Warning when precompiling a module which uses ParallelAccelerator - julia
Whenever I try to precompile a module which includes using ParallelAccelerator, I get the following warning,
WARNING: eval from module J2CArray to Foo:
Expr(:block, Expr(:line, 69, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :j2c_array_size, Expr(:::, :arr, Expr(:curly, :Ptr, :Void)::Any)::Any, Expr(:::, :dim, :Int)::Any)::Any, Expr(:block, Expr(:line, 70, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :l = Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :j2c_array_size)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, :Cuint, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any, :Cuint)::Any, :arr, Expr(:call, :convert, :Cuint, :dim)::Any)::Any, Expr(:line, 72, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:return, Expr(:call, :convert, :Int, :l)::Any)::Any)::Any)::Any, Expr(:line, 77, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :j2c_array_to_pointer, Expr(:::, :arr, Expr(:curly, :Ptr, :Void)::Any)::Any, Expr(:::, :own, :Bool)::Any)::Any, Expr(:block, Expr(:line, 78, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :j2c_array_to_pointer)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, Expr(:curly, :Ptr, :Void)::Any, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any, :Bool)::Any, :arr, :own)::Any)::Any)::Any, Expr(:line, 84, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :j2c_array_get, Expr(:::, :arr, Expr(:curly, :Ptr, :Void)::Any)::Any, Expr(:::, :idx, :Int)::Any, Expr(:::, :T, :Type)::Any)::Any, Expr(:block, Expr(:line, 85, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :nbytes = Expr(:if, Expr(:call, :===, :T, Expr(:curly, :Ptr, :Void)::Any)::Any, 0, Expr(:call, :sizeof, :T)::Any)::Any, Expr(:line, 86, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :_value = Expr(:call, Expr(:curly, :Array, :T)::Any, 1)::Any, Expr(:line, 87, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :j2c_array_get)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, :Void, Expr(:tuple, :Cint, Expr(:curly, :Ptr, :Void)::Any, :Cuint, Expr(:curly, :Ptr, :Void)::Any)::Any, Expr(:call, :convert, :Cint, :nbytes)::Any, :arr, Expr(:call, :convert, :Cuint, :idx)::Any, Expr(:call, :convert, Expr(:curly, :Ptr, :Void)::Any, Expr(:call, :pointer, :_value)::Any)::Any)::Any, Expr(:line, 89, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:return, Expr(:ref, :_value, 1)::Any)::Any)::Any)::Any, Expr(:line, 94, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, Expr(:curly, :j2c_array_set, :T)::Any, Expr(:::, :arr, Expr(:curly, :Ptr, :Void)::Any)::Any, Expr(:::, :idx, :Int)::Any, Expr(:::, :value, :T)::Any)::Any, Expr(:block, Expr(:line, 95, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :nbytes = Expr(:if, Expr(:call, :===, :T, Expr(:curly, :Ptr, :Void)::Any)::Any, 0, Expr(:call, :sizeof, :T)::Any)::Any, Expr(:line, 96, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :_value = Expr(:if, Expr(:call, :==, :nbytes, 0)::Any, :value, Expr(:call, :convert, Expr(:curly, :Ptr, :Void)::Any, Expr(:call, :pointer, Expr(:ref, :T, :value)::Any)::Any)::Any)::Any, Expr(:line, 97, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :j2c_array_set)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, :Void, Expr(:tuple, :Cint, Expr(:curly, :Ptr, :Void)::Any, :Cuint, Expr(:curly, :Ptr, :Void)::Any)::Any, Expr(:call, :convert, :Cint, :nbytes)::Any, :arr, Expr(:call, :convert, :Cuint, :idx)::Any, :_value)::Any)::Any)::Any, Expr(:line, 107, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :j2c_array_delete, Expr(:::, :arr, Expr(:curly, :Ptr, :Void)::Any)::Any)::Any, Expr(:block, Expr(:line, 108, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :j2c_array_delete)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, :Void, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any)::Any, :arr)::Any)::Any)::Any, Expr(:line, 115, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :j2c_array_deref, Expr(:::, :arr, Expr(:curly, :Ptr, :Void)::Any)::Any)::Any, Expr(:block, Expr(:line, 116, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :j2c_array_deref)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, :Void, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any)::Any, :arr)::Any)::Any)::Any, Expr(:line, 119, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:if, Expr(:call, :>=, :VERSION, Expr(:macrocall, :#v_str, "0.6.0-pre")::Any)::Any, Expr(:block, Expr(:line, 121, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :to_j2c_array, Expr(:::, :inp, :AbstractString)::Any, :ptr_array_dict, :mapAtypeKey, :j2c_array_new)::Any, Expr(:block, Expr(:line, 122, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :arr = Expr(:call, :to_j2c_array, Expr(:call, Expr(:curly, :Vector, :UInt8)::Any, :inp)::Any, :ptr_array_dict, :mapAtypeKey, :j2c_array_new)::Any, Expr(:line, 123, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :new_ascii_string)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, Expr(:curly, :Ptr, :Void)::Any, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any)::Any, :arr)::Any)::Any)::Any)::Any, Expr(:block, Expr(:line, 127, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :to_j2c_array, Expr(:::, :inp, :AbstractString)::Any, :ptr_array_dict, :mapAtypeKey, :j2c_array_new)::Any, Expr(:block, Expr(:line, 128, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :arr = Expr(:call, :to_j2c_array, Expr(:., :inp, :data)::Any, :ptr_array_dict, :mapAtypeKey, :j2c_array_new)::Any, Expr(:line, 129, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :new_ascii_string)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, Expr(:curly, :Ptr, :Void)::Any, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any)::Any, :arr)::Any)::Any)::Any)::Any)::Any, Expr(:line, 134, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:function, Expr(:call, :from_ascii_string, Expr(:::, :str, Expr(:curly, :Ptr, :Void)::Any)::Any, :ptr_array_dict)::Any, Expr(:block, Expr(:line, 135, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :data = Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :from_ascii_string)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, Expr(:curly, :Ptr, :Void)::Any, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any)::Any, :str)::Any, Expr(:line, 136, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, :arr = Expr(:call, :_from_j2c_array, :data, :UInt8, 1, :ptr_array_dict)::Any, Expr(:line, 137, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:call, :ccall, Expr(:tuple, Expr(:quote, :delete_ascii_string)::Any, "/home/miguel/.julia/v0.6/ParallelAccelerator/src/../deps/libj2carray.so.1.0")::Any, :Void, Expr(:tuple, Expr(:curly, :Ptr, :Void)::Any)::Any, :str)::Any, Expr(:line, 138, Symbol("/home/miguel/.julia/v0.6/ParallelAccelerator/src/j2c-array.jl"))::Any, Expr(:return, :arr)::Any)::Any)::Any)::Any
** incremental compilation may be broken for this module **
I can reproduce this warning with 5 lines of code. First I create the module:
Foo.jl
__precompile__()
module Foo
using ParallelAccelerator
end
then I create a test script:
test.jl
using Foo
Finally I run the test script with Julia 0.6.4
$ julia test.jl
Is this a problem with ParallelAccelerator or the setup of my system?
Related
How to reorder day of year so that seasons line up
I am working with seasonal data in Australia that spans from their Spring to Fall. I would like to have a column with day of year. However, when I use the yday function from the lubridate, I receive a distribution like this:
The issue here is that the Australian summer (peak time for data collection) goes through the new year. If I tried to add this variable into the model, it would produce incorrect results.
So my question is:
How can I get a seasonally appropriate 'day of year' column for this dataset
I have included some data here:
structure(list(Date = structure(c(11323, 11323, 11323, 11323,
12784, 11324, 12785, 11324, 12419, 12785, 12786, 12786, 13151,
13151, 13151, 12787, 12421, 15343, 15709, 12787, 14614, 15344,
11327, 15344, 14614, 13154, 15711, 15711, 11328, 13154, 16442,
15712, 15712, 16442, 16442, 13886, 16443, 16443, 16443, 16443,
16444, 16444, 13887, 13157, 13157, 13158, 13158, 11697, 16445,
13158, 13889, 16446, 11698, 15716, 11333, 16447, 13525, 13525,
13160, 15351, 11335, 12430, 14257, 13161, 12796, 13162, 12066,
13162, 13892, 13162, 13893, 16085, 14989, 16815, 16085, 16086,
13164, 16086, 17182, 13894, 17183, 15356, 17183, 13165, 11704,
14992, 13166, 14262, 17184, 12435, 11706, 17185, 14628, 14993,
14628, 14629, 14629, 14994, 13168, 16455, 16456, 12804, 16456,
16456, 13899, 12805, 14996, 14996, 16822, 16457, 13171, 16458,
14997, 12806, 14997, 14998, 13172, 13172, 13172, 14998, 13173,
13173, 12808, 12808, 13173, 17192, 13904, 13174, 13174, 13174,
14271, 13175, 13175, 15001, 17193, 12445, 17194, 13176, 12445,
14272, 16464, 16464, 15734, 13907, 14638, 14639, 13178, 13178,
16465, 14639, 16466, 13179, 13179, 16101, 13179, 13910, 12449,
16467, 13910, 16467, 13181, 15372, 13181, 14277, 16468, 14278,
13182, 14278, 12451, 16469, 16470, 13183, 14279, 16835, 14644,
15010, 13549, 14645, 12819, 12819, 17203, 17203, 12454, 12820,
17203, 12821, 15012, 13916, 13916, 12821, 13917, 15013, 15013,
13187, 13917, 13918, 13553, 17206, 14284, 12457, 16476, 14285,
15380, 14285, 16476, 12825, 12459, 12459, 13920, 16477, 13921,
12460, 16478, 12460, 14287, 16114, 14288, 16844, 11731, 16479,
16480, 11367, 11367, 11367, 16845, 16481, 16481, 16846, 16846,
12829, 12830, 11003, 16482, 13925, 11369, 12465, 16483, 16483,
16483, 14657, 17215, 11371, 14658, 16484, 16484, 17216, 16485,
12467, 16850, 11737, 14295, 12834, 12468, 14295, 16851, 13200,
13200, 16487, 11374, 15026, 11375, 16488, 16488, 12836, 16488,
11376, 14663, 13202, 14663, 14663, 14664, 11742, 15029, 11377,
13203, 16491, 16491, 16491, 13569, 13204, 13205, 14301, 11744,
16492, 13935, 16493, 11380, 16493, 11014, 13936, 16494, 13937,
13937, 13572, 16494, 13208, 11382, 13938, 11016, 13208, 11748,
14670, 16496, 14670, 14670, 11384, 14671, 14306, 14671, 12479,
14672, 16498, 16498, 14672, 14672, 14673, 13942, 12481, 13942,
12481, 14309, 16500, 12482, 13943, 13943, 12849, 12483, 15771,
13944, 16501, 16867, 16502, 13945, 12484, 15406, 17234, 16868,
11024, 16503, 17234, 17235, 17235, 17235, 17235, 16869, 17236,
13948, 13948, 13948, 17236, 15045, 15410, 13949, 16141, 13219,
15046, 12489, 15411, 13950, 13950, 11395, 11395, 11395, 12856,
11395, 15779, 15048, 16874, 12857, 16874, 16875, 16510, 16510,
15049, 15049, 14685, 12859, 16511, 15050, 16146, 16512, 14686,
12860, 12860, 12860, 16513, 16878, 14322, 12861, 11400, 15784,
16514, 15784, 13227, 12862, 16515, 12497, 16515, 16515, 14689,
13594, 14690, 12498, 12864, 12864, 11404, 13595, 11404, 12865,
12499, 12500, 12866, 14327, 16518, 16518, 14693, 14328, 14693,
16519, 16519, 12502, 16520, 14329, 14329, 14329, 12503, 11042,
16521, 11042, 13234, 11409, 12504, 14331, 12504, 12504, 15427,
15427, 13601, 15427, 11775, 13602, 16889, 17255, 12506, 12506,
16160, 12873, 15429, 14334, 12507, 12508, 12508, 11047, 13969,
12508, 13970, 11414, 14336, 13605, 13970, 12876, 17259, 16893,
16163, 13606, 13972, 16894, 16894, 11416, 13607, 13973, 12878,
13243, 11417, 13973, 17262, 12513, 13974, 12513, 12513, 16897,
12514, 12514, 16897, 11419, 13976, 16898, 16898, 13976, 16898,
16169, 12151, 15804, 11421, 16899, 16170, 16535, 12883, 16170,
16535, 11057, 11057, 16171, 13979, 16171, 13980, 13250, 14711,
16537, 13980, 14712, 13981, 15808, 13981, 14712, 14713, 14713,
14713, 16174, 14713, 16905, 15444, 16905, 14349, 14349, 13619,
14715, 12523, 16906, 14350, 14716, 13620, 15446, 12524, 12524,
13621, 13621, 13621, 14352, 12525, 12526, 12526, 12526, 12526,
14718, 12527, 11066, 16910, 12527, 13623, 16911, 12528, 16911,
14720, 16911, 14721, 12529, 13990, 11068, 13990, 14357, 12530,
14722, 14357, 13626, 13627, 12531, 13627, 13992, 13992, 11071,
13993, 11071, 11071, 14359, 13994, 11072, 13994, 14360, 13994,
14361, 11073, 14361, 14361, 14361, 16188, 16188, 14362, 14362,
14362, 11075, 13997, 14363, 14363, 14363, 14364, 16190, 14364,
16190, 16190, 14365, 16191, 16191, 11808, 16191, 14366, 14366,
16192, 11078, 14366, 16193, 16193, 16193, 16193, 16193, 16194,
16194, 16194, 16194, 16194, 12542, 16195, 16195, 12542, 12542,
12543, 12543, 12543, 12543, 16196, 14005, 14371, 16197, 14005,
14005, 12545, 14006, 12545, 14006, 12545, 14007, 14007, 12546,
12546, 12546, 14739, 14008, 12547, 12547, 14008, 14009, 14009,
14740, 12548, 15470, 14741, 14741, 15471, 14741, 14741, 14742,
14742, 14742, 16203, 14742, 14743, 14743, 14743, 14743, 14743,
16205, 16205, 13283, 16205, 16205, 16206, 13284, 13284, 16206,
13284, 16207, 16207, 16207, 16207, 15476, 15477, 15477, 15477,
15477, 15477, 15478, 15478, 15478, 14748, 15478, 15479, 14749,
11827, 14749, 14749, 11828, 11828, 14750, 11828, 14750, 16943,
16943, 16943, 16943, 16943, 11100, 11100, 11100, 11100, 11100,
16945, 16945, 16945, 16945, 16945, 16946, 16946, 16946, 16946,
16946, 16947, 16947, 16947, 16947, 16947, 16948, 16948, 16948,
16948, 16948, 16949, 16949, 16949, 16949, 16949, 16314, 16314,
14853, 16314, 16314, 16315, 16315, 14489, 14489, 14489, 16316,
16316, 16316, 14855, 14490, 16317, 13760, 16317, 16317, 16317,
16318, 16318, 13761, 14126, 13761, 13762, 15223, 13762, 16319,
16319, 12667, 14859, 14859, 17050, 12667, 16321, 11207, 11207,
11207, 11207, 11208, 11208, 14496, 13400, 13400, 16323, 14497,
16323, 13401, 14497, 14498, 16324, 14498, 16324, 13402, 16325,
15229, 11211, 15229, 13403, 15230, 15230, 15961, 17056, 15230,
14866, 15231, 13040, 15231, 15962, 13771, 14867, 14502, 15963,
11580, 14868, 14868, 15233, 16694, 16694, 15965, 14869, 15965,
13043, 14504, 14139, 16696, 12678, 14505, 15966, 14871, 13045,
16697, 16697, 14140, 14872, 11219, 11219, 14872, 13411, 13412,
11220, 15969, 14873, 14873, 14509, 14874, 14509, 14874, 12682,
14875, 12683, 14875, 14875, 14875, 12684, 11223, 14511, 14511,
14876, 14877, 12685, 14877, 14877, 12685, 12686, 16339, 14878,
14147, 14878, 16340, 14879, 14879, 14879, 14879, 11227, 11227,
14880, 14880, 14880, 14881, 14881, 16342, 14881, 14881, 16343,
14882, 14882, 14882, 14882, 11230, 14883, 12691, 14883, 12691,
16345, 14153, 16710, 14884, 14884, 14154, 15615, 14885, 12693,
11232, 14886, 14155, 14155, 14886, 14886, 14887, 14887, 14887,
14887, 14887, 14888, 14888, 14888, 12696, 14523, 14889, 16350,
14889, 15619, 14889, 14890, 14525, 14890, 14525, 14525, 14526,
14891, 14891, 14891, 14526, 14892, 14892, 16353, 16353, 12335,
14893, 14893, 14893, 14893, 14893, 13798, 17085, 14894, 17085,
13068, 14895, 13069, 14895, 14895, 14895, 17087, 14896, 17087,
14896, 12339, 17088, 16358, 16358, 14532, 16358, 14898, 12706,
14898, 14898, 14898, 16360, 14899, 14899, 11246, 14899, 14900,
12708, 14900, 14900, 14900, 14901, 14901, 14901, 16362, 14901,
14902, 14902, 14902, 14537, 14902, 14903, 14903, 14903, 14903,
12711, 14904, 12712, 14904, 12712, 14904, 15635, 14905, 11252,
14905, 11252, 14906, 14906, 12349, 14906, 14906, 14542, 14907,
14542, 14542, 16368, 16369, 14908, 14908, 14177, 16369, 16370,
14909, 14909, 12352, 16370, 14910, 17101, 14910, 14545, 14910,
14911, 11258, 16737, 12719, 14911, 16373, 16373, 14912, 14912,
12720, 14913, 14913, 14913, 14913, 11260, 14914, 16375, 11992,
11627, 14914, 14915, 14915, 14915, 14550, 15280, 14916, 13820,
14551, 14916, 14916, 14917, 14917, 14917, 14917, 14917, 11265,
14553, 16014, 14918, 12726, 11266, 14919, 11266, 16380, 14919,
14920, 12728, 12363, 12363, 14920, 14921, 14921, 17112, 14921,
14921, 14922, 14922, 14922, 11269, 14922, 14923, 14923, 14923,
16384, 14923, 14924, 16385, 14924, 14924, 11271, 16386, 14194,
14925, 16386, 14925, 14926, 16387, 13465, 14926, 11273, 11640,
12005, 14196, 14927, 14196, 14197, 12736, 14928, 14928, 14928,
12737, 13833, 14929, 14929, 11642, 14930, 14930, 14930, 14930,
14930, 17122, 12739, 13470, 13470, 12374, 14932, 12375, 14932,
13471, 13471, 11280, 14933, 14933, 14933, 14202, 14934, 14934,
14203, 14934, 14934, 14204, 14935, 14935, 14935, 14935, 12744,
14936, 14936, 14936, 14936, 16398, 11284, 16398, 16398, 16398,
11285, 14938, 16764, 14938, 12381, 16400, 16400, 12747, 12382,
14208, 14209, 11287, 14209, 16401, 13844, 16402, 15671, 14210,
16402, 12749, 12750, 13846, 16403, 16403, 14211, 15673, 15673,
16404, 11656, 16404, 17135, 17135, 16405, 16405, 17135, 14580,
16771, 12753, 12388, 14580, 13485, 17137, 14581, 14581, 12389,
16773, 16773, 15677, 13486, 11294, 12391, 16044, 14217, 14217,
13122, 12757, 13853, 14218, 17140, 14949, 16776, 17141, 17141,
16046, 14950, 15681, 12759, 15681, 16412, 12759, 14587, 13126,
13126, 16413, 16413, 16414, 16414, 16414, 13492, 16414, 16415,
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366, 366)), row.names = c(NA, -1345L), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), groups = structure(list(DOY = c(1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
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1326:1330, 1331:1335, 1336:1340, 1341:1345)), row.names = c(NA,
-269L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))
The sample data you gave was not very illustrative, since it contained one instance each of the same day each year. We'll create some data that is perhaps more representative:
library(ggplot2)
set.seed(69)
dates <- as.Date("2015-01-01") +
lubridate::days(round(rnorm(5000, 0, 30))) +
lubridate::years(sample(0:5, 5000, TRUE))
df <- data.frame(Date = dates, DOY = lubridate::yday(dates))
ggplot(df, aes(DOY)) + geom_histogram(binwidth = 1)
The way to get rid of this gap is simply to add 6 months and take the result modulo 366:
ggplot(df, aes((DOY + 183) %% 366)) +
geom_histogram(binwidth = 1) +
lims(x = c(0, 366)) +
labs(x = "DOY")
Created on 2020-08-02 by the reprex package (v0.3.0)
How to add a variable in a data frame using another variable for indices?
Data I have a data frame "veh" with a variable "Tim": > dput(veh$Tim) c(169.7, 169.8, 169.9, 170, 170.1, 170.2, 170.3, 170.4, 170.5, 170.6, 170.7, 170.8, 170.9, 171, 171.1, 171.2, 171.3, 171.4, 171.5, 171.6, 171.7, 171.8, 171.9, 172, 172.1, 172.2, 172.3, 172.4, 172.5, 172.6, 172.7, 172.8, 172.9, 173, 173.1, 173.2, 173.3, 173.4, 173.5, 173.6, 173.7, 173.8, 173.9, 174, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.7, 174.8, 174.9, 175, 175.1, 175.2, 175.3, 175.4, 175.5, 175.6, 175.7, 175.8, 175.9, 176, 176.1, 176.2, 176.3, 176.4, 176.5, 176.6, 176.7, 176.8, 176.9, 177, 177.1, 177.2, 177.3, 177.4, 177.5, 177.6, 177.7, 177.8, 177.9, 178, 178.1, 178.2, 178.3, 178.4, 178.5, 178.6, 178.7, 178.8, 178.9, 179, 179.1, 179.2, 179.3, 179.4, 179.5, 179.6, 179.7, 179.8, 179.9, 180, 180.1, 180.2, 180.3, 180.4, 180.5, 180.6, 180.7, 180.8, 180.9, 181, 181.1, 181.2, 181.3, 181.4, 181.5, 181.6, 181.7, 181.8, 181.9, 182, 182.1, 182.2, 182.3, 182.4, 182.5, 182.6, 182.7, 182.8, 182.9, 183, 183.1, 183.2, 183.3, 183.4, 183.5, 183.6, 183.7, 183.8, 183.9, 184, 184.1, 184.2, 184.3, 184.4, 184.5, 184.6, 184.7, 184.8, 184.9, 185, 185.1, 185.2) Also, I have a vector "slopezz": > slopezz [1] -2.1920 0.7034 0.6113 -1.2540 0.7513 2.3250 0.0791 -0.9713 1.1010 1.9490 [11] -1.4290 2.2500 0.8775 and another one-column data frame, "x": > x psi psi1.Tim 171.4 psi2.Tim 171.8 psi3.Tim 175.1 psi4.Tim 175.7 psi5.Tim 176.3 psi6.Tim 177.8 psi7.Tim 178.7 psi8.Tim 180.1 psi9.Tim 181.5 psi10.Tim 182.4 psi11.Tim 183.8 psi12.Tim 184.8 Goal There are 13 values in the "slopezz" and 12 in x$psi. In the data frame "veh", I want to add a new column "slope" that contains the values from "slopezz" but at the indices from x$psi. Example: The first value in "slopezz" is -2.1920 and in x$psi is 171.4. x$psi corresponds to veh$Tim. So, between 169.7 (first value in veh$Time) and 171.4, the "slope" variable contains the first value of -2.1920. Then, between 171.4 and 171.8 the second value of slope, 0.7034. And so on. What I have tried I can successfully create the new column by using ifelse and putting in the values of x$psi and "slopezz" manually. ## Example: library(dplyr) veh <- veh %>% mutate(slope = ifelse(Tim<=171.4,slopezz[1], ifelse(Tim>171.4 & Tim<=171.8, slopezz[2], .... Code was long, so not putting the entire thing here. But is there a better method where I don't have to manually put the Tim values taken from x$psi?
You had the right idea using dput() for veh$Tim; it would have helped had you used it for slopezz and x as well. Here's a two line solution (where ix is a temporary index variable): ix <- sapply(veh$Time, function(z) which.max(z <= c(x$psi, Inf))) veh$slope <- slopezz[ix] You were a bit ambiguous about what value of slopezz to use when, for example, veh$Tim equals 171.4. The code above uses intervals closed on the right.
You need joins, and something like tidyr::fill:
library(dplyr)
library(tidyr)
x %>% mutate(slopezz = slopezz[1:n()]) %>%
right_join(veh, by = c('psi' = 'Tim')) %>%
fill(slopezz, .direction = 'up')
# psi slopezz
# 1 169.7 -2.1920
# 2 169.8 -2.1920
# 3 169.9 -2.1920
# 4 170.0 -2.1920
# 5 170.1 -2.1920
# 6 170.2 -2.1920
# . ... ...
Note that this will leave the last four values as NA as you're filling up. If you want to then fill down, just add on %>% fill(slopezz).
Data
x <- structure(list(psi = c(171.4, 171.8, 175.1, 175.7, 176.3, 177.8,
178.7, 180.1, 181.5, 182.4, 183.8, 184.8)), .Names = "psi", class = "data.frame", row.names = c(NA, -12L))
slopezz <- c(-2.192, 0.7034, 0.6113, -1.254, 0.7513, 2.325, 0.0791, -0.9713,
1.101, 1.949, -1.429, 2.25, 0.8775)
veh <- structure(list(Tim = c(169.7, 169.8, 169.9, 170, 170.1, 170.2,
170.3, 170.4, 170.5, 170.6, 170.7, 170.8, 170.9, 171, 171.1,
171.2, 171.3, 171.4, 171.5, 171.6, 171.7, 171.8, 171.9, 172,
172.1, 172.2, 172.3, 172.4, 172.5, 172.6, 172.7, 172.8, 172.9,
173, 173.1, 173.2, 173.3, 173.4, 173.5, 173.6, 173.7, 173.8,
173.9, 174, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.7,
174.8, 174.9, 175, 175.1, 175.2, 175.3, 175.4, 175.5, 175.6,
175.7, 175.8, 175.9, 176, 176.1, 176.2, 176.3, 176.4, 176.5,
176.6, 176.7, 176.8, 176.9, 177, 177.1, 177.2, 177.3, 177.4,
177.5, 177.6, 177.7, 177.8, 177.9, 178, 178.1, 178.2, 178.3,
178.4, 178.5, 178.6, 178.7, 178.8, 178.9, 179, 179.1, 179.2,
179.3, 179.4, 179.5, 179.6, 179.7, 179.8, 179.9, 180, 180.1,
180.2, 180.3, 180.4, 180.5, 180.6, 180.7, 180.8, 180.9, 181,
181.1, 181.2, 181.3, 181.4, 181.5, 181.6, 181.7, 181.8, 181.9,
182, 182.1, 182.2, 182.3, 182.4, 182.5, 182.6, 182.7, 182.8,
182.9, 183, 183.1, 183.2, 183.3, 183.4, 183.5, 183.6, 183.7,
183.8, 183.9, 184, 184.1, 184.2, 184.3, 184.4, 184.5, 184.6,
184.7, 184.8, 184.9, 185, 185.1, 185.2)), .Names = "Tim", row.names = c(NA,
-156L), class = "data.frame")
Here is a solution using the cut function from base R. The data: veh<-data.frame(Tim=c(169.7, 169.8, 169.9, 170, 170.1, 170.2, 170.3, 170.4, 170.5, 170.6, 170.7, 170.8, 170.9, 171, 171.1, 171.2, 171.3, 171.4, 171.5, 171.6, 171.7, 171.8, 171.9, 172, 172.1, 172.2, 172.3, 172.4, 172.5, 172.6, 172.7, 172.8, 172.9, 173, 173.1, 173.2, 173.3, 173.4, 173.5, 173.6, 173.7, 173.8, 173.9, 174, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.7, 174.8, 174.9, 175, 175.1, 175.2, 175.3, 175.4, 175.5, 175.6, 175.7, 175.8, 175.9, 176, 176.1, 176.2, 176.3, 176.4, 176.5, 176.6, 176.7, 176.8, 176.9, 177, 177.1, 177.2, 177.3, 177.4, 177.5, 177.6, 177.7, 177.8, 177.9, 178, 178.1, 178.2, 178.3, 178.4, 178.5, 178.6, 178.7, 178.8, 178.9, 179, 179.1, 179.2, 179.3, 179.4, 179.5, 179.6, 179.7, 179.8, 179.9, 180, 180.1, 180.2, 180.3, 180.4, 180.5, 180.6, 180.7, 180.8, 180.9, 181, 181.1, 181.2, 181.3, 181.4, 181.5, 181.6, 181.7, 181.8, 181.9, 182, 182.1, 182.2, 182.3, 182.4, 182.5, 182.6, 182.7, 182.8, 182.9, 183, 183.1, 183.2, 183.3, 183.4, 183.5, 183.6, 183.7, 183.8, 183.9, 184, 184.1, 184.2, 184.3, 184.4, 184.5, 184.6, 184.7, 184.8, 184.9, 185, 185.1, 185.2)) slopezz<-c(-2.1920, 0.7034, 0.6113, -1.2540, 0.7513, 2.3250, 0.0791, -0.9713, 1.1010, 1.9490, -1.4290, 2.2500, 0.8775) x<-c(171.4, 171.8, 175.1, 175.7, 176.3, 177.8, 178.7, 180.1, 181.5, 182.4, 183.8, 184.8) Now define x to encompass the entire range of Tim: x<-c(0,x,200) veh$slope<-slopezz[cut(veh$Tim, breaks=x)] The final dataframe for this example will be the column Tim and the new column slope.
A brute force way would be veh$slope = rep(slopes[1], length(veh$Tim)) for (j in 1:12) veh$slope[ veh$Tim>x$psi[j] ] = slopes[j+1]
Reformat daily stock and return data to weekly/monthly
I have some stock data together with some returns that are presented below.
Now I would like to coerce both the daily price changes (open, high, low, close, volume, adj. close) and the returns given to weekly or monthly values.
I know that the weekly prices can be obtained by xts::to.weekly(), but this drops the return. I don't know the exact mechanism behind the to.weekly function, but the returns need to be summarized with the sum function (I'm thinking of using xts::apply.weekly()), but then this would not be consistent with the stock price data....
How can this transition to weekly or monthly timescale be achieved efficiently?
Data <-
structure(c(64.5, 67, 72, 76, 75.75, 72, 75.5, 76, 76, 78, 78,
77.5, 79.25, 80, 76.25, 84, 89.75, 90.75, 92.25, 95.75, 94.5,
95, 92, 95.75, 100, 98, 104.25, 101.25, 100.25, 96.5, 94.75,
89, 94, 91.25, 99.25, 100.25, 100.25, 98, 96.5, 94.75, 97.5,
96.25, 99.25, 97, 98, 98.75, 97.25, 98.75, 100.25, 100.25, 103.25,
105.75, 108.5, 108.25, 103.75, 101.5, 99.75, 100, 99, 94.5, 99,
101.5, 105, 64.75, 73.25, 76.5, 76.75, 76, 75.75, 76, 76, 76,
80.75, 79, 79.5, 83, 80.5, 76.75, 91.5, 92.75, 94.75, 100.25,
96, 97.5, 96.75, 92, 100, 101.75, 104, 105, 103.25, 100.75, 99,
95.5, 92.75, 94, 97.75, 103.75, 101, 100.25, 99.5, 97.75, 96.75,
98, 99, 100, 100.75, 99.25, 98.75, 98, 102.25, 101, 103, 105,
109, 110.5, 108.25, 105, 102.25, 100.75, 100, 99.75, 98.75, 102.5,
103, 107.25, 60.75, 66.75, 71.25, 74.25, 72.25, 71.5, 74.25,
76, 76, 77, 75.75, 76, 79, 75.75, 73.25, 82, 88.75, 89.5, 91.75,
92.25, 92.75, 92, 92, 94, 97.75, 96.5, 98.5, 99.25, 93.5, 94,
88.25, 87.5, 91, 91, 98.75, 98.5, 100.25, 95.75, 95.5, 90.75,
96.75, 96, 95.25, 97, 95.75, 95.75, 96.75, 98.5, 97.25, 100.25,
102.5, 105.5, 107.5, 103.5, 102.75, 97.5, 98.25, 100, 94, 94.25,
98, 100, 103.75, 64.25, 72.75, 75.75, 75, 73.75, 75.5, 76, 76,
76, 79, 76, 79.25, 81.25, 76.75, 75.75, 88, 90.25, 93.75, 97.5,
95, 95, 92, 92, 100, 97.75, 102.75, 99.75, 100.25, 97.25, 94.5,
89, 91.75, 92.5, 97, 99.5, 100.25, 100.25, 96.75, 96, 96, 97.5,
98, 95.5, 100.75, 98, 96.75, 98, 100.25, 99.5, 102.25, 103.5,
108, 109.25, 104, 102.75, 99, 100, 100, 94.5, 97.5, 102, 103,
104.5, 6808900, 8180500, 5628500, 3238900, 3765800, 3177100,
887600, 0, 0, 3923200, 2425700, 3331200, 4058600, 3682800, 3293500,
10525000, 5664200, 3982600, 4702300, 6479800, 2565300, 2480500,
0, 3653000, 3400, 4010500, 5145800, 2782200, 3925100, 2770700,
4618500, 2712300, 1675400, 3331500, 5343000, 1169700, 0, 2095400,
1016600, 3642600, 3729400, 3575300, 3396900, 1963400, 3547300,
1865100, 2496300, 3226800, 2333200, 4285900, 3933000, 7400700,
3325200, 1848400, 21000, 4273700, 1910400, 0, 3168000, 2356000,
2184800, 1950300, 2649900, 51.5865, 58.4112, 60.8199, 60.2178,
59.2141, 60.6192, 61.0207, 61.0207, 61.0207, 63.4294, 61.0207,
63.6301, 65.2359, 61.6228, 60.8199, 70.6555, 72.462, 75.2722,
78.2831, 76.2758, 76.2758, 73.8671, 73.8671, 80.2903, 78.4838,
82.4983, 80.0896, 80.4911, 78.0824, 75.8744, 71.4584, 73.6664,
74.2686, 77.8816, 79.8889, 80.4911, 80.4911, 77.6809, 77.0787,
77.0787, 78.2831, 78.6845, 76.6773, 80.8925, 78.6845, 77.6809,
78.6845, 80.4911, 79.8889, 82.0969, 83.1005, 86.7136, 87.7172,
83.5019, 82.4983, 79.4874, 80.2903, 80.2903, 75.8744, 78.2831,
81.8961, 82.699, 83.9034, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.086956169, -0.022499605, 0.051150683,
-0.029196965, 0.005013135, -0.029925048, -0.028277819, -0.058201449,
0.030899097, 0.00817469, 0.048647746, 0.025773739, 0.007537968,
0, -0.034913177, -0.007752227, 0, 0.015625588, 0.005127543, -0.025509471,
0.054973245, -0.027295485, -0.012754736, 0.012919521, 0.022960049,
-0.007481572, 0.027638383, 0.012224579, 0.043478679, 0.011573732,
-0.048055569, -0.012018888, -0.03649651, 0.010100972, 0, 0, 0,
0.046153001, 0.009803886, 0.014563659), .Dim = c(63L, 7L), .Dimnames = list(
NULL, c("Open", "High", "Low", "Close", "Volume", "Adj.Close",
"Return")), index = structure(c(1238544000, 1238630400, 1238716800,
1238976000, 1239062400, 1239148800, 1239235200, 1239321600, 1239580800,
1239667200, 1239753600, 1239840000, 1239926400, 1240185600, 1240272000,
1240358400, 1240444800, 1240531200, 1240790400, 1240876800, 1240963200,
1241049600, 1241136000, 1241395200, 1241481600, 1241568000, 1241654400,
1241740800, 1.242e+09, 1242086400, 1242172800, 1242259200, 1242345600,
1242604800, 1242691200, 1242777600, 1242864000, 1242950400, 1243209600,
1243296000, 1243382400, 1243468800, 1243555200, 1243814400, 1243900800,
1243987200, 1244073600, 1244160000, 1244419200, 1244505600, 1244592000,
1244678400, 1244764800, 1245024000, 1245110400, 1245196800, 1245283200,
1245369600, 1245628800, 1245715200, 1245801600, 1245888000, 1245974400),
tzone = "UTC", tclass = "Date"), class = c("xts", "zoo"), .indexCLASS = "Date",
tclass = "Date", .indexTZ = "UTC", tzone = "UTC")
Use period.apply (or apply.daily, apply.weekly, etc.) with your own custom function. Something like:
library(quantmod) # for Op, Hi, Lo, Cl, and Vo functions
myFun <- function(x) {
# need coredata, so c.xts will not be dispatched
cx <- coredata(x)
c(Open = first(Op(cx)),
Hi = max(Hi(cx)),
Low = min(Lo(cx)),
Close = last(cx[,"Close"]),
Volume = sum(Vo(cx)),
Adj.Close = last(cx[,"Adj.Close"]),
Return = sum(cx[,"Return"]))
}
out <- period.apply(Data, endpoints(Data, "months"), myFun)
# Open Hi Low Close Volume Adj.Close Return
# 2009-04-30 64.5 100.25 60.75 92.0 88802000 73.8671 0.0000000
# 2009-05-29 92.0 105.00 87.50 95.5 58597300 76.6773 0.0486306
# 2009-06-26 97.0 110.50 94.00 104.5 54739400 83.9034 0.1222869
ggvis barchart using dates as x axis
I have been switching over from ggplot to ggvis when working with shiny apps. I have figured out a lot but am really stumped when it comes to bar graphs. I have a timeseries with dates and values. I simply want bars instead of points for each value (I would ideally like to be able to plot multiple semi-transparent bars if anyone has had success there please share) but here I wanted to get one bar working.
Works with layer_points()
df %>% ggvis(~date, ~x) %>% layer_points() %>% scale_datetime("x")
Doesnt work with layer_bars()
df %>% ggvis(~date, ~x) %>% layer_bars() %>% scale_datetime("x")
Data I am using...
structure(list(date = structure(c(7680, 7687, 7694, 7701, 7708,
7715, 7722, 7729, 7736, 7743, 7750, 7757, 7764, 7771, 7778, 7785,
7792, 7799, 7806, 7813, 7820, 7827, 7834, 7841, 7848, 7855, 7862,
7869, 7876, 7883, 7890, 7897, 7904, 7911, 7918, 7925, 7932, 7939,
7946, 7953, 7960, 7967, 7974, 7981, 7988, 7995, 8002, 8009, 8016,
8023, 8030, 8037, 8044, 8051, 8058, 8065, 8072, 8079, 8086, 8093,
8100, 8107, 8114, 8121, 8128, 8135, 8142, 8149, 8156, 8163, 8170,
8177, 8184, 8191, 8198, 8205, 8212, 8219, 8226, 8233, 8240, 8247,
8254, 8261, 8268, 8275, 8282, 8289, 8296, 8303, 8310, 8317, 8324,
8331, 8338, 8345, 8352, 8359, 8366, 8373, 8380, 8387, 8394, 8401,
8408, 8415, 8422, 8429, 8436, 8443, 8450, 8457, 8464, 8471, 8478,
8485, 8492, 8499, 8506, 8513, 8520, 8527, 8534, 8541, 8548, 8555,
8562, 8569, 8576, 8583, 8590, 8597, 8604, 8611, 8618, 8625, 8632,
8639, 8646, 8653, 8660, 8667, 8674, 8681, 8688, 8695, 8702, 8709,
8716, 8723, 8730, 8737, 8744, 8751, 8758, 8765, 8772, 8779, 8786,
8793, 8800, 8807, 8814, 8821, 8828, 8835, 8842, 8849, 8856, 8863,
8870, 8877, 8884, 8891, 8898, 8905, 8912, 8919, 8926, 8933, 8940,
8947, 8954, 8961, 8968, 8975, 8982, 8989, 8996, 9003, 9010, 9017,
9024, 9031, 9038, 9045, 9052, 9059, 9066, 9073), class = "Date"),
x = c(-0.034038302, 0.122310949, -0.002797319, 0.026515253,
0.039961798, 0.034473263, 0.00549937, -0.024125944, 0.000132490000000001,
0.011038357, -0.02135072, 0.030663311, -0.008915551, 0.004855042,
0.01563688, -0.007397493, 0.013569146, -0.004968811, -0.00250391,
0.014624532, 0.036937453, -0.023685917, 0.018921356, -0.003066779,
-0.009217771, 0.005317513, 0.010378968, 0.001580798, -0.015085972,
-0.000121644000000001, 0.020468644, 0.007925229, 0.007721276,
-0.003123545, -0.018317891, -0.014900591, 0.003260844, -0.001565358,
-0.014833886, 0.00366766, 0.014297139, -0.00725552, 0.012207931,
0.024035152, -0.024195095, -0.0043564, 0.000847468, 0.033031596,
0.023685033, 0.025143071, 0.046264348, 0.038285177, -0.009180356,
-0.01630399, -0.010131294, -0.009939386, -0.007620427, 0.013062259,
0.009912238, 0.000192973, -0.01683559, -0.002627549, 0.019836063,
-0.019946159, -0.020124331, 0.012921737, 0.034604405, -0.020774015,
0.00334805, 0.002271156, -0.018676732, 0.019160923, -0.01945997,
-0.014342636, -0.004867796, -0.010002446, -0.004372991, 0.023164369,
0.019824112, -0.00321832, -0.015785746, 0.040836652, 0.00148831,
0.012084485, -0.009603897, -0.004642148, -0.008399234, 0.010463218,
0.000256571000000001, -0.01978405, -0.003439498, -0.015669975,
0.026180724, 0.020373255, 0.019160773, 0.00692683, 0.010215506,
0.010861939, 0.012041143, 0.025734568, -0.004828156, 0.006914552,
-0.00720089, -0.000538489999999999, -0.008479448, 0.022926604,
0.002131842, -0.003688597, 0.025325639, -0.009562293, -0.024336741,
0.012907537, 0.004339383, 0.010744364, -0.013058765, -0.003672014,
-0.023887493, 0.01062259, 0.02088054, -0.035249878, -0.001462821,
0.01904368, -0.001308787, 0.009203217, 0.019856479, 0.011296979,
0.010039545, -0.01559142, 0.006083419, -0.017958978, -0.007488063,
0.01236649, -0.004459064, -0.004375386, 0.025500722, 0.005557851,
0.008444321, 0.002827649, 0.020320308, 0.031611803, -0.010199803,
-0.009425874, 0.007942729, -2.59379999999999e-05, 0.016669077,
-0.011666062, 0.022835386, -0.025599107, 0.013562535, -0.018365192,
0.018148786, 0.016649144, -0.009530455, 0.012996597, 0.002034778,
-0.005926478, -0.004897238, -0.004419719, 0.010848926, -0.006039757,
-0.030287605, 0.019221837, 0.001808161, -0.009566133, 0.005009292,
0.005365023, -0.004879922, -0.024637933, -0.0186584, 0.004786059,
-0.008245254, -0.000106243, -0.001714888, -0.017804006, -0.021200061,
0.003812757, 0.021940886, 0.002270448, -0.015417493, -0.045754612,
-0.003468442, -0.006242659, 0.022383824, -0.018753927, 0.008577571,
0.008655048, 0.02374636, 0.029522811, 0.009946946, 0.015419714,
-0.016714623, -0.014616188, 0.019670855, -0.038979063, 0.020491563,
-0.009640674, 0.046051144, -0.021434575, 0.000190443999999998,
-0.029013969), id = 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)), .Names = c("date", "x", "id"), row.names = 53:252, class = "data.frame")
Set format df$date as character: df$date <- as.character(df$date) and then: df %>% ggvis(~date, ~x) %>% layer_bars()
Selecting just some rows of a RGList
I am using the package Limma to analyse some data. After reading the raw data with read.maimagenes I get a RGList object. R.cut and G.cut are a value of class numeric and I want the values above them. I tried something like this:
RG$R <- RG$R[RG$R>R.cut]
RG$G <- RG$G[RG$G>G.cut]
But this converts the class of RG$R from matrix to numeric how could I keep the matrix class (I don't know if introducing some NA values would affect the rest of the analyse). I have tried subset like this:
RG.probe$R <- subset(x=RG, subset=RG$R>R.cut)
But it keeps returning an error Error: Two subscripts required
How can this be done?
Aside: If I would like to just get the rows that are above the R.cut and G.cut?
Possible solution:
I have found that with ifelse I can do it although I must introduce a value for the ones that are under the cut, and I haven't found the way to check if both R and G channels are above each cut.
RG$R<-ifelse(RG$R>R.cut, RG$R, '')
RG$G<-ifelse(RG$G>G.cut, RG$G, '')
Although it is converted to character, and therefore I cannot do further analysis.
Data to make it reproducible:
library(limma)
RG<-read.maimages(path, source='agilent')
class(RG)
[1] "RGList"
attr(,"package")
[1] "limma"
dput(head(RG$R))
structure(c(2893, 81.5, 80.5, 140208, 4512, 6272, 4934, 195,
184.5, 164092, 11819, 10569, 1689.5, 83, 82, 68996, 2260.5, 3603,
2470, 84, 77, 96750, 3203, 5223, 3246, 85.5, 104.5, 54773, 519.5,
8244.5, 1807, 86.5, 88, 204574, 15693, 8939.5, 2040, 87, 95,
131880, 7346, 9922.5, 1445, 76, 85.5, 125598, 3863, 5758.5, 2626,
87.5, 85, 180266, 18173, 20171.5, 1811.5, 84, 87.5, 122498, 3993,
5857, 1799, 87.5, 82, 123220, 3780, 5964, 1706, 77.5, 80, 124463,
3390, 5070, 3787, 81.5, 88, 65874, 269, 781.5, 1476, 90, 89,
122445, 4232, 6479, 2788, 82, 87.5, 80669, 791, 7440.5, 1503,
81, 88, 124702, 4270, 6111, 2012.5, 93.5, 90, 215820, 4555, 3101,
1727.5, 102, 109, 131316, 4284, 6638, 2009, 95.5, 111.5, 175474,
12665, 17213, 1532, 87.5, 84.5, 117568, 4098, 6100, 1436, 83,
91, 118472, 4067.5, 6114, 1651.5, 83, 82, 127308, 4150, 6277,
2028.5, 85.5, 89, 74816, 896.5, 7697, 2698, 84, 92.5, 99431,
1273, 9182.5, 1833.5, 100, 104, 163604, 15582, 12146, 2359, 102,
109, 159301, 17229, 9822.5, 1857, 86, 88, 130319, 4354.5, 6266.5,
1887, 87, 87, 133386, 11639.5, 8931, 2304.5, 86.5, 87, 91022,
1011, 14524, 1353, 84, 88, 114282, 3935, 5944, 1487, 83, 87,
125507, 4138, 5804, 3379, 86.5, 88, 63703.5, 331, 1167, 1778,
87, 83.5, 123988, 4366, 6670, 1862, 94.5, 92, 134174, 4558, 6881,
2388.5, 82, 91.5, 174744, 8570, 10677, 4374, 94, 94, 179579,
12753, 10869, 3747.5, 115, 144.5, 133809, 3710, 5406, 5062, 93.5,
92, 207843, 13220, 6774, 3294, 78, 82.5, 149764, 3774, 5582,
5303, 93, 100, 93479.5, 803, 6709, 2969, 86.5, 101, 149011, 4043,
5407, 5488, 106, 118.5, 191053, 9990.5, 12194, 4308, 89, 85,
143087, 3926.5, 5370.5, 5168, 87, 91.5, 137415, 4028, 5671, 4649.5,
91, 90, 147328, 4102, 5614.5, 7225, 87, 85, 179052, 15612, 16908,
5815.5, 84, 88, 200883, 13229, 11482, 3551, 101, 125, 224012,
20461, 16149.5, 3992, 98, 83, 134744, 3569, 5068, 4817, 97, 92,
142087, 4203, 5678, 5436, 108, 84.5, 195104, 11299, 13246), .Dim = c(6L,
51L), .Dimnames = list(NULL, c("US23502326_253482110017_S01_GE2_1105_Oct12_1_1",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_1", "US23502326_253482110017_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_3", "US23502326_253482110017_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_1", "US23502326_253482110027_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_3", "US23502326_253482110027_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_1", "US23502326_253482110027_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_3", "US23502326_253482110027_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_1", "US23502326_253482110028_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_4", "US23502326_253482110028_S01_GE2_1105_Oct12_2_1",
"US23502326_253482110028_S01_GE2_1105_Oct12_2_2", "US23502326_253482110028_S01_GE2_1105_Oct12_2_3",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_1", "US23502326_253482110029_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_3", "US23502326_253482110029_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_1", "US23502326_253482110029_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_3", "US23502326_253482110029_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_1", "US23502326_253482110030_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_3", "US23502326_253482110030_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_1", "US23502326_253482110030_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_3", "US23502326_253482110030_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_1", "US23502326_253482110031_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_3", "US23502326_253482110031_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_1", "US23502326_253482110031_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_3", "US23502326_253482110031_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_1", "US23502326_253482110049_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_3", "US23502326_253482110049_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_1", "US23502326_253482110049_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_3", "US23502326_253482110049_S01_GE2_1105_Oct12_2_4"
)))
dput(head(RG$G))
structure(c(2324, 58, 52, 98015, 9800, 5284, 1472, 114, 92.5,
27879, 2296, 3272.5, 3637, 216, 204.5, 34898, 731, 5084, 3466,
77, 74, 32543, 497, 7416, 1344, 79.5, 99, 52753, 2363, 3457,
686, 39, 44.5, 32866, 2937, 4324, 910, 42, 40, 42361, 2780, 4072,
1587, 83.5, 97, 79659, 7667, 10103, 754, 49.5, 44, 23664, 2962,
4166, 1390.5, 136, 156.5, 70132.5, 7914, 4876, 1609, 99, 125,
25923.5, 610, 5125, 1526, 198.5, 157, 94640.5, 10408, 9233, 1060,
42, 37, 70033, 3144, 4355.5, 1465, 89, 91, 99188, 9587, 7547,
743, 61.5, 60, 65888, 3247, 4676.5, 1931.5, 89, 84, 65967, 11226,
7757, 873.5, 56, 66, 20126.5, 3291, 4736, 1339.5, 298, 300, 75324,
6712, 8500, 894, 65, 86, 26341.5, 3132.5, 4647, 2372, 80, 81.5,
73418.5, 5026, 7612, 1564, 70, 73, 77180, 7802.5, 9454, 1315,
90, 85, 20562, 340, 5337, 868.5, 49, 55, 64712.5, 2947, 4260,
798, 46, 48, 52505, 3380, 4663.5, 904, 69.5, 80, 33371.5, 3300,
4997, 813, 73, 81, 29552, 2932, 4632.5, 1696.5, 187, 324, 63647,
6407, 8571.5, 872, 39.5, 52, 24518, 3094, 4387, 752.5, 54, 52,
48299.5, 3221, 4278, 2631, 61, 72, 27229, 513, 5019, 1256.5,
61, 63, 74560, 11016, 9019, 942, 57, 55, 70933.5, 3526.5, 5383,
1457.5, 162, 193.5, 86276, 8154, 12084, 1590, 213, 293, 66871,
6580, 9535, 833.5, 57, 62.5, 36416, 3229, 4600, 2161.5, 53.5,
42.5, 39157.5, 2952, 3977, 3481, 67, 68, 18675, 152, 536, 1977,
57, 55.5, 32861, 2785, 3812, 4739.5, 112.5, 113.5, 104923, 6231,
8198, 1907, 57.5, 69, 76674.5, 3219, 4244, 3879.5, 183.5, 171,
110822, 9582, 8426, 1746, 74.5, 74, 33327, 2774, 4017, 3333,
187, 270.5, 83696, 6616, 7080, 4737, 38, 37, 30041, 429.5, 3970,
3347, 45, 46, 106822, 8003.5, 7137.5, 2431, 32, 35, 32985, 3121,
4179, 2535.5, 28, 34, 36131.5, 3135, 4126, 1929, 42, 65, 47428,
3300, 4626.5, 5371, 54.5, 43.5, 108175, 9983, 6182, 5139.5, 34,
28, 26774, 152, 518, 2621, 48, 32, 44499, 3409, 4643), .Dim = c(6L,
51L), .Dimnames = list(NULL, c("US23502326_253482110017_S01_GE2_1105_Oct12_1_1",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_1", "US23502326_253482110017_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110017_S01_GE2_1105_Oct12_2_3", "US23502326_253482110017_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_1", "US23502326_253482110027_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_1_3", "US23502326_253482110027_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_1", "US23502326_253482110027_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110027_S01_GE2_1105_Oct12_2_3", "US23502326_253482110027_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_1", "US23502326_253482110028_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110028_S01_GE2_1105_Oct12_1_4", "US23502326_253482110028_S01_GE2_1105_Oct12_2_1",
"US23502326_253482110028_S01_GE2_1105_Oct12_2_2", "US23502326_253482110028_S01_GE2_1105_Oct12_2_3",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_1", "US23502326_253482110029_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_1_3", "US23502326_253482110029_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_1", "US23502326_253482110029_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110029_S01_GE2_1105_Oct12_2_3", "US23502326_253482110029_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_1", "US23502326_253482110030_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_1_3", "US23502326_253482110030_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_1", "US23502326_253482110030_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110030_S01_GE2_1105_Oct12_2_3", "US23502326_253482110030_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_1", "US23502326_253482110031_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_1_3", "US23502326_253482110031_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_1", "US23502326_253482110031_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110031_S01_GE2_1105_Oct12_2_3", "US23502326_253482110031_S01_GE2_1105_Oct12_2_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_1", "US23502326_253482110049_S01_GE2_1105_Oct12_1_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_1_3", "US23502326_253482110049_S01_GE2_1105_Oct12_1_4",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_1", "US23502326_253482110049_S01_GE2_1105_Oct12_2_2",
"US23502326_253482110049_S01_GE2_1105_Oct12_2_3", "US23502326_253482110049_S01_GE2_1105_Oct12_2_4"
)))
From the dput you can create an RGList by new("RGList") I know it is to much data but as I previously asked I don't know how to short the output.
The solution that finally I got is doing almost the same: RG$G <- ifelse(RG$R>R.cut, RG$G, NA) RG$R <- ifelse(RG$G>G.cut, RG$R, NA) Deleting this values seems to increase the Fold Change of each gene as already happened comparing the original RG with the RG with deleted values as in the question.