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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.
I need to add missing rows from "count" based on the "numberclass" that is missing. "numberclass" is the column of the "count" dataset that should go from 1 to 652, but misses some numbers and ends at 645.
To achieve that, I made an index vector that goes from 1 to 652 called c1.
How can I use rbind to add the missing rows that are missing in "count"?
Those missing rows should contain the appropriate number in "numberclass" that is missing and a 0 on the column "sum" in the "count" data frame.
visual example
count
numberclass sum
1 1 3.45
2 2 32.45
3 3 23.11
4 5 21.33
5 6 1.54
c1
V1
1 1
2 2
3 3
4 4
5 5
6 6
finalcount
numberclass sum
1 1 3.45
2 2 32.45
3 3 23.11
4 4 0
5 5 21.33
6 6 1.54
dput(c1)
1:652
> dput(count)
structure(list(numberclass = 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, 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, 251, 252, 253, 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, 307, 308, 309, 310, 311, 312, 313, 314, 315,
316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328,
329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,
342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354,
355, 356, 357, 358, 360, 361, 362, 363, 364, 365, 366, 367, 368,
369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381,
382, 383, 384, 385, 386, 387, 388, 389, 391, 392, 393, 394, 395,
396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408,
409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421,
422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434,
435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447,
448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460,
461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473,
474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486,
487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499,
500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512,
513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525,
526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538,
539, 540, 541, 542, 543, 545, 546, 547, 548, 549, 550, 551, 552,
554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566,
567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579,
580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592,
593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605,
606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618,
619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631,
632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644,
645, 646, 647, 648, 649, 650, 651, 652), sum = c(237.750666386555,
189.540342857143, 351.867604761905, 195.005685714286, 308.574686424686,
18.2691666666667, 85.6063492063492, 330.872041913642, 12.5832666666667,
81.3559523809524, 940.085002447968, 38.9222222222222, 67.6095238095238,
52.4340924675325, 48.9761904761905, 190.221922510823, 67.2384948051948,
106.311044372294, 50.4888222222222, 40.4883365079365, 146.992341452991,
43.6190142857143, 133.421034293119, 234.662733903319, 41.3940476190476,
27.5869769119769, 4.77619047619048, 1.14404345238095, 33.7083333333333,
44.2833333333333, 22.9526315789474, 21.5833333333333, 10.65,
2.75, 73.0113858363858, 9.41666666666667, 10.9, 30.3830128205128,
58.9269230769231, 1.39285714285714, 267.691666666667, 58.0575757575758,
48.1547008547009, 82.8479908979909, 57.6404761904762, 0.333333333333333,
15.0952380952381, 62.5674603174603, 155.280158730159, 39.9, 82.6307359307359,
24.6282467532468, 301.294040989729, 336.528306349206, 19.0833333333333,
110.152380952381, 151.278584609835, 27.3151515151515, 326.42688974359,
148.124206349206, 250.934674989716, 791.586193783953, 284.357225111163,
26.3166666666667, 689.571152020736, 211.649312496276, 143.23373015873,
104.389479365079, 1977.09488512611, 278.063024283429, 635.353051458803,
255.639689709121, 182.388611918596, 121.218055555556, 53.5880285714286,
29.8071514529915, 289.396377133977, 261.427877777778, 13.0333333333333,
120.082323232323, 26.4499333333333, 118.030555555556, 3.16666666666667,
3.5, 1.27692307692308, 1327.43098544718, 359.099526103064, 886.03077133796,
77.9476163059163, 3.7, 204.405522222222, 42.3193805944056, 83.1319512987013,
32.0430735930736, 100.999933333333, 41.4505205838606, 359.286551817072,
134.815597663857, 120.851665339892, 68.6170634920635, 120.464757456432,
98.7313991341991, 138.937179487179, 18.4913941580642, 8.9984237984238,
238.521621356421, 123.083044733045, 363.372644000444, 39.2380952380952,
3.16666666666667, 19.6226551226551, 53.5838383838384, 34.581746031746,
4.95, 131.300949206349, 445.728384935065, 109.100990092656, 364.408721825397,
61.5416666666667, 222.299498645799, 16.0214285714286, 13.5833333333333,
35.9928238095238, 522.570385291901, 92.072619047619, 451.015331590632,
276.63253968254, 61.6666666666667, 56.875, 246.15873015873, 52.5833,
73.5964119047619, 28.1214646464646, 30.1333333333333, 53.9054945054945,
206.796237085137, 111.121428571429, 182.169199264787, 59.3175971087736,
64.3332722832723, 16.9333333333333, 13.9166666666667, 23.3833333333333,
33.8173992673993, 1.50952380952381, 1.1, 47.9876584126984, 33.6666666666667,
31.7166666666667, 42.5094738594739, 193.209163059163, 36.8706349206349,
56.4786214285714, 125.781411481369, 1326.37051628773, 128.802066528312,
176.118690340834, 124.811656943091, 221.328297720058, 92.4357277483439,
5.54453781512605, 11.934710550887, 34.1893281555046, 297.559209282097,
10.45, 15.9714285714286, 0.333333333333333, 404.635647619048,
1.33333333333333, 423.917088383838, 31.725, 22.2334666666667,
126.991549902454, 46.2095071428571, 19.9333333333333, 9.41666666666667,
36.1666666666667, 101.691628950685, 88.0833333333333, 1.08333333333333,
60.5678571428571, 44.5857142857143, 10.3333333333333, 27.9333333333333,
59.6450530463688, 33.0823773448773, 15.2018740031898, 139.796428571429,
302.865200865801, 58.4464285714286, 7.50238095238095, 253.278364368964,
98.456746031746, 275.551738539239, 224.303773488182, 43.4340004939634,
14.475, 252.068551587302, 193.944014285714, 97.1103202020202,
522.762237662338, 152.027922077922, 495.599785289496, 15.45,
44.4584599224305, 2.63932178932179, 76.913480952381, 18.5944333333333,
80.5424963924964, 52.8404761904762, 19.602380952381, 21.7789854538307,
2.09285714285714, 15.6, 57.8281523809524, 114.880233333333, 2.5,
582.268982688364, 22.8928571428571, 43.5, 71.0449134199134, 13.45,
71.4832666666667, 382.793654822955, 57.6023587301587, 17.8666666666667,
134.694036507937, 8.65833333333333, 6.48333333333333, 167.456313131313,
108.970238095238, 38.0944444444444, 41.4536075036075, 644.437984476377,
64.2714285714286, 1630.6914617297, 81.8621387218045, 977.944218315018,
825.631676469739, 76.9720238095238, 161.353968253968, 70.9142857142857,
122.307142857143, 49.1575757575758, 38.9833333333333, 119.23980017316,
9.5, 7, 9.03333333333333, 0.285714285714286, 2.81558441558442,
34.3352130179203, 423.489491888615, 26.7138582972583, 20.2610666666667,
70.2504356560596, 84.3197993439266, 133.202467136288, 452.717995233655,
320.773420116725, 209.525511634406, 641.329055345934, 9.29166666666667,
20.0666666666667, 23.4825757575758, 42.336926961927, 21.5083333333333,
48.472619047619, 5.68452380952381, 3.61666666666667, 2.66666666666667,
22.6410952702853, 2596.19741576659, 3701.15679179432, 458.475674942574,
0.177777777777778, 236.511739558926, 178.846204916721, 554.69148345371,
109.069139904866, 27.9428571428571, 865.353323873349, 1315.57171181985,
4.94494734487734, 367.766031285642, 519.099162156913, 703.569199879477,
570.161782712288, 55.7592247797747, 424.061781409081, 4.14444444444444,
7.85, 1.5, 203.543559424236, 417.414520853467, 118.026934176934,
13.8930333333333, 5.3, 195.214038429218, 2, 125.901590928837,
20.183510972172, 174.23474402697, 115.783354224877, 20.9589971153889,
64.2541744390332, 30.1928142135642, 653.283386817422, 45.4998949579832,
2.28333333333333, 35.7234848484849, 13.4766233766234, 1, 1, 151.923361772117,
466.496416114588, 241.639269088134, 208.697684171547, 37.1753432142857,
32.7720180265813, 28.2666666666667, 32.9353202020202, 29.3107466063348,
52.1338661616162, 92.2408604474645, 143.825094880675, 146.094892496393,
185.56378660516, 229.435060026582, 35.8161587301587, 358.75152088854,
9.54144989396568, 100.579542891096, 48.5654928571429, 182.120363315018,
92.411123015873, 213.978268831169, 30.4477001960784, 133.023283627484,
1.48156826833297, 8.58333333333333, 4.44443333333333, 38.2468253968254,
56.047481038406, 67.3214285714286, 123.833316666667, 72.7440476190476,
4.04166666666667, 15.0999833333333, 66.4499333333333, 200.083454172494,
6.04285714285714, 160.691602741703, 6.19924242424242, 1.33333333333333,
108.082979449584, 106.752280952381, 14.5075757575758, 17.3920634920635,
131.341230952381, 44.2768897435897, 313.758134920635, 2.16666666666667,
16.6477124183007, 4.75, 23.7767065934066, 114.554377815518, 67.8246376228347,
127.12717047619, 8.01590909090909, 62.9999458874459, 24.5385558774559,
25.4267800865801, 64.9809956302521, 26.8670829004329, 144.936510045837,
18.2714285714286, 181.673313930514, 6.37619047619048, 122.4944,
163.107067798868, 62.2391525974026, 100.821861471861, 66.6090659340659,
151.295802741703, 227.115548340548, 161.469246031746, 20.8428571428571,
98.9682406349206, 84.2357142857143, 63.5107142857143, 587.042635340803,
291.116304438862, 217.717193917194, 314.73560018413, 198.123701298701,
236.697900710401, 410.192568542569, 118.817857142857, 143.350727050727,
81.387055999556, 43.8719696969697, 203.429180541681, 517.788687667888,
61.2261904761905, 382.272785934066, 75.7309523809524, 112.349503174603,
22.7539682539683, 31.7878787878788, 71.6388888888889, 116.672591197691,
31.4399816686581, 139.147260092848, 38.9365079365079, 142.327696091318,
73.9474025974026, 353.130164019063, 49.7790027560675, 247.005519209059,
98.4489704073704, 22.8163324675325, 49.0166666666667, 398.237265694185,
20.0119047619048, 127.929437229437, 29.906746031746, 11.4833333333333,
29.5477994227994, 17.2627344877345, 1, 275.39396990232, 155.285052380952,
191.24167394958, 17.5547619047619, 32.6397907647908, 48.0516145404303,
20.0202991341991, 296.087292678082, 6.05553333333333, 6.30952380952381,
550.020158730159, 398.502413950429, 697.700455175612, 342.769086313686,
100.248412698413, 578.569767384318, 323.557284593185, 578.870478870574,
799.803117448702, 66.4497474747475, 52.7964285714286, 28.2440476190476,
1, 9.15, 0.333333333333333, 101.279396149946, 20.4504329004329,
2, 0.342857142857143, 11.0416666666667, 114.264102564103, 148.394093406593,
17.3285625923784, 10.2605680868839, 109.262121733822, 5.68568095238095,
4.91666666666667, 27.8404512154512, 95.3755683538683, 134.882303769841,
61.262513966589, 16.5333333333333, 64.6593323051948, 37.6535103785104,
42.0317820956821, 17.3730092063492, 81.8735937673438, 44.7111111111111,
17.4607142857143, 70.0927904761905, 148.696792063492, 170.374507625708,
185.520274170274, 177.809072871573, 86.3721112221112, 176.200008488178,
15.1166666666667, 136.109471067821, 48.0101062250443, 166.262856565657,
148.329752057299, 151.820306375846, 4.18642884892885, 13.65,
17.5384920634921, 158.262582783883, 255.417342568543, 29.2134920634921,
197.809798534799, 29.85, 16.9095238095238, 20.8333333333333,
113.602744444444, 44.002380952381, 36.0333333333333, 318.15949047619,
116.7, 9.73333333333333, 459.457291197691, 200.920720879121,
314.905574729437, 468.928687626263, 127.85367965368, 34.46829004329,
127.564573784059, 168.830957864358, 276.134640779221, 201.892396392496,
1946.09400347577, 201.03562536075, 0.54047619047619, 782.099165160003,
425.714983516484, 89.7872682539683, 146.385452539683, 10.6666666666667,
1025.68925909923, 116.007914285714, 276.85727701204, 289.008666233766,
251.763574012009, 83.7539682539683, 348.782956092124, 241.232478499278,
35.9951548451548, 23.8844904761905, 16.75, 15.6583166666667,
23.4777777777778, 5.83333333333333, 262.787474045562, 285.537711241699,
63.2683473389356, 66.3647186147186, 2, 8.83323333333333, 751.311316139416,
20.0833333333333, 3.48333333333333, 313.547763557495, 24.6952380952381,
2.33333333333333, 60.6101524475524, 111.872585714286, 52.7153693528694,
181.421808730159, 86.6900043290043, 223.108003141303, 16.0825757575758,
304.663375396825, 48.2595238095238, 53.0539682539683, 117.610714285714,
3.1, 1.83333333333333, 305.834008148714, 197.169349200473, 0.5,
8, 33.7777777777778, 1.2, 5.58333333333333, 42.6051282051282,
144.887301587302, 65.7499666666667, 963.598530853141, 217.737908305747,
19.827380952381, 3.775, 229.018578571429, 7.19166666666667, 186.860334126984,
9.33333333333333, 0.75, 1, 43.8273809523809, 62.2753634920635,
301.048005944774, 89.4083452763611, 374.762004736842, 166.820046453546,
1058.5261360623, 872.182726540127, 54.4082666666667, 1227.53727689429,
321.227890629965, 148.721916971917, 277.273484848485, 897.280942113442,
226.137230929597, 72.7005952380952, 140.310317460317, 317.511606180094,
209.189406410256, 104.605501434676, 437.805596256685, 273.362576312576,
8.47222222222222, 227.129921804748, 0.943686868686869, 67.7638777888778,
20.4856893106893, 99.1611000111, 166.165773015873, 82.3694444444444,
227.211077777778, 72.4857142857143, 461.993158401598, 78.8, 210.535976984127,
428.665560794761, 35.797619047619, 133.786890638528, 20.4904761904762,
577.348705757576, 404.170196392496, 1101.04344335286, 270.924821327561,
196.366666666667, 5.83333333333333, 81.6839466089466, 516.43132186441,
2.33333333333333, 10.9095238095238, 54.1369047619048, 48.2956349206349,
676.496237656507, 137.799728238428, 14.4768149941046, 355.509695218997,
422.28376567026, 213.912283405959, 177.353159198024, 14.0459013125763
)), row.names = c(NA, -645L), class = c("tbl_df", "tbl", "data.frame"
))
I've found a solution with the tidyr package:
library(tidyr)
count <- as.data.frame(count)
count <- count %>% complete(numberclass = full_seq(numberclass, period = 1),fill=list(sum=0))
No need for an extra index vector.
The package fpp2 has a wonderful function ggseaonplot(). I would like to make use of it by turning my xts object into a matrix where "Years" are the row names and the 12 months are the columns names.
#here is the data. This is stock data from yahoo finance
library(quantmod)
getSymbols("SPY")
adjusted = Ad(SPY)
head(adjusted)
#here is an example of what I would like the data to look like using the
AirPassengers matrix.
dput(AirPassengers)
structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119,
104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114,
140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166,
171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196,
196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188,
235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267,
269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313,
318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355,
422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435,
491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548,
559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606,
508, 461, 390, 432), .Tsp = c(1949, 1960.91666666667, 12), class = "ts")
Here is what the final project should look similar to.
library(fpp2)
ggseasonplot(AirPassengers)
class(adjusted)
#[1] "xts" "zoo"
adjusted is of type xts but ggseasonplot needs object of type ts.
Here is an alternative with ggplot2 though
Prepare the data
library(zoo)
df <- data.frame(date = index(adjusted),value = coredata(adjusted),row.names = NULL)
df$month <- factor(format(df$date, "%b"), levels = month.abb)
df$year <- format(df$date, "%Y")
Plot the data
library(ggplot2)
ggplot(df) +
aes(month, SPY.Adjusted, group = year, color = year) +
geom_line()
My task is to plot cumulative distribution function of asymptotic Kernels. For this purpose, i prepared following R-code for cdf of Log-normal kernel; but my problem is that cdf graph is moving downward. After I used transformation provided by author of kernel (Lognormal kernel ).
Kindly provide suggestions/correction in this problem.
R code:
k <- 200
y <- c(306, 455, 210, 883, 310, 361, 218, 166, 170, 654, 728, 71, 567, 144, 613, 707, 61, 88, 301, 81, 624, 371, 394, 520, 574, 118, 390, 12, 473, 26, 533, 107, 53, 122, 814, 93,731, 460, 153, 433, 145, 583, 95, 303, 519, 643, 765, 735, 189, 53, 246, 689, 65, 5,132, 687, 345, 444, 223, 175, 60, 163, 65, 208, 428, 230, 305, 11, 132, 226, 426, 705,363, 11, 176, 791, 95, 167, 284, 641, 147, 163, 655, 239, 88, 245, 30, 179, 310, 477,166, 450, 364, 107, 177, 156, 11, 429, 351, 15, 181, 283, 201, 524, 13, 212, 524, 288, 363, 442, 199, 550, 54, 558, 207, 92, 60, 293, 202, 353, 267, 371, 387, 457, 337, 201, 222, 62, 353, 163, 31, 340, 229, 182, 156, 329, 291, 179, 268, 142, 194, 320, 181, 285,348, 197, 180, 186, 145, 350, 285, 110, 286, 270, 81, 131, 269, 135, 79, 59, 105, 239, 13, 183, 116)
n <- length(y)
h <- 0.79 * IQR(y) * length(y) ^ (-1/5)
x <- seq(min(y) + 0.05, max(y), length = k)
Fhat <- rep(0, k)
for (j in 1:k) {
for (i in 1:n) {
PhiLN <- matrix(rep(0, k * n), ncol = k)
PhiLN[i, j] <- pnorm((log(y[i])-log(x[j]))/(sqrt(4*log(1+h))))
}
Fhat[j] <- 1/n * (sum(PhiLN[, j]))
}
plot(x, Fhat, type = "l")