如何将每日时间序列转换为平均每周？

Question

我希望(算术上)平均每日数据,从而将我的每日时间序列转换为每周一次.

遵循这个主题:如何使用R计算每列数据的平均值？,我正在使用xts库.

# Averages daily time series into weekly time series
# where my source is a zoo object
source.w <- apply.weekly(source, colMeans)

我遇到的问题是,这个系列的平均值是星期二到下一个星期一的数据.

我正在寻找从星期一到星期五平均每日数据的选项.

任何提示？

这里有一点:

    # here is part of my data, from a "blé colza.txt" file


    24/07/2012  250.5   499
    23/07/2012  264.75  518.25
    20/07/2012  269.25  525.25
    19/07/2012  267 522.5
    18/07/2012  261.25  517
    17/07/2012  265.75  522.25
    16/07/2012  264.25  523.25
    13/07/2012  258.25  517
    12/07/2012  253.75  513
    11/07/2012  246.25  512.75
    10/07/2012  248 515
    09/07/2012  247 519.25
    06/07/2012  243.25  508.25
    05/07/2012  245 508.5
    04/07/2012  236 500.5
    03/07/2012  234 497.75
    02/07/2012  234.25  489.75
    29/06/2012  229 490.25
    28/06/2012  229.75  487.25
    27/06/2012  229.75  493
    26/06/2012  226.5   486
    25/06/2012  220 482.25
    22/06/2012  214.25  472.5
    21/06/2012  212 469.5
    20/06/2012  210.25  473.75
    19/06/2012  208 472.75
    18/06/2012  206.75  462.5
    15/06/2012  203 456.5
    14/06/2012  205.25  460.5
    13/06/2012  205.25  465.25
    12/06/2012  205.25  469
    11/06/2012  208 471.5
    08/06/2012  208 468.5
    07/06/2012  208 471.25
    06/06/2012  208 467
    05/06/2012  208 458.75
    04/06/2012  208 457.5
    01/06/2012  208 463.5
    31/05/2012  208 466.75
    30/05/2012  208 468
    29/05/2012  212.75  469.75
    28/05/2012  212.75  469.75
    25/05/2012  212.75  465.5



# Loads external libraries
library("zoo") # or require("zoo")
library("xts") # or require("xts")

# Loads data as a zoo object
source <- read.zoo("blé colza.txt", sep=",", dec=".", header=T, na.strings="NA",     format="%d/%m/%Y")

# Averages daily time series into weekly time series
# https://stackoverflow.com/questions/11129562/how-does-one-compute-the-mean-of-weekly-    data-by-column-using-r
source.w <- apply.weekly(source, colMeans) 

Answer 1

mrdwab的回答恰好起作用,因为它们与OP共享时区(或其特征).为了显示:

Lines <- 
    "24/07/2012  250.5   499
    23/07/2012  264.75  518.25
    20/07/2012  269.25  525.25
    19/07/2012  267 522.5
    18/07/2012  261.25  517
    17/07/2012  265.75  522.25
    16/07/2012  264.25  523.25
    13/07/2012  258.25  517
    12/07/2012  253.75  513
    11/07/2012  246.25  512.75
    10/07/2012  248 515
    09/07/2012  247 519.25
    06/07/2012  243.25  508.25
    05/07/2012  245 508.5
    04/07/2012  236 500.5
    03/07/2012  234 497.75
    02/07/2012  234.25  489.75
    29/06/2012  229 490.25
    28/06/2012  229.75  487.25
    27/06/2012  229.75  493
    26/06/2012  226.5   486
    25/06/2012  220 482.25
    22/06/2012  214.25  472.5
    21/06/2012  212 469.5
    20/06/2012  210.25  473.75
    19/06/2012  208 472.75
    18/06/2012  206.75  462.5
    15/06/2012  203 456.5
    14/06/2012  205.25  460.5
    13/06/2012  205.25  465.25
    12/06/2012  205.25  469
    11/06/2012  208 471.5
    08/06/2012  208 468.5
    07/06/2012  208 471.25
    06/06/2012  208 467
    05/06/2012  208 458.75
    04/06/2012  208 457.5
    01/06/2012  208 463.5
    31/05/2012  208 466.75
    30/05/2012  208 468
    29/05/2012  212.75  469.75
    28/05/2012  212.75  469.75
    25/05/2012  212.75  465.5"

# Get R's timezone information (from ?Sys.timezone)
tzfile <- file.path(R.home("share"), "zoneinfo", "zone.tab")
tzones <- read.delim(tzfile, row.names = NULL, header = FALSE,
  col.names = c("country", "coords", "name", "comments"),
  as.is = TRUE, fill = TRUE, comment.char = "#")

# Run the analysis on each timezone
out <- list()
library(xts)
for(i in seq_along(tzones$name)) {
  tzn <- tzones$name[i]
  Sys.setenv(TZ=tzn)
  con <- textConnection(Lines)
  Source <- read.zoo(con, format="%d/%m/%Y")
  out[[tzn]] <- apply.weekly(Source, colMeans)
}

现在您可以运行head(out,5)并查看某些输出因使用的时区而异:

head(out,5)
$`Europe/Andorra`
               V2      V3
2012-05-27 212.75 467.625
2012-06-03 208.95 465.100
2012-06-10 208.00 467.400
2012-06-17 205.10 462.750
2012-06-24 212.90 474.150
2012-07-01 229.85 489.250
2012-07-08 241.05 506.850
2012-07-15 254.10 516.200
2012-07-22 265.60 521.050
2012-07-23 250.50 499.000

$`Asia/Dubai`
               V2      V3
2012-05-27 212.75 467.625
2012-06-03 208.95 465.100
2012-06-10 208.00 467.400
2012-06-17 205.10 462.750
2012-06-24 212.90 474.150
2012-07-01 229.85 489.250
2012-07-08 241.05 506.850
2012-07-15 254.10 516.200
2012-07-22 265.60 521.050
2012-07-23 250.50 499.000

$`Asia/Kabul`
               V2      V3
2012-05-27 212.75 467.625
2012-06-03 208.95 465.100
2012-06-10 208.00 467.400
2012-06-17 205.10 462.750
2012-06-24 212.90 474.150
2012-07-01 229.85 489.250
2012-07-08 241.05 506.850
2012-07-15 254.10 516.200
2012-07-22 265.60 521.050
2012-07-23 250.50 499.000

$`America/Antigua`
                V2      V3
2012-05-25 212.750 465.500
2012-06-01 209.900 467.550
2012-06-08 208.000 464.600
2012-06-15 205.350 464.550
2012-06-22 210.250 470.200
2012-06-29 227.000 487.750
2012-07-06 238.500 500.950
2012-07-13 250.650 515.400
2012-07-20 265.500 522.050
2012-07-24 257.625 508.625

$`America/Anguilla`
                V2      V3
2012-05-25 212.750 465.500
2012-06-01 209.900 467.550
2012-06-08 208.000 464.600
2012-06-15 205.350 464.550
2012-06-22 210.250 470.200
2012-06-29 227.000 487.750
2012-07-06 238.500 500.950
2012-07-13 250.650 515.400
2012-07-20 265.500 522.050
2012-07-24 257.625 508.625

更强大的解决方案是确保正确表示您的时区,方法是使用Sys.setenv(TZ="<yourTZ>")全局indexTZ(Source) <- "<yourTZ>"设置或为每个单独的对象设置它.