Add a column with repeating values taken from another data.frame - r

everybody! I have the following data:
> dput(test)
structure(list(CNTRYID = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Czech Republic",
"Denmark", "France", "Greece", "Hungary", "Italy", "Lithuania",
"Netherlands", "Poland", "Slovak Republic", "Slovenia", "Spain",
"United Kingdom"), class = "factor"), Gender = structure(c(2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 2L), .Label = c("Male", "Female"), class = "factor"),
PVLIT1 = c(341.73556, 327.75647, 333.99133, 299.23201, 274.26806,
330.20199, 290.3974, 245.60594, 305.04734, 322.12211, 264.19379,
283.34121, 286.62649, 301.27084, 204.67276, 339.7475, 350.74884,
300.41595, 338.76949, 340.92763, 323.86579, 320.03587, 276.43592,
307.12426, 290.60296, 322.23858, 299.0517, 328.35244, 267.82705,
245.1432, 322.43248, 352.37208, 275.75408, 317.75532, 332.75897,
321.23265, 315.338, 303.61999, 309.63271, 315.13702, 349.31912,
330.28415, 291.53027, 238.86247, 336.98836, 312.80759, 312.32254,
238.765, 268.14566, 326.59843, 298.99782, 372.8563, 314.59135,
267.68085, 358.00259, 293.74775, 257.99369, 352.46286, 277.74005,
340.21297, 343.90215, 365.2711, 298.95923, 347.24665, 288.41685,
267.28314, 299.62821, 288.50113, 346.33, 348.62584, 304.37408,
288.28595, 209.42411, 308.3979, 317.11152, 339.05234, 338.79844,
273.4862, 317.05362, 321.9683, 298.49502, 315.31917, 285.24075,
260.5942, 292.17127, 278.49578, 351.55043, 291.29336, 345.61643,
252.87387, 318.81766, 359.97134, 334.8103, 282.96817, 347.12409,
266.44512, 284.23384, 303.45264, 228.6119, 248.62507, 445.14543,
262.78094, 330.89695, 276.94543, 340.09174, 370.30567, 254.49489,
267.31821, 235.36262, 268.87887, 293.34609, 323.89632, 332.1124,
270.03193, 302.92495, 290.86264, 330.11303, 225.08377, 306.51173,
393.41635, 296.14069, 290.8399, 249.71853, 273.45073, 391.61765,
229.62298, 368.49384, 352.84141, 276.27584, 271.89748, 345.33152,
279.98931, 322.87181, 262.99515, 251.44921, 291.01078, 302.7094,
288.9321, 335.60061, 353.84158, 225.1918, 267.17191, 261.80763,
291.8125, 354.75112, 263.19101, 300.22572, 309.91597, 323.37047,
371.69579, 288.89204, 344.10129, 211.20385, 268.50285, 347.76109,
223.8669, 280.59382, 341.03251, 330.07897, 297.91223, 382.22934,
346.63016, 279.97218, 296.75031, 279.52446, 239.17915, 307.47267,
310.66321, 339.9095, 288.07157, 313.38334, 294.49606, 301.16477,
330.97285, 306.29201, 258.9723, 305.16945, 220.56537, 293.92923,
316.1579, 294.9318, 231.12861, 327.08462, 295.45707, 212.56843,
343.58854, 321.65331, 347.51206, 327.05139, 270.26912, 245.49499,
284.13733, 346.38385, 262.40319, 305.82358, 268.60343, 314.58164,
247.77339, 346.95623, 326.82632, 296.7529, 252.09381, 307.30177,
262.73407, 261.99723, 265.99006, 297.02533, 221.36089, 342.78459,
259.3387, 308.40893, 345.39162, 252.01223, 249.46163, 232.65253,
295.11341, 269.32542, 270.33917, 311.52152, 295.45142, 299.57599,
264.36488, 253.78787, 277.27982, 184.86342, 343.89483, 304.73262,
315.47804, 277.46603, 294.02264, 278.49927, 288.03195, 261.12088,
258.48768, 286.84427, 232.02021, 239.09253, 249.53217, 341.65779,
199.08805, 348.26952, 292.84918, 233.47102, 280.34833, 266.45888,
300.40607, 287.81728, 293.79174, 257.01624, 375.42354, 212.49236,
326.45861, 266.21237, 282.09158, 273.21422, 226.8083, 320.00889,
300.76454, 261.39701, 263.16525, 325.74916, 285.175, 297.7344,
266.98718, 287.35748, 295.33917, 310.84685, 235.88418, 335.12738,
338.0637, 263.95686, 341.24316, 344.17478, 277.19368, 292.86124,
306.71616, 302.24125, 292.9568, 327.03543, 273.10249, 246.17394,
329.32818, 289.45179, 329.58524, 316.57873, 254.53541, 307.42027,
340.46333, 260.72247, 177.1923, 298.05925, 229.80109, 299.93516,
298.97657, 296.32594, 331.81735, 335.73543, 348.6484, 224.40826,
339.58894, 230.45043, 297.69392, 250.64399, 216.37015, 248.48309,
315.3377, 259.91751, 343.96253, 238.19071, 268.7662, 195.30995,
295.58828, 282.90717, 257.23455, 312.86206, 255.33727, 293.08026,
321.61584, 352.64202, 331.90932, 291.72882, 301.87908, 299.83755,
276.29271, 288.31371, 173.19914, 235.98186, 267.68055, 273.57641,
259.38568, 282.82247, 255.2055, 259.92553, 324.37237, 323.27084,
297.1294, 293.29913, 319.26816, 296.19763, 287.20711, 120.7633,
299.14635, 324.02645, 272.24852, 280.46404, 363.56726, 213.18303,
318.84395, 307.23832, 337.49138, 352.61447, 295.99838, 245.2863,
303.07131, 200.14136, 314.2698, 290.86413, 289.6412, 232.13515,
328.96702, 250.57396, 251.36983, 259.23267, 277.98361, 306.61626,
298.90864, 336.54659, 270.34128, 295.23666, 289.28454, 314.25576,
305.90498, 223.59499, 297.3472, 302.97199, 306.82586, 293.8622,
277.52691, 274.99887, 309.28188, 243.9094, 287.04775, 332.49858,
316.85995, 227.84818, 338.34644, 231.03199, 280.42949, 326.23743,
287.90662, 308.44719, 224.65553, 255.51269, 320.01916, 284.76909,
310.07218, 305.64163, 267.90448, 291.91266, 311.95645, 234.77883,
288.47911, 337.66548, 235.67435, 328.26341, 217.05384, 271.18711,
256.92855, 306.35037, 298.57583, 220.25599, 305.70531, 397.23308,
243.34537, 318.81272, 287.84564, 291.19244, 260.50231, 309.29317,
288.32053, 285.65509, 270.71887, 349.55511, 266.04791, 305.22095,
259.29735, 283.99375, 281.30021, 368.59316, 300.66732, 253.21196,
277.61171, 313.79851, 310.08222, 288.8469, 329.40954, 324.01478,
283.84872, 293.73975, 175.58543, 287.87392, 304.40044, 294.23277,
268.17933, 309.02819, 253.19897, 265.68756, 290.41261, 333.8446,
248.33467, 298.56631, 196.18183, 249.01214, 239.07566, 272.0436,
266.38371, 244.71529, 230.62195, 280.98915, 327.92842, 291.64174,
290.58153, 278.05094, 242.61752, 290.2778, 349.53173, 206.13951,
293.64674, 201.95774, 353.00234, 301.41944, 266.58184, 313.45438,
290.98948, 287.79774, 334.35567, 276.27736, 271.85967, 233.80236,
275.925, 292.44499, 275.67027, 306.44843, 293.76468, 300.68248,
277.37136, 258.36308, 215.93238, 304.53151, 282.98405, 284.24563,
267.54523, 278.81698, 293.47491, 274.20848, 325.56625, 338.29286,
237.04901, 321.03634, 284.59905), PVLIT2 = c(329.96437, 309.7071,
323.76923, 311.6131, 296.0494, 320.40846, 316.35148, 284.25026,
305.10127, 317.73826, 279.00676, 311.32111, 263.20577, 295.35872,
247.77471, 312.83461, 338.78073, 306.69291, 323.277, 324.9266,
286.49722, 317.47413, 294.87445, 332.89844, 285.07891, 294.75732,
312.38012, 307.31727, 281.80926, 299.38988, 307.64408, 359.26622,
269.06506, 311.84518, 335.81958, 320.7753, 261.46395, 254.00522,
322.33784, 304.60692, 330.96759, 348.84054, 308.38673, 246.73035,
330.95077, 351.57083, 307.36752, 255.48044, 269.63236, 347.14345,
315.88441, 400.94726, 296.03271, 276.19477, 363.93767, 309.51741,
249.73923, 359.06376, 268.23778, 340.45045, 347.93015, 331.94389,
265.17793, 339.92872, 302.63463, 271.15349, 287.00579, 245.92875,
333.74443, 395.13695, 319.19868, 280.50088, 274.27643, 298.57925,
301.34704, 343.32807, 322.24002, 260.04914, 305.97704, 299.22391,
311.31108, 314.82843, 284.82475, 263.54816, 305.83341, 275.05002,
291.21003, 295.85667, 325.4418, 297.35548, 328.91424, 348.46255,
374.91294, 278.72346, 336.15884, 284.42308, 301.46666, 280.60555,
221.60585, 240.95952, 352.39105, 244.17594, 331.78841, 288.21081,
311.62633, 360.55586, 252.44882, 275.11625, 224.22946, 263.59549,
275.29046, 321.90792, 332.09749, 282.92641, 282.89338, 292.94236,
329.33191, 242.67135, 284.59736, 372.01791, 284.58945, 304.33581,
262.18477, 263.41397, 397.0077, 233.13107, 354.51546, 330.58595,
267.95011, 272.97652, 320.41706, 269.92821, 311.25004, 248.15808,
260.19179, 278.38501, 314.36786, 296.30594, 295.8064, 362.28497,
236.54304, 280.39167, 268.16006, 282.61124, 328.43024, 266.80519,
272.71165, 317.02057, 302.61842, 313.83703, 299.44275, 347.81048,
248.96742, 244.98621, 314.78989, 228.19594, 277.08832, 321.66255,
307.59243, 285.20515, 329.42832, 327.38645, 266.39655, 284.52323,
253.67779, 239.9784, 315.34247, 324.64586, 339.56843, 334.15503,
324.9195, 289.38614, 279.53691, 346.34106, 299.6327, 264.50415,
329.80362, 257.43317, 310.62546, 325.04705, 266.05947, 252.10474,
377.03047, 284.46253, 252.06975, 307.29189, 325.21219, 318.36119,
313.92387, 266.27629, 236.96554, 308.785, 327.27422, 260.62932,
298.31588, 266.69611, 343.64082, 249.20663, 368.61185, 314.38577,
296.44633, 301.93329, 305.82456, 234.26794, 285.09993, 264.74247,
291.16644, 261.86653, 326.92465, 271.35729, 298.4598, 315.83039,
251.59546, 248.09457, 250.90401, 313.28557, 281.10513, 289.41966,
322.99908, 308.48391, 303.46002, 259.34959, 285.41517, 276.54708,
215.90873, 320.46273, 307.92637, 329.45411, 280.43586, 319.99516,
295.35201, 270.81816, 260.6502, 277.0142, 285.64532, 217.5607,
245.98812, 226.29258, 309.23674, 220.03202, 387.01331, 268.84682,
248.05849, 279.63382, 285.34802, 284.09757, 273.3005, 259.08111,
264.10347, 386.52038, 210.39955, 320.20292, 245.43105, 301.72215,
262.78873, 207.51776, 329.13312, 321.57565, 258.19365, 291.23977,
310.97158, 272.66014, 259.28731, 246.29506, 319.95987, 294.63771,
311.32836, 312.20071, 350.12412, 308.50961, 237.55051, 343.6281,
340.66271, 297.44784, 331.91189, 276.27969, 301.48287, 321.79642,
285.85259, 256.98003, 267.14263, 334.84721, 348.23387, 322.91747,
268.42791, 269.33266, 334.05385, 319.43236, 270.38822, 168.24661,
327.46855, 248.39576, 294.05205, 318.50606, 312.98079, 287.35827,
361.43941, 342.54578, 247.997, 316.69669, 232.59609, 285.6277,
261.74517, 230.28962, 254.88764, 302.1326, 267.05801, 385.75184,
267.35567, 313.20042, 203.28131, 282.90967, 290.01701, 282.52713,
339.58608, 266.53576, 297.53657, 278.7528, 348.01977, 328.51913,
289.18677, 319.62583, 311.31862, 271.67348, 279.98333, 227.36405,
276.27979, 290.28081, 280.66062, 255.47229, 291.26037, 264.89911,
304.19635, 330.39094, 334.60314, 272.9507, 294.44286, 346.35474,
332.11805, 313.19078, 104.71963, 293.40003, 329.98007, 247.70109,
259.13664, 325.72004, 239.1354, 294.74629, 291.78074, 328.38065,
334.70175, 312.16118, 215.75044, 297.27003, 202.36576, 271.84798,
291.59486, 303.22928, 221.18819, 315.37282, 238.00923, 233.12403,
269.9957, 259.90847, 311.6859, 272.51505, 346.11677, 260.70023,
275.42651, 290.61255, 307.04942, 323.60677, 219.96542, 305.55666,
283.99912, 267.52644, 285.16624, 301.02858, 260.05581, 283.41617,
244.09153, 290.04512, 353.63745, 317.37715, 241.34487, 321.14259,
258.36885, 261.37509, 348.62232, 300.73864, 327.22453, 228.55662,
268.91226, 314.09536, 260.56452, 283.06898, 319.64052, 275.96927,
296.46456, 295.56963, 276.68877, 289.44363, 296.45405, 240.71778,
310.15017, 237.00255, 240.61887, 244.14616, 316.83501, 253.25562,
230.76127, 304.57009, 364.11619, 267.0067, 308.55546, 275.38505,
293.96026, 243.73364, 321.01363, 316.79925, 277.67496, 289.51552,
307.85001, 279.01546, 317.36872, 242.91248, 279.70631, 226.23475,
386.36518, 326.81052, 235.98733, 288.59577, 305.13604, 353.10836,
274.67248, 327.51103, 322.45477, 315.17773, 316.73504, 189.05388,
267.47862, 316.6865, 272.59329, 281.07099, 327.16369, 280.24953,
253.59041, 314.05915, 341.81589, 286.40082, 323.2246, 218.71734,
256.56632, 239.05101, 292.73646, 262.55946, 280.23444, 219.2759,
293.2041, 342.50186, 251.37571, 321.82169, 290.32648, 233.07586,
293.06438, 333.05446, 222.39499, 298.17628, 201.93622, 354.07415,
294.31312, 301.73295, 328.2708, 278.76013, 289.11145, 313.0081,
275.55262, 287.71408, 272.14237, 300.88391, 308.66827, 284.93954,
312.92511, 335.31426, 276.17927, 320.79382, 273.71349, 255.89581,
300.24576, 292.25961, 288.96434, 303.44888, 253.07684, 248.08721,
281.36322, 326.35893, 335.85343, 253.90892, 328.49694, 272.70062
), PVLIT3 = c(347.03202, 297.99638, 308.9164, 300.68665,
302.42232, 318.3244, 315.16283, 273.26457, 306.98393, 342.15998,
271.74761, 330.01561, 239.3113, 292.38404, 220.32939, 320.98693,
328.77846, 312.25874, 320.16997, 315.01401, 278.54675, 297.93796,
289.05429, 306.41476, 292.88945, 287.35494, 289.7173, 327.61128,
256.59233, 270.40046, 299.88817, 347.61574, 271.24062, 300.58625,
389.47902, 323.26406, 303.8882, 287.85783, 320.78071, 260.5962,
329.16759, 322.44865, 288.50371, 230.97053, 341.00067, 317.60536,
334.04519, 242.21659, 273.88791, 319.75284, 303.14072, 369.25668,
315.38962, 238.82989, 389.86658, 291.40597, 256.2491, 328.59089,
270.08341, 346.88612, 351.05896, 313.98005, 276.49836, 341.01902,
304.44641, 279.94384, 297.38058, 260.15889, 301.88881, 385.34273,
281.3716, 305.96796, 235.56287, 313.06217, 242.71572, 354.28779,
305.7085, 256.87025, 288.9405, 291.11581, 307.62043, 321.73213,
283.82863, 248.89211, 291.42861, 273.85537, 307.50928, 316.5518,
321.39642, 289.61352, 319.72759, 339.1263, 364.61416, 277.72976,
295.74977, 274.09616, 272.9924, 272.49014, 251.05889, 251.01214,
386.34787, 245.74296, 303.75875, 284.2808, 350.74965, 354.9783,
229.63546, 282.53014, 233.23552, 252.86831, 264.47331, 320.17187,
357.86769, 281.28431, 291.52807, 309.28932, 335.37317, 250.08053,
279.77425, 370.20707, 313.44683, 293.07481, 250.40199, 270.51998,
391.40329, 234.55338, 376.94785, 332.07567, 246.3079, 299.29627,
313.21714, 285.05673, 285.967, 272.23878, 254.95618, 291.76139,
311.07348, 291.55214, 330.95787, 368.524, 230.85077, 259.26676,
269.73751, 284.4568, 331.74769, 267.65832, 297.71321, 327.10192,
281.02533, 317.82234, 277.43442, 347.61242, 242.73154, 273.46411,
326.03792, 228.83492, 292.92737, 304.29623, 275.46119, 298.80275,
343.8432, 343.49854, 280.61476, 290.29079, 251.7561, 249.89728,
297.3227, 298.18224, 355.51642, 310.46884, 337.82139, 307.61772,
313.36828, 334.1722, 305.46306, 278.88311, 278.72036, 267.1024,
320.97308, 343.18164, 263.62036, 256.22528, 309.85014, 288.59665,
235.3395, 314.77392, 321.25776, 300.35306, 371.13057, 259.11446,
258.48696, 306.43744, 323.78058, 280.04595, 297.34385, 263.67336,
329.24681, 233.97186, 346.51321, 286.97003, 306.86708, 297.15846,
337.82102, 262.12682, 291.70902, 281.91251, 297.9659, 217.23621,
359.04224, 265.45122, 324.9777, 334.42103, 237.77726, 228.74243,
252.22045, 290.07852, 283.7953, 270.72377, 295.45991, 302.80527,
304.19675, 268.4273, 244.94332, 263.11038, 207.48832, 326.08576,
312.77998, 318.79277, 244.6571, 332.0414, 259.87963, 296.28998,
239.8572, 248.63712, 312.60629, 247.72389, 242.67078, 271.98961,
322.73757, 197.99485, 342.45941, 246.40288, 246.54248, 270.31797,
279.19136, 283.27782, 256.21676, 298.22446, 245.34207, 363.90223,
206.35297, 297.69943, 250.76662, 299.09484, 296.26682, 217.5044,
323.62796, 290.34726, 246.24697, 290.60326, 313.243, 254.3545,
273.66298, 265.30058, 304.26679, 309.53028, 310.93268, 318.41836,
318.59416, 344.05085, 247.21871, 319.13724, 314.71633, 262.51803,
277.58658, 240.33522, 283.6671, 315.50305, 284.70172, 260.21957,
247.34008, 347.11825, 315.84204, 310.46298, 278.06065, 264.27623,
336.07573, 326.01364, 245.67, 149.19113, 298.48698, 222.68479,
303.39236, 305.14083, 303.49802, 261.84537, 341.96969, 334.29589,
221.43909, 307.37339, 258.84784, 319.13495, 241.58692, 239.09068,
242.53418, 330.78695, 280.48138, 310.41672, 263.1502, 267.58253,
187.53822, 262.62308, 295.44728, 266.01227, 313.69862, 253.52788,
256.41986, 323.6009, 337.53842, 333.99872, 303.51809, 312.28169,
294.92838, 300.70826, 300.24336, 187.73782, 276.88791, 286.49432,
292.1261, 254.85939, 277.5201, 243.95193, 251.41231, 300.05949,
307.55581, 308.63016, 292.26692, 322.59732, 314.09203, 279.01204,
104.22403, 282.82249, 310.19937, 265.4532, 298.59688, 327.82834,
216.9004, 304.42974, 308.75751, 353.56875, 323.77259, 301.02944,
243.21455, 299.93867, 239.32609, 288.85644, 321.70498, 300.7652,
214.00534, 330.58371, 218.42779, 226.38351, 269.54573, 281.0985,
313.38796, 315.12442, 338.55295, 287.76669, 276.50012, 283.05571,
293.54309, 313.50773, 251.84096, 306.30878, 298.6039, 282.81368,
280.57491, 246.21204, 228.88241, 278.85273, 250.53517, 308.06586,
337.51522, 322.46427, 249.82595, 326.33555, 223.46005, 292.20952,
326.24258, 284.81759, 361.99513, 225.74007, 265.54327, 301.95898,
289.31911, 306.88864, 312.2168, 227.28386, 288.40244, 274.2207,
281.3788, 295.98457, 291.69465, 233.11237, 327.41621, 238.52888,
246.48451, 272.98087, 300.54158, 265.74598, 205.12706, 296.53577,
350.87861, 247.59947, 315.72462, 285.2722, 269.48033, 243.84168,
283.6546, 275.47676, 290.5681, 252.84893, 341.91242, 268.79573,
304.94915, 251.56578, 263.95641, 243.55776, 338.59661, 303.00255,
212.9379, 292.68837, 294.13636, 388.07221, 303.7387, 313.72581,
355.13609, 317.09882, 331.7978, 218.43844, 262.96991, 306.87193,
268.31627, 265.33588, 320.67174, 247.42967, 276.86054, 273.34922,
348.14517, 267.12308, 306.68901, 177.57379, 242.70165, 216.52883,
255.14138, 224.94714, 252.34893, 235.99393, 311.64778, 336.18343,
287.39269, 336.26041, 289.42778, 192.66316, 276.65478, 306.98647,
198.9643, 302.39132, 182.62885, 346.2712, 309.66977, 264.90717,
302.69623, 264.56688, 277.51619, 339.16197, 280.54847, 277.54282,
283.37459, 292.15455, 292.34224, 252.94767, 301.60766, 301.46605,
289.27154, 286.94408, 288.05704, 233.64855, 304.36141, 278.99481,
260.61019, 257.88718, 279.99577, 269.7465, 293.24509, 323.32979,
315.02618, 246.47989, 327.3044, 274.86892)), row.names = c(29L,
47L, 59L, 116L, 125L, 155L, 238L, 245L, 335L, 354L, 393L, 410L,
420L, 443L, 478L, 514L, 584L, 665L, 676L, 720L, 737L, 766L, 782L,
839L, 840L, 842L, 857L, 866L, 885L, 887L, 1017L, 1041L, 1083L,
1086L, 1153L, 1207L, 1219L, 1231L, 1276L, 1283L, 1389L, 1405L,
1423L, 1469L, 1517L, 1525L, 1544L, 1686L, 1766L, 1769L, 1773L,
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2105L, 2169L, 2262L, 2291L, 2293L, 2310L, 2349L, 2353L, 2358L,
2385L, 2441L, 2456L, 2490L, 2516L, 2545L, 2552L, 2557L, 2676L,
2683L, 2702L, 2733L, 2761L, 2801L, 2851L, 2876L, 2881L, 2882L,
2913L, 2927L, 2928L, 2991L, 3001L, 3018L, 3042L, 3071L, 3209L,
3239L, 3296L, 3298L, 3371L, 3502L, 3546L, 3559L, 3560L, 3561L,
3566L, 3589L, 3659L, 3669L, 3714L, 3717L, 3738L, 3744L, 3765L,
3768L, 3778L, 3782L, 3826L, 3840L, 3842L, 3848L, 3894L, 3968L,
3972L, 3984L, 4007L, 4064L, 4103L, 4160L, 4209L, 4212L, 4337L,
4377L, 4432L, 4671L, 4674L, 4675L, 4686L, 4714L, 4724L, 4761L,
4773L, 4853L, 4871L, 4908L, 4931L, 4993L, 5000L, 5039L, 5055L,
5081L, 5100L, 5173L, 5260L, 5265L, 5281L, 5331L, 5368L, 5412L,
5419L, 5435L, 5476L, 5482L, 5485L, 5500L, 5561L, 5591L, 5618L,
5629L, 5641L, 5745L, 5767L, 5783L, 5833L, 5876L, 5884L, 5933L,
5938L, 5943L, 5960L, 5997L, 6000L, 6016L, 6021L, 6109L, 6116L,
6124L, 6135L, 6157L, 6166L, 6172L, 6173L, 6176L, 6181L, 6214L,
6216L, 6226L, 6229L, 6264L, 6280L, 6282L, 6315L, 6330L, 6358L,
6366L, 6368L, 6375L, 6397L, 6463L, 6541L, 6543L, 6598L, 6604L,
6644L, 6649L, 6669L, 6690L, 6692L, 6702L, 6711L, 6716L, 6718L,
6725L, 6740L, 6769L, 6775L, 6781L, 6829L, 6841L, 6853L, 6860L,
6872L, 6890L, 6891L, 6902L, 6905L, 6913L, 6914L, 6927L, 6934L,
6937L, 6947L, 6965L, 6980L, 6981L, 7021L, 7072L, 7094L, 7104L,
7121L, 7136L, 7138L, 7148L, 7149L, 7168L, 7169L, 7222L, 7258L,
7263L, 7266L, 7292L, 7347L, 7350L, 7360L, 7382L, 7390L, 7419L,
7423L, 7455L, 7463L, 7471L, 7482L, 7509L, 7528L, 7543L, 7577L,
7583L, 7587L, 7593L, 7615L, 7616L, 7617L, 7633L, 7650L, 7654L,
7674L, 7688L, 7706L, 7718L, 7723L, 7748L, 7766L, 7797L, 7811L,
7813L, 7820L, 7839L, 7893L, 7899L, 7910L, 7926L, 7944L, 7945L,
7947L, 7954L, 7955L, 7959L, 7982L, 7990L, 8025L, 8031L, 8041L,
8044L, 8049L, 8062L, 8069L, 8072L, 8085L, 8090L, 8110L, 8122L,
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8236L, 8271L, 8273L, 8276L, 8284L, 8307L, 8328L, 8333L, 8354L,
8367L, 8386L, 8412L, 8421L, 8441L, 8452L, 8478L, 8488L, 8504L,
8509L, 8518L, 8534L, 8548L, 8558L, 8574L, 8578L, 8585L, 8620L,
8654L, 8660L, 8662L, 8663L, 8704L, 8707L, 8733L, 8751L, 8757L,
8817L, 8822L, 8824L, 8831L, 8840L, 8841L, 8851L, 8858L, 8877L,
8904L, 8919L, 8944L, 8945L, 8951L, 8954L, 8959L, 8966L, 8986L,
9049L, 9058L, 9075L, 9088L, 9111L, 9124L, 9132L, 9138L, 9148L,
9171L, 9183L, 9188L, 9207L, 9257L, 9280L, 9284L, 9288L, 9292L,
9316L, 9335L, 9341L, 9354L, 9392L, 9428L, 9439L, 9451L, 9483L,
9484L, 9493L, 9498L, 9532L, 9542L, 9566L, 9569L, 9582L, 9592L,
9613L, 9628L, 9634L, 9643L, 9647L, 9659L, 9665L, 9687L, 9697L,
9708L, 9713L, 9762L, 9773L, 9815L, 9825L, 9829L, 9830L, 9839L,
9842L, 9848L, 9854L, 9865L, 9868L, 9891L, 9896L, 9925L, 9945L,
10012L, 10023L, 10035L, 10046L, 10059L, 10067L, 10096L, 10105L,
10106L, 10137L, 10146L, 10154L, 10173L, 10178L, 10195L, 10201L,
10202L, 10233L, 10243L, 10259L, 10273L, 10277L, 10287L, 10288L,
10315L, 10316L, 10342L, 10345L, 10348L, 10354L, 10361L, 10372L,
10417L, 10420L, 10430L, 10454L, 10476L, 10477L, 10490L, 10498L,
10499L, 10532L, 10533L, 10550L, 10553L, 10557L, 10560L, 10572L,
10616L, 10663L, 10667L, 10687L, 10712L), class = "data.frame")
and I want to add another column here with values taken from another data. The other data is this one:
> dput(new)
structure(list(CNTRYID = structure(1:2, .Label = c("Czech Republic",
"Denmark", "France", "Greece", "Hungary", "Italy", "Lithuania",
"Netherlands", "Poland", "Slovak Republic", "Slovenia", "Spain",
"United Kingdom"), class = "factor"), Freq = c(184, 518), Mean = c(299.71,
294.09), s.e. = c(4.39, 2.28), SD = c(36.73, 44.8), s.e = c(3.4,
2.12)), row.names = 1:2, class = c("intsvy.mean", "data.frame"
))
What I want to do is to take the value under Mean from the second data.frame that is associated with, e.g. the Czech Republic, and put it in the first data.frame so that everywhere where CNTRYID is Czech Republic the value 299.71 to appear in a new column, say, test$MEAN. Then 294.09 to appear in every row where the CNTRYID is Denmark and so on. You might require package "intsvy" since one of the classes here is "intsvy.mean". I have extracted this from a large dataset I am working with, so there might be some additional labels etc. but, overall, you should be able to reproduce it. Thank you in advance!


You are looking for merge.
result <- merge(test,new,by="CNTRYID")

Related

How to compare the slope and intercept of two total least squares regressions in R?

I have a dataset consisting of the two numerical, measured variables D and O, and a third variable that indicates sampling locations (East or West), in total 310 observation.
D and O are linearly correlated, I have used odregress() in R to create total least square regressions. Doing this for both sampling locations seperately leads to fairly similar regression lines.
I would like to statistically test if slope and/or intercept are different for these two locations. If I had used a simple linear regression I would use an ANOVA for this. But as far as I know this doesnt work for total least square approaches, is that correct? I have googled a lot but couldnt find any solution to this. Someone suggested bootstrapping both groups separately, getting 100 different slopes and intercepts and then to t.test these slope and intercept groups, but I feel like there should be an easier way of doing this?
I would appreciate any help!
EDIT: Here is the dataset
structure(list(O = c(0.7324, 0.78124, 0.78556, 0.78704, 0.81002,
0.87443, 0.8924, 0.91224, 0.92517, 0.92573, 0.92924, 0.93397,
0.95854, 0.96477, 0.98186, 0.99257, 0.9931, 0.99488, 0.99901,
1.0071, 1.0119, 1.0275, 1.0453, 1.0467, 1.0526, 1.0622, 1.0644,
1.0694, 1.0781, 1.0785, 1.0808, 1.0847, 1.0855, 1.0871, 1.0871,
1.0872, 1.0935, 1.1028, 1.1067, 1.1106, 1.1207, 1.1251, 1.131,
1.1359, 1.1359, 1.1387, 1.1419, 1.1496, 1.151, 1.1526, 1.1536,
1.1538, 1.1592, 1.1595, 1.1645, 1.1705, 1.1766, 1.1842, 1.1997,
1.2, 1.2011, 1.2077, 1.2085, 1.2175, 1.2183, 1.2287, 1.2301,
1.2362, 1.2449, 1.248, 1.2508, 1.254, 1.2638, 1.2721, 1.2745,
1.2858, 1.3039, 1.306, 1.3082, 1.3151, 1.3162, 1.3195, 1.3365,
1.3392, 1.3413, 1.355, 1.3614, 1.3675, 1.3826, 1.3882, 1.3926,
1.4075, 1.4094, 1.4109, 1.4155, 1.4414, 1.4487, 1.4554, 1.4642,
1.4771, 1.4782, 1.4845, 1.5, 1.5136, 1.6112, 1.6173, 1.6228,
1.6761, 1.7301, 1.7614, 1.8407, 1.893, 2.1033, 2.433, 0.13104,
0.18361, 0.25999, 0.26253, 0.26939, 0.36762, 0.37335, 0.38632,
0.39468, 0.48303, 0.50917, 0.5375, 0.54697, 0.55499, 0.55638,
0.55957, 0.56313, 0.58666, 0.58995, 0.61187, 0.63833, 0.63971,
0.65251, 0.66876, 0.67105, 0.67192, 0.67333, 0.67489, 0.69292,
0.69587, 0.71104, 0.71439, 0.715, 0.72289, 0.72526, 0.75811,
0.75894, 0.76593, 0.76717, 0.77036, 0.7803, 0.78315, 0.78472,
0.78818, 0.78862, 0.79408, 0.79605, 0.79814, 0.80054, 0.81471,
0.81491, 0.8154, 0.81859, 0.82125, 0.8259, 0.8261, 0.83919, 0.8415,
0.84694, 0.85396, 0.86645, 0.86752, 0.86833, 0.87828, 0.88269,
0.88637, 0.8927, 0.90286, 0.90783, 0.91026, 0.91418, 0.91427,
0.91478, 0.9166, 0.91863, 0.91865, 0.92676, 0.9295, 0.9334, 0.93429,
0.93487, 0.93529, 0.94348, 0.95148, 0.952, 0.95354, 0.95403,
0.9548, 0.96222, 0.96533, 0.96921, 0.97023, 0.97109, 0.97247,
0.97251, 0.98598, 0.98738, 0.99089, 0.99475, 0.99679, 0.99814,
1.0051, 1.0061, 1.0074, 1.0097, 1.0189, 1.0231, 1.0305, 1.0379,
1.0452, 1.0467, 1.0469, 1.0473, 1.0474, 1.0476, 1.0521, 1.06,
1.0652, 1.0673, 1.0737, 1.0739, 1.0796, 1.0832, 1.0864, 1.0922,
1.0923, 1.0928, 1.0987, 1.1098, 1.1108, 1.112, 1.119, 1.1319,
1.135, 1.1355, 1.1368, 1.1406, 1.15, 1.1532, 1.1548, 1.1561,
1.1563, 1.161, 1.1628, 1.1657, 1.1695, 1.1726, 1.1762, 1.1847,
1.1851, 1.186, 1.1861, 1.1864, 1.1892, 1.1944, 1.2014, 1.2034,
1.2035, 1.2088, 1.209, 1.2093, 1.21, 1.226, 1.234, 1.2457, 1.258,
1.2597, 1.2601, 1.261, 1.2619, 1.2648, 1.2724, 1.2785, 1.3138,
1.3148, 1.3392, 1.3452, 1.3537, 1.3553, 1.3613, 1.3634, 1.374,
1.3804, 1.39, 1.3964, 1.4017, 1.4033, 1.4124, 1.454, 1.4909,
1.4933, 1.5095, 1.6554, 2.2905, 2.2935), D = c(6.7335, 5.9842,
7.2607, 6.6692, 6.7883, 6.3851, 6.9412, 6.0943, 6.617, 6.3907,
7.8515, 7.5698, 7.0378, 7.4205, 7.499, 6.8394, 7.0227, 7.5943,
7.6416, 6.2831, 8.1137, 6.5366, 8.5699, 7.9497, 6.6017, 6.6698,
7.4538, 8.0064, 7.9721, 8.618, 8.0632, 7.9414, 7.1566, 6.7663,
8.7359, 6.8296, 7.0741, 7.7438, 6.97, 8.4848, 7.6823, 7.8706,
7.2941, 7.6861, 7.8883, 7.2856, 7.7869, 7.6533, 8.2157, 8.0779,
7.4342, 6.9185, 8.6697, 8.1597, 6.869, 7.8173, 8.0279, 7.3248,
8.1733, 8.8169, 8.1503, 8.6909, 8.7486, 6.9067, 8.4934, 7.891,
8.5693, 8.9372, 8.5297, 8.1161, 8.1002, 7.764, 7.4613, 8.3119,
8.1478, 8.0479, 8.0527, 8.6343, 7.8803, 8.032, 7.2934, 7.7577,
10.055, 6.9696, 8.2901, 9.4509, 8.6809, 8.3964, 9.8273, 8.0222,
8.933, 8.6679, 9.4189, 9.7793, 9.468, 9.4953, 8.9932, 9.9725,
9.2968, 9.9642, 9.3904, 8.9943, 8.8995, 9.4839, 9.9091, 9.3051,
9.8542, 8.5494, 8.5515, 8.287, 10.293, 8.591, 7.8362, 11.147,
2.8316, 3.8897, 4.5759, 3.7706, 4.225, 4.8624, 3.444, 4.0051,
4.3401, 2.938, 4.8966, 3.5711, 4.4281, 5.0723, 5.4119, 5.472,
4.9745, 5.52, 5.5544, 6.9943, 4.8439, 5.0446, 4.5001, 5.1235,
6.3628, 4.7419, 5.8969, 5.5802, 5.6402, 5.5854, 4.9522, 4.7576,
4.1654, 5.571, 5.6993, 4.6309, 5.115, 5.5524, 5.6906, 5.7571,
5.6431, 5.1011, 5.0844, 5.6666, 4.5314, 6.456, 5.1582, 5.3766,
4.7862, 5.4651, 5.914, 5.345, 5.494, 6.9188, 5.7707, 4.9756,
5.1671, 6.1472, 5.2446, 6.1542, 5.9616, 5.9502, 5.4772, 6.1042,
5.7241, 5.592, 4.9474, 6.8122, 7.1245, 6.4829, 5.0722, 6.7933,
6.2212, 6.2546, 6.434, 6.9884, 6.4172, 5.7726, 5.5066, 5.8896,
6.9811, 5.0706, 6.2065, 6.6623, 6.0453, 6.6251, 5.7937, 6.889,
6.4776, 6.3118, 6.2194, 4.9703, 6.4035, 7.5873, 6.4064, 7.1442,
6.461, 5.4649, 5.3957, 6.7057, 6.4148, 6.52, 5.878, 6.8155, 7.4694,
6.4654, 6.081, 8.053, 6.5501, 6.6835, 6.8489, 6.2467, 7.4948,
7.1152, 7.1818, 6.4375, 7.3438, 7.2218, 5.4177, 7.0612, 6.8986,
6.9722, 6.5899, 6.876, 6.817, 6.9595, 7.674, 6.3334, 6.9009,
6.236, 7.1216, 6.4549, 6.8034, 6.379, 6.6753, 6.9686, 7.4758,
7.2485, 6.9605, 6.7682, 6.7202, 7.3145, 7.266, 6.1579, 7.5649,
7.1079, 7.0922, 6.886, 6.9401, 6.8369, 6.8474, 7.2315, 6.3774,
5.6486, 7.1576, 7.5174, 7.3115, 7.998, 8.4278, 7.5225, 7.5302,
7.196, 7.5353, 6.5144, 7.8133, 6.4237, 7.8706, 7.9279, 6.9488,
7.5694, 7.078, 6.9277, 8.1135, 7.7531, 6.6827, 6.9672, 7.6023,
8.957, 7.3327, 7.6301, 8.0807, 6.824, 9.4569, 8.8401, 7.1244,
8.6603, 7.8307, 7.8158, 6.5451, 8.2186, 8.4406, 8.3064, 6.3104,
7.3834, 7.3139), Location = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label =
c("East",
"West"), class = "factor")), class = "data.frame", row.names = c(NA,
-309L))

Difference between densityPlot and geom_density

I am plotting the same data with both geom_density and base R's densityPlot:
head(df)
min_dist iteration
<dbl> <fct>
1 -79277 1
2 -68987 1
3 -98661 1
4 -98145 1
5 -222408 1
6 -217409 1
geom_density:
ggplot(df) + geom_density(aes(min_dist)) + xlim(c(-1e4, 1e4)) + geom_rug(aes(min_dist))
densityPlot:
densityPlot(df$min_dist, xlim = c(-1e4, 1e4))
And they are strikingly different!
I was under the impression that geom_density was a wrapper for stat_density.
Is this just a matter of different adjust values?
df <- structure(list(min_dist = c(-79277, -68987, -98661, -98145, -222408,
-217409, -10759, -10363, 48034, 48525, 617038, 617069, -19656,
-17396, -479, -333, -48721, -47691, -19761, 380, 31429, 33953,
-9220, -61971, -609, -560, 6228, 13082, -217137, -216639, 99268,
102365, -119574, -53795, -329407, -111401, 1018733, -559898,
-1346, -15, -71577, -2883, 19490, -12321, -12240, -8470, -29804,
28724, 40673, -57818, -41421, -39126, 136783, 165727, -52989,
-51058, 58995, -12226, 8057, 44217, 13280, 16828, -80410, 11324,
975, 2630, 14250, 17108, -29341, -27033, -79277, -68987, -98661,
-98145, -222408, -217409, -10759, -10363, 48034, 48525, 617038,
617069, -19656, -17396, -479, -333, -48721, -47691, -19761, 380,
31429, 33953, -9220, -61971, -609, -560, 6228, 13082, -217137,
-216639, 99268, 102365, -119574, -53795, -329407, -111401, 1018733,
-559898, -1346, -15, -71577, -2883, 19490, -12321, -12240, -8470,
-29804, 28724, 40673, -57818, -41421, -39126, 136783, 165727,
-52989, -51058, 58995, -12226, 8057, 44217, 13280, 16828, -80410,
11324, 975, 2630, 14250, 17108, -29341, -27033), iteration = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor")), row.names = c(NA, -140L), class = c("tbl_df",
"tbl", "data.frame"))

How to determine similar values around a particular row in R?

Goal
I want to find the duration of lane changes of vehicles as shown in the plot, using lateral position data.
Data
Following is the data for only one vehicle:
> dput(a)
structure(list(Frame.ID = 526:1058, xcoord = c(14.346, 14.367,
14.388, 14.419, 14.458, 14.503, 14.55, 14.6, 14.65, 14.702, 14.754,
14.807, 14.86, 14.913, 14.966, 15.02, 15.072, 15.125, 15.178,
15.23, 15.282, 15.333, 15.384, 15.434, 15.482, 15.529, 15.574,
15.617, 15.657, 15.694, 15.727, 15.755, 15.78, 15.802, 15.823,
15.841, 15.858, 15.874, 15.889, 15.903, 15.917, 15.93, 15.942,
15.955, 15.967, 15.978, 15.989, 16, 16.011, 16.022, 16.033, 16.044,
16.055, 16.065, 16.075, 16.085, 16.095, 16.104, 16.112, 16.12,
16.129, 16.139, 16.151, 16.164, 16.178, 16.195, 16.212, 16.231,
16.25, 16.27, 16.291, 16.312, 16.333, 16.356, 16.379, 16.403,
16.428, 16.455, 16.482, 16.511, 16.542, 16.574, 16.609, 16.646,
16.687, 16.732, 16.783, 16.839, 16.902, 16.967, 17.033, 17.1,
17.168, 17.232, 17.294, 17.354, 17.41, 17.464, 17.513, 17.559,
17.6, 17.636, 17.665, 17.685, 17.694, 17.7, 17.708, 17.725, 17.751,
17.782, 17.817, 17.856, 17.897, 17.939, 17.982, 18.025, 18.067,
18.108, 18.145, 18.178, 18.207, 18.232, 18.255, 18.274, 18.292,
18.308, 18.323, 18.336, 18.349, 18.361, 18.372, 18.383, 18.393,
18.403, 18.413, 18.422, 18.432, 18.441, 18.451, 18.46, 18.469,
18.479, 18.488, 18.496, 18.505, 18.513, 18.521, 18.529, 18.537,
18.544, 18.55, 18.556, 18.562, 18.567, 18.574, 18.58, 18.588,
18.597, 18.609, 18.623, 18.64, 18.662, 18.69, 18.722, 18.76,
18.802, 18.849, 18.899, 18.953, 19.012, 19.076, 19.144, 19.218,
19.299, 19.386, 19.479, 19.574, 19.669, 19.763, 19.855, 19.945,
20.031, 20.112, 20.187, 20.254, 20.31, 20.352, 20.385, 20.412,
20.435, 20.45, 20.455, 20.449, 20.436, 20.416, 20.39, 20.361,
20.328, 20.293, 20.256, 20.217, 20.178, 20.139, 20.1, 20.063,
20.026, 19.99, 19.957, 19.925, 19.895, 19.867, 19.842, 19.819,
19.796, 19.774, 19.751, 19.729, 19.707, 19.685, 19.662, 19.64,
19.617, 19.594, 19.571, 19.547, 19.523, 19.499, 19.473, 19.449,
19.426, 19.404, 19.382, 19.359, 19.336, 19.312, 19.288, 19.263,
19.237, 19.211, 19.184, 19.156, 19.127, 19.097, 19.066, 19.033,
18.998, 18.961, 18.921, 18.878, 18.831, 18.781, 18.727, 18.67,
18.612, 18.554, 18.498, 18.446, 18.397, 18.349, 18.304, 18.264,
18.233, 18.21, 18.194, 18.182, 18.175, 18.171, 18.17, 18.172,
18.177, 18.183, 18.192, 18.202, 18.213, 18.226, 18.241, 18.258,
18.277, 18.298, 18.321, 18.346, 18.371, 18.396, 18.422, 18.447,
18.471, 18.495, 18.518, 18.54, 18.559, 18.577, 18.591, 18.601,
18.606, 18.605, 18.6, 18.593, 18.584, 18.579, 18.58, 18.59, 18.607,
18.629, 18.655, 18.682, 18.711, 18.739, 18.766, 18.792, 18.818,
18.842, 18.864, 18.885, 18.905, 18.924, 18.943, 18.961, 18.98,
19, 19.02, 19.038, 19.054, 19.068, 19.081, 19.092, 19.103, 19.112,
19.121, 19.129, 19.137, 19.144, 19.15, 19.156, 19.161, 19.166,
19.169, 19.172, 19.173, 19.173, 19.171, 19.168, 19.163, 19.156,
19.147, 19.136, 19.123, 19.109, 19.093, 19.078, 19.061, 19.041,
19.017, 18.988, 18.954, 18.918, 18.878, 18.836, 18.795, 18.756,
18.722, 18.693, 18.671, 18.655, 18.642, 18.633, 18.625, 18.619,
18.613, 18.608, 18.602, 18.593, 18.58, 18.562, 18.537, 18.504,
18.46, 18.403, 18.33, 18.234, 18.115, 17.972, 17.806, 17.623,
17.427, 17.223, 17.013, 16.802, 16.592, 16.389, 16.191, 15.998,
15.806, 15.604, 15.386, 15.149, 14.891, 14.617, 14.328, 14.029,
13.722, 13.412, 13.097, 12.773, 12.436, 12.084, 11.723, 11.361,
11.006, 10.663, 10.334, 10.02, 9.723, 9.453, 9.219, 9.027, 8.874,
8.753, 8.657, 8.583, 8.525, 8.481, 8.448, 8.421, 8.4, 8.384,
8.371, 8.36, 8.351, 8.345, 8.338, 8.33, 8.319, 8.304, 8.284,
8.258, 8.224, 8.183, 8.136, 8.084, 8.029, 7.971, 7.912, 7.853,
7.794, 7.736, 7.681, 7.629, 7.581, 7.54, 7.506, 7.482, 7.468,
7.46, 7.459, 7.462, 7.468, 7.477, 7.489, 7.501, 7.514, 7.526,
7.539, 7.55, 7.562, 7.573, 7.584, 7.595, 7.607, 7.62, 7.636,
7.654, 7.675, 7.702, 7.734, 7.773, 7.823, 7.885, 7.96, 8.046,
8.134, 8.213, 8.278, 8.322, 8.342, 8.338, 8.308, 8.258, 8.192,
8.112, 8.023, 7.927, 7.827, 7.725, 7.623, 7.522, 7.424, 7.334,
7.252, 7.183, 7.128, 7.093, 7.078, 7.085, 7.117, 7.177, 7.267,
7.385, 7.525, 7.679, 7.839, 8, 8.155, 8.296, 8.418, 8.519, 8.606,
8.682, 8.749, 8.82, 8.891, 8.956, 9.012, 9.057, 9.09, 9.126,
9.162, 9.197, 9.227, 9.249, 9.257, 9.254, 9.251, 9.247), Lane = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L)), row.names = c(NA, -533L), .Names = c("Frame.ID",
"xcoord", "Lane"), class = c("tbl_df", "tbl", "data.frame"))
Method
To estimate the lane change duration, I want to start from the row where the target lane number is reported first time. In the example the target lane is 1 and the number 1 is reported first time when the vehicle touches the pavement marking, as shown in the plot. This point can be added in the data as:
a$rows <- as.numeric(row.names(a)) # Row numbers
a$lch <- a$xcoord[match(head(which(a$Lane==1),1), a$rows)]
For this point I want to find the difference from every other point.
a$difference <- abs(a$lch - a$xcoord)
Then, comparing the differences in both directions, there will be some rows where the differences will be quite similar indicating that lateral position was almost similar. The first instances in both directions will be treated as the limits of lane change maneuver.
How can I achieve this comparison part in R? I don't know how to 'move' from Frame.ID 929 (this example) upwards and downwards to compare where the differences become similar.

Not sure if it's the best/fastest solution, but the idea is to calculate the differences between two consecutive "difference" columns you created and pick a small value as a threshold to flag that they started becoming similar. Then get the row closest to the time point of the change for each lane.
Note that I renamed your dataset as dt. Run the process step by step to see how the process works. If you are happy with it you can make the script shorter by combining some commands.
dt$rows <- as.numeric(row.names(dt)) # Row numbers
dt$lch <- dt$xcoord[match(head(which(dt$Lane==1),1), dt$rows)]
dt$difference <- abs(dt$lch - dt$xcoord)
library(dplyr)
dt %>%
mutate(frameIDchange = Frame.ID[difference==0], # spot the frame ID of the change
diff_diff = difference - lag(difference, default=difference[1]), # find the difference of two consequtive differences
flag = ifelse(abs(diff_diff) <= 0.01,1,0)) %>% # flag if the difference of two consequtive differences is equal or less to our threshold (0.01)
filter(flag==1) %>% # keep only rows with threshold difference
mutate(frameIDdiff = abs(Frame.ID-frameIDchange)) %>% # calculate distance of frame IDs
group_by(Lane) %>% # for each lane
filter(frameIDdiff== min(abs(frameIDdiff))) # return the frame id closest to the change frame id with a difference equal or less to our threshold
# Frame.ID xcoord Lane rows lch difference frameIDchange diff_diff flag frameIDdiff
# 1 896 18.593 2 371 12.436 6.157 929 -0.009 1 33
# 2 953 8.351 1 428 12.436 4.085 929 0.009 1 24
This tells you that the frame ids where the differences started to become similar (0.01 threshold) are 896 and 953 for lanes 2 and 1 respectively. Obviously the frame id of the change (929) belongs between those frames as expected.
You can also experiment with slightly higher/less threshold values to see how the results change.

How to add significance letters (anova) to ggplot geom_bar with facet wrap

When I make a barplot with significance letters from anova above the bars, I use following code:
anova_NDW_geel<-aov(nodule_dry_weight~treatment,inoculatieproef_geel_variety2)
HSD_NDW_geel <- HSD.test(anova_NDW_geel,"treatment",alpha=0.05,group=TRUE)$groups
HSD_NDW_means_geel <- HSD.test(anova_NDW_geel,"treatment",alpha=0.05,group=TRUE)$means
HSD_NDW_means_geel <- HSD_NDW_means_geel[order(-HSD_NDW_means_geel$nodule_dry_weight),]
p_HSD_NDW_geel <- ggplot(aes(x=treatment, y=NDW_mean_geel, width=0.6), data=inoculatieproef_mean_geel)+
geom_bar(stat="identity", data=HSD_NDW_geel, aes(x=trt, y=means), fill="gray40")+
geom_text(data=HSD_NDW_geel, aes(x=trt, y=means, label=M), size=5, vjust=-1, hjust=1)+
ggtitle("Zand")+
ylab("Droog gewicht wortelknolletjes (g)")+
xlab("Behandeling")+
geom_errorbar(aes(ymin=NDW_mean_geel-NDW_sd_geel,ymax=NDW_mean_geel+NDW_sd_geel),
position=position_dodge(width=0.5),width=0.1,size=0.3)+
theme_bw() +
theme(axis.line = element_line(colour="black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
scale_y_continuous(expand = c(0, 0))+
theme(axis.text.x = element_text(angle = 0, hjust = 1, vjust = 0.5))+
theme(text = element_text(size=12))
which results in following graph: http://i.stack.imgur.com/bZidZ.png
This is probably not the best way to do this and when I want to add the letters to the barplots with facet wrap.
Here is a sample of the data I want to make a facet wrap with significance letters with:
structure(list(treatment = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("1", "2",
"3", "4", "5", "6", "7", "8"), class = "factor"), block = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("I",
"II", "III", "IV"), class = "factor"), position = structure(c(2L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("b",
"gem(ab)"), class = "factor"), variety = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), location = structure(c(2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Geel",
"Merelbeke"), class = "factor"), year = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("2014",
"2015"), class = "factor"), nodule_dry_weight = c(0, 0.0467,
0.0328, 0.0885, 0.0081, 0.1086, 0.0788, 0.0267, 0, 0.0128, 0.0143,
0.0333, 0.006, 0.098, 0.0286, 0.011, 0, 0.0627, 0.0769, 0.0784,
0.023, 0.1504, 0.1026, 0.0254, 0, 0.0597, 0.0158, 0.0354, 0.0226,
0.3261, 0.0436, 0, 0, 0.0203, 0.0469, 0.0904, 0.1593, 0.0836,
0.056, 0.0037, 0, 0.0534, 0.0901, 0.0435, 0.0248, 0.0435, 0.0279,
0.0029, 0, 0.0545, 0.038, 0.0991, 0.0099, 0.1453, 0.1096, 0.0272,
0, 0.0319, 0.0624, 0.0508, 0.0415, 0.11, 0.0079, 0, 0, 0.1257,
0.1242, 0.2899, 0.024, 0.2175, 0.2979, 0.0396, 0, 0.1583, 0.2935,
0.2541, 0.1027, 0.4196, 0.2059, 0.0396, 0, 0.0891, 0.167, 0.0907,
0.2153, 0.3063, 0.2921, 0.0528, 0, 0.0928, 0.2109, 0.1514, 0.0821,
0.3607, 0.0996, 0.0069, 0, 0.0685, 0.3109, 0.1862, 0.0393, 0.286,
0.3418, 0.0459, 0, 0.0765, 0.3486, 0.3988, 0.1155, 0.6341, 0.3653,
0.039, 0, 0.0766, 0.3112, 0.1988, 0.05, 0.2856, 0.34, 0.0862,
0, 0.2621, 0.1146, 0.393, 0.1644, 0.3415, 0.1343, 0.019, 0, 0.0976,
0.1853, 0.0691, 0.0248, 0.1764, 0.1244, 0.1525, 0, 0.1529, 0.1069,
0.2833, 0.0204, 0.2966, 0.2371, 0.1464, 0, 0.0691, 0.2094, 0.1633,
0.0264, 0.1344, 0.0694, 0.1175, 0, 0.1783, 0.1434, 0.2136, 0.0873,
0.19, 0.1683, 0.1927, 0, 0.0571, 0.0599, 0.1061, 0.0244, 0.1256,
0.0894, 0.0123, 0, 0.1696, 0.1046, 0.2164, 0.0939, 0.1552, 0.2942,
0.1652, 0, 0.0844, 0.102, 0.0227, 0.025, 0.0654, 0.1234, 0.0702,
0, 0.0979, 0.1246, 0.0958, 0.0867, 0.1104, 0.1969, 0.227, 0,
0.3704, 0.4727, 0.2527, 0.2078, 0.3377, 0.308, 0.1293, 0, 0.2417,
0.3744, 0.2916, 0.1773, 0.433, 0.2446, 0.1382, 0, 0.4718, 0.4271,
0.4882, 0.1799, 0.4178, 0.518, 0.3915, 0, 0.3421, 0.3804, 0.2112,
0.4292, 0.3829, 0.1315, 0.2719, 0, 0.3197, 0.6867, 0.414, 0.3112,
0.2914, 0.4994, 0.369, 0.0256, 0.1494, 0.5577, 0.2538, 0.3854,
0.4151, 0.544, 0.4009, 0, 0.5208, 0.2962, 0.4175, 0.2689, 0.3374,
0.5075, 0.3601, 0, 0.704, 0.4631, 0.4573, 0.154, 0.5087, 0.4319,
0.4155)), .Names = c("treatment", "block", "position", "variety",
"location", "year", "nodule_dry_weight"), row.names = c(NA, -256L
), class = "data.frame")
I use following code for my graph with facet wrap:
inoculatieproef <- inoculatieproef %>%
group_by(treatment, location, variety, year) %>%
mutate(NDW_mean = mean(nodule_dry_weight),
NDW_sd = sd(nodule_dry_weight))
ggplot(data=inoculatieproef,aes(x=treatment, y=NDW_mean))+
facet_wrap(~location*variety*year,ncol=2)+
geom_bar(position="dodge", stat="identity")+
geom_errorbar(aes(ymin = NDW_mean - NDW_sd,
ymax = NDW_mean + NDW_sd),
width=0.1,size=0.3,
color = "darkgrey")+
theme_bw() +
theme(axis.line = element_line(colour="black"),
panel.grid.minor = element_blank(),
panel.background = element_blank())
How do I add on each barplot the significance letters (anova) in de the facet wrap graph?

No idea if the test fits your data distribution, but you can start with that:
library(tidyverse)
stat_pvalue <- dd %>%
group_by(location, variety, year) %>%
rstatix::t_test(nodule_dry_weight~treatment) %>%
filter(p < 0.05) %>%
group_by(location, variety, year) %>%
rstatix::add_significance("p") %>%
rstatix::add_y_position() %>%
mutate(y.position = seq(min(y.position), max(y.position),length.out = n())*1.1) %>%
ungroup()
ggplot(data=dd,aes(x=treatment, y=nodule_dry_weight))+
geom_boxplot() +
facet_wrap(~location + variety + year,ncol=2, scales = "free_y") +
ggpubr::stat_pvalue_manual(stat_pvalue, label = "p")

Bar chart for factorial designs in R

I'm currently trying to create a clustered bar chart using ggplot2. It's basically just mean response times for a 2x2x2 factorial design. The three factors are load, compatibility and salience. I'm having a hard time jamming the third factor (salience) in there though. It shouldn't be a stacked graph though
This is what I currently have
bar+stat_summary(fun.y = mean, geom = "bar", position = "dodge") +
+ stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width = 0.90), width = 0.2)+
+ labs(x = "Compatibility", y = "Mean RT", fill = "Load")
Here's a small sample of the data I'm trying to graph:
ID load comp sal rt
1 1 High Incompatible Non_Salient 787
2 1 Low Compatible Salient 754
3 2 High Incompatible Salient 654
I've seen graphs like these numerous times before but I have no idea how to get ggplot2 to display three independent variables at the same time.
I've tried splitting the graphs by adding
+ facet_wrap( ~ sal)
but that doesn't work either. It just says "Invalid argument to unary operator"
Any help would be appreciated.

Is this the kind of plot you are looking for?
I used the Wii data from the book "Discovering Statistics Using R", which is in a similar format to yours.
structure(list(athlete = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), .Label = c("Athlete", "Non-Athlete"), class = "factor"),
stretch = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("No Stretching", "Stretching"
), class = "factor"), wii = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Playing Wii",
"Watching Wii"), class = "factor"), injury = c(2L, 2L, 1L,
2L, 0L, 1L, 2L, 0L, 2L, 2L, 2L, 1L, 4L, 2L, 2L, 0L, 0L, 3L,
3L, 3L, 2L, 1L, 0L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 4L, 1L,
2L, 2L, 2L, 1L, 4L, 4L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 1L, 0L, 3L, 3L, 2L, 1L, 2L, 4L, 1L, 2L, 5L, 5L, 3L,
6L, 4L, 3L, 4L, 5L, 5L, 2L, 6L, 4L, 4L, 4L, 3L, 4L, 3L, 2L,
1L, 4L, 3L, 2L, 2L, 1L, 3L, 1L, 1L, 3L, 4L, 2L, 7L, 8L, 6L,
9L, 4L, 7L, 5L, 9L, 6L, 4L, 8L, 5L, 4L, 7L, 10L, 1L, 3L,
2L, 1L, 3L, 3L, 2L, 3L, 4L, 2L, 0L, 1L, 3L, 2L, 0L)),
.Names = c("athlete", "stretch", "wii", "injury"),
class = "data.frame", row.names = c(NA, -120L))
Here is how to produce the plot.
library(ggplot2)
library(Hmisc)
ggplot(data=Wii, aes(x=stretch, y=injury, fill=wii)) +
facet_wrap(~athlete) +
stat_summary(fun.y = mean, geom = "bar", position = "dodge") +
stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width = 0.90), width = 0.2)

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