plotting multiple time series within a tibble using ggplot
Ana
I've seen answers that are close, but not quite the same. Apologies if I missed something! I would love a ggplot solution if possible.
So I have a tibble with four columns and a whole bunch of rows. I'll put the first few rows at the end of the post. The first column is called id, and each row has a unique id. The next column isdoy.series and the third column is called smooth.series Each entry in the doy.series and smooth.series columns are lists. The last column is just called doy which is an integer.
So what I want is to plot the doy.series against the smooth.series for each row, but plot all of those as lines on the same plot. I'd also like the lines to be colored by the doy. I would like the highest doy values to be red gradually transitioning to the lowest doyvalues which I want to be blue.
I've tried: ggplot(df, aes(x = doy.series, y = smooth.series, colour = doy)) + geom_line() but I get an error Error in order(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :unimplemented type 'list' in 'listgreater'
Part of the data:
df =structure(list(id = c("431", "432", "337"), smooth.series = list(
c(0.43777430245429, 0.426019133975921, 0.415206313498503,
0.405280883116371, 0.396187884923862, 0.387872361015312,
0.38031119938733, 0.373592663283192, 0.367828857667235, 0.363131887503796,
0.359613857757211, 0.356607134781236, 0.353704035469539,
0.35140659059632, 0.350216830935782, 0.350636787262128, 0.35316849034956,
0.358517061158787, 0.366484449461701, 0.376217929249277,
0.386864774512491, 0.39757225924232, 0.409883801658293, 0.425118711223808,
0.442088098795645, 0.459603075230582, 0.4764747513854, 0.491514238116876,
0.506146464703748, 0.521890696446379, 0.537699548233988,
0.552525634955796, 0.565321571501024, 0.576605881716668,
0.587480851125326, 0.597774737129616, 0.60731579713216, 0.615932288535578,
0.623452468742488, 0.629552270021279, 0.63428577220539, 0.638048454045027,
0.641235794290393, 0.644243271691694, 0.647354596944719,
0.650361974787858, 0.652923625035218, 0.6546977675009, 0.655342621999009,
0.65451640834365, 0.652487512684198, 0.649700033698352, 0.646075759992703,
0.64153648017384, 0.636003982848354, 0.629488244830451, 0.622014048803483,
0.613525392177759, 0.603966272363588, 0.593280686771281,
0.581412632811147, 0.567266510394374, 0.550635047828052,
0.532794735116347, 0.515022062263421, 0.498593519273441,
0.483883609978582, 0.46991423121304, 0.455932962484007, 0.44118738329868,
0.424925073164253, 0.40639361158792, 0.383767843946444, 0.357314491283398,
0.329418946606192, 0.302466602922238, 0.278842853238944,
0.255899788147355, 0.230850391635965, 0.205871396808358,
0.183139536768122, 0.164831544618842, 0.149568001323752,
0.134962706326001, 0.121682220449047, 0.110393104516348,
0.101761919351363, 0.0981488455530686, 0.100045680674816,
0.10531641097162, 0.111825022698496, 0.11743550211046, 0.120011835462528,
0.120333320859239, 0.120618157926921, 0.120888685203272,
0.121167241225986, 0.12147616453276, 0.121505910782356, 0.121126366388396,
0.120629016020871, 0.120305344349771, 0.120446836045087,
0.121344975776808, 0.123390133852811, 0.126410803293804,
0.129855652839032, 0.133173351227739, 0.135812567199168,
0.138057473114274, 0.140495834481054, 0.143031709759322,
0.145569157408891, 0.148012235889573, 0.150265003661183,
0.152290006630133, 0.154126926530363, 0.155795525562979,
0.157315565929083, 0.158706809829782, 0.160250815875207,
0.162078771150788, 0.163984880570575, 0.165763349048621,
0.167208381498974, 0.168114182835688, 0.16850832706102, 0.168567502729141,
0.168309586581966, 0.16775245536141, 0.16691398580939, 0.165203276117212,
0.162385787113404, 0.159013939495705, 0.155640153961858,
0.152816851209604, 0.151096451936684, 0.150114294258756,
0.149209308831738, 0.148489305163764, 0.148062092762967,
0.148035481137483, 0.148749174278873, 0.150258581427476,
0.152245069964593, 0.154390007271523, 0.156374760729566,
0.157880697720023, 0.159221858871935, 0.160811179021899,
0.162478366862545, 0.164053131086502, 0.1653651803864, 0.166423570261033,
0.167358821255243, 0.168182839630726, 0.168907531649179,
0.169544803572298, 0.170106561661781, 0.171006085382971,
0.172297499704099, 0.173453979141192, 0.173948698210276,
0.17325483142738, 0.171654728251965, 0.169714589410648, 0.16733336632019,
0.164410010397352, 0.160843473058894, 0.156569065531064,
0.15162033080968, 0.146043567965858, 0.139885076070716, 0.133191154195369
), c(0.457739384166388, 0.446283951227014, 0.435458731659352,
0.425174728440119, 0.415342944546034, 0.405874382953813,
0.396721863270565, 0.388014493526017, 0.379927709330819,
0.372636946295622, 0.366317640031077, 0.360178421838646,
0.353702781615852, 0.347478442867435, 0.342093129098133,
0.338134563812685, 0.336190470515832, 0.336041945239129,
0.337140469384323, 0.339579344511437, 0.343451872180496,
0.348851353951523, 0.357307167516573, 0.369360100304132,
0.383620568478867, 0.398698988205442, 0.413205775648526,
0.425751346972783, 0.438198108716855, 0.452357405811074,
0.466763062007647, 0.479948901058777, 0.490448746716669,
0.499269968838016, 0.508060504095011, 0.516315026463113,
0.523528209917781, 0.529194728434472, 0.532809255988646,
0.532560415067928, 0.528443894744577, 0.522664968871, 0.517428911299606,
0.514940995882802, 0.514237611010666, 0.513096947813341,
0.51183698425333, 0.510775698293136, 0.510231067895263, 0.510521071022212,
0.513660749308306, 0.519899994047127, 0.526909057690073,
0.53235819268854, 0.533917651493925, 0.532181861007955, 0.529401757828773,
0.525732358952399, 0.521328681374857, 0.516345742092168,
0.510938558100352, 0.503456562618317, 0.493099310232067,
0.481296999988271, 0.469479830933597, 0.459078002114715,
0.451541771202268, 0.446332623697785, 0.441897600394263,
0.436683742084698, 0.429138089562085, 0.41770768361942, 0.402085327505209,
0.383720408000677, 0.36369484119791, 0.343090543188989, 0.322989430065998,
0.300005231528961, 0.271886288912299, 0.241416435759692,
0.211379505614818, 0.184559332021355, 0.163739748522983,
0.147630103790859, 0.133129608496548, 0.12030688918434, 0.109230572398524,
0.0999692846833883, 0.094199063728592, 0.0926170037645956,
0.0938233161468934, 0.0964182122309796, 0.0990019033723484,
0.100174600926494, 0.100939656974712, 0.102835148042118,
0.105263372149938, 0.1076266273194, 0.109327211571729, 0.110659617793555,
0.112276556788702, 0.114117653454184, 0.116122532687017,
0.118230819384217, 0.1203821384428, 0.122547432824039, 0.124750983614669,
0.127012422899894, 0.129351382764921, 0.131787495294954,
0.134376519682738, 0.13714379196334, 0.140063309485449, 0.143109069597754,
0.146255069648946, 0.149475306987713, 0.153299187306221,
0.15787431971218, 0.162602006677591, 0.166883550674454, 0.170120254174771,
0.172788929210748, 0.175585494211257, 0.178239911243302,
0.180482142373889, 0.182042149670021, 0.182649895198703,
0.181405045617177, 0.178312697326645, 0.174460957010051,
0.170937931350339, 0.168831727030453, 0.167152106850496,
0.164632203670552, 0.161945179263534, 0.159764195402356,
0.158762413859935, 0.159612996409183, 0.162977004175573,
0.168327634609843, 0.174556253972805, 0.180554228525275,
0.185212924528066, 0.190718693570446, 0.198886756126355,
0.208051781783614, 0.216548440130045, 0.222711400753468,
0.224875333241705, 0.222735845081612, 0.217800784976475,
0.211123184338759, 0.203756074580929, 0.196752487115451,
0.187581281663291, 0.174488379633314, 0.159692404969832,
0.145411981617157, 0.133865733519603, 0.127272284621481,
0.124232380130641, 0.122075598245104, 0.121113812394444,
0.121658896008234, 0.124022722516045, 0.127934377121999,
0.133041703809836, 0.139534714178564, 0.14760341982719, 0.157437832354721,
0.16909197009469, 0.182448635194993, 0.197441014976182, 0.214002296758808,
0.232065667863423), c(0.439295372199582, 0.439274974986675,
0.439428581777065, 0.439769584911677, 0.440311376731435,
0.441067349577262, 0.442050682434608, 0.44324523656069, 0.444620427704081,
0.446145671613356, 0.447790384037087, 0.449665408397616,
0.451847221910088, 0.45425369101228, 0.456802682141973, 0.459412061736945,
0.461999696234976, 0.464831683666793, 0.468057954385313,
0.471422123311602, 0.474667805366727, 0.477538615471753,
0.480053511602743, 0.482425851243634, 0.484690848577301,
0.486883717786618, 0.489039673054462, 0.491193928563707,
0.493956982558061, 0.497481497979686, 0.501115936059141,
0.504208758026984, 0.506108425113776, 0.507280984619562,
0.508369165880557, 0.508986453181571, 0.508746330807411,
0.507262283042889, 0.504147794172811, 0.496936291357725,
0.485461135551454, 0.47278571172872, 0.461973404864246, 0.456087599932756,
0.454930941860552, 0.455688514963464, 0.457497363300498,
0.459494530930665, 0.460817061912972, 0.460602000306429,
0.459562814204646, 0.459004047420431, 0.45894935759158, 0.459422402355893,
0.460446839351168, 0.462009766461302, 0.463937197600506,
0.466011164452051, 0.468013698699211, 0.469726832025256,
0.470932596113459, 0.47296997490044, 0.476459839069827, 0.480107050425403,
0.482616470770949, 0.482692961910247, 0.483734851275993,
0.48814977919746, 0.493156576823297, 0.495974075302154, 0.493821105782682,
0.483916499413531, 0.462612304326225, 0.431585642670544,
0.396042956622516, 0.361190688358171, 0.332235280053538,
0.30442706641557, 0.271581322127357, 0.236751899329133, 0.20299265016113,
0.173357426763582, 0.15090008127672, 0.13494796346089, 0.122349580442357,
0.112440520410242, 0.104556371553664, 0.0980327220617412,
0.0957763306533094, 0.0994930336492738, 0.10671703564186,
0.114982541223294, 0.1218237549858, 0.124774881521606, 0.124717986794219,
0.124302397813276, 0.123713210740194, 0.123135521736389,
0.122754426963278, 0.121973514647147, 0.120376930722341,
0.118420607912366, 0.116560478940727, 0.115252476530928,
0.114952533406475, 0.115616528001641, 0.116810285085568,
0.118404656884062, 0.12027049562293, 0.122278653527979, 0.12480637140669,
0.128127218628048, 0.131956540260109, 0.136009681370931,
0.140001987028574, 0.143648802301094, 0.147492847541756,
0.151910365858572, 0.156366986451721, 0.16032833852138, 0.163260051267728,
0.165524631797851, 0.167755659620206, 0.16982537253703, 0.171606008350561,
0.172969804863034, 0.173788999876686, 0.172946930763641,
0.170221200699518, 0.166825446191799, 0.163973303747963,
0.162878409875491, 0.163154513456275, 0.163651138819633,
0.164435585644181, 0.165575153608539, 0.167137142391325,
0.169188851671156, 0.173030459047731, 0.178959544638302,
0.185539511897822, 0.191333764281243, 0.194905705243516,
0.197368161364743, 0.20034276288299, 0.203156118740162, 0.205134837878162,
0.205605529238895, 0.203894801764263, 0.198859358795474,
0.190828959110254, 0.181339794805284, 0.171928057977248,
0.164129940722826, 0.155917106935194, 0.145306319929596,
0.133901420935815, 0.123306251183633, 0.115124651902835,
0.110960464323204, 0.109903453921255, 0.110038554987629,
0.111462361973299, 0.11427146932924, 0.118562471506423, 0.124211946172795,
0.131104334512566, 0.139348026251107, 0.149051411113789,
0.160322878825983, 0.173179242302362, 0.187543184114871,
0.203381898474785, 0.220662579593383, 0.239352421681939)),
doy.series = list(c(39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106,
107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,
131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,
167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178,
179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202,
203, 204, 205, 206, 207, 208, 209, 210, 211, 212), c(38,
39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,
99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,
111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,
123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,
159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,
171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,
183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,
207, 208, 209, 210, 211, 212), c(38, 39, 40, 41, 42, 43,
44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,
103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,
115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,
175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,
187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,
199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210,
211, 212)), doy = c(121, 121, 121)), .Names = c("id", "smooth.series",
"doy.series", "doy"), row.names = c(NA, -3L), class = c("tbl_df",
"tbl", "data.frame"))
www
A solution using tidyverse. The key is you need to unnest your list column to use ggplot2. In addition, you mentioned you want to map the color by doy but you only had one doy value in your example. So I changed your doy column to be 121:123 to make an example. Finally, to plot the gradient color, we can use scale_color_gradient with low and high value specified to be "blue" and "red".
library(tidyverse)
df2 <- df %>%
mutate(doy= 121:123) %>%
unnest()
ggplot(df2, aes(x = doy.series, y = smooth.series, color = doy, group = doy)) +
geom_line() +
scale_color_gradient(low = "blue", high = "red")
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