[R] Plotting symbols and colors based upon data values
Brian Diggs
diggsb at ohsu.edu
Mon Mar 14 20:16:43 CET 2011
On 3/13/2011 6:46 PM, David Winsemius wrote:
>
> On Mar 13, 2011, at 8:51 PM, Mark Linderman wrote:
>
>> David, thank you for your quick reply. I spent a few minutes getting your
>> command to work with some sparse synthetic data, and then spent several
>> hours trying to figure out why my data didn't work (at least for symbols,
>> colors look okay). I have massaged my data to where it is practically
>> indistinguishable from the synthetic data - yet it still doesn't work.
>> Attached are the two data files that can be plotted as follows:
>>
>> broken = read.table("broken.table",header=TRUE)
>> works = read.table("works.table",header=TRUE)
>> xyplot(Y ~ X | A, data=works, pch=works$C , col=works$B)
>> xyplot(Y ~ X | A, data=broken, pch=broken$C , col=broken$B)
>
> I get the same problem and after experimenting for a while I think I can
> solve it by randomizing the order of the entries:
>
> > broken <- broken[sample(417), ]
>
> > xyplot(Y ~ X | A, data=broken, pch=broken$C, col=broken$B)
>
> Why xyplot should fail to properly assign pch values just because all
> "1"'s are at the beginning seems to me to be a bug.
>
Using a completely different approach (ggplot2 versus lattice) and
David's dput output for the data.frame "broken", this can be done with:
broken <- structure(list(rown = c(91L, 193L, 128L, 8L, 143L, 46L, 60L,
99L, 112L, 67L, 25L, 15L, 188L, 93L, 115L, 4L, 190L, 64L, 147L,
119L, 82L, 120L, 23L, 139L, 28L, 42L, 180L, 24L, 145L, 71L, 13L,
95L, 94L, 104L, 149L, 74L, 32L, 184L, 11L, 114L, 90L, 70L, 63L,
141L, 192L, 126L, 153L, 172L, 26L, 151L, 109L, 133L, 79L, 35L,
61L, 43L, 52L, 29L, 30L, 80L, 154L, 7L, 121L, 122L, 106L, 182L,
16L, 2L, 175L, 34L, 102L, 174L, 117L, 178L, 100L, 68L, 48L, 31L,
53L, 168L, 59L, 165L, 123L, 69L, 55L, 62L, 163L, 39L, 108L, 96L,
97L, 113L, 87L, 164L, 169L, 33L, 118L, 45L, 148L, 129L, 22L,
116L, 101L, 157L, 191L, 89L, 75L, 156L, 137L, 183L, 98L, 150L,
124L, 144L, 127L, 155L, 57L, 36L, 14L, 161L, 187L, 138L, 111L,
146L, 20L, 107L, 140L, 110L, 125L, 41L, 105L, 159L, 103L, 132L,
44L, 166L, 56L, 171L, 195L, 40L, 135L, 5L, 58L, 37L, 54L, 83L,
17L, 142L, 77L, 162L, 170L, 160L, 78L, 38L, 194L, 21L, 167L,
27L, 81L, 185L, 47L, 66L, 73L, 3L, 134L, 158L, 51L, 173L, 50L,
18L, 12L, 6L, 189L, 72L, 85L, 65L, 92L, 179L, 86L, 49L, 130L,
177L, 152L, 176L, 9L, 10L, 76L, 88L, 131L, 181L, 19L, 186L, 136L,
1L, 84L, 366L, 235L, 196L, 224L, 206L, 288L, 204L, 274L, 199L,
239L, 271L, 295L, 266L, 305L, 284L, 340L, 268L, 296L, 293L, 262L,
300L, 212L, 336L, 208L, 358L, 242L, 221L, 237L, 369L, 292L, 201L,
338L, 233L, 217L, 227L, 225L, 270L, 267L, 345L, 205L, 219L, 278L,
337L, 230L, 380L, 291L, 229L, 367L, 339L, 241L, 228L, 263L, 349L,
348L, 371L, 202L, 207L, 351L, 282L, 222L, 200L, 213L, 285L, 375L,
302L, 231L, 223L, 386L, 352L, 363L, 353L, 357L, 359L, 350L, 283L,
362L, 218L, 198L, 374L, 301L, 286L, 364L, 368L, 220L, 298L, 280L,
214L, 273L, 303L, 382L, 354L, 238L, 373L, 234L, 356L, 216L, 289L,
370L, 381L, 343L, 361L, 306L, 281L, 203L, 341L, 355L, 346L, 272L,
264L, 360L, 334L, 210L, 197L, 342L, 299L, 378L, 236L, 333L, 294L,
347L, 275L, 385L, 365L, 209L, 297L, 240L, 265L, 379L, 304L, 269L,
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416L, 327L, 393L, 322L, 390L, 317L, 389L, 249L, 325L, 329L, 398L,
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316L, 394L, 257L, 404L, 243L, 252L, 410L, 313L, 256L, 330L, 321L,
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0.69323132908903, 0.454046661267057, 0.0532575217075646, 0.864072882803157,
0.108720119809732, 0.941018613055348, 0.68864845763892, 0.106074716662988,
0.59618656639941, 0.983284626156092, 0.447163598611951, 0.323577877366915,
0.43200644152239, 0.230878660688177, 0.330381851643324, 0.228483391692862,
0.14531171717681, 0.947160384617746, 0.0644653548952192, 0.933858478674665,
0.867367077618837, 0.960713072912768, 0.0106419362127781,
0.0958437097724527,
0.318360113538802, 0.89607287850231, 0.194603573530912, 0.0140892933122814,
0.78282424225472, 0.154727570246905, 0.762999390950426, 0.489407370565459,
0.423035337822512, 0.19474891689606, 0.681294421199709, 0.225796846672893,
0.6234319685027, 0.636501412605867, 0.159554290119559, 0.553764832671732,
0.0210408298298717, 0.68121306411922, 0.995034355204552, 0.399561397265643,
0.403975014341995, 0.852582210907713, 0.125726311234757, 0.992505229078233,
0.422545881476253, 0.662919480586424, 0.316766428994015, 0.618335535051301,
0.441973085515201, 0.851687799440697, 0.842560255667195, 0.633133036317304,
0.324292129138485, 0.381184502737597, 0.78097918536514, 0.238214250188321,
0.927592432824895, 0.841349175665528, 0.8883684319444, 0.848109701182693,
0.215758525533602, 0.440561114577577, 0.667430794099346, 0.555521365720779,
0.0360126029700041, 0.355077777989209, 0.802172212162986,
0.0323124176356941,
0.764302607392892, 0.556403563823551, 0.0513982712291181, 0.167700330493972,
0.275772945489734, 0.121303298510611, 0.355494713177904, 0.619331127265468,
0.722270094789565, 0.13826255640015, 0.83197070308961, 0.208093572407961,
0.417050289222971, 0.552277022507042, 0.532353505026549, 0.825634842040017,
0.0584846271667629, 0.206079388037324, 0.55847840802744, 0.787833330687135,
0.265674144495279, 0.632736003026366, 0.935361107578501, 0.892695550108328,
0.810330303385854, 0.504313444718719, 0.484284303616732, 0.268657196313143,
0.79852058342658, 0.837990865344182, 0.0854433339554816, 0.457756362855434,
0.930622784886509, 0.30546832550317, 0.364406730281189, 0.895903791300952,
0.61879310826771, 0.705111734103411, 0.229004139779136, 0.153806942980736,
0.388366017956287, 0.384744216687977, 0.131829720223323, 0.933241792721674,
0.828655388206244, 0.478957881452516, 0.163506358396262, 0.202536955475807,
0.521721071796492, 0.934954703785479, 0.922832843149081, 0.0890378498006612,
0.744039923418313, 0.342938947491348, 0.829126243945211, 0.438954021781683,
0.342147971037775, 0.904643931658939, 0.618884094292298, 0.53136019455269,
0.28578051389195, 0.261097583221272, 0.731547623872757, 0.925990937277675,
0.392090859822929, 0.344719064421952, 0.566447681514546, 0.676267065340653,
0.0889970965217799, 0.79778384277597, 0.454307504929602, 0.0324128807988018,
0.367819492006674, 0.748151563573629, 0.117547858972102, 0.609072768129408,
0.0297117948066443, 0.425113417906687, 0.59103324636817, 0.89295660564676,
0.961610725149512, 0.844706527423114, 0.538759749848396, 0.818922623759136,
0.549228129675612, 0.126476648263633, 0.861659712390974, 0.613700804766268,
0.409116324270144, 0.686794322915375, 0.438312869053334, 0.878093276405707,
0.755687783006579, 0.00695069995708764, 0.138217013562098,
0.411313445540145,
0.907310962677002, 0.701067975489423, 0.1852589645423, 0.150231995387003,
0.934385694563389, 0.353562438627705, 0.464768649777398, 0.765283492859453,
0.905872487463057, 0.0849798938725144, 0.773121788864955,
0.0939909212756902,
0.596990453079343, 0.725830281618983, 0.598506234120578, 0.458693271037191,
0.281013652216643, 0.458665299229324, 0.0339348095003515, 0.799791351892054,
0.000570902600884438, 0.804609716171399, 0.812421002890915,
0.886078594485298,
0.525463038356975, 0.145692070946097, 0.78209150978364, 0.905050198314711
), A = structure(c(1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 3L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 3L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L,
2L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 2L,
1L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L,
2L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 1L, 1L,
3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 3L, 3L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 3L, 1L, 3L, 3L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 1L, 3L,
1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 2L, 3L, 1L, 3L, 2L, 1L, 1L, 6L, 4L, 4L, 4L, 4L, 5L, 4L, 5L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 4L, 6L, 4L,
6L, 4L, 4L, 4L, 6L, 5L, 4L, 6L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 4L,
4L, 5L, 6L, 4L, 6L, 5L, 4L, 6L, 6L, 4L, 4L, 5L, 6L, 6L, 6L, 4L,
4L, 6L, 5L, 4L, 4L, 4L, 5L, 6L, 5L, 4L, 4L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 5L, 6L, 4L, 4L, 6L, 5L, 5L, 6L, 6L, 4L, 5L, 5L, 4L, 5L,
5L, 6L, 6L, 4L, 6L, 4L, 6L, 4L, 5L, 6L, 6L, 6L, 6L, 5L, 5L, 4L,
6L, 6L, 6L, 5L, 5L, 6L, 6L, 4L, 4L, 6L, 5L, 6L, 4L, 6L, 5L, 6L,
5L, 6L, 6L, 4L, 5L, 4L, 5L, 6L, 5L, 5L, 6L, 6L, 6L, 5L, 6L, 6L,
5L, 6L, 6L, 4L, 4L, 5L, 5L, 4L, 5L, 6L, 4L, 5L, 6L, 5L, 6L, 6L,
6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 6L, 5L, 5L, 4L, 4L, 4L, 6L, 5L,
5L, 6L, 6L, 4L, 5L, 6L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 4L, 5L, 5L,
6L, 6L, 5L, 6L, 4L, 6L, 4L, 6L, 6L, 4L, 6L, 5L, 6L, 6L, 5L, 4L,
4L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 6L, 4L, 6L, 4L, 4L, 6L, 5L, 4L,
5L, 5L, 4L, 6L, 4L, 5L), .Label = c("Cat A", "Cat B", "Cat C",
"Cat D", "Cat E", "Cat F"), class = "factor"), B = structure(c(3L,
1L, 1L, 1L, 4L, 3L, 3L, 4L, 4L, 1L, 4L, 4L, 1L, 1L, 3L, 1L, 3L,
4L, 1L, 1L, 1L, 3L, 4L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 3L, 4L, 4L, 3L, 1L, 4L, 4L, 4L, 1L, 1L, 4L, 3L, 3L, 4L, 4L,
4L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 1L, 4L, 4L,
4L, 3L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 3L, 1L, 1L, 4L, 1L, 4L,
3L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
4L, 1L, 3L, 4L, 1L, 4L, 1L, 4L, 4L, 3L, 1L, 4L, 3L, 4L, 3L, 4L,
3L, 4L, 3L, 1L, 3L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 4L, 3L, 3L,
4L, 4L, 4L, 1L, 1L, 1L, 4L, 3L, 4L, 3L, 4L, 4L, 4L, 4L, 3L, 4L,
4L, 3L, 1L, 4L, 3L, 4L, 1L, 4L, 3L, 3L, 4L, 1L, 4L, 4L, 1L, 1L,
1L, 3L, 1L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 4L,
4L, 4L, 3L, 3L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 4L, 3L, 3L, 4L, 4L,
4L, 4L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L,
3L, 4L, 3L, 3L, 4L, 3L, 3L, 3L, 4L, 4L, 3L, 2L, 2L, 2L, 3L, 3L,
2L, 4L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L,
2L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 4L, 2L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 4L, 3L, 3L, 2L, 1L, 3L, 2L, 2L,
1L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L,
3L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 4L, 1L, 3L, 2L, 3L, 4L, 2L, 4L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 3L,
4L, 3L, 4L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L,
3L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 4L, 3L,
3L, 4L, 3L, 4L, 2L, 2L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 4L, 3L, 3L,
4L, 3L, 4L, 3L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 3L, 4L, 3L, 3L
), .Label = c("black", "blue", "orange", "red"), class = "factor"),
C = 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, 1L,
1L, 1L, 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, 4L, 4L, 4L, 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,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L)), .Names =
c("rown",
"X", "Y", "A", "B", "C"), row.names = c(91L, 193L, 128L, 8L,
143L, 46L, 60L, 99L, 112L, 67L, 25L, 15L, 188L, 93L, 115L, 4L,
190L, 64L, 147L, 119L, 82L, 120L, 23L, 139L, 28L, 42L, 180L,
24L, 145L, 71L, 13L, 95L, 94L, 104L, 149L, 74L, 32L, 184L, 11L,
114L, 90L, 70L, 63L, 141L, 192L, 126L, 153L, 172L, 26L, 151L,
109L, 133L, 79L, 35L, 61L, 43L, 52L, 29L, 30L, 80L, 154L, 7L,
121L, 122L, 106L, 182L, 16L, 2L, 175L, 34L, 102L, 174L, 117L,
178L, 100L, 68L, 48L, 31L, 53L, 168L, 59L, 165L, 123L, 69L, 55L,
62L, 163L, 39L, 108L, 96L, 97L, 113L, 87L, 164L, 169L, 33L, 118L,
45L, 148L, 129L, 22L, 116L, 101L, 157L, 191L, 89L, 75L, 156L,
137L, 183L, 98L, 150L, 124L, 144L, 127L, 155L, 57L, 36L, 14L,
161L, 187L, 138L, 111L, 146L, 20L, 107L, 140L, 110L, 125L, 41L,
105L, 159L, 103L, 132L, 44L, 166L, 56L, 171L, 195L, 40L, 135L,
5L, 58L, 37L, 54L, 83L, 17L, 142L, 77L, 162L, 170L, 160L, 78L,
38L, 194L, 21L, 167L, 27L, 81L, 185L, 47L, 66L, 73L, 3L, 134L,
158L, 51L, 173L, 50L, 18L, 12L, 6L, 189L, 72L, 85L, 65L, 92L,
179L, 86L, 49L, 130L, 177L, 152L, 176L, 9L, 10L, 76L, 88L, 131L,
181L, 19L, 186L, 136L, 1L, 84L, 366L, 235L, 196L, 224L, 206L,
288L, 204L, 274L, 199L, 239L, 271L, 295L, 266L, 305L, 284L, 340L,
268L, 296L, 293L, 262L, 300L, 212L, 336L, 208L, 358L, 242L, 221L,
237L, 369L, 292L, 201L, 338L, 233L, 217L, 227L, 225L, 270L, 267L,
345L, 205L, 219L, 278L, 337L, 230L, 380L, 291L, 229L, 367L, 339L,
241L, 228L, 263L, 349L, 348L, 371L, 202L, 207L, 351L, 282L, 222L,
200L, 213L, 285L, 375L, 302L, 231L, 223L, 386L, 352L, 363L, 353L,
357L, 359L, 350L, 283L, 362L, 218L, 198L, 374L, 301L, 286L, 364L,
368L, 220L, 298L, 280L, 214L, 273L, 303L, 382L, 354L, 238L, 373L,
234L, 356L, 216L, 289L, 370L, 381L, 343L, 361L, 306L, 281L, 203L,
341L, 355L, 346L, 272L, 264L, 360L, 334L, 210L, 197L, 342L, 299L,
378L, 236L, 333L, 294L, 347L, 275L, 385L, 365L, 209L, 297L, 240L,
265L, 379L, 304L, 269L, 372L, 384L, 344L, 287L, 332L, 376L, 261L,
377L, 383L, 215L, 232L, 277L, 276L, 211L, 290L, 335L, 226L, 279L,
399L, 307L, 395L, 400L, 411L, 388L, 319L, 403L, 320L, 309L, 318L,
407L, 402L, 308L, 326L, 251L, 260L, 246L, 408L, 331L, 312L, 387L,
414L, 253L, 315L, 413L, 416L, 327L, 393L, 322L, 390L, 317L, 389L,
249L, 325L, 329L, 398L, 397L, 323L, 396L, 255L, 415L, 245L, 391L,
412L, 259L, 417L, 311L, 392L, 409L, 328L, 254L, 248L, 310L, 258L,
405L, 324L, 250L, 406L, 316L, 394L, 257L, 404L, 243L, 252L, 410L,
313L, 256L, 330L, 321L, 244L, 401L, 247L, 314L), class = "data.frame")
library("ggplot2")
ggplot(data=broken) +
geom_point(aes(x=X, y=Y, shape=C, colour=B)) +
facet_wrap(~A)
ggplot2 makes this (mapping different data values to different
aesthetics (e.g. color, shape, etc.) of the plot) relatively
straightforward.
--
Brian S. Diggs, PhD
Senior Research Associate, Department of Surgery
Oregon Health & Science University
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