如何计算间隔为 10 的 table 中每年的标准偏差？
How to calculate standard deviation for each year in a table in interval of 10?
我有一个table。行代表年份 (2016,2017,2018,2019,2020)，列代表 juliandays（1 到 365 天）。我们的值是从 1 到 365 天，但是，我已经超出了写的字数限制，所以我摘录了 350 列。
我想计算每年的标准偏差，但步长为 10（juliandays 列中的间隔为 10 天）
请问如何使用循环计算这个？
有人可以帮我吗？
我的 table 在这里 :
dput(fAPAR_smoothed[1:5,1:250])
structure(c(0.122500582083785, 0.29427544459643, 0.223506623031398,
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0.710575988938223, 0.78060894079382, 0.732605075889282, 0.709008314174368,
0.784093485374338, 0.710396076812338, 0.778545253074824, 0.731220795817246,
0.709709439814997, 0.785246188017508, 0.710350864664868, 0.776527347624918,
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0.774562323006881, 0.728613423446487, 0.711324603983489, 0.787255251416557,
0.710641707183546, 0.772656475975333, 0.727404160236508, 0.712218555103088,
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0.7131541867611, 0.788843531451631, 0.71138152934357, 0.769042826642096,
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0.790345465412374, 0.71312720496631, 0.764165178302632, 0.722466895664008,
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0.761281784290697, 0.721034984862097, 0.718811310593743, 0.790649878840733,
0.715315739392031, 0.759943193738371, 0.720432156235881, 0.71960646909055,
0.790459969339718, 0.716081060179766, 0.758666054997089, 0.719901153762111,
0.720314334675844, 0.790117018365815, 0.716843283381805, 0.757442541673293,
0.719438393202841, 0.720919373340759, 0.789617684709366, 0.717591001655842,
0.756263111717257, 0.719039653577692, 0.721407099418396, 0.788959502444147,
0.71831390631762, 0.755116703587182, 0.718700140188424, 0.721764219148072,
0.788140996853189, 0.719002843823386, 0.753990932413287, 0.718414547643507,
0.721978774239583, 0.787161800354588, 0.719649872252329, 0.752872286161905,
0.718177122882703, 0.722040285437457, 0.786022768427323, 0.720248317789026,
0.751746321799579, 0.717981728201636, 0.721939896085211, 0.784726326547061,
0.720792935367653, 0.750598088433132, 0.717821912555189, 0.721670515689609,
0.783276817121947, 0.721280069316188, 0.749412550449746, 0.717690982860895,
0.72122696348492, 0.781680233856119, 0.721707708350468, 0.748174765453578,
0.71758207530233, 0.72060611199717, 0.779943956113207, 0.722075540568228,
0.74687006220239, 0.717488226632511, 0.719807030608406, 0.778076483279837,
0.722385008443152, 0.745484218544168, 0.717402445477285, 0.718830817523893,
0.776087339664835, 0.722639301829306, 0.744003723368089, 0.71731778363873,
0.717680431739323, 0.773986979398426, 0.722843350965568, 0.74241603855547,
0.717227407398546, 0.716360351478335, 0.771786602362918, 0.723003480796795,
0.740709646439553, 0.717124668821455, 0.714876232630052, 0.769497970123383,
0.723127065295006, 0.738874097265286, 0.717003177058603, 0.713234567186606,
0.767133221858344, 0.723222181780549, 0.736900056649105, 0.716856869650955,
0.711442672084187, 0.764704741356475, 0.72329726524328, 0.734779353038717,
0.716680052279332, 0.709508678044102, 0.762225024013313, 0.723360762663733,
0.732505025172883, 0.716467438514453, 0.707441518413822, 0.759706543827961,
0.723420787334297, 0.730071369541197, 0.716214189566969, 0.705250918008038,
0.757161620399798, 0.723484773180396, 0.727473987843868, 0.715915954037502,
0.702947381949723, 0.754602285925185, 0.723559129081655, 0.724709834451509,
0.715568907666681, 0.700542189726354, 0.752040152194173, 0.723649155534245,
0.721777263864922, 0.715169793085183, 0.698047389246153, 0.749486277587218,
0.723758961313215, 0.718676078174886, 0.714715959563765, 0.69547553236811,
0.746951034071887, 0.723891150659456, 0.715407574521941, 0.714205113785683,
0.692839410432002, 0.744443974199575, 0.724046510466666, 0.711974592556176,
0.713635070619101, 0.690151789788423, 0.741973698102213, 0.724223697468319,
0.708381561897018, 0.713003503889507, 0.687425442217445, 0.739547945199311,
0.724419541802812, 0.704634761535462, 0.712307945736596, 0.684673175347285,
0.737173685904985, 0.72462935057862, 0.700742579236306, 0.71154578597115,
0.681907807390831, 0.734857078671981, 0.724847170076432, 0.696715515822678,
0.710714271431922, 0.679142141882164, 0.732603427035687, 0.725066047951282,
0.692566189460571, 0.709810505342516, 0.676388942413085, 0.730417136658153,
0.725278295434689, 0.68830933994337, 0.708831446668265, 0.673660907369642,
0.728301801609538, 0.725475749536789, 0.683961532664851, 0.707773909473119,
0.670970644668651, 0.726260290649554, 0.725650035248473, 0.679540862592175,
0.706634562276514, 0.668330646494225, 0.724294833508911, 0.725792618793141,
0.675066632665642, 0.70541005745808, 0.665753264034301, 0.722407107170761,
0.725894860878465, 0.670559032198432, 0.704097160662347, 0.663250682217163,
0.720598322152139, 0.725948069948145, 0.666038815276351, 0.70269288020344,
0.660834894447964, 0.718869361333807, 0.725943555433669, 0.661526979157582,
0.701194596469787, 0.658517677345255, 0.71722091879009, 0.725872681006075,
0.657044442672429, 0.699600191328815, 0.656310432384337, 0.715653486201941,
0.725726917827706, 0.652611724623066, 0.697908177531652, 0.654224027540619,
0.71416733927, 0.725497897803973, 0.648248622183291, 0.696117828117821,
0.652268638932969, 0.712762524127661, 0.725177466835113, 0.643973889298266,
0.694229305819949, 0.650453592467073, 0.71143884375413, 0.724757738067951,
0.639805169201532, 0.692243792468457, 0.648787205478782, 0.710195844387489,
0.724231145147664, 0.635758926932014, 0.690163618396267, 0.647276628377471,
0.709032561162971, 0.723590705910877, 0.631850381851025, 0.687992202536576,
0.64592768628939, 0.707947263751228, 0.722830286078761, 0.62809344015928,
0.685733992520634, 0.644744720701018, 0.706937201996604, 0.721944862950135,
0.624500627413897, 0.683394404775524, 0.643730723735348, 0.705998711747714,
0.720930789094558, 0.621083251549228, 0.680979764621941, 0.642887472428173,
0.705127320688011, 0.719786056045427, 0.617851565897687, 0.678497246371966,
0.642215663004369, 0.70431785416636, 0.718510563041713, 0.614814907883497,
0.675954813426841, 0.641715045154181, 0.703564541027608, 0.7171063857697,
0.611981837716444, 0.673361158374748, 0.64138455630951, 0.70286111944316,
0.71557804510472, 0.609360277085623, 0.67072564308858, 0.641222455920195,
0.702200942741548, 0.713932302025208, 0.60695747626894, 0.668058238823713,
0.641226459730308, 0.701577085239013, 0.712177952526762, 0.604779981242614,
0.66536946631578, 0.641393874054435, 0.700982448070075, 0.710325622536197,
0.602833600790677, 0.662670309668224, 0.641721730053974, 0.700409865018109,
0.708387562825601, 0.601123373614478, 0.659972130239842, 0.642206918013419,
0.699852208345925, 0.706377443926391, 0.599653535442188, 0.65728658053234,
0.642846321616653, 0.699302720450811, 0.704310202432958, 0.598427388074301,
0.654625518077879, 0.643636952223237, 0.698755345519584, 0.702201887306308,
0.597447168429141, 0.652000919326621, 0.644575788013852, 0.69820487717238,
0.700069350352095, 0.59671391758836, 0.649424793534284, 0.645659613135754,
0.697647106106444, 0.697929936698657, 0.596227349842446, 0.646909096649695,
0.646884856848223, 0.697078967739922, 0.695801175275049, 0.595985721736223,
0.64446564520234, 0.648247464825912, 0.696498492437792, 0.693700469289074,
0.595985839720184, 0.642106030189918, 0.649742770462198, 0.695904755737797,
0.691644786705316, 0.596223067801817, 0.639841530965896, 0.651365164621128,
0.69529782857638, 0.689650350723165, 0.596691116120784, 0.63768288951685,
0.65310776538936, 0.694678727514616, 0.687732330254854, 0.597381829524094,
0.635640084739809, 0.654962087828103, 0.694049364964146, 0.685904530403488,
0.598284976141281, 0.633722106719599, 0.656918021910071, 0.693412499413118,
0.684179438377916, 0.59938838860409, 0.631936979568404, 0.658963810456427,
0.692771685652114, 0.6825682694076, 0.600678105266165, 0.630291784265325,
0.661086027073734, 0.692131225000089, 0.681081012657484, 0.602138707472509,
0.62879268149594, 0.663269554090901, 0.691496115530307, 0.679726477142865,
0.603753656828942, 0.627444934491862, 0.665497560496134, 0.690872002296272,
0.67851233764427, 0.605505632471562, 0.626252931870302, 0.667751479873891,
0.690264975936618, 0.677445180622324, 0.607376659589943, 0.625220210473622,
0.670010988341832, 0.689681371279992, 0.676530550132623, 0.609348237950327,
0.624349478208897, 0.672253982487778, 0.689127565949943, 0.675772791530688,
0.611401470418823, 0.623642491150168, 0.674456557306661, 0.688609778969806,
0.675174895176909, 0.6135171914846, 0.623099930640708, 0.676592984137485,
0.688133869367594, 0.674738340141494, 0.615676095783084), .Dim = c(5L,
250L))
这将计算每年每 10 天的标准偏差。
t(apply(fAPAR_smoothed, 1, function(x) tapply(x, ceiling(seq_along(x)/10), sd)))
从@Ronak 那里得到提示并假设您的数据名称是 fAPAR
sapply(split.default(as.data.frame(fAPAR), ceiling(seq_along(as.data.frame(fAPAR))/10)),
function(x) matrixStats::rowSds(as.matrix(x, c(nrow(as.data.frame(fAPAR)), 10))))
会给你同样的结果。
我最喜欢的 tidyverse 语法风格
map_dfc(seq_len(ncol(fAPAR)/10), ~fAPAR %>% as.data.frame() %>%
select(!!(.x * seq_len(10))) %>% rowwise %>%
summarise(!!paste0('X', .x) := sd(c_across(everything())), .groups = 'drop'))
# A tibble: 5 x 25
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.0279 0.0471 0.0578 0.0606 0.0570 0.0504 0.0460 0.0436 0.0400 0.0443 0.0728 0.115 0.154 0.183 0.200 0.210 0.215
2 0.0100 0.0126 0.0104 0.0136 0.0262 0.0394 0.0465 0.0454 0.0467 0.0701 0.108 0.144 0.171 0.187 0.197 0.204 0.209
3 0.00713 0.0118 0.0139 0.0136 0.0118 0.0113 0.0121 0.0114 0.0177 0.0445 0.0861 0.130 0.166 0.192 0.212 0.227 0.237
4 0.00130 0.00440 0.00964 0.0173 0.0247 0.0285 0.0282 0.0263 0.0276 0.0433 0.0770 0.119 0.156 0.182 0.198 0.207 0.214
5 0.0153 0.0199 0.0172 0.0141 0.0136 0.0129 0.0118 0.0125 0.0119 0.0324 0.0796 0.132 0.175 0.203 0.219 0.227 0.231
# ... with 8 more variables: X18 <dbl>, X19 <dbl>, X20 <dbl>, X21 <dbl>, X22 <dbl>, X23 <dbl>, X24 <dbl>, X25 <dbl>
我有一个table。行代表年份 (2016,2017,2018,2019,2020)，列代表 juliandays（1 到 365 天）。我们的值是从 1 到 365 天，但是，我已经超出了写的字数限制，所以我摘录了 350 列。
我想计算每年的标准偏差，但步长为 10（juliandays 列中的间隔为 10 天）
请问如何使用循环计算这个？
有人可以帮我吗？
我的 table 在这里 :
dput(fAPAR_smoothed[1:5,1:250])
structure(c(0.122500582083785, 0.29427544459643, 0.223506623031398,
0.305543938017955, 0.3555346545493, 0.13276591893586, 0.29851456949357,
0.226163385898154, 0.305334527655609, 0.349099920643732, 0.142765775203754,
0.302517405153014, 0.228743825400136, 0.305071530853091, 0.343012833399477,
0.152500150887466, 0.30628395157476, 0.231247941537343, 0.304754947610403,
0.337273392816537, 0.161969045986996, 0.309814208758809, 0.233675734309774,
0.304384777927542, 0.331881598894911, 0.171172460502345, 0.313108193745883,
0.236027203717432, 0.30396104662241, 0.326837451634599, 0.180110394433513,
0.316165957658146, 0.238302349760315, 0.303483828148701, 0.322140951035602,
0.188782847780499, 0.318987602739927, 0.240501172438425, 0.302953256861566,
0.317791977513919, 0.197189820543304, 0.321573299398441, 0.242623671751759,
0.302369537283266, 0.313790172317547, 0.205331312721925, 0.323923303244507,
0.244669847700318, 0.301732954368829, 0.310134817942479, 0.213207324316361,
0.326037972133275, 0.246639700284097, 0.301043883771709, 0.306824677535159,
0.220817855326607, 0.327917783204944, 0.248533229503092, 0.300302802109442,
0.30385783429494, 0.228162905752658, 0.329563349925485, 0.250350435357295,
0.299510128025495, 0.301231530876531, 0.235242475594506, 0.330975439127363,
0.252091317846694, 0.298666063251117, 0.298942008792457, 0.242056564852138,
0.332154988050262, 0.253755876971275, 0.297770433667191, 0.296984347815508,
0.248605173525543, 0.3331031213818, 0.255344077035364, 0.296822530366082,
0.295352305381194, 0.254888301614705, 0.333821168298262, 0.25685581095197,
0.295820950713493, 0.294038155990196, 0.260905949119606, 0.334310679505311,
0.258290864547128, 0.294763718181327, 0.29303253061082, 0.266658116040223,
0.334573444278715, 0.259648880864242, 0.293648402180558, 0.292324256081446,
0.272144802376533, 0.334611507505068, 0.260929324468427, 0.292472237894095,
0.29190019451298, 0.277366008128505, 0.334427186722508, 0.262131445750849,
0.291232246109652, 0.29174545781661, 0.282321733296109, 0.334023089161438,
0.263254245233068, 0.289925353052612, 0.291843622231568, 0.287011977879309,
0.33340212878525, 0.264296437871382, 0.288548510218893, 0.29217694285288,
0.291436741878066, 0.332567543331044, 0.265256417361163, 0.28709881420782,
0.292726568159131, 0.295596025292336, 0.331522911350348, 0.266132220441202,
0.285573626554987, 0.293472754540217, 0.299489828122074, 0.330272169249841,
0.266921653895772, 0.283970693565133, 0.294395080825109, 0.30311815036723,
0.328819628332072, 0.267622421556679, 0.282288266145008, 0.295472662809605,
0.306480992027753, 0.32716999183618, 0.268232251305332, 0.280525371105845,
0.296684367784092, 0.309578353103588, 0.325328371978616, 0.268749022074797,
0.278682082465839, 0.298009029061305, 0.31241023359468, 0.323300306993861,
0.269170890851861, 0.276759792752618, 0.299425660504082, 0.314976633500972,
0.321091778175144, 0.269496360401606, 0.27476145142608, 0.300913671053126,
0.317277552822408, 0.318709226915169, 0.269724346991979, 0.272691803301225,
0.30245307925476, 0.319312991558926, 0.316159571746827, 0.269854177560595,
0.270557267102268, 0.304024727788694, 0.321082949710463, 0.313450225383922,
0.269885586881538, 0.268365814016749, 0.305610497995776, 0.32258742727695,
0.31058911176189, 0.26981871473216, 0.266126846249641, 0.307193524405759,
0.323826424258312, 0.307584683078521, 0.269654103059881, 0.263851075577464,
0.308758409265061, 0.324799940654468, 0.304445936834679, 0.269392693148992,
0.261550401902396, 0.310291437064519, 0.325507976465329, 0.301182432875025,
0.269035822787454, 0.259237720941508, 0.311780408602389, 0.325950687955043,
0.297804310428734, 0.268585223433699, 0.256926731916016, 0.313214475047339,
0.326128543916233, 0.294322305150219, 0.268043017383431, 0.254631745240517,
0.31458397200144, 0.326042481934244, 0.290747766159853, 0.267411784894658,
0.252367490212232, 0.315880326567261, 0.325694064651384, 0.287092673084685,
0.266694631312714, 0.25014892270025, 0.317095964414956, 0.325085636031168,
0.283369653099159, 0.26589517822546, 0.247991032834772, 0.318224216849358,
0.324220477622561, 0.279591997965842, 0.265017554618472, 0.245908652696348,
0.319259227877065, 0.323102964824218, 0.275773681076135, 0.264066388030237,
0.243916264005123, 0.320195861273532, 0.321738723148725, 0.271929374491003,
0.263046795707343, 0.242027805810076, 0.321029607650156, 0.320134784486837,
0.268074465981687, 0.261964375759676, 0.240256482178265, 0.321756491521366,
0.318299743371724, 0.264225076070435, 0.260825198315607, 0.238614569884064,
0.322372962063665, 0.316243679297106, 0.260398075071212, 0.259635796677185,
0.237113226098407, 0.322875783874669, 0.313978079035401, 0.256611100130432,
0.258403158475333, 0.235762296078024, 0.323261927732155, 0.311515758955865,
0.252882572267672, 0.257134716825033, 0.234570241267049, 0.3235284613531,
0.308870787342738, 0.249231713416398, 0.255838341480526, 0.233544067398609,
0.323672440152728, 0.306058406713381, 0.245678349309024, 0.254522329990498,
0.232689252596435, 0.323690845289486, 0.303094956136427, 0.242242712361977,
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0.685733992520634, 0.644744720701018, 0.706937201996604, 0.721944862950135,
0.624500627413897, 0.683394404775524, 0.643730723735348, 0.705998711747714,
0.720930789094558, 0.621083251549228, 0.680979764621941, 0.642887472428173,
0.705127320688011, 0.719786056045427, 0.617851565897687, 0.678497246371966,
0.642215663004369, 0.70431785416636, 0.718510563041713, 0.614814907883497,
0.675954813426841, 0.641715045154181, 0.703564541027608, 0.7171063857697,
0.611981837716444, 0.673361158374748, 0.64138455630951, 0.70286111944316,
0.71557804510472, 0.609360277085623, 0.67072564308858, 0.641222455920195,
0.702200942741548, 0.713932302025208, 0.60695747626894, 0.668058238823713,
0.641226459730308, 0.701577085239013, 0.712177952526762, 0.604779981242614,
0.66536946631578, 0.641393874054435, 0.700982448070075, 0.710325622536197,
0.602833600790677, 0.662670309668224, 0.641721730053974, 0.700409865018109,
0.708387562825601, 0.601123373614478, 0.659972130239842, 0.642206918013419,
0.699852208345925, 0.706377443926391, 0.599653535442188, 0.65728658053234,
0.642846321616653, 0.699302720450811, 0.704310202432958, 0.598427388074301,
0.654625518077879, 0.643636952223237, 0.698755345519584, 0.702201887306308,
0.597447168429141, 0.652000919326621, 0.644575788013852, 0.69820487717238,
0.700069350352095, 0.59671391758836, 0.649424793534284, 0.645659613135754,
0.697647106106444, 0.697929936698657, 0.596227349842446, 0.646909096649695,
0.646884856848223, 0.697078967739922, 0.695801175275049, 0.595985721736223,
0.64446564520234, 0.648247464825912, 0.696498492437792, 0.693700469289074,
0.595985839720184, 0.642106030189918, 0.649742770462198, 0.695904755737797,
0.691644786705316, 0.596223067801817, 0.639841530965896, 0.651365164621128,
0.69529782857638, 0.689650350723165, 0.596691116120784, 0.63768288951685,
0.65310776538936, 0.694678727514616, 0.687732330254854, 0.597381829524094,
0.635640084739809, 0.654962087828103, 0.694049364964146, 0.685904530403488,
0.598284976141281, 0.633722106719599, 0.656918021910071, 0.693412499413118,
0.684179438377916, 0.59938838860409, 0.631936979568404, 0.658963810456427,
0.692771685652114, 0.6825682694076, 0.600678105266165, 0.630291784265325,
0.661086027073734, 0.692131225000089, 0.681081012657484, 0.602138707472509,
0.62879268149594, 0.663269554090901, 0.691496115530307, 0.679726477142865,
0.603753656828942, 0.627444934491862, 0.665497560496134, 0.690872002296272,
0.67851233764427, 0.605505632471562, 0.626252931870302, 0.667751479873891,
0.690264975936618, 0.677445180622324, 0.607376659589943, 0.625220210473622,
0.670010988341832, 0.689681371279992, 0.676530550132623, 0.609348237950327,
0.624349478208897, 0.672253982487778, 0.689127565949943, 0.675772791530688,
0.611401470418823, 0.623642491150168, 0.674456557306661, 0.688609778969806,
0.675174895176909, 0.6135171914846, 0.623099930640708, 0.676592984137485,
0.688133869367594, 0.674738340141494, 0.615676095783084), .Dim = c(5L,
250L))
这将计算每年每 10 天的标准偏差。
t(apply(fAPAR_smoothed, 1, function(x) tapply(x, ceiling(seq_along(x)/10), sd)))
从@Ronak 那里得到提示并假设您的数据名称是 fAPAR
sapply(split.default(as.data.frame(fAPAR), ceiling(seq_along(as.data.frame(fAPAR))/10)),
function(x) matrixStats::rowSds(as.matrix(x, c(nrow(as.data.frame(fAPAR)), 10))))
会给你同样的结果。
我最喜欢的 tidyverse 语法风格
map_dfc(seq_len(ncol(fAPAR)/10), ~fAPAR %>% as.data.frame() %>%
select(!!(.x * seq_len(10))) %>% rowwise %>%
summarise(!!paste0('X', .x) := sd(c_across(everything())), .groups = 'drop'))
# A tibble: 5 x 25
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.0279 0.0471 0.0578 0.0606 0.0570 0.0504 0.0460 0.0436 0.0400 0.0443 0.0728 0.115 0.154 0.183 0.200 0.210 0.215
2 0.0100 0.0126 0.0104 0.0136 0.0262 0.0394 0.0465 0.0454 0.0467 0.0701 0.108 0.144 0.171 0.187 0.197 0.204 0.209
3 0.00713 0.0118 0.0139 0.0136 0.0118 0.0113 0.0121 0.0114 0.0177 0.0445 0.0861 0.130 0.166 0.192 0.212 0.227 0.237
4 0.00130 0.00440 0.00964 0.0173 0.0247 0.0285 0.0282 0.0263 0.0276 0.0433 0.0770 0.119 0.156 0.182 0.198 0.207 0.214
5 0.0153 0.0199 0.0172 0.0141 0.0136 0.0129 0.0118 0.0125 0.0119 0.0324 0.0796 0.132 0.175 0.203 0.219 0.227 0.231
# ... with 8 more variables: X18 <dbl>, X19 <dbl>, X20 <dbl>, X21 <dbl>, X22 <dbl>, X23 <dbl>, X24 <dbl>, X25 <dbl>