Informe #2
2024-09-18
Producción mensual de energía en Colombia.
El conjunto de datos proporciona estadísticas mensuales de electricidad de la Agencia Internacional de Energía (AIE) para múltiples países y territorios en todo el mundo. Incluye información sobre la generación de electricidad mensual desde 2010 hasta 2022. La producción de energía se mide en gigavatios-hora (GWh) y abarca una variedad de productos energéticos, incluyendo hidroeléctrica, eólica, solar, geotérmica, nuclear, combustibles fósiles y otros. Estos datos pueden ser útiles para analizar las tendencias de consumo y producción de electricidad a nivel mundial y regional, así como para evaluar el impacto ambiental de la industria eléctrica.
Se procede a cargar la base de datos
library(readxl)
library(readr)
library(readr)
df<- read_csv("dataset_energy.csv") ## Rows: 47159 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Se visualiza la base de datos
str(df)## spc_tbl_ [47,159 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ COUNTRY : chr [1:47159] "Argentina" "Argentina" "Argentina" "Argentina" ...
## $ CODE_TIME : chr [1:47159] "JAN2020" "JAN2020" "JAN2020" "JAN2020" ...
## $ TIME : chr [1:47159] "January 2020" "January 2020" "January 2020" "January 2020" ...
## $ YEAR : num [1:47159] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ MONTH : num [1:47159] 1 1 1 1 1 1 1 1 1 1 ...
## $ MONTH_NAME : chr [1:47159] "January" "January" "January" "January" ...
## $ PRODUCT : chr [1:47159] "Hydro" "Wind" "Solar" "Geothermal" ...
## $ VALUE : num [1:47159] 2393 677 106 0 0 ...
## $ DISPLAY_ORDER : num [1:47159] 1 2 3 4 5 6 7 8 9 10 ...
## $ yearToDate : num [1:47159] 23614 9318 1331 0 0 ...
## $ previousYearToDate: num [1:47159] 2694.1 285.6 40.1 0 0 ...
## $ share : num [1:47159] 0.192 0.0543 0.0085 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. COUNTRY = col_character(),
## .. CODE_TIME = col_character(),
## .. TIME = col_character(),
## .. YEAR = col_double(),
## .. MONTH = col_double(),
## .. MONTH_NAME = col_character(),
## .. PRODUCT = col_character(),
## .. VALUE = col_double(),
## .. DISPLAY_ORDER = col_double(),
## .. yearToDate = col_double(),
## .. previousYearToDate = col_double(),
## .. share = col_double()
## .. )
## - attr(*, "problems")=<externalptr>unique(df$COUNTRY)## [1] "Argentina" "Australia" "Austria"
## [4] "Belgium" "Brazil" "Bulgaria"
## [7] "Canada" "Chile" "Colombia"
## [10] "Croatia" "Cyprus" "Czech Republic"
## [13] "Denmark" "Estonia" "Finland"
## [16] "France" "Germany" "Greece"
## [19] "Hungary" "IEA Total" "Iceland"
## [22] "India" "Ireland" "Italy"
## [25] "Japan" "Korea" "Latvia"
## [28] "Lithuania" "Luxembourg" "Malta"
## [31] "Mexico" "Netherlands" "New Zealand"
## [34] "North Macedonia" "Norway" "OECD Americas"
## [37] "OECD Asia Oceania" "OECD Europe" "OECD Total"
## [40] "Poland" "Portugal" "Republic of Turkiye"
## [43] "Romania" "Serbia" "Slovak Republic"
## [46] "Slovenia" "Spain" "Sweden"
## [49] "Switzerland" "United Kingdom" "United States"
## [52] "Costa Rica"Favor seguir continuar con los codigos de Kaggle
Actividad
Tomar la base de datos y aplicar las distribuciones de la media de la
variable Value en cualquier pais de tu interes.
Modelo de ejemplo.
Planteamiento del problema: Tomando como pais de referencia se toman los datos correspondientes a Colombia en la base de datos. El objetivo es entender la variabilidad de las muestras tomadas de la variable VALUE relacionada con los valores de producción/consumo de energía para Colombia.
Pregunta del problema: ¿Cuál es la media esperada de la variable VALUE para Colombia, y cuál es la distribución muestral de la media si tomamos muestras de tamaño 𝑛=40?
Este análisis permitirá estimar con mayor precisión el valor promedio de energía en Colombia y analizar la variabilidad de las medias obtenidas a partir de diferentes muestras.
Pasos a seguir:
Filtrar los datos para Colombia.
Calcular la media y desviación estándar de la variable VALUE para Colombia.
Simular la distribución muestral de la media con un tamaño de muestra de n=40.
Calcular la media y error estándar de la distribución muestral.
Graficar la distribución muestral de la media.
# Paso 1: Cargar los datos y filtrar por Colombia
colombia_data <- subset(df, COUNTRY == "Colombia")
# Paso 2: Calcular la media y desviación estándar poblacional
population_mean_col <- mean(colombia_data$VALUE, na.rm = TRUE)
population_std_col <- sd(colombia_data$VALUE, na.rm = TRUE)
# Paso 3: Configurar el tamaño de muestra y número de simulaciones
sample_size <- 40
num_samples <- 1000
set.seed(123) # Para reproducibilidad
# Simular la distribución muestral de la media
sample_means_col <- replicate(num_samples, {
sample_data <- sample(colombia_data$VALUE, sample_size, replace = TRUE)
mean(sample_data)
})
# Paso 4: Calcular la media muestral y el error estándar
sampling_mean_col <- mean(sample_means_col)
sampling_std_error_col <- sd(sample_means_col)
# Paso 5: Graficar la distribución muestral de la media
hist(sample_means_col, breaks = 30, main = "Distribución Muestral de la Media (Colombia)",
xlab = "Media Muestral", col = "lightgreen", border = "black")
abline(v = sampling_mean_col, col = "red", lwd = 2, lty = 2)
Favor replicar con otro pais.
# Cargar las librerías necesarias
library(readr)
# Cargar los datos
df <- read_csv("dataset_energy.csv")## Rows: 47159 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Ver los países únicos en la columna COUNTRY
unique_countries <- unique(df$COUNTRY)
# Mostrar la lista de países
print(unique_countries)## [1] "Argentina" "Australia" "Austria"
## [4] "Belgium" "Brazil" "Bulgaria"
## [7] "Canada" "Chile" "Colombia"
## [10] "Croatia" "Cyprus" "Czech Republic"
## [13] "Denmark" "Estonia" "Finland"
## [16] "France" "Germany" "Greece"
## [19] "Hungary" "IEA Total" "Iceland"
## [22] "India" "Ireland" "Italy"
## [25] "Japan" "Korea" "Latvia"
## [28] "Lithuania" "Luxembourg" "Malta"
## [31] "Mexico" "Netherlands" "New Zealand"
## [34] "North Macedonia" "Norway" "OECD Americas"
## [37] "OECD Asia Oceania" "OECD Europe" "OECD Total"
## [40] "Poland" "Portugal" "Republic of Turkiye"
## [43] "Romania" "Serbia" "Slovak Republic"
## [46] "Slovenia" "Spain" "Sweden"
## [49] "Switzerland" "United Kingdom" "United States"
## [52] "Costa Rica"# Paso 1: Cargar las librerías necesarias
library(readr)
# Paso 2: Cargar los datos
df <- read_csv("dataset_energy.csv")## Rows: 47159 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Paso 3: Filtrar los datos para Brasil
brazil_data <- subset(df, COUNTRY == "Brazil")
# Paso 4: Visualizar los datos
str(brazil_data)## tibble [864 × 12] (S3: tbl_df/tbl/data.frame)
## $ COUNTRY : chr [1:864] "Brazil" "Brazil" "Brazil" "Brazil" ...
## $ CODE_TIME : chr [1:864] "JAN2020" "JAN2020" "JAN2020" "JAN2020" ...
## $ TIME : chr [1:864] "January 2020" "January 2020" "January 2020" "January 2020" ...
## $ YEAR : num [1:864] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ MONTH : num [1:864] 1 1 1 1 1 1 1 1 1 1 ...
## $ MONTH_NAME : chr [1:864] "January" "January" "January" "January" ...
## $ PRODUCT : chr [1:864] "Hydro" "Wind" "Solar" "Geothermal" ...
## $ VALUE : num [1:864] 36956 2797 789 0 0 ...
## $ DISPLAY_ORDER : num [1:864] 1 2 3 4 5 6 7 8 9 10 ...
## $ yearToDate : num [1:864] 392364 56480 10642 0 0 ...
## $ previousYearToDate: num [1:864] 41203 4234 452 0 0 ...
## $ share : num [1:864] 0.6905 0.0523 0.0147 0 0 ...head(brazil_data) # Ver primeras filas## # A tibble: 6 × 12
## COUNTRY CODE_TIME TIME YEAR MONTH MONTH_NAME PRODUCT VALUE DISPLAY_ORDER
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 Brazil JAN2020 January… 2020 1 January Hydro 36956. 1
## 2 Brazil JAN2020 January… 2020 1 January Wind 2797. 2
## 3 Brazil JAN2020 January… 2020 1 January Solar 789. 3
## 4 Brazil JAN2020 January… 2020 1 January Geothe… 0 4
## 5 Brazil JAN2020 January… 2020 1 January Other … 0 5
## 6 Brazil JAN2020 January… 2020 1 January Nuclear 1089. 6
## # ℹ 3 more variables: yearToDate <dbl>, previousYearToDate <dbl>, share <dbl># Paso 5: Definir el umbral (ejemplo: producción de energía superior a 1000 GWh)
umbral <- 1000
print(umbral) # Ver el umbral## [1] 1000# Paso 6: Calcular la probabilidad de éxito
n <- nrow(brazil_data) # Tamaño de la muestra
p <- mean(brazil_data$VALUE > umbral, na.rm = TRUE) # Probabilidad de éxito
print(n) # Ver tamaño de la muestra## [1] 864print(p) # Ver probabilidad de éxito## [1] 0.619213# Paso 7: Definir el rango de éxitos posibles (0 a n)
k <- 0:n
print(k) # Ver el rango de éxitos posibles## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [19] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## [37] 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
## [55] 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
## [73] 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## [91] 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
## [109] 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
## [127] 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
## [145] 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
## [163] 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
## [181] 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
## [199] 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
## [217] 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
## [235] 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
## [253] 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
## [271] 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
## [289] 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
## [307] 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
## [325] 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
## [343] 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
## [361] 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
## [379] 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395
## [397] 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
## [415] 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
## [433] 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449
## [451] 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467
## [469] 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485
## [487] 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503
## [505] 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521
## [523] 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539
## [541] 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557
## [559] 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575
## [577] 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593
## [595] 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611
## [613] 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629
## [631] 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647
## [649] 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665
## [667] 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683
## [685] 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701
## [703] 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719
## [721] 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737
## [739] 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755
## [757] 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773
## [775] 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791
## [793] 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809
## [811] 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827
## [829] 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845
## [847] 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863
## [865] 864# Paso 8: Calcular la distribución binomial
binomial_distribution <- dbinom(k, size = n, prob = p)
print(binomial_distribution) # Ver la distribución binomial## [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [16] 0.000000e+00 0.000000e+00 4.940656e-324 3.063207e-322 2.205509e-320
## [21] 1.515161e-318 9.902389e-317 6.170250e-315 3.673203e-313 2.093088e-311
## [26] 1.143628e-309 6.001105e-308 3.028791e-306 1.472294e-304 6.901775e-303
## [31] 3.123805e-301 1.366615e-299 5.784943e-298 2.371736e-296 9.426410e-295
## [36] 3.635082e-293 1.361207e-291 4.953483e-290 1.753035e-288 6.037593e-287
## [41] 2.024956e-285 6.617849e-284 2.108751e-282 6.555209e-281 1.989001e-279
## [46] 5.893783e-278 1.706389e-276 4.829381e-275 1.336688e-273 3.619779e-272
## [51] 9.594616e-271 2.490231e-269 6.331182e-268 1.577331e-266 3.852191e-265
## [56] 9.225460e-264 2.167237e-262 4.995751e-261 1.130327e-259 2.510991e-258
## [61] 5.478323e-257 1.174172e-255 2.472939e-254 5.119236e-253 1.041875e-251
## [66] 2.085212e-250 4.104978e-249 7.950544e-248 1.515319e-246 2.842667e-245
## [71] 5.249923e-244 9.547133e-243 1.709903e-241 3.016697e-240 5.243659e-239
## [76] 8.981691e-238 1.516280e-236 2.523323e-235 4.140100e-234 6.698293e-233
## [81] 1.068813e-231 1.682250e-230 2.612140e-229 4.002054e-228 6.050797e-227
## [86] 9.029135e-226 1.329974e-224 1.934021e-223 2.776883e-222 3.937197e-221
## [91] 5.513206e-220 7.625383e-219 1.041864e-217 1.406382e-216 1.875806e-215
## [96] 2.472366e-214 3.220519e-213 4.146419e-212 5.277161e-211 6.639752e-210
## [101] 8.259831e-209 1.016017e-207 1.235900e-206 1.486822e-205 1.769164e-204
## [106] 2.082333e-203 2.424622e-202 2.793106e-201 3.183592e-200 3.590629e-199
## [111] 4.007585e-198 4.426791e-197 4.839760e-196 5.237465e-195 5.610661e-194
## [116] 5.950252e-193 6.247656e-192 6.495173e-191 6.686320e-190 6.816115e-189
## [121] 6.881290e-188 6.880425e-187 6.814000e-186 6.684345e-185 6.495510e-184
## [126] 6.253063e-183 5.963821e-182 5.635535e-181 5.276556e-180 4.895494e-179
## [131] 4.500890e-178 4.100918e-177 3.703130e-176 3.314258e-175 2.940064e-174
## [136] 2.585257e-173 2.253462e-172 1.947238e-171 1.668139e-170 1.416811e-169
## [141] 1.193108e-168 9.962241e-168 8.248308e-167 6.772109e-166 5.513845e-165
## [146] 4.452224e-164 3.565421e-163 2.831887e-162 2.230957e-161 1.743315e-160
## [151] 1.351290e-159 1.039029e-158 7.925588e-158 5.997606e-157 4.502821e-156
## [156] 3.354048e-155 2.478841e-154 1.817775e-153 1.322697e-152 9.550495e-152
## [161] 6.843101e-151 4.865837e-150 3.433648e-149 2.404713e-148 1.671457e-147
## [166] 1.153101e-146 7.895764e-146 5.366499e-145 3.620532e-144 2.424671e-143
## [171] 1.611931e-142 1.063819e-141 6.969963e-141 4.533654e-140 2.927757e-139
## [176] 1.877172e-138 1.195001e-137 7.553382e-137 4.740630e-136 2.954371e-135
## [181] 1.828273e-134 1.123509e-133 6.856202e-133 4.155036e-132 2.500702e-131
## [186] 1.494710e-130 8.873026e-130 5.231398e-129 3.063419e-128 1.781760e-127
## [191] 1.029336e-126 5.906656e-126 3.366770e-125 1.906265e-124 1.072166e-123
## [196] 5.990457e-123 3.324971e-122 1.833395e-121 1.004326e-120 5.465798e-120
## [201] 2.955311e-119 1.587571e-118 8.473310e-118 4.493378e-117 2.367566e-116
## [206] 1.239510e-115 6.448016e-115 3.333033e-114 1.711983e-113 8.738058e-113
## [211] 4.431950e-112 2.233819e-111 1.118879e-110 5.569408e-110 2.755080e-109
## [216] 1.354462e-108 6.617833e-108 3.213579e-107 1.550939e-106 7.439451e-106
## [221] 3.546793e-105 1.680689e-104 7.915952e-104 3.705879e-103 1.724484e-102
## [226] 7.976538e-102 3.667455e-101 1.676168e-100 7.615176e-100 3.439216e-99
## [231] 1.544057e-98 6.891256e-98 3.057539e-97 1.348624e-96 5.913743e-96
## [236] 2.578060e-95 1.117351e-94 4.814583e-94 2.062563e-93 8.784994e-93
## [241] 3.720216e-92 1.566367e-91 6.557286e-91 2.729393e-90 1.129603e-89
## [246] 4.648462e-89 1.902053e-88 7.738773e-88 3.130859e-87 1.259513e-86
## [251] 5.038436e-86 2.004233e-85 7.928044e-85 3.118565e-84 1.219889e-83
## [256] 4.745345e-83 1.835705e-82 7.062059e-82 2.701831e-81 1.027990e-80
## [261] 3.889814e-80 1.463805e-79 5.478442e-79 2.039181e-78 7.548908e-78
## [266] 2.779377e-77 1.017772e-76 3.706792e-76 1.342752e-75 4.837796e-75
## [271] 1.733639e-74 6.179220e-74 2.190672e-73 7.724925e-73 2.709500e-72
## [276] 9.452927e-72 3.280427e-71 1.132363e-70 3.888096e-70 1.327970e-69
## [281] 4.511740e-69 1.524784e-68 5.126085e-68 1.714274e-67 5.702902e-67
## [286] 1.887282e-66 6.213085e-66 2.034750e-65 6.629066e-65 2.148498e-64
## [291] 6.927278e-64 2.221976e-63 7.090373e-63 2.250896e-62 7.108894e-62
## [296] 2.233638e-61 6.982182e-61 2.171406e-60 6.718396e-60 2.068087e-59
## [301] 6.333647e-59 1.929853e-58 5.850374e-58 1.764555e-57 5.295200e-57
## [306] 1.580988e-56 4.696526e-56 1.388131e-55 4.082186e-55 1.194447e-54
## [311] 3.477412e-54 1.007311e-53 2.903300e-53 8.326155e-53 2.375879e-52
## [316] 6.745813e-52 1.905801e-51 5.357432e-51 1.498562e-50 4.170946e-50
## [321] 1.155152e-49 3.183397e-49 8.729565e-49 2.382030e-48 6.467812e-48
## [326] 1.747534e-47 4.698451e-47 1.257034e-46 3.346613e-46 8.866092e-46
## [331] 2.337383e-45 6.131985e-45 1.600841e-44 4.158851e-44 1.075175e-43
## [336] 2.766102e-43 7.081782e-43 1.804282e-42 4.574633e-42 1.154251e-41
## [341] 2.898267e-41 7.242245e-41 1.800971e-40 4.456983e-40 1.097686e-39
## [346] 2.690421e-39 6.562502e-39 1.593043e-38 3.848547e-38 9.252923e-38
## [351] 2.213992e-37 5.272176e-37 1.249460e-36 2.946969e-36 6.917531e-36
## [356] 1.616035e-35 3.757305e-35 8.694205e-35 2.002224e-34 4.589091e-34
## [361] 1.046823e-33 2.376592e-33 5.369971e-33 1.207611e-32 2.702845e-32
## [366] 6.020827e-32 1.334853e-31 2.945470e-31 6.468761e-31 1.413951e-30
## [371] 3.076066e-30 6.660496e-30 1.435385e-29 3.078808e-29 6.572800e-29
## [376] 1.396603e-28 2.953602e-28 6.217107e-28 1.302517e-27 2.716054e-27
## [381] 5.637082e-27 1.164482e-26 2.394276e-26 4.899823e-26 9.980497e-26
## [386] 2.023441e-25 4.083162e-25 8.201088e-25 1.639517e-24 3.262355e-24
## [391] 6.461273e-24 1.273731e-23 2.499255e-23 4.881102e-23 9.488563e-23
## [396] 1.835943e-22 3.535857e-22 6.778098e-22 1.293301e-21 2.456238e-21
## [401] 4.643242e-21 8.736808e-21 1.636310e-20 3.050426e-20 5.660280e-20
## [406] 1.045439e-19 1.921955e-19 3.516998e-19 6.405986e-19 1.161410e-18
## [411] 2.095901e-18 3.764807e-18 6.731341e-18 1.197975e-17 2.122179e-17
## [416] 3.742004e-17 6.567728e-17 1.147400e-16 1.995281e-16 3.453686e-16
## [421] 5.950473e-16 1.020493e-15 1.742045e-15 2.960051e-15 5.006449e-15
## [426] 8.428522e-15 1.412421e-14 2.355962e-14 3.911685e-14 6.464739e-14
## [431] 1.063481e-13 1.741406e-13 2.838324e-13 4.604853e-13 7.436373e-13
## [436] 1.195359e-12 1.912612e-12 3.046121e-12 4.829018e-12 7.620120e-12
## [441] 1.196895e-11 1.871290e-11 2.912172e-11 4.511113e-11 6.955697e-11
## [446] 1.067549e-10 1.630891e-10 2.479999e-10 3.753768e-10 5.655517e-10
## [451] 8.481366e-10 1.266040e-09 1.881122e-09 2.782107e-09 4.095602e-09
## [456] 6.001338e-09 8.753151e-09 1.270768e-08 1.836340e-08 2.641340e-08
## [461] 3.781633e-08 5.389118e-08 7.644317e-08 1.079299e-07 1.516792e-07
## [466] 2.121733e-07 2.954171e-07 4.094112e-07 5.647579e-07 7.754299e-07
## [471] 1.059741e-06 1.441560e-06 1.951827e-06 2.630414e-06 3.528421e-06
## [476] 4.710958e-06 6.260511e-06 8.280967e-06 1.090240e-05 1.428669e-05
## [481] 1.863413e-05 2.419096e-05 3.125811e-05 4.020099e-05 5.146054e-05
## [486] 6.556530e-05 8.314476e-05 1.049435e-04 1.318363e-04 1.648435e-04
## [491] 2.051469e-04 2.541048e-04 3.132669e-04 3.843868e-04 4.694327e-04
## [496] 5.705947e-04 6.902879e-04 8.311504e-04 9.960341e-04 1.187990e-03
## [501] 1.410241e-03 1.666153e-03 1.959186e-03 2.292842e-03 2.670599e-03
## [506] 3.095834e-03 3.571738e-03 4.101215e-03 4.686783e-03 5.330464e-03
## [511] 6.033663e-03 6.797063e-03 7.620508e-03 8.502901e-03 9.442108e-03
## [516] 1.043489e-02 1.147681e-02 1.256226e-02 1.368439e-02 1.483516e-02
## [521] 1.600537e-02 1.718479e-02 1.836225e-02 1.952577e-02 2.066280e-02
## [526] 2.176039e-02 2.280544e-02 2.378496e-02 2.468632e-02 2.549751e-02
## [531] 2.620744e-02 2.680615e-02 2.728506e-02 2.763718e-02 2.785725e-02
## [536] 2.794193e-02 2.788980e-02 2.770147e-02 2.737954e-02 2.692854e-02
## [541] 2.635484e-02 2.566646e-02 2.487294e-02 2.398507e-02 2.301468e-02
## [546] 2.197437e-02 2.087720e-02 1.973649e-02 1.856550e-02 1.737718e-02
## [551] 1.618397e-02 1.499757e-02 1.382878e-02 1.268736e-02 1.158190e-02
## [556] 1.051978e-02 9.507100e-03 8.548727e-03 7.648275e-03 6.808180e-03
## [561] 6.029770e-03 5.313361e-03 4.658365e-03 4.063403e-03 3.526430e-03
## [566] 3.044850e-03 2.615644e-03 2.235474e-03 1.900797e-03 1.607952e-03
## [571] 1.353250e-03 1.133045e-03 9.437927e-04 7.821008e-04 6.447654e-04
## [576] 5.287979e-04 4.314425e-04 3.501850e-04 2.827545e-04 2.271197e-04
## [581] 1.814803e-04 1.442546e-04 1.140646e-04 8.972004e-05 7.020060e-05
## [586] 5.463876e-05 4.230243e-05 3.257841e-05 2.495688e-05 1.901700e-05
## [591] 1.441387e-05 1.086679e-05 8.148924e-06 6.078163e-06 4.509341e-06
## [596] 3.327498e-06 2.442202e-06 1.782789e-06 1.294398e-06 9.347181e-07
## [601] 6.713256e-07 4.795352e-07 3.406730e-07 2.407019e-07 1.691379e-07
## [606] 1.181998e-07 8.214892e-08 5.677939e-08 3.902817e-08 2.667834e-08
## [611] 1.813539e-08 1.225963e-08 8.241464e-09 5.509374e-09 3.662398e-09
## [616] 2.420964e-09 1.591347e-09 1.040134e-09 6.760138e-10 4.368757e-10
## [621] 2.807309e-10 1.793686e-10 1.139515e-10 7.197891e-11 4.520595e-11
## [626] 2.822830e-11 1.752533e-11 1.081766e-11 6.638655e-12 4.050413e-12
## [631] 2.456883e-12 1.481593e-12 8.882304e-13 5.293803e-13 3.136522e-13
## [636] 1.847398e-13 1.081675e-13 6.295786e-14 3.642613e-14 2.094973e-14
## [641] 1.197675e-14 6.805919e-15 3.844275e-15 2.158313e-15 1.204422e-15
## [646] 6.680354e-16 3.682722e-16 2.017805e-16 1.098808e-16 5.946873e-17
## [651] 3.198686e-17 1.709867e-17 9.083478e-18 4.795487e-18 2.515911e-18
## [656] 1.311690e-18 6.795659e-19 3.498544e-19 1.789739e-19 9.097654e-20
## [661] 4.595124e-20 2.306130e-20 1.149952e-20 5.697384e-21 2.804538e-21
## [666] 1.371600e-21 6.664450e-22 3.217078e-22 1.542798e-22 7.350164e-23
## [671] 3.478682e-23 1.635505e-23 7.638318e-24 3.543576e-24 1.632951e-24
## [676] 7.474478e-25 3.398238e-25 1.534550e-25 6.882571e-26 3.065856e-26
## [681] 1.356352e-26 5.959380e-27 2.600313e-27 1.126767e-27 4.848580e-28
## [686] 2.071831e-28 8.791059e-29 3.703926e-29 1.549548e-29 6.436598e-30
## [691] 2.654625e-30 1.087007e-30 4.419063e-31 1.783542e-31 7.146244e-32
## [696] 2.842496e-32 1.122368e-32 4.399158e-33 1.711546e-33 6.609632e-34
## [701] 2.533501e-34 9.638397e-35 3.639261e-35 1.363737e-35 5.071563e-36
## [706] 1.871676e-36 6.854581e-37 2.491016e-37 8.982573e-38 3.213934e-38
## [711] 1.140954e-38 4.018623e-39 1.404257e-39 4.868089e-40 1.674153e-40
## [716] 5.711343e-41 1.932722e-41 6.487387e-42 2.159835e-42 7.131854e-43
## [721] 2.335586e-43 7.585442e-44 2.443080e-44 7.802712e-45 2.471064e-45
## [726] 7.759467e-46 2.415839e-46 7.457110e-47 2.282010e-47 6.922881e-48
## [731] 2.081880e-48 6.205846e-49 1.833578e-49 5.369419e-50 1.558333e-50
## [736] 4.482025e-51 1.277450e-51 3.607816e-52 1.009601e-52 2.799198e-53
## [741] 7.688999e-54 2.092335e-54 5.640145e-55 1.505980e-55 3.982809e-56
## [746] 1.043211e-56 2.706066e-57 6.951166e-58 1.768071e-58 4.452807e-59
## [751] 1.110269e-59 2.740635e-60 6.696835e-61 1.619760e-61 3.877574e-62
## [756] 9.186788e-63 2.153904e-63 4.997031e-64 1.147056e-64 2.604992e-65
## [761] 5.852482e-66 1.300609e-66 2.858820e-67 6.214701e-68 1.335999e-68
## [766] 2.839897e-69 5.968524e-70 1.240097e-70 2.546971e-71 5.170430e-72
## [771] 1.037331e-72 2.056595e-73 4.028769e-74 7.797198e-75 1.490723e-75
## [776] 2.815111e-76 5.250271e-77 9.669439e-78 1.758324e-78 3.156587e-79
## [781] 5.593710e-80 9.783315e-81 1.688555e-81 2.875579e-82 4.831167e-83
## [786] 8.006271e-84 1.308557e-84 2.108970e-85 3.351142e-86 5.249130e-87
## [791] 8.103626e-88 1.232800e-88 1.847771e-89 2.728131e-90 3.966989e-91
## [796] 5.680019e-92 8.006519e-93 1.110842e-93 1.516638e-94 2.037216e-95
## [801] 2.691648e-96 3.497225e-97 4.467326e-98 5.608952e-99 6.920116e-100
## [806] 8.387386e-101 9.983929e-102 1.166847e-102 1.338552e-103 1.506720e-104
## [811] 1.663674e-105 1.801354e-106 1.911951e-107 1.988600e-108 2.026054e-109
## [816] 2.021256e-110 1.973718e-111 1.885655e-112 1.761832e-113 1.609150e-114
## [821] 1.435995e-115 1.251470e-116 1.064572e-117 8.834505e-119 7.148201e-120
## [826] 5.635866e-121 4.327162e-122 3.233249e-123 2.349462e-124 1.659106e-125
## [831] 1.137685e-126 7.569337e-128 4.882096e-129 3.049785e-130 1.843413e-131
## [836] 1.076999e-132 6.075262e-134 3.304878e-135 1.731542e-136 8.725742e-138
## [841] 4.222999e-139 1.959720e-140 8.704979e-142 3.694203e-143 1.494705e-144
## [846] 5.752897e-146 2.101008e-147 7.260637e-149 2.366931e-150 7.253637e-152
## [851] 2.081546e-153 5.568554e-155 1.381669e-156 3.160779e-158 6.620440e-160
## [856] 1.259153e-161 2.152808e-163 3.267927e-165 4.335517e-167 4.924441e-169
## [861] 4.655715e-171 3.517232e-173 1.990549e-175 7.501533e-178 1.411868e-180# Paso 9: Graficar la distribución binomial
barplot(binomial_distribution, names.arg = k,
main = "Distribución Binomial de la Producción de Energía en Brasil",
xlab = "Número de Meses con Producción > 1000 GWh",
ylab = "Probabilidad",
col = "lightblue")
# Paso 1: Cargar las librerías necesarias
library(readr)
# Paso 2: Cargar los datos
df <- read_csv("dataset_energy.csv")## Rows: 47159 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Paso 3: Filtrar los datos para Brasil
brazil_data <- subset(df, COUNTRY == "Brazil")
# Paso 4: Visualizar los datos
str(brazil_data)## tibble [864 × 12] (S3: tbl_df/tbl/data.frame)
## $ COUNTRY : chr [1:864] "Brazil" "Brazil" "Brazil" "Brazil" ...
## $ CODE_TIME : chr [1:864] "JAN2020" "JAN2020" "JAN2020" "JAN2020" ...
## $ TIME : chr [1:864] "January 2020" "January 2020" "January 2020" "January 2020" ...
## $ YEAR : num [1:864] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ MONTH : num [1:864] 1 1 1 1 1 1 1 1 1 1 ...
## $ MONTH_NAME : chr [1:864] "January" "January" "January" "January" ...
## $ PRODUCT : chr [1:864] "Hydro" "Wind" "Solar" "Geothermal" ...
## $ VALUE : num [1:864] 36956 2797 789 0 0 ...
## $ DISPLAY_ORDER : num [1:864] 1 2 3 4 5 6 7 8 9 10 ...
## $ yearToDate : num [1:864] 392364 56480 10642 0 0 ...
## $ previousYearToDate: num [1:864] 41203 4234 452 0 0 ...
## $ share : num [1:864] 0.6905 0.0523 0.0147 0 0 ...head(brazil_data) # Ver primeras filas de los datos## # A tibble: 6 × 12
## COUNTRY CODE_TIME TIME YEAR MONTH MONTH_NAME PRODUCT VALUE DISPLAY_ORDER
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 Brazil JAN2020 January… 2020 1 January Hydro 36956. 1
## 2 Brazil JAN2020 January… 2020 1 January Wind 2797. 2
## 3 Brazil JAN2020 January… 2020 1 January Solar 789. 3
## 4 Brazil JAN2020 January… 2020 1 January Geothe… 0 4
## 5 Brazil JAN2020 January… 2020 1 January Other … 0 5
## 6 Brazil JAN2020 January… 2020 1 January Nuclear 1089. 6
## # ℹ 3 more variables: yearToDate <dbl>, previousYearToDate <dbl>, share <dbl># Paso 5: Definir el umbral (ejemplo: producción de energía superior a 1000 GWh)
umbral <- 1000
print(umbral) # Ver el valor del umbral## [1] 1000# Paso 6: Contar los meses donde la producción supera el umbral
meses_exitosos <- sum(brazil_data$VALUE > umbral, na.rm = TRUE)
print(meses_exitosos) # Ver número de meses con producción superior al umbral## [1] 535# Paso 7: Calcular la tasa de eventos (lambda)
lambda <- mean(brazil_data$VALUE > umbral, na.rm = TRUE) * nrow(brazil_data)
print(lambda) # Ver la tasa de eventos (lambda)## [1] 535# Paso 8: Definir el rango de eventos posibles (0 a un número razonable)
k <- 0:max(meses_exitosos, 10) # Ajustar el límite si es necesario
print(k) # Ver el rango de eventos posibles## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [19] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## [37] 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
## [55] 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
## [73] 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## [91] 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
## [109] 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
## [127] 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
## [145] 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
## [163] 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
## [181] 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
## [199] 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
## [217] 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
## [235] 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
## [253] 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
## [271] 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
## [289] 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
## [307] 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
## [325] 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
## [343] 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
## [361] 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
## [379] 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395
## [397] 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
## [415] 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
## [433] 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449
## [451] 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467
## [469] 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485
## [487] 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503
## [505] 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521
## [523] 522 523 524 525 526 527 528 529 530 531 532 533 534 535# Paso 9: Calcular la distribución de Poisson
poisson_distribution <- dpois(k, lambda)
print(poisson_distribution) # Ver la distribución de Poisson## [1] 4.492129e-233 2.403289e-230 6.428798e-228 1.146469e-225 1.533402e-223
## [6] 1.640740e-221 1.462993e-219 1.118145e-217 7.477595e-216 4.445015e-214
## [11] 2.378083e-212 1.156613e-210 5.156566e-209 2.122125e-207 8.109550e-206
## [16] 2.892406e-204 9.671484e-203 3.043673e-201 9.046472e-200 2.547296e-198
## [21] 6.814017e-197 1.735952e-195 4.221519e-194 9.819621e-193 2.188957e-191
## [26] 4.684369e-190 9.638989e-189 1.909948e-187 3.649365e-186 6.732449e-185
## [31] 1.200620e-183 2.072038e-182 3.464188e-181 5.616184e-180 8.837230e-179
## [36] 1.350834e-177 2.007489e-176 2.902721e-175 4.086725e-174 5.606149e-173
## [41] 7.498224e-172 9.784268e-171 1.246329e-169 1.550666e-168 1.885468e-167
## [46] 2.241612e-166 2.607093e-165 2.967648e-164 3.307691e-163 3.611459e-162
## [51] 3.864261e-161 4.053685e-160 4.170618e-159 4.209964e-158 4.170983e-157
## [56] 4.057229e-156 3.876102e-155 3.638096e-154 3.355830e-153 3.042998e-152
## [61] 2.713340e-151 2.379733e-150 2.053479e-149 1.743828e-148 1.457731e-147
## [66] 1.199825e-146 9.725851e-146 7.766164e-145 6.110144e-144 4.737576e-143
## [71] 3.620861e-142 2.728395e-141 2.027349e-140 1.485797e-139 1.074191e-138
## [76] 7.662564e-138 5.394042e-137 3.747808e-136 2.570612e-135 1.740858e-134
## [81] 1.164198e-133 7.689459e-133 5.016903e-132 3.233787e-131 2.059614e-130
## [86] 1.296345e-129 8.064475e-129 4.959188e-128 3.014961e-127 1.812364e-126
## [91] 1.077350e-125 6.333870e-125 3.683283e-124 2.118878e-123 1.205957e-122
## [96] 6.791443e-122 3.784815e-121 2.087501e-120 1.139605e-119 6.158472e-119
## [101] 3.294782e-118 1.745256e-117 9.154039e-117 4.754768e-116 2.445962e-115
## [106] 1.246276e-114 6.290167e-114 3.145083e-113 1.557981e-112 7.646971e-112
## [111] 3.719209e-111 1.792592e-110 8.562826e-110 4.054081e-109 1.902573e-108
## [116] 8.851102e-108 4.082189e-107 1.866642e-106 8.463165e-106 3.804868e-105
## [121] 1.696337e-104 7.500334e-104 3.289081e-103 1.430616e-102 6.172418e-102
## [126] 2.641795e-101 1.121714e-100 4.725333e-100 1.975041e-99 8.191063e-99
## [131] 3.370937e-98 1.376681e-97 5.579728e-97 2.244477e-96 8.961158e-96
## [136] 3.551274e-95 1.397008e-94 5.455471e-94 2.114983e-93 8.140403e-93
## [141] 3.110797e-92 1.180338e-91 4.447048e-91 1.663756e-90 6.181314e-90
## [146] 2.280692e-89 8.357329e-89 3.041613e-88 1.099502e-87 3.947876e-87
## [151] 1.408076e-86 4.988878e-86 1.755954e-85 6.140100e-85 2.133087e-84
## [156] 7.362590e-84 2.524991e-83 8.604268e-83 2.913470e-82 9.803187e-82
## [161] 3.277940e-81 1.089254e-80 3.597226e-80 1.180685e-79 3.851624e-79
## [166] 1.248860e-78 4.024940e-78 1.289427e-77 4.106210e-77 1.299895e-76
## [171] 4.090846e-76 1.279885e-75 3.981036e-75 1.231130e-74 3.785370e-74
## [176] 1.157242e-73 3.517752e-73 1.063275e-72 3.195799e-72 9.551691e-72
## [181] 2.838975e-71 8.391445e-71 2.466716e-70 7.211437e-70 2.096804e-69
## [186] 6.063730e-69 1.744137e-68 4.989911e-68 1.420001e-67 4.019581e-67
## [191] 1.131829e-66 3.170307e-66 8.833929e-66 2.448783e-65 6.753088e-65
## [196] 1.852770e-64 5.057307e-64 1.373431e-63 3.711038e-63 9.976912e-63
## [201] 2.668824e-62 7.103586e-62 1.881395e-61 4.958357e-61 1.300354e-60
## [206] 3.393606e-60 8.813490e-60 2.277883e-59 5.858977e-59 1.499786e-58
## [211] 3.820883e-58 9.688022e-58 2.444855e-57 6.140832e-57 1.535208e-56
## [216] 3.820169e-56 9.461992e-56 2.332795e-55 5.724979e-55 1.398568e-54
## [221] 3.401063e-54 8.233343e-54 1.984161e-53 4.760208e-53 1.136925e-52
## [226] 2.703354e-52 6.399533e-52 1.508260e-51 3.539119e-51 8.268247e-51
## [231] 1.923266e-50 4.454318e-50 1.027181e-49 2.358549e-49 5.392408e-49
## [236] 1.227633e-48 2.782983e-48 6.282260e-48 1.412189e-47 3.161176e-47
## [241] 7.046787e-47 1.564328e-46 3.458329e-46 7.614017e-46 1.669467e-45
## [246] 3.645570e-45 7.928374e-45 1.717279e-44 3.704615e-44 7.959715e-44
## [251] 1.703379e-43 3.630708e-43 7.708051e-43 1.629963e-42 3.433191e-42
## [256] 7.202969e-42 1.505308e-41 3.133618e-41 6.498005e-41 1.342252e-40
## [261] 2.761942e-40 5.661452e-40 1.156060e-39 2.351681e-39 4.765716e-39
## [266] 9.621352e-39 1.935121e-38 3.877491e-38 7.740513e-38 1.539470e-37
## [271] 3.050431e-37 6.022069e-37 1.184488e-36 2.321249e-36 4.532366e-36
## [276] 8.817512e-36 1.709192e-35 3.301146e-35 6.352926e-35 1.218213e-34
## [281] 2.327658e-34 4.431661e-34 8.407584e-34 1.589420e-33 2.994153e-33
## [286] 5.620603e-33 1.051407e-32 1.959939e-32 3.640859e-32 6.739999e-32
## [291] 1.243414e-31 2.286001e-31 4.188392e-31 7.647747e-31 1.391682e-30
## [296] 2.523897e-30 4.561774e-30 8.217337e-30 1.475260e-29 2.639680e-29
## [301] 4.707429e-29 8.367024e-29 1.482238e-28 2.617152e-28 4.605844e-28
## [306] 8.079103e-28 1.412523e-27 2.461563e-27 4.275767e-27 7.403026e-27
## [311] 1.277619e-26 2.197833e-26 3.768721e-26 6.441743e-26 1.097558e-25
## [316] 1.864107e-25 3.156003e-25 5.326378e-25 8.961045e-25 1.502871e-24
## [321] 2.512613e-24 4.187688e-24 6.957804e-24 1.152454e-23 1.902971e-23
## [326] 3.132584e-23 5.140896e-23 8.410947e-23 1.371907e-22 2.230913e-22
## [331] 3.616784e-22 5.845859e-22 9.420285e-22 1.513469e-21 2.424269e-21
## [336] 3.871595e-21 6.164592e-21 9.786519e-21 1.549050e-20 2.444665e-20
## [341] 3.846753e-20 6.035228e-20 9.441073e-20 1.472587e-19 2.290216e-19
## [346] 3.551494e-19 5.491472e-19 8.466678e-19 1.301630e-18 1.995336e-18
## [351] 3.050013e-18 4.648880e-18 7.065769e-18 1.070874e-17 1.618412e-17
## [356] 2.439015e-17 3.665374e-17 5.492927e-17 8.208704e-17 1.223303e-16
## [361] 1.817964e-16 2.694212e-16 3.981778e-16 5.868460e-16 8.625346e-16
## [366] 1.264263e-15 1.848035e-15 2.694002e-15 3.916552e-15 5.678469e-15
## [371] 8.210759e-15 1.184031e-14 1.702841e-14 2.442412e-14 3.493825e-14
## [376] 4.984524e-14 7.092341e-14 1.006473e-13 1.424505e-13 2.010845e-13
## [381] 2.831058e-13 3.975370e-13 5.567600e-13 7.777196e-13 1.083542e-12
## [386] 1.505701e-12 2.086917e-12 2.885014e-12 3.978048e-12 5.471094e-12
## [391] 7.505218e-12 1.026929e-11 1.401548e-11 1.907960e-11 2.590758e-11
## [396] 3.509002e-11 4.740696e-11 6.388596e-11 8.587686e-11 1.151482e-10
## [401] 1.540107e-10 2.054756e-10 2.734563e-10 3.630251e-10 4.807387e-10
## [406] 6.350499e-10 8.368269e-10 1.100006e-09 1.442410e-09 1.886770e-09
## [411] 2.462005e-09 3.204800e-09 4.161573e-09 5.390900e-09 6.966501e-09
## [416] 8.980911e-09 1.154997e-08 1.481831e-08 1.896601e-08 2.421675e-08
## [421] 3.084752e-08 3.920054e-08 4.969736e-08 6.285600e-08 7.931123e-08
## [426] 9.983884e-08 1.253845e-07 1.570976e-07 1.963720e-07 2.448929e-07
## [431] 3.046923e-07 3.782143e-07 4.683904e-07 5.787272e-07 7.134079e-07
## [436] 8.774097e-07 1.076638e-06 1.318081e-06 1.609985e-06 1.962054e-06
## [441] 2.385680e-06 2.894192e-06 3.503151e-06 4.230667e-06 5.097764e-06
## [446] 6.128772e-06 7.351778e-06 8.799108e-06 1.050786e-05 1.252051e-05
## [451] 1.488549e-05 1.765795e-05 2.090045e-05 2.468376e-05 2.908769e-05
## [456] 3.420201e-05 4.012736e-05 4.697623e-05 5.487398e-05 6.395986e-05
## [461] 7.438810e-05 8.632893e-05 9.996965e-05 1.155157e-04 1.331916e-04
## [466] 1.532419e-04 1.759322e-04 2.015498e-04 2.304041e-04 2.628277e-04
## [471] 2.991763e-04 3.398287e-04 3.851871e-04 4.356768e-04 4.917449e-04
## [476] 5.538601e-04 6.225108e-04 6.982039e-04 7.814625e-04 8.728235e-04
## [481] 9.728345e-04 1.082051e-03 1.201032e-03 1.330335e-03 1.470515e-03
## [486] 1.622115e-03 1.785661e-03 1.961661e-03 2.150591e-03 2.352896e-03
## [491] 2.568978e-03 2.799192e-03 3.043837e-03 3.303150e-03 3.577298e-03
## [496] 3.866373e-03 4.170382e-03 4.489244e-03 4.822782e-03 5.170718e-03
## [501] 5.532669e-03 5.908139e-03 6.296523e-03 6.697097e-03 7.109021e-03
## [506] 7.531339e-03 7.962978e-03 8.402747e-03 8.849350e-03 9.301380e-03
## [511] 9.757330e-03 1.021560e-02 1.067450e-02 1.113228e-02 1.158710e-02
## [516] 1.203708e-02 1.248031e-02 1.291483e-02 1.333867e-02 1.374989e-02
## [521] 1.414652e-02 1.452665e-02 1.488843e-02 1.523004e-02 1.554975e-02
## [526] 1.584594e-02 1.611707e-02 1.636173e-02 1.657864e-02 1.676668e-02
## [531] 1.692486e-02 1.705235e-02 1.714851e-02 1.721286e-02 1.724509e-02
## [536] 1.724509e-02# Paso 10: Graficar la distribución de Poisson
barplot(poisson_distribution, names.arg = k,
main = "Distribución de Poisson de la Producción de Energía en Brasil",
xlab = "Número de Meses con Producción > 1000 GWh",
ylab = "Probabilidad",
col = "lightgreen")
# Paso 1: Cargar las librerías necesarias
library(readr)
library(ggplot2)
# Paso 2: Cargar los datos
df <- read_csv("dataset_energy.csv")## Rows: 47159 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Paso 3: Filtrar los datos para Brasil
brazil_data <- subset(df, COUNTRY == "Brazil")
# Paso 4: Calcular la media y desviación estándar de la variable VALUE
mean_brazil <- mean(brazil_data$VALUE, na.rm = TRUE)
sd_brazil <- sd(brazil_data$VALUE, na.rm = TRUE)
# Ver la media y la desviación estándar
print(mean_brazil) # Mostrar la media## [1] 12319.11print(sd_brazil) # Mostrar la desviación estándar## [1] 18427.41# Paso 5: Crear una secuencia de valores para la curva normal
x_values <- seq(mean_brazil - 4 * sd_brazil, mean_brazil + 4 * sd_brazil, length.out = 100)
y_values <- dnorm(x_values, mean = mean_brazil, sd = sd_brazil)
# Ver las primeras filas de x_values y y_values
head(x_values) # Mostrar los primeros valores de x_values## [1] -61390.55 -59901.46 -58412.38 -56923.29 -55434.21 -53945.13head(y_values) # Mostrar los primeros valores de y_values## [1] 7.262561e-09 1.000115e-08 1.368277e-08 1.859783e-08 2.511392e-08
## [6] 3.369232e-08# Paso 6: Crear un dataframe para ggplot
normal_df <- data.frame(x_values, y_values)
# Paso 7: Graficar la distribución normal
p <- ggplot(normal_df, aes(x = x_values, y = y_values)) +
geom_line(color = "blue") +
labs(title = "Distribución Normal para Brasil",
x = "Valores de Producción de Energía (GWh)",
y = "Densidad") +
theme_minimal()
# Paso 8: Añadir la media y la desviación estándar en la gráfica
p <- p +
geom_vline(xintercept = mean_brazil, color = "red", linetype = "dashed", size = 1) +
geom_vline(xintercept = mean_brazil + sd_brazil, color = "green", linetype = "dashed", size = 1) +
geom_vline(xintercept = mean_brazil - sd_brazil, color = "green", linetype = "dashed", size = 1)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Mostrar el gráfico
print(p)
# Cargar librerías necesarias
library(readr)
library(ggplot2)
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Paso 1: Cargar los datos
df <- read_csv("dataset_energy.csv")## Rows: 47159 Columns: 12## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Paso 2: Filtrar los datos para Brasil
brazil_data <- subset(df, COUNTRY == "Brazil")
# Paso 3: Calcular la media y desviación estándar
population_mean <- mean(brazil_data$VALUE, na.rm = TRUE)
population_sd <- sd(brazil_data$VALUE, na.rm = TRUE)
# Paso 4: Simular distribuciones muestrales
set.seed(123) # Para reproducibilidad
sample_size <- 40
num_samples <- 1000
# Generar medias de las muestras
sample_means <- replicate(num_samples, {
sample_data <- sample(brazil_data$VALUE, sample_size, replace = TRUE)
mean(sample_data)
})
# Paso 5: Graficar la distribución muestral de la media
ggplot(data.frame(sample_means), aes(x = sample_means)) +
geom_histogram(bins = 30, fill = "lightgreen", color = "black", alpha = 0.7) +
geom_vline(aes(xintercept = mean(sample_means)), color = "red", linetype = "dashed", size = 1) +
labs(title = "Distribución Muestral de la Media (Brasil)",
x = "Media Muestral",
y = "Frecuencia") +
theme_minimal()
# Paso 6: Graficar la distribución normal
x_values <- seq(population_mean - 4 * population_sd, population_mean + 4 * population_sd, by = 0.1)
y_values <- dnorm(x_values, mean = population_mean, sd = population_sd)
normal_df <- data.frame(x_values, y_values)
ggplot(normal_df, aes(x = x_values, y = y_values)) +
geom_line(color = "blue") +
labs(title = "Distribución Normal (Brasil)",
x = "Valores",
y = "Densidad") +
theme_minimal()
# Paso 7: Graficar la distribución chi-cuadrado
degrees_of_freedom <- sample_size - 1
chi_square_values <- seq(0, 60, by = 0.1)
chi_square_y_values <- dchisq(chi_square_values, df = degrees_of_freedom)
chi_square_df <- data.frame(chi_square_values, chi_square_y_values)
ggplot(chi_square_df, aes(x = chi_square_values, y = chi_square_y_values)) +
geom_line(color = "orange") +
labs(title = paste("Distribución Chi-Cuadrado (df =", degrees_of_freedom, ")"),
x = "Valores",
y = "Densidad") +
theme_minimal()
head(sample_means)## [1] 16397.871 11623.662 7626.163 11939.368 12195.383 9944.622head(normal_df)## x_values y_values
## 1 -61390.55 7.262561e-09
## 2 -61390.45 7.262719e-09
## 3 -61390.35 7.262877e-09
## 4 -61390.25 7.263034e-09
## 5 -61390.15 7.263192e-09
## 6 -61390.05 7.263350e-09head(chi_square_df)## chi_square_values chi_square_y_values
## 1 0.0 0.000000e+00
## 2 0.1 1.463320e-41
## 3 0.2 5.160355e-36
## 4 0.3 8.884913e-33
## 5 0.4 1.731032e-30
## 6 0.5 1.021940e-28# Asegurarse de que las librerías necesarias estén cargadas
library(ggplot2)
# Paso 1: Calcular la tasa de ocurrencia (lambda) para la distribución exponencial
lambda <- 1 / mean(brazil_data$VALUE, na.rm = TRUE)
# Ver el valor de lambda
print(lambda)## [1] 8.117469e-05# Paso 2: Simular distribuciones muestrales exponenciales
sample_expo_means <- replicate(num_samples, {
sample_data <- rexp(sample_size, rate = lambda)
mean(sample_data)
})
# Ver las primeras seis medias muestrales exponenciales
head(sample_expo_means)## [1] 10465.135 9696.541 11142.449 14242.650 11139.138 7997.707# Ver el resumen estadístico de las medias muestrales exponenciales
summary(sample_expo_means)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7485 10954 12180 12268 13537 18623# Ver todas las medias muestrales exponenciales generadas
print(sample_expo_means)## [1] 10465.135 9696.541 11142.449 14242.650 11139.138 7997.707 15239.533
## [8] 10467.813 11768.712 11038.702 12282.691 11288.453 13213.361 11118.560
## [15] 10295.349 8423.661 11879.311 11991.985 14886.762 13038.035 10704.134
## [22] 11993.778 14682.574 11717.000 10671.805 16666.525 10581.075 14476.725
## [29] 13768.158 12965.178 14159.568 12184.818 12495.910 15708.201 13466.330
## [36] 13115.602 11079.326 13168.511 13671.319 12164.138 12791.668 11100.909
## [43] 11122.219 12388.480 12368.820 12680.592 11108.979 8222.021 12881.650
## [50] 12274.348 14020.949 13540.050 11939.921 11831.025 14675.052 15004.868
## [57] 13191.706 14053.585 13321.407 10526.317 11804.556 15343.544 11228.380
## [64] 9511.394 12229.057 14959.586 13910.398 12734.967 11491.297 11145.406
## [71] 11862.190 8640.799 14393.066 13301.196 14580.367 13158.321 16369.155
## [78] 14283.562 9253.689 10681.362 15737.470 12939.804 12459.005 8881.237
## [85] 13742.169 9661.518 17205.863 11633.616 13692.442 9689.093 11326.424
## [92] 13984.287 14266.847 15012.583 11178.544 9285.883 11524.713 13223.095
## [99] 11974.671 10598.585 15179.245 13281.084 9821.461 15484.967 10671.312
## [106] 13744.144 13095.062 9779.546 13757.341 12525.973 14273.023 12061.873
## [113] 12234.724 11907.532 14922.445 10826.781 10098.810 13040.992 12716.625
## [120] 12671.261 9188.386 15556.131 12393.384 10748.777 8146.978 14938.779
## [127] 14060.654 10889.790 11469.285 15850.345 12787.938 12983.612 12507.047
## [134] 10229.854 12713.243 12537.771 8628.239 12743.208 14478.519 11267.320
## [141] 11710.322 10975.428 11062.724 12892.232 11689.921 11304.696 11007.250
## [148] 14087.573 10643.227 12114.234 10389.227 12201.575 11235.509 12991.486
## [155] 11710.267 15237.459 10379.215 14304.821 11840.898 12720.503 11170.421
## [162] 13914.829 10938.537 12781.860 11547.343 12572.839 12351.154 13709.598
## [169] 12866.355 15359.124 10755.460 15014.674 10747.327 12848.109 9173.030
## [176] 11183.631 8341.526 12305.442 13929.431 11302.836 13277.581 10104.351
## [183] 11772.783 8846.189 11605.037 10989.275 13104.740 13281.305 12162.360
## [190] 11375.155 11660.734 13360.901 10963.217 14232.013 12052.372 11498.057
## [197] 12790.670 10345.822 12386.869 12700.554 15295.820 11801.221 13593.020
## [204] 11581.631 12312.481 14411.553 14747.186 15363.351 11493.927 10873.056
## [211] 9802.060 10953.160 11097.601 10650.121 11230.301 10088.170 10831.855
## [218] 11763.266 13699.709 11329.639 13003.161 9831.010 13862.491 11966.899
## [225] 13168.812 9941.656 9672.964 12325.753 11802.190 13718.330 15534.715
## [232] 14886.122 11301.513 11863.262 14072.646 10997.458 13918.948 10941.821
## [239] 13827.270 10220.260 15462.342 15374.880 13295.536 16180.839 13205.488
## [246] 14075.563 11053.390 14876.017 14537.941 10099.849 10275.369 10738.475
## [253] 13533.586 10882.697 10374.302 12762.149 9459.372 12612.782 11787.688
## [260] 13538.108 14650.726 11307.416 12143.438 14426.791 11990.092 11439.329
## [267] 14673.938 14348.758 14186.755 15842.350 15070.211 11787.243 13004.080
## [274] 11438.004 9828.104 9854.445 12231.680 11679.623 11679.446 11512.702
## [281] 10396.429 12660.583 13033.384 11049.775 12188.749 11254.923 14346.557
## [288] 10853.454 10954.730 10683.119 13913.652 12391.448 12857.155 12690.137
## [295] 10916.054 13869.975 13550.646 8720.365 13766.829 11324.303 14391.716
## [302] 10827.040 11463.995 15162.855 8746.382 11668.080 13684.081 14464.628
## [309] 12174.344 11793.817 15459.046 12261.812 13449.582 14914.576 10306.402
## [316] 12425.555 13537.102 12072.393 11107.385 13929.817 11125.530 14928.841
## [323] 12451.022 12000.057 11368.028 14495.546 16521.880 10360.933 15500.904
## [330] 14128.516 13132.539 10877.240 13803.073 9974.511 13231.687 16190.626
## [337] 13047.577 9685.692 11423.798 13357.255 12332.560 16284.632 14024.465
## [344] 11502.204 12095.565 14781.454 11136.494 13461.476 12982.369 11229.934
## [351] 10657.578 12096.409 13505.839 13344.724 10935.083 13801.551 12108.252
## [358] 11931.764 11609.374 10716.551 12459.375 13922.409 11129.121 15761.785
## [365] 11776.702 12275.276 13856.153 11409.496 12564.998 11964.031 12489.368
## [372] 16103.931 11368.264 12438.896 13689.244 11068.707 10744.852 11610.203
## [379] 8295.724 15445.635 13020.991 12700.295 16018.337 15421.581 12427.105
## [386] 10120.020 11905.139 8809.645 12731.052 13986.319 12139.654 11436.067
## [393] 10685.049 10431.621 11972.017 13237.331 14468.957 10198.810 13137.658
## [400] 16140.687 11889.632 11319.331 11927.556 14827.271 13295.973 11131.331
## [407] 9534.522 15324.408 13611.069 11471.282 12341.446 12791.643 13795.516
## [414] 12827.054 13983.575 11244.553 12980.452 10767.069 12634.409 10940.470
## [421] 12611.769 11223.460 9156.236 11153.679 12687.217 13088.063 11132.499
## [428] 13992.944 11284.097 12324.961 13550.537 10629.722 14704.718 10573.912
## [435] 11727.528 15565.679 14177.134 9927.053 10649.828 8856.628 13087.742
## [442] 10291.286 10223.594 11842.990 13804.537 15493.274 11171.000 12148.342
## [449] 9300.641 12315.698 8550.172 11348.035 9112.767 11352.817 10640.593
## [456] 14531.559 13065.422 15315.942 9552.892 14832.892 11949.869 14001.811
## [463] 13151.663 15453.778 12730.614 14367.385 14014.079 9642.312 11371.212
## [470] 13990.255 11526.033 13695.907 11535.378 12344.313 15691.127 11194.149
## [477] 13746.683 9400.163 9152.646 11670.786 9808.803 9180.928 13969.691
## [484] 10403.312 12977.511 13711.958 14704.205 13258.599 13922.706 11174.206
## [491] 11684.214 10717.638 13990.394 13069.501 13323.089 17081.071 14519.036
## [498] 8890.852 15055.407 11148.100 9382.174 13893.822 9938.456 10494.314
## [505] 10871.132 12679.974 13483.855 10194.719 12968.809 12412.867 17017.782
## [512] 10557.372 13976.060 10558.377 11154.452 13004.228 13509.683 13740.905
## [519] 8769.204 13158.911 12223.459 12716.148 9356.392 14280.063 13838.816
## [526] 13313.067 12166.271 13608.342 12551.521 12095.155 12023.113 7831.248
## [533] 12883.507 17058.522 11691.758 11877.738 8382.383 10147.285 15324.270
## [540] 11604.328 10474.721 12483.714 12537.569 14007.719 11912.003 12238.200
## [547] 12993.162 11184.111 11754.134 13824.926 9521.993 10792.132 12935.497
## [554] 11679.089 9090.440 11205.224 11910.127 8561.338 9640.244 8711.061
## [561] 8963.092 13731.654 17295.014 15184.760 13829.644 9192.806 11952.829
## [568] 11199.573 11496.068 14886.352 13905.371 9614.706 10401.532 11405.161
## [575] 9795.203 10655.294 13456.520 12911.442 13285.811 13846.972 9427.675
## [582] 13903.774 9705.031 13651.616 10953.952 10221.397 12334.510 9839.071
## [589] 14809.910 13164.835 11717.289 13020.828 8477.837 10020.080 10590.003
## [596] 10981.823 11827.315 12805.795 14576.856 11594.457 9867.684 12678.596
## [603] 11936.918 13919.005 9603.794 12365.302 13765.750 13102.001 7952.825
## [610] 14162.404 12482.848 12425.044 10915.783 9431.144 12916.564 10808.599
## [617] 9602.609 14541.520 11621.755 12058.424 16007.827 11371.026 12877.001
## [624] 12831.208 14234.561 11845.430 14364.640 11674.154 13435.584 16472.028
## [631] 13023.962 11051.707 14519.450 15478.279 13946.685 12880.916 9901.710
## [638] 11557.340 10817.197 11413.604 11806.576 12089.464 11856.452 10200.209
## [645] 10504.606 14343.583 14035.230 15110.142 10355.275 9539.682 15679.350
## [652] 13590.254 14708.679 10431.708 10704.296 10142.264 11055.623 16873.823
## [659] 12881.968 11850.509 11546.578 13032.720 16076.755 13349.623 12996.455
## [666] 16560.659 11348.549 12159.834 10891.628 13184.647 15721.137 10617.871
## [673] 12562.443 11646.445 10749.918 13063.196 11208.575 12762.720 13061.217
## [680] 9964.445 12498.350 11659.476 8988.599 13046.309 12665.600 12740.902
## [687] 9588.066 10929.185 10689.767 11303.769 16436.500 13268.874 12346.312
## [694] 11079.738 12904.605 9419.030 12803.702 10229.901 15711.546 11145.961
## [701] 10820.852 12704.962 12671.567 12329.778 11910.282 10757.470 10204.027
## [708] 13701.339 12326.052 12313.826 10890.962 15381.413 14379.223 13115.548
## [715] 9603.566 11196.094 11183.591 12330.082 15968.331 14665.623 10409.301
## [722] 11174.451 10324.860 8767.544 10344.824 11157.428 10157.399 14140.765
## [729] 14860.144 12171.751 11627.462 10706.854 10211.837 11688.352 10623.294
## [736] 14620.989 11115.574 11054.775 13919.387 12080.606 13023.690 12476.615
## [743] 12235.585 11390.247 11034.847 12973.198 12348.101 10069.393 12203.675
## [750] 12325.122 11976.862 11029.602 13056.499 12439.016 9218.504 11399.123
## [757] 13722.652 10464.342 11701.910 11952.096 11443.061 10363.104 8460.113
## [764] 12496.782 15251.550 14122.570 9350.976 13935.336 15505.878 7485.097
## [771] 15066.024 11683.017 10368.942 10173.996 13395.507 11089.888 11772.025
## [778] 11145.561 10750.919 14132.710 12906.603 13172.477 13937.361 14186.998
## [785] 13668.712 10507.656 11641.248 11957.352 12286.342 11930.632 12707.604
## [792] 10290.696 8482.095 10438.184 12055.826 12315.593 8437.808 12669.070
## [799] 12357.679 13089.613 12064.297 9521.194 9649.775 11502.655 14210.173
## [806] 14551.136 11243.671 10614.024 12068.078 14578.133 13540.009 11712.752
## [813] 12798.821 11869.261 16370.835 9310.218 10099.469 10601.132 12615.912
## [820] 10215.576 11763.893 10471.937 12575.307 9467.828 9140.180 9276.532
## [827] 12121.716 10643.322 14331.977 14977.405 12961.005 9705.850 12662.189
## [834] 14530.608 13391.364 9698.340 9420.736 13427.494 10737.067 9977.981
## [841] 13158.191 14799.331 11662.789 12188.389 13185.104 11394.692 12638.391
## [848] 12200.603 12137.916 11689.054 17064.398 9578.266 13139.327 9227.055
## [855] 15544.989 11363.850 9353.662 12996.019 12560.702 13220.861 13095.199
## [862] 13948.787 15603.720 10321.236 10506.897 15773.136 10975.011 11127.435
## [869] 18622.990 14084.320 10596.770 12467.410 10077.226 11315.766 11106.072
## [876] 13221.139 12893.983 11686.309 14450.882 11394.890 13483.125 13064.117
## [883] 12802.934 12498.871 10518.103 10535.486 9923.702 10872.433 13284.595
## [890] 14472.675 18549.156 13055.853 12648.497 11353.964 11775.275 9516.876
## [897] 15115.430 10818.124 13370.849 13481.386 14079.820 12850.878 11162.680
## [904] 10096.860 13173.202 13670.283 12435.792 10700.533 10854.050 12420.678
## [911] 14391.619 16555.862 10382.594 10008.669 11890.706 10885.127 13244.693
## [918] 9520.100 11241.367 12827.164 12156.681 11837.802 11016.490 16120.937
## [925] 10858.480 13383.480 11415.777 17254.501 10968.194 11929.001 13840.870
## [932] 13606.075 9397.906 14340.609 11211.517 13293.632 11480.027 10502.219
## [939] 16782.124 10496.471 10644.412 13348.328 14744.748 8596.108 8231.976
## [946] 12778.259 14328.689 12907.610 13434.086 11029.609 12786.806 16401.186
## [953] 12375.408 9920.108 11484.563 10103.831 8050.441 9962.448 11513.096
## [960] 16509.576 12497.468 13281.481 13811.542 11288.223 12042.994 12192.643
## [967] 12437.258 12301.140 13816.098 11281.300 11705.012 13951.372 12501.364
## [974] 11357.577 10024.475 12355.431 11752.379 9014.587 12804.334 12588.679
## [981] 10417.169 11243.524 17328.811 13589.074 14779.666 12432.270 11566.848
## [988] 10806.121 12420.164 10315.003 13032.503 15459.722 17888.469 14563.118
## [995] 10764.128 14628.558 10917.267 8795.014 10870.578 13757.931# Paso 3: Graficar la distribución muestral de la media exponencial
ggplot(data.frame(sample_expo_means), aes(x = sample_expo_means)) +
geom_histogram(bins = 30, fill = "lightblue", color = "black", alpha = 0.7) +
geom_vline(aes(xintercept = mean(sample_expo_means)), color = "red", linetype = "dashed", size = 1) +
labs(title = "Distribución Muestral de la Media Exponencial (Brasil)",
x = "Media Muestral",
y = "Frecuencia") +
theme_minimal()
# Paso 1: Cargar las librerías necesarias
library(readr)
library(ggplot2)
# Paso 2: Cargar los datos
df <- read_csv("dataset_energy.csv")## Rows: 47159 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): COUNTRY, CODE_TIME, TIME, MONTH_NAME, PRODUCT
## dbl (7): YEAR, MONTH, VALUE, DISPLAY_ORDER, yearToDate, previousYearToDate, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Paso 3: Filtrar los datos para Brasil
brazil_data <- subset(df, COUNTRY == "Brazil")
# Paso 4: Calcular la probabilidad de éxito (p) para la distribución geométrica
success_prob <- mean(brazil_data$VALUE, na.rm = TRUE) / max(brazil_data$VALUE, na.rm = TRUE)
# Mostrar la probabilidad de éxito
print(success_prob) # Mostrar la probabilidad de éxito## [1] 0.2183413# Paso 5: Simular distribuciones muestrales geométricas
set.seed(123) # Para reproducibilidad
sample_size <- 40
num_samples <- 1000
sample_geom_means <- replicate(num_samples, {
sample_data <- rgeom(sample_size, prob = success_prob)
mean(sample_data)
})
# Paso 6: Ver las primeras filas de las medias muestrales geométricas
head(sample_geom_means) # Mostrar los primeros valores de sample_geom_means## [1] 4.700 2.800 3.575 2.875 2.750 3.375# Paso 7: Graficar la distribución muestral de la media geométrica
ggplot(data.frame(sample_geom_means), aes(x = sample_geom_means)) +
geom_histogram(bins = 30, fill = "lightcoral", color = "black", alpha = 0.7) +
geom_vline(aes(xintercept = mean(sample_geom_means)), color = "red", linetype = "dashed", size = 1) +
labs(title = "Distribución Muestral de la Media Geométrica (Brasil)",
x = "Media Muestral",
y = "Frecuencia") +
theme_minimal()