor 11M 7.2M 1.4M 3.6K
## 48 48 miladmirg Milad 6.4M 11.6M 920.7K 3.6K
## 49 49 mmmjoemele Joe Mele 24.1M 10.6M 1M 2.4K
## 50 50 thezachchoi Zach Choi 8M 9.2M 789.2K 2.7K
## 51 51 thezachchoi Zach Choi 8M 9.2M 789.2K 2.7K
## 52 52 morimura MoriMura🍕 9.5M 4.8M 772.6K 5.8K
## 53 53 yano4kaa.aaa yano4kaa 4.7M 9.2M 1M 1.6K
## 54 54 salzabilll_ s a l z a b i l l x 🔮 8.1M 8.5M 709K 4.6K
## 55 55 elpugaa Puga 10.3M 6.7M 680.1K 2.3K
## 56 56 kukombo H 4.8M 11.4M 888.1K 3K
## 57 57 brookemonk_ Brooke Monk 26.3M 8.2M 852K 3.5K
## 58 58 yvesbissonsturgeonco yves 1.2M 8.2M 320K 3K
## 59 59 mewsuppasit21 mewsuppasit 1.5M 4.2M 864.8K 6.8K
## 60 60 therock The Rock 64.3M 3.6M 626K 8K
## 61 61 elina_karimovaa 🐚Elina_리나대장님🤍 12.7M 7.2M 783.6K 3.1K
## 62 62 bigchungus.tik BigChungus 9.2M 10.3M 909.6K 2.5K
## 63 63 domelipa domelipa 62M 5.8M 779.9K 5.3K
## 64 64 mikaylahau Mikaylah 5.8M 6.1M 415.6K 10.3K
## 65 65 mrnigelng Nigel Ng (Uncle Roger) 7.3M 4.6M 676.6K 3.1K
## 66 66 jakefresca Fresca Fresh 387.5K 3M 560.7K 3.8K
## 67 67 bellaamtz Bella 1M 5.5M 813.5K 3K
## 68 68 guiedits_ guieditss 149.6K 3M 653.9K 3.2K
## 69 69 niallhoran Niall Horan 4.8M 2.2M 701K 9.2K
## 70 70 shellyclouds Shelly Clouds 1.5M 11.1M 695.5K 506
## 71 71 jayandsharon Jay & Sharon 2.4M 4.2M 488.6K 2.1K
## 72 72 kane kane 3.2M 4.6M 685.4K 2K
## 73 73 future_millionaires Future Millionaires 754.8K 5.7M 790.2K 4.1K
## 74 74 kyliejenner Kylie Jenner 50.4M 7.5M 793.1K 2.8K
## 75 75 rubentuestaok Ruben Tuesta 25.4M 5.7M 648.5K 1.9K
## 76 76 ferxxo444 Feid 5.5M 3.6M 715.7K 2.9K
## 77 77 onwardwanna Wanna🥊 8.4M 4.7M 827.1K 4.8K
## 78 78 montpantoja Montpantoja 36.9M 7M 1.2M 2K
## 79 79 nekoglai Николай 10M 4.7M 440.5K 9.3K
## 80 80 esnyrrr Esnyr 6M 5.1M 813.8K 2K
## 81 81 skythedogtrainer skythedogtrainer 31.4K 14M 1.2M 678
## 82 82 mclomaofficiall mclomaofficial 6M 6.7M 1M 2.2K
## 83 83 lechilinh88 Lê Chí Linh 4.1M 7.5M 513.3K 4.4K
## 84 84 pongamoslo_a_prueba Pongámoslo a Prueba 38.2M 7.5M 898.8K 1.8K
## 85 85 nianaguerrero Niana Guerrero 35.1M 7.9M 725.8K 2.1K
## 86 86 druskitv DRUSKI 3.8M 2.8M 533.6K 4K
## 87 87 jaykindafunny8 Jaykindafunny 25M 8.4M 582.8K 2.1K
## 88 88 zodiac.boyfriend Zodiac Boyfriend🪐🔮 2.7M 4M 386.7K 7.4K
## 89 89 andrewlepage23 Andrew Le Page 209K 4.4M 390.3K 3.6K
## 90 90 pandkourt Kourtney-Penelope 5M 10.1M 846K 0
## 91 91 pinkpantheress 😘🙈☺️ 1.7M 3.4M 732.4K 5.8K
## 92 92 bellapoarch Bella Poarch 92.6M 6M 482.2K 4.8K
## 93 93 thethinktok The Think Tok 782.4K 2.8M 284.6K 5.1K
## 94 94 livvy Olivia Dunne 6.5M 4.9M 550.8K 4.6K
## 95 95 falcopunch Falco 13.2M 3.7M 522.6K 4.8K
## 96 96 vibin.wit.tay Tay 5.4M 2.5M 486.4K 3.2K
## 97 97 swagboygorringe Daniel Gorringe 7M 3.1M 347.2K 6K
## 98 98 sadiafza Sadia 2.7M 5.3M 749K 2.2K
## 99 99 ryanbakery 𝙍𝙮𝙖𝙣𝘽𝙖𝙠𝙚𝙧𝙮 3.2M 3.8M 466.8K 4.3K
## 100 100 zachking Zach King 72.1M 11.5M 466.4K 1.6K
## 101 101 docdami Doc Dami 4.1M 4.7M 759.1K 2.2K
## 102 102 mdmotivator Zachery Dereniowski 11.3M 4.9M 834.2K 2.7K
## 103 103 cool__bad Kuan - Aisha 🔎 2.8M 9.4M 565.4K 1.7K
## 104 104 kallmekris Kris HC 47.6M 3.6M 715.5K 3.8K
## 105 105 angelaaguilar_ Angela Aguilar :) 10.7M 5.4M 469.8K 2.3K
## 106 106 joshtgodfrey Josh Godfrey 1.1M 3.7M 464.6K 3.9K
## 107 107 kieram.litchfield Kieram Litchfield 2.3M 5.9M 708.8K 2.2K
## 108 108 kervo.dolo Kervo.dolo 10.5M 3.1M 515.7K 3.1K
## 109 109 winnermaxyt WinnerMax 7.1M 6.4M 421.5K 1.7K
## 110 110 olisboa LISBOA 3.9M 4.3M 740.3K 1.1K
## 111 111 imeyhou Meyden 5M 4.8M 802.4K 2.7K
## 112 112 lilireinhart Lili Reinhart 5.6M 4.2M 602.9K 1.8K
## 113 113 therealemilylin Emily Lin 238.9K 5.2M 655.3K 3.5K
## 114 114 nourmar5 nourmar5 14.7M 3.1M 190.4K 10K
## 115 115 selenagomez Selena Gomez 44.9M 4.5M 545.6K 3.7K
## 116 116 kumulator Kumulátor zábavy 123K 2.3M 207K 4.6K
## 117 117 spencer_serafica Xspencer 14.1M 4.2M 497.6K 3.7K
## 118 118 jesusnalgas JESUSNALGAS 3.9M 3.4M 325.9K 4.3K
## 119 119 makeup_rhk makeup_rhk 860.3K 4.3M 812.8K 556
## 120 120 duncanyounot Duncan Joseph 4.4M 2.9M 560.1K 2.7K
## S
## 20 5.2K
## 21 6.5K
## 22 34.3K
## 23 5.7K
## 24 2.4K
## 25 5.9K
## 26 1.4K
## 27 1.3K
## 28 11.2K
## 29 3.8K
## 30 1.9K
## 31 3.9K
## 32 6.1K
## 33 3.9K
## 34 2.5K
## 35 13.3K
## 36 22.7K
## 37 6.7K
## 38 3K
## 39 6K
## 40 9.8K
## 41 2.1K
## 42 192
## 43 9.4K
## 44 3.8K
## 45 2.7K
## 46 1.4K
## 47 1.3K
## 48 944
## 49 2.1K
## 50 3.8K
## 51 3.8K
## 52 6.9K
## 53 3.1K
## 54 2.4K
## 55 6.9K
## 56 1.2K
## 57 2.2K
## 58 11.8K
## 59 3.2K
## 60 7.8K
## 61 3.2K
## 62 1.3K
## 63 2.5K
## 64 3.3K
## 65 6.8K
## 66 14.2K
## 67 3.4K
## 68 11.4K
## 69 9.3K
## 70 3.1K
## 71 11.4K
## 72 6.3K
## 73 1.7K
## 74 1.6K
## 75 5.1K
## 76 6.6K
## 77 1.4K
## 78 1K
## 79 2.4K
## 80 3.7K
## 81 705
## 82 1.2K
## 83 1.3K
## 84 1.6K
## 85 1.8K
## 86 10.4K
## 87 2.2K
## 88 5.9K
## 89 7.8K
## 90 2.3K
## 91 1.7K
## 92 1.5K
## 93 18.2K
## 94 2.1K
## 95 4.4K
## 96 11.4K
## 97 10.5K
## 98 2.1K
## 99 5.2K
## 100 1.5K
## 101 2.3K
## 102 1.2K
## 103 1.2K
## 104 2.1K
## 105 3.6K
## 106 4.7K
## 107 1.2K
## 108 5.7K
## 109 3.4K
## 110 3K
## 111 658
## 112 3.3K
## 113 258
## 114 11.6K
## 115 1.3K
## 116 20.8K
## 117 2.1K
## 118 6.3K
## 119 2.8K
## 120 4.2K
nati3 <- nv22[nv22$L== '998.4K',]
nati3
## R RN TN F V L C S
## 3 3 yzn47 يزن الأسمر 8.9M 48.9M 998.4K 16.3K 60.9K