If-Else Statement in R with 3 conditions

I am trying to use an if-else statement to create a column in my data set. I want this if-else statement to create a column called "Surgical" in the df "option1" that displays the value of the column "Duration" subtracted by 20 ONLY IF the value in Duration is above 625, AND the factor "Single" is indicated in the column "Variability". I have tried the following code:

option1$Surgical <- ifelse(option1$Variability == "Single", option1$Duration - 20, option1$Duration)

Any insight into how to specify the "only if the value is greater than 625" portion is appreciated!!

Df "option 1" for reference.

dput(option1) structure(list(Stimulus = structure(c(36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 7L, 9L, 12L, 18L, 28L, 26L, 51L, 37L, 3L, 2L, 19L, 14L, 27L, 23L, 65L, 77L, 7L, 9L, 12L, 18L, 28L, 26L, 51L, 37L, 3L, 2L, 19L, 14L, 27L, 23L, 65L, 77L, 5L, 11L, 20L, 16L, 30L, 25L, 35L, 33L, 7L, 9L, 12L, 18L, 28L, 26L, 51L, 37L, 5L, 11L, 20L, 16L, 30L, 25L, 35L, 33L, 7L, 9L, 12L, 18L, 28L, 26L, 51L, 37L), .Label = c("t1_block2_hoed3.mp3", "t1_block2_whod3.mp3", "t1_block2_whod4.mp3", "t1_block2_whod5.mp3", "t1_block3_heed2.mp3", "t1_block3_heed5.mp3", "t1_block3_hoed1.mp3", "t1_block3_hoed2.mp3", "t1_block3_hoed4.mp3", "t1_block3_whod3.mp3", "t1_block4_heed5.mp3", "t2_block1_hoed3.mp3", "t2_block1_whod1.mp3", "t2_block1_whod2.mp3", "t2_block1_whod4.mp3", "t2_block2_heed3.mp3", "t2_block2_hoed5.mp3", "t2_block3_hoed1.mp3", "t2_block3_whod1.mp3", "t2_block4_heed2.mp3", "t2_block4_heed5.mp3", "t3_block1_heed1.mp3", "t3_block1_whod2.mp3", "t3_block1_whod5.mp3", "t3_block2_heed5.mp3", "t3_block2_hoed2.mp3", "t3_block2_whod5.mp3", "t3_block3_hoed1.mp3", "t3_block3_hoed4.mp3", "t3_block4_heed3.mp3", "t4_block1_heed1.mp3", "t4_block1_heed2.mp3", "t4_block1_heed3.mp3", "t4_block1_heed4.mp3", "t4_block1_heed5.mp3", "t4_block1_hoed1.mp3", "t4_block1_hoed2.mp3", "t4_block1_hoed3.mp3", "t4_block1_hoed4.mp3", "t4_block1_hoed5.mp3", "t4_block1_whod1.mp3", "t4_block1_whod2.mp3", "t4_block1_whod3.mp3", "t4_block1_whod4.mp3", "t4_block1_whod5.mp3", "t4_block2_heed1.mp3", "t4_block2_heed2.mp3", "t4_block2_heed4.mp3", "t4_block2_heed5.mp3", "t4_block2_hoed1.mp3", "t4_block2_hoed3.mp3", "t4_block2_hoed4.mp3", "t4_block2_hoed5.mp3", "t4_block2_whod2.mp3", "t4_block2_whod4.mp3", "t4_block2_whod5.mp3", "t4_block3_heed1.mp3", "t4_block3_heed4.mp3", "t4_block3_heed5.mp3", "t4_block3_hoed1.mp3", "t4_block3_hoed2.mp3", "t4_block3_hoed4.mp3", "t4_block3_hoed5.mp3", "t4_block3_whod1.mp3", "t4_block3_whod2.mp3", "t4_block3_whod3.mp3", "t4_block3_whod5.mp3", "t4_block4_heed1.mp3", "t4_block4_heed2.mp3", "t4_block4_heed3.mp3", "t4_block4_heed4.mp3", "t4_block4_heed5.mp3", "t4_block4_hoed1.mp3", "t4_block4_hoed2.mp3", "t4_block4_hoed3.mp3", "t4_block4_whod1.mp3", "t4_block4_whod2.mp3", "t4_block4_whod3.mp3", "t4_block4_whod5.mp3"), class = "factor"), Duration = c(497L, 517L, 580L, 563L, 569L, 486L, 506L, 536L, 545L, 554L, 516L, 600L, 607L, 577L, 537L, 583L, 544L, 566L, 567L, 616L, 652L, 564L, 517L, 612L, 564L, 632L, 662L, 565L, 594L, 622L, 552L, 542L, 539L, 554L, 600L, 607L, 577L, 497L, 517L, 580L, 563L, 569L, 594L, 563L, 623L, 602L, 516L, 600L, 607L, 577L, 531L, 642L, 624L, 566L, 567L, 616L, 652L, 654L, 576L, 556L, 608L, 632L, 662L, 565L, 497L, 517L, 580L, 563L, 569L, 486L, 506L, 536L, 545L, 554L, 516L, 600L, 607L, 577L, 537L, 583L, 544L, 566L, 567L, 616L, 652L, 564L, 517L, 612L, 564L, 632L, 662L, 565L, 594L, 622L, 552L, 542L, 539L, 554L, 600L, 607L, 577L, 497L, 517L, 580L, 563L, 569L, 594L, 563L, 623L, 602L, 516L, 600L, 607L, 577L, 531L, 642L, 624L, 566L, 567L, 616L, 652L, 654L, 576L, 556L, 608L, 632L, 662L, 565L, 452L, 547L, 510L, 663L, 470L, 503L, 600L, 517L, 491L, 505L, 641L, 581L, 520L, 485L, 517L, 622L, 452L, 547L, 510L, 663L, 470L, 503L, 600L, 517L, 491L, 505L, 641L, 581L, 520L, 485L, 517L, 622L, 510L, 458L, 558L, 638L, 483L, 538L, 577L, 600L, 452L, 547L, 510L, 663L, 470L, 503L, 600L, 517L, 510L, 458L, 558L, 638L, 483L, 538L, 577L, 600L, 452L, 547L, 510L, 663L, 470L, 503L, 600L, 517L), F0 = c(196L, 204L, 204L, 197L, 203L, 216L, 208L, 223L, 213L, 219L, 196L, 202L, 205L, 202L, 208L, 205L, 206L, 197L, 202L, 195L, 200L, 201L, 210L, 202L, 208L, 195L, 196L, 195L, 205L, 208L, 203L, 203L, 212L, 213L, 210L, 206L, 204L, 196L, 204L, 204L, 197L, 203L, 201L, 198L, 199L, 203L, 196L, 202L, 205L, 202L, 193L, 195L, 208L, 197L, 202L, 195L, 200L, 201L, 195L, 205L, 202L, 195L, 196L, 195L, 196L, 204L, 204L, 197L, 203L, 216L, 208L, 223L, 213L, 219L, 196L, 202L, 205L, 202L, 208L, 205L, 206L, 197L, 202L, 195L, 200L, 201L, 210L, 202L, 208L, 195L, 196L, 195L, 205L, 208L, 203L, 203L, 212L, 213L, 210L, 206L, 204L, 196L, 204L, 204L, 197L, 203L, 201L, 198L, 199L, 203L, 196L, 202L, 205L, 202L, 193L, 195L, 208L, 197L, 202L, 195L, 200L, 201L, 195L, 205L, 202L, 195L, 196L, 195L, 215L, 219L, 219L, 220L, 199L, 202L, 202L, 204L, 224L, 231L, 238L, 240L, 217L, 212L, 210L, 208L, 215L, 219L, 219L, 220L, 199L, 202L, 202L, 204L, 224L, 231L, 238L, 240L, 217L, 212L, 210L, 208L, 230L, 223L, 219L, 221L, 199L, 200L, 204L, 210L, 215L, 219L, 219L, 220L, 199L, 202L, 202L, 204L, 230L, 223L, 219L, 221L, 199L, 200L, 204L, 210L, 215L, 219L, 219L, 220L, 199L, 202L, 202L, 204L), F1 = c(576L, 553L, 579L, 586L, 601L, 398L, 390L, 398L, 389L, 404L, 587L, 560L, 562L, 553L, 393L, 397L, 382L, 553L, 592L, 556L, 571L, 387L, 392L, 398L, 400L, 580L, 580L, 554L, 403L, 391L, 388L, 393L, 382L, 375L, 384L, 392L, 388L, 576L, 553L, 579L, 586L, 601L, 387L, 393L, 402L, 406L, 587L, 560L, 562L, 553L, 388L, 391L, 412L, 553L, 592L, 556L, 571L, 410L, 404L, 401L, 420L, 580L, 580L, 554L, 576L, 553L, 579L, 586L, 601L, 398L, 390L, 398L, 389L, 404L, 587L, 560L, 562L, 553L, 393L, 397L, 382L, 553L, 592L, 556L, 571L, 387L, 392L, 398L, 400L, 580L, 580L, 554L, 403L, 391L, 388L, 393L, 382L, 375L, 384L, 392L, 388L, 576L, 553L, 579L, 586L, 601L, 387L, 393L, 402L, 406L, 587L, 560L, 562L, 553L, 388L, 391L, 412L, 553L, 592L, 556L, 571L, 410L, 404L, 401L, 420L, 580L, 580L, 554L, 620L, 630L, 602L, 605L, 571L, 573L, 560L, 553L, 434L, 417L, 306L, 319L, 414L, 419L, 392L, 391L, 620L, 630L, 602L, 605L, 571L, 573L, 560L, 553L, 434L, 417L, 306L, 319L, 414L, 419L, 392L, 391L, 448L, 441L, 293L, 291L, 420L, 420L, 388L, 384L, 620L, 630L, 602L, 605L, 571L, 573L, 560L, 553L, 448L, 441L, 293L, 291L, 420L, 420L, 388L, 384L, 620L, 630L, 602L, 605L, 571L, 573L, 560L, 553L ), F2 = c(1339L, 1381L, 1381L, 1347L, 1394L, 1484L, 1521L, 1539L, 1430L, 1454L, 1353L, 1378L, 1325L, 1357L, 1424L, 1563L, 1578L, 1350L, 1397L, 1273L, 1319L, 1548L, 1452L, 1499L, 1515L, 1358L, 1347L, 1248L, 1575L, 1438L, 1414L, 1548L, 3001L, 2916L, 2948L, 2973L, 2947L, 1339L, 1381L, 1381L, 1347L, 1394L, 2943L, 2913L, 2987L, 2940L, 1353L, 1378L, 1325L, 1357L, 3010L, 3008L, 2972L, 1350L, 1397L, 1273L, 1319L, 2963L, 2991L, 3007L, 2989L, 1358L, 1347L, 1248L, 1339L, 1381L, 1381L, 1347L, 1394L, 1484L, 1521L, 1539L, 1430L, 1454L, 1353L, 1378L, 1325L, 1357L, 1424L, 1563L, 1578L, 1350L, 1397L, 1273L, 1319L, 1548L, 1452L, 1499L, 1515L, 1358L, 1347L, 1248L, 1575L, 1438L, 1414L, 1548L, 3001L, 2916L, 2948L, 2973L, 2947L, 1339L, 1381L, 1381L, 1347L, 1394L, 2943L, 2913L, 2987L, 2940L, 1353L, 1378L, 1325L, 1357L, 3010L, 3008L, 2972L, 1350L, 1397L, 1273L, 1319L, 2963L, 2991L, 3007L, 2989L, 1358L, 1347L, 1248L, 1247L, 1244L, 1293L, 1264L, 1348L, 1354L, 1378L, 1381L, 1314L, 1233L, 1190L, 1208L, 1643L, 1659L, 1452L, 1438L, 1247L, 1244L, 1293L, 1264L, 1348L, 1354L, 1378L, 1381L, 1314L, 1233L, 1190L, 1208L, 1643L, 1659L, 1452L, 1438L, 2837L, 2816L, 2780L, 2776L, 2684L, 2718L, 2947L, 2948L, 1247L, 1244L, 1293L, 1264L, 1348L, 1354L, 1378L, 1381L, 2837L, 2816L, 2780L, 2776L, 2684L, 2718L, 2947L, 2948L, 1247L, 1244L, 1293L, 1264L, 1348L, 1354L, 1378L, 1381L), F3 = c(2831L, 2779L, 2915L, 2875L, 2712L, 2730L, 2793L, 2779L, 2772L, 2692L, 2718L, 2856L, 2674L, 2659L, 2717L, 2584L, 2829L, 2726L, 2685L, 2866L, 2793L, 2614L, 2636L, 2907L, 2822L, 2932L, 2882L, 2882L, 2650L, 2929L, 2809L, 2737L, 3623L, 3607L, 3584L, 3576L, 3680L, 2831L, 2779L, 2915L, 2875L, 2712L, 3641L, 3590L, 3556L, 3584L, 2718L, 2856L, 2674L, 2659L, 3640L, 3656L, 3686L, 2726L, 2685L, 2866L, 2793L, 3516L, 3552L, 3513L, 3579L, 2932L, 2882L, 2882L, 2831L, 2779L, 2915L, 2875L, 2712L, 2730L, 2793L, 2779L, 2772L, 2692L, 2718L, 2856L, 2674L, 2659L, 2717L, 2584L, 2829L, 2726L, 2685L, 2866L, 2793L, 2614L, 2636L, 2907L, 2822L, 2932L, 2882L, 2882L, 2650L, 2929L, 2809L, 2737L, 3623L, 3607L, 3584L, 3576L, 3680L, 2831L, 2779L, 2915L, 2875L, 2712L, 3641L, 3590L, 3556L, 3584L, 2718L, 2856L, 2674L, 2659L, 3640L, 3656L, 3686L, 2726L, 2685L, 2866L, 2793L, 3516L, 3552L, 3513L, 3579L, 2932L, 2882L, 2882L, 2711L, 3129L, 2786L, 2833L, 2754L, 2771L, 2856L, 2779L, 2909L, 2750L, 2866L, 2863L, 2804L, 2704L, 2636L, 2929L, 2711L, 3129L, 2786L, 2833L, 2754L, 2771L, 2856L, 2779L, 2909L, 2750L, 2866L, 2863L, 2804L, 2704L, 2636L, 2929L, 3226L, 3121L, 3867L, 3319L, 3426L, 3269L, 3680L, 3357L, 2711L, 3129L, 2786L, 2833L, 2754L, 2771L, 2856L, 2779L, 3226L, 3121L, 3867L, 3319L, 3426L, 3269L, 3680L, 3357L, 2711L, 3129L, 2786L, 2833L, 2754L, 2771L, 2856L, 2779L), Word = structure(c(2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("heed", "hoed", "hoed ", "whod" ), class = "factor"), Vowel = structure(c(2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("i", "o", "o ", "u"), class = "factor"), F1.Mean = c(564L, 564L, 564L, 564L, 564L, 394L, 394L, 394L, 394L, 394L, 564L, 564L, 564L, 564L, 394L, 394L, 394L, 564L, 564L, 564L, 564L, 394L, 394L, 394L, 394L, 564L, 564L, 564L, 394L, 394L, 394L, 394L, 398L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 564L, 564L, 564L, 564L, 394L, 394L, 394L, 394L, 394L, 564L, 564L, 564L, 564L, 394L, 394L, 394L, 564L, 564L, 564L, 564L, 394L, 394L, 394L, 394L, 564L, 564L, 564L, 394L, 394L, 394L, 394L, 398L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 627L, 627L, 614L, 614L, 614L, 614L, 566L, 566L, 432L, 432L, 327L, 327L, 415L, 415L, 393L, 393L, 627L, 627L, 614L, 614L, 614L, 614L, 566L, 566L, 432L, 432L, 327L, 327L, 415L, 415L, 393L, 393L, 397L, 397L, 292L, 292L, 417L, 417L, 398L, 398L, 627L, 627L, 614L, 614L, 614L, 614L, 566L, 566L, 397L, 397L, 292L, 292L, 417L, 417L, 398L, 398L, 627L, 627L, 614L, 614L, 614L, 614L, 566L, 566L), F2.Mean = c(1328L, 1328L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1496L, 1496L, 1328L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1328L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1496L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1496L, 2969L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 1328L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1496L, 1496L, 1328L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1328L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1496L, 1328L, 1328L, 1328L, 1496L, 1496L, 1496L, 1496L, 2969L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 1250L, 1250L, 1247L, 1247L, 1247L, 1247L, 1357L, 1357L, 1292L, 1292L, 1157L, 1157L, 1746L, 1746L, 1455L, 1455L, 1250L, 1250L, 1247L, 1247L, 1247L, 1247L, 1357L, 1357L, 1292L, 1292L, 1157L, 1157L, 1746L, 1746L, 1455L, 1455L, 2828L, 2828L, 2763L, 2763L, 2721L, 2721L, 2969L, 2969L, 1250L, 1250L, 1247L, 1247L, 1247L, 1247L, 1357L, 1357L, 2828L, 2828L, 2763L, 2763L, 2721L, 2721L, 2969L, 2969L, 1250L, 1250L, 1247L, 1247L, 1247L, 1247L, 1357L, 1357L), Distance = c(16L, 54L, 55L, 29L, 76L, 13L, 25L, 43L, 66L, 43L, 34L, 50L, 4L, 31L, 72L, 67L, 83L, 25L, 74L, 56L, 11L, 52L, 44L, 5L, 20L, 34L, 25L, 81L, 80L, 58L, 82L, 52L, 36L, 58L, 25L, 7L, 24L, 16L, 54L, 55L, 29L, 76L, 28L, 56L, 18L, 30L, 34L, 50L, 4L, 31L, 42L, 40L, 14L, 25L, 74L, 56L, 11L, 13L, 23L, 38L, 30L, 34L, 25L, 81L, 16L, 54L, 55L, 29L, 76L, 13L, 25L, 43L, 66L, 43L, 34L, 50L, 4L, 31L, 72L, 67L, 83L, 25L, 74L, 56L, 11L, 52L, 44L, 5L, 20L, 34L, 25L, 81L, 80L, 58L, 82L, 52L, 36L, 58L, 25L, 7L, 24L, 16L, 54L, 55L, 29L, 76L, 28L, 56L, 18L, 30L, 34L, 50L, 4L, 31L, 42L, 40L, 14L, 25L, 74L, 56L, 11L, 13L, 23L, 38L, 30L, 34L, 25L, 81L, 8L, 7L, 48L, 19L, 110L, 115L, 22L, 27L, 22L, 61L, 39L, 52L, 103L, 87L, 3L, 17L, 8L, 7L, 48L, 19L, 110L, 115L, 22L, 27L, 22L, 61L, 39L, 52L, 103L, 87L, 3L, 17L, 52L, 46L, 17L, 13L, 37L, 4L, 24L, 25L, 8L, 7L, 48L, 19L, 110L, 115L, 22L, 27L, 52L, 46L, 17L, 13L, 37L, 4L, 24L, 25L, 8L, 7L, 48L, 19L, 110L, 115L, 22L, 27L), Included = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Yes", class = "factor"), Talker = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L), .Label = c("T1 ", "T2", "T3", "T4"), class = "factor"), Ambiguity = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("High", "Low"), class = "factor"), Variability = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Mixed", "Single"), class = "factor"), Consistency = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Consistent", "Inconsistent"), class = "factor"), Fake = c(477, 497, 560, 543, 549, 466, 486, 516, 525, 534, 496, 580, 587, 557, 517, 563, 524, 546, 547, 596, 632, 544, 497, 592, 544, 612, 642, 545, 574, 602, 532, 522, 519, 534, 580, 587, 557, 477, 497, 560, 543, 549, 574, 543, 603, 582, 496, 580, 587, 557, 511, 622, 604, 546, 547, 596, 632, 634, 556, 536, 588, 612, 642, 545, 477, 497, 560, 543, 549, 466, 486, 516, 525, 534, 496, 580, 587, 557, 517, 563, 524, 546, 547, 596, 632, 544, 497, 592, 544, 612, 642, 545, 574, 602, 532, 522, 519, 534, 580, 587, 557, 477, 497, 560, 543, 549, 574, 543, 603, 582, 496, 580, 587, 557, 511, 622, 604, 546, 547, 596, 632, 634, 556, 536, 588, 612, 642, 545, 452, 547, 510, 663, 470, 503, 600, 517, 491, 505, 641, 581, 520, 485, 517, 622, 452, 547, 510, 663, 470, 503, 600, 517, 491, 505, 641, 581, 520, 485, 517, 622, 510, 458, 558, 638, 483, 538, 577, 600, 452, 547, 510, 663, 470, 503, 600, 517, 510, 458, 558, 638, 483, 538, 577, 600, 452, 547, 510, 663, 470, 503, 600, 517), Check = c(20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -192L), class = "data.frame")