Eseguire il seguente codice: cosa fa?
library("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.2.0
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary("ISLR")
data("Credit",package="ISLR")
# i è una variabile che prende gli indici di 35 righe prese in maniera casuale tramite l'ausilio della funzione sample
i = sample(1:nrow(Credit), size = 35)
# Crea un nuovo dataset chiamato Credit_na che è identico a Credit
Credit_na = Credit
# Gli indici delle righe contenute nella variabile i, sono utilizzate per richiamare tali righe nel dataset Credit_na, # per di più di quelle righe contenute nella variabile i sono prese le colonne Ethnicity, Income e Age ed i valori
# delle colonne contenute nelle righe di i sono trasformati in NA cioè valori mancanti
Credit_na[i,c("Ethnicity", "Income", "Age")] <- NAIndividuare una variabile numerica con NA e rimpiazzare i valori con la media aritmetica
class(Credit_na$Income)## [1] "numeric"Credit_na %>%
mutate(Income = ifelse(is.na(Income),mean(Income,na.rm=T),Income))## ID Income Limit Rating Cards Age Education Gender Student Married
## 1 1 14.89100 3606 283 2 34 11 Male No Yes
## 2 2 106.02500 6645 483 3 82 15 Female Yes Yes
## 3 3 104.59300 7075 514 4 71 11 Male No No
## 4 4 148.92400 9504 681 3 36 11 Female No No
## 5 5 55.88200 4897 357 2 68 16 Male No Yes
## 6 6 80.18000 8047 569 4 77 10 Male No No
## 7 7 20.99600 3388 259 2 37 12 Female No No
## 8 8 71.40800 7114 512 2 87 9 Male No No
## 9 9 15.12500 3300 266 5 66 13 Female No No
## 10 10 71.06100 6819 491 3 41 19 Female Yes Yes
## 11 11 44.82378 8117 589 4 NA 14 Male No Yes
## 12 12 15.04500 1311 138 3 64 16 Male No No
## 13 13 80.61600 5308 394 1 57 7 Female No Yes
## 14 14 43.68200 6922 511 1 49 9 Male No Yes
## 15 15 19.14400 3291 269 2 75 13 Female No No
## 16 16 20.08900 2525 200 3 57 15 Female No Yes
## 17 17 53.59800 3714 286 3 73 17 Female No Yes
## 18 18 36.49600 4378 339 3 69 15 Female No Yes
## 19 19 44.82378 6384 448 1 NA 9 Female No Yes
## 20 20 42.07900 6626 479 2 44 9 Male No No
## 21 21 17.70000 2860 235 4 63 16 Female No No
## 22 22 37.34800 6378 458 1 72 17 Female No No
## 23 23 20.10300 2631 213 3 61 10 Male No Yes
## 24 24 64.02700 5179 398 5 48 8 Male No Yes
## 25 25 10.74200 1757 156 3 57 15 Female No No
## 26 26 14.09000 4323 326 5 25 16 Female No Yes
## 27 27 42.47100 3625 289 6 44 12 Female Yes No
## 28 28 32.79300 4534 333 2 44 16 Male No No
## 29 29 186.63400 13414 949 2 41 14 Female No Yes
## 30 30 26.81300 5611 411 4 55 16 Female No No
## 31 31 34.14200 5666 413 4 47 5 Female No Yes
## 32 32 28.94100 2733 210 5 43 16 Male No Yes
## 33 33 134.18100 7838 563 2 48 13 Female No No
## 34 34 31.36700 1829 162 4 30 10 Male No Yes
## 35 35 20.15000 2646 199 2 25 14 Female No Yes
## 36 36 23.35000 2558 220 3 49 12 Female Yes No
## 37 37 62.41300 6457 455 2 71 11 Female No Yes
## 38 38 30.00700 6481 462 2 69 9 Female No Yes
## 39 39 11.79500 3899 300 4 25 10 Female No No
## 40 40 13.64700 3461 264 4 47 14 Male No Yes
## 41 41 34.95000 3327 253 3 54 14 Female No No
## 42 42 113.65900 7659 538 2 66 15 Male Yes Yes
## 43 43 44.15800 4763 351 2 66 13 Female No Yes
## 44 44 36.92900 6257 445 1 24 14 Female No Yes
## 45 45 31.86100 6375 469 3 25 16 Female No Yes
## 46 46 77.38000 7569 564 3 50 12 Female No Yes
## 47 47 19.53100 5043 376 2 64 16 Female Yes Yes
## 48 48 44.64600 4431 320 2 49 15 Male Yes Yes
## 49 49 44.52200 2252 205 6 72 15 Male No Yes
## 50 50 43.47900 4569 354 4 49 13 Male Yes Yes
## 51 51 36.36200 5183 376 3 49 15 Male No Yes
## 52 52 39.70500 3969 301 2 27 20 Male No Yes
## 53 53 44.20500 5441 394 1 32 12 Male No Yes
## 54 54 44.82378 5466 413 4 NA 10 Male No Yes
## 55 55 15.33300 1499 138 2 47 9 Female No Yes
## 56 56 32.91600 1786 154 2 60 8 Female No Yes
## 57 57 57.10000 4742 372 7 79 18 Female No Yes
## 58 58 76.27300 4779 367 4 65 14 Female No Yes
## 59 59 10.35400 3480 281 2 70 17 Male No Yes
## 60 60 51.87200 5294 390 4 81 17 Female No No
## 61 61 35.51000 5198 364 2 35 20 Female No No
## 62 62 21.23800 3089 254 3 59 10 Female No No
## 63 63 30.68200 1671 160 2 77 7 Female No No
## 64 64 14.13200 2998 251 4 75 17 Male No No
## 65 65 32.16400 2937 223 2 79 15 Female No Yes
## 66 66 12.00000 4160 320 4 28 14 Female No Yes
## 67 67 113.82900 9704 694 4 38 13 Female No Yes
## 68 68 44.82378 5099 380 4 NA 16 Female No No
## 69 69 27.84700 5619 418 2 78 15 Female No Yes
## 70 70 49.50200 6819 505 4 55 14 Male No Yes
## 71 71 24.88900 3954 318 4 75 12 Male No Yes
## 72 72 58.78100 7402 538 2 81 12 Female No Yes
## 73 73 22.93900 4923 355 1 47 18 Female No Yes
## 74 74 23.98900 4523 338 4 31 15 Male No No
## 75 75 16.10300 5390 418 4 45 10 Female No Yes
## 76 76 33.01700 3180 224 2 28 16 Male No Yes
## 77 77 30.62200 3293 251 1 68 16 Male Yes No
## 78 78 20.93600 3254 253 1 30 15 Female No No
## 79 79 110.96800 6662 468 3 45 11 Female No Yes
## 80 80 15.35400 2101 171 2 65 14 Male No No
## 81 81 27.36900 3449 288 3 40 9 Female No Yes
## 82 82 53.48000 4263 317 1 83 15 Male No No
## 83 83 44.82378 4433 344 3 NA 11 Male No No
## 84 84 19.22500 1433 122 3 38 14 Female No No
## 85 85 43.54000 2906 232 4 69 11 Male No No
## 86 86 152.29800 12066 828 4 41 12 Female No Yes
## 87 87 55.36700 6340 448 1 33 15 Male No Yes
## 88 88 11.74100 2271 182 4 59 12 Female No No
## 89 89 15.56000 4307 352 4 57 8 Male No Yes
## 90 90 44.82378 7518 543 3 NA 9 Female No No
## 91 91 20.19100 5767 431 4 42 16 Male No Yes
## 92 92 48.49800 6040 456 3 47 16 Male No Yes
## 93 93 30.73300 2832 249 4 51 13 Male No No
## 94 94 16.47900 5435 388 2 26 16 Male No No
## 95 95 38.00900 3075 245 3 45 15 Female No No
## 96 96 14.08400 855 120 5 46 17 Female No Yes
## 97 97 14.31200 5382 367 1 59 17 Male Yes No
## 98 98 26.06700 3388 266 4 74 17 Female No Yes
## 99 99 36.29500 2963 241 2 68 14 Female Yes No
## 100 100 83.85100 8494 607 5 47 18 Male No No
## 101 101 21.15300 3736 256 1 41 11 Male No No
## 102 102 17.97600 2433 190 3 70 16 Female Yes No
## 103 103 68.71300 7582 531 2 56 16 Male Yes No
## 104 104 44.82378 9540 682 6 NA 15 Male No No
## 105 105 15.84600 4768 365 4 53 12 Female No No
## 106 106 12.03100 3182 259 2 58 18 Female No Yes
## 107 107 16.81900 1337 115 2 74 15 Male No Yes
## 108 108 39.11000 3189 263 3 72 12 Male No No
## 109 109 107.98600 6033 449 4 64 14 Male No Yes
## 110 110 13.56100 3261 279 5 37 19 Male No Yes
## 111 111 44.82378 3271 250 3 NA 17 Female No Yes
## 112 112 28.57500 2959 231 2 60 11 Female No No
## 113 113 46.00700 6637 491 4 42 14 Male No Yes
## 114 114 69.25100 6386 474 4 30 12 Female No Yes
## 115 115 16.48200 3326 268 4 41 15 Male No No
## 116 116 40.44200 4828 369 5 81 8 Female No No
## 117 117 35.17700 2117 186 3 62 16 Female No No
## 118 118 91.36200 9113 626 1 47 17 Male No Yes
## 119 119 27.03900 2161 173 3 40 17 Female No No
## 120 120 23.01200 1410 137 3 81 16 Male No No
## 121 121 27.24100 1402 128 2 67 15 Female No Yes
## 122 122 148.08000 8157 599 2 83 13 Male No Yes
## 123 123 62.60200 7056 481 1 84 11 Female No No
## 124 124 11.80800 1300 117 3 77 14 Female No No
## 125 125 29.56400 2529 192 1 30 12 Female No Yes
## 126 126 27.57800 2531 195 1 34 15 Female No Yes
## 127 127 26.42700 5533 433 5 50 15 Female Yes Yes
## 128 128 57.20200 3411 259 3 72 11 Female No No
## 129 129 123.29900 8376 610 2 89 17 Male Yes No
## 130 130 18.14500 3461 279 3 56 15 Male No Yes
## 131 131 23.79300 3821 281 4 56 12 Female Yes Yes
## 132 132 10.72600 1568 162 5 46 19 Male No Yes
## 133 133 23.28300 5443 407 4 49 13 Male No Yes
## 134 134 44.82378 5829 427 4 NA 12 Female No Yes
## 135 135 34.66400 5835 452 3 77 15 Female No Yes
## 136 136 44.47300 3500 257 3 81 16 Female No No
## 137 137 54.66300 4116 314 2 70 8 Female No No
## 138 138 36.35500 3613 278 4 35 9 Male No Yes
## 139 139 21.37400 2073 175 2 74 11 Female No Yes
## 140 140 107.84100 10384 728 3 87 7 Male No No
## 141 141 39.83100 6045 459 3 32 12 Female Yes Yes
## 142 142 44.82378 6754 483 2 NA 10 Male No Yes
## 143 143 103.89300 7416 549 3 84 17 Male No No
## 144 144 44.82378 4896 387 3 NA 10 Female No No
## 145 145 17.39200 2748 228 3 32 14 Male No Yes
## 146 146 19.52900 4673 341 2 51 14 Male No No
## 147 147 44.82378 5110 371 3 NA 15 Female No Yes
## 148 148 23.85700 1501 150 3 56 16 Male No Yes
## 149 149 15.18400 2420 192 2 69 11 Female No Yes
## 150 150 13.44400 886 121 5 44 10 Male No Yes
## 151 151 63.93100 5728 435 3 28 14 Female No Yes
## 152 152 35.86400 4831 353 3 66 13 Female No Yes
## 153 153 41.41900 2120 184 4 24 11 Female Yes No
## 154 154 92.11200 4612 344 3 32 17 Male No No
## 155 155 55.05600 3155 235 2 31 16 Male No Yes
## 156 156 19.53700 1362 143 4 34 9 Female No Yes
## 157 157 31.81100 4284 338 5 75 13 Female No Yes
## 158 158 56.25600 5521 406 2 72 16 Female Yes Yes
## 159 159 42.35700 5550 406 2 83 12 Female No Yes
## 160 160 53.31900 3000 235 3 53 13 Male No No
## 161 161 12.23800 4865 381 5 67 11 Female No No
## 162 162 31.35300 1705 160 3 81 14 Male No Yes
## 163 163 63.80900 7530 515 1 56 12 Male No Yes
## 164 164 13.67600 2330 203 5 80 16 Female No No
## 165 165 76.78200 5977 429 4 44 12 Male No Yes
## 166 166 25.38300 4527 367 4 46 11 Male No Yes
## 167 167 35.69100 2880 214 2 35 15 Male No No
## 168 168 29.40300 2327 178 1 37 14 Female No Yes
## 169 169 27.47000 2820 219 1 32 11 Female No Yes
## 170 170 27.33000 6179 459 4 36 12 Female No Yes
## 171 171 34.77200 2021 167 3 57 9 Male No No
## 172 172 36.93400 4270 299 1 63 9 Female No Yes
## 173 173 76.34800 4697 344 4 60 18 Male No No
## 174 174 44.82378 4745 339 3 NA 12 Male No Yes
## 175 175 121.83400 10673 750 3 54 16 Male No No
## 176 176 30.13200 2168 206 3 52 17 Male No No
## 177 177 24.05000 2607 221 4 32 18 Male No Yes
## 178 178 22.37900 3965 292 2 34 14 Female No Yes
## 179 179 28.31600 4391 316 2 29 10 Female No No
## 180 180 58.02600 7499 560 5 67 11 Female No No
## 181 181 10.63500 3584 294 5 69 16 Male No Yes
## 182 182 46.10200 5180 382 3 81 12 Male No Yes
## 183 183 58.92900 6420 459 2 66 9 Female No Yes
## 184 184 80.86100 4090 335 3 29 15 Female No Yes
## 185 185 158.88900 11589 805 1 62 17 Female No Yes
## 186 186 30.42000 4442 316 1 30 14 Female No No
## 187 187 36.47200 3806 309 2 52 13 Male No No
## 188 188 23.36500 2179 167 2 75 15 Male No No
## 189 189 83.86900 7667 554 2 83 11 Male No No
## 190 190 58.35100 4411 326 2 85 16 Female No Yes
## 191 191 55.18700 5352 385 4 50 17 Female No Yes
## 192 192 124.29000 9560 701 3 52 17 Female Yes No
## 193 193 28.50800 3933 287 4 56 14 Male No Yes
## 194 194 130.20900 10088 730 7 39 19 Female No Yes
## 195 195 30.40600 2120 181 2 79 14 Male No Yes
## 196 196 23.88300 5384 398 2 73 16 Female No Yes
## 197 197 93.03900 7398 517 1 67 12 Male No Yes
## 198 198 50.69900 3977 304 2 84 17 Female No No
## 199 199 27.34900 2000 169 4 51 16 Female No Yes
## 200 200 10.40300 4159 310 3 43 7 Male No Yes
## 201 201 23.94900 5343 383 2 40 18 Male No Yes
## 202 202 73.91400 7333 529 6 67 15 Female No Yes
## 203 203 21.03800 1448 145 2 58 13 Female No Yes
## 204 204 68.20600 6784 499 5 40 16 Female Yes No
## 205 205 57.33700 5310 392 2 45 7 Female No No
## 206 206 10.79300 3878 321 8 29 13 Male No No
## 207 207 23.45000 2450 180 2 78 13 Male No No
## 208 208 44.82378 4391 358 5 NA 10 Female Yes Yes
## 209 209 51.34500 4327 320 3 46 15 Male No No
## 210 210 151.94700 9156 642 2 91 11 Female No Yes
## 211 211 24.54300 3206 243 2 62 12 Female No Yes
## 212 212 44.82378 5309 397 3 NA 15 Male No No
## 213 213 39.14500 4351 323 2 66 13 Male No Yes
## 214 214 39.42200 5245 383 2 44 19 Male No No
## 215 215 34.90900 5289 410 2 62 16 Female No Yes
## 216 216 41.02500 4229 337 3 79 19 Female No Yes
## 217 217 15.47600 2762 215 3 60 18 Male No No
## 218 218 44.82378 5395 392 3 NA 14 Male No Yes
## 219 219 10.62700 1647 149 2 71 10 Female Yes Yes
## 220 220 38.95400 5222 370 4 76 13 Female No No
## 221 221 44.84700 5765 437 3 53 13 Female Yes No
## 222 222 98.51500 8760 633 5 78 11 Female No No
## 223 223 33.43700 6207 451 4 44 9 Male Yes No
## 224 224 27.51200 4613 344 5 72 17 Male No Yes
## 225 225 44.82378 7818 584 4 NA 6 Male No Yes
## 226 226 15.07900 5673 411 4 28 15 Female No Yes
## 227 227 44.82378 6906 527 6 NA 15 Female No No
## 228 228 66.98900 5614 430 3 47 14 Female No Yes
## 229 229 44.82378 4668 341 2 NA 11 Female No No
## 230 230 44.82378 7555 547 3 NA 9 Male No Yes
## 231 231 44.82378 5137 387 3 NA 9 Male No No
## 232 232 25.12400 4776 378 4 29 12 Male No Yes
## 233 233 15.74100 4788 360 1 39 14 Male No Yes
## 234 234 11.60300 2278 187 3 71 11 Male No Yes
## 235 235 69.65600 8244 579 3 41 14 Male No Yes
## 236 236 10.50300 2923 232 3 25 18 Female No Yes
## 237 237 42.52900 4986 369 2 37 11 Male No Yes
## 238 238 60.57900 5149 388 5 38 15 Male No Yes
## 239 239 26.53200 2910 236 6 58 19 Female No Yes
## 240 240 27.95200 3557 263 1 35 13 Female No Yes
## 241 241 29.70500 3351 262 5 71 14 Female No Yes
## 242 242 15.60200 906 103 2 36 11 Male No Yes
## 243 243 44.82378 1233 128 3 NA 18 Female Yes Yes
## 244 244 44.82378 6617 460 1 NA 12 Female No Yes
## 245 245 44.82378 1787 147 4 NA 15 Female No No
## 246 246 34.50900 2001 189 5 80 18 Female No Yes
## 247 247 44.82378 3211 265 4 NA 14 Female No No
## 248 248 36.36400 2220 188 3 50 19 Male No No
## 249 249 15.71700 905 93 1 38 16 Male Yes Yes
## 250 250 44.82378 1551 134 3 NA 13 Female Yes Yes
## 251 251 10.36300 2430 191 2 47 18 Female No Yes
## 252 252 28.47400 3202 267 5 66 12 Male No Yes
## 253 253 72.94500 8603 621 3 64 8 Female No No
## 254 254 85.42500 5182 402 6 60 12 Male No Yes
## 255 255 36.50800 6386 469 4 79 6 Female No Yes
## 256 256 58.06300 4221 304 3 50 8 Male No No
## 257 257 25.93600 1774 135 2 71 14 Female No No
## 258 258 15.62900 2493 186 1 60 14 Male No Yes
## 259 259 41.40000 2561 215 2 36 14 Male No Yes
## 260 260 33.65700 6196 450 6 55 9 Female No No
## 261 261 67.93700 5184 383 4 63 12 Male No Yes
## 262 262 44.82378 9310 665 3 NA 8 Female Yes Yes
## 263 263 10.58800 4049 296 1 66 13 Female No Yes
## 264 264 29.72500 3536 270 2 52 15 Female No No
## 265 265 27.99900 5107 380 1 55 10 Male No Yes
## 266 266 44.82378 5013 379 3 NA 13 Female No Yes
## 267 267 88.83000 4952 360 4 86 16 Female No Yes
## 268 268 29.63800 5833 433 3 29 15 Female No Yes
## 269 269 25.98800 1349 142 4 82 12 Male No No
## 270 270 39.05500 5565 410 4 48 18 Female No Yes
## 271 271 15.86600 3085 217 1 39 13 Male No No
## 272 272 44.97800 4866 347 1 30 10 Female No No
## 273 273 30.41300 3690 299 2 25 15 Female Yes No
## 274 274 16.75100 4706 353 6 48 14 Male Yes No
## 275 275 44.82378 5869 439 5 NA 9 Female No No
## 276 276 44.82378 8732 636 3 NA 14 Male No Yes
## 277 277 23.10600 3476 257 2 50 15 Female No No
## 278 278 41.53200 5000 353 2 50 12 Male No Yes
## 279 279 128.04000 6982 518 2 78 11 Female No Yes
## 280 280 54.31900 3063 248 3 59 8 Female Yes No
## 281 281 53.40100 5319 377 3 35 12 Female No No
## 282 282 36.14200 1852 183 3 33 13 Female No No
## 283 283 63.53400 8100 581 2 50 17 Female No Yes
## 284 284 49.92700 6396 485 3 75 17 Female No Yes
## 285 285 14.71100 2047 167 2 67 6 Male No Yes
## 286 286 18.96700 1626 156 2 41 11 Female No Yes
## 287 287 18.03600 1552 142 2 48 15 Female No No
## 288 288 60.44900 3098 272 4 69 8 Male No Yes
## 289 289 16.71100 5274 387 3 42 16 Female No Yes
## 290 290 10.85200 3907 296 2 30 9 Male No No
## 291 291 26.37000 3235 268 5 78 11 Male No Yes
## 292 292 24.08800 3665 287 4 56 13 Female No Yes
## 293 293 51.53200 5096 380 2 31 15 Male No Yes
## 294 294 140.67200 11200 817 7 46 9 Male No Yes
## 295 295 42.91500 2532 205 4 42 13 Male No Yes
## 296 296 27.27200 1389 149 5 67 10 Female No Yes
## 297 297 65.89600 5140 370 1 49 17 Female No Yes
## 298 298 55.05400 4381 321 3 74 17 Male No Yes
## 299 299 20.79100 2672 204 1 70 18 Female No No
## 300 300 44.82378 5051 372 3 NA 11 Female No Yes
## 301 301 21.78600 4632 355 1 50 17 Male No Yes
## 302 302 31.33500 3526 289 3 38 7 Female No No
## 303 303 59.85500 4964 365 1 46 13 Female No Yes
## 304 304 44.06100 4970 352 1 79 11 Male No Yes
## 305 305 82.70600 7506 536 2 64 13 Female No Yes
## 306 306 24.46000 1924 165 2 50 14 Female No Yes
## 307 307 45.12000 3762 287 3 80 8 Male No Yes
## 308 308 75.40600 3874 298 3 41 14 Female No Yes
## 309 309 14.95600 4640 332 2 33 6 Male No No
## 310 310 75.25700 7010 494 3 34 18 Female No Yes
## 311 311 33.69400 4891 369 1 52 16 Male Yes No
## 312 312 23.37500 5429 396 3 57 15 Female No Yes
## 313 313 27.82500 5227 386 6 63 11 Male No Yes
## 314 314 92.38600 7685 534 2 75 18 Female No Yes
## 315 315 115.52000 9272 656 2 69 14 Male No No
## 316 316 14.47900 3907 296 3 43 16 Male No Yes
## 317 317 52.17900 7306 522 2 57 14 Male No No
## 318 318 68.46200 4712 340 2 71 16 Male No Yes
## 319 319 18.95100 1485 129 3 82 13 Female No No
## 320 320 27.59000 2586 229 5 54 16 Male No Yes
## 321 321 44.82378 1160 126 3 NA 13 Male Yes Yes
## 322 322 25.07800 3096 236 2 27 15 Female No Yes
## 323 323 44.82378 3484 282 6 NA 11 Male No No
## 324 324 182.72800 13913 982 4 98 17 Male No Yes
## 325 325 31.02900 2863 223 2 66 17 Male Yes Yes
## 326 326 17.76500 5072 364 1 66 12 Female No Yes
## 327 327 125.48000 10230 721 3 82 16 Male No Yes
## 328 328 49.16600 6662 508 3 68 14 Female No No
## 329 329 41.19200 3673 297 3 54 16 Female No Yes
## 330 330 94.19300 7576 527 2 44 16 Female No Yes
## 331 331 20.40500 4543 329 2 72 17 Male Yes No
## 332 332 12.58100 3976 291 2 48 16 Male No Yes
## 333 333 62.32800 5228 377 3 83 15 Male No No
## 334 334 21.01100 3402 261 2 68 17 Male No Yes
## 335 335 24.23000 4756 351 2 64 15 Female No Yes
## 336 336 24.31400 3409 270 2 23 7 Female No Yes
## 337 337 32.85600 5884 438 4 68 13 Male No No
## 338 338 12.41400 855 119 3 32 12 Male No Yes
## 339 339 41.36500 5303 377 1 45 14 Male No No
## 340 340 149.31600 10278 707 1 80 16 Male No No
## 341 341 27.79400 3807 301 4 35 8 Female No Yes
## 342 342 13.23400 3922 299 2 77 17 Female No Yes
## 343 343 14.59500 2955 260 5 37 9 Male No Yes
## 344 344 10.73500 3746 280 2 44 17 Female No Yes
## 345 345 48.21800 5199 401 7 39 10 Male No Yes
## 346 346 30.01200 1511 137 2 33 17 Male No Yes
## 347 347 21.55100 5380 420 5 51 18 Male No Yes
## 348 348 160.23100 10748 754 2 69 17 Male No No
## 349 349 13.43300 1134 112 3 70 14 Male No Yes
## 350 350 48.57700 5145 389 3 71 13 Female No Yes
## 351 351 30.00200 1561 155 4 70 13 Female No Yes
## 352 352 61.62000 5140 374 1 71 9 Male No Yes
## 353 353 44.82378 7140 507 2 NA 14 Male No Yes
## 354 354 41.86800 4716 342 2 47 18 Male No No
## 355 355 12.06800 3873 292 1 44 18 Female No Yes
## 356 356 180.68200 11966 832 2 58 8 Female No Yes
## 357 357 34.48000 6090 442 3 36 14 Male No No
## 358 358 39.60900 2539 188 1 40 14 Male No Yes
## 359 359 30.11100 4336 339 1 81 18 Male No Yes
## 360 360 12.33500 4471 344 3 79 12 Male No Yes
## 361 361 53.56600 5891 434 4 82 10 Female No No
## 362 362 53.21700 4943 362 2 46 16 Female No Yes
## 363 363 26.16200 5101 382 3 62 19 Female No No
## 364 364 64.17300 6127 433 1 80 10 Male No Yes
## 365 365 128.66900 9824 685 3 67 16 Male No Yes
## 366 366 113.77200 6442 489 4 69 15 Male Yes Yes
## 367 367 61.06900 7871 564 3 56 14 Male No Yes
## 368 368 23.79300 3615 263 2 70 14 Male No No
## 369 369 89.00000 5759 440 3 37 6 Female No No
## 370 370 71.68200 8028 599 3 57 16 Male No Yes
## 371 371 35.61000 6135 466 4 40 12 Male No No
## 372 372 39.11600 2150 173 4 75 15 Male No No
## 373 373 19.78200 3782 293 2 46 16 Female Yes No
## 374 374 55.41200 5354 383 2 37 16 Female Yes Yes
## 375 375 29.40000 4840 368 3 76 18 Female No Yes
## 376 376 20.97400 5673 413 5 44 16 Female No Yes
## 377 377 87.62500 7167 515 2 46 10 Female No No
## 378 378 28.14400 1567 142 3 51 10 Male No Yes
## 379 379 19.34900 4941 366 1 33 19 Male No Yes
## 380 380 53.30800 2860 214 1 84 10 Male No Yes
## 381 381 115.12300 7760 538 3 83 14 Female No No
## 382 382 101.78800 8029 574 2 84 11 Male No Yes
## 383 383 24.82400 5495 409 1 33 9 Male Yes No
## 384 384 14.29200 3274 282 9 64 9 Male No Yes
## 385 385 20.08800 1870 180 3 76 16 Male No No
## 386 386 26.40000 5640 398 3 58 15 Female No No
## 387 387 44.82378 3683 287 4 NA 10 Male No No
## 388 388 16.52900 1357 126 3 62 9 Male No No
## 389 389 37.87800 6827 482 2 80 13 Female No No
## 390 390 83.94800 7100 503 2 44 18 Male No No
## 391 391 135.11800 10578 747 3 81 15 Female No Yes
## 392 392 73.32700 6555 472 2 43 15 Female No No
## 393 393 25.97400 2308 196 2 24 10 Male No No
## 394 394 17.31600 1335 138 2 65 13 Male No No
## 395 395 49.79400 5758 410 4 40 8 Male No No
## 396 396 12.09600 4100 307 3 32 13 Male No Yes
## 397 397 13.36400 3838 296 5 65 17 Male No No
## 398 398 57.87200 4171 321 5 67 12 Female No Yes
## 399 399 37.72800 2525 192 1 44 13 Male No Yes
## 400 400 18.70100 5524 415 5 64 7 Female No No
## Ethnicity Balance
## 1 Caucasian 333
## 2 Asian 903
## 3 Asian 580
## 4 Asian 964
## 5 Caucasian 331
## 6 Caucasian 1151
## 7 African American 203
## 8 Asian 872
## 9 Caucasian 279
## 10 African American 1350
## 11 <NA> 1407
## 12 Caucasian 0
## 13 Asian 204
## 14 Caucasian 1081
## 15 African American 148
## 16 African American 0
## 17 African American 0
## 18 Asian 368
## 19 <NA> 891
## 20 Asian 1048
## 21 Asian 89
## 22 Caucasian 968
## 23 African American 0
## 24 African American 411
## 25 Caucasian 0
## 26 African American 671
## 27 Caucasian 654
## 28 African American 467
## 29 African American 1809
## 30 Caucasian 915
## 31 Caucasian 863
## 32 Asian 0
## 33 Caucasian 526
## 34 Caucasian 0
## 35 Asian 0
## 36 Caucasian 419
## 37 Caucasian 762
## 38 Caucasian 1093
## 39 Caucasian 531
## 40 Caucasian 344
## 41 African American 50
## 42 African American 1155
## 43 Asian 385
## 44 Asian 976
## 45 Caucasian 1120
## 46 Caucasian 997
## 47 Asian 1241
## 48 Caucasian 797
## 49 Asian 0
## 50 African American 902
## 51 African American 654
## 52 African American 211
## 53 Caucasian 607
## 54 <NA> 957
## 55 Asian 0
## 56 Asian 0
## 57 Asian 379
## 58 Caucasian 133
## 59 Caucasian 333
## 60 Caucasian 531
## 61 Asian 631
## 62 Caucasian 108
## 63 Caucasian 0
## 64 Caucasian 133
## 65 African American 0
## 66 Caucasian 602
## 67 Asian 1388
## 68 <NA> 889
## 69 Caucasian 822
## 70 Caucasian 1084
## 71 Caucasian 357
## 72 Asian 1103
## 73 Asian 663
## 74 Caucasian 601
## 75 Caucasian 945
## 76 African American 29
## 77 Caucasian 532
## 78 Asian 145
## 79 Caucasian 391
## 80 Asian 0
## 81 Caucasian 162
## 82 Caucasian 99
## 83 <NA> 503
## 84 Caucasian 0
## 85 Caucasian 0
## 86 Asian 1779
## 87 Caucasian 815
## 88 Asian 0
## 89 African American 579
## 90 <NA> 1176
## 91 African American 1023
## 92 Caucasian 812
## 93 Caucasian 0
## 94 African American 937
## 95 African American 0
## 96 African American 0
## 97 Asian 1380
## 98 African American 155
## 99 African American 375
## 100 Caucasian 1311
## 101 Caucasian 298
## 102 Caucasian 431
## 103 Caucasian 1587
## 104 <NA> 1050
## 105 Caucasian 745
## 106 Caucasian 210
## 107 Asian 0
## 108 Asian 0
## 109 Caucasian 227
## 110 Asian 297
## 111 <NA> 47
## 112 African American 0
## 113 Caucasian 1046
## 114 Asian 768
## 115 Caucasian 271
## 116 African American 510
## 117 Caucasian 0
## 118 Asian 1341
## 119 Caucasian 0
## 120 Caucasian 0
## 121 Asian 0
## 122 Caucasian 454
## 123 Caucasian 904
## 124 African American 0
## 125 Caucasian 0
## 126 Caucasian 0
## 127 Asian 1404
## 128 Caucasian 0
## 129 African American 1259
## 130 African American 255
## 131 African American 868
## 132 Asian 0
## 133 African American 912
## 134 <NA> 1018
## 135 African American 835
## 136 African American 8
## 137 African American 75
## 138 Asian 187
## 139 Caucasian 0
## 140 African American 1597
## 141 African American 1425
## 142 <NA> 605
## 143 Asian 669
## 144 <NA> 710
## 145 Caucasian 68
## 146 Asian 642
## 147 <NA> 805
## 148 Caucasian 0
## 149 Caucasian 0
## 150 Asian 0
## 151 African American 581
## 152 Caucasian 534
## 153 Caucasian 156
## 154 Caucasian 0
## 155 African American 0
## 156 Asian 0
## 157 Caucasian 429
## 158 Caucasian 1020
## 159 Asian 653
## 160 Asian 0
## 161 Caucasian 836
## 162 Caucasian 0
## 163 Caucasian 1086
## 164 African American 0
## 165 Asian 548
## 166 Caucasian 570
## 167 African American 0
## 168 Caucasian 0
## 169 Asian 0
## 170 Caucasian 1099
## 171 Asian 0
## 172 Caucasian 283
## 173 Asian 108
## 174 <NA> 724
## 175 African American 1573
## 176 Caucasian 0
## 177 Caucasian 0
## 178 Asian 384
## 179 Caucasian 453
## 180 Caucasian 1237
## 181 Asian 423
## 182 African American 516
## 183 African American 789
## 184 Asian 0
## 185 Caucasian 1448
## 186 African American 450
## 187 African American 188
## 188 Asian 0
## 189 African American 930
## 190 Caucasian 126
## 191 Caucasian 538
## 192 Asian 1687
## 193 Asian 336
## 194 Caucasian 1426
## 195 African American 0
## 196 African American 802
## 197 African American 749
## 198 African American 69
## 199 African American 0
## 200 Asian 571
## 201 African American 829
## 202 Caucasian 1048
## 203 Caucasian 0
## 204 African American 1411
## 205 Caucasian 456
## 206 Caucasian 638
## 207 Caucasian 0
## 208 <NA> 1216
## 209 African American 230
## 210 African American 732
## 211 Caucasian 95
## 212 <NA> 799
## 213 Caucasian 308
## 214 African American 637
## 215 Caucasian 681
## 216 Caucasian 246
## 217 Asian 52
## 218 <NA> 955
## 219 Asian 195
## 220 Caucasian 653
## 221 Asian 1246
## 222 African American 1230
## 223 Caucasian 1549
## 224 Asian 573
## 225 <NA> 701
## 226 Asian 1075
## 227 <NA> 1032
## 228 Caucasian 482
## 229 <NA> 156
## 230 <NA> 1058
## 231 <NA> 661
## 232 Caucasian 657
## 233 Asian 689
## 234 Caucasian 0
## 235 African American 1329
## 236 African American 191
## 237 Asian 489
## 238 Asian 443
## 239 Caucasian 52
## 240 Asian 163
## 241 Asian 148
## 242 African American 0
## 243 <NA> 16
## 244 <NA> 856
## 245 <NA> 0
## 246 African American 0
## 247 <NA> 199
## 248 Caucasian 0
## 249 Caucasian 0
## 250 <NA> 98
## 251 Asian 0
## 252 Caucasian 132
## 253 Caucasian 1355
## 254 African American 218
## 255 Caucasian 1048
## 256 African American 118
## 257 Asian 0
## 258 Asian 0
## 259 Caucasian 0
## 260 Caucasian 1092
## 261 Asian 345
## 262 <NA> 1050
## 263 Caucasian 465
## 264 African American 133
## 265 Caucasian 651
## 266 <NA> 549
## 267 Caucasian 15
## 268 Asian 942
## 269 Caucasian 0
## 270 Caucasian 772
## 271 Caucasian 136
## 272 Caucasian 436
## 273 Asian 728
## 274 Asian 1255
## 275 <NA> 967
## 276 <NA> 529
## 277 Caucasian 209
## 278 Caucasian 531
## 279 Caucasian 250
## 280 Caucasian 269
## 281 African American 541
## 282 African American 0
## 283 Caucasian 1298
## 284 Caucasian 890
## 285 Caucasian 0
## 286 Asian 0
## 287 Caucasian 0
## 288 Caucasian 0
## 289 Asian 863
## 290 Caucasian 485
## 291 Asian 159
## 292 Caucasian 309
## 293 Caucasian 481
## 294 African American 1677
## 295 Asian 0
## 296 Caucasian 0
## 297 Caucasian 293
## 298 Asian 188
## 299 African American 0
## 300 <NA> 711
## 301 Caucasian 580
## 302 Caucasian 172
## 303 Caucasian 295
## 304 African American 414
## 305 Asian 905
## 306 Asian 0
## 307 Caucasian 70
## 308 Asian 0
## 309 Asian 681
## 310 Caucasian 885
## 311 African American 1036
## 312 Caucasian 844
## 313 Caucasian 823
## 314 Asian 843
## 315 African American 1140
## 316 Caucasian 463
## 317 Asian 1142
## 318 Caucasian 136
## 319 Caucasian 0
## 320 African American 0
## 321 <NA> 5
## 322 Caucasian 81
## 323 <NA> 265
## 324 Caucasian 1999
## 325 Asian 415
## 326 Caucasian 732
## 327 Caucasian 1361
## 328 Asian 984
## 329 Caucasian 121
## 330 Caucasian 846
## 331 Asian 1054
## 332 Caucasian 474
## 333 Caucasian 380
## 334 African American 182
## 335 Caucasian 594
## 336 Caucasian 194
## 337 Caucasian 926
## 338 African American 0
## 339 Caucasian 606
## 340 African American 1107
## 341 African American 320
## 342 Caucasian 426
## 343 African American 204
## 344 Caucasian 410
## 345 Asian 633
## 346 Caucasian 0
## 347 Asian 907
## 348 Caucasian 1192
## 349 Caucasian 0
## 350 Asian 503
## 351 Caucasian 0
## 352 Caucasian 302
## 353 <NA> 583
## 354 Caucasian 425
## 355 Asian 413
## 356 African American 1405
## 357 Caucasian 962
## 358 Asian 0
## 359 Caucasian 347
## 360 African American 611
## 361 Caucasian 712
## 362 Asian 382
## 363 African American 710
## 364 Caucasian 578
## 365 Asian 1243
## 366 Caucasian 790
## 367 Caucasian 1264
## 368 African American 216
## 369 Caucasian 345
## 370 Caucasian 1208
## 371 Caucasian 992
## 372 Caucasian 0
## 373 Caucasian 840
## 374 Caucasian 1003
## 375 Caucasian 588
## 376 Caucasian 1000
## 377 African American 767
## 378 Caucasian 0
## 379 Caucasian 717
## 380 Caucasian 0
## 381 African American 661
## 382 Caucasian 849
## 383 Caucasian 1352
## 384 Caucasian 382
## 385 African American 0
## 386 Asian 905
## 387 <NA> 371
## 388 Asian 0
## 389 Caucasian 1129
## 390 Caucasian 806
## 391 Asian 1393
## 392 Caucasian 721
## 393 Asian 0
## 394 African American 0
## 395 Caucasian 734
## 396 Caucasian 560
## 397 African American 480
## 398 Caucasian 138
## 399 Caucasian 0
## 400 Asian 966Definire una nuova categoria “valore mancante” per gli NA sulla variabile Ethnicity e creare una nuova variabile.(suggerimento: per evitare errori, convertire prima la variabile in charachter, rimpiazzare poi i valori mancanti con la nuovamodalità e infine riconvertire la variabile in un factor)
Credit_na<-Credit_na%>%
mutate(Ethnicity2=as.character(Ethnicity)) %>%
mutate(Ethnicity2=ifelse(is.na(Ethnicity2),"Valore Mancante",Ethnicity2)) %>%
mutate(Ethnicity2=as.factor(Ethnicity2))Verificare che la nuova variabile sia un factor e cambia i nomi delle categorie della variabile traducendo in italiano le etichette delle modalità
class(Credit_na$Ethnicity2)## [1] "factor"Credit_na<-Credit_na %>%
mutate(Ethnicity2=recode(Ethnicity2,"Asian"="Asiatico","Caucasian"="Caucasico","African American"="Afroamericano"))
levels(Credit_na$Ethnicity2)## [1] "Afroamericano" "Asiatico" "Caucasico" "Valore Mancante"crea un nuovo dataset eliminando tutte le righe con valori mancanti per la variabile Age
Credit_no_na <- Credit_na %>%
drop_na(Age)