Homework_10
Alexander Kissonergis
2024-04-03
- Using a for loop, write a function to calculate the number of zeroes in a numeric vector. Before entering the loop, set up a counter variable counter <- 0. Inside the loop, add 1 to counter each time you have a zero in the vector. Finally, use return(counter) for the output.
library(ggplot2)
#setting counter to zero
counter <- 0
#creating requested function
counting_zeroes <- function(vect) {
counter <- 0
for (num in vect) {
if (num == 0) {
counter <- counter + 1
}
}
return(counter)
}
#testing 1 2 1 2
exvector <- c(0,2,3,0,0,3,3)
ex2vector <-c(0,2,1,1,1,1,1)
counting_zeroes(exvector)## [1] 3counting_zeroes(ex2vector)## [1] 1#this confirms it is working#2: Use subsetting instead of a loop to rewrite the function as a single line of code.
#resetting counter to 0 just to make sure
counter <- 0
counting_zeroes_2 <-function(vec) {
#this just tells the # of 0s in the vector using the sum function
sum(vec ==0)
}
#testing with same vectors as before
counting_zeroes_2(exvector)## [1] 3counting_zeroes_2(ex2vector)## [1] 1#3: Write a function that takes as input two integers representing the number of rows and columns in a matrix. The output is a matrix of these dimensions in which each element is the product of the row number x the column number.
#creating the function
matrix_dimensions <-function(int1,int2){
#below, I am creating sequences of numbers that are equal to the inputs of int1 and int2 respectively
y <- 1:int1
z<- 1:int2
#This outer function essentially mulitplying the row # by column #, as ditinguished by the third argument
matrix <- outer(y, z, "*")
return(matrix)
}
#testing, seems like it works.
matrix_dimensions(5,7)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1 2 3 4 5 6 7
## [2,] 2 4 6 8 10 12 14
## [3,] 3 6 9 12 15 18 21
## [4,] 4 8 12 16 20 24 28
## [5,] 5 10 15 20 25 30 35#testing again, and seems like it works again.
matrix_dimensions(17,44)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 2 3 4 5 6 7 8 9 10 11 12 13
## [2,] 2 4 6 8 10 12 14 16 18 20 22 24 26
## [3,] 3 6 9 12 15 18 21 24 27 30 33 36 39
## [4,] 4 8 12 16 20 24 28 32 36 40 44 48 52
## [5,] 5 10 15 20 25 30 35 40 45 50 55 60 65
## [6,] 6 12 18 24 30 36 42 48 54 60 66 72 78
## [7,] 7 14 21 28 35 42 49 56 63 70 77 84 91
## [8,] 8 16 24 32 40 48 56 64 72 80 88 96 104
## [9,] 9 18 27 36 45 54 63 72 81 90 99 108 117
## [10,] 10 20 30 40 50 60 70 80 90 100 110 120 130
## [11,] 11 22 33 44 55 66 77 88 99 110 121 132 143
## [12,] 12 24 36 48 60 72 84 96 108 120 132 144 156
## [13,] 13 26 39 52 65 78 91 104 117 130 143 156 169
## [14,] 14 28 42 56 70 84 98 112 126 140 154 168 182
## [15,] 15 30 45 60 75 90 105 120 135 150 165 180 195
## [16,] 16 32 48 64 80 96 112 128 144 160 176 192 208
## [17,] 17 34 51 68 85 102 119 136 153 170 187 204 221
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## [1,] 14 15 16 17 18 19 20 21 22 23 24 25
## [2,] 28 30 32 34 36 38 40 42 44 46 48 50
## [3,] 42 45 48 51 54 57 60 63 66 69 72 75
## [4,] 56 60 64 68 72 76 80 84 88 92 96 100
## [5,] 70 75 80 85 90 95 100 105 110 115 120 125
## [6,] 84 90 96 102 108 114 120 126 132 138 144 150
## [7,] 98 105 112 119 126 133 140 147 154 161 168 175
## [8,] 112 120 128 136 144 152 160 168 176 184 192 200
## [9,] 126 135 144 153 162 171 180 189 198 207 216 225
## [10,] 140 150 160 170 180 190 200 210 220 230 240 250
## [11,] 154 165 176 187 198 209 220 231 242 253 264 275
## [12,] 168 180 192 204 216 228 240 252 264 276 288 300
## [13,] 182 195 208 221 234 247 260 273 286 299 312 325
## [14,] 196 210 224 238 252 266 280 294 308 322 336 350
## [15,] 210 225 240 255 270 285 300 315 330 345 360 375
## [16,] 224 240 256 272 288 304 320 336 352 368 384 400
## [17,] 238 255 272 289 306 323 340 357 374 391 408 425
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## [1,] 26 27 28 29 30 31 32 33 34 35 36 37
## [2,] 52 54 56 58 60 62 64 66 68 70 72 74
## [3,] 78 81 84 87 90 93 96 99 102 105 108 111
## [4,] 104 108 112 116 120 124 128 132 136 140 144 148
## [5,] 130 135 140 145 150 155 160 165 170 175 180 185
## [6,] 156 162 168 174 180 186 192 198 204 210 216 222
## [7,] 182 189 196 203 210 217 224 231 238 245 252 259
## [8,] 208 216 224 232 240 248 256 264 272 280 288 296
## [9,] 234 243 252 261 270 279 288 297 306 315 324 333
## [10,] 260 270 280 290 300 310 320 330 340 350 360 370
## [11,] 286 297 308 319 330 341 352 363 374 385 396 407
## [12,] 312 324 336 348 360 372 384 396 408 420 432 444
## [13,] 338 351 364 377 390 403 416 429 442 455 468 481
## [14,] 364 378 392 406 420 434 448 462 476 490 504 518
## [15,] 390 405 420 435 450 465 480 495 510 525 540 555
## [16,] 416 432 448 464 480 496 512 528 544 560 576 592
## [17,] 442 459 476 493 510 527 544 561 578 595 612 629
## [,38] [,39] [,40] [,41] [,42] [,43] [,44]
## [1,] 38 39 40 41 42 43 44
## [2,] 76 78 80 82 84 86 88
## [3,] 114 117 120 123 126 129 132
## [4,] 152 156 160 164 168 172 176
## [5,] 190 195 200 205 210 215 220
## [6,] 228 234 240 246 252 258 264
## [7,] 266 273 280 287 294 301 308
## [8,] 304 312 320 328 336 344 352
## [9,] 342 351 360 369 378 387 396
## [10,] 380 390 400 410 420 430 440
## [11,] 418 429 440 451 462 473 484
## [12,] 456 468 480 492 504 516 528
## [13,] 494 507 520 533 546 559 572
## [14,] 532 546 560 574 588 602 616
## [15,] 570 585 600 615 630 645 660
## [16,] 608 624 640 656 672 688 704
## [17,] 646 663 680 697 714 731 748#now realizing you may have wanted a forloop. I will email you. #4a: Simulate a dataset with 3 groups of data, each group drawn from a distribution with a different mean. The final data frame should have 1 column for group and 1 column for the response variable.
#setting seed for reproducibility
set.seed(487)
#setting the size for each
size <- 1000
#creating the means for each group
means <- c(147, 465, 765)
#creating the groups, each with 1000 observations
groups <- rep(1:3, each = size)
#creating the distributions around each mean using rnorm
cell_count <- c(rnorm(size, means[1], 3),
rnorm(size, means[2], 3),
rnorm(size, means[3], 3)
)
#creating the actual dataframe
z <- data.frame(Group = groups, Cell_Count = cell_count)
#let's see
print(z)## Group Cell_Count
## 1 1 147.9520
## 2 1 149.2385
## 3 1 143.4585
## 4 1 151.8173
## 5 1 144.0203
## 6 1 147.3182
## 7 1 147.4601
## 8 1 151.3212
## 9 1 146.2643
## 10 1 148.1393
## 11 1 144.6566
## 12 1 149.4381
## 13 1 147.0516
## 14 1 142.2583
## 15 1 146.6764
## 16 1 149.7816
## 17 1 139.9553
## 18 1 146.9587
## 19 1 149.4394
## 20 1 144.6255
## 21 1 148.1561
## 22 1 150.9236
## 23 1 149.4836
## 24 1 139.4259
## 25 1 143.4339
## 26 1 146.8229
## 27 1 150.3677
## 28 1 146.5538
## 29 1 151.6160
## 30 1 149.8578
## 31 1 151.5846
## 32 1 142.5675
## 33 1 146.1937
## 34 1 148.1734
## 35 1 141.5909
## 36 1 152.3183
## 37 1 141.3950
## 38 1 149.5178
## 39 1 148.9442
## 40 1 146.3608
## 41 1 146.8692
## 42 1 145.0417
## 43 1 146.9748
## 44 1 148.3269
## 45 1 145.7795
## 46 1 148.4403
## 47 1 148.0260
## 48 1 144.8543
## 49 1 143.4784
## 50 1 144.5636
## 51 1 145.8103
## 52 1 148.2592
## 53 1 149.0375
## 54 1 148.0327
## 55 1 152.6881
## 56 1 141.2943
## 57 1 146.3652
## 58 1 140.6077
## 59 1 150.8787
## 60 1 148.2476
## 61 1 149.6346
## 62 1 147.0994
## 63 1 143.5555
## 64 1 147.2957
## 65 1 151.5087
## 66 1 146.3384
## 67 1 146.0121
## 68 1 147.9927
## 69 1 152.2752
## 70 1 145.7988
## 71 1 145.1723
## 72 1 145.5752
## 73 1 141.7815
## 74 1 144.9038
## 75 1 148.6396
## 76 1 146.6282
## 77 1 148.2362
## 78 1 141.9688
## 79 1 145.3292
## 80 1 143.5371
## 81 1 148.7871
## 82 1 147.3245
## 83 1 145.6699
## 84 1 146.6599
## 85 1 140.0229
## 86 1 146.7838
## 87 1 144.5478
## 88 1 146.8516
## 89 1 145.0140
## 90 1 144.2426
## 91 1 148.0028
## 92 1 143.7419
## 93 1 144.0112
## 94 1 147.5165
## 95 1 149.1176
## 96 1 147.7769
## 97 1 153.9695
## 98 1 149.1003
## 99 1 149.5198
## 100 1 142.9510
## 101 1 147.6543
## 102 1 147.0584
## 103 1 148.3296
## 104 1 145.6190
## 105 1 151.0340
## 106 1 145.3024
## 107 1 151.3738
## 108 1 149.3315
## 109 1 148.4672
## 110 1 149.6360
## 111 1 150.7754
## 112 1 145.6371
## 113 1 152.9905
## 114 1 145.8886
## 115 1 148.2548
## 116 1 147.5689
## 117 1 142.9527
## 118 1 148.6690
## 119 1 149.6421
## 120 1 146.4552
## 121 1 149.8167
## 122 1 144.5927
## 123 1 148.6829
## 124 1 148.5609
## 125 1 148.0905
## 126 1 147.5798
## 127 1 148.4374
## 128 1 150.1404
## 129 1 148.8060
## 130 1 149.5370
## 131 1 147.4547
## 132 1 143.6386
## 133 1 148.3645
## 134 1 146.8698
## 135 1 145.7917
## 136 1 150.0816
## 137 1 145.2574
## 138 1 139.5068
## 139 1 147.6718
## 140 1 149.1039
## 141 1 154.5831
## 142 1 150.6302
## 143 1 147.0895
## 144 1 144.6646
## 145 1 144.3944
## 146 1 145.7282
## 147 1 148.4125
## 148 1 147.2970
## 149 1 145.4419
## 150 1 146.7921
## 151 1 149.3057
## 152 1 144.5657
## 153 1 146.8230
## 154 1 148.6717
## 155 1 146.7799
## 156 1 144.7498
## 157 1 149.8842
## 158 1 143.4784
## 159 1 149.6316
## 160 1 146.3967
## 161 1 144.4438
## 162 1 143.4110
## 163 1 147.8785
## 164 1 150.6738
## 165 1 139.6396
## 166 1 149.7568
## 167 1 140.5594
## 168 1 145.6185
## 169 1 155.5272
## 170 1 147.5802
## 171 1 145.5420
## 172 1 149.7834
## 173 1 146.2837
## 174 1 146.3779
## 175 1 147.9546
## 176 1 147.1937
## 177 1 152.3727
## 178 1 147.1001
## 179 1 148.6258
## 180 1 145.7340
## 181 1 146.4508
## 182 1 148.3992
## 183 1 143.6699
## 184 1 147.1525
## 185 1 143.7123
## 186 1 146.1897
## 187 1 147.1052
## 188 1 150.1839
## 189 1 149.4669
## 190 1 141.7576
## 191 1 151.9274
## 192 1 146.2960
## 193 1 141.9046
## 194 1 147.0781
## 195 1 153.2019
## 196 1 142.8850
## 197 1 146.5506
## 198 1 142.3572
## 199 1 145.6122
## 200 1 147.5235
## 201 1 149.0658
## 202 1 148.4667
## 203 1 139.6748
## 204 1 153.4796
## 205 1 146.7828
## 206 1 148.9593
## 207 1 150.0671
## 208 1 147.2529
## 209 1 151.4985
## 210 1 144.8870
## 211 1 147.9550
## 212 1 143.8163
## 213 1 146.0115
## 214 1 141.7876
## 215 1 145.1762
## 216 1 149.8850
## 217 1 148.5274
## 218 1 146.5094
## 219 1 150.6780
## 220 1 147.8215
## 221 1 147.3515
## 222 1 143.7092
## 223 1 143.0384
## 224 1 144.9672
## 225 1 142.9036
## 226 1 147.1561
## 227 1 149.7565
## 228 1 144.9958
## 229 1 146.5921
## 230 1 144.4928
## 231 1 154.3869
## 232 1 148.3483
## 233 1 151.0437
## 234 1 148.3962
## 235 1 145.5307
## 236 1 142.9429
## 237 1 147.9228
## 238 1 146.0758
## 239 1 147.8974
## 240 1 148.8553
## 241 1 143.8086
## 242 1 144.2201
## 243 1 146.1491
## 244 1 143.7053
## 245 1 148.7877
## 246 1 150.7936
## 247 1 147.0109
## 248 1 144.8703
## 249 1 142.5335
## 250 1 145.5548
## 251 1 146.7045
## 252 1 148.3907
## 253 1 150.4102
## 254 1 140.6979
## 255 1 146.3201
## 256 1 145.8644
## 257 1 146.7653
## 258 1 150.2382
## 259 1 150.9061
## 260 1 147.6521
## 261 1 150.6088
## 262 1 146.3593
## 263 1 140.8390
## 264 1 148.7236
## 265 1 142.5797
## 266 1 149.4772
## 267 1 154.7931
## 268 1 153.2182
## 269 1 147.7541
## 270 1 145.8801
## 271 1 149.2848
## 272 1 150.7197
## 273 1 145.6605
## 274 1 143.3028
## 275 1 147.3666
## 276 1 148.4120
## 277 1 144.0809
## 278 1 143.4851
## 279 1 148.1413
## 280 1 144.8034
## 281 1 142.7912
## 282 1 145.6499
## 283 1 147.5062
## 284 1 145.6027
## 285 1 145.0784
## 286 1 149.7036
## 287 1 142.4010
## 288 1 151.4620
## 289 1 144.5456
## 290 1 144.7910
## 291 1 153.2156
## 292 1 148.5563
## 293 1 146.1794
## 294 1 145.1455
## 295 1 150.9458
## 296 1 148.5486
## 297 1 149.8199
## 298 1 146.1884
## 299 1 148.1961
## 300 1 142.4724
## 301 1 150.3148
## 302 1 141.2328
## 303 1 144.8060
## 304 1 147.0690
## 305 1 152.1671
## 306 1 149.2539
## 307 1 148.6845
## 308 1 147.6940
## 309 1 146.2059
## 310 1 153.5225
## 311 1 144.5108
## 312 1 144.5310
## 313 1 143.8855
## 314 1 148.4445
## 315 1 144.3092
## 316 1 147.6726
## 317 1 143.2699
## 318 1 146.3929
## 319 1 146.2615
## 320 1 147.7387
## 321 1 144.8587
## 322 1 149.3735
## 323 1 151.1545
## 324 1 149.9008
## 325 1 139.1884
## 326 1 143.4346
## 327 1 147.9128
## 328 1 148.6518
## 329 1 142.7872
## 330 1 144.0357
## 331 1 147.2879
## 332 1 153.4365
## 333 1 151.1495
## 334 1 150.9076
## 335 1 149.1011
## 336 1 144.4353
## 337 1 144.6012
## 338 1 150.0236
## 339 1 148.4448
## 340 1 145.1928
## 341 1 144.6690
## 342 1 147.2634
## 343 1 146.8621
## 344 1 149.9165
## 345 1 149.9311
## 346 1 147.0826
## 347 1 144.6150
## 348 1 147.5166
## 349 1 147.0404
## 350 1 143.8261
## 351 1 150.4565
## 352 1 149.9458
## 353 1 148.4808
## 354 1 147.8481
## 355 1 149.7095
## 356 1 146.6543
## 357 1 149.3303
## 358 1 151.4792
## 359 1 142.3966
## 360 1 142.8496
## 361 1 142.8682
## 362 1 145.1431
## 363 1 146.0012
## 364 1 147.7451
## 365 1 150.1506
## 366 1 152.9871
## 367 1 152.4609
## 368 1 144.7300
## 369 1 145.5793
## 370 1 145.5690
## 371 1 145.1203
## 372 1 148.2109
## 373 1 145.2317
## 374 1 146.0693
## 375 1 145.8542
## 376 1 151.1971
## 377 1 149.6704
## 378 1 147.7490
## 379 1 144.3854
## 380 1 147.7910
## 381 1 143.6877
## 382 1 151.2997
## 383 1 154.4853
## 384 1 145.4649
## 385 1 142.7753
## 386 1 148.4794
## 387 1 149.7794
## 388 1 148.8866
## 389 1 154.3028
## 390 1 150.4439
## 391 1 148.4315
## 392 1 141.3974
## 393 1 149.3981
## 394 1 148.8554
## 395 1 151.6148
## 396 1 146.0989
## 397 1 150.1650
## 398 1 145.7339
## 399 1 144.2768
## 400 1 147.0215
## 401 1 147.2075
## 402 1 145.8685
## 403 1 148.7870
## 404 1 140.3743
## 405 1 143.2270
## 406 1 150.9084
## 407 1 149.0114
## 408 1 146.0071
## 409 1 142.7322
## 410 1 148.0297
## 411 1 140.0817
## 412 1 142.5577
## 413 1 140.2881
## 414 1 150.1634
## 415 1 146.2592
## 416 1 148.6875
## 417 1 148.6150
## 418 1 151.1662
## 419 1 148.2996
## 420 1 147.3425
## 421 1 148.7984
## 422 1 145.0124
## 423 1 150.5285
## 424 1 145.7741
## 425 1 145.1765
## 426 1 139.3806
## 427 1 149.0765
## 428 1 144.4653
## 429 1 145.7778
## 430 1 150.8543
## 431 1 142.9770
## 432 1 151.3836
## 433 1 140.9419
## 434 1 150.2476
## 435 1 145.2386
## 436 1 142.9107
## 437 1 147.6680
## 438 1 148.1862
## 439 1 147.4566
## 440 1 146.5490
## 441 1 144.2026
## 442 1 146.4554
## 443 1 150.0078
## 444 1 144.6752
## 445 1 143.3746
## 446 1 147.2398
## 447 1 145.6287
## 448 1 144.8921
## 449 1 144.3311
## 450 1 140.1696
## 451 1 152.4061
## 452 1 145.3601
## 453 1 148.4516
## 454 1 145.6456
## 455 1 151.1895
## 456 1 146.0843
## 457 1 149.0243
## 458 1 152.0896
## 459 1 143.0301
## 460 1 143.9403
## 461 1 147.7337
## 462 1 147.6303
## 463 1 146.7055
## 464 1 149.1687
## 465 1 146.3406
## 466 1 144.9925
## 467 1 144.3920
## 468 1 145.8478
## 469 1 147.6500
## 470 1 146.1453
## 471 1 146.6632
## 472 1 149.3625
## 473 1 146.3075
## 474 1 148.2435
## 475 1 143.0734
## 476 1 144.1129
## 477 1 145.8406
## 478 1 149.1339
## 479 1 145.9564
## 480 1 148.9481
## 481 1 144.8046
## 482 1 148.8606
## 483 1 148.9599
## 484 1 140.3507
## 485 1 151.1910
## 486 1 150.2747
## 487 1 151.9161
## 488 1 148.0189
## 489 1 146.3862
## 490 1 146.1599
## 491 1 142.6397
## 492 1 149.5131
## 493 1 145.8029
## 494 1 144.9579
## 495 1 150.1177
## 496 1 142.3325
## 497 1 150.0431
## 498 1 152.5291
## 499 1 146.0948
## 500 1 143.7696
## 501 1 150.4856
## 502 1 145.2744
## 503 1 148.9920
## 504 1 148.9917
## 505 1 141.1830
## 506 1 148.1534
## 507 1 146.8590
## 508 1 148.1899
## 509 1 149.3993
## 510 1 152.9750
## 511 1 150.8214
## 512 1 146.9415
## 513 1 144.1449
## 514 1 144.1022
## 515 1 142.9906
## 516 1 155.3915
## 517 1 150.1279
## 518 1 150.3018
## 519 1 143.2855
## 520 1 147.9720
## 521 1 149.0120
## 522 1 144.9128
## 523 1 148.4010
## 524 1 144.7480
## 525 1 151.3674
## 526 1 147.1327
## 527 1 147.7418
## 528 1 146.1559
## 529 1 145.3207
## 530 1 150.8559
## 531 1 147.8870
## 532 1 146.7457
## 533 1 154.2843
## 534 1 143.6451
## 535 1 146.5423
## 536 1 146.7260
## 537 1 149.7136
## 538 1 146.2153
## 539 1 146.0333
## 540 1 150.8617
## 541 1 147.5177
## 542 1 151.9226
## 543 1 145.6865
## 544 1 150.0149
## 545 1 146.1117
## 546 1 144.2849
## 547 1 150.3173
## 548 1 147.9385
## 549 1 148.9207
## 550 1 141.6345
## 551 1 145.7875
## 552 1 145.4428
## 553 1 146.1997
## 554 1 147.7805
## 555 1 147.8918
## 556 1 151.7230
## 557 1 147.8104
## 558 1 148.8021
## 559 1 146.5203
## 560 1 146.4026
## 561 1 147.1515
## 562 1 152.2627
## 563 1 145.3770
## 564 1 144.3554
## 565 1 150.4487
## 566 1 146.3600
## 567 1 148.5224
## 568 1 143.2585
## 569 1 147.0045
## 570 1 146.6063
## 571 1 142.0318
## 572 1 142.2559
## 573 1 150.9169
## 574 1 148.2406
## 575 1 144.5800
## 576 1 148.0159
## 577 1 146.9910
## 578 1 144.7217
## 579 1 152.2027
## 580 1 145.8902
## 581 1 152.2306
## 582 1 146.6733
## 583 1 144.3207
## 584 1 150.2133
## 585 1 151.1457
## 586 1 149.3772
## 587 1 147.9058
## 588 1 150.1439
## 589 1 146.2015
## 590 1 147.2326
## 591 1 147.6898
## 592 1 146.7439
## 593 1 150.3931
## 594 1 147.1809
## 595 1 142.1160
## 596 1 150.5306
## 597 1 148.7870
## 598 1 146.3854
## 599 1 145.5547
## 600 1 149.7426
## 601 1 148.1360
## 602 1 144.5544
## 603 1 149.7674
## 604 1 145.3516
## 605 1 153.2843
## 606 1 150.6737
## 607 1 142.3974
## 608 1 148.7969
## 609 1 146.6645
## 610 1 145.7058
## 611 1 149.3367
## 612 1 149.3435
## 613 1 148.1692
## 614 1 144.0490
## 615 1 146.1189
## 616 1 150.1029
## 617 1 146.9218
## 618 1 145.5391
## 619 1 145.3482
## 620 1 148.8555
## 621 1 149.9064
## 622 1 151.1396
## 623 1 148.1741
## 624 1 145.3427
## 625 1 148.5091
## 626 1 146.5362
## 627 1 149.5309
## 628 1 146.1041
## 629 1 143.2385
## 630 1 148.0706
## 631 1 151.9876
## 632 1 145.1908
## 633 1 146.1289
## 634 1 149.3331
## 635 1 144.0959
## 636 1 146.7381
## 637 1 148.3865
## 638 1 145.3007
## 639 1 145.0613
## 640 1 146.2497
## 641 1 151.4200
## 642 1 140.9974
## 643 1 147.3544
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## 2998 3 764.3172
## 2999 3 765.5532
## 3000 3 767.3819#everything seems to be working#4b: Write a custom function that 1) reshuffles the response variable, and 2) calculates the mean of each group in the reshuffled data. Store the means in a vector of length 3.
#creating custom function "reshuffle"
reshuffle <- function(dataframe){
reshuffled_cell_counts <- sample(dataframe$Cell_Count)
#this is dual purposed,to initially create and then adjust for the new means
reshuffled_means <- 3
#a for loop to iterate through the creation of the means
for (i in 1:3){
reshuffled_means[i] <- mean(reshuffled_cell_counts[dataframe$Group == i])
}
#this needs to be outside of the second nest or it will only return the first group.
return(reshuffled_means)
}
zreshuffled <- reshuffle(z)
print(zreshuffled)## [1] 455.8343 457.8905 463.5304#4c: Use a for loop to repeat the function in b 100 times. Store the results in a data frame that has 1 column indicating the replicate number and 1 column for each new group mean, for a total of 4 columns.
#first creating an empty df
hundred_replicates <- data.frame(Replicate = 1:100,
Group1 = 100,
Group2 = 100,
Group3 = 100)
#this is the for loop to create all of the additional replicates
for (i in 1:100){
#using reshuffle function during iteration
additional_means <- reshuffle(z)
#updates each column 2:4 during iteration
hundred_replicates[i, 2:4] <- additional_means
}
print(hundred_replicates)## Replicate Group1 Group2 Group3
## 1 1 450.7707 460.7572 465.7273
## 2 2 454.3532 455.7325 467.1695
## 3 3 458.8935 460.4801 457.8815
## 4 4 461.9083 460.5924 454.7545
## 5 5 465.6724 460.7907 450.7921
## 6 6 459.1980 453.5768 464.4804
## 7 7 466.3592 463.6931 447.2029
## 8 8 463.3662 463.7625 450.1265
## 9 9 466.7351 449.2371 461.2831
## 10 10 449.6354 470.5569 457.0628
## 11 11 467.5708 464.8188 444.8656
## 12 12 455.0292 455.3351 466.8909
## 13 13 460.7638 462.7972 453.6942
## 14 14 467.5484 450.7534 458.9534
## 15 15 464.2756 452.9221 460.0575
## 16 16 456.6316 456.3636 464.2599
## 17 17 457.9606 463.4468 455.8479
## 18 18 453.7259 463.6921 459.8372
## 19 19 456.4504 451.9394 468.8654
## 20 20 461.1933 448.6675 467.3944
## 21 21 450.8398 463.6092 462.8062
## 22 22 462.3393 456.0704 458.8455
## 23 23 473.4277 452.0531 451.7744
## 24 24 460.8693 453.2374 463.1485
## 25 25 463.1010 462.3196 451.8346
## 26 26 455.0308 462.2735 459.9509
## 27 27 447.8983 464.3239 465.0330
## 28 28 449.9679 463.5370 463.7503
## 29 29 454.5544 465.4337 457.2671
## 30 30 465.1228 455.0706 457.0618
## 31 31 460.1822 457.8412 459.2317
## 32 32 462.9539 449.3632 464.9381
## 33 33 464.8250 454.6871 457.7431
## 34 34 459.9126 455.4127 461.9300
## 35 35 457.4573 454.1004 465.6975
## 36 36 460.9528 457.6030 458.6993
## 37 37 464.2862 457.9278 455.0412
## 38 38 455.7979 457.7642 463.6932
## 39 39 466.2583 462.2077 448.7892
## 40 40 463.8137 452.7709 460.6706
## 41 41 458.0243 455.2461 463.9848
## 42 42 454.1666 456.0754 467.0132
## 43 43 458.2711 465.4264 453.5578
## 44 44 474.5161 450.5973 452.1418
## 45 45 455.7991 463.1384 458.3178
## 46 46 464.9486 455.3602 456.9464
## 47 47 462.6386 457.2351 457.3815
## 48 48 470.2557 456.5232 450.4763
## 49 49 460.7563 464.9563 451.5426
## 50 50 446.2491 465.5316 465.4745
## 51 51 464.7088 463.6064 448.9400
## 52 52 454.2040 463.3572 459.6939
## 53 53 450.7324 463.1167 463.4061
## 54 54 451.5066 460.9489 464.7998
## 55 55 462.7263 461.3241 453.2048
## 56 56 460.0563 460.4229 456.7760
## 57 57 457.6163 460.6570 458.9819
## 58 58 461.2211 466.6071 449.4269
## 59 59 453.1826 468.0695 456.0032
## 60 60 449.0856 460.8894 467.2802
## 61 61 466.7949 456.2401 454.2201
## 62 62 455.9698 458.9384 462.3470
## 63 63 458.7242 458.4270 460.1041
## 64 64 463.8329 464.3102 449.1122
## 65 65 459.2486 458.8944 459.1122
## 66 66 457.3674 467.7459 452.1419
## 67 67 453.5335 459.3490 464.3726
## 68 68 455.6286 455.1236 466.5031
## 69 69 452.9175 454.2998 470.0379
## 70 70 457.2769 470.6496 449.3287
## 71 71 476.4561 452.3947 448.4043
## 72 72 449.0217 457.6383 470.5952
## 73 73 467.8073 472.2416 437.2063
## 74 74 448.3248 464.9260 464.0043
## 75 75 457.1973 464.6798 455.3781
## 76 76 467.2232 453.0054 457.0266
## 77 77 458.2800 453.4171 465.5581
## 78 78 461.5982 465.8406 449.8164
## 79 79 437.7116 467.8455 471.6981
## 80 80 451.8104 453.4095 472.0353
## 81 81 461.8661 454.2103 461.1788
## 82 82 446.5000 473.3350 457.4202
## 83 83 462.6478 453.0255 461.5820
## 84 84 461.3395 457.7956 458.1201
## 85 85 452.8812 461.1477 463.2263
## 86 86 454.4720 454.0943 468.6888
## 87 87 461.5548 454.4315 461.2689
## 88 88 462.8849 459.7752 454.5951
## 89 89 457.6076 459.5045 460.1431
## 90 90 469.3216 448.8108 459.1228
## 91 91 462.9314 466.4680 447.8558
## 92 92 458.1505 462.6030 456.5017
## 93 93 462.3619 458.5365 456.3568
## 94 94 464.5930 477.9356 434.7266
## 95 95 465.6830 453.7130 457.8592
## 96 96 445.3910 466.7413 465.1229
## 97 97 461.2680 450.1208 465.8665
## 98 98 468.5460 458.4571 450.2521
## 99 99 461.2990 456.6931 459.2631
## 100 100 466.2562 457.0272 453.9718#4d: Use qplot() to create a histogram of the means for each reshuffled group. Or, if you want a challenge, use ggplot() to overlay all 3 histograms in the same figure. How do the distributions of reshuffled means compare to the original means?
#for whatever reason I cannot figure out how to get the outline done on this for the boxes
qplot(data = hundred_replicates, x = Group1, fill = "Group1", geom = "histogram", bins = 20) +
labs(x = "Mean", y = "Frequency", title = "Histogram of Reshuffled Means (Group 1)")## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
qplot(data = hundred_replicates, x = Group2, fill = "Group2", geom = "histogram", bins = 20) +
labs(x = "Mean", y = "Frequency", title = "Histogram of Reshuffled Means (Group 2)")
qplot(data = hundred_replicates, x = Group3, fill = "Group3", geom = "histogram", bins = 20) +
labs(x = "Mean", y = "Frequency", title = "Histogram of Reshuffled Means (Group 3)")