Related
My dataset contains 2 variables Y and X. Y was measured every 1.0 seconds.
My Data:
dput(Dataexample)
structure(list(X = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,
121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,
186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,
199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,
212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,
225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,
238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,
251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,
264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,
277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302,
303, 304, 305, 306), Y = c(71756.2344, 71745.85, 70882.42, 71025.61,
70539.02, 70602.3047, 70811.87, 70514.125, 69998.63, 70531.76,
70424.9141, 70663.51, 70075.375, 69731.0859, 70029.74, 70519.31,
69858.63, 69987.23, 70080.56, 69970.63, 69829.6, 69872.12, 69775.68,
69679.24, 69814.05, 69639.84, 69645.02, 69344.35, 69430.41, 70078.49,
69239.65, 69734.1953, 69736.27, 69549.63, 69506.0859, 69108,
69669.91, 69516.45, 69490.54, 69609.77, 69314.29, 69454.25, 69590.07,
69721.76, 69525.79, 69736.27, 69303.92, 69171.23, 69294.59, 69430.41,
69457.36, 69462.54, 69144.27, 69590.07, 69446.99, 70083.67, 69358.87,
69800.56, 69680.28, 69332.95, 69723.83, 69942.63, 69772.56, 69969.59,
69808.86, 70043.23, 70208.13, 70077.45, 69856.56, 70423.875,
69490.54, 69984.12, 70175.98, 70192.58, 70279.7, 70480.93, 70594,
70792.16, 70234.06, 70165.61, 70249.62, 70564.95, 70403.13, 70444.625,
70426.99, 69907.375, 70327.4141, 70686.3359, 70473.67, 71031.83,
70864.78, 70710.1953, 70691.52, 70703.97, 70826.39, 70708.12,
70595.04, 70946.75, 71319.27, 70977.875, 70475.74, 70612.68,
70680.11, 70527.61, 70461.22, 70877.2344, 70631.35, 70723.68,
70677, 70433.21, 70306.6641, 71246.63, 70375.125, 70416.62, 70150.0547,
70733.0156, 70583.63, 70866.86, 70580.5156, 70433.21, 70377.2,
70114.79, 70347.12, 70613.71, 70576.37, 70599.19, 70407.28, 70581.5547,
70650.02, 71122.11, 70909.4, 70694.63, 71076.45, 70650.02, 71133.52,
70810.83, 71240.41, 70630.31, 71144.94, 71493.63, 71117.95, 71374.28,
71143.9, 70805.64, 71349.375, 71208.2344, 71322.39, 71727.1641,
71060.88, 71546.56, 71569.4, 70984.1, 72032.37, 71573.55, 71787.375,
71469.76, 71398.15, 71683.57, 71709.52, 71637.9, 71556.9453,
71870.4141, 71612.99, 71953.47, 71515.43, 71315.125, 72007.4453,
72021.9844, 71549.68, 72001.22, 71359.75, 71775.95, 72327.23,
71949.31, 71844.47, 71857.96, 72128.9141, 72147.6, 71501.94,
72268.05, 72104, 72217.1641, 72253.51, 72198.48, 72908.78, 72084.27,
72653.29, 72431.06, 72858.92, 72512.0547, 72632.5156, 72700.02,
72335.53, 72713.52, 73065.62, 72818.42, 73004.3359, 72458.06,
73436.48, 73231.82, 73002.26, 73313.89, 73213.125, 72980.4453,
72948.25, 73106.13, 72931.625, 73409.47, 73057.31, 73141.4453,
73218.32, 73216.24, 73273.375, 73701.42, 73486.35, 72574.37,
73229.74, 73576.74, 73195.46, 73697.2656, 73115.48, 73065.62,
73062.5, 73111.32, 73988.23, 73619.3359, 73874.95, 73683.76,
73674.41, 73550.7656, 74166.9844, 73875.99, 74013.17, 74092.16,
73872.875, 74015.25, 73984.07, 73911.33, 73606.87, 74082.8, 73866.64,
74550.53, 74271.95, 73980.95, 74502.71, 74901.92, 74753.25, 74310.4141,
75178.51, 74748.05, 74756.37, 75194.1, 74797.95, 75531.0547,
75549.77, 75293.94, 75378.17, 75457.21, 75676.67, 76087.56, 76141.6641,
76008.5, 76241.55, 76585.96, 76091.73, 76880.4844, 76898.18,
77005.38, 77080.32, 77548.78, 77337.4453, 77000.18, 77448.8359,
76997.0547, 77314.54, 77919.47, 77185.46, 78127.75, 77464.45,
78349.59, 77824.71, 77465.49, 77818.46, 78140.25, 78547.51, 77850.74,
78236.06, 78341.2656, 78104.8359, 78464.17, 77888.23, 78392.3,
78686.0547, 78149.625, 78623.5547, 78672.5156, 78810.03, 78498.55,
78652.72, 78717.31, 78831.91, 78882.96, 78715.23, 78499.5859,
78892.3359, 78372.51)), row.names = c(NA, -306L), class = c("tbl_df",
"tbl", "data.frame"))
I have used ggplot to plot the data and used a loop to calculate the average slope within a moving 60-second-window for the entire duration of the dataset to find the 60 consecutive seconds where the slope is greatest.
Code:
library(readr)
library(ggplot2)
Dataexample<- read_csv("HF-6.csv", skip = 3)
Dataexample<- head(Dataexample, -1)
Dataexample$X <- as.numeric(Dataexample$X)
df <- data.frame(Dataexample)
ggplot(data=df, aes(x=X, y=Y, group=1)) +
geom_line()
slopes <- rep(NA, nrow(Dataexample)-59)
for( i in 1:length(slopes)){
slopes[i] <- lm(Y ~ X, data=Dataexample[i:(i+59), ])$coefficients[2]
}
print(slopes)
which.max(slopes)
max(slopes)
My questions is how can I then take the results of my loop that show the consecutive 60 seconds where the slope is highest and change the color of the line in the plot during those 60 seconds to highlight where slope is greatest.
This should work:
maxslope_ind <- which.max(slope)
Dataexample$highlight <- ifelse(Dataexample$X %in% maxslope_ind:(maxslope_ind+59), 1, 0)
library(ggplot2)
ggplot(data=Dataexample, aes(x=X, y=Y, group=1)) +
geom_line(aes(colour=as.factor(highlight)), show.legend=FALSE) +
scale_colour_manual(values=c("black", "red"))
I am new to 'for' loops in R, and I'm trying to create a new column in the dataframe and pass a formula through it, with the output being a calculated value in each cell. Here is the data I am working with:
data <- data.frame(plot1=c(133,
134,
115,
135,
119,
41,
96,
147,
64,
18,
123,
198,
40,
142,
131,
175,
52,
7,
100,
30,
197,
200,
45,
149,
170,
87,
191,
90,
50,
168,
153,
70,
98,
76,
12,
83,
101,
26,
62,
96,
52,
97,
90,
65,
9,
146,
182,
174,
15,
78,
165,
29,
186,
132,
167,
6,
43,
135,
148,
49,
64,
102,
195,
103,
105,
75,
189,
28,
117,
112,
187,
13,
109,
123,
61,
13,
160,
172,
125,
34,
75,
89,
19,
121,
136,
3,
32,
161,
111,
158,
135,
78,
133,
7,
182,
159,
99,
155,
80,
186,
120,
74,
119,
105,
157,
187,
46,
92,
25,
48,
129,
156,
180,
106,
115,
188,
120,
133,
61,
152,
152,
31,
84,
8,
20,
40,
59,
136,
84,
111,
167,
53,
40,
121,
29,
127,
135,
177,
54,
103,
41,
15,
21,
108,
105,
139,
73,
126,
59,
165,
91,
56,
53,
126,
26,
124,
128,
86,
109,
50,
40,
8,
21,
177,
198,
128,
160,
97,
12,
128,
102,
57,
197,
167,
90,
33,
59,
79,
167,
5,
52,
58,
22,
200,
38,
163,
77,
188,
136,
26,
10,
37,
179,
98,
170,
35,
30,
144,
61,
58,
141,
165,
37,
98,
2,
103,
133,
88,
172,
114,
132,
89,
170,
62,
12,
27),
plot2=c(412,
303,
10,
45,
161,
181,
450,
647,
43,
407,
170,
470,
404,
505,
293,
173,
217,
238,
318,
443,
378,
317,
29,
64,
192,
249,
550,
328,
605,
552,
58,
226,
502,
193,
506,
589,
160,
261,
515,
563,
550,
645,
488,
547,
434,
652,
70,
573,
623,
351,
450,
384,
82,
315,
517,
626,
524,
650,
466,
8,
452,
624,
306,
431,
460,
587,
619,
139,
43,
124,
38,
394,
53,
436,
625,
173,
509,
32,
115,
156,
465,
656,
584,
636,
255,
212,
317,
154,
153,
111,
554,
653,
15,
32,
118,
532,
415,
615,
124,
416,
546,
51,
503,
448,
612,
551,
286,
347,
52,
435,
592,
138,
182,
622,
646,
5,
158,
473,
640,
500,
7,
578,
1,
644,
230,
374,
452,
307,
271,
462,
647,
648,
358,
262,
614,
79,
278,
534,
68,
359,
357,
106,
589,
575,
457,
555,
252,
418,
371,
654,
88,
609,
589,
215,
435,
82,
600,
266,
510,
307,
537,
148,
536,
290,
36,
301,
27,
129,
77,
595,
506,
431,
293,
232,
108,
246,
354,
347,
460,
384,
452,
261,
132,
499,
463,
187,
422,
319,
48,
284,
241,
151,
611,
496,
572,
501,
644,
387,
480,
221,
146,
39,
400,
361,
531,
69,
539,
295,
249,
638,
289,
83,
404,
289,
547,
324))
The new column created is data$newCol, and I'm trying to calculate the area of a circle using the data in data$plot2 and then multiply that value by data$plot1. The result should be in the data$newCol column.
Here is what I have tried:
data$newCol <- for(i in data$newCol) {
((3.14(data$plot2^2)) * (data$plot1)
}
However, I keep getting this error:
Error: attempt to apply non-function
Can someone help me properly write this loop?
I see a few issues with what you're doing:
To multiply 3.14 by something you need to supply a multiplication operator *. When you write 3.14(x) that is the construction of a function where 3.14 is the name of the function and x is it's arguments. That's what's causing the error
Error: attempt to apply non-function
If data$newCol doesn't exist yet you can't loop over it.
This doesn't need to be a loop because those mathematical operations are vectorized so they will just act element by element down the vectors supplied if they are the same length.
R has a built in constant for pi so you can get better precision than 3.14 if you're interested.
So with that in mind, try this:
data$newCol <- pi * (data$plot2^2) * data$plot1
If I understood you correctly you are looking for:
data$newCol <- pi*data$plot2^2 * data$plot1
The above would give you a new column. In R, these operations are vectorised, thus, loops are not needed.
This question already has an answer here:
extract values from a data frame based on a vector of row numbers in R
(1 answer)
Closed 2 years ago.
I have a sample data.frame below (subset of a very large cyclic database)
> dput(try)
structure(list(Actuator.Force = c(-402.57388, -400.83463, -402.72595,
-404.24283, -404.07663, -403.83575, -407.55435, -418.7684, -435.86246,
-462.38239, -504.09146, -558.40039, -618.46674, -681.58704, -748.87347,
-814.95032, -880.57739, -946.11627, -1012.9043, -1075.2557, -1141.4972,
-1209.1968, -1272.8707, -1336.021, -1400.5078, -1465.5786, -1528.6499,
-1589.5626, -1654.6541, -1717.825, -1780.0903, -1839.9329, -1902.9841,
-1964.1945, -2025.569, -2085.9578, -2148.239, -2207.5295, -2267.5806,
-2328.6467, -2388.4958, -2447.5298, -2506.7534, -2567.687, -2625.7661,
-2682.866, -2741.3511, -2802.1934, -2858.2546, -2915.1028, -2972.7683,
-3030.8093, -3089.2439, -3145.5701, -3199.8442, -3259.2087, -3315.8582,
-3371.958, -3426.5596, -3484.3855, -3541.2642, -3595.3362, -3650.0208,
-3708.3748, -3763.8076, -3820.0623, -3875.3044, -3932.9504, -3989.6238,
-4047.5957, -4104.8169, -4164.8237, -4223.5444, -4283.3813, -4341.3989,
-4403.166, -4462.1479, -4522.5728, -4584.0186, -4644.7656, -4704.3525,
-4762.6826, -4821.8706, -4878.8818, -4924.1021, -4959.0415, -4985.9517,
-5005.4531, -5017.8027, -5026.0757, -5032.3428, -5036.8042, -5038.9292,
-5039.5361, -5043.021, -5043.0981, -5043.0415, -5042.627, -5014.4199,
-4853.5854, -4566.9771, -4198.7612, -3774.5527, -3317.6958, -2847.5229,
-2364.7585, -1880.9485, -1405.4272, -930.289, -467.04822, -18.867363,
421.17499, 838.86719, 1239.9121, 1626.0669, 1990.6389, 2334.0852,
2655.344, 2962.0227, 3243.7817, 3506.2249, 3744.2622, 3959.8271,
4156.7061, 4324.9048, 4469.229, 4591.6689, 4687.4194, 4764.0801,
4814.6167, 4840.313, 4846.0181, 4826.3135, 4777.6553, 4696.0791,
4583.854, 4442.457, 4272.5254, 4076.7224, 3851.1211, 3603.1853,
3330.7456, 3038.3157, 2724.115, 2386.5476, 2032.5809, 1660.0547,
1268.0084, 859.16675, 432.4075, -14.131592, -479.29309, -955.67108,
-1444.614, -1937.2562, -2437.0085, -2941.8914, -3450.9009, -3959.9597,
-4468.9795, -4981.2549, -5492.6997, -6002.334, -6510.5425, -7016.2432,
-7517.8286, -8013.1348, -8500.4199, -8974.8867, -9439.5479, -9890.5938,
-10326.367, -10744.421, -11147.754, -11534.83, -11902.651, -12248.997,
-12577.919, -12885.458, -13172.309, -13441.554, -13691.502, -13922.634,
-14127.116, -14305.272, -14458.267, -14582.934, -14685.274, -14758.539,
-14806.058, -14830.719, -14836.625, -14822.204, -14773.916, -14700.484,
-14597.968, -14469.834, -14312.099, -14126.422, -13915.136, -13676.505,
-13412.388, -13120.703, -12807.961, -12473.883, -12115.751, -11740.082,
-11342.633, -10929.945, -10502.158, -10062.869, -9611.8271, -9146.6006,
-8673.3545, -8191.7417, -7700.769, -7200.9346, -6695.8809, -6185.2378,
-5670.8711, -5154.9995, -4643.4414, -4135.0015, -3629.2859, -3125.657,
-2626.541, -2134.0662, -1646.4242, -1168.816, -699.63068, -245.34488,
192.7984, 618.76703, 1033.223, 1428.922, 1807.2645, 2165.6274,
2507.6655, 2826.2754, 3120.4724, 3395.2593, 3647.6946, 3879.4983,
4086.3855, 4265.1323, 4421.6831, 4554.3594, 4657.8184, 4736.9561,
4792.6724, 4822.3784, 4830.3091, 4815.9038, 4773.9692, 4706.4736,
4614.8379, 4491.3198, 4337.8892, 4158.002, 3949.3147, 3713.4622,
3453.9114, 3167.8179, 2861.2598, 2536.3259, 2187.3623, 1822.752,
1437.5449, 1034.8208, 617.23962, 183.35637, -270.79733, -738.95618,
-1220.1345, -1710.7787, -2206.1941, -2706.4871, -3210.8625, -3721.0002,
-4233.6387, -4747.7271, -5258.7578, -5771.3071, -6280.7759, -6791.0166,
-7295.0229, -7794.4199, -8287.4189, -8771.6377, -9243.3457, -9702.2559,
-10146.865, -10577.053, -10989.863, -11385.981, -11760.477, -12116.938,
-12456.351, -12772.688, -13071.995), No.Rows = c(1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152,
153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165,
166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178,
179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204,
205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,
218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230,
231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243,
244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256,
257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269,
270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282,
283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295,
296, 297, 298, 299, 300)), row.names = c(NA, 300L), class = "data.frame") class =
"data.frame")
I find the peaks and valleys of the data using:
library(quantmod)
max <- findPeaks(try$Actuator.Force)
min <- findValleys(try$Actuator.Force)
The result is the row.number of the try data.frame corresponding to the peaks and valleys. What I want is a vector of the Actuator.Force peak values corresponding to the row.numbers that the findPeaks and findValleys function find.
If the min and max values are the row index of try data frame, you can get a subset of try:
> try[min, ]
Actuator.Force No.Rows
5 -404.0766 5
97 -5043.0415 97
193 -14822.2040 193
> try[max, ]
Actuator.Force No.Rows
3 -402.7260 3
7 -407.5543 7
133 4826.3135 133
253 4815.9038 253
If you want to get only the Actuator.Force values for max and min row index:
> try[min, "Actuator.Force"]
[1] -404.0766 -5043.0415 -14822.2040
> try[max, "Actuator.Force"]
[1] -402.7260 -407.5543 4826.3135 4815.9038
This is sort of a repost with an update, because the person who replied to the original post was able to solve part of the problem, but we discovered a new issue that needed to be solved and I haven't found other posts addressing this. If this isn't allowed, let me know!
So I have a tibble with four columns and a whole bunch of rows. I'll put the first few rows at the end of the post. The first column is called id, and each row has a unique id. The next column is doy.series and the third column is called smooth.series Each entry in the doy.series and smooth.series columns are lists. The last column is just called doy which is an integer.
So what I want is to plot the doy.series against the smooth.series for each row, but plot all of those as lines on the same plot. I'd also like the lines to be colored by the doy. I would like the highest doy values to be red gradually transitioning to the lowest doy values which I want to be blue.
The issue is that the length of the two lists varies slightly row to row (so the doy.series and smooth.series lists for a given row have the same number of elements, but the number of elements varies from row to row). So if I try to do this:
library(tidyverse)
df2 <- df %>%
unnest()
ggplot(df2, aes(x = doy.series, y = smooth.series, color = doy, group = doy)) +
geom_line() +
scale_color_gradient(low = "blue", high = "red")
I get Error: All nested columns must have the same number of elements.
Any ideas of how to solve this?
Sample of data:
df=structure(list(id = c("1", "2", "3"), doy = c(152, 158, 142),
smooth.series = list(c(0.356716711457841, 0.370050893258325,
0.383236999766461, 0.396376974233949, 0.40957275991249, 0.422784291482468,
0.435895856103075, 0.448895925744217, 0.461772972375802,
0.474515467967738, 0.48722268616777, 0.499933470515835, 0.512545647820125,
0.524957044888832, 0.537065488530148, 0.549189274496968,
0.561532939869938, 0.573823673448877, 0.5857886640336, 0.597155100423927,
0.608751798005646, 0.621116663488914, 0.633540522660091,
0.645314201305544, 0.655728525211634, 0.665571086939856,
0.675708836320647, 0.685551635043781, 0.694509344799033,
0.701991827276177, 0.70938842013153, 0.717660871422796, 0.725577658441836,
0.731907258480512, 0.735418148830686, 0.737609381488737,
0.740068708326791, 0.741697656450321, 0.741397752964802,
0.738070524975708, 0.730787113459408, 0.720275348839784,
0.707921792393576, 0.695113005397529, 0.683235549128384,
0.66854065601544, 0.648565682783239, 0.626626377151392, 0.606038486839507,
0.590117759567193, 0.575248354822936, 0.557183338977548,
0.538291820074129, 0.520942906155777, 0.507505705265592,
0.497170423227522, 0.487542218972326, 0.478612630203321,
0.470373194623822, 0.462815449937146, 0.458831683827816,
0.459466542404155, 0.461940101005184, 0.463472434969922,
0.461283619637389, 0.458826926942516, 0.459760482491641,
0.461611642130895, 0.461907761706409, 0.458176197064313,
0.45041527548862, 0.440794234319326, 0.430096794486539, 0.419106676920368,
0.408607602550923, 0.396242242656226, 0.380503346606902,
0.363449752471964, 0.347140298320423, 0.333633822221291,
0.321253095838767, 0.307606088569194, 0.293679435079791,
0.28045977003778, 0.268933728110384, 0.258699817638372, 0.248739536036072,
0.239114001581039, 0.22988433255083, 0.221111647222998, 0.213576575535607,
0.207511807976447, 0.202156553647666, 0.196750021651411,
0.19053142108983, 0.183900129705502, 0.177683584689661, 0.17176308431744,
0.166019926863971, 0.160335410604389, 0.153743267353215,
0.146014563103421, 0.138136597397814, 0.131096669779199,
0.125882079790385, 0.121448622919517, 0.116554575980903,
0.111890960506595, 0.10814879802864, 0.106019110079089, 0.105661169696536,
0.106498694582266, 0.108119373262358, 0.110110894262892,
0.11206094610995, 0.11540233539241, 0.120997725074791, 0.127579588246629,
0.133880397997461, 0.138632627416822, 0.143475963087052,
0.150098990228934, 0.157307529889666, 0.163907403116449,
0.168704430956481, 0.172368187413333, 0.176173416587563,
0.179833694671856, 0.183062597858895, 0.185573702341366,
0.187052218702838, 0.187638597324987, 0.187729274097655,
0.187720684910687, 0.188009265653923, 0.188094043051569,
0.187473296399738, 0.186542340446139, 0.185696489938482,
0.185331059624476, 0.18519846898834, 0.184852749545972, 0.184391634092598,
0.183912855423446, 0.183514146333744, 0.183117312589153,
0.182606699408324, 0.182023848765966, 0.181410302636787,
0.180807602995498, 0.180212334083628, 0.179591966640683,
0.178944372388341, 0.178267423048277, 0.177558990342169,
0.176816945991692, 0.176039161718523, 0.175223509244339,
0.174367860290815), c(0.774610362619149, 0.746412269781788,
0.719913789191898, 0.695420287796062, 0.673237132540861,
0.653273968452586, 0.635200894750251, 0.618963959669522,
0.604509211446066, 0.59178269831555, 0.581143108860635, 0.572741206185169,
0.566211150306591, 0.561187101242345, 0.557303219009872,
0.555232501533965, 0.555534753213423, 0.557674343776691,
0.561115642952215, 0.565323020468442, 0.573372729704498,
0.586895414376992, 0.603187029720595, 0.619543530969977,
0.63326087335981, 0.649100787927623, 0.670935206211495, 0.694725384196925,
0.716432577869408, 0.732018043214443, 0.745048344614319,
0.760240534670835, 0.775281601698759, 0.787858534012854,
0.795658319927888, 0.799986340749408, 0.803308852568436,
0.805054155877942, 0.804650551170897, 0.801526338940272,
0.794278541385548, 0.783000246619136, 0.769363854003397,
0.755041762900692, 0.741706372673384, 0.725475019829043,
0.703589930868272, 0.679410840142977, 0.656297482005064,
0.637609590806439, 0.619522671663228, 0.59768917697572, 0.574684262022063,
0.553083082080407, 0.535460792428901, 0.520724989583801,
0.506208924554847, 0.492126234360422, 0.478690556018906,
0.466115526548682, 0.456163205377972, 0.449307080301381,
0.443827930886861, 0.438006536702364, 0.430123677315843,
0.422161025148951, 0.416341337462143, 0.411307021081574,
0.405700482833399, 0.398164129543771, 0.388509330252805,
0.377833000710694, 0.366705867808713, 0.355698658438137,
0.34538209949024, 0.334476225402477, 0.322019919293152, 0.309062181074817,
0.296652010660021, 0.285838407961315, 0.276447235011443,
0.267512949813937, 0.258897446237528, 0.250462618150946,
0.242070359422922, 0.234129641095434, 0.226902750204945,
0.220031554611916, 0.21315792217681, 0.205923720760086, 0.19852372751621,
0.191343478746417, 0.184310981031329, 0.177354240951568,
0.170401265087756, 0.163192040497278, 0.155702671847874,
0.148215342135701, 0.141012234356916, 0.134375531507676,
0.127758694427545, 0.120727232121655, 0.113731399834516,
0.107221452810637, 0.101647646294526, 0.0965005694542742,
0.091209569652388, 0.0861330394250022, 0.0816293713082512,
0.0780569578382695, 0.075371315752509, 0.0732346851768886,
0.0715655437302434, 0.0702823690314086, 0.0693036386992192,
0.0691863250953481, 0.0701883842483067, 0.0717797692771877,
0.073430433301084, 0.0746103294390886, 0.0761784204310707,
0.0788956524181342, 0.0820849536212236, 0.0850692522612836,
0.0871714765592586, 0.0890972731947091, 0.0916871981841642,
0.094466543754022, 0.0969606021306803, 0.0986946655405372,
0.0998521594454551, 0.100914181761071, 0.101852533394445,
0.102639015252636, 0.103245428242704, 0.103462264899288,
0.103250578499722, 0.102838011065113, 0.102452204616565,
0.102320801175184, 0.102287042959656, 0.102088178523127,
0.101792859388387, 0.10146973707823, 0.101187463115447, 0.100889974984667,
0.100495657145597, 0.100034831583652, 0.0995378202842451,
0.0990349452327908, 0.0985243275768939, 0.097982837008049,
0.0974089183275044, 0.0968010163365085, 0.0961575758363094,
0.0954770416281557, 0.0947578585132956, 0.0939984712929773,
0.0931973247684494), c(0.754994105046569, 0.759262980856892,
0.763248462599852, 0.767062652758686, 0.77081765381663, 0.774472838830307,
0.777902459086919, 0.781090934415562, 0.784022684645335,
0.786682129605334, 0.789179136777192, 0.791558707699299,
0.793707963285894, 0.795514024451214, 0.796864012109498,
0.798059444416928, 0.799307737538445, 0.800354859401417,
0.800946777933214, 0.800829461061205, 0.800535183022801,
0.800489794522664, 0.800279628189041, 0.799491016650182,
0.797710292534332, 0.793727807721641, 0.787531875152631,
0.780505769046113, 0.774032763620899, 0.7694961330958, 0.766814565429895,
0.764644322900798, 0.76247504120512, 0.759796356039472, 0.756097903100465,
0.753105409945246, 0.751812834849642, 0.750612159588286,
0.747895365935809, 0.742054435666847, 0.734349211270437,
0.726724708968583, 0.718600671135361, 0.709396840144846,
0.698532958371117, 0.685958066059214, 0.672217339551, 0.657624725365782,
0.642494170022865, 0.627139620041553, 0.608952937377657,
0.586693889874402, 0.562728282882226, 0.539421921751564,
0.519140611832852, 0.498827185487661, 0.475313450129851,
0.450798673688293, 0.427482124091856, 0.40756306926941, 0.391594783774196,
0.377888811456193, 0.365281824189383, 0.352610493847746,
0.338711492305265, 0.324742577633052, 0.312133619351959,
0.300129456510569, 0.287974928157469, 0.274914873341241,
0.260271155053797, 0.244483391182105, 0.228473053117332,
0.21316161225065, 0.199470539973226, 0.18615371420193, 0.171962954821233,
0.157816646173702, 0.144633172601908, 0.13333091844842, 0.12330701757161,
0.113524600030087, 0.104251044466771, 0.0957537295245833,
0.0883000338464436, 0.0824702507214148, 0.0782344478172879,
0.0749446653450757, 0.0719529435157916, 0.0686113225404485,
0.0661995394292459, 0.0657367472597934, 0.0661841103442199,
0.0665027929946539, 0.0656539595232242, 0.0644681965998428,
0.0641406846621584, 0.0641794786739045, 0.0640926335988149,
0.0633882044006231, 0.0619833956742033, 0.0602788637336122,
0.058507841121357, 0.0569035603799449, 0.0556992540518834,
0.0546528395507013, 0.0534742168963767, 0.0523245925410911,
0.0513651729370255, 0.0507571645363614, 0.0504376622860656,
0.050241048436331, 0.0501744603941783, 0.0502450355666279,
0.0504599113607003, 0.0510283221178971, 0.052012828131863,
0.0532005557378333, 0.0543786312710432, 0.055334181066728,
0.0564245098836434, 0.057962991953209, 0.0596708578196554,
0.0612693380272137, 0.0624796631201147, 0.0635479377810315,
0.0648035693895804, 0.0660927425354069, 0.0672616418081562,
0.0681564517974736, 0.0687850088878122, 0.0692835297177245,
0.0696907692580061, 0.0700454824794523, 0.0703864243528584,
0.0706277104270338, 0.0707098566767007, 0.0707112187115236,
0.0707101521411668, 0.0707850125752948, 0.0708833573425856,
0.0709155836751044, 0.0709043070375961, 0.0708721428948058,
0.0708417067114787, 0.070795102344694, 0.0707055967085618,
0.0705825498533734, 0.0704353218294197, 0.0702732726869918,
0.0700959115439309, 0.0698961835839182, 0.069673602956005,
0.0694276838092427, 0.0691579402926828, 0.0688638865553766,
0.0685450367463756, 0.0682009050147312, 0.0678310055094947
)), doy.series = list(c(55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106,
107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,
131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,
167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178,
179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202,
203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213), c(55,
56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124,
125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,
137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,
161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,
185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
209, 210, 211, 212, 213), c(55, 56, 57, 58, 59, 60, 61, 62,
63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,
106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,
118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,
142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165,
166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,
190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,
202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213
)), year = c("2000", "2000", "2000"), geometry = structure(list(
structure(c(-164.047259999849, -164.044659999559, -164.044719999628,
-164.038089999654, -164.028189999968, -164.019179999957,
-164.005899999985, -164.004819999643, -164.006060000169,
-164.01439999986, -164.020739999951, 62.9589599997043,
62.9570600002189, 62.9551799998571, 62.9500200002229,
62.9453699998257, 62.9321099998767, 62.9228599995894,
62.9198900002234, 62.9182900001834, 62.9161899995689,
62.9119300000695), .Dim = c(11L, 2L), class = c("XY",
"LINESTRING", "sfg")), structure(c(-163.950299999945,
-163.929679999632, -163.91427000036, -163.903839999616,
-163.892950000142, -163.874760000374, -163.857260000049,
-163.83827000026, -163.831219999803, -163.826049999708,
-163.831939999731, -163.830590000428, -163.822, -163.815322687912,
62.7265500001824, 62.7286899999436, 62.7327399996513,
62.7292899997337, 62.7222099996918, 62.7222000001299,
62.7196300003243, 62.7251300003493, 62.7253400001409,
62.7224699999905, 62.7144400002059, 62.7114699999406,
62.7062799998222, 62.7052201090963), .Dim = c(14L, 2L
), class = c("XY", "LINESTRING", "sfg")), structure(c(-163.815322687912,
-163.798689999744, -163.782269999761, -163.768690000343,
-163.762120000438, -163.757980000177, -163.754040000146,
-163.750479999652, -163.741150000172, -163.731440000256,
-163.727959999854, -163.716170000245, -163.707080000142,
-163.69419999973, -163.687290000333, -163.670841577631,
62.7052201090963, 62.7025800000671, 62.7027099997667,
62.7047399998511, 62.7076500000475, 62.7154200004327,
62.7186199996133, 62.7195300002094, 62.718240000076,
62.7119499995929, 62.7111100004263, 62.7123500000526,
62.71900000005, 62.7184800003518, 62.7155900001783, 62.7051667771758
), .Dim = c(16L, 2L), class = c("XY", "LINESTRING", "sfg"
))), class = c("sfc_LINESTRING", "sfc"), precision = 0, bbox = structure(c(-164.047259999849,
62.7025800000671, -163.670841577631, 62.9589599997043), .Names = c("xmin",
"ymin", "xmax", "ymax"), class = "bbox"), crs = structure(list(
epsg = 4326L, proj4string = "+proj=longlat +datum=WGS84 +no_defs"), .Names = c("epsg",
"proj4string"), class = "crs"), n_empty = 0L)), .Names = c("id",
"doy", "smooth.series", "doy.series", "year", "geometry"), row.names = c(NA,
3L), class = c("sf", "data.frame"), sf_column = "geometry", agr = structure(c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_), .Names = c("id",
"doy", "smooth.series", "doy.series", "year"), .Label = c("constant",
"aggregate", "identity"), class = "factor"))
With the small example you gave, I only added select(-geometry) :
library(tidyverse)
df3 <- df %>%
select(-geometry) %>%
unnest()
df3 %>%
ggplot(aes(x = doy.series, y = smooth.series, color = doy, group = doy)) +
geom_line() +
scale_color_gradient(low = "blue", high = "red")
I have tried for several days to just flip a dendrogram so that the last gene is the first in the figure and the first the last. But even when I have managed to move leaves around the internal ordering is not the same. Here is my script:
cluster.hosts <- read.table("Norm_0_to1_heatmap.txt", header = TRUE, sep="", quote="/", row.names = 1)
# A table with 8 columnns and 229 rows cirresponding to gene expression
hosts.dist <- dist(cluster.hosts, method = "euclidean", diag = FALSE, upper = FALSE, p = 2)
hc <- hclust(hosts.dist, method = "average")
dd <- as.dendrogram(hc)
order.dendrogram(dd)
X11()
par(cex=0.5,font=3)
plot(dd, main="Dendrogram of Syn9 genes")
order.dd <- order.dendrogram(dd) #the numbers in the order indicate the position of the gene in the original table
#Then I generate a vector with the opposed order to the one obtained
y <- c(206, 204, 210, 209, 213, 212, 211, 207, 208, 94, 199, 192, 195, 198, 193, 201, 203, 200, 185, 61, 191, 190, 197, 189, 188, 196, 187, 215, 214, 202, 217, 220, 219, 218, 95, 180, 179, 181, 182, 186, 178, 132, 133, 122, 66, 65, 64, 58, 91, 88, 92, 89, 62, 184, 103, 128, 127, 229, 231, 230, 148, 63, 228, 116, 134, 104, 221, 78, 20, 232, 160, 159, 225, 112, 167, 164, 166, 140, 222, 51, 149, 227, 79, 68, 90, 131, 130, 136, 135, 105, 147, 172, 150, 176, 175, 174, 177, 152, 151, 165, 137, 168, 163, 52, 146, 141, 145, 82, 81, 56, 161, 120, 144, 129, 84, 1, 173, 143, 142, 86, 85, 83, 194, 183, 111, 55, 53, 54, 224, 171, 170, 223, 169, 93, 59, 60, 123, 121, 124, 87, 125, 226, 3, 158, 47, 10, 162, 138, 139, 154, 153, 119, 118, 117, 106, 80, 45, 70, 69, 126, 205, 77, 67, 19, 102, 46, 13, 108, 107, 109, 72, 71, 73, 23, 22, 25, 57, 48, 216, 155, 29, 24, 101, 35, 113, 115, 36, 37, 114, 110, 2, 14, 6, 16, 15, 17, 18, 74, 31, 30, 76, 12, 75, 8, 11, 5, 7, 99, 98, 100, 39, 38, 33, 32, 97, 96, 49, 44, 34, 50, 156, 26, 157, 42, 41, 43, 4, 28, 27, 9, 40, 21)
rx <- reorder(dd, y, agglo.FUN=mean)
order.rx <- order.dendrogram(rx)
write(order.rx, file="order_hosts_rx.txt", sep="\t")
write(labels(rx), file="labels_order_hosts_rx.txt", sep="\t")
X11()
par(cex=0.5)
plot(rx, main="Dendrogram of Syn9 genes")
I guess it has something to do with the heights of the leaves but I just want to flip the dendrogram...
Thanks in advance!
Miguel
You can use rev(dd); rev.dendrogram simply returns the dendrogram with reversed nodes:
hc <- hclust(dist(USArrests), "ave")
dd <- as.dendrogram(hc)
plot(dd)
plot(rev(dd))