How to reorder day of year so that seasons line up - r
I am working with seasonal data in Australia that spans from their Spring to Fall. I would like to have a column with day of year. However, when I use the yday function from the lubridate, I receive a distribution like this:
The issue here is that the Australian summer (peak time for data collection) goes through the new year. If I tried to add this variable into the model, it would produce incorrect results.
So my question is:
How can I get a seasonally appropriate 'day of year' column for this dataset
I have included some data here:
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191:195, 196:200, 201:205, 206:210, 211:215, 216:220, 221:225,
226:230, 231:235, 236:240, 241:245, 246:250, 251:255, 256:260,
261:265, 266:270, 271:275, 276:280, 281:285, 286:290, 291:295,
296:300, 301:305, 306:310, 311:315, 316:320, 321:325, 326:330,
331:335, 336:340, 341:345, 346:350, 351:355, 356:360, 361:365,
366:370, 371:375, 376:380, 381:385, 386:390, 391:395, 396:400,
401:405, 406:410, 411:415, 416:420, 421:425, 426:430, 431:435,
436:440, 441:445, 446:450, 451:455, 456:460, 461:465, 466:470,
471:475, 476:480, 481:485, 486:490, 491:495, 496:500, 501:505,
506:510, 511:515, 516:520, 521:525, 526:530, 531:535, 536:540,
541:545, 546:550, 551:555, 556:560, 561:565, 566:570, 571:575,
576:580, 581:585, 586:590, 591:595, 596:600, 601:605, 606:610,
611:615, 616:620, 621:625, 626:630, 631:635, 636:640, 641:645,
646:650, 651:655, 656:660, 661:665, 666:670, 671:675, 676:680,
681:685, 686:690, 691:695, 696:700, 701:705, 706:710, 711:715,
716:720, 721:725, 726:730, 731:735, 736:740, 741:745, 746:750,
751:755, 756:760, 761:765, 766:770, 771:775, 776:780, 781:785,
786:790, 791:795, 796:800, 801:805, 806:810, 811:815, 816:820,
821:825, 826:830, 831:835, 836:840, 841:845, 846:850, 851:855,
856:860, 861:865, 866:870, 871:875, 876:880, 881:885, 886:890,
891:895, 896:900, 901:905, 906:910, 911:915, 916:920, 921:925,
926:930, 931:935, 936:940, 941:945, 946:950, 951:955, 956:960,
961:965, 966:970, 971:975, 976:980, 981:985, 986:990, 991:995,
996:1000, 1001:1005, 1006:1010, 1011:1015, 1016:1020, 1021:1025,
1026:1030, 1031:1035, 1036:1040, 1041:1045, 1046:1050, 1051:1055,
1056:1060, 1061:1065, 1066:1070, 1071:1075, 1076:1080, 1081:1085,
1086:1090, 1091:1095, 1096:1100, 1101:1105, 1106:1110, 1111:1115,
1116:1120, 1121:1125, 1126:1130, 1131:1135, 1136:1140, 1141:1145,
1146:1150, 1151:1155, 1156:1160, 1161:1165, 1166:1170, 1171:1175,
1176:1180, 1181:1185, 1186:1190, 1191:1195, 1196:1200, 1201:1205,
1206:1210, 1211:1215, 1216:1220, 1221:1225, 1226:1230, 1231:1235,
1236:1240, 1241:1245, 1246:1250, 1251:1255, 1256:1260, 1261:1265,
1266:1270, 1271:1275, 1276:1280, 1281:1285, 1286:1290, 1291:1295,
1296:1300, 1301:1305, 1306:1310, 1311:1315, 1316:1320, 1321:1325,
1326:1330, 1331:1335, 1336:1340, 1341:1345)), row.names = c(NA,
-269L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))
The sample data you gave was not very illustrative, since it contained one instance each of the same day each year. We'll create some data that is perhaps more representative:
library(ggplot2)
set.seed(69)
dates <- as.Date("2015-01-01") +
lubridate::days(round(rnorm(5000, 0, 30))) +
lubridate::years(sample(0:5, 5000, TRUE))
df <- data.frame(Date = dates, DOY = lubridate::yday(dates))
ggplot(df, aes(DOY)) + geom_histogram(binwidth = 1)
The way to get rid of this gap is simply to add 6 months and take the result modulo 366:
ggplot(df, aes((DOY + 183) %% 366)) +
geom_histogram(binwidth = 1) +
lims(x = c(0, 366)) +
labs(x = "DOY")
Created on 2020-08-02 by the reprex package (v0.3.0)
Related
how do I add keys of a sorted dictionary sequentially having no intersection with other value lists
I have a dictionary which contains key pair values as key interprets "nodes " and value is a list of communities it belongs to. arranging them according to non-increasing order based on length of list value ,i need to create a list of keys starting from the top rank key and iterate over all keys to find keys with no intersection in their list values with previously added key
this is a dictionary
" {'2179': [15, 197, 363, 594, 766, 865, 1150, 1417, 1575, 1615, 1617, 1618, 1621, 1623, 1624, 1625, 1627], '2188': [15, 363, 766, 1150, 1417, 1616, 1617, 1618, 1619, 1620, 1622, 1624, 1625, 1626, 1629], '2180': [197, 594, 1150, 1575, 1616, 1617, 1618, 1619, 1620, 1622, 1624, 1625, 1626, 1629, 2201], '2195': [1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629], '2452': [1757, 1758, 1759, 1760, 1761, 1762, 1763, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772], '238': [57, 65, 76, 213, 251, 1080, 1126, 1448, 1896, 1897, 1898, 1899, 1900], '6974': [14, 122, 137, 491, 641, 660, 675, 1046, 1800, 2054, 2371], '124': [19, 66, 70, 113, 123, 159, 276, 297, 826, 2122], '3224': [18, 36, 44, 215, 230, 419, 1139, 1259, 2153], '100': [19, 66, 113, 297, 635, 826, 1356, 2122], '553': [40, 50, 133, 135, 192, 526, 1677,
1829]}"
.
I need to add keys iteratively to the list which have no intersection with list values of previously added key and next key to be added. this is a code i tried
"this is a code i tried.
k=len(new_dict)
seed=list(new_dict.keys())[0]
print(seed)
CummunitySet=[]
CommunitySet=set(new_dict.get(seed))
print(CommunitySet)
seedSet=set(seed)
Index=1
while ((seedCount < k) & (Index < count)):
seed=list(new_dict.keys())[Index]
if(set(new_dict.get(seed)).difference(CommunitySet)!=set()):
CommunitySet = CommunitySet.union(new_dict.get(seed))
print(CommunitySet)
seedSet = seedSet.union(set(seed))
Index=Index+1
seedCount=seedCount+1
else:
Index=Index+1
Index=Index+1
print(seedSet)
thankyou.
Maybe you could utilize set.intersection:
def main() -> None:
data = {
'2179': [15, 197, 363, 594, 766, 865, 1150, 1417, 1575, 1615, 1617, 1618, 1621, 1623, 1624, 1625, 1627],
'2188': [15, 363, 766, 1150, 1417, 1616, 1617, 1618, 1619, 1620, 1622, 1624, 1625, 1626, 1629],
'2180': [197, 594, 1150, 1575, 1616, 1617, 1618, 1619, 1620, 1622, 1624, 1625, 1626, 1629, 2201],
'2195': [1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629],
'2452': [1757, 1758, 1759, 1760, 1761, 1762, 1763, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772],
'238': [57, 65, 76, 213, 251, 1080, 1126, 1448, 1896, 1897, 1898, 1899, 1900],
'6974': [14, 122, 137, 491, 641, 660, 675, 1046, 1800, 2054, 2371],
'124': [19, 66, 70, 113, 123, 159, 276, 297, 826, 2122],
'3224': [18, 36, 44, 215, 230, 419, 1139, 1259, 2153],
'100': [19, 66, 113, 297, 635, 826, 1356, 2122],
'553': [40, 50, 133, 135, 192, 526, 1677, 1829]
}
new_data = {}
used_values = set()
for key, values in data.items():
values_set = set(values)
if values_set.intersection(used_values): # Equivalant to `values_set & used_values`.
continue
used_values |= values_set
new_data[key] = values
print(new_data)
if __name__ == '__main__':
main()
Output:
{
'2179': [15, 197, 363, 594, 766, 865, 1150, 1417, 1575, 1615, 1617, 1618, 1621, 1623, 1624, 1625, 1627],
'2452': [1757, 1758, 1759, 1760, 1761, 1762, 1763, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772],
'238': [57, 65, 76, 213, 251, 1080, 1126, 1448, 1896, 1897, 1898, 1899, 1900],
'6974': [14, 122, 137, 491, 641, 660, 675, 1046, 1800, 2054, 2371],
'124': [19, 66, 70, 113, 123, 159, 276, 297, 826, 2122],
'3224': [18, 36, 44, 215, 230, 419, 1139, 1259, 2153],
'553': [40, 50, 133, 135, 192, 526, 1677, 1829]
}
Note: Indentation in output has been added manually for readability.
Replace a single value in data.table column if it doesn't meet a specific criteria
I have the following data.table:
> head(sample)
WeekEndingDate Totals_1 Totals_2
1: 2021-06-05 0 0
2: 2021-06-12 0 0
3: 2021-06-19 0 0
4: 2021-06-26 0 0
5: 2021-07-03 0 0
6: 2021-07-10 0 0
> dput(sample)
structure(list(WeekEndingDate = structure(c(18783, 18790, 18797,
18804, 18811, 18818, 18825, 18832, 18839, 18846, 18853, 18860,
18867, 18874, 18881, 18888, 18895, 18902, 18909, 18916, 18923,
18930, 18937, 18944, 18951, 18958, 18965, 18972, 18979, 18986,
18993, 19000, 19007, 19014, 19021, 19028, 19035, 19042, 19049,
19056, 19063, 19070, 19077, 19084, 19091, 19098, 19105, 19112,
19119, 19126, 19133, 19140, 19147, 19154, 19161, 19168, 19175,
19182, 19189, 19196, 19203, 19210, 19217, 19224, 19231, 19238,
19245, 19252, 19259, 19266, 19273, 19280, 19287, 19294, 19301,
19308, 19315, 19322, 19329, 19336, 19343, 19350, 19357, 19364,
19371, 19378, 19385, 19392, 19399, 19406, 19413, 19420, 19427,
19434, 19441, 19448, 19455, 19462, 19469, 19476, 19483, 19490,
19497, 19504, 19511, 19518, 19525), class = "Date"), Totals_1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 3, 5, 6, 8,
10, 12, 15, 18, 21, 24, 27, 27, 28, 32, 37, 42, 48, 54, 61, 68,
74, 81, 90, 98, 106, 116, 123, 123, 128, 136, 145, 155, 164,
173, 181, 191, 200, 208, 216, 225, 236, 243, 247, 251, 254, 259,
266, 271, 278, 288, 296, 304, 313, 326, 333, 341, 351, 360, 369,
376, 384, 392, 400, 409, 409, 412, 423, 428, 432, 436, 440, 442,
443, 443, 444, 445, 446, 446, 446, 446, 446, 446, 446, 447, 447,
447, 447, 447, 447), Totals_2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 5, 6, 8, 10, 13, 15, 18,
18, 19, 22, 26, 30, 35, 40, 45, 51, 57, 63, 69, 76, 83, 91, 98,
98, 100, 108, 117, 124, 131, 140, 146, 155, 163, 170, 178, 185,
192, 199, 207, 210, 213, 217, 221, 229, 236, 244, 253, 260, 268,
276, 284, 292, 300, 308, 316, 323, 330, 339, 347, 355, 355, 357,
364, 372, 381, 389, 398, 407, 413, 418, 422, 424, 426, 427, 427,
428, 429, 429, 429, 429, 429, 429, 429, 429, 429)), row.names = c(NA,
-107L), class = c("data.table", "data.frame"))
Within the same environment I have defined a variable x_date which has a specific date. For this example the value is 2023-09-30. Now I have been able to identify the row with the max value using the statement below:
> class(x_date)
[1] "Date"
> x_date
[1] "2023-09-30"
> sample[sample[,.I[which.max(WeekEndingDate)]]]
WeekEndingDate Totals_1 Totals_2
1: 2023-06-17 447 429
However, what I'm trying to do is if the maximum WeekEndingDate is before x_date
than I want to update that value to x_date otherwise the maximum value is fine. I was thinking something like this would work, but this replaces all values in the column instead of just the max:
max(sample$WeekEndingDate) <- ifelse(max(sample$WeekEndingDate) < x_date, x_date, max(sample$WeekEndingDate))
library(data.table)
x_date <- as.Date('2023-09-30')
sample[WeekEndingDate==max(WeekEndingDate),WeekEndingDate:=ifelse(WeekEndingDate<x_date,x_date,WeekEndingDate)]
How to identify the main peaks in data that contains multiple broad peaks
I've got some data that looks like it contains 23 peaks. The x-axis is time and y-axis is peak. However, each peak is quite broad, including several smaller peaks. I'd like to remove the smaller peaks, so I'm just left with the time for each main peak. I'd be very grateful for some help! time <- c(1562, 1563, 1564, 1565, 1566, 1810, 1811, 1812, 1813, 1814, 2058, 2059, 2060, 2061, 2306, 2307, 2308, 2309, 2310, 2560, 2561, 2562, 2563, 2564, 3064, 3065, 3066, 3067, 3580, 3581, 3582, 3583, 3584, 4095, 4096, 4097, 4098, 4099, 4610, 4611, 4612, 4613, 4614, 5128, 5129, 5130, 5131, 5132, 5133, 5637, 5638, 5639, 5640, 5641, 5876, 5877, 5878, 5879, 5880, 5881, 5882, 6125, 6126, 6127, 6128, 6129, 6130, 6607, 6608, 6609, 6610, 6611, 6612, 6613, 7072, 7073, 7074, 7075, 7076, 7077, 7078, 7079, 7519, 7520, 7521, 7522, 7523, 7524, 7525, 7526, 7527, 7528, 7941, 7942, 7943, 7944, 7945, 7946, 7947, 7948, 7949, 8342, 8343, 8344, 8345, 8346, 8347, 8348, 8349, 8350, 8351, 8708, 8709, 8710, 8711, 8712, 8713, 8714, 8715, 8716, 8717, 8718, 9045, 9046, 9047, 9048, 9049, 9050, 9051, 9052, 9053, 9054, 9055, 9352, 9353, 9354, 9355, 9356, 9357, 9358, 9359, 9360, 9361, 9362, 9363, 9624, 9625, 9626, 9627, 9628, 9629, 9630, 9631, 9632, 9633, 9634, 9867, 9868, 9869, 9870, 9871, 9872, 9873, 9874, 9875, 9876) peak <- c(509, 672, 758, 686, 584, 559, 727, 759, 688, 528, 562, 711, 768, 678, 644, 750, 822, 693, 531, 566, 738, 793, 730, 511, 587, 739, 761, 651, 579, 747, 768, 705, 544, 551, 687, 756, 749, 645, 564, 680, 724, 691, 596, 535, 625, 685, 689, 612, 512, 537, 616, 657, 653, 573, 506, 598, 675, 685, 668, 609, 515, 575, 656, 687, 678, 626, 533, 509, 587, 641, 680, 663, 602, 515, 505, 583, 646, 693, 696, 684, 630, 549, 500, 572, 637, 681, 725, 736, 736, 703, 649, 556, 568, 637, 682, 743, 765, 767, 709, 660, 587, 548, 622, 690, 761, 779, 764, 749, 694, 631, 525, 571, 646, 724, 788, 811, 834, 818, 776, 712, 616, 536, 556, 649, 738, 801, 857, 866, 837, 808, 718, 647, 568, 508, 605, 714, 823, 872, 917, 916, 890, 825, 742, 642, 543, 549, 656, 766, 851, 921, 947, 951, 892, 830, 730, 617, 586, 675, 760, 804, 816, 795, 740, 690, 613, 522) data <- data.frame(time = time, peak = peak) ggplot(data, aes(x=time, y=peak)) + geom_line() ADDITIONAL QUESTION: I've been trying to apply this to another data set and it picks out too many peaks. The new data contains a broad normal distribution. I'd like it to pick out just the very highest peak in this distribution, but as you can see from the image, it picks out every individual peak on either side. Is there a modification that can be made to the code to pick them out? data2 <- data.frame(time = c(5001, 5002, 5003, 5004, 5005, 5006, 5007, 5008, 5009, 5010, 5011, 5012, 5013, 5014, 5015, 5016, 5017, 5018, 5019, 5020, 5021, 5022, 5023, 5024, 5025, 5026, 5027, 5028, 5029, 5030, 5031, 5032, 5033, 5034, 5035, 5036, 5037, 5038, 5039, 5040, 5041, 5042, 5043, 5044, 5045, 5046, 5047, 5048, 5049, 5050, 5051, 5052, 5053, 5054, 5055, 5056, 5057, 5058, 5059, 5060, 5061, 5062, 5063, 5064, 5065, 5066, 5067, 5068, 5069, 5070, 5071, 5072, 5073, 5074, 5075, 5076, 5077, 5078, 5079, 5080, 5081, 5082, 5083, 5084, 5085, 5086, 5087, 5088, 5089, 5090, 5091, 5092, 5093, 5094, 5095, 5096, 5097, 5098, 5099, 5100, 5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120, 5121, 5122, 5123, 5124, 5125, 5126, 5127, 5135, 5136, 5137, 5138, 5139, 5140, 5141, 5142, 5143, 5144, 5145, 5146, 5147, 5148, 5149, 5150, 5151, 5152, 5153, 5154, 5155, 5156, 5157, 5158, 5159, 5160, 5161, 5162, 5163, 5164, 5165, 5166, 5167, 5168, 5169, 5170, 5171, 5172, 5173, 5174, 5175, 5176, 5177, 5178, 5179, 5180, 5181, 5182, 5183, 5184, 5185, 5186, 5187, 5188, 5189, 5190, 5191, 5192, 5193, 5194, 5195, 5196, 5197, 5198, 5199, 5200, 5201, 5202, 5203, 5204, 5205, 5206, 5207, 5208, 5209, 5210, 5211, 5212, 5213, 5214, 5215, 5216, 5217, 5218, 5219, 5220, 5221, 5222, 5223, 5224, 5225, 5226, 5227, 5228, 5229, 5230, 5231, 5232, 5233, 5234, 5235, 5236, 5237, 5238, 5239, 5240, 5241, 5242, 5243, 5244, 5245, 5246, 5247, 5248, 5249, 5250, 5251, 5252, 5253, 5254, 5255, 5256, 5257, 5258, 5259, 5260, 5261, 5262, 5263, 5264, 5265, 5266, 5267, 5268, 5269, 5270, 5271, 5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281, 5282, 5283, 5284, 5285, 5286, 5287, 5288, 5289, 5290, 5291, 5292, 5293, 5294, 5295, 5296, 5297, 5298, 5299, 5300, 5301, 5302, 5303, 5304, 5305, 5306, 5307, 5308, 5309, 5310, 5311, 5312, 5313, 5314, 5315, 5316, 5317, 5318, 5319, 5320, 5321, 5322, 5323, 5324, 5325, 5326, 5327, 5328, 5329, 5330, 5331, 5332, 5333, 5334, 5335, 5336, 5337, 5338, 5339, 5340, 5341, 5342, 5343, 5344, 5345, 5346, 5347, 5348, 5349, 5350, 5351, 5352, 5353, 5354, 5355, 5356, 5357, 5358, 5359, 5360, 5361, 5362, 5363, 5364, 5365, 5366, 5367, 5368, 5369, 5370, 5371, 5372, 5373, 5374, 5375, 5376, 5377, 5378, 5379, 5380, 5381, 5382, 5383, 5384, 5385, 5386, 5387, 5388, 5389, 5390, 5391, 5392, 5393, 5394, 5395, 5396, 5397, 5398, 5399, 5400, 5401, 5402, 5403, 5404, 5405, 5406, 5407, 5408, 5409, 5410, 5411, 5412, 5413, 5414, 5415, 5416, 5417, 5418, 5419, 5420, 5421, 5422, 5423, 5424, 5425, 5426, 5427, 5428, 5429, 5430, 5431, 5432, 5433, 5434, 5435, 5436, 5437, 5438, 5439, 5440, 5441, 5442, 5443, 5444, 5445, 5446, 5447, 5448, 5449, 5450, 5451, 5452, 5453, 5454, 5455, 5456, 5457, 5458, 5459, 5460, 5461, 5462, 5463, 5464, 5465, 5466, 5467, 5468, 5469, 5470, 5471, 5472, 5473, 5474, 5475, 5476, 5477, 5478, 5479, 5480, 5481, 5482, 5483, 5484, 5485, 5486, 5487, 5488, 5489, 5490, 5491, 5492, 5493, 5494, 5495, 5496, 5497, 5498, 5499, 5500, 5501, 5502, 5503, 5504, 5505, 5506, 5507, 5508, 5509, 5510, 5511, 5512, 5513, 5514, 5515, 5516, 5517, 5518, 5519, 5520, 5521, 5522, 5523, 5524, 5525, 5526, 5527, 5528, 5529, 5530, 5531, 5532, 5533, 5534, 5535, 5536, 5537, 5538, 5539, 5540, 5541, 5542, 5543, 5544, 5545, 5546, 5547, 5548, 5549, 5550, 5551, 5552, 5553, 5554, 5555, 5556, 5557, 5558, 5559, 5560, 5561, 5562, 5563, 5564, 5565, 5566, 5567, 5568, 5569, 5570, 5571, 5572, 5573, 5574, 5575, 5576, 5577, 5578, 5579, 5580, 5581, 5582, 5583, 5584, 5585, 5586, 5587, 5588, 5589, 5590, 5591, 5592, 5593, 5594, 5595, 5596, 5597, 5598, 5599, 5600, 5601, 5602, 5603, 5604, 5605, 5606, 5607, 5608, 5609, 5610, 5611, 5612, 5613, 5614, 5615, 5616, 5617, 5618, 5619, 5620, 5621, 5622, 5623, 5624, 5625, 5626, 5627, 5628, 5629, 5630, 5631, 5632, 5633, 5634, 5635, 5636, 5637, 5638, 5639, 5640, 5641, 5642, 5643, 5644, 5645, 5646, 5647, 5648, 5649, 5650, 5651, 5652, 5653, 5654, 5655, 5656, 5657, 5658, 5659, 5660, 5661, 5662, 5663, 5664, 5665, 5666, 5667, 5668, 5669, 5670, 5671, 5672, 5673, 5674, 5675, 5676, 5677, 5678, 5679, 5680, 5681, 5682, 5683, 5684, 5685, 5686, 5687, 5688, 5689, 5690, 5691, 5692, 5693, 5694, 5695, 5696, 5697, 5698, 5699, 5700, 5701, 5702, 5703, 5704, 5705, 5706, 5707, 5708, 5709, 5710, 5711, 5712, 5713, 5714, 5715, 5716, 5717, 5718, 5719, 5720, 5721, 5722, 5723, 5724, 5725, 5726, 5727, 5728, 5729, 5730, 5731, 5732, 5733, 5734, 5735, 5736, 5737, 5738, 5739, 5740, 5741, 5742, 5743, 5744, 5745, 5746, 5747, 5748, 5749, 5750, 5751, 5752, 5753, 5754, 5755, 5756, 5757, 5758, 5759, 5760, 5761, 5762, 5763, 5764, 5765, 5766, 5767, 5768, 5769, 5770, 5771, 5772, 5773, 5774, 5775, 5776, 5777, 5778, 5779, 5780, 5781, 5782, 5783, 5784, 5785, 5786, 5787, 5788, 5789, 5790, 5791, 5792, 5793, 5794, 5795, 5796, 5797, 5798, 5799, 5800, 5801, 5802, 5803, 5804, 5805, 5806, 5807, 5808, 5809, 5810, 5811, 5812, 5813, 5814, 5815, 5816, 5817, 5818, 5819, 5820, 5821, 5822, 5823, 5824, 5825, 5826, 5827, 5828, 5829, 5830, 5831, 5832, 5833, 5834, 5835, 5836, 5837, 5838, 5839, 5840, 5841, 5842, 5843, 5844, 5845, 5846, 5847, 5848, 5849, 5850, 5851, 5852, 5853, 5854, 5855, 5856, 5857, 5858, 5859, 5860, 5861, 5862, 5863, 5864, 5865, 5866, 5867, 5868, 5869, 5870, 5871, 5872, 5873, 5874, 5875, 5876, 5885, 5886, 5887, 5888, 5889, 5890, 5891, 5892, 5893, 5894, 5895, 5896, 5897, 5898, 5899, 5900, 5901, 5902, 5903, 5904, 5905, 5906, 5907, 5908, 5909, 5910, 5911, 5912, 5913, 5914, 5915, 5916, 5917, 5918, 5919, 5920, 5921, 5922, 5923, 5924, 5925, 5926, 5927, 5928, 5929, 5930, 5931, 5932, 5933, 5934, 5935, 5936, 5937, 5938, 5939, 5940, 5941, 5942, 5943, 5944, 5945, 5946, 5947, 5948, 5949, 5950, 5951, 5952, 5953, 5954, 5955, 5956, 5957, 5958, 5959, 5960, 5961, 5962, 5963, 5964, 5965, 5966, 5967, 5968, 5969, 5970, 5971, 5972, 5973, 5974, 5975, 5976, 5977, 5978, 5979, 5980, 5981, 5982, 5983, 5984, 5985, 5986, 5987, 5988, 5989, 5990, 5991, 5992, 5993, 5994, 5995, 5996, 5997, 5998, 5999), peak = c(245, 236, 220, 189, 173, 154, 142, 124, 118, 105, 107, 99, 100, 98, 111, 116, 115, 126, 129, 128, 128, 132, 134, 145, 158, 170, 188, 207, 225, 242, 237, 229, 211, 189, 173, 151, 131, 116, 113, 98, 105, 96, 101, 112, 106, 112, 113, 111, 124, 131, 127, 130, 137, 149, 146, 164, 184, 210, 228, 239, 228, 225, 217, 185, 168, 149, 140, 126, 110, 101, 104, 100, 102, 103, 109, 105, 109, 114, 117, 122, 128, 119, 130, 142, 148, 166, 182, 204, 226, 235, 247, 226, 224, 193, 174, 153, 137, 124, 119, 110, 99, 105, 96, 98, 102, 116, 122, 117, 116, 132, 123, 130, 139, 138, 145, 166, 182, 191, 215, 221, 212, 207, 186, 153, 112, 66, 8, 27, 68, 86, 113, 125, 130, 139, 148, 153, 159, 178, 188, 213, 230, 249, 259, 246, 234, 219, 197, 175, 151, 133, 126, 117, 122, 120, 115, 109, 115, 116, 125, 133, 141, 139, 142, 148, 147, 155, 170, 178, 203, 226, 254, 259, 273, 257, 249, 221, 191, 173, 152, 145, 130, 121, 127, 118, 121, 119, 124, 130, 136, 146, 144, 154, 166, 155, 163, 168, 190, 215, 223, 252, 273, 282, 275, 268, 237, 220, 191, 168, 159, 140, 137, 130, 122, 131, 120, 134, 132, 134, 137, 142, 151, 153, 164, 162, 176, 190, 196, 217, 243, 266, 271, 262, 257, 237, 214, 187, 169, 147, 138, 126, 118, 116, 114, 117, 116, 123, 123, 123, 129, 140, 146, 152, 156, 166, 178, 185, 209, 241, 264, 264, 261, 262, 232, 204, 178, 162, 140, 125, 117, 110, 112, 103, 98, 105, 108, 116, 117, 130, 128, 142, 135, 138, 149, 161, 169, 193, 222, 244, 256, 265, 246, 222, 201, 174, 147, 130, 125, 110, 102, 97, 95, 90, 98, 99, 107, 117, 115, 119, 128, 131, 137, 135, 144, 173, 188, 209, 232, 251, 251, 244, 217, 190, 162, 144, 122, 116, 103, 94, 87, 88, 92, 97, 101, 97, 98, 108, 121, 122, 118, 128, 132, 144, 162, 180, 209, 230, 246, 233, 230, 218, 187, 159, 125, 124, 105, 92, 89, 84, 81, 85, 76, 78, 92, 92, 102, 102, 109, 115, 126, 134, 137, 164, 179, 208, 227, 241, 258, 247, 223, 203, 173, 140, 121, 111, 94, 98, 85, 87, 89, 91, 84, 91, 106, 103, 107, 111, 123, 126, 142, 150, 166, 194, 221, 242, 247, 254, 237, 229, 194, 170, 140, 130, 110, 94, 85, 84, 84, 79, 82, 88, 89, 100, 108, 111, 114, 121, 122, 142, 149, 175, 198, 236, 252, 268, 279, 257, 232, 194, 170, 151, 127, 108, 104, 95, 91, 81, 83, 94, 92, 96, 106, 111, 122, 115, 131, 142, 152, 167, 183, 222, 248, 282, 290, 296, 279, 249, 226, 190, 163, 136, 116, 109, 93, 95, 92, 86, 86, 91, 108, 109, 120, 121, 131, 140, 141, 157, 187, 216, 240, 276, 311, 319, 333, 309, 281, 237, 203, 167, 134, 123, 112, 104, 92, 95, 99, 103, 112, 110, 114, 128, 138, 140, 161, 180, 193, 223, 242, 276, 317, 352, 372, 383, 354, 318, 285, 222, 190, 163, 144, 124, 114, 100, 98, 107, 105, 117, 121, 133, 144, 166, 176, 185, 199, 225, 268, 306, 348, 384, 439, 466, 463, 427, 392, 329, 274, 221, 181, 161, 141, 131, 123, 127, 128, 131, 138, 147, 163, 173, 188, 208, 221, 242, 294, 338, 380, 468, 518, 568, 596, 604, 554, 488, 403, 336, 275, 229, 182, 159, 141, 143, 140, 143, 160, 163, 183, 204, 230, 241, 244, 281, 316, 382, 448, 515, 624, 720, 790, 840, 790, 709, 616, 505, 412, 329, 267, 218, 186, 166, 155, 148, 158, 171, 183, 204, 211, 230, 243, 235, 268, 280, 315, 395, 525, 690, 881, 986, 1037, 1002, 930, 790, 668, 556, 444, 335, 276, 236, 218, 201, 201, 216, 227, 257, 290, 331, 368, 401, 451, 525, 599, 693, 823, 988, 1160, 1354, 1466, 1468, 1399, 1220, 1016, 824, 648, 501, 402, 326, 273, 253, 233, 235, 266, 271, 300, 359, 380, 422, 462, 532, 592, 703, 886, 1062, 1253, 1444, 1588, 1701, 1691, 1551, 1372, 1141, 929, 713, 563, 445, 362, 305, 258, 245, 240, 254, 280, 319, 348, 394, 416, 478, 537, 608, 717, 829, 989, 1197, 1426, 1551, 1579, 1563, 1447, 1244, 1026, 801, 638, 500, 379, 303, 265, 230, 221, 202, 203, 225, 246, 289, 303, 327, 374, 396, 462, 539, 625, 744, 899, 1017, 1094, 1138, 1058, 946, 832, 677, 543, 415, 337, 272, 230, 200, 182, 173, 170, 173, 174, 196, 208, 218, 236, 250, 280, 324, 364, 424, 505, 567, 627, 649, 660, 626, 568, 484, 400, 325, 276, 222, 185, 159, 143, 136, 129, 130, 131, 135, 142, 153, 162, 178, 191, 208, 228, 245, 285, 317, 347, 378, 397, 380, 367, 323, 288, 242, 202, 171, 151, 130, 112, 112, 103, 101, 103, 100, 113, 119, 118, 127, 135, 143, 155, 158, 177, 195, 207, 226, 247, 253, 244, 224, 201, 177, 156, 134, 118, 113, 102, 100, 90, 88, 84, 94, 87, 94, 102, 103, 101, 108, 107, 121, 120, 131, 136, 127, 135, 122, 91, 41, 3, 5, 32, 48, 50, 61, 71, 75, 80, 87, 90, 98, 108, 107, 127, 136, 139, 145, 149, 138, 139, 129, 117, 106, 94, 91, 84, 74, 75, 70, 71, 74, 72, 74, 81, 78, 87, 85, 81, 91, 93, 98, 102, 122, 120, 127, 129, 126, 133, 113, 103, 97, 90, 85, 75, 79, 69, 63, 65, 68, 68, 71, 74, 73, 78, 71, 81, 78, 87, 92, 104, 109, 118, 115, 130, 121, 122, 111, 97, 89, 87, 85, 75, 71, 70, 62, 65, 60, 69, 60, 68, 64, 66, 70, 81, 82, 85, 89, 90, 96, 105, 122, 115, 126, 114, 119, 105, 96, 94, 85, 72, 68, 62, 70, 58, 59)) blue.peaks <- tibble(time = data2$time, value = data2$peak) blue.peaks <- blue.peaks %>% mutate(left_v = c(0.01, diff(value)), right_v = -lead(left_v, default = -0.01), is_peak = if_else(left_v > 0 & right_v >= 0, TRUE, FALSE)) data2$keep <- blue.peaks$is_peak data2$keep <- ifelse(data2$keep == TRUE, data2$peak, NA) ggplot(data2, aes(x=time, y=peak)) + geom_col() + geom_point(aes(x=time, y=keep))
BEFORE EDIT data (only peak values): peak <- c( 509, 672 ,758, 686 ,584, 559, 727, 759, 688, 528, 562, 711, 768, 678, 644, 750, 822, 693, 531, 566, 738, 793, 730, 511, 587 ,739, 761, 651, 579, 747, 768, 705, 544, 551, 687, 756, 749, 645, 564, 680, 724, 691, 596, 535, 625, 685, 689, 612, 512, 537, 616, 657, 653 ,573, 506, 598, 675, 685, 668, 609 ,515, 575, 656, 687, 678, 626, 533, 509, 587, 641, 680, 663, 602, 515 ,505, 583, 646, 693, 696, 684, 630, 549, 500 ,572, 637, 681, 725, 736 ,736, 703, 649, 556, 568, 637, 682, 743, 765, 767, 709, 660, 587, 548, 622, 690, 761, 779, 764, 749, 694, 631 ,525 ,571 ,646, 724, 788, 811, 834, 818, 776, 712, 616, 536, 556, 649, 738, 801, 857, 866, 837, 808, 718 ,647 ,568, 508, 605, 714, 823, 872, 917 ,916, 890 ,825, 742, 642, 543, 549, 656, 766, 851, 921, 947, 951, 892, 830, 730, 617, 586, 675, 760, 804, 816 ,795, 740, 690, 613, 522) code: peak_data <- tibble( value = peak ) peak_data <- peak_data %>% mutate( left_v = c(0.01, diff(value)), right_v = -lead(left_v, default = -0.01), peak = if_else( (left_v > 0 & right_v >= 0) | (left_v >= 0 & right_v > 0), TRUE, FALSE ) ) %>% filter(peak == TRUE) %>% select(-left_v, -right_v, -peak) I've put some postive number (0.01) for cheking if edges are peaks. I've got 24 peaks (local max) from this code: peak_data %>% nrow() [1] 24 I'm still not exactly sure what you mean by peak, for example vector: x <- c(5, 6, 6, 4) has 2 peaks in my code. Its easy to adapt it to have 0 peaks with change: (left_v > 0 & right_v >= 0) | (left_v >= 0 & right_v > 0) to: left_v > 0 & right_v > 0 then I get 22 peaks. Or you can count it as 1 by putting one of: left_v >= 0 & right_v > 0 left_v > 0 & right_v >= 0 depending what side you want to count for 23 peaks. AFTER EDIT AND COMMENT peak_data <- tibble( peak = peak, time = time ) peak_data <- peak_data %>% mutate( left_v = c(0.01, diff(peak)), right_v = -lead(left_v, default = -0.01), is_peak = if_else( left_v > 0 & right_v >= 0, TRUE, FALSE ) ) %>% filter(is_peak == TRUE) %>% select(peak, time) gives peaks and peaks time.
Unused argument in GA package
I'm trying to use TSP package with GA. I want to do something similar to this
My code:
library(GA)
library(globalOptTests)
library(TSP)
data("USCA50")
fitFun <-
function(x)
-tour_length(solve_TSP(USCA50))
dist <- as.matrix(USCA50)
GA <- ga(
type = "permutation",
fitness = fitFun,
distMatrix = dist,
min =1,
max = 50
)
The error I get:
Error in fitness(Pop[i, ], ...) :
unused argument (distMatrix = c(0, 1167, 1579, 437, 3575, 1453, 226, 2976, 1107, 1006, 1046, 891, 1488, 1030, 1803, 190, 1122, 1373, 1860, 523, 1047, 1152, 370, 1453, 1629, 1323, 1032, 654, 1462, 752, 993, 813, 1178, 1705, 816, 1206, 1285, 1641, 1578, 1703, 1343, 1317, 1647, 1157, 1479, 1703, 1166, 1211, 795, 1572, 1167, 0, 413, 1422, 2895, 316, 1172, 3094, 140, 382, 189, 530, 392, 526, 635, 1174, 2056, 286, 692, 910, 207, 211, 1035, 303, 2046, 2164, 1385, 845, 297, 597, 1033, 393, 1766, 546, 386, 1076,
153, 476, 432, 546, 184, 184, 481, 1579, 1686, 543, 20, 2008, 527, 434, 1579, 413, 0, 1832, 2766, 167, 1585, 3265, 508, 677, 547, 842, 229, 775, 229, 1575, 2451, 275, 289, 1277, 582, 514, 1420, 207, 2347, 2544, 1720, 1189, 116, 947, 1350, 800, 2117, 138, 777, 1338, 334, 62, 106, 145, 260, 312, 128, 1911, 1961, 136, 413, 2384, 913, 131, 437, 1422, 1832, 0, 3437, 1732, 272, 2607, 1327, 1355, 1345, 1269, 1787, 1409, 2041, 615, 697, 1670, 2093, 954, 1256, 1345, 807, 1672, 1242, 8
Is there something wrong with my GA package? RStudio doesn't show me this parameter but somehow others are able to run it.
Selecting just some rows of a RGList
I am using the package Limma to analyse some data. After reading the raw data with read.maimagenes I get a RGList object. R.cut and G.cut are a value of class numeric and I want the values above them. I tried something like this:
RG$R <- RG$R[RG$R>R.cut]
RG$G <- RG$G[RG$G>G.cut]
But this converts the class of RG$R from matrix to numeric how could I keep the matrix class (I don't know if introducing some NA values would affect the rest of the analyse). I have tried subset like this:
RG.probe$R <- subset(x=RG, subset=RG$R>R.cut)
But it keeps returning an error Error: Two subscripts required
How can this be done?
Aside: If I would like to just get the rows that are above the R.cut and G.cut?
Possible solution:
I have found that with ifelse I can do it although I must introduce a value for the ones that are under the cut, and I haven't found the way to check if both R and G channels are above each cut.
RG$R<-ifelse(RG$R>R.cut, RG$R, '')
RG$G<-ifelse(RG$G>G.cut, RG$G, '')
Although it is converted to character, and therefore I cannot do further analysis.
Data to make it reproducible:
library(limma)
RG<-read.maimages(path, source='agilent')
class(RG)
[1] "RGList"
attr(,"package")
[1] "limma"
dput(head(RG$R))
structure(c(2893, 81.5, 80.5, 140208, 4512, 6272, 4934, 195,
184.5, 164092, 11819, 10569, 1689.5, 83, 82, 68996, 2260.5, 3603,
2470, 84, 77, 96750, 3203, 5223, 3246, 85.5, 104.5, 54773, 519.5,
8244.5, 1807, 86.5, 88, 204574, 15693, 8939.5, 2040, 87, 95,
131880, 7346, 9922.5, 1445, 76, 85.5, 125598, 3863, 5758.5, 2626,
87.5, 85, 180266, 18173, 20171.5, 1811.5, 84, 87.5, 122498, 3993,
5857, 1799, 87.5, 82, 123220, 3780, 5964, 1706, 77.5, 80, 124463,
3390, 5070, 3787, 81.5, 88, 65874, 269, 781.5, 1476, 90, 89,
122445, 4232, 6479, 2788, 82, 87.5, 80669, 791, 7440.5, 1503,
81, 88, 124702, 4270, 6111, 2012.5, 93.5, 90, 215820, 4555, 3101,
1727.5, 102, 109, 131316, 4284, 6638, 2009, 95.5, 111.5, 175474,
12665, 17213, 1532, 87.5, 84.5, 117568, 4098, 6100, 1436, 83,
91, 118472, 4067.5, 6114, 1651.5, 83, 82, 127308, 4150, 6277,
2028.5, 85.5, 89, 74816, 896.5, 7697, 2698, 84, 92.5, 99431,
1273, 9182.5, 1833.5, 100, 104, 163604, 15582, 12146, 2359, 102,
109, 159301, 17229, 9822.5, 1857, 86, 88, 130319, 4354.5, 6266.5,
1887, 87, 87, 133386, 11639.5, 8931, 2304.5, 86.5, 87, 91022,
1011, 14524, 1353, 84, 88, 114282, 3935, 5944, 1487, 83, 87,
125507, 4138, 5804, 3379, 86.5, 88, 63703.5, 331, 1167, 1778,
87, 83.5, 123988, 4366, 6670, 1862, 94.5, 92, 134174, 4558, 6881,
2388.5, 82, 91.5, 174744, 8570, 10677, 4374, 94, 94, 179579,
12753, 10869, 3747.5, 115, 144.5, 133809, 3710, 5406, 5062, 93.5,
92, 207843, 13220, 6774, 3294, 78, 82.5, 149764, 3774, 5582,
5303, 93, 100, 93479.5, 803, 6709, 2969, 86.5, 101, 149011, 4043,
5407, 5488, 106, 118.5, 191053, 9990.5, 12194, 4308, 89, 85,
143087, 3926.5, 5370.5, 5168, 87, 91.5, 137415, 4028, 5671, 4649.5,
91, 90, 147328, 4102, 5614.5, 7225, 87, 85, 179052, 15612, 16908,
5815.5, 84, 88, 200883, 13229, 11482, 3551, 101, 125, 224012,
20461, 16149.5, 3992, 98, 83, 134744, 3569, 5068, 4817, 97, 92,
142087, 4203, 5678, 5436, 108, 84.5, 195104, 11299, 13246), .Dim = c(6L,
51L), .Dimnames = list(NULL, c("US23502326_253482110017_S01_GE2_1105_Oct12_1_1",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_1", "US23502326_253482110017_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_3", "US23502326_253482110017_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_1", "US23502326_253482110027_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_3", "US23502326_253482110027_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_1", "US23502326_253482110027_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_3", "US23502326_253482110027_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_1", "US23502326_253482110028_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_4", "US23502326_253482110028_S01_GE2_1105_Oct12_2_1",
"US23502326_253482110028_S01_GE2_1105_Oct12_2_2", "US23502326_253482110028_S01_GE2_1105_Oct12_2_3",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_1", "US23502326_253482110029_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_3", "US23502326_253482110029_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_1", "US23502326_253482110029_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_3", "US23502326_253482110029_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_1", "US23502326_253482110030_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_3", "US23502326_253482110030_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_1", "US23502326_253482110030_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_3", "US23502326_253482110030_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_1", "US23502326_253482110031_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_3", "US23502326_253482110031_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_1", "US23502326_253482110031_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_3", "US23502326_253482110031_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_1", "US23502326_253482110049_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_3", "US23502326_253482110049_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_1", "US23502326_253482110049_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_3", "US23502326_253482110049_S01_GE2_1105_Oct12_2_4"
)))
dput(head(RG$G))
structure(c(2324, 58, 52, 98015, 9800, 5284, 1472, 114, 92.5,
27879, 2296, 3272.5, 3637, 216, 204.5, 34898, 731, 5084, 3466,
77, 74, 32543, 497, 7416, 1344, 79.5, 99, 52753, 2363, 3457,
686, 39, 44.5, 32866, 2937, 4324, 910, 42, 40, 42361, 2780, 4072,
1587, 83.5, 97, 79659, 7667, 10103, 754, 49.5, 44, 23664, 2962,
4166, 1390.5, 136, 156.5, 70132.5, 7914, 4876, 1609, 99, 125,
25923.5, 610, 5125, 1526, 198.5, 157, 94640.5, 10408, 9233, 1060,
42, 37, 70033, 3144, 4355.5, 1465, 89, 91, 99188, 9587, 7547,
743, 61.5, 60, 65888, 3247, 4676.5, 1931.5, 89, 84, 65967, 11226,
7757, 873.5, 56, 66, 20126.5, 3291, 4736, 1339.5, 298, 300, 75324,
6712, 8500, 894, 65, 86, 26341.5, 3132.5, 4647, 2372, 80, 81.5,
73418.5, 5026, 7612, 1564, 70, 73, 77180, 7802.5, 9454, 1315,
90, 85, 20562, 340, 5337, 868.5, 49, 55, 64712.5, 2947, 4260,
798, 46, 48, 52505, 3380, 4663.5, 904, 69.5, 80, 33371.5, 3300,
4997, 813, 73, 81, 29552, 2932, 4632.5, 1696.5, 187, 324, 63647,
6407, 8571.5, 872, 39.5, 52, 24518, 3094, 4387, 752.5, 54, 52,
48299.5, 3221, 4278, 2631, 61, 72, 27229, 513, 5019, 1256.5,
61, 63, 74560, 11016, 9019, 942, 57, 55, 70933.5, 3526.5, 5383,
1457.5, 162, 193.5, 86276, 8154, 12084, 1590, 213, 293, 66871,
6580, 9535, 833.5, 57, 62.5, 36416, 3229, 4600, 2161.5, 53.5,
42.5, 39157.5, 2952, 3977, 3481, 67, 68, 18675, 152, 536, 1977,
57, 55.5, 32861, 2785, 3812, 4739.5, 112.5, 113.5, 104923, 6231,
8198, 1907, 57.5, 69, 76674.5, 3219, 4244, 3879.5, 183.5, 171,
110822, 9582, 8426, 1746, 74.5, 74, 33327, 2774, 4017, 3333,
187, 270.5, 83696, 6616, 7080, 4737, 38, 37, 30041, 429.5, 3970,
3347, 45, 46, 106822, 8003.5, 7137.5, 2431, 32, 35, 32985, 3121,
4179, 2535.5, 28, 34, 36131.5, 3135, 4126, 1929, 42, 65, 47428,
3300, 4626.5, 5371, 54.5, 43.5, 108175, 9983, 6182, 5139.5, 34,
28, 26774, 152, 518, 2621, 48, 32, 44499, 3409, 4643), .Dim = c(6L,
51L), .Dimnames = list(NULL, c("US23502326_253482110017_S01_GE2_1105_Oct12_1_1",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_1", "US23502326_253482110017_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_3", "US23502326_253482110017_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_1", "US23502326_253482110027_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_3", "US23502326_253482110027_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_1", "US23502326_253482110027_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_3", "US23502326_253482110027_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_1", "US23502326_253482110028_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_4", "US23502326_253482110028_S01_GE2_1105_Oct12_2_1",
"US23502326_253482110028_S01_GE2_1105_Oct12_2_2", "US23502326_253482110028_S01_GE2_1105_Oct12_2_3",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_1", "US23502326_253482110029_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_3", "US23502326_253482110029_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_1", "US23502326_253482110029_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_3", "US23502326_253482110029_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_1", "US23502326_253482110030_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_3", "US23502326_253482110030_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_1", "US23502326_253482110030_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_3", "US23502326_253482110030_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_1", "US23502326_253482110031_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_3", "US23502326_253482110031_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_1", "US23502326_253482110031_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_3", "US23502326_253482110031_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_1", "US23502326_253482110049_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_3", "US23502326_253482110049_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_1", "US23502326_253482110049_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_3", "US23502326_253482110049_S01_GE2_1105_Oct12_2_4"
)))
From the dput you can create an RGList by new("RGList") I know it is to much data but as I previously asked I don't know how to short the output.
The solution that finally I got is doing almost the same: RG$G <- ifelse(RG$R>R.cut, RG$G, NA) RG$R <- ifelse(RG$G>G.cut, RG$R, NA) Deleting this values seems to increase the Fold Change of each gene as already happened comparing the original RG with the RG with deleted values as in the question.