Overlapping Confidence Interval Alphas in ggplot2

Question

I'm trying to visualize the confidence intervals for a set of Monte Carlo draws and am having some issues with my approach using alpha levels.

I'm running a loess regression for each draw and I wanted to try to visualize how the confidence intervals from each draw overlapped by displaying them on top of one another with reduced alphas to produce a gradient effect.
Essentially in the end produce the darkest areas would show where the confidence intervals overlapped the most.

As you can see in the example, the central portions are heavily obscured. While this pattern is to be expected, there isn't as much differentiation shown in the central portions as I need. I tried reducing the alpha level, but the result remained the same. After some looking around I found that ggplot2 doesn't display alphas at levels lower than 0.1.

Edit
As mentioned in the comment, the alpha can in fact be set below 0.1, and from my own quick testing it seems anything set at 0.005 is where it fails to display. Even though this is quite low, I would like to display the alpha at an even lower level or use an alternative approach if possible.

The included sample set is a little long, but in the initial 20 draw sample the problem wasn't as clear. This set shown here includes 50 draws, but the actual analysis consists of 1000 draws, so the effect is even more pronounced.

I'd like to know if there are any workaround to achieve an effect that is similar to what I'm looking for, or if there is another way to visualize this.

Sample plot image set at 0.1

Sample plot image set at 0.006

Sample code

ggplot(data = subset(play, !is.na(y)), 
       aes(x = -x, y = y, group = RunID)) +
  geom_smooth(method = "loess", linetype = 0, alpha = 0.1) +
  scale_x_reverse() +
  theme_minimal()

Reproducible sample

dput(play)
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0.244791666666667, 0, 0.2, 0.166666666666667, 0.184027777777778, 
0.130434782608696, 0.5, 0.191919191919192, 0, 0.4, 0.133333333333333, 
0.2, 0.296296296296296, 0.133333333333333, 0.125, 0, 0.241803278688029, 
0.2, 0.0588235294117647, 0.244791666666667, 0, 0.2, 0.166666666666667, 
0.184027777777778, 0.5, 0.191919191919192, 0.4, 0.133333333333333, 
0.2, 0.296296296296296, 0.133333333333333, 0.241803278688029, 
0.2, 0.0588235294117647, 0.244791666666667, 0, 0.2, 0.166666666666667, 
0.184027777777778, 0.5, 0.191919191919192, 0, 0.4, 0.133333333333333, 
0.2, 0.296296296296296, 0.241803278688029, 0.2, 0.0588235294117647, 
0.244791666666667, 0, 0.2, 0.166666666666667, 0.184027777777778, 
0.5, 0.191919191919192, 0, 0.133333333333333, 0.2, 0.296296296296296, 
0.133333333333333, 0.160493827160494, 0.241803278688029, 0.2, 
0.0588235294117647, 0.244791666666667, 0, 0.2, 0.166666666666667, 
0.184027777777778, 0.5, 0.191919191919192, 0, 0.4, 0.133333333333333, 
0.2, 0.160493827160494, 0.241803278688029, 0.2, 0.0588235294117647, 
0.244791666666667, 0.22884012539185, 0, 0.2, 0.166666666666667, 
0.184027777777778, 0.5, 0.191919191919192, 0, 0.4, 0.133333333333333, 
0.2, 0.296296296296296, 0.133333333333333, 0.241803278688029, 
0.2, 0.0588235294117647, 0.244791666666667, 0, 0.2, 0.193548387096774, 
0.166666666666667, 0.184027777777778, 0.5, 0.191919191919192, 
0, 0.4, 0.2, 0.296296296296296, 0.133333333333333, 0.241803278688029, 
0.2, 0.0588235294117647, 0.244791666666667, 0, 0.2, 0.166666666666667, 
0.184027777777778, 0.130434782608696, 0.5, 0.191919191919192, 
0, 0.4, 0.133333333333333, 0.2, 0.296296296296296, 0.241803278688029, 
0.2, 0.0588235294117647, 0.244791666666667, 0, 0.2, 0.166666666666667, 
0.184027777777778, 0.130434782608696, 0.5, 0.191919191919192, 
0, 0.4, 0.133333333333333, 0.126984126984127, 0.2, 0.296296296296296, 
0.133333333333333, 0.241803278688029, 0.2, 0.0588235294117647, 
0.244791666666667, 0, 0.2, 0.193548387096774, 0.166666666666667, 
0.184027777777778, 0.5, 0.191919191919192, 0, 0.4, 0.126984126984127, 
0.2, 0.296296296296296, 0.133333333333333, 0.160493827160494, 
0.241803278688029, 0.2, 0.0588235294117647, 0.244791666666667, 
0, 0.2, 0.166666666666667, 0.184027777777778, 0.5, 0.191919191919192, 
0, 0.4, 0.2, 0.296296296296296, 0, 0.241803278688029, 0.2, 0.0588235294117647, 
0.244791666666667, 0, 0.2, 0.166666666666667, 0.184027777777778, 
0.5), RunID = c(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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 
31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 
32L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 
33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 
34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 
35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 
36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 38L, 38L, 
38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 
38L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 
39L, 39L, 39L, 39L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 42L, 42L, 
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 
44L, 44L, 44L, 44L, 44L, 44L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 46L, 46L, 46L, 
46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 
47L, 47L, 47L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 
48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 49L, 49L, 49L, 49L, 49L, 
49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 50L, 
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 
50L)), groups = structure(list(.rows = structure(list(1L, 2L, 
    3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
    16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
    28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 
    40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 
    52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 
    64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 
    76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 
    88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 
    100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 
    110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 
    120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 
    130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 
    140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 
    150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 
    160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 
    170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 
    180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 
    190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 
    200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 
    210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 
    220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 
    230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 
    240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 
    250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 
    260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 
    270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 
    280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 
    290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 
    300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 
    310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 
    320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 
    330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 
    340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 
    350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 
    360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 
    370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 
    380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 
    390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 
    400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 
    410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 
    420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 
    430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 
    440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 
    450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 
    460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 
    470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 
    480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 
    490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 
    500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 
    510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 
    520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 
    530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 
    540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 
    550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 
    560L, 561L, 562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 
    570L, 571L, 572L, 573L, 574L, 575L, 576L, 577L, 578L, 579L, 
    580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 589L, 
    590L, 591L, 592L, 593L, 594L, 595L, 596L, 597L, 598L, 599L, 
    600L, 601L, 602L, 603L, 604L, 605L, 606L, 607L, 608L, 609L, 
    610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L, 618L, 619L, 
    620L, 621L, 622L, 623L, 624L, 625L, 626L, 627L, 628L, 629L, 
    630L, 631L, 632L, 633L, 634L, 635L, 636L, 637L, 638L, 639L, 
    640L, 641L, 642L, 643L, 644L, 645L, 646L, 647L, 648L, 649L, 
    650L, 651L, 652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L, 
    660L, 661L, 662L, 663L, 664L, 665L, 666L, 667L, 668L, 669L, 
    670L, 671L, 672L, 673L, 674L, 675L, 676L, 677L, 678L, 679L, 
    680L, 681L, 682L, 683L, 684L, 685L, 686L, 687L, 688L, 689L, 
    690L, 691L, 692L, 693L, 694L, 695L, 696L, 697L, 698L, 699L, 
    700L, 701L, 702L, 703L, 704L, 705L, 706L, 707L, 708L, 709L, 
    710L, 711L, 712L, 713L, 714L, 715L, 716L, 717L, 718L, 719L, 
    720L, 721L, 722L, 723L, 724L, 725L, 726L, 727L, 728L, 729L, 
    730L, 731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 
    740L, 741L, 742L, 743L, 744L, 745L, 746L, 747L, 748L, 749L, 
    750L, 751L, 752L, 753L, 754L, 755L, 756L, 757L, 758L, 759L, 
    760L, 761L, 762L, 763L, 764L, 765L, 766L, 767L, 768L, 769L, 
    770L, 771L, 772L, 773L, 774L, 775L, 776L, 777L, 778L, 779L, 
    780L, 781L, 782L, 783L, 784L, 785L, 786L, 787L, 788L, 789L, 
    790L, 791L, 792L, 793L, 794L, 795L, 796L, 797L, 798L, 799L, 
    800L, 801L, 802L, 803L), ptype = integer(0), class = c("vctrs_list_of", 
"vctrs_vctr", "list"))), row.names = c(NA, -803L), class = c("tbl_df", 
"tbl", "data.frame")), row.names = c(NA, -803L), class = c("rowwise_df", 
"tbl_df", "tbl", "data.frame"))

Answer 1

ggplot2 does recognise alpha values less than 0.1.

For example:

ggplot(data = subset(play, !is.na(y)), aes(x = -x, y = y, group = RunID)) +
    geom_smooth(method = "loess", linetype = 0, alpha = 0.01) +
    scale_x_reverse() +
    theme_minimal()

produces

Only when you use an alpha transparency scale through an aesthetic, values are mapped to alpha values in the interval [0.1, 1].