Examen LungCapData
Joaquin Iturria
2025-05-28
#install.packages("readxl")
library(readxl)
LungCapData <- read_excel("C:/Users/joaqu/Downloads/LungCapData.xls")LungCapData <- as.data.frame(LungCapData)
library(ggplot2)
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
## ✔ lubridate 1.9.4 ✔ tibble 3.2.1
## ✔ purrr 1.0.4 ✔ tidyr 1.3.1
## ── 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 errorslibrary(devtools)## Cargando paquete requerido: usethislibrary(sjPlot)## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!library(plotly)##
## Adjuntando el paquete: 'plotly'
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layoutlibrary(gtsummary)
library(hrbrthemes)
library(ggExtra)`
LungCapData <- as.data.frame(LungCapData)
base::as.data.frame(LungCapData)## LungCap Age Height Smoke Gender Caesarean
## 1 6.475 6 62.1 no male no
## 2 10.125 18 74.7 yes female no
## 3 9.550 16 69.7 no female yes
## 4 11.125 14 71.0 no male no
## 5 4.800 5 56.9 no male no
## 6 6.225 11 58.7 no female no
## 7 4.950 8 63.3 no male yes
## 8 7.325 11 70.4 no male no
## 9 8.875 15 70.5 no male no
## 10 6.800 11 59.2 no male no
## 11 11.500 19 76.4 no male yes
## 12 10.925 17 71.7 no male no
## 13 6.525 12 57.5 no male no
## 14 6.000 10 61.1 no female no
## 15 7.825 10 61.2 no male no
## 16 9.525 13 63.5 no male yes
## 17 7.875 15 59.2 no male no
## 18 5.050 8 56.1 no male no
## 19 7.025 11 61.2 yes female no
## 20 9.525 14 70.6 no female no
## 21 3.975 6 57.3 no male no
## 22 5.325 8 59.7 no female no
## 23 10.025 16 72.4 no male no
## 24 8.725 11 68.0 no male yes
## 25 9.375 11 65.7 no female no
## 26 8.350 12 61.3 no male yes
## 27 6.750 12 60.7 no female no
## 28 9.025 9 65.6 no male no
## 29 1.125 4 48.7 no female no
## 30 10.475 18 72.0 yes female no
## 31 4.650 4 53.7 no female no
## 32 7.725 13 64.7 no male no
## 33 10.600 13 69.3 no male no
## 34 11.025 13 65.6 no male yes
## 35 8.650 12 67.8 no male no
## 36 8.825 10 65.5 no male no
## 37 4.200 6 52.7 no male no
## 38 8.775 9 63.6 no male no
## 39 6.325 11 64.6 no female no
## 40 11.325 17 77.7 no male no
## 41 8.225 14 65.4 no female no
## 42 10.725 17 72.5 no female yes
## 43 5.875 8 58.9 no female no
## 44 7.275 12 67.7 no male no
## 45 1.575 6 49.3 no male no
## 46 6.700 11 62.6 no female no
## 47 7.650 11 61.7 no male yes
## 48 8.000 12 64.7 no female no
## 49 12.950 17 74.9 no male no
## 50 7.350 7 61.6 no male no
## 51 9.625 15 66.4 no male no
## 52 12.425 15 74.1 no male no
## 53 7.400 11 65.3 no male no
## 54 4.875 10 61.4 no male no
## 55 12.225 18 79.6 no male no
## 56 4.250 6 52.9 no male no
## 57 8.200 13 65.6 no male no
## 58 11.400 19 79.1 no male no
## 59 4.625 9 56.8 no female no
## 60 7.825 12 65.4 no male yes
## 61 6.700 12 57.9 no female no
## 62 9.200 14 68.2 no male no
## 63 6.950 9 61.4 no female no
## 64 6.850 13 58.7 no female no
## 65 8.450 13 65.1 no male no
## 66 7.350 13 67.5 yes female no
## 67 5.375 11 59.3 yes female no
## 68 7.375 11 63.0 no female no
## 69 8.600 11 64.4 no female no
## 70 7.900 12 68.0 no male no
## 71 8.500 14 61.4 no female yes
## 72 9.700 11 72.4 no male no
## 73 5.125 11 51.5 no female yes
## 74 7.825 13 71.0 no female no
## 75 6.250 13 61.8 no female no
## 76 4.975 12 62.6 yes female yes
## 77 7.500 14 66.6 no male no
## 78 5.875 9 59.0 no female no
## 79 10.050 17 70.4 no female no
## 80 10.800 11 69.8 no male no
## 81 7.350 12 63.0 no female no
## 82 11.900 16 69.3 no male no
## 83 12.050 17 72.2 no male no
## 84 11.575 19 78.2 no female no
## 85 6.200 14 61.1 no female no
## 86 6.125 12 63.3 no female yes
## 87 13.875 19 78.4 no male yes
## 88 7.750 11 63.5 no female no
## 89 7.475 15 63.0 yes female no
## 90 11.575 19 75.5 no male no
## 91 6.950 9 63.9 no male yes
## 92 9.200 14 69.4 no male no
## 93 9.750 13 72.8 yes male no
## 94 9.650 14 65.2 no female no
## 95 11.750 19 78.0 yes female yes
## 96 10.825 18 75.7 no female no
## 97 7.550 16 71.1 yes male no
## 98 6.950 7 64.7 no male no
## 99 10.675 16 74.9 no male no
## 100 6.100 10 57.0 no male no
## 101 8.025 13 66.2 yes male no
## 102 9.225 14 66.9 no male no
## 103 3.450 13 58.5 no female yes
## 104 10.725 16 75.6 no female no
## 105 7.950 16 67.3 no female no
## 106 3.425 5 51.7 no female no
## 107 10.875 16 75.5 no male no
## 108 8.625 12 64.8 no male no
## 109 6.450 7 63.2 no male no
## 110 3.100 7 52.1 no male no
## 111 10.425 15 70.6 no male no
## 112 12.150 18 76.3 no female no
## 113 1.850 8 49.8 no female no
## 114 5.875 3 55.9 no male no
## 115 9.125 15 73.4 no male no
## 116 8.975 15 67.5 no female yes
## 117 3.750 7 50.3 no female no
## 118 10.275 18 71.0 no male no
## 119 6.675 8 54.9 no female no
## 120 11.775 17 76.9 yes male no
## 121 8.550 16 67.9 no male no
## 122 6.450 12 61.0 yes male yes
## 123 13.200 17 78.6 no male yes
## 124 11.550 16 75.7 no male no
## 125 12.950 19 79.6 no male yes
## 126 7.825 12 67.5 no female yes
## 127 10.550 17 71.8 no male yes
## 128 11.700 19 76.2 no female yes
## 129 3.650 12 56.6 no male yes
## 130 6.650 12 60.0 yes female yes
## 131 10.425 15 67.2 no male no
## 132 12.925 17 75.7 no female no
## 133 7.450 13 61.1 no male no
## 134 8.600 12 60.1 no female no
## 135 10.650 16 74.4 no male no
## 136 4.725 13 65.5 no female no
## 137 7.550 15 69.3 no female no
## 138 10.175 15 71.4 no female no
## 139 6.450 14 61.4 no male no
## 140 9.475 13 67.4 no male no
## 141 4.975 6 58.4 no male no
## 142 9.900 18 70.9 no female no
## 143 10.200 18 68.6 no female no
## 144 12.400 18 81.8 no male no
## 145 6.850 9 65.7 no female yes
## 146 11.825 17 73.9 no female no
## 147 8.625 14 66.8 no female yes
## 148 11.350 14 70.5 no male no
## 149 8.225 14 64.0 no female no
## 150 0.507 3 51.6 no female yes
## 151 5.075 11 61.2 no male no
## 152 6.450 8 62.7 no male no
## 153 6.725 9 56.1 no male no
## 154 4.525 8 55.5 no female no
## 155 9.275 16 67.2 no female no
## 156 2.850 7 51.4 no female no
## 157 9.350 11 71.2 yes male no
## 158 5.550 5 55.8 no female yes
## 159 10.350 16 73.5 no male yes
## 160 6.625 11 62.4 no male yes
## 161 9.725 16 68.6 no female yes
## 162 4.900 10 56.8 no female no
## 163 10.475 12 69.7 no male no
## 164 10.850 19 70.9 no male no
## 165 5.150 7 58.4 no female no
## 166 4.425 8 56.6 no male no
## 167 7.550 13 66.1 no male no
## 168 8.350 17 66.2 no male no
## 169 6.050 9 62.1 no male no
## 170 4.325 8 54.8 no female yes
## 171 8.850 15 68.9 no male no
## 172 4.325 6 56.6 no male no
## 173 8.775 14 67.7 no female no
## 174 6.450 10 66.5 yes female no
## 175 9.175 17 72.5 no female no
## 176 9.450 15 65.1 yes male no
## 177 6.700 10 65.4 no male no
## 178 10.950 16 74.3 no female no
## 179 9.850 17 72.4 yes female yes
## 180 9.825 17 65.5 no female yes
## 181 8.500 13 62.9 no male no
## 182 11.875 17 78.9 no male yes
## 183 10.475 15 72.3 no female no
## 184 9.925 16 75.4 no male no
## 185 8.500 18 68.2 no female no
## 186 7.200 10 64.3 no male yes
## 187 8.375 16 67.3 no female no
## 188 5.500 8 58.5 no male no
## 189 7.925 14 65.3 no female yes
## 190 2.875 4 55.4 no male no
## 191 10.350 17 71.8 no male no
## 192 9.325 15 67.6 no female no
## 193 7.450 10 61.5 no female no
## 194 8.625 13 60.6 no female no
## 195 11.225 16 72.8 no male no
## 196 12.125 17 73.3 no male yes
## 197 13.025 19 75.5 no male no
## 198 7.850 11 66.0 no male yes
## 199 2.550 8 49.9 no female no
## 200 7.575 12 66.4 no female no
## 201 9.550 13 68.6 no male no
## 202 12.225 16 76.2 no female no
## 203 11.625 15 74.0 no male no
## 204 10.000 18 73.5 no female no
## 205 3.900 8 55.5 no female no
## 206 5.850 5 55.1 no male no
## 207 5.025 10 60.1 no female no
## 208 5.950 8 58.6 no female no
## 209 9.475 13 66.1 no male no
## 210 6.400 7 60.4 no male no
## 211 8.225 11 63.6 no male no
## 212 10.775 15 75.2 no male no
## 213 6.150 10 62.0 no male no
## 214 9.400 15 66.4 no female no
## 215 7.250 8 59.9 no male no
## 216 5.850 7 58.8 no male no
## 217 4.125 10 55.5 no female yes
## 218 10.925 18 77.6 no male no
## 219 7.350 15 61.9 yes female yes
## 220 10.475 19 76.0 no male no
## 221 6.575 14 59.7 yes female yes
## 222 1.175 3 51.9 no male no
## 223 2.950 6 47.8 no male yes
## 224 9.000 12 62.0 no female no
## 225 8.100 14 61.6 no female no
## 226 7.275 8 64.7 no male yes
## 227 11.075 15 69.9 no male no
## 228 3.100 5 47.4 no female no
## 229 4.700 3 52.7 no male no
## 230 5.850 11 60.5 no female no
## 231 7.925 11 65.1 no male no
## 232 7.300 13 61.5 no female no
## 233 9.275 18 67.6 no female no
## 234 11.125 19 71.5 no male no
## 235 8.625 15 68.4 no female yes
## 236 9.825 18 70.8 no male no
## 237 5.725 8 58.3 no female yes
## 238 7.925 11 67.1 yes male no
## 239 9.475 17 64.8 yes female no
## 240 10.900 14 71.3 no female yes
## 241 9.675 12 70.9 no male no
## 242 9.950 14 71.6 no female no
## 243 6.575 8 59.8 no female no
## 244 8.600 12 61.6 no male yes
## 245 9.500 11 63.4 no male no
## 246 3.825 6 55.1 no female no
## 247 8.000 9 66.4 no male no
## 248 7.475 11 60.7 no male yes
## 249 10.700 18 74.2 yes male no
## 250 10.125 18 71.0 no female no
## 251 11.225 19 74.8 no female no
## 252 7.275 9 63.7 no male no
## 253 12.500 18 80.3 no male no
## 254 7.625 9 60.0 no female no
## 255 5.300 7 57.3 no male yes
## 256 5.600 8 58.5 no female no
## 257 11.050 18 72.6 yes female no
## 258 8.025 11 65.5 no male yes
## 259 9.950 12 68.2 no female no
## 260 7.400 11 63.3 yes female no
## 261 8.850 14 65.1 no female no
## 262 11.875 14 71.2 no male no
## 263 4.850 5 53.5 no male no
## 264 5.175 12 56.6 no female yes
## 265 10.175 19 69.2 no male yes
## 266 5.225 9 53.7 no female no
## 267 8.975 17 70.9 no male yes
## 268 7.350 10 61.0 no female no
## 269 7.975 9 61.9 no male no
## 270 8.775 9 59.2 no female no
## 271 6.100 10 57.4 no female no
## 272 8.800 14 70.5 no female no
## 273 8.700 14 73.1 no male yes
## 274 6.975 12 58.8 no male no
## 275 9.375 17 69.7 no male no
## 276 7.400 10 59.7 no male yes
## 277 8.800 12 65.9 no female no
## 278 6.700 13 66.0 no male yes
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## 280 9.725 12 71.4 no male no
## 281 8.500 17 72.5 yes male no
## 282 10.975 14 71.2 no male no
## 283 9.700 15 64.8 no female no
## 284 4.775 8 55.7 no female no
## 285 10.325 13 69.3 no male no
## 286 6.200 11 66.9 no female yes
## 287 8.000 10 63.4 no male no
## 288 3.025 6 47.4 no female no
## 289 6.125 10 64.7 no male no
## 290 9.550 18 70.8 yes female no
## 291 7.050 6 56.7 no male no
## 292 10.450 18 72.9 yes male yes
## 293 5.475 3 52.9 no male no
## 294 11.400 15 74.6 no male no
## 295 7.150 13 63.4 no female no
## 296 12.200 19 72.2 no female no
## 297 6.725 11 59.4 no female no
## 298 8.725 13 66.6 no female yes
## 299 2.625 5 49.0 no male no
## 300 7.400 8 62.1 no male no
## 301 8.200 18 69.0 yes female yes
## 302 6.850 13 63.3 no male yes
## 303 6.550 10 59.7 no male no
## 304 10.525 18 71.9 no female no
## 305 6.625 12 59.8 no female no
## 306 6.550 9 58.7 no female no
## 307 8.475 15 69.3 yes male no
## 308 10.925 14 69.4 no female no
## 309 6.075 8 58.4 no female no
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## 330 4.500 8 60.0 no female no
## 331 4.150 7 53.3 no female yes
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## 334 10.750 14 70.4 no male no
## 335 7.375 14 69.1 no female yes
## 336 5.250 9 55.5 no male yes
## 337 8.875 11 66.9 no male yes
## 338 10.900 13 71.5 no male yes
## 339 9.800 15 73.5 no male yes
## 340 10.250 18 72.5 no female no
## 341 7.425 15 62.6 no female no
## 342 6.325 13 59.9 yes male yes
## 343 5.550 9 58.7 no male no
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## 346 10.100 17 74.2 no female no
## 347 4.725 11 59.2 no female yes
## 348 5.025 12 55.0 no female no
## 349 3.425 9 51.0 no male no
## 350 8.475 14 62.2 no female yes
## 351 5.650 8 53.7 no female yes
## 352 11.675 16 72.7 no male no
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## 355 7.575 12 61.5 no female no
## 356 7.750 19 69.2 no female yes
## 357 11.700 17 71.8 no male no
## 358 8.325 15 63.3 no male no
## 359 6.650 12 58.9 yes female yes
## 360 3.675 7 56.3 no male no
## 361 3.975 8 56.8 no female no
## 362 10.050 15 70.2 no male no
## 363 10.200 18 73.5 yes female yes
## 364 7.275 11 60.1 no female no
## 365 6.050 10 58.3 no female no
## 366 6.225 9 63.5 no male no
## 367 9.300 11 64.4 no female no
## 368 8.975 16 69.1 no female yes
## 369 8.650 16 74.3 yes female no
## 370 11.650 18 73.3 no male no
## 371 11.075 16 68.7 no female yes
## 372 7.700 10 62.1 no male no
## 373 6.925 13 62.4 no female yes
## 374 10.725 16 77.4 no female no
## 375 14.375 18 80.8 no male no
## 376 6.175 9 63.9 no female yes
## 377 7.825 13 62.0 no female no
## 378 8.625 11 70.2 no male yes
## 379 4.900 5 60.4 no male no
## 380 4.200 6 56.1 no male no
## 381 5.850 12 62.6 no male no
## 382 7.375 9 59.9 no female no
## 383 8.775 15 64.7 yes female no
## 384 8.000 13 69.4 no female no
## 385 11.075 16 68.8 no male no
## 386 10.600 18 70.0 no female yes
## 387 5.150 11 59.3 no male no
## 388 6.475 13 66.3 yes female no
## 389 5.375 7 56.6 no male no
## 390 3.400 4 55.6 no male no
## 391 6.525 13 61.4 no female no
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## 397 6.825 11 61.9 no female yes
## 398 10.500 16 70.1 no female no
## 399 8.725 13 69.0 no male yes
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## 408 12.150 15 74.0 no male no
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## 411 5.650 7 57.4 no female no
## 412 8.675 13 68.8 yes female no
## 413 9.275 18 70.1 no female no
## 414 11.000 19 75.8 no female yes
## 415 8.125 10 66.8 no male no
## 416 3.175 6 56.2 no female no
## 417 7.450 13 63.6 no female no
## 418 9.100 14 68.4 yes male no
## 419 9.875 15 71.6 yes male no
## 420 2.250 5 57.2 no female yes
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## 423 5.950 8 59.4 no male no
## 424 7.800 12 73.3 yes female yes
## 425 8.550 12 68.5 no male no
## 426 5.875 12 61.5 no male no
## 427 5.425 7 61.1 no female yes
## 428 12.275 19 75.9 no male yes
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## 442 9.750 19 71.5 no female yes
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## 539 12.325 15 73.8 no male yes
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## 542 6.850 10 60.6 no male no
## 543 6.925 6 62.1 no male no
## 544 7.875 12 71.4 no female no
## 545 11.175 14 73.8 no male no
## 546 9.575 15 61.8 no male no
## 547 8.425 8 69.3 no male yes
## 548 10.775 19 71.7 no female yes
## 549 7.625 12 65.0 no male no
## 550 7.650 7 60.4 no male no
## 551 10.300 17 71.9 yes female no
## 552 1.775 7 51.2 no female no
## 553 9.025 12 65.7 no female no
## 554 8.025 13 69.0 no female no
## 555 8.575 15 65.4 no female no
## 556 8.850 15 68.8 no male no
## 557 2.725 6 56.5 no female yes
## 558 7.100 13 64.6 no female no
## 559 10.575 19 75.0 no female no
## 560 6.575 12 60.4 no female yes
## 561 7.900 9 62.4 no female yes
## 562 8.275 17 73.6 yes female no
## 563 10.200 19 71.4 no male yes
## 564 4.550 10 57.4 no female yes
## 565 8.000 12 64.5 yes male no
## 566 7.225 14 63.5 no male no
## 567 8.700 12 67.8 no female no
## 568 10.700 18 72.5 no female yes
## 569 1.325 5 48.9 no female no
## 570 13.375 19 75.6 no male yes
## 571 11.900 17 75.8 no male yes
## 572 8.625 13 67.8 yes female no
## 573 6.900 9 60.6 no female no
## 574 2.250 7 53.2 no male no
## 575 10.025 16 67.6 no female no
## 576 13.050 16 73.0 no male no
## 577 12.325 17 78.4 no male yes
## 578 10.700 17 72.6 yes female no
## 579 9.100 18 68.3 no male yes
## 580 8.225 7 65.7 no male no
## 581 5.150 7 58.0 no male no
## 582 8.000 12 64.1 no female no
## 583 6.050 12 60.3 no female no
## 584 4.450 8 55.1 no female no
## 585 5.350 8 55.2 no male no
## 586 8.825 14 71.9 no male no
## 587 8.925 11 65.6 yes male yes
## 588 12.375 17 71.1 no male no
## 589 9.475 13 69.6 no male no
## 590 8.350 13 67.9 no female no
## 591 9.150 15 65.8 no female no
## 592 2.250 5 53.0 no female no
## 593 8.025 10 61.4 no female no
## 594 8.700 15 63.7 no female no
## 595 7.825 11 62.8 no male no
## 596 1.450 3 45.3 no female no
## 597 3.925 6 56.4 no female no
## 598 2.825 7 51.9 no female yes
## 599 8.975 12 65.9 no female yes
## 600 6.275 14 61.1 no male no
## 601 11.800 13 73.5 no female no
## 602 11.800 19 74.6 no female no
## 603 9.375 15 73.1 no female no
## 604 7.675 14 66.4 no female no
## 605 2.650 7 51.5 no female no
## 606 8.575 16 67.1 no male yes
## 607 9.875 16 66.3 no female no
## 608 10.825 16 78.9 yes male no
## 609 4.825 7 55.4 no male no
## 610 4.250 7 51.1 no female no
## 611 9.750 16 73.7 no male yes
## 612 8.550 15 63.2 no female no
## 613 8.600 12 68.6 no female yes
## 614 3.700 6 52.7 no female no
## 615 8.275 11 61.8 no female no
## 616 8.125 13 63.3 no female no
## 617 10.675 15 76.6 no male yes
## 618 7.650 13 62.5 no female no
## 619 9.175 18 69.4 yes male no
## 620 8.250 15 69.4 no female no
## 621 3.675 3 54.2 no male yes
## 622 10.625 16 74.2 yes male yes
## 623 8.200 12 66.3 no female no
## 624 7.675 12 64.1 no male no
## 625 2.725 5 50.5 no male no
## 626 8.350 12 66.0 no male yes
## 627 9.225 14 75.1 no female no
## 628 2.925 6 49.2 no female no
## 629 8.350 19 66.3 no female no
## 630 3.700 9 57.3 no female no
## 631 6.425 11 63.5 no male no
## 632 8.775 10 66.5 no female no
## 633 5.650 7 56.7 no male no
## 634 10.200 16 68.7 no male no
## 635 2.025 5 52.8 no female no
## 636 6.175 8 54.9 no female no
## 637 9.050 15 65.3 no male yes
## 638 7.000 10 62.8 yes male yes
## 639 5.025 10 64.4 no female no
## 640 7.550 13 65.0 no female no
## 641 14.550 18 74.9 no male no
## 642 6.975 12 64.0 no male yes
## 643 7.950 13 66.0 yes female no
## 644 10.875 19 73.2 yes male no
## 645 7.325 10 66.9 no male no
## 646 13.375 19 76.8 no male no
## 647 7.850 16 68.9 no female no
## 648 9.100 13 65.0 no female no
## 649 6.825 13 60.2 no male no
## 650 6.450 14 62.8 no female no
## 651 6.550 9 60.4 no male no
## 652 6.175 8 55.9 no female no
## 653 5.575 10 58.8 no female no
## 654 7.800 13 65.4 no female no
## 655 8.900 12 65.8 no male yes
## 656 4.625 6 56.9 no female no
## 657 5.625 14 59.5 no female no
## 658 10.025 18 72.0 no male no
## 659 6.500 14 61.7 yes female no
## 660 3.250 3 52.0 no male no
## 661 11.225 16 73.6 no female no
## 662 7.325 10 55.7 no female no
## 663 8.425 12 69.2 no male no
## 664 5.550 10 58.0 no female no
## 665 10.400 16 69.6 no male no
## 666 7.825 10 64.4 no male no
## 667 7.550 8 67.3 no male no
## 668 8.050 10 65.4 no male no
## 669 2.625 5 51.7 no male no
## 670 4.575 8 56.5 no female no
## 671 4.975 8 54.9 no male no
## 672 11.275 18 73.0 yes male yes
## 673 6.125 6 59.2 no male no
## 674 5.950 10 62.5 no male no
## 675 7.475 14 62.8 no male no
## 676 12.125 17 69.4 no male no
## 677 13.325 18 76.9 yes male no
## 678 9.200 14 68.7 no female yes
## 679 8.125 14 66.6 yes female yes
## 680 3.925 10 60.8 no female no
## 681 10.025 13 68.4 no male no
## 682 9.825 18 72.4 no female no
## 683 5.825 11 60.0 no male yes
## 684 8.525 15 65.3 yes female no
## 685 12.700 19 76.1 no female yes
## 686 8.800 16 64.3 no female no
## 687 6.150 16 66.0 no female yes
## 688 7.475 15 64.0 no female no
## 689 4.450 8 55.6 no male no
## 690 4.700 7 55.6 no female no
## 691 4.425 12 62.0 no female no
## 692 6.200 10 61.9 no male no
## 693 6.050 13 58.4 no female no
## 694 1.950 7 48.8 no male no
## 695 6.750 13 65.3 no female no
## 696 8.250 14 67.5 no male yes
## 697 6.375 9 59.9 no female yes
## 698 6.450 16 66.5 yes male no
## 699 7.950 15 67.3 yes female no
## 700 7.425 13 62.7 no male no
## 701 6.600 10 58.4 no female no
## 702 10.400 14 71.9 no female no
## 703 9.675 12 66.0 no male no
## 704 3.600 7 53.9 no male no
## 705 6.225 15 58.9 no female no
## 706 12.425 19 73.6 no female yes
## 707 9.175 15 71.1 no male no
## 708 5.275 12 58.5 no female yes
## 709 6.900 15 64.5 no female no
## 710 4.850 10 63.7 no female no
## 711 12.325 17 73.5 no male no
## 712 4.625 5 55.6 no female yes
## 713 3.425 3 51.0 no male yes
## 714 9.325 14 65.5 no male no
## 715 9.925 16 68.3 no female no
## 716 8.725 19 68.4 no female no
## 717 7.075 11 66.7 no male yes
## 718 8.825 16 71.3 yes female no
## 719 7.175 17 68.8 no male yes
## 720 7.325 9 66.3 no male no
## 721 5.725 9 56.0 no female no
## 722 9.050 18 72.0 yes male yes
## 723 3.850 11 60.5 yes female no
## 724 9.825 15 64.9 no female no
## 725 7.100 10 67.7 no male noclass(LungCapData)## [1] "data.frame"str(LungCapData)## 'data.frame': 725 obs. of 6 variables:
## $ LungCap : num 6.47 10.12 9.55 11.12 4.8 ...
## $ Age : num 6 18 16 14 5 11 8 11 15 11 ...
## $ Height : num 62.1 74.7 69.7 71 56.9 58.7 63.3 70.4 70.5 59.2 ...
## $ Smoke : chr "no" "yes" "no" "no" ...
## $ Gender : chr "male" "female" "female" "male" ...
## $ Caesarean: chr "no" "no" "yes" "no" ...head(LungCapData)## LungCap Age Height Smoke Gender Caesarean
## 1 6.475 6 62.1 no male no
## 2 10.125 18 74.7 yes female no
## 3 9.550 16 69.7 no female yes
## 4 11.125 14 71.0 no male no
## 5 4.800 5 56.9 no male no
## 6 6.225 11 58.7 no female noLungCapData <- as.data.frame(LungCapData)
class(LungCapData)## [1] "data.frame"str(LungCapData)## 'data.frame': 725 obs. of 6 variables:
## $ LungCap : num 6.47 10.12 9.55 11.12 4.8 ...
## $ Age : num 6 18 16 14 5 11 8 11 15 11 ...
## $ Height : num 62.1 74.7 69.7 71 56.9 58.7 63.3 70.4 70.5 59.2 ...
## $ Smoke : chr "no" "yes" "no" "no" ...
## $ Gender : chr "male" "female" "female" "male" ...
## $ Caesarean: chr "no" "no" "yes" "no" ...View(LungCapData)
str(LungCapData)## 'data.frame': 725 obs. of 6 variables:
## $ LungCap : num 6.47 10.12 9.55 11.12 4.8 ...
## $ Age : num 6 18 16 14 5 11 8 11 15 11 ...
## $ Height : num 62.1 74.7 69.7 71 56.9 58.7 63.3 70.4 70.5 59.2 ...
## $ Smoke : chr "no" "yes" "no" "no" ...
## $ Gender : chr "male" "female" "female" "male" ...
## $ Caesarean: chr "no" "no" "yes" "no" ...glimpse(LungCapData)## Rows: 725
## Columns: 6
## $ LungCap <dbl> 6.475, 10.125, 9.550, 11.125, 4.800, 6.225, 4.950, 7.325, 8.…
## $ Age <dbl> 6, 18, 16, 14, 5, 11, 8, 11, 15, 11, 19, 17, 12, 10, 10, 13,…
## $ Height <dbl> 62.1, 74.7, 69.7, 71.0, 56.9, 58.7, 63.3, 70.4, 70.5, 59.2, …
## $ Smoke <chr> "no", "yes", "no", "no", "no", "no", "no", "no", "no", "no",…
## $ Gender <chr> "male", "female", "female", "male", "male", "female", "male"…
## $ Caesarean <chr> "no", "no", "yes", "no", "no", "no", "yes", "no", "no", "no"…#skimr::skim(LungCapData)
ls()## [1] "LungCapData"exists("LungCapData")## [1] TRUEDataExplorer::plot_missing(LungCapData)
#ANALISIS UNIVARIADO DE VARIABLES NUMERICAS
#LUNGCAP
summary(LungCapData)## LungCap Age Height Smoke
## Min. : 0.507 Min. : 3.00 Min. :45.30 Length:725
## 1st Qu.: 6.150 1st Qu.: 9.00 1st Qu.:59.90 Class :character
## Median : 8.000 Median :13.00 Median :65.40 Mode :character
## Mean : 7.863 Mean :12.33 Mean :64.84
## 3rd Qu.: 9.800 3rd Qu.:15.00 3rd Qu.:70.30
## Max. :14.675 Max. :19.00 Max. :81.80
## Gender Caesarean
## Length:725 Length:725
## Class :character Class :character
## Mode :character Mode :character
##
##
## summary(LungCapData$LungCap)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.507 6.150 8.000 7.863 9.800 14.675#ANALITICO
shapiro.test(LungCapData$LungCap)##
## Shapiro-Wilk normality test
##
## data: LungCapData$LungCap
## W = 0.99305, p-value = 0.001886#GRAFICO
hist(LungCapData$LungCap)
LungCapData %>%
ggplot(aes(x = LungCap)) +
geom_histogram(bins = 10, color = "lightblue", alpha = 0.6) +
theme_ipsum() +
labs(x = "Capacidad Pulmonar")## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
boxplot(LungCapData$LungCap , col = "blue")
qqnorm(LungCapData$LungCap)
#qqline(LungCapData$LungCap col = "blue", lwd = 3)
par(mfrow = c(1, 1))
# Gráfico de Densidad
LungCapData %>%
ggplot(aes(LungCap)) +
geom_density()
ggplot(LungCapData, aes(x=LungCap, fill = Smoke)) +
geom_density(alpha=0.3) +
scale_x_continuous(limits = c(0, 15))
#AGE
#LUNGCAP
summary(LungCapData)## LungCap Age Height Smoke
## Min. : 0.507 Min. : 3.00 Min. :45.30 Length:725
## 1st Qu.: 6.150 1st Qu.: 9.00 1st Qu.:59.90 Class :character
## Median : 8.000 Median :13.00 Median :65.40 Mode :character
## Mean : 7.863 Mean :12.33 Mean :64.84
## 3rd Qu.: 9.800 3rd Qu.:15.00 3rd Qu.:70.30
## Max. :14.675 Max. :19.00 Max. :81.80
## Gender Caesarean
## Length:725 Length:725
## Class :character Class :character
## Mode :character Mode :character
##
##
## summary(LungCapData$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 9.00 13.00 12.33 15.00 19.00#ANALITICO
shapiro.test(LungCapData$Age)##
## Shapiro-Wilk normality test
##
## data: LungCapData$Age
## W = 0.97231, p-value = 1.763e-10#GRAFICO
hist(LungCapData$Age)
LungCapData %>%
ggplot(aes(x = Age)) +
geom_histogram(bins = 10, color = "lightblue", alpha = 0.6) +
theme_ipsum() +
labs(x = "Edad")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
boxplot(LungCapData$Age , col = "blue")
qqnorm(LungCapData$Age)
#qqline(LungCapData$Age col = "blue", lwd = 3)
par(mfrow = c(1, 1))
# Gráfico de Densidad
LungCapData %>%
ggplot(aes(Age)) +
geom_density()
ggplot(LungCapData, aes(x=Age, fill = Smoke)) +
geom_density(alpha=0.3) +
scale_x_continuous(limits = c(0, 25))
#HEIGHT
#LUNGCAP
summary(LungCapData)## LungCap Age Height Smoke
## Min. : 0.507 Min. : 3.00 Min. :45.30 Length:725
## 1st Qu.: 6.150 1st Qu.: 9.00 1st Qu.:59.90 Class :character
## Median : 8.000 Median :13.00 Median :65.40 Mode :character
## Mean : 7.863 Mean :12.33 Mean :64.84
## 3rd Qu.: 9.800 3rd Qu.:15.00 3rd Qu.:70.30
## Max. :14.675 Max. :19.00 Max. :81.80
## Gender Caesarean
## Length:725 Length:725
## Class :character Class :character
## Mode :character Mode :character
##
##
## summary(LungCapData$Height)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 45.30 59.90 65.40 64.84 70.30 81.80#ANALITICO
shapiro.test(LungCapData$Height)##
## Shapiro-Wilk normality test
##
## data: LungCapData$Height
## W = 0.99027, p-value = 0.0001006#GRAFICO
hist(LungCapData$Height)
LungCapData %>%
ggplot(aes(x = Height)) +
geom_histogram(bins = 10, color = "lightblue", alpha = 0.6) +
theme_ipsum() +
labs(x = "Altura")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
boxplot(LungCapData$Height , col = "blue")
qqnorm(LungCapData$Height)
#qqline(LungCapData$Height col = "blue", lwd = 3)
par(mfrow = c(1, 1))
# Gráfico de Densidad
LungCapData %>%
ggplot(aes(Height)) +
geom_density()
ggplot(LungCapData, aes(x=Height, fill = Gender)) +
geom_density(alpha=0.3) +
scale_x_continuous(limits = c(40, 90))
#ANALISIS UNIVARIADO DE VARIABLES CATEGORICAS
table(LungCapData$Smoke) ##
## no yes
## 648 77prop.table(table(LungCapData$Smoke)) ##
## no yes
## 0.8937931 0.1062069chisq.test(table(LungCapData$Smoke)) #p valor bajo: distribución normal##
## Chi-squared test for given probabilities
##
## data: table(LungCapData$Smoke)
## X-squared = 449.71, df = 1, p-value < 2.2e-16#analisis grafico
barplot(table(LungCapData$Smoke), col = rainbow(3))
ggplot(data = LungCapData, aes(x = Smoke)) +
geom_bar(color = 'darkslategray', fill = 'steelblue') +
xlab("Fumador") +
ylab("Total de pacientes") 
LungCapData %>%
plot_frq(Smoke) 
#GENDER
table(LungCapData$Gender)##
## female male
## 358 367prop.table(table(LungCapData$Gender))##
## female male
## 0.4937931 0.5062069chisq.test(table(LungCapData$Gender)) #p valor alto: distribución NO normal##
## Chi-squared test for given probabilities
##
## data: table(LungCapData$Gender)
## X-squared = 0.11172, df = 1, p-value = 0.7382#analisis grafico
barplot(table(LungCapData$Gender), col = rainbow(3))
ggplot(data = LungCapData, aes(x = Gender)) +
geom_bar(color = 'darkslategray', fill = 'steelblue') +
xlab("Género") +
ylab("Total de pacientes") 
LungCapData %>%
plot_frq(Gender) 
#Caesarean
table(LungCapData$Caesarean)##
## no yes
## 561 164prop.table(table(LungCapData$Caesarean)) ##
## no yes
## 0.7737931 0.2262069chisq.test(table(LungCapData$Caesarean)) #p valor bajo: distribución normal##
## Chi-squared test for given probabilities
##
## data: table(LungCapData$Caesarean)
## X-squared = 217.39, df = 1, p-value < 2.2e-16#analisis grafico
barplot(table(LungCapData$Caesarean), col = rainbow(3))
ggplot(data = LungCapData, aes(x = Caesarean)) +
geom_bar(color = 'darkslategray', fill = 'steelblue') +
xlab("Nacidos por Cesárea") +
ylab("Total de pacientes") 
LungCapData %>%
plot_frq(Caesarean) 
#ANALISIS BIVARIADO
#CAPACIDAD PULMONAR VS EDAD
cor.test(LungCapData$LungCap, LungCapData$Age, method = "pearson") #Fuerte correlación positiva##
## Pearson's product-moment correlation
##
## data: LungCapData$LungCap and LungCapData$Age
## t = 38.476, df = 723, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7942660 0.8422217
## sample estimates:
## cor
## 0.8196749ggplot(LungCapData, aes(x = LungCap, y = Age,)) +
geom_point() +
labs(title = "Relación entre Capacidad Pulmonar y Edad",
x = "Capacidad Pulmonar",
y = "Edad") +
theme_minimal()
ggplot(LungCapData, aes(x = LungCap, y = Age, colour = Gender)) +
geom_point() +
labs(title = "Relación entre Capacidad Pulmonar y Edad",
x = "Capacidad Pulmonar",
y = "Edad") +
theme_minimal()
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2ggpairs(LungCapData, columns = c("LungCap", "Age"),
aes(color = Gender, alpha = 0.5)) +
theme_minimal()
boxplot(LungCapData$LungCap ~ LungCapData$Age,
col = "lightblue",
las = 1,
main = "Boxplot",
xlab = "Capacidad Pulmonar",
ylab = "Edad")
#CAPACIDAD PULMONAR VS GENERO
wilcox.test(LungCapData$LungCap ~ LungCapData$Gender)##
## Wilcoxon rank sum test with continuity correction
##
## data: LungCapData$LungCap by LungCapData$Gender
## W = 53624, p-value = 1.864e-05
## alternative hypothesis: true location shift is not equal to 0ggplot(LungCapData, aes(x = LungCap, y = Gender,)) +
geom_point() +
labs(title = "Relación entre Capacidad Pulmonar y Género",
x = "Capacidad Pulmonar",
y = "Género") +
theme_minimal()
ggplot(LungCapData, aes(x = LungCap, y = Gender, colour = Smoke)) +
geom_point() +
labs(title = "Relación entre Capacidad Pulmonar y Género",
x = "Capacidad Pulmonar",
y = "Género") +
theme_minimal()
library(GGally)
ggpairs(LungCapData, columns = c("LungCap", "Gender"),
aes(color = Smoke, alpha = 0.5)) +
theme_minimal()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#GENERO vs HABITO TABAQUICO
chisq.test(LungCapData$Gender, LungCapData$Smoke)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: LungCapData$Gender and LungCapData$Smoke
## X-squared = 1.7443, df = 1, p-value = 0.1866tabla <- table(LungCapData$Gender, LungCapData$Smoke)
print(tabla)##
## no yes
## female 314 44
## male 334 33prueba_chi <- chisq.test(tabla)
print(prueba_chi)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla
## X-squared = 1.7443, df = 1, p-value = 0.1866ggplot(LungCapData, aes(x = Gender, fill = Smoke)) +
geom_bar(position = "dodge") +
labs(title = "Distribución de fumadores por género", x = "Género", y = "Frecuencia") +
theme_minimal()
ggplot(LungCapData, aes(x = Gender, fill = Smoke)) +
geom_bar(position = "fill") +
labs(title = "Proporción de fumadores por género", x = "Género", y = "Proporción") +
theme_minimal()
#install.packages("vcd")
library(vcd)## Cargando paquete requerido: gridmosaicplot(table(LungCapData$Gender, LungCapData$Smoke), main = "Relación entre Género y Tabaquismo",
color = TRUE)
table(LungCapData$trt)## < table of extent 0 >library(gtsummary)
glimpse(LungCapData)## Rows: 725
## Columns: 6
## $ LungCap <dbl> 6.475, 10.125, 9.550, 11.125, 4.800, 6.225, 4.950, 7.325, 8.…
## $ Age <dbl> 6, 18, 16, 14, 5, 11, 8, 11, 15, 11, 19, 17, 12, 10, 10, 13,…
## $ Height <dbl> 62.1, 74.7, 69.7, 71.0, 56.9, 58.7, 63.3, 70.4, 70.5, 59.2, …
## $ Smoke <chr> "no", "yes", "no", "no", "no", "no", "no", "no", "no", "no",…
## $ Gender <chr> "male", "female", "female", "male", "male", "female", "male"…
## $ Caesarean <chr> "no", "no", "yes", "no", "no", "no", "yes", "no", "no", "no"…LungCapData %>%
tbl_summary()| Characteristic | N = 7251 |
|---|---|
| LungCap | 8.00 (6.15, 9.80) |
| Age | 13.0 (9.0, 15.0) |
| Height | 65 (60, 70) |
| Smoke | 77 (11%) |
| Gender | |
| female | 358 (49%) |
| male | 367 (51%) |
| Caesarean | 164 (23%) |
| 1 Median (Q1, Q3); n (%) | |
LungCapData %>%
select(LungCap, Age, Height, Smoke, Gender, Caesarean) %>%
tbl_summary()| Characteristic | N = 7251 |
|---|---|
| LungCap | 8.00 (6.15, 9.80) |
| Age | 13.0 (9.0, 15.0) |
| Height | 65 (60, 70) |
| Smoke | 77 (11%) |
| Gender | |
| female | 358 (49%) |
| male | 367 (51%) |
| Caesarean | 164 (23%) |
| 1 Median (Q1, Q3); n (%) | |