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r按月份和年份平均每日库存数据，并附加到数据集

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r按月份和年份平均每日库存数据，并附加到数据集,r,dplyr,R,Dplyr,我有以下十年的每日数据： library(lubridate) library(dplyr) head(infy_close_subset,24) date INFY.NS.Close 1 2007-01-02 568.162 2 2007-01-03 577.838 3 2007-01-04 571.325 4 2007-01-05 568.763 5 2007-01-08 551.400 6 2007

我有以下十年的每日数据：

library(lubridate)
library(dplyr)

head(infy_close_subset,24)
         date INFY.NS.Close
1  2007-01-02       568.162
2  2007-01-03       577.838
3  2007-01-04       571.325
4  2007-01-05       568.763
5  2007-01-08       551.400
6  2007-01-09       547.525
7  2007-01-10       541.112
8  2007-01-11       545.750
9  2007-01-12       555.850
10 2007-01-15       560.737
11 2007-01-16       555.550
12 2007-01-17       551.362
13 2007-01-18       556.037
14 2007-01-19       550.588
15 2007-01-22       563.500
16 2007-01-23       558.787
17 2007-01-24       558.513
18 2007-01-25       560.250
19 2007-01-29       561.100
20 2007-01-31       561.825
21 2007-02-01       567.237
22 2007-02-02       566.388
23 2007-02-05       567.325
24 2007-02-06       568.237
我正在尝试按年份和月份创建一个新的平均列，如下所示：

Infy_monthlyAvg <- infy_close_subset %>% 
  group_by(yr = year(date), mon = month(date)) %>% 
  summarize(mean_close = mean(INFY.NS.Close))

head(Infy_monthlyAvg)
  mean_close
1   731.6223
我想在
infy\u close\u子集
dataframe之后添加一列
mean\u close

        date INFY.NS.Close    yr   mon  mean_close
      <date>         <dbl> <dbl> <dbl>
1 2007-01-02       568.162  2007     1   731.6223
2 2007-01-03       577.838  2007     1   731.6223
3 2007-01-04       571.325  2007     1   731.6223
4 2007-01-05       568.763  2007     1   731.6223
5 2007-01-08       551.400  2007     1   731.6223
6 2007-01-09       547.525  2007     1   731.6223
.................

999 2017-09-08       988.400  2007     9   921.3333
1000 2017-09-09      977.525  2007     9   921.3333

date INFY.NS.Close yr mon mean\u Close
1 2007-01-02       568.162  2007     1   731.6223
2 2007-01-03       577.838  2007     1   731.6223
3 2007-01-04       571.325  2007     1   731.6223
4 2007-01-05       568.763  2007     1   731.6223
5 2007-01-08       551.400  2007     1   731.6223
6 2007-01-09       547.525  2007     1   731.6223
.................
999 2017-09-08       988.400  2007     9   921.3333
1000 2017-09-09      977.525  2007     9   921.3333
如果将
yr
和
mon
列添加到原始数据帧中：

infy_close_subset = infy_close_subset %>% 
    mutate(yr = year(date), mon = month(date))
然后您可以通过
yr
和
mon
合并两个结果表：

answer = merge(infy_close_subset, Infy_monthlyAvg, by = c("yr", "mon")
我想你想要每月的收入。如果您想要总体平均值，那么答案很简单：

answer = infy_close_subset %>% 
    mutate(mean_close = mean(infy_close_subset$INFY.NS.Close))
没有分组、汇总和合并的中间步骤。

如果将
yr
和
mon
列添加到原始数据帧：

infy_close_subset = infy_close_subset %>% 
    mutate(yr = year(date), mon = month(date))
然后您可以通过
yr
和
mon
合并两个结果表：

answer = merge(infy_close_subset, Infy_monthlyAvg, by = c("yr", "mon")
我想你想要每月的收入。如果您想要总体平均值，那么答案很简单：

answer = infy_close_subset %>% 
    mutate(mean_close = mean(infy_close_subset$INFY.NS.Close))
没有分组、总结和合并的中间步骤。

我倾向于制作一个句点栏

df <- left_join(
  infy_close_subset %>%
    mutate(
      period = format(date, "%Y-%m"),
      yr = year(date),
      mon = month(date)
    ),
  infy_close_subset %>%
    mutate(period = format(date, "%Y-%m")) %>% 
    group_by(period) %>% 
    summarise(mean_close = mean(INFY.NS.Close)
  ),
  by = "period"
) %>%
select(-period)

#          date INFY.NS.Close   yr mon mean_close
# 1  2007-01-02       568.162 2007   1   558.2987
# 2  2007-01-03       577.838 2007   1   558.2987
# 3  2007-01-04       571.325 2007   1   558.2987
# 4  2007-01-05       568.763 2007   1   558.2987
# 5  2007-01-08       551.400 2007   1   558.2987
# 6  2007-01-09       547.525 2007   1   558.2987
# 7  2007-01-10       541.112 2007   1   558.2987
# 8  2007-01-11       545.750 2007   1   558.2987
# 9  2007-01-12       555.850 2007   1   558.2987
# 10 2007-01-15       560.737 2007   1   558.2987
# 11 2007-01-16       555.550 2007   1   558.2987
# 12 2007-01-17       551.362 2007   1   558.2987
# 13 2007-01-18       556.037 2007   1   558.2987
# 14 2007-01-19       550.588 2007   1   558.2987
# 15 2007-01-22       563.500 2007   1   558.2987
# 16 2007-01-23       558.787 2007   1   558.2987
# 17 2007-01-24       558.513 2007   1   558.2987
# 18 2007-01-25       560.250 2007   1   558.2987
# 19 2007-01-29       561.100 2007   1   558.2987
# 20 2007-01-31       561.825 2007   1   558.2987
# 21 2007-02-01       567.237 2007   2   567.2967
# 22 2007-02-02       566.388 2007   2   567.2967
# 23 2007-02-05       567.325 2007   2   567.2967
# 24 2007-02-06       568.237 2007   2   567.2967

df%
变异(
期间=格式（日期，“%Y-%m”），
年=年（日期），
周一=月（日）
),
通知关闭子集%>%
变异（期间=格式（日期，“%Y-%m”））%>%
分组单位（期间）%>%
总结（平均值=平均值（信息结束）
),
by=“期间”
) %>%
选择（-period）
#日期信息N.S.Close yr mon mean\U Close
# 1  2007-01-02       568.162 2007   1   558.2987
# 2  2007-01-03       577.838 2007   1   558.2987
# 3  2007-01-04       571.325 2007   1   558.2987
# 4  2007-01-05       568.763 2007   1   558.2987
# 5  2007-01-08       551.400 2007   1   558.2987
# 6  2007-01-09       547.525 2007   1   558.2987
# 7  2007-01-10       541.112 2007   1   558.2987
# 8  2007-01-11       545.750 2007   1   558.2987
# 9  2007-01-12       555.850 2007   1   558.2987
# 10 2007-01-15       560.737 2007   1   558.2987
# 11 2007-01-16       555.550 2007   1   558.2987
# 12 2007-01-17       551.362 2007   1   558.2987
# 13 2007-01-18       556.037 2007   1   558.2987
# 14 2007-01-19       550.588 2007   1   558.2987
# 15 2007-01-22       563.500 2007   1   558.2987
# 16 2007-01-23       558.787 2007   1   558.2987
# 17 2007-01-24       558.513 2007   1   558.2987
# 18 2007-01-25       560.250 2007   1   558.2987
# 19 2007-01-29       561.100 2007   1   558.2987
# 20 2007-01-31       561.825 2007   1   558.2987
# 21 2007-02-01       567.237 2007   2   567.2967
# 22 2007-02-02       566.388 2007   2   567.2967
# 23 2007-02-05       567.325 2007   2   567.2967
# 24 2007-02-06       568.237 2007   2   567.2967
我倾向于做一个句号列

df <- left_join(
  infy_close_subset %>%
    mutate(
      period = format(date, "%Y-%m"),
      yr = year(date),
      mon = month(date)
    ),
  infy_close_subset %>%
    mutate(period = format(date, "%Y-%m")) %>% 
    group_by(period) %>% 
    summarise(mean_close = mean(INFY.NS.Close)
  ),
  by = "period"
) %>%
select(-period)

#          date INFY.NS.Close   yr mon mean_close
# 1  2007-01-02       568.162 2007   1   558.2987
# 2  2007-01-03       577.838 2007   1   558.2987
# 3  2007-01-04       571.325 2007   1   558.2987
# 4  2007-01-05       568.763 2007   1   558.2987
# 5  2007-01-08       551.400 2007   1   558.2987
# 6  2007-01-09       547.525 2007   1   558.2987
# 7  2007-01-10       541.112 2007   1   558.2987
# 8  2007-01-11       545.750 2007   1   558.2987
# 9  2007-01-12       555.850 2007   1   558.2987
# 10 2007-01-15       560.737 2007   1   558.2987
# 11 2007-01-16       555.550 2007   1   558.2987
# 12 2007-01-17       551.362 2007   1   558.2987
# 13 2007-01-18       556.037 2007   1   558.2987
# 14 2007-01-19       550.588 2007   1   558.2987
# 15 2007-01-22       563.500 2007   1   558.2987
# 16 2007-01-23       558.787 2007   1   558.2987
# 17 2007-01-24       558.513 2007   1   558.2987
# 18 2007-01-25       560.250 2007   1   558.2987
# 19 2007-01-29       561.100 2007   1   558.2987
# 20 2007-01-31       561.825 2007   1   558.2987
# 21 2007-02-01       567.237 2007   2   567.2967
# 22 2007-02-02       566.388 2007   2   567.2967
# 23 2007-02-05       567.325 2007   2   567.2967
# 24 2007-02-06       568.237 2007   2   567.2967

df%
变异(
期间=格式（日期，“%Y-%m”），
年=年（日期），
周一=月（日）
),
通知关闭子集%>%
变异（期间=格式（日期，“%Y-%m”））%>%
分组单位（期间）%>%
总结（平均值=平均值（信息结束）
),
by=“期间”
) %>%
选择（-period）
#日期信息N.S.Close yr mon mean\U Close
# 1  2007-01-02       568.162 2007   1   558.2987
# 2  2007-01-03       577.838 2007   1   558.2987
# 3  2007-01-04       571.325 2007   1   558.2987
# 4  2007-01-05       568.763 2007   1   558.2987
# 5  2007-01-08       551.400 2007   1   558.2987
# 6  2007-01-09       547.525 2007   1   558.2987
# 7  2007-01-10       541.112 2007   1   558.2987
# 8  2007-01-11       545.750 2007   1   558.2987
# 9  2007-01-12       555.850 2007   1   558.2987
# 10 2007-01-15       560.737 2007   1   558.2987
# 11 2007-01-16       555.550 2007   1   558.2987
# 12 2007-01-17       551.362 2007   1   558.2987
# 13 2007-01-18       556.037 2007   1   558.2987
# 14 2007-01-19       550.588 2007   1   558.2987
# 15 2007-01-22       563.500 2007   1   558.2987
# 16 2007-01-23       558.787 2007   1   558.2987
# 17 2007-01-24       558.513 2007   1   558.2987
# 18 2007-01-25       560.250 2007   1   558.2987
# 19 2007-01-29       561.100 2007   1   558.2987
# 20 2007-01-31       561.825 2007   1   558.2987
# 21 2007-02-01       567.237 2007   2   567.2967
# 22 2007-02-02       566.388 2007   2   567.2967
# 23 2007-02-05       567.325 2007   2   567.2967
# 24 2007-02-06       568.237 2007   2   567.2967
利用
数据的解决方案。表
：

library(data.table)
setDT(infy_close_subset)
infy_close_subset[, mean_close := mean(INFY.NS.Close), by = format(date, "%Y-%m")]
利用
数据的解决方案。表
：

library(data.table)
setDT(infy_close_subset)
infy_close_subset[, mean_close := mean(INFY.NS.Close), by = format(date, "%Y-%m")]
您只能得到一个结果，因为您是按年份和月份分组的，只有有效的组
2017-01
。您是否使用lubridate软件包中的
year
&
month
功能？请指定，因为它们不是系统库的一部分。您只能得到一个结果，因为您是按年份和月份分组的，只有有效的组
2017-01
。您是否使用lubridate软件包中的
year
和
month
功能？请指定，因为它们不是系统库的一部分。