1

如果我们有三年的数据:

数据= (x1:x1096)

我想以这种方式计算平均值:

     [x1+x366(the first day in the second year)+ x731(the first day in the third year)]/3
     [x2+x367(the second day in the second year)+ x732(the second day in the third year)]/3

依此类推,直到第 365 天:

     [x365+x730(the last day in the second year)+ x1096(the last day in the third year)]/3

最后我会365 values摆脱它。

     dat= c(1:1096) 

关于如何做到这一点的任何想法?

4

3 回答 3

3

data.table在这里非常方便:(即使基本R解决方案是完全可行的!):

> set.seed(1)
> dat <- data.table(date=seq(as.Date("2010-01-01"), as.Date("2012-12-31"), "days"),
+                   var=rnorm(1096))
> dat
            date          var
   1: 2010-01-01 -0.626453811
   2: 2010-01-02  0.183643324
   3: 2010-01-03 -0.835628612
   4: 2010-01-04  1.595280802
   5: 2010-01-05  0.329507772
  ---                        
1092: 2012-12-27  0.711213964
1093: 2012-12-28 -0.337691156
1094: 2012-12-29 -0.009148952
1095: 2012-12-30 -0.125309208
1096: 2012-12-31 -2.090846097

> dat[, mean(var), by=list(month=month(date), mday(date))]
     month mday          V1
  1:     1    1 -0.16755484
  2:     1    2  0.59942582
  3:     1    3 -0.44336168
  4:     1    4  0.01297244
  5:     1    5 -0.20317854
 ---                       
362:    12   28 -0.18076284
363:    12   29  0.07302903
364:    12   30 -0.01790655
365:    12   31 -0.87164859
366:     2   29 -0.78859794

2 月 29 日结束,因为[.data.table那天的组是什么时候找到的最后一个唯一组合 (ofmonth(date)mday(date)),因为它在 2012 年第一次出现。一旦你得到你的结果,你可以分配键并对表格进行排序:

> result <- dat[, mean(var), by=list(month=month(date), mday(date))]
> setkey(result, month, mday)
> result
     month mday          V1
  1:     1    1 -0.16755484
  2:     1    2  0.59942582
  3:     1    3 -0.44336168
  4:     1    4  0.01297244
  5:     1    5 -0.20317854
 ---                       
362:    12   27 -0.60348463
363:    12   28 -0.18076284
364:    12   29  0.07302903
365:    12   30 -0.01790655
366:    12   31 -0.87164859
于 2013-10-09T11:44:10.930 回答
2

也许像这样?我在一个比你的向量略小的例子上进行了尝试1:1096——我每年使用 5 个值。

# the data, here 3 years with 5 values per year. 
dat <- 1:15

# put your vector in a matrix
# by default, the matrix is filled column-wise
# thus, each column corresponds to a year, and each row to day of year
mm <- matrix(dat, ncol = 3)

# calculate row means
mm <- cbind(mm, rowMeans(mm))
mm
#      [,1] [,2] [,3] [,4]
# [1,]    1    6   11    6
# [2,]    2    7   12    7
# [3,]    3    8   13    8
# [4,]    4    9   14    9
# [5,]    5   10   15   10

更新 另一个base考虑闰年的替代方案,使用set.seed(1)来自@Michele 回答的相同(即)“完整”数据:

df2 <- aggregate(var ~ format(date, "%m-%d"), data = dat, FUN = mean)
head(df2)

#   format(date, "%m-%d")         var
# 1                 01-01 -0.16755484
# 2                 01-02  0.59942582
# 3                 01-03 -0.44336168
# 4                 01-04  0.01297244
# 5                 01-05 -0.20317854
# 6                 01-06 -0.55350137
于 2013-10-09T10:33:31.260 回答
2

这是一个base考虑闰年的解决方案:

# First your data
set.seed(1)
dat <- rnorm(1096) #Value for each day
day <- seq(as.Date("2010-01-01"), as.Date("2012-12-31"), "days") #Corresponding days

sapply(split(dat,format(day,"%m-%d")),mean)
       01-01        01-02        01-03        01-04        01-05        01-06        01-07        01-08        01-09 
-0.167554841  0.599425816 -0.443361675  0.012972442 -0.203178536 -0.553501370  0.563475994 -0.094459075  0.567263811 
       01-10        01-11        01-12        01-13        01-14        01-15        01-16        01-17        01-18 
-0.325835336 -0.247226807 -0.272224241  0.171886332 -0.562604980  0.640473418 -0.209380261  0.709635402 -0.263715734 
       01-19        01-20        01-21        01-22        01-23        01-24        01-25        01-26        01-27 
 0.929096171  1.173422823 -0.197411808 -0.730959553 -0.277022971 -1.075673025 -0.494038031 -0.255709319  0.827062779 
       01-28        01-29        01-30        01-31        02-01        02-02        02-03        02-04        02-05 
 0.208963353  0.215192803 -0.118735162  0.141028516  0.703267761 -0.282852177 -0.297731589 -0.112031601  0.784073396 
       02-06        02-07        02-08        02-09        02-10        02-11        02-12        02-13        02-14 
 0.714499179  0.206640777  0.283234842 -0.255182989 -0.293285997 -0.761585755  0.443379228  1.138436815 -0.483004921 
       02-15        02-16        02-17        02-18        02-19        02-20        02-21        02-22        02-23 
-0.692188333  0.701422889  0.677544133 -0.423576371  0.498868978  0.053960271  0.518228979 -0.250840385 -0.722647734 
       02-24        02-25        02-26        02-27        02-28        02-29        03-01        03-02        03-03 
 1.344507325  0.693403586 -0.226489715 -0.406929668 -0.171335064 -0.788597935  0.115894011  1.798749522 -0.502676829 
       03-04        03-05        03-06        03-07        03-08        03-09        03-10        03-11        03-12 
 0.244453933 -0.278023124 -0.817932086 -0.618472996 -0.842995408 -0.887451556  0.432459430  0.559562525 -0.516256302 
       03-13        03-14        03-15        03-16        03-17        03-18        03-19        03-20        03-21 
 0.392447923  0.191049834 -0.727128826 -0.261740657 -0.189455949  0.775326029  0.236835450 -0.266491426 -0.010319849 
       03-22        03-23        03-24        03-25        03-26        03-27        03-28        03-29        03-30 
-0.949967889 -0.277676523 -0.556777524 -0.507373521  0.076952129  0.697147181 -0.416867359 -0.906909972 -0.231494410 
       03-31        04-01        04-02        04-03        04-04        04-05        04-06        04-07        04-08 
-0.453616811  0.158367456  0.670354625 -0.285493660 -0.040162162  0.762953404 -0.388049908  1.079423205 -0.246508050 
       04-09        04-10        04-11        04-12        04-13        04-14        04-15        04-16        04-17 
-0.215358691 -0.337611847  0.486368813  0.115883308 -0.282207017  0.614554509  0.531435739  1.063455284 -0.199968099 
       04-18        04-19        04-20        04-21        04-22        04-23        04-24        04-25        04-26 
-0.080662691 -0.052822528  1.679629547 -1.341639141  0.986160744  0.468143827  0.029621883 -0.025910053  0.061093981 
       04-27        04-28        04-29        04-30        05-01        05-02        05-03        05-04        05-05 
-0.387992910 -0.917561336  0.161867089  0.874549452  0.866708261  0.048304939 -1.209756576 -0.825689257 -0.176605953 
       05-06        05-07        05-08        05-09        05-10        05-11        05-12        05-13        05-14 
-0.381265758  0.419105218 -0.440418731 -0.293923704  1.427366374 -0.020773738 -0.358619841 -0.294738750 -0.269765222 
       05-15        05-16        05-17        05-18        05-19        05-20        05-21        05-22        05-23 
 0.277361477 -0.505072373 -0.765572754 -0.493223200 -0.253297588  0.902399037  0.007676731 -0.273059247 -0.784701888 
       05-24        05-25        05-26        05-27        05-28        05-29        05-30        05-31        06-01 
 0.063532445 -0.681369105 -1.034300631  0.689037398 -0.209889037 -0.535166412 -0.994984541  0.438795387 -0.167806908 
       06-02        06-03        06-04        06-05        06-06        06-07        06-08        06-09        06-10 
 0.079629296 -0.063908968  0.484892252 -0.922112094  0.978258635 -0.790949931 -0.303356059  0.681310315 -0.512109593 
       06-11        06-12        06-13        06-14        06-15        06-16        06-17        06-18        06-19 
 0.337126461  0.526594905  0.742784618 -0.163083706  0.027435241  0.709630255 -1.144544436 -0.374108608  0.102721328 
       06-20        06-21        06-22        06-23        06-24        06-25        06-26        06-27        06-28 
 0.577569049  0.224528626  0.206667019  0.392007605 -0.557974448  0.068685789  0.460201512  1.101334023  0.035838933 
       06-29        06-30        07-01        07-02        07-03        07-04        07-05        07-06        07-07 
 0.873903793 -0.586658280 -0.395094221  0.303312480 -0.631756580  0.088308518  0.046129624  0.642985443 -0.615693218 
       07-08        07-09        07-10        07-11        07-12        07-13        07-14        07-15        07-16 
 0.372776652  0.453644860  0.466905164 -0.526930331 -0.351139797  0.250132593 -0.881175203 -1.090136940  0.409708249 
       07-17        07-18        07-19        07-20        07-21        07-22        07-23        07-24        07-25 
 0.206436178  0.056134229 -0.057927905  0.807127686  0.423170493 -0.325181464 -0.053593067  0.261438323  0.520617153 
       07-26        07-27        07-28        07-29        07-30        07-31        08-01        08-02        08-03 
 0.053800701  0.326492953 -0.471839346  0.438963172  0.499502012  0.620917026  0.619923442 -1.422177067  0.212056501 
       08-04        08-05        08-06        08-07        08-08        08-09        08-10        08-11        08-12 
 0.497181456  0.703607380 -0.054104370  0.931407619  0.545759743 -0.323646872  0.127371847  0.017697636 -0.033060879 
       08-13        08-14        08-15        08-16        08-17        08-18        08-19        08-20        08-21 
-0.583034512  0.824859915 -0.019064796 -0.226035270 -1.026526076 -0.882074229 -0.079167867 -2.073168805  0.378121135 
       08-22        08-23        08-24        08-25        08-26        08-27        08-28        08-29        08-30 
-0.004516521 -0.661187139  0.339497500 -0.042210229  0.026970585  0.431653210  0.104619786  0.149562359 -0.473661114 
       08-31        09-01        09-02        09-03        09-04        09-05        09-06        09-07        09-08 
-0.235250025 -0.624645896  0.141205349 -0.485201261  0.097633486  0.462059099 -0.500082678  1.386621118 -0.070895288 
       09-09        09-10        09-11        09-12        09-13        09-14        09-15        09-16        09-17 
-0.126090048 -0.371028573 -0.010479329  0.192555782  0.025085776 -1.410061589  1.046273116  0.938254501 -0.072773342 
       09-18        09-19        09-20        09-21        09-22        09-23        09-24        09-25        09-26 
-0.272947102  0.279357832  0.172702983  0.219560592  0.922992902 -0.612832806 -0.450896711 -1.134353324 -0.336199724 
       09-27        09-28        09-29        09-30        10-01        10-02        10-03        10-04        10-05 
-0.459242718  0.049888664  0.079844541 -0.058636867  0.581553407 -0.315806482 -0.163864166 -1.513984901  0.069093641 
       10-06        10-07        10-08        10-09        10-10        10-11        10-12        10-13        10-14 
-0.325709367  0.114176104 -0.470510646 -0.393891025 -0.659031395 -0.224657523 -0.336803115 -0.510526475 -0.941899166 
       10-15        10-16        10-17        10-18        10-19        10-20        10-21        10-22        10-23 
 0.559205646  0.346629848  0.310935589 -0.851962382  0.387930834  0.505692192 -0.738722861  0.410302113 -0.181359914 
       10-24        10-25        10-26        10-27        10-28        10-29        10-30        10-31        11-01 
 0.831105889 -0.398852239 -0.164535170 -0.870295447  0.057609116 -1.058556114  0.809784093  0.188277796  1.432543613 
       11-02        11-03        11-04        11-05        11-06        11-07        11-08        11-09        11-10 
 0.040680316  0.711553107  0.565285429 -0.829181807  0.455487776 -0.037182199 -0.644669824 -0.704611643  0.491631958 
       11-11        11-12        11-13        11-14        11-15        11-16        11-17        11-18        11-19 
-0.051188454  0.963031185 -0.511791970  0.193671830 -0.333065645 -0.176479500  0.367566807 -0.056534518  1.391773053 
       11-20        11-21        11-22        11-23        11-24        11-25        11-26        11-27        11-28 
 0.162741879 -0.269991630  0.866532461 -0.352034768 -0.028515790 -0.671437717 -0.393703641  0.394041604 -0.959721458 
       11-29        11-30        12-01        12-02        12-03        12-04        12-05        12-06        12-07 
-0.187149463  0.203037321 -0.824439261 -0.081277243  0.361409692 -0.300022665 -0.067589145 -0.265877741 -0.474834675 
       12-08        12-09        12-10        12-11        12-12        12-13        12-14        12-15        12-16 
-0.903405316  0.026396956  0.930117145 -0.489879346 -0.481598661  0.122388492  0.042287328 -0.160328704  0.777249363 
       12-17        12-18        12-19        12-20        12-21        12-22        12-23        12-24        12-25 
-0.359802827  0.252189848  0.754686655 -0.012767780  0.683605939  0.782528149 -0.786087093  0.751560196 -0.610885984 
       12-26        12-27        12-28        12-29        12-30        12-31 
 0.203570612 -0.603484627 -0.180762839  0.073029026 -0.017906554 -0.871648586 

这个想法是根据一年中的日期(%d-%m)进行拆分,并对每个子组进行平均。

编辑 - 米歇尔(我认为最好将此答案改进为完全base相关,而不是我的):

如果上面的向量被用来创建一个,data.frame那么这个解决方案是一个很好的选择:

dat <- data.frame(date=day, var=dat)

> ddply(dat, .(day=format(date,"%m-%d")), summarise, result=mean(var))
      day       result
1   01-01 -0.167554841
2   01-02  0.599425816
3   01-03 -0.443361675
4   01-04  0.012972442
5   01-05 -0.203178536
6   01-06 -0.553501370

注意:对不起,它实际上使用plyr包,但它仍然使用data.frame并且ddply可以被byfrom basepackage 替换。

于 2013-10-09T12:02:24.733 回答