Loading in packages and reading data
library(tidyverse)
library(knitr)
full_data <- read_csv("uncertain.csv") #whole dataset
print(full_data)## # A tibble: 18 × 156
## PID Gender Age Shock `Calibrate 0` `Calibrate 100` `Calibrate 50` A
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 1 18 0.25 -1 -1 0.526 pink
## 2 2 1 19 1.5 -1 -1 0.526 orange
## 3 3 2 19 1.5 -1 -1 0.526 orange
## 4 4 2 18 0.75 -1 -1 0.526 pink
## 5 5 1 18 1.5 -1 -1 0.526 orange
## 6 6 1 22 2 -1 -1 0.526 blue
## 7 7 2 23 1 -1 -1 0.526 yellow
## 8 8 2 19 2 -1 -1 0.526 blue
## 9 9 2 19 1.5 -1 -1 0.526 black
## 10 10 2 18 1.5 -1 -1 0.526 orange
## 11 11 1 23 0.75 -1 -1 0.526 pink
## 12 12 1 18 1.5 -1 -1 0.526 pink
## 13 13 2 20 1.5 -1 -1 0.526 yellow
## 14 14 1 18 1 -1 -1 0.526 blue
## 15 15 1 18 1.5 -1 -1 0.526 orange
## 16 16 1 19 1.5 -1 -1 0.526 orange
## 17 17 1 19 1.5 -1 -1 0.526 orange
## 18 18 1 19 0.75 -1 -1 0.526 black
## # ℹ 148 more variables: B <chr>, C <chr>, D <chr>, E <chr>, TT1 <dbl>,
## # TT2 <dbl>, TT3 <dbl>, TT4 <dbl>, TT5 <dbl>, TT6 <dbl>, TT7 <dbl>,
## # TT8 <dbl>, TT9 <dbl>, TT10 <dbl>, TT11 <dbl>, TT12 <dbl>, TT13 <dbl>,
## # TT14 <dbl>, TT15 <dbl>, TT16 <dbl>, TT17 <dbl>, TT18 <dbl>, TT19 <dbl>,
## # TT20 <dbl>, TT21 <dbl>, TT22 <dbl>, TT23 <dbl>, TT24 <dbl>, TT25 <dbl>,
## # TT26 <dbl>, TT27 <dbl>, TT28 <dbl>, TT29 <dbl>, TT30 <dbl>, TT31 <dbl>,
## # TT32 <dbl>, TT33 <dbl>, TT34 <dbl>, TT35 <dbl>, TT36 <dbl>, Anx1 <dbl>, …Just keeping the columns that are relevant (getting rid of gender, cue colours, calibration, and habituation)
data <- full_data %>%
subset(select = -c(2:12, 13:22, 49:58, 85:94, 121:130)) #removing irrelevant columns
print(data)## # A tibble: 18 × 105
## PID TT11 TT12 TT13 TT14 TT15 TT16 TT17 TT18 TT19 TT20 TT21 TT22
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 4 4 1 3 4 1 3 3 2 4 4 1
## 2 2 1 4 1 1 4 3 2 3 4 4 3 1
## 3 3 4 1 4 3 4 1 3 1 1 1 3 4
## 4 4 1 4 4 3 4 3 1 1 2 2 2 3
## 5 5 4 1 1 1 2 4 4 3 3 2 4 2
## 6 6 4 1 1 4 3 1 2 3 3 4 2 4
## 7 7 4 1 4 3 1 2 1 4 3 1 1 4
## 8 8 3 4 2 1 4 3 2 3 1 2 4 1
## 9 9 4 2 4 2 1 1 1 3 4 1 3 4
## 10 10 3 3 4 1 1 2 1 1 4 3 4 4
## 11 11 4 1 4 3 4 2 1 2 2 3 4 1
## 12 12 2 3 2 1 4 2 4 1 3 4 1 1
## 13 13 1 1 4 1 3 1 4 2 3 4 2 1
## 14 14 1 1 3 1 4 2 2 3 1 4 3 4
## 15 15 1 3 4 4 2 3 4 1 2 3 4 1
## 16 16 4 1 1 1 3 4 4 1 4 2 4 1
## 17 17 2 2 4 3 1 4 1 1 2 1 1 4
## 18 18 4 4 3 4 4 2 1 2 1 4 2 3
## # ℹ 92 more variables: TT23 <dbl>, TT24 <dbl>, TT25 <dbl>, TT26 <dbl>,
## # TT27 <dbl>, TT28 <dbl>, TT29 <dbl>, TT30 <dbl>, TT31 <dbl>, TT32 <dbl>,
## # TT33 <dbl>, TT34 <dbl>, TT35 <dbl>, TT36 <dbl>, Anx11 <dbl>, Anx12 <dbl>,
## # Anx13 <dbl>, Anx14 <dbl>, Anx15 <dbl>, Anx16 <dbl>, Anx17 <dbl>,
## # Anx18 <dbl>, Anx19 <dbl>, Anx20 <dbl>, Anx21 <dbl>, Anx22 <dbl>,
## # Anx23 <dbl>, Anx24 <dbl>, Anx25 <dbl>, Anx26 <dbl>, Anx27 <dbl>,
## # Anx28 <dbl>, Anx29 <dbl>, Anx30 <dbl>, Anx31 <dbl>, Anx32 <dbl>, …Isolating trial type information and anxiety information for each of the trials then combining them together
trial_data <- data %>%
subset(select= c(1, 2:27)) %>% #taking trial type only
rename_at(vars(2:27), ~as.character(11:36)) %>% #renaming columns to just be trial number
pivot_longer(cols = 2:27, names_to = "trial", values_to = "type") #pivoting to get trial number and trial type values
print(trial_data)## # A tibble: 468 × 3
## PID trial type
## <dbl> <chr> <dbl>
## 1 1 11 4
## 2 1 12 4
## 3 1 13 1
## 4 1 14 3
## 5 1 15 4
## 6 1 16 1
## 7 1 17 3
## 8 1 18 3
## 9 1 19 2
## 10 1 20 4
## # ℹ 458 more rowsanx_data <- data %>%
subset(select= c(1, 28:53)) %>% #taking anxiety only
rename_at(vars(2:27), ~as.character(11:36)) %>% #renaming columns to just be trial number
pivot_longer(cols = 2:27, names_to = "trial", values_to = "anx") #pivoting to get trial number and anxiety values
print(anx_data)## # A tibble: 468 × 3
## PID trial anx
## <dbl> <chr> <dbl>
## 1 1 11 37
## 2 1 12 89
## 3 1 13 35
## 4 1 14 18
## 5 1 15 91
## 6 1 16 30
## 7 1 17 89
## 8 1 18 100
## 9 1 19 94
## 10 1 20 89
## # ℹ 458 more rowscomb_data <- cbind(trial_data, anx = anx_data$anx) %>% #combining dataframes into one for later
arrange(PID) #arranging by PID
print(comb_data)## PID trial type anx
## 1 1 11 4 37
## 2 1 12 4 89
## 3 1 13 1 35
## 4 1 14 3 18
## 5 1 15 4 91
## 6 1 16 1 30
## 7 1 17 3 89
## 8 1 18 3 100
## 9 1 19 2 94
## 10 1 20 4 89
## 11 1 21 4 89
## 12 1 22 1 22
## 13 1 23 1 21
## 14 1 24 2 75
## 15 1 25 2 69
## 16 1 26 1 22
## 17 1 27 4 90
## 18 1 28 1 11
## 19 1 29 5 39
## 20 1 30 3 49
## 21 1 31 1 20
## 22 1 32 4 86
## 23 1 33 3 86
## 24 1 34 6 11
## 25 1 35 1 10
## 26 1 36 4 89
## 27 2 11 1 55
## 28 2 12 4 65
## 29 2 13 1 72
## 30 2 14 1 69
## 31 2 15 4 72
## 32 2 16 3 61
## 33 2 17 2 66
## 34 2 18 3 63
## 35 2 19 4 39
## 36 2 20 4 43
## 37 2 21 3 44
## 38 2 22 1 28
## 39 2 23 2 39
## 40 2 24 2 48
## 41 2 25 1 35
## 42 2 26 4 39
## 43 2 27 4 46
## 44 2 28 1 17
## 45 2 29 5 16
## 46 2 30 3 29
## 47 2 31 1 17
## 48 2 32 4 18
## 49 2 33 3 20
## 50 2 34 6 9
## 51 2 35 4 33
## 52 2 36 1 22
## 53 3 11 4 26
## 54 3 12 1 36
## 55 3 13 4 50
## 56 3 14 3 34
## 57 3 15 4 57
## 58 3 16 1 21
## 59 3 17 3 47
## 60 3 18 1 26
## 61 3 19 1 27
## 62 3 20 1 15
## 63 3 21 3 40
## 64 3 22 4 53
## 65 3 23 2 49
## 66 3 24 2 37
## 67 3 25 2 25
## 68 3 26 4 55
## 69 3 27 4 61
## 70 3 28 1 27
## 71 3 29 3 26
## 72 3 30 5 27
## 73 3 31 1 20
## 74 3 32 4 56
## 75 3 33 3 65
## 76 3 34 6 48
## 77 3 35 1 20
## 78 3 36 4 88
## 79 4 11 1 13
## 80 4 12 4 51
## 81 4 13 4 81
## 82 4 14 3 51
## 83 4 15 4 83
## 84 4 16 3 86
## 85 4 17 1 0
## 86 4 18 1 0
## 87 4 19 2 100
## 88 4 20 2 100
## 89 4 21 2 80
## 90 4 22 3 48
## 91 4 23 4 100
## 92 4 24 1 0
## 93 4 25 4 100
## 94 4 26 1 0
## 95 4 27 4 100
## 96 4 28 1 0
## 97 4 29 5 48
## 98 4 30 3 49
## 99 4 31 4 100
## 100 4 32 1 0
## 101 4 33 6 49
## 102 4 34 3 50
## 103 4 35 4 100
## 104 4 36 1 0
## 105 5 11 4 38
## 106 5 12 1 31
## 107 5 13 1 23
## 108 5 14 1 21
## 109 5 15 2 16
## 110 5 16 4 42
## 111 5 17 4 59
## 112 5 18 3 23
## 113 5 19 3 39
## 114 5 20 2 38
## 115 5 21 4 51
## 116 5 22 2 43
## 117 5 23 4 43
## 118 5 24 1 38
## 119 5 25 1 35
## 120 5 26 3 25
## 121 5 27 4 35
## 122 5 28 1 36
## 123 5 29 5 49
## 124 5 30 3 -99
## 125 5 31 4 29
## 126 5 32 1 40
## 127 5 33 3 93
## 128 5 34 6 23
## 129 5 35 1 20
## 130 5 36 4 100
## 131 6 11 4 51
## 132 6 12 1 39
## 133 6 13 1 23
## 134 6 14 4 35
## 135 6 15 3 33
## 136 6 16 1 24
## 137 6 17 2 22
## 138 6 18 3 10
## 139 6 19 3 6
## 140 6 20 4 15
## 141 6 21 2 40
## 142 6 22 4 51
## 143 6 23 1 52
## 144 6 24 4 -99
## 145 6 25 2 51
## 146 6 26 1 63
## 147 6 27 4 33
## 148 6 28 1 51
## 149 6 29 5 51
## 150 6 30 3 29
## 151 6 31 4 7
## 152 6 32 1 51
## 153 6 33 6 52
## 154 6 34 3 12
## 155 6 35 1 27
## 156 6 36 4 0
## 157 7 11 4 15
## 158 7 12 1 35
## 159 7 13 4 50
## 160 7 14 3 32
## 161 7 15 1 16
## 162 7 16 2 50
## 163 7 17 1 14
## 164 7 18 4 50
## 165 7 19 3 16
## 166 7 20 1 4
## 167 7 21 1 3
## 168 7 22 4 12
## 169 7 23 3 15
## 170 7 24 4 22
## 171 7 25 2 24
## 172 7 26 2 25
## 173 7 27 1 0
## 174 7 28 4 24
## 175 7 29 5 49
## 176 7 30 3 22
## 177 7 31 4 24
## 178 7 32 1 0
## 179 7 33 6 50
## 180 7 34 3 78
## 181 7 35 4 100
## 182 7 36 1 0
## 183 8 11 3 30
## 184 8 12 4 15
## 185 8 13 2 16
## 186 8 14 1 11
## 187 8 15 4 8
## 188 8 16 3 9
## 189 8 17 2 6
## 190 8 18 3 6
## 191 8 19 1 6
## 192 8 20 2 5
## 193 8 21 4 5
## 194 8 22 1 4
## 195 8 23 4 5
## 196 8 24 4 3
## 197 8 25 1 3
## 198 8 26 1 3
## 199 8 27 4 2
## 200 8 28 1 4
## 201 8 29 3 2
## 202 8 30 5 3
## 203 8 31 1 0
## 204 8 32 4 2
## 205 8 33 6 0
## 206 8 34 3 51
## 207 8 35 4 100
## 208 8 36 1 0
## 209 9 11 4 100
## 210 9 12 2 100
## 211 9 13 4 89
## 212 9 14 2 41
## 213 9 15 1 42
## 214 9 16 1 52
## 215 9 17 1 21
## 216 9 18 3 20
## 217 9 19 4 93
## 218 9 20 1 57
## 219 9 21 3 88
## 220 9 22 4 80
## 221 9 23 4 90
## 222 9 24 3 59
## 223 9 25 1 12
## 224 9 26 2 41
## 225 9 27 1 88
## 226 9 28 4 71
## 227 9 29 3 59
## 228 9 30 5 100
## 229 9 31 4 43
## 230 9 32 1 75
## 231 9 33 3 78
## 232 9 34 6 53
## 233 9 35 4 100
## 234 9 36 1 0
## 235 10 11 3 4
## 236 10 12 3 4
## 237 10 13 4 6
## 238 10 14 1 5
## 239 10 15 1 5
## 240 10 16 2 7
## 241 10 17 1 7
## 242 10 18 1 7
## 243 10 19 4 8
## 244 10 20 3 9
## 245 10 21 4 7
## 246 10 22 4 6
## 247 10 23 1 6
## 248 10 24 2 6
## 249 10 25 2 9
## 250 10 26 4 6
## 251 10 27 1 5
## 252 10 28 4 7
## 253 10 29 3 6
## 254 10 30 5 6
## 255 10 31 4 6
## 256 10 32 1 7
## 257 10 33 3 70
## 258 10 34 6 31
## 259 10 35 1 51
## 260 10 36 4 65
## 261 11 11 4 0
## 262 11 12 1 8
## 263 11 13 4 31
## 264 11 14 3 19
## 265 11 15 4 38
## 266 11 16 2 30
## 267 11 17 1 9
## 268 11 18 2 7
## 269 11 19 2 0
## 270 11 20 3 2
## 271 11 21 4 28
## 272 11 22 1 3
## 273 11 23 3 20
## 274 11 24 1 0
## 275 11 25 1 3
## 276 11 26 4 21
## 277 11 27 1 0
## 278 11 28 4 25
## 279 11 29 5 28
## 280 11 30 3 24
## 281 11 31 1 0
## 282 11 32 4 15
## 283 11 33 3 -99
## 284 11 34 6 33
## 285 11 35 1 0
## 286 11 36 4 100
## 287 12 11 2 20
## 288 12 12 3 11
## 289 12 13 2 36
## 290 12 14 1 14
## 291 12 15 4 21
## 292 12 16 2 50
## 293 12 17 4 49
## 294 12 18 1 12
## 295 12 19 3 43
## 296 12 20 4 54
## 297 12 21 1 0
## 298 12 22 1 6
## 299 12 23 1 4
## 300 12 24 3 60
## 301 12 25 4 59
## 302 12 26 4 65
## 303 12 27 1 0
## 304 12 28 4 64
## 305 12 29 5 24
## 306 12 30 3 55
## 307 12 31 1 4
## 308 12 32 4 54
## 309 12 33 3 100
## 310 12 34 6 28
## 311 12 35 4 100
## 312 12 36 1 0
## 313 13 11 1 24
## 314 13 12 1 20
## 315 13 13 4 25
## 316 13 14 1 11
## 317 13 15 3 28
## 318 13 16 1 0
## 319 13 17 4 38
## 320 13 18 2 49
## 321 13 19 3 33
## 322 13 20 4 45
## 323 13 21 2 45
## 324 13 22 1 0
## 325 13 23 3 52
## 326 13 24 4 34
## 327 13 25 2 37
## 328 13 26 4 30
## 329 13 27 1 3
## 330 13 28 4 30
## 331 13 29 5 34
## 332 13 30 3 41
## 333 13 31 4 27
## 334 13 32 1 0
## 335 13 33 3 52
## 336 13 34 6 50
## 337 13 35 4 100
## 338 13 36 1 0
## 339 14 11 1 5
## 340 14 12 1 5
## 341 14 13 3 -99
## 342 14 14 1 3
## 343 14 15 4 9
## 344 14 16 2 50
## 345 14 17 2 74
## 346 14 18 3 93
## 347 14 19 1 0
## 348 14 20 4 49
## 349 14 21 3 91
## 350 14 22 4 94
## 351 14 23 4 93
## 352 14 24 1 15
## 353 14 25 4 71
## 354 14 26 2 87
## 355 14 27 4 83
## 356 14 28 1 0
## 357 14 29 3 93
## 358 14 30 5 49
## 359 14 31 1 0
## 360 14 32 4 93
## 361 14 33 3 74
## 362 14 34 6 38
## 363 14 35 4 73
## 364 14 36 1 4
## 365 15 11 1 37
## 366 15 12 3 44
## 367 15 13 4 53
## 368 15 14 4 78
## 369 15 15 2 71
## 370 15 16 3 51
## 371 15 17 4 71
## 372 15 18 1 8
## 373 15 19 2 36
## 374 15 20 3 49
## 375 15 21 4 78
## 376 15 22 1 0
## 377 15 23 1 0
## 378 15 24 1 13
## 379 15 25 4 67
## 380 15 26 2 -99
## 381 15 27 1 0
## 382 15 28 4 75
## 383 15 29 5 39
## 384 15 30 3 46
## 385 15 31 4 66
## 386 15 32 1 5
## 387 15 33 3 51
## 388 15 34 6 50
## 389 15 35 4 99
## 390 15 36 1 0
## 391 16 11 4 12
## 392 16 12 1 15
## 393 16 13 1 16
## 394 16 14 1 5
## 395 16 15 3 22
## 396 16 16 4 0
## 397 16 17 4 32
## 398 16 18 1 0
## 399 16 19 4 62
## 400 16 20 2 79
## 401 16 21 4 91
## 402 16 22 1 3
## 403 16 23 2 71
## 404 16 24 3 67
## 405 16 25 3 86
## 406 16 26 2 89
## 407 16 27 4 89
## 408 16 28 1 0
## 409 16 29 5 55
## 410 16 30 3 70
## 411 16 31 4 95
## 412 16 32 1 0
## 413 16 33 6 49
## 414 16 34 3 80
## 415 16 35 4 100
## 416 16 36 1 0
## 417 17 11 2 0
## 418 17 12 2 0
## 419 17 13 4 12
## 420 17 14 3 0
## 421 17 15 1 -99
## 422 17 16 4 13
## 423 17 17 1 18
## 424 17 18 1 15
## 425 17 19 2 6
## 426 17 20 1 22
## 427 17 21 1 22
## 428 17 22 4 20
## 429 17 23 4 17
## 430 17 24 3 14
## 431 17 25 4 29
## 432 17 26 3 27
## 433 17 27 1 16
## 434 17 28 4 32
## 435 17 29 3 32
## 436 17 30 5 16
## 437 17 31 1 16
## 438 17 32 4 20
## 439 17 33 3 100
## 440 17 34 6 0
## 441 17 35 4 -99
## 442 17 36 1 21
## 443 18 11 4 -99
## 444 18 12 4 -99
## 445 18 13 3 -99
## 446 18 14 4 -99
## 447 18 15 4 -99
## 448 18 16 2 -99
## 449 18 17 1 -99
## 450 18 18 2 -99
## 451 18 19 1 -99
## 452 18 20 4 -99
## 453 18 21 2 -99
## 454 18 22 3 -99
## 455 18 23 1 -99
## 456 18 24 1 -99
## 457 18 25 3 -99
## 458 18 26 1 -99
## 459 18 27 1 -99
## 460 18 28 4 -99
## 461 18 29 3 -99
## 462 18 30 5 -99
## 463 18 31 1 -99
## 464 18 32 4 -99
## 465 18 33 6 -99
## 466 18 34 3 -99
## 467 18 35 4 -99
## 468 18 36 1 -99Separating by type of trial (0%, 50%, 100%, and ambiguous) and then finding mean
mean_0_data <- comb_data %>%
filter(type == 1) %>% #only want 0% cue trials
group_by(PID) %>% #applying mean function to each PID
summarise(mean_0 = mean(anx)) #summarizing mean for anxiety
print(mean_0_data)## # A tibble: 18 × 2
## PID mean_0
## <dbl> <dbl>
## 1 1 21.4
## 2 2 39.4
## 3 3 24
## 4 4 1.62
## 5 5 30.5
## 6 6 41.2
## 7 7 9
## 8 8 3.88
## 9 9 43.4
## 10 10 11.6
## 11 11 2.88
## 12 12 5
## 13 13 7.25
## 14 14 4
## 15 15 7.88
## 16 16 4.88
## 17 17 3.88
## 18 18 -99mean_50_data <- comb_data %>%
filter(type == 2 | type == 3) %>% #only want 50% cue trials
group_by(PID) %>%
summarise(mean_50 = mean(anx))
print(mean_50_data)## # A tibble: 18 × 2
## PID mean_50
## <dbl> <dbl>
## 1 1 72.5
## 2 2 46.2
## 3 3 40.4
## 4 4 70.5
## 5 5 22.2
## 6 6 25.4
## 7 7 32.8
## 8 8 15.6
## 9 9 60.8
## 10 10 14.4
## 11 11 0.375
## 12 12 46.9
## 13 13 42.1
## 14 14 57.9
## 15 15 31.1
## 16 16 70.5
## 17 17 22.4
## 18 18 -99mean_100_data <- comb_data %>%
filter(type == 4) %>% #only want 100% cue trials
group_by(PID) %>%
summarise(mean_100 = mean(anx))
print(mean_100_data)## # A tibble: 18 × 2
## PID mean_100
## <dbl> <dbl>
## 1 1 82.5
## 2 2 44.4
## 3 3 55.8
## 4 4 89.4
## 5 5 49.6
## 6 6 11.6
## 7 7 37.1
## 8 8 17.5
## 9 9 83.2
## 10 10 13.9
## 11 11 32.2
## 12 12 58.2
## 13 13 41.1
## 14 14 70.6
## 15 15 73.4
## 16 16 60.1
## 17 17 5.5
## 18 18 -99mean_amb_data <- comb_data %>%
filter(type == 5) %>% #only want ambiguous cue trials
group_by(PID) %>%
summarise(mean_amb = mean(anx))
print(mean_amb_data)## # A tibble: 18 × 2
## PID mean_amb
## <dbl> <dbl>
## 1 1 39
## 2 2 16
## 3 3 27
## 4 4 48
## 5 5 49
## 6 6 51
## 7 7 49
## 8 8 3
## 9 9 100
## 10 10 6
## 11 11 28
## 12 12 24
## 13 13 34
## 14 14 49
## 15 15 39
## 16 16 55
## 17 17 16
## 18 18 -99#for below - combining all the above datasets
mean_data <- cbind(mean_0_data, mean_50 = mean_50_data$mean_50, mean_100 = mean_100_data$mean_100, mean_amb = mean_amb_data$mean_amb)
kable(mean_data)| PID | mean_0 | mean_50 | mean_100 | mean_amb |
|---|---|---|---|---|
| 1 | 21.375 | 72.500 | 82.500 | 39 |
| 2 | 39.375 | 46.250 | 44.375 | 16 |
| 3 | 24.000 | 40.375 | 55.750 | 27 |
| 4 | 1.625 | 70.500 | 89.375 | 48 |
| 5 | 30.500 | 22.250 | 49.625 | 49 |
| 6 | 41.250 | 25.375 | 11.625 | 51 |
| 7 | 9.000 | 32.750 | 37.125 | 49 |
| 8 | 3.875 | 15.625 | 17.500 | 3 |
| 9 | 43.375 | 60.750 | 83.250 | 100 |
| 10 | 11.625 | 14.375 | 13.875 | 6 |
| 11 | 2.875 | 0.375 | 32.250 | 28 |
| 12 | 5.000 | 46.875 | 58.250 | 24 |
| 13 | 7.250 | 42.125 | 41.125 | 34 |
| 14 | 4.000 | 57.875 | 70.625 | 49 |
| 15 | 7.875 | 31.125 | 73.375 | 39 |
| 16 | 4.875 | 70.500 | 60.125 | 55 |
| 17 | 3.875 | 22.375 | 5.500 | 16 |
| 18 | -99.000 | -99.000 | -99.000 | -99 |
Mean anxiety for cues across participanst (excluding 18 because they used dial incorrectly)
total_mean_data <- mean_data %>%
filter(PID != 18) %>% #taking out PID 18
summarise(mean_0 = mean(mean_0), #mean for each cue
mean_50 = mean(mean_50),
mean_100 = mean(mean_100),
mean_amb = mean(mean_amb))
kable(total_mean_data)| mean_0 | mean_50 | mean_100 | mean_amb |
|---|---|---|---|
| 15.39706 | 39.52941 | 48.60294 | 37.23529 |