如何每 30 天计算和绘制相关系数
How to calculate and plot correlation coefficient every 30 days
我想每 30 天计算一次相关系数，然后将其绘制为线图。我的数据table是基于2018年至今的每日时间数据。数据 table 如下所示：
应该计算 prod1 和 prod2 列之间的相关性。
它的代码是什么样的？ 30天只是一个例子，也可能是我想每100天计算一次相关系数，然后绘制出来。该图应在 x 轴上显示时间数据，在 y 轴上显示相关系数。
试试这个解决方案。我将使用一些虚拟数据。 zoo 包中的函数 rollapplyr() 可用于您想要的内容，您可以在其中定义 window 的大小以进行关联。这里的代码：
library(dplyr)
library(zoo)
library(ggplot2)
#Seed for randomness in dummy data
set.seed(123)
#Data
df <- data.frame(Date=seq(as.Date('2020-01-01'),by='1 day',length.out = 180),
Germany=rnorm(180,2,1),
WeeklyMeanSpot=rnorm(180,5,2))
#Compute correlation
#Correlation function
corf <- function(z) cor(z[,1],z[,2])
#Compute
df2 <- df %>%
mutate(Cor = rollapplyr(cbind(Germany, WeeklyMeanSpot),
30, corf, by.column = FALSE, fill = NA))
#Plot
ggplot(df2,aes(x=Date,y=Cor,group=1))+
geom_line()
输出：
df2 的计算相关性的输出是下一个：
Date Germany WeeklyMeanSpot Cor
1 2020-01-01 1.19566840 3.6946726 NA
2 2020-01-02 0.94347435 6.6512156 NA
3 2020-01-03 0.96460422 6.4999068 NA
4 2020-01-04 0.81443965 5.4603047 NA
5 2020-01-05 1.49956049 8.3968345 NA
6 2020-01-06 1.47501129 5.6317127 NA
7 2020-01-07 1.69756704 1.2517788 NA
8 2020-01-08 2.47196813 6.2125895 NA
9 2020-01-09 1.75161605 6.7580369 NA
10 2020-01-10 3.25931803 2.8137961 NA
11 2020-01-11 2.35092096 4.5250905 NA
12 2020-01-12 2.86331855 4.0167231 NA
13 2020-01-13 1.52108869 6.2325693 NA
14 2020-01-14 0.81152078 4.5071424 NA
15 2020-01-15 1.73143363 6.8472478 NA
16 2020-01-16 1.91977894 3.5537041 NA
17 2020-01-17 1.87676431 6.2689551 NA
18 2020-01-18 1.90056019 7.6625696 NA
19 2020-01-19 1.16524602 6.8619390 NA
20 2020-01-20 3.85777954 8.2171442 NA
21 2020-01-21 1.50160298 4.4852652 NA
22 2020-01-22 0.72994539 3.7480259 NA
23 2020-01-23 1.06305629 4.3896943 NA
24 2020-01-24 2.53343924 4.6650263 NA
25 2020-01-25 0.38323689 6.9921718 NA
26 2020-01-26 1.68294336 7.3718035 NA
27 2020-01-27 1.58023632 3.2206476 NA
28 2020-01-28 1.81914418 5.0806198 NA
29 2020-01-29 2.80181121 3.7171291 NA
30 2020-01-30 1.24608210 5.1656254 -0.069328611
31 2020-01-31 2.17777470 9.6932622 -0.041998773
32 2020-02-01 2.20834285 4.4412259 -0.031939643
33 2020-02-02 2.80313722 -0.5411659 -0.126551727
34 2020-02-03 0.40491851 5.3073543 -0.120457790
35 2020-02-04 3.59958366 4.4275978 -0.118749657
36 2020-02-05 2.49927063 5.6215082 -0.108412518
37 2020-02-06 2.36761410 4.1901185 -0.143451075
38 2020-02-07 1.58946984 4.6777601 -0.151660231
39 2020-02-08 2.92735042 6.3879691 -0.118660216
40 2020-02-09 2.86451608 7.5402940 -0.014142570
41 2020-02-10 2.72215229 6.5314283 0.010730700
42 2020-02-11 2.27126555 3.0690963 0.020957694
43 2020-02-12 2.31410815 -0.5702206 -0.009844143
44 2020-02-13 2.75552375 4.8392687 -0.027487761
45 2020-02-14 1.81084036 7.4972097 -0.027565466
46 2020-02-15 2.37724269 4.6652200 -0.034667100
47 2020-02-16 1.59176705 2.6149900 -0.009356898
48 2020-02-17 1.73390789 2.8832420 0.009686187
49 2020-02-18 2.08626516 5.0319071 0.043278282
50 2020-02-19 1.80148730 4.4002390 -0.081224484
51 2020-02-20 2.56108773 1.9128418 -0.111529257
52 2020-02-21 2.44717246 6.4163411 -0.129383738
53 2020-02-22 1.73245612 2.3592017 -0.118695448
54 2020-02-23 1.68611598 5.9183319 -0.129440483
55 2020-02-24 1.38920248 5.4855287 -0.061359523
56 2020-02-25 2.11631857 7.2027532 -0.032894093
57 2020-02-26 1.19588499 4.9261229 -0.057696575
58 2020-02-27 1.79937433 3.7816037 -0.047373869
59 2020-02-28 2.33899698 4.2787117 -0.034553964
60 2020-02-29 2.33822137 6.2447491 -0.016695285
61 2020-03-01 1.29077058 3.3391708 0.005120906
62 2020-03-02 2.52296888 5.8291996 0.019002552
63 2020-03-03 1.64635472 5.2335154 0.122045846
64 2020-03-04 1.22192124 1.3847397 0.255058702
65 2020-03-05 2.79730323 3.0493643 0.250388765
66 2020-03-06 1.09454848 4.8693789 0.214052761
67 2020-03-07 1.03896452 5.6941608 0.171494681
68 2020-03-08 1.74266416 2.8415849 0.185865239
69 2020-03-09 2.41083638 5.6567791 0.153216529
70 2020-03-10 1.60495980 3.9097297 0.073893995
71 2020-03-11 3.08204424 5.9867381 0.077400278
72 2020-03-12 2.05235965 7.9561615 0.099486113
73 2020-03-13 3.25492035 4.1286298 0.147580143
74 2020-03-14 2.02207206 4.5089341 0.147214275
75 2020-03-15 3.11233886 2.4308661 0.073639439
76 2020-03-16 1.64179508 2.7206389 0.090696545
77 2020-03-17 3.93310396 7.5960730 0.223265766
78 2020-03-18 1.88740774 3.5307611 0.215280006
79 2020-03-19 2.83203023 5.5233247 0.229482931
80 2020-03-20 2.98156351 4.4886564 0.218710223
81 2020-03-21 3.04108648 4.3510577 0.250144487
82 2020-03-22 1.94415267 7.3240372 0.215852160
83 2020-03-23 2.17571710 5.6210955 0.196962431
84 2020-03-24 1.08907376 7.2746353 0.129827357
85 2020-03-25 0.46736629 6.2506416 0.075280966
86 2020-03-26 3.04382316 3.8902802 0.051033629
87 2020-03-27 3.26713772 2.0326508 -0.022771486
88 2020-03-28 1.25425759 4.1685972 -0.018611666
89 2020-03-29 1.78537906 4.5814061 -0.015141877
90 2020-03-30 0.55916826 6.3867915 -0.079959328
91 2020-03-31 3.03646229 7.9588403 -0.041039748
92 2020-04-01 2.18654230 3.3342883 -0.050067885
93 2020-04-02 1.98364701 7.1449628 -0.052829309
94 2020-04-03 0.87784007 8.2796654 -0.210683405
95 2020-04-04 3.21831762 1.4485581 -0.249303841
96 2020-04-05 1.79057989 5.5392876 -0.262353845
97 2020-04-06 2.31089465 3.7248119 -0.256082115
98 2020-04-07 1.89472095 4.6288934 -0.282282546
99 2020-04-08 3.09624860 4.7436530 -0.286120837
100 2020-04-09 1.75106130 2.8165431 -0.274227411
101 2020-04-10 1.49548872 3.6162198 -0.271835083
102 2020-04-11 3.29498591 6.0214158 -0.240059217
103 2020-04-12 2.15258515 5.2863815 -0.229939274
104 2020-04-13 1.68842563 2.2941041 -0.195512846
105 2020-04-14 0.92979959 3.6129904 -0.117033776
106 2020-04-15 2.56985846 6.3892148 -0.128667936
107 2020-04-16 1.51054432 7.0069700 -0.277398502
108 2020-04-17 2.25076461 0.3918448 -0.275443670
109 2020-04-18 1.73878714 5.5785622 -0.294250216
110 2020-04-19 2.26153230 6.3358410 -0.282754674
111 2020-04-20 3.43107897 1.9195776 -0.339707067
112 2020-04-21 2.39608484 6.0239955 -0.331781212
113 2020-04-22 1.52481529 4.0114004 -0.324084396
114 2020-04-23 3.78863274 4.5602722 -0.272620718
115 2020-04-24 1.94889006 6.0793982 -0.240532336
116 2020-04-25 1.26054150 7.4817879 -0.268735652
117 2020-04-26 4.07936526 5.3875341 -0.173697707
118 2020-04-27 2.37667279 7.2425724 -0.176916649
119 2020-04-28 -0.28948775 6.6484258 -0.228180064
120 2020-04-29 3.18482679 6.6674888 -0.165084799
121 2020-04-30 2.30542690 4.3040041 -0.223958480
122 2020-05-01 2.64462958 5.5283809 -0.220184800
123 2020-05-02 3.16372687 6.7292477 -0.177536333
124 2020-05-03 2.93533418 9.4057928 -0.034177715
125 2020-05-04 2.09798281 2.0753314 0.042028799
126 2020-05-05 2.76823535 5.8257415 0.053708351
127 2020-05-06 3.10826971 3.5786374 0.029750660
128 2020-05-07 1.61457004 2.5527381 0.057862907
129 2020-05-08 2.40305722 4.3785420 0.061111764
130 2020-05-09 1.72254121 7.2454087 0.015646994
131 2020-05-10 2.55731351 7.1460101 0.001279747
132 2020-05-11 1.80334653 6.7058641 -0.026780358
133 2020-05-12 1.91806736 4.4419938 -0.021189986
134 2020-05-13 2.66234636 2.5769939 -0.075241124
135 2020-05-14 1.63385336 7.0378847 -0.147585480
136 2020-05-15 2.59522443 6.0830823 -0.149085725
137 2020-05-16 1.18439450 5.2343399 -0.119715185
138 2020-05-17 1.09319397 7.8585397 -0.192218899
139 2020-05-18 0.57639316 5.5739832 -0.182888162
140 2020-05-19 2.35030333 1.0771646 -0.177972434
141 2020-05-20 2.15803757 2.7999814 -0.102354986
142 2020-05-21 -0.07277026 3.6667601 -0.028759350
143 2020-05-22 2.82160575 4.0788493 -0.057996081
144 2020-05-23 1.40711360 3.7210454 -0.016449436
145 2020-05-24 1.02022598 2.8656543 0.028624385
146 2020-05-25 2.16204124 10.7421682 0.064280682
147 2020-05-26 2.60690903 3.8405760 0.051142268
148 2020-05-27 1.33978888 3.3738355 0.058259625
149 2020-05-28 1.49997767 3.5574732 0.150644740
150 2020-05-29 1.50356134 5.3614688 0.113900956
151 2020-05-30 0.10573719 2.3644583 0.192329330
152 2020-05-31 1.49020770 6.8182643 0.170441249
153 2020-06-01 1.22237027 5.4754067 0.127232194
154 2020-06-02 2.73645899 7.2688418 0.077586823
155 2020-06-03 3.35740356 3.5303725 0.049992953
156 2020-06-04 2.01387953 8.0645219 0.044162424
157 2020-06-05 0.49527608 9.3677753 -0.022251816
158 2020-06-06 2.89302696 5.9954997 -0.012804587
159 2020-06-07 1.56870826 6.5999076 -0.006541896
160 2020-06-08 2.42222369 4.0573725 -0.020046142
161 2020-06-09 1.66013636 6.8790633 -0.051388421
162 2020-06-10 1.50189792 1.8699504 -0.038053991
163 2020-06-11 0.96986890 8.0014292 -0.071007772
164 2020-06-12 2.75003594 6.9345386 0.003553137
165 2020-06-13 1.41120282 2.6551424 0.018304975
166 2020-06-14 3.35648599 4.5510642 -0.012458258
167 2020-06-15 2.64145069 5.1257506 -0.011624235
168 2020-06-16 3.11170119 7.6395649 0.071641902
169 2020-06-17 1.11459462 5.7198230 0.075340442
170 2020-06-18 0.96105868 1.1754863 0.164378201
171 2020-06-19 2.98615773 4.5273782 0.163922599
172 2020-06-20 1.30131703 6.4458046 0.112625203
173 2020-06-21 1.15387132 4.6452171 0.142764013
174 2020-06-22 1.58012656 10.2545247 0.102730292
175 2020-06-23 2.27452047 4.3093313 0.059121441
176 2020-06-24 3.78758803 2.9486305 -0.043807907
177 2020-06-25 3.08603766 5.2541586 -0.028202974
178 2020-06-26 0.94733431 4.5851817 -0.035074437
179 2020-06-27 2.28741992 3.1251400 -0.061364354
180 2020-06-28 3.10617185 6.3849361 -0.042380000
如果我理解你，你只想提取月份最后一天的相关性。所以你可以使用下一个代码来提取这些值：
#Create dates for filter
i1 <- seq(as.Date("2020-01-01"), length=13, by="1 month") - 1
#Avoid day of 2019
i1 <- i1[-1]
#Filter
dfs <- df2[df2$Date %in% i1,]
输出：
Date Germany WeeklyMeanSpot Cor
31 2020-01-31 2.426464 5.238490 0.06685690
60 2020-02-29 2.215942 3.436927 0.06675448
91 2020-03-31 2.993504 6.690026 0.06052142
121 2020-04-30 2.117647 3.569516 0.08070001
152 2020-05-31 2.769042 2.623132 0.22305907
我想每 30 天计算一次相关系数，然后将其绘制为线图。我的数据table是基于2018年至今的每日时间数据。数据 table 如下所示：
应该计算 prod1 和 prod2 列之间的相关性。
它的代码是什么样的？ 30天只是一个例子，也可能是我想每100天计算一次相关系数，然后绘制出来。该图应在 x 轴上显示时间数据，在 y 轴上显示相关系数。
试试这个解决方案。我将使用一些虚拟数据。 zoo 包中的函数 rollapplyr() 可用于您想要的内容，您可以在其中定义 window 的大小以进行关联。这里的代码：
library(dplyr)
library(zoo)
library(ggplot2)
#Seed for randomness in dummy data
set.seed(123)
#Data
df <- data.frame(Date=seq(as.Date('2020-01-01'),by='1 day',length.out = 180),
Germany=rnorm(180,2,1),
WeeklyMeanSpot=rnorm(180,5,2))
#Compute correlation
#Correlation function
corf <- function(z) cor(z[,1],z[,2])
#Compute
df2 <- df %>%
mutate(Cor = rollapplyr(cbind(Germany, WeeklyMeanSpot),
30, corf, by.column = FALSE, fill = NA))
#Plot
ggplot(df2,aes(x=Date,y=Cor,group=1))+
geom_line()
输出：
df2 的计算相关性的输出是下一个：
Date Germany WeeklyMeanSpot Cor
1 2020-01-01 1.19566840 3.6946726 NA
2 2020-01-02 0.94347435 6.6512156 NA
3 2020-01-03 0.96460422 6.4999068 NA
4 2020-01-04 0.81443965 5.4603047 NA
5 2020-01-05 1.49956049 8.3968345 NA
6 2020-01-06 1.47501129 5.6317127 NA
7 2020-01-07 1.69756704 1.2517788 NA
8 2020-01-08 2.47196813 6.2125895 NA
9 2020-01-09 1.75161605 6.7580369 NA
10 2020-01-10 3.25931803 2.8137961 NA
11 2020-01-11 2.35092096 4.5250905 NA
12 2020-01-12 2.86331855 4.0167231 NA
13 2020-01-13 1.52108869 6.2325693 NA
14 2020-01-14 0.81152078 4.5071424 NA
15 2020-01-15 1.73143363 6.8472478 NA
16 2020-01-16 1.91977894 3.5537041 NA
17 2020-01-17 1.87676431 6.2689551 NA
18 2020-01-18 1.90056019 7.6625696 NA
19 2020-01-19 1.16524602 6.8619390 NA
20 2020-01-20 3.85777954 8.2171442 NA
21 2020-01-21 1.50160298 4.4852652 NA
22 2020-01-22 0.72994539 3.7480259 NA
23 2020-01-23 1.06305629 4.3896943 NA
24 2020-01-24 2.53343924 4.6650263 NA
25 2020-01-25 0.38323689 6.9921718 NA
26 2020-01-26 1.68294336 7.3718035 NA
27 2020-01-27 1.58023632 3.2206476 NA
28 2020-01-28 1.81914418 5.0806198 NA
29 2020-01-29 2.80181121 3.7171291 NA
30 2020-01-30 1.24608210 5.1656254 -0.069328611
31 2020-01-31 2.17777470 9.6932622 -0.041998773
32 2020-02-01 2.20834285 4.4412259 -0.031939643
33 2020-02-02 2.80313722 -0.5411659 -0.126551727
34 2020-02-03 0.40491851 5.3073543 -0.120457790
35 2020-02-04 3.59958366 4.4275978 -0.118749657
36 2020-02-05 2.49927063 5.6215082 -0.108412518
37 2020-02-06 2.36761410 4.1901185 -0.143451075
38 2020-02-07 1.58946984 4.6777601 -0.151660231
39 2020-02-08 2.92735042 6.3879691 -0.118660216
40 2020-02-09 2.86451608 7.5402940 -0.014142570
41 2020-02-10 2.72215229 6.5314283 0.010730700
42 2020-02-11 2.27126555 3.0690963 0.020957694
43 2020-02-12 2.31410815 -0.5702206 -0.009844143
44 2020-02-13 2.75552375 4.8392687 -0.027487761
45 2020-02-14 1.81084036 7.4972097 -0.027565466
46 2020-02-15 2.37724269 4.6652200 -0.034667100
47 2020-02-16 1.59176705 2.6149900 -0.009356898
48 2020-02-17 1.73390789 2.8832420 0.009686187
49 2020-02-18 2.08626516 5.0319071 0.043278282
50 2020-02-19 1.80148730 4.4002390 -0.081224484
51 2020-02-20 2.56108773 1.9128418 -0.111529257
52 2020-02-21 2.44717246 6.4163411 -0.129383738
53 2020-02-22 1.73245612 2.3592017 -0.118695448
54 2020-02-23 1.68611598 5.9183319 -0.129440483
55 2020-02-24 1.38920248 5.4855287 -0.061359523
56 2020-02-25 2.11631857 7.2027532 -0.032894093
57 2020-02-26 1.19588499 4.9261229 -0.057696575
58 2020-02-27 1.79937433 3.7816037 -0.047373869
59 2020-02-28 2.33899698 4.2787117 -0.034553964
60 2020-02-29 2.33822137 6.2447491 -0.016695285
61 2020-03-01 1.29077058 3.3391708 0.005120906
62 2020-03-02 2.52296888 5.8291996 0.019002552
63 2020-03-03 1.64635472 5.2335154 0.122045846
64 2020-03-04 1.22192124 1.3847397 0.255058702
65 2020-03-05 2.79730323 3.0493643 0.250388765
66 2020-03-06 1.09454848 4.8693789 0.214052761
67 2020-03-07 1.03896452 5.6941608 0.171494681
68 2020-03-08 1.74266416 2.8415849 0.185865239
69 2020-03-09 2.41083638 5.6567791 0.153216529
70 2020-03-10 1.60495980 3.9097297 0.073893995
71 2020-03-11 3.08204424 5.9867381 0.077400278
72 2020-03-12 2.05235965 7.9561615 0.099486113
73 2020-03-13 3.25492035 4.1286298 0.147580143
74 2020-03-14 2.02207206 4.5089341 0.147214275
75 2020-03-15 3.11233886 2.4308661 0.073639439
76 2020-03-16 1.64179508 2.7206389 0.090696545
77 2020-03-17 3.93310396 7.5960730 0.223265766
78 2020-03-18 1.88740774 3.5307611 0.215280006
79 2020-03-19 2.83203023 5.5233247 0.229482931
80 2020-03-20 2.98156351 4.4886564 0.218710223
81 2020-03-21 3.04108648 4.3510577 0.250144487
82 2020-03-22 1.94415267 7.3240372 0.215852160
83 2020-03-23 2.17571710 5.6210955 0.196962431
84 2020-03-24 1.08907376 7.2746353 0.129827357
85 2020-03-25 0.46736629 6.2506416 0.075280966
86 2020-03-26 3.04382316 3.8902802 0.051033629
87 2020-03-27 3.26713772 2.0326508 -0.022771486
88 2020-03-28 1.25425759 4.1685972 -0.018611666
89 2020-03-29 1.78537906 4.5814061 -0.015141877
90 2020-03-30 0.55916826 6.3867915 -0.079959328
91 2020-03-31 3.03646229 7.9588403 -0.041039748
92 2020-04-01 2.18654230 3.3342883 -0.050067885
93 2020-04-02 1.98364701 7.1449628 -0.052829309
94 2020-04-03 0.87784007 8.2796654 -0.210683405
95 2020-04-04 3.21831762 1.4485581 -0.249303841
96 2020-04-05 1.79057989 5.5392876 -0.262353845
97 2020-04-06 2.31089465 3.7248119 -0.256082115
98 2020-04-07 1.89472095 4.6288934 -0.282282546
99 2020-04-08 3.09624860 4.7436530 -0.286120837
100 2020-04-09 1.75106130 2.8165431 -0.274227411
101 2020-04-10 1.49548872 3.6162198 -0.271835083
102 2020-04-11 3.29498591 6.0214158 -0.240059217
103 2020-04-12 2.15258515 5.2863815 -0.229939274
104 2020-04-13 1.68842563 2.2941041 -0.195512846
105 2020-04-14 0.92979959 3.6129904 -0.117033776
106 2020-04-15 2.56985846 6.3892148 -0.128667936
107 2020-04-16 1.51054432 7.0069700 -0.277398502
108 2020-04-17 2.25076461 0.3918448 -0.275443670
109 2020-04-18 1.73878714 5.5785622 -0.294250216
110 2020-04-19 2.26153230 6.3358410 -0.282754674
111 2020-04-20 3.43107897 1.9195776 -0.339707067
112 2020-04-21 2.39608484 6.0239955 -0.331781212
113 2020-04-22 1.52481529 4.0114004 -0.324084396
114 2020-04-23 3.78863274 4.5602722 -0.272620718
115 2020-04-24 1.94889006 6.0793982 -0.240532336
116 2020-04-25 1.26054150 7.4817879 -0.268735652
117 2020-04-26 4.07936526 5.3875341 -0.173697707
118 2020-04-27 2.37667279 7.2425724 -0.176916649
119 2020-04-28 -0.28948775 6.6484258 -0.228180064
120 2020-04-29 3.18482679 6.6674888 -0.165084799
121 2020-04-30 2.30542690 4.3040041 -0.223958480
122 2020-05-01 2.64462958 5.5283809 -0.220184800
123 2020-05-02 3.16372687 6.7292477 -0.177536333
124 2020-05-03 2.93533418 9.4057928 -0.034177715
125 2020-05-04 2.09798281 2.0753314 0.042028799
126 2020-05-05 2.76823535 5.8257415 0.053708351
127 2020-05-06 3.10826971 3.5786374 0.029750660
128 2020-05-07 1.61457004 2.5527381 0.057862907
129 2020-05-08 2.40305722 4.3785420 0.061111764
130 2020-05-09 1.72254121 7.2454087 0.015646994
131 2020-05-10 2.55731351 7.1460101 0.001279747
132 2020-05-11 1.80334653 6.7058641 -0.026780358
133 2020-05-12 1.91806736 4.4419938 -0.021189986
134 2020-05-13 2.66234636 2.5769939 -0.075241124
135 2020-05-14 1.63385336 7.0378847 -0.147585480
136 2020-05-15 2.59522443 6.0830823 -0.149085725
137 2020-05-16 1.18439450 5.2343399 -0.119715185
138 2020-05-17 1.09319397 7.8585397 -0.192218899
139 2020-05-18 0.57639316 5.5739832 -0.182888162
140 2020-05-19 2.35030333 1.0771646 -0.177972434
141 2020-05-20 2.15803757 2.7999814 -0.102354986
142 2020-05-21 -0.07277026 3.6667601 -0.028759350
143 2020-05-22 2.82160575 4.0788493 -0.057996081
144 2020-05-23 1.40711360 3.7210454 -0.016449436
145 2020-05-24 1.02022598 2.8656543 0.028624385
146 2020-05-25 2.16204124 10.7421682 0.064280682
147 2020-05-26 2.60690903 3.8405760 0.051142268
148 2020-05-27 1.33978888 3.3738355 0.058259625
149 2020-05-28 1.49997767 3.5574732 0.150644740
150 2020-05-29 1.50356134 5.3614688 0.113900956
151 2020-05-30 0.10573719 2.3644583 0.192329330
152 2020-05-31 1.49020770 6.8182643 0.170441249
153 2020-06-01 1.22237027 5.4754067 0.127232194
154 2020-06-02 2.73645899 7.2688418 0.077586823
155 2020-06-03 3.35740356 3.5303725 0.049992953
156 2020-06-04 2.01387953 8.0645219 0.044162424
157 2020-06-05 0.49527608 9.3677753 -0.022251816
158 2020-06-06 2.89302696 5.9954997 -0.012804587
159 2020-06-07 1.56870826 6.5999076 -0.006541896
160 2020-06-08 2.42222369 4.0573725 -0.020046142
161 2020-06-09 1.66013636 6.8790633 -0.051388421
162 2020-06-10 1.50189792 1.8699504 -0.038053991
163 2020-06-11 0.96986890 8.0014292 -0.071007772
164 2020-06-12 2.75003594 6.9345386 0.003553137
165 2020-06-13 1.41120282 2.6551424 0.018304975
166 2020-06-14 3.35648599 4.5510642 -0.012458258
167 2020-06-15 2.64145069 5.1257506 -0.011624235
168 2020-06-16 3.11170119 7.6395649 0.071641902
169 2020-06-17 1.11459462 5.7198230 0.075340442
170 2020-06-18 0.96105868 1.1754863 0.164378201
171 2020-06-19 2.98615773 4.5273782 0.163922599
172 2020-06-20 1.30131703 6.4458046 0.112625203
173 2020-06-21 1.15387132 4.6452171 0.142764013
174 2020-06-22 1.58012656 10.2545247 0.102730292
175 2020-06-23 2.27452047 4.3093313 0.059121441
176 2020-06-24 3.78758803 2.9486305 -0.043807907
177 2020-06-25 3.08603766 5.2541586 -0.028202974
178 2020-06-26 0.94733431 4.5851817 -0.035074437
179 2020-06-27 2.28741992 3.1251400 -0.061364354
180 2020-06-28 3.10617185 6.3849361 -0.042380000
如果我理解你，你只想提取月份最后一天的相关性。所以你可以使用下一个代码来提取这些值：
#Create dates for filter
i1 <- seq(as.Date("2020-01-01"), length=13, by="1 month") - 1
#Avoid day of 2019
i1 <- i1[-1]
#Filter
dfs <- df2[df2$Date %in% i1,]
输出：
Date Germany WeeklyMeanSpot Cor
31 2020-01-31 2.426464 5.238490 0.06685690
60 2020-02-29 2.215942 3.436927 0.06675448
91 2020-03-31 2.993504 6.690026 0.06052142
121 2020-04-30 2.117647 3.569516 0.08070001
152 2020-05-31 2.769042 2.623132 0.22305907