很抱歉，如果已发布，但我看起来很辛苦，找不到任何东西。
我正在进行为期30年的月度温度观测，包括1960年1月至1989年12月。它看起来像这样：
> head(df)
date temp
1 1960-01-01 22.92235
2 1960-02-01 23.07059
3 1960-03-01 23.10941
4 1960-04-01 20.78353
5 1960-05-01 17.45176
6 1960-06-01 17.31765
首先，我需要做的是在整个时期内平均所有的游戏，费城，游行等。
然后，我想在特定的时间段（3年，5年，10年等）做同样的事情。
例如，
最终结果将包括月份，期间和温度，如下所示：
Month Period Temp
Jan 1960-1989 17
Feb 1960-1989 12
Mar 1960-1989 7
Apr 1960-1989 9
May 1960-1989 15
Jun 1960-1989 12
Jul 1960-1989 17
Aug 1960-1989 22
Sep 1960-1989 21
Oct 1960-1989 21
Nov 1960-1989 18
Dec 1960-1989 17
Jan 1960-1964 17
Feb 1960-1964 12
Mar 1960-1964 7
Apr 1960-1964 9
May 1960-1964 9
Jun 1960-1964 11
Jul 1960-1964 14
Aug 1960-1964 18
Sep 1960-1964 13
Oct 1960-1964 12
Nov 1960-1964 17
Dec 1960-1964 11
关于如何做到这一点的任何想法？
如果您觉得有用，这里是我的数据集的一个克隆：
df <- structure(list(date = structure(c(-3653, -3622, -3593, -3562,
-3532, -3501, -3471, -3440, -3409, -3379, -3348, -3318, -3287,
-3256, -3228, -3197, -3167, -3136, -3106, -3075, -3044, -3014,
-2983, -2953, -2922, -2891, -2863, -2832, -2802, -2771, -2741,
-2710, -2679, -2649, -2618, -2588, -2557, -2526, -2498, -2467,
-2437, -2406, -2376, -2345, -2314, -2284, -2253, -2223, -2192,
-2161, -2132, -2101, -2071, -2040, -2010, -1979, -1948, -1918,
-1887, -1857, -1826, -1795, -1767, -1736, -1706, -1675, -1645,
-1614, -1583, -1553, -1522, -1492, -1461, -1430, -1402, -1371,
-1341, -1310, -1280, -1249, -1218, -1188, -1157, -1127, -1096,
-1065, -1037, -1006, -976, -945, -915, -884, -853, -823, -792,
-762, -731, -700, -671, -640, -610, -579, -549, -518, -487, -457,
-426, -396, -365, -334, -306, -275, -245, -214, -184, -153, -122,
-92, -61, -31, 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304,
334, 365, 396, 424, 455, 485, 516, 546, 577, 608, 638, 669, 699,
730, 761, 790, 821, 851, 882, 912, 943, 974, 1004, 1035, 1065,
1096, 1127, 1155, 1186, 1216, 1247, 1277, 1308, 1339, 1369, 1400,
1430, 1461, 1492, 1520, 1551, 1581, 1612, 1642, 1673, 1704, 1734,
1765, 1795, 1826, 1857, 1885, 1916, 1946, 1977, 2007, 2038, 2069,
2099, 2130, 2160, 2191, 2222, 2251, 2282, 2312, 2343, 2373, 2404,
2435, 2465, 2496, 2526, 2557, 2588, 2616, 2647, 2677, 2708, 2738,
2769, 2800, 2830, 2861, 2891, 2922, 2953, 2981, 3012, 3042, 3073,
3103, 3134, 3165, 3195, 3226, 3256, 3287, 3318, 3346, 3377, 3407,
3438, 3468, 3499, 3530, 3560, 3591, 3621, 3652, 3683, 3712, 3743,
3773, 3804, 3834, 3865, 3896, 3926, 3957, 3987, 4018, 4049, 4077,
4108, 4138, 4169, 4199, 4230, 4261, 4291, 4322, 4352, 4383, 4414,
4442, 4473, 4503, 4534, 4564, 4595, 4626, 4656, 4687, 4717, 4748,
4779, 4807, 4838, 4868, 4899, 4929, 4960, 4991, 5021, 5052, 5082,
5113, 5144, 5173, 5204, 5234, 5265, 5295, 5326, 5357, 5387, 5418,
5448, 5479, 5510, 5538, 5569, 5599, 5630, 5660, 5691, 5722, 5752,
5783, 5813, 5844, 5875, 5903, 5934, 5964, 5995, 6025, 6056, 6087,
6117, 6148, 6178, 6209, 6240, 6268, 6299, 6329, 6360, 6390, 6421,
6452, 6482, 6513, 6543, 6574, 6605, 6634, 6665, 6695, 6726, 6756,
6787, 6818, 6848, 6879, 6909, 6940, 6971, 6999, 7030, 7060, 7091,
7121, 7152, 7183, 7213, 7244, 7274), class = "Date"), temp = c(22.9223529411765,
23.0705882352941, 23.1094117647059, 20.7835294117647, 17.4517647058824,
17.3176470588235, 18.0494117647059, 19.6188235294118, 21.3023529411765,
23.1105882352941, 22.2364705882353, 22.7482352941176, 23.5870588235294,
24.0023529411765, 23.0094117647059, 22.0176470588235, 19.4917647058824,
18.1011764705882, 18.3164705882353, 20.0623529411765, 22.8717647058824,
23.2576470588235, 23.68, 22.3694117647059, 22.9517647058824,
23.6976470588235, 23.3294117647059, 20.8564705882353, 18.16,
15.8988235294118, 15.7988235294118, 18.4176470588235, 20.8423529411765,
20.3247058823529, 22.3070588235294, 22.2035294117647, 24.2235294117647,
23.6976470588235, 24.4082352941176, 21.1752941176471, 18.1023529411765,
16.1211764705882, 18.3164705882353, 19.7635294117647, 23.1294117647059,
22.9964705882353, 23.6552941176471, 22.6964705882353, 23.6011764705882,
23.6517647058824, 23.7035294117647, 22.4352941176471, 18.5835294117647,
16.5976470588235, 15.7741176470588, 19.2541176470588, 20.8776470588235,
20.5729411764706, 21.1729411764706, 21.5870588235294, 22.4576470588235,
23.6058823529412, 21.84, 21.6694117647059, 19.2458823529412,
18.7517647058824, 17.7811764705882, 19.4764705882353, 21.9270588235294,
21.5470588235294, 22.88, 23.2458823529412, 24.2776470588235,
25.2470588235294, 23.4694117647059, 21.4435294117647, 19.3941176470588,
18.5447058823529, 17.6, 18.3764705882353, 19.8529411764706, 22.0823529411765,
22.7294117647059, 23.4011764705882, 23.3611764705882, 24.2505882352941,
23.2870588235294, 21.9482352941176, 20.5552941176471, 18.0788235294118,
18.5929411764706, 20.8752941176471, 21.9023529411765, 23.6105882352941,
22.4070588235294, 21.5635294117647, 23.3129411764706, 22.9741176470588,
23.3670588235294, 19.6105882352941, 16.9941176470588, 17.7670588235294,
17.4858823529412, 17.8517647058824, 20.26, 22.1576470588235,
23.8364705882353, 23.4447058823529, 24.8129411764706, 25.1764705882353,
24.2694117647059, 21.5035294117647, 20.0458823529412, 18.4694117647059,
18.4541176470588, 19.5388235294118, 22.02, 20.5364705882353,
22.9858823529412, 21.9752941176471, 23.7729411764706, 24.0576470588235,
24.0941176470588, 22.1552941176471, 21.2329411764706, 19.5611764705882,
17.8788235294118, 18.6823529411765, 20.1541176470588, 21.6258823529412,
21.5211764705882, 23.9811764705882, 24.8352941176471, 24.5882352941176,
24.1729411764706, 21.1035294117647, 19.0435294117647, 17.08,
17.4529411764706, 19.1458823529412, 20.4447058823529, 20.7129411764706,
21.5047058823529, 22.6952941176471, 23.4364705882353, 23.1, 24.1847058823529,
19.8105882352941, 19.9847058823529, 20.5188235294118, 17.7658823529412,
19.4435294117647, 20.7588235294118, 21.7835294117647, 22.7788235294118,
23.2388235294118, 24.9129411764706, 25.6, 23.5647058823529, 24.0058823529412,
19.7823529411765, 19.3152941176471, 18.7741176470588, 19.0305882352941,
20.5576470588235, 21.3611764705882, 21.4247058823529, 23.4811764705882,
23.6505882352941, 25.1870588235294, 23.3541176470588, 21.4823529411765,
18.7364705882353, 17.7235294117647, 18.3976470588235, 19.7235294117647,
21.0741176470588, 21.6094117647059, 22.9635294117647, 22.4011764705882,
23.4152941176471, 24.7741176470588, 24.3270588235294, 20.7976470588235,
18.8764705882353, 17.7788235294118, 16.4129411764706, 21.4117647058824,
22.3317647058824, 21.66, 22.3694117647059, 23.0917647058824,
24.4541176470588, 23.2847058823529, 23.3164705882353, 21.2529411764706,
19.1258823529412, 17.3882352941176, 17.3823529411765, 19.0529411764706,
19.6576470588235, 20.2976470588235, 21.9023529411765, 23.3094117647059,
24.0117647058824, 25.5611764705882, 24.9129411764706, 21.3964705882353,
19.9870588235294, 18.3929411764706, 20.9917647058824, 20.3058823529412,
21.4435294117647, 23.1941176470588, 22.8388235294118, 22.5176470588235,
24.6317647058824, 24.6541176470588, 24.2, 20.84, 18.4576470588235,
17.5011764705882, 19.16, 20.54, 20.1517647058824, 22.6776470588235,
22.7470588235294, 22.7882352941176, 22.0811764705882, 24.2152941176471,
22.9235294117647, 20.8411764705882, 19.6188235294118, 17.16,
16.0529411764706, 20.3223529411765, 19.9752941176471, 22.5152941176471,
22.2705882352941, 23.1541176470588, 23.1047058823529, 23.9517647058824,
24.8176470588235, 22.18, 20.5023529411765, 17.3505882352941,
19.1917647058824, 19.9894117647059, 19.0235294117647, 22.8235294117647,
22.7094117647059, 23.8741176470588, 24.0517647058824, 25.1764705882353,
23.9235294117647, 21.2929411764706, 20.6117647058824, 17.1305882352941,
16.3470588235294, 19.6470588235294, 21.3341176470588, 20.2176470588235,
23.7435294117647, 22.6741176470588, 22.9070588235294, 24.7152941176471,
23.2905882352941, 20.5776470588235, 18.9635294117647, 19.0658823529412,
18.8423529411765, 20.0729411764706, 21.3047058823529, 22.1588235294118,
24.0388235294118, 22.1917647058824, 24.0517647058824, 24.8729411764706,
23.0117647058824, 23, 21.3094117647059, 19.4105882352941, 20.3470588235294,
19.4482352941176, 20.0670588235294, 21.6364705882353, 23.4211764705882,
23.16, 25.4788235294118, 26.4741176470588, 24.0482352941176,
21.4176470588235, 21.7164705882353, 19.0905882352941, 19.6752941176471,
18.1611764705882, 20.0482352941176, 23.4917647058824, 23.4894117647059,
22.5482352941176, 23.1376470588235, 24.9811764705882, 24.1552941176471,
22.8423529411765, 19.7435294117647, 16.4, 17.3105882352941, 20.5235294117647,
21.0494117647059, 23.1352941176471, 23.9435294117647, 23.9058823529412,
24.9835294117647, 24.6952941176471, 24.0047058823529, 23.3164705882353,
21.5823529411765, 18.3447058823529, 18.1964705882353, 20.0035294117647,
20.7152941176471, 22.5705882352941, 24.6541176470588, 23.2329411764706,
25.0517647058824, 24.3329411764706, 23.5811764705882, 22.9988235294118,
19.4976470588235, 17.3188235294118, 19.5635294117647, 19.0211764705882,
19.7223529411765, 22.6858823529412, 23.9423529411765, 23.6905882352941,
25.7129411764706, 23.9505882352941, 24.4376470588235, 22.6070588235294,
19.8882352941176, 17.2058823529412, 16.4211764705882, 20.02,
21.9458823529412, 21.9341176470588, 22.74, 23.8, 23.9611764705882,
24.4564705882353, 24, 23.2129411764706, 19.4729411764706, 17.7105882352941,
16.9682352941176, 19.0341176470588, 20.2917647058824, 20.7776470588235,
22.9364705882353, 22.7894117647059)), .Names = c("date", "temp"
), row.names = c(NA, -360L), class = "data.frame")
答案 0 :(得分：2)
一种选择是data.table使用年级分组cut或findInterval。对于第一种情况，即。每个月汇总mean，我们将“日期”转换为Date类并提取months，将其用作分组变量并获取mean '临时'。请注意，我们首先将'data.frame'转换为'data.table'（setDT(df)）。
library(data.table)
setDT(df)[, list(Temp=mean(temp)) , by = .(Months= months(as.Date(date), abbr=TRUE))]
# Months Temp
# 1: Jan 23.90506
# 2: Feb 24.40012
# 3: Mar 23.73714
# 4: Apr 21.68584
# 5: May 19.53863
# 6: Jun 17.90322
# 7: Jul 17.97675
# 8: Aug 19.56051
# 9: Sep 20.90125
#10: Oct 21.96886
#11: Nov 22.86102
#12: Dec 22.92537
对于按期和按月分组，我们需要创建一个期间列。一种方法是cut或findInterval。例如，如果我们正在寻找一个5年的窗口，即。 1960-1964,1965-1969等，我们使用vec在findInterval中创建seq来创建“期间”列，更改从findInterval派生的数字索引来自paste创建的'lbl'。使用'月'和'句号'作为分组变量，其余部分与之前相同。
setDT(df)[, c('Month', 'Period') := {tmp <- as.Date(date)
tmp1 <- as.numeric(format(tmp, '%Y'))
tmp2 <- months(tmp, abbr=TRUE)
i1 <- seq(min(tmp1), max(tmp1)+4, by=5)
i2 <- i1+4
lbl <-paste(i1, i2, sep='-')
list(tmp2, lbl[findInterval(tmp1, i1)])
}]
df[, list(Temp= mean(temp)), .(Month, Period)]
# Month Period Temp
# 1: Jan 1960-1964 23.45718
# 2: Feb 1960-1964 23.62400
# 3: Mar 1960-1964 23.51200
# 4: Apr 1960-1964 21.45365
# 5: May 1960-1964 18.35788
# 6: Jun 1960-1964 16.80729
# 7: Jul 1960-1964 17.25106
# 8: Aug 1960-1964 19.42329
# 9: Sep 1960-1964 21.80471
#10: Oct 1960-1964 22.05247
#11: Nov 1960-1964 22.61035
#12: Dec 1960-1964 22.32094
#13: Jan 1965-1969 23.64447
#14: Feb 1965-1969 24.25082
#15: Mar 1965-1969 23.24659
#16: Apr 1965-1969 21.23506
#17: May 1965-1969 19.24706
#18: Jun 1965-1969 18.32235
#19: Jul 1965-1969 17.98282
#20: Aug 1965-1969 19.22376
#21: Sep 1965-1969 21.19247
#22: Oct 1965-1969 21.98682
#23: Nov 1965-1969 22.96776
#24: Dec 1965-1969 22.72612
#25: Jan 1970-1974 24.12165
#26: Feb 1970-1974 24.50659
#27: Mar 1970-1974 23.87412
#28: Apr 1970-1974 21.71153
#29: May 1970-1974 19.75600
#30: Jun 1970-1974 18.83976
#31: Jul 1970-1974 18.05388
#32: Aug 1970-1974 19.20518
#33: Sep 1970-1974 20.59788
#34: Oct 1970-1974 21.41859
#35: Nov 1970-1974 22.03859
#36: Dec 1970-1974 23.15953
#37: Jan 1975-1979 23.71882
#38: Feb 1975-1979 24.49788
#39: Mar 1975-1979 23.93600
#40: Apr 1975-1979 21.02565
#41: May 1975-1979 19.21318
#42: Jun 1975-1979 17.64424
#43: Jul 1975-1979 18.00000
#44: Aug 1975-1979 20.32659
#45: Sep 1975-1979 20.71200
#46: Oct 1975-1979 22.06894
#47: Nov 1975-1979 22.42565
#48: Dec 1975-1979 22.97224
#49: Jan 1980-1984 23.91882
#50: Feb 1980-1984 25.03812
#51: Mar 1980-1984 23.81835
#52: Apr 1980-1984 21.69365
#53: May 1980-1984 20.62071
#54: Jun 1980-1984 18.40965
#55: Jul 1980-1984 18.88071
#56: Aug 1980-1984 19.46376
#57: Sep 1980-1984 20.35553
#58: Oct 1980-1984 22.06565
#59: Nov 1980-1984 23.48047
#60: Dec 1980-1984 22.88965
#61: Jan 1985-1989 24.56941
#62: Feb 1985-1989 24.48329
#63: Mar 1985-1989 24.03576
#64: Apr 1985-1989 22.99553
#65: May 1985-1989 20.03694
#66: Jun 1985-1989 17.39600
#67: Jul 1985-1989 17.69200
#68: Aug 1985-1989 19.72047
#69: Sep 1985-1989 20.74494
#70: Oct 1985-1989 22.22071
#71: Nov 1985-1989 23.64329
#72: Dec 1985-1989 23.48376
# Month Period Temp
以同样的方式，我们可以获得10年或其他窗口。
答案 1 :(得分：1)
问题的第一部分 平均所有januaries，februaries，你可以得到 -
library(data.table)#v1.9.6+
setDT(train)[,list(n_doc_line_num = uniqueN(DOC_LINE_NUM),
sum_comb_hours = sum(COMBINED_HOURS)),
by = DOC_NUM]
第二部分获得特定范围的平均值，您可以尝试
monthly_data <- aggregate(df$temp,by=list(strftime(df$date, "%m")),mean)
cbind(monthly_data[2], Month = month.abb, Period = "1960-1989")
# x Month Period
# 1 23.90506 Jan 1960-1989
# 2 24.40012 Feb 1960-1989
# 3 23.73714 Mar 1960-1989
# 4 21.68584 Apr 1960-1989
# 5 19.53863 May 1960-1989
# 6 17.90322 Jun 1960-1989
# 7 17.97675 Jul 1960-1989
# 8 19.56051 Aug 1960-1989
# 9 20.90125 Sep 1960-1989
# 10 21.96886 Oct 1960-1989
# 11 22.86102 Nov 1960-1989
# 12 22.92537 Dec 1960-1989
我在1960年至1964年期间证明了这一点。同样，您可以在任何给定时间段内执行此操作。