R:repeating 面板设置中每日值的汇总值
R:repeating aggregate values for daily values in a panel setting
我有一个 data frame 每 5 天（在数据集中称为一周）的汇总数据。
> dput(Sample3)
structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "AUS",
"AUS", "AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ"), Week = c(1,
2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5), TotalSales = c(200,
155, 195, 150, 155, 140, 145, 150, 155, 160, 120, 125, 130, 135,
140)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-15L), .Names = c("Firm", "Week", "TotalSales"))
我想加入另一个data frame，它有三个生产部门 AP、BG 和 CK 的每日产出值。
> dput(Sample2)
structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ"), Date = structure(c(1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400, 1451865600, 1451952000, 1452038400, 1452124800,
1452211200, 1452470400, 1452556800, 1452643200, 1452729600, 1452816000,
1453075200, 1453161600, 1453248000, 1453334400, 1453420800, 1453680000,
1453852800, 1453939200, 1454025600, 1454284800, 1454371200, 1454457600,
1454544000, 1454630400, 1454889600, 1454976000, 1455062400, 1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
AP = c(75451.0611, 70753.7178, 72662.012, 77799.73, 79414.4391,
81469.5286, 81029.1539, 82203.4883, 90183.0465, 91850.0159,
92516.8063, 94350.4702, 91683.3215, 95017.2605, 97184.3196,
97684.4091, 98184.5051, 99017.9898, 100851.6537, 99684.7738,
99518.0794, 100351.5641, 99684.7738, 99518.0794, 100018.1689,
101185.0489, 103852.1975, 81469.5286, 81029.1539, 82203.4883,
90183.0465, 91850.0159, 104852.383, 106019.2565, 100518.2585,
98017.8043, 108686.4052, 81469.5286, 81029.1539, 82203.4883,
90183.0465, 91850.0159, 110686.7762, 111853.6497, 115354.2831,
115854.379, 116687.8638, 104852.383, 106019.2565, 100518.2585,
98017.8043, 81469.5286, 81029.1539, 82203.4883, 75451.0611,
70753.7178, 72662.012, 77799.73, 79414.4391, 81469.5286,
81029.1539, 82203.4883, 90183.0465, 91850.0159, 92516.8063,
94350.4702, 91683.3215, 95017.2605, 97184.3196, 97684.4091,
98184.5051, 99017.9898, 100851.6537, 99684.7738, 99518.0794,
100351.5641, 99684.7738, 99518.0794, 100018.1689, 101185.0489,
103852.1975), BG = c(104852.383, 106019.2565, 100518.2585,
98017.8043, 108686.4052, 114854.1999, 116021.0734, 117354.6541,
113020.536, 114020.7151, 110686.7762, 111853.6497, 115354.2831,
115854.379, 116687.8638, 117021.2526, 115187.5887, 114354.104,
113520.6192, 109853.2914, 109186.5011, 107686.226, 107352.8372,
106852.7413, 116687.8638, 117021.2526, 115187.5887, 75451.0611,
70753.7178, 72662.012, 77799.73, 79414.4391, 81469.5286,
81029.1539, 82203.4883, 90183.0465, 91850.0159, 92516.8063,
94350.4702, 91683.3215, 95017.2605, 97184.3196, 97684.4091,
98184.5051, 99017.9898, 100851.6537, 99684.7738, 99518.0794,
100351.5641, 99684.7738, 99518.0794, 100018.1689, 101185.0489,
103852.1975, 81469.5286, 81029.1539, 82203.4883, 90183.0465,
91850.0159, 104852.383, 106019.2565, 100518.2585, 98017.8043,
108686.4052, 81469.5286, 81029.1539, 82203.4883, 90183.0465,
91850.0159, 110686.7762, 111853.6497, 115354.2831, 115854.379,
116687.8638, 104852.383, 106019.2565, 100518.2585, 98017.8043,
81469.5286, 81029.1539, 82203.4883), CK = c(81469.5286, 81029.1539,
82203.4883, 90183.0465, 91850.0159, 104852.383, 106019.2565,
100518.2585, 98017.8043, 108686.4052, 81469.5286, 81029.1539,
82203.4883, 90183.0465, 91850.0159, 110686.7762, 111853.6497,
115354.2831, 115854.379, 116687.8638, 104852.383, 106019.2565,
100518.2585, 98017.8043, 81469.5286, 81029.1539, 82203.4883,
104852.383, 106019.2565, 100518.2585, 98017.8043, 108686.4052,
114854.1999, 116021.0734, 117354.6541, 113020.536, 114020.7151,
110686.7762, 111853.6497, 115354.2831, 115854.379, 116687.8638,
117021.2526, 115187.5887, 114354.104, 113520.6192, 109853.2914,
109186.5011, 107686.226, 107352.8372, 106852.7413, 116687.8638,
117021.2526, 115187.5887, 75451.0611, 70753.7178, 72662.012,
77799.73, 79414.4391, 81469.5286, 81029.1539, 82203.4883,
90183.0465, 91850.0159, 92516.8063, 94350.4702, 91683.3215,
95017.2605, 97184.3196, 97684.4091, 98184.5051, 99017.9898,
100851.6537, 99684.7738, 99518.0794, 100351.5641, 99684.7738,
99518.0794, 100018.1689, 101185.0489, 103852.1975), Week = c(1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,
5, 5, 5, 5, 5, 6, 6, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 1, 1, 1, 1,
1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5,
5, 5, 6, 6)), .Names = c("Firm", "Date", "AP", "BG", "CK",
"Week"), row.names = c(NA, -81L), class = c("tbl_df", "tbl",
"data.frame"))
我想制作一个看起来像 this
的数据框
> dput(SampleFinal)
structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ"), Date = structure(c(1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400, 1451865600, 1451952000, 1452038400, 1452124800,
1452211200, 1452470400, 1452556800, 1452643200, 1452729600, 1452816000,
1453075200, 1453161600, 1453248000, 1453334400, 1453420800, 1453680000,
1453852800, 1453939200, 1454025600, 1454284800, 1454371200, 1454457600,
1454544000, 1454630400, 1454889600, 1454976000, 1455062400, 1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
Week = c("1", "1", "1", "1", "1", "2", "2", "2", "2", "2",
"3", "3", "3", "3", "3", "4", "4", "4", "4", "4", "5", "5",
"5", "5", "5", "NA", "NA", "1", "1", "1", "1", "1", "2",
"2", "2", "2", "2", "3", "3", "3", "3", "3", "4", "4", "4",
"4", "4", "5", "5", "5", "5", "5", "NA", "NA", "1", "1",
"1", "1", "1", "2", "2", "2", "2", "2", "3", "3", "3", "3",
"3", "4", "4", "4", "4", "4", "5", "5", "5", "5", "5", "NA",
"NA"), TotalSales = c("200", "200", "200", "200", "200",
"155", "155", "155", "155", "155", "195", "195", "195", "195",
"195", "150", "150", "150", "150", "150", "155", "155", "155",
"155", "155", "NA", "NA", "140", "140", "140", "140", "140",
"145", "145", "145", "145", "145", "150", "150", "150", "150",
"150", "155", "155", "155", "155", "155", "160", "160", "160",
"160", "160", "NA", "NA", "120", "120", "120", "120", "120",
"125", "125", "125", "125", "125", "130", "130", "130", "130",
"130", "135", "135", "135", "135", "135", "140", "140", "140",
"140", "140", "NA", "NA"), AP = c("75451.061100000006", "70753.717799999999",
"72662.012000000002", "77799.73", "79414.439100000003", "81469.528600000005",
"81029.153900000005", "82203.488299999997", "90183.046499999997",
"91850.015899999999", "92516.806299999997", "94350.470199999996",
"91683.321500000005", "95017.260500000004", "97184.319600000003",
"97684.409100000004", "98184.505099999995", "99017.989799999996",
"100851.6537", "99684.773799999995", "99518.079400000002",
"100351.5641", "99684.773799999995", "99518.079400000002",
"100018.1689", "NA", "NA", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"104852.383", "106019.2565", "100518.2585", "98017.804300000003",
"108686.40519999999", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"110686.77619999999", "111853.64969999999", "115354.2831",
"115854.379", "116687.86380000001", "104852.383", "106019.2565",
"100518.2585", "98017.804300000003", "81469.528600000005",
"NA", "NA", "75451.061100000006", "70753.717799999999", "72662.012000000002",
"77799.73", "79414.439100000003", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"92516.806299999997", "94350.470199999996", "91683.321500000005",
"95017.260500000004", "97184.319600000003", "97684.409100000004",
"98184.505099999995", "99017.989799999996", "100851.6537",
"99684.773799999995", "99518.079400000002", "100351.5641",
"99684.773799999995", "99518.079400000002", "100018.1689",
"NA", "NA"), BG = c("104852.383", "106019.2565", "100518.2585",
"98017.804300000003", "108686.40519999999", "114854.19990000001",
"116021.07339999999", "117354.6541", "113020.53599999999",
"114020.7151", "110686.77619999999", "111853.64969999999",
"115354.2831", "115854.379", "116687.86380000001", "117021.25260000001",
"115187.58869999999", "114354.10400000001", "113520.6192",
"109853.2914", "109186.50109999999", "107686.226", "107352.83719999999",
"106852.74129999999", "116687.86380000001", "NA", "NA", "75451.061100000006",
"70753.717799999999", "72662.012000000002", "77799.73", "79414.439100000003",
"81469.528600000005", "81029.153900000005", "82203.488299999997",
"90183.046499999997", "91850.015899999999", "92516.806299999997",
"94350.470199999996", "91683.321500000005", "95017.260500000004",
"97184.319600000003", "97684.409100000004", "98184.505099999995",
"99017.989799999996", "100851.6537", "99684.773799999995",
"99518.079400000002", "100351.5641", "99684.773799999995",
"99518.079400000002", "100018.1689", "NA", "NA", "81469.528600000005",
"81029.153900000005", "82203.488299999997", "90183.046499999997",
"91850.015899999999", "104852.383", "106019.2565", "100518.2585",
"98017.804300000003", "108686.40519999999", "81469.528600000005",
"81029.153900000005", "82203.488299999997", "90183.046499999997",
"91850.015899999999", "110686.77619999999", "111853.64969999999",
"115354.2831", "115854.379", "116687.86380000001", "104852.383",
"106019.2565", "100518.2585", "98017.804300000003", "81469.528600000005",
"NA", "NA"), CK = c("81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"104852.383", "106019.2565", "100518.2585", "98017.804300000003",
"108686.40519999999", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"110686.77619999999", "111853.64969999999", "115354.2831",
"115854.379", "116687.86380000001", "104852.383", "106019.2565",
"100518.2585", "98017.804300000003", "81469.528600000005",
"NA", "NA", "104852.383", "106019.2565", "100518.2585", "98017.804300000003",
"108686.40519999999", "114854.19990000001", "116021.07339999999",
"117354.6541", "113020.53599999999", "114020.7151", "110686.77619999999",
"111853.64969999999", "115354.2831", "115854.379", "116687.86380000001",
"117021.25260000001", "115187.58869999999", "114354.10400000001",
"113520.6192", "109853.2914", "109186.50109999999", "107686.226",
"107352.83719999999", "106852.74129999999", "116687.86380000001",
"NA", "NA", "75451.061100000006", "70753.717799999999", "72662.012000000002",
"77799.73", "79414.439100000003", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"92516.806299999997", "94350.470199999996", "91683.321500000005",
"95017.260500000004", "97184.319600000003", "97684.409100000004",
"98184.505099999995", "99017.989799999996", "100851.6537",
"99684.773799999995", "99518.079400000002", "100351.5641",
"99684.773799999995", "99518.079400000002", "100018.1689",
"NA", "NA")), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-81L), .Names = c("Firm", "Date", "Week", "TotalSales", "AP",
"BG", "CK"))
它具有每个公司重复 5 个日期值的每周销售值。新数据框应包含 Sample2 中的所有其他列。
还建议当数据框（示例 2）的每五个日期值与数据框（示例 3）的每周销售值匹配时如何制作数据框。数据框很大，所以我提供了所需的链接。
请提出没有 Data.table 包的解决方案。非常感谢。
请不要在评论前忽略问题的面板设置。
我们可以使用基础 R merge() 函数，传递适当的 "all" 参数（all.x with Sample2 as x）来保留键无法加入 Sample3。
然后，我们可以通过将 NA 显式分配给您要在合并后取消的单元格来实现您的特殊要求。
res <- merge(Sample2,Sample3,all.x=T);
res[is.na(res$TotalSales),c('Week','AP','BG','CK')] <- NA;
res;
## Firm Week Date AP BG CK TotalSales
## 1 AUS 1 2016-01-06 82203.49 72662.01 100518.26 140
## 2 AUS 1 2016-01-07 90183.05 77799.73 98017.80 140
## 3 AUS 1 2016-01-05 81029.15 70753.72 106019.26 140
## 4 AUS 1 2016-01-08 91850.02 79414.44 108686.41 140
## 5 AUS 1 2016-01-04 81469.53 75451.06 104852.38 140
## 6 AUS 2 2016-01-13 100518.26 82203.49 117354.65 145
## 7 AUS 2 2016-01-11 104852.38 81469.53 114854.20 145
## 8 AUS 2 2016-01-14 98017.80 90183.05 113020.54 145
## 9 AUS 2 2016-01-15 108686.41 91850.02 114020.72 145
## 10 AUS 2 2016-01-12 106019.26 81029.15 116021.07 145
## 11 AUS 3 2016-01-19 81029.15 94350.47 111853.65 150
## 12 AUS 3 2016-01-22 91850.02 97184.32 116687.86 150
## 13 AUS 3 2016-01-20 82203.49 91683.32 115354.28 150
## 14 AUS 3 2016-01-18 81469.53 92516.81 110686.78 150
## 15 AUS 3 2016-01-21 90183.05 95017.26 115854.38 150
## 16 AUS 4 2016-01-27 111853.65 98184.51 115187.59 155
## 17 AUS 4 2016-01-28 115354.28 99017.99 114354.10 155
## 18 AUS 4 2016-01-29 115854.38 100851.65 113520.62 155
## 19 AUS 4 2016-01-25 110686.78 97684.41 117021.25 155
## 20 AUS 4 2016-02-01 116687.86 99684.77 109853.29 155
## 21 AUS 5 2016-02-02 104852.38 99518.08 109186.50 160
## 22 AUS 5 2016-02-08 81469.53 100018.17 116687.86 160
## 23 AUS 5 2016-02-03 106019.26 100351.56 107686.23 160
## 24 AUS 5 2016-02-04 100518.26 99684.77 107352.84 160
## 25 AUS 5 2016-02-05 98017.80 99518.08 106852.74 160
## 26 AUS NA 2016-02-10 NA NA NA NA
## 27 AUS NA 2016-02-09 NA NA NA NA
## 28 ENG 1 2016-01-07 77799.73 98017.80 90183.05 200
## 29 ENG 1 2016-01-06 72662.01 100518.26 82203.49 200
## 30 ENG 1 2016-01-08 79414.44 108686.41 91850.02 200
## 31 ENG 1 2016-01-04 75451.06 104852.38 81469.53 200
## 32 ENG 1 2016-01-05 70753.72 106019.26 81029.15 200
## 33 ENG 2 2016-01-11 81469.53 114854.20 104852.38 155
## 34 ENG 2 2016-01-14 90183.05 113020.54 98017.80 155
## 35 ENG 2 2016-01-15 91850.02 114020.72 108686.41 155
## 36 ENG 2 2016-01-12 81029.15 116021.07 106019.26 155
## 37 ENG 2 2016-01-13 82203.49 117354.65 100518.26 155
## 38 ENG 3 2016-01-22 97184.32 116687.86 91850.02 195
## 39 ENG 3 2016-01-20 91683.32 115354.28 82203.49 195
## 40 ENG 3 2016-01-18 92516.81 110686.78 81469.53 195
## 41 ENG 3 2016-01-19 94350.47 111853.65 81029.15 195
## 42 ENG 3 2016-01-21 95017.26 115854.38 90183.05 195
## 43 ENG 4 2016-01-27 98184.51 115187.59 111853.65 150
## 44 ENG 4 2016-01-28 99017.99 114354.10 115354.28 150
## 45 ENG 4 2016-01-29 100851.65 113520.62 115854.38 150
## 46 ENG 4 2016-01-25 97684.41 117021.25 110686.78 150
## 47 ENG 4 2016-02-01 99684.77 109853.29 116687.86 150
## 48 ENG 5 2016-02-03 100351.56 107686.23 106019.26 155
## 49 ENG 5 2016-02-04 99684.77 107352.84 100518.26 155
## 50 ENG 5 2016-02-02 99518.08 109186.50 104852.38 155
## 51 ENG 5 2016-02-05 99518.08 106852.74 98017.80 155
## 52 ENG 5 2016-02-08 100018.17 116687.86 81469.53 155
## 53 ENG NA 2016-02-10 NA NA NA NA
## 54 ENG NA 2016-02-09 NA NA NA NA
## 55 NZ 1 2016-01-05 70753.72 81029.15 70753.72 120
## 56 NZ 1 2016-01-06 72662.01 82203.49 72662.01 120
## 57 NZ 1 2016-01-07 77799.73 90183.05 77799.73 120
## 58 NZ 1 2016-01-04 75451.06 81469.53 75451.06 120
## 59 NZ 1 2016-01-08 79414.44 91850.02 79414.44 120
## 60 NZ 2 2016-01-12 81029.15 106019.26 81029.15 125
## 61 NZ 2 2016-01-13 82203.49 100518.26 82203.49 125
## 62 NZ 2 2016-01-11 81469.53 104852.38 81469.53 125
## 63 NZ 2 2016-01-14 90183.05 98017.80 90183.05 125
## 64 NZ 2 2016-01-15 91850.02 108686.41 91850.02 125
## 65 NZ 3 2016-01-18 92516.81 81469.53 92516.81 130
## 66 NZ 3 2016-01-19 94350.47 81029.15 94350.47 130
## 67 NZ 3 2016-01-22 97184.32 91850.02 97184.32 130
## 68 NZ 3 2016-01-20 91683.32 82203.49 91683.32 130
## 69 NZ 3 2016-01-21 95017.26 90183.05 95017.26 130
## 70 NZ 4 2016-01-25 97684.41 110686.78 97684.41 135
## 71 NZ 4 2016-01-27 98184.51 111853.65 98184.51 135
## 72 NZ 4 2016-02-01 99684.77 116687.86 99684.77 135
## 73 NZ 4 2016-01-28 99017.99 115354.28 99017.99 135
## 74 NZ 4 2016-01-29 100851.65 115854.38 100851.65 135
## 75 NZ 5 2016-02-02 99518.08 104852.38 99518.08 140
## 76 NZ 5 2016-02-05 99518.08 98017.80 99518.08 140
## 77 NZ 5 2016-02-08 100018.17 81469.53 100018.17 140
## 78 NZ 5 2016-02-03 100351.56 106019.26 100351.56 140
## 79 NZ 5 2016-02-04 99684.77 100518.26 99684.77 140
## 80 NZ NA 2016-02-10 NA NA NA NA
## 81 NZ NA 2016-02-09 NA NA NA NA
之后我们可以手动修复行顺序，如果需要，也可以修复行名：
res <- res[order(match(res$Firm,unique(Sample3$Firm)),res$Date),];
rownames(res) <- NULL;
res;
## Firm Week Date AP BG CK TotalSales
## 1 ENG 1 2016-01-04 75451.06 104852.38 81469.53 200
## 2 ENG 1 2016-01-05 70753.72 106019.26 81029.15 200
## 3 ENG 1 2016-01-06 72662.01 100518.26 82203.49 200
## 4 ENG 1 2016-01-07 77799.73 98017.80 90183.05 200
## 5 ENG 1 2016-01-08 79414.44 108686.41 91850.02 200
## 6 ENG 2 2016-01-11 81469.53 114854.20 104852.38 155
## 7 ENG 2 2016-01-12 81029.15 116021.07 106019.26 155
## 8 ENG 2 2016-01-13 82203.49 117354.65 100518.26 155
## 9 ENG 2 2016-01-14 90183.05 113020.54 98017.80 155
## 10 ENG 2 2016-01-15 91850.02 114020.72 108686.41 155
## 11 ENG 3 2016-01-18 92516.81 110686.78 81469.53 195
## 12 ENG 3 2016-01-19 94350.47 111853.65 81029.15 195
## 13 ENG 3 2016-01-20 91683.32 115354.28 82203.49 195
## 14 ENG 3 2016-01-21 95017.26 115854.38 90183.05 195
## 15 ENG 3 2016-01-22 97184.32 116687.86 91850.02 195
## 16 ENG 4 2016-01-25 97684.41 117021.25 110686.78 150
## 17 ENG 4 2016-01-27 98184.51 115187.59 111853.65 150
## 18 ENG 4 2016-01-28 99017.99 114354.10 115354.28 150
## 19 ENG 4 2016-01-29 100851.65 113520.62 115854.38 150
## 20 ENG 4 2016-02-01 99684.77 109853.29 116687.86 150
## 21 ENG 5 2016-02-02 99518.08 109186.50 104852.38 155
## 22 ENG 5 2016-02-03 100351.56 107686.23 106019.26 155
## 23 ENG 5 2016-02-04 99684.77 107352.84 100518.26 155
## 24 ENG 5 2016-02-05 99518.08 106852.74 98017.80 155
## 25 ENG 5 2016-02-08 100018.17 116687.86 81469.53 155
## 26 ENG NA 2016-02-09 NA NA NA NA
## 27 ENG NA 2016-02-10 NA NA NA NA
## 28 AUS 1 2016-01-04 81469.53 75451.06 104852.38 140
## 29 AUS 1 2016-01-05 81029.15 70753.72 106019.26 140
## 30 AUS 1 2016-01-06 82203.49 72662.01 100518.26 140
## 31 AUS 1 2016-01-07 90183.05 77799.73 98017.80 140
## 32 AUS 1 2016-01-08 91850.02 79414.44 108686.41 140
## 33 AUS 2 2016-01-11 104852.38 81469.53 114854.20 145
## 34 AUS 2 2016-01-12 106019.26 81029.15 116021.07 145
## 35 AUS 2 2016-01-13 100518.26 82203.49 117354.65 145
## 36 AUS 2 2016-01-14 98017.80 90183.05 113020.54 145
## 37 AUS 2 2016-01-15 108686.41 91850.02 114020.72 145
## 38 AUS 3 2016-01-18 81469.53 92516.81 110686.78 150
## 39 AUS 3 2016-01-19 81029.15 94350.47 111853.65 150
## 40 AUS 3 2016-01-20 82203.49 91683.32 115354.28 150
## 41 AUS 3 2016-01-21 90183.05 95017.26 115854.38 150
## 42 AUS 3 2016-01-22 91850.02 97184.32 116687.86 150
## 43 AUS 4 2016-01-25 110686.78 97684.41 117021.25 155
## 44 AUS 4 2016-01-27 111853.65 98184.51 115187.59 155
## 45 AUS 4 2016-01-28 115354.28 99017.99 114354.10 155
## 46 AUS 4 2016-01-29 115854.38 100851.65 113520.62 155
## 47 AUS 4 2016-02-01 116687.86 99684.77 109853.29 155
## 48 AUS 5 2016-02-02 104852.38 99518.08 109186.50 160
## 49 AUS 5 2016-02-03 106019.26 100351.56 107686.23 160
## 50 AUS 5 2016-02-04 100518.26 99684.77 107352.84 160
## 51 AUS 5 2016-02-05 98017.80 99518.08 106852.74 160
## 52 AUS 5 2016-02-08 81469.53 100018.17 116687.86 160
## 53 AUS NA 2016-02-09 NA NA NA NA
## 54 AUS NA 2016-02-10 NA NA NA NA
## 55 NZ 1 2016-01-04 75451.06 81469.53 75451.06 120
## 56 NZ 1 2016-01-05 70753.72 81029.15 70753.72 120
## 57 NZ 1 2016-01-06 72662.01 82203.49 72662.01 120
## 58 NZ 1 2016-01-07 77799.73 90183.05 77799.73 120
## 59 NZ 1 2016-01-08 79414.44 91850.02 79414.44 120
## 60 NZ 2 2016-01-11 81469.53 104852.38 81469.53 125
## 61 NZ 2 2016-01-12 81029.15 106019.26 81029.15 125
## 62 NZ 2 2016-01-13 82203.49 100518.26 82203.49 125
## 63 NZ 2 2016-01-14 90183.05 98017.80 90183.05 125
## 64 NZ 2 2016-01-15 91850.02 108686.41 91850.02 125
## 65 NZ 3 2016-01-18 92516.81 81469.53 92516.81 130
## 66 NZ 3 2016-01-19 94350.47 81029.15 94350.47 130
## 67 NZ 3 2016-01-20 91683.32 82203.49 91683.32 130
## 68 NZ 3 2016-01-21 95017.26 90183.05 95017.26 130
## 69 NZ 3 2016-01-22 97184.32 91850.02 97184.32 130
## 70 NZ 4 2016-01-25 97684.41 110686.78 97684.41 135
## 71 NZ 4 2016-01-27 98184.51 111853.65 98184.51 135
## 72 NZ 4 2016-01-28 99017.99 115354.28 99017.99 135
## 73 NZ 4 2016-01-29 100851.65 115854.38 100851.65 135
## 74 NZ 4 2016-02-01 99684.77 116687.86 99684.77 135
## 75 NZ 5 2016-02-02 99518.08 104852.38 99518.08 140
## 76 NZ 5 2016-02-03 100351.56 106019.26 100351.56 140
## 77 NZ 5 2016-02-04 99684.77 100518.26 99684.77 140
## 78 NZ 5 2016-02-05 99518.08 98017.80 99518.08 140
## 79 NZ 5 2016-02-08 100018.17 81469.53 100018.17 140
## 80 NZ NA 2016-02-09 NA NA NA NA
## 81 NZ NA 2016-02-10 NA NA NA NA
等价性证明：
identical(res,transform(as.data.frame(SampleFinal),Week=as.double(replace(Week,Week=='NA',NA)),TotalSales=as.double(replace(TotalSales,TotalSales=='NA',NA)),AP=as.double(replace(AP,AP=='NA',NA)),BG=as.double(replace(BG,BG=='NA',NA)),CK=as.double(replace(CK,CK=='NA',NA)))[,match(names(res),names(SampleFinal))]);
## [1] TRUE
我有一个 data frame 每 5 天（在数据集中称为一周）的汇总数据。
> dput(Sample3)
structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "AUS",
"AUS", "AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ"), Week = c(1,
2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5), TotalSales = c(200,
155, 195, 150, 155, 140, 145, 150, 155, 160, 120, 125, 130, 135,
140)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-15L), .Names = c("Firm", "Week", "TotalSales"))
我想加入另一个data frame，它有三个生产部门 AP、BG 和 CK 的每日产出值。
> dput(Sample2)
structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ"), Date = structure(c(1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400, 1451865600, 1451952000, 1452038400, 1452124800,
1452211200, 1452470400, 1452556800, 1452643200, 1452729600, 1452816000,
1453075200, 1453161600, 1453248000, 1453334400, 1453420800, 1453680000,
1453852800, 1453939200, 1454025600, 1454284800, 1454371200, 1454457600,
1454544000, 1454630400, 1454889600, 1454976000, 1455062400, 1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
AP = c(75451.0611, 70753.7178, 72662.012, 77799.73, 79414.4391,
81469.5286, 81029.1539, 82203.4883, 90183.0465, 91850.0159,
92516.8063, 94350.4702, 91683.3215, 95017.2605, 97184.3196,
97684.4091, 98184.5051, 99017.9898, 100851.6537, 99684.7738,
99518.0794, 100351.5641, 99684.7738, 99518.0794, 100018.1689,
101185.0489, 103852.1975, 81469.5286, 81029.1539, 82203.4883,
90183.0465, 91850.0159, 104852.383, 106019.2565, 100518.2585,
98017.8043, 108686.4052, 81469.5286, 81029.1539, 82203.4883,
90183.0465, 91850.0159, 110686.7762, 111853.6497, 115354.2831,
115854.379, 116687.8638, 104852.383, 106019.2565, 100518.2585,
98017.8043, 81469.5286, 81029.1539, 82203.4883, 75451.0611,
70753.7178, 72662.012, 77799.73, 79414.4391, 81469.5286,
81029.1539, 82203.4883, 90183.0465, 91850.0159, 92516.8063,
94350.4702, 91683.3215, 95017.2605, 97184.3196, 97684.4091,
98184.5051, 99017.9898, 100851.6537, 99684.7738, 99518.0794,
100351.5641, 99684.7738, 99518.0794, 100018.1689, 101185.0489,
103852.1975), BG = c(104852.383, 106019.2565, 100518.2585,
98017.8043, 108686.4052, 114854.1999, 116021.0734, 117354.6541,
113020.536, 114020.7151, 110686.7762, 111853.6497, 115354.2831,
115854.379, 116687.8638, 117021.2526, 115187.5887, 114354.104,
113520.6192, 109853.2914, 109186.5011, 107686.226, 107352.8372,
106852.7413, 116687.8638, 117021.2526, 115187.5887, 75451.0611,
70753.7178, 72662.012, 77799.73, 79414.4391, 81469.5286,
81029.1539, 82203.4883, 90183.0465, 91850.0159, 92516.8063,
94350.4702, 91683.3215, 95017.2605, 97184.3196, 97684.4091,
98184.5051, 99017.9898, 100851.6537, 99684.7738, 99518.0794,
100351.5641, 99684.7738, 99518.0794, 100018.1689, 101185.0489,
103852.1975, 81469.5286, 81029.1539, 82203.4883, 90183.0465,
91850.0159, 104852.383, 106019.2565, 100518.2585, 98017.8043,
108686.4052, 81469.5286, 81029.1539, 82203.4883, 90183.0465,
91850.0159, 110686.7762, 111853.6497, 115354.2831, 115854.379,
116687.8638, 104852.383, 106019.2565, 100518.2585, 98017.8043,
81469.5286, 81029.1539, 82203.4883), CK = c(81469.5286, 81029.1539,
82203.4883, 90183.0465, 91850.0159, 104852.383, 106019.2565,
100518.2585, 98017.8043, 108686.4052, 81469.5286, 81029.1539,
82203.4883, 90183.0465, 91850.0159, 110686.7762, 111853.6497,
115354.2831, 115854.379, 116687.8638, 104852.383, 106019.2565,
100518.2585, 98017.8043, 81469.5286, 81029.1539, 82203.4883,
104852.383, 106019.2565, 100518.2585, 98017.8043, 108686.4052,
114854.1999, 116021.0734, 117354.6541, 113020.536, 114020.7151,
110686.7762, 111853.6497, 115354.2831, 115854.379, 116687.8638,
117021.2526, 115187.5887, 114354.104, 113520.6192, 109853.2914,
109186.5011, 107686.226, 107352.8372, 106852.7413, 116687.8638,
117021.2526, 115187.5887, 75451.0611, 70753.7178, 72662.012,
77799.73, 79414.4391, 81469.5286, 81029.1539, 82203.4883,
90183.0465, 91850.0159, 92516.8063, 94350.4702, 91683.3215,
95017.2605, 97184.3196, 97684.4091, 98184.5051, 99017.9898,
100851.6537, 99684.7738, 99518.0794, 100351.5641, 99684.7738,
99518.0794, 100018.1689, 101185.0489, 103852.1975), Week = c(1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,
5, 5, 5, 5, 5, 6, 6, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 1, 1, 1, 1,
1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5,
5, 5, 6, 6)), .Names = c("Firm", "Date", "AP", "BG", "CK",
"Week"), row.names = c(NA, -81L), class = c("tbl_df", "tbl",
"data.frame"))
我想制作一个看起来像 this
的数据框 > dput(SampleFinal)
structure(list(Firm = c("ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "ENG",
"ENG", "ENG", "ENG", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS", "AUS",
"AUS", "AUS", "AUS", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ",
"NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ", "NZ"), Date = structure(c(1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400, 1451865600, 1451952000, 1452038400, 1452124800,
1452211200, 1452470400, 1452556800, 1452643200, 1452729600, 1452816000,
1453075200, 1453161600, 1453248000, 1453334400, 1453420800, 1453680000,
1453852800, 1453939200, 1454025600, 1454284800, 1454371200, 1454457600,
1454544000, 1454630400, 1454889600, 1454976000, 1455062400, 1451865600,
1451952000, 1452038400, 1452124800, 1452211200, 1452470400, 1452556800,
1452643200, 1452729600, 1452816000, 1453075200, 1453161600, 1453248000,
1453334400, 1453420800, 1453680000, 1453852800, 1453939200, 1454025600,
1454284800, 1454371200, 1454457600, 1454544000, 1454630400, 1454889600,
1454976000, 1455062400), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
Week = c("1", "1", "1", "1", "1", "2", "2", "2", "2", "2",
"3", "3", "3", "3", "3", "4", "4", "4", "4", "4", "5", "5",
"5", "5", "5", "NA", "NA", "1", "1", "1", "1", "1", "2",
"2", "2", "2", "2", "3", "3", "3", "3", "3", "4", "4", "4",
"4", "4", "5", "5", "5", "5", "5", "NA", "NA", "1", "1",
"1", "1", "1", "2", "2", "2", "2", "2", "3", "3", "3", "3",
"3", "4", "4", "4", "4", "4", "5", "5", "5", "5", "5", "NA",
"NA"), TotalSales = c("200", "200", "200", "200", "200",
"155", "155", "155", "155", "155", "195", "195", "195", "195",
"195", "150", "150", "150", "150", "150", "155", "155", "155",
"155", "155", "NA", "NA", "140", "140", "140", "140", "140",
"145", "145", "145", "145", "145", "150", "150", "150", "150",
"150", "155", "155", "155", "155", "155", "160", "160", "160",
"160", "160", "NA", "NA", "120", "120", "120", "120", "120",
"125", "125", "125", "125", "125", "130", "130", "130", "130",
"130", "135", "135", "135", "135", "135", "140", "140", "140",
"140", "140", "NA", "NA"), AP = c("75451.061100000006", "70753.717799999999",
"72662.012000000002", "77799.73", "79414.439100000003", "81469.528600000005",
"81029.153900000005", "82203.488299999997", "90183.046499999997",
"91850.015899999999", "92516.806299999997", "94350.470199999996",
"91683.321500000005", "95017.260500000004", "97184.319600000003",
"97684.409100000004", "98184.505099999995", "99017.989799999996",
"100851.6537", "99684.773799999995", "99518.079400000002",
"100351.5641", "99684.773799999995", "99518.079400000002",
"100018.1689", "NA", "NA", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"104852.383", "106019.2565", "100518.2585", "98017.804300000003",
"108686.40519999999", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"110686.77619999999", "111853.64969999999", "115354.2831",
"115854.379", "116687.86380000001", "104852.383", "106019.2565",
"100518.2585", "98017.804300000003", "81469.528600000005",
"NA", "NA", "75451.061100000006", "70753.717799999999", "72662.012000000002",
"77799.73", "79414.439100000003", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"92516.806299999997", "94350.470199999996", "91683.321500000005",
"95017.260500000004", "97184.319600000003", "97684.409100000004",
"98184.505099999995", "99017.989799999996", "100851.6537",
"99684.773799999995", "99518.079400000002", "100351.5641",
"99684.773799999995", "99518.079400000002", "100018.1689",
"NA", "NA"), BG = c("104852.383", "106019.2565", "100518.2585",
"98017.804300000003", "108686.40519999999", "114854.19990000001",
"116021.07339999999", "117354.6541", "113020.53599999999",
"114020.7151", "110686.77619999999", "111853.64969999999",
"115354.2831", "115854.379", "116687.86380000001", "117021.25260000001",
"115187.58869999999", "114354.10400000001", "113520.6192",
"109853.2914", "109186.50109999999", "107686.226", "107352.83719999999",
"106852.74129999999", "116687.86380000001", "NA", "NA", "75451.061100000006",
"70753.717799999999", "72662.012000000002", "77799.73", "79414.439100000003",
"81469.528600000005", "81029.153900000005", "82203.488299999997",
"90183.046499999997", "91850.015899999999", "92516.806299999997",
"94350.470199999996", "91683.321500000005", "95017.260500000004",
"97184.319600000003", "97684.409100000004", "98184.505099999995",
"99017.989799999996", "100851.6537", "99684.773799999995",
"99518.079400000002", "100351.5641", "99684.773799999995",
"99518.079400000002", "100018.1689", "NA", "NA", "81469.528600000005",
"81029.153900000005", "82203.488299999997", "90183.046499999997",
"91850.015899999999", "104852.383", "106019.2565", "100518.2585",
"98017.804300000003", "108686.40519999999", "81469.528600000005",
"81029.153900000005", "82203.488299999997", "90183.046499999997",
"91850.015899999999", "110686.77619999999", "111853.64969999999",
"115354.2831", "115854.379", "116687.86380000001", "104852.383",
"106019.2565", "100518.2585", "98017.804300000003", "81469.528600000005",
"NA", "NA"), CK = c("81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"104852.383", "106019.2565", "100518.2585", "98017.804300000003",
"108686.40519999999", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"110686.77619999999", "111853.64969999999", "115354.2831",
"115854.379", "116687.86380000001", "104852.383", "106019.2565",
"100518.2585", "98017.804300000003", "81469.528600000005",
"NA", "NA", "104852.383", "106019.2565", "100518.2585", "98017.804300000003",
"108686.40519999999", "114854.19990000001", "116021.07339999999",
"117354.6541", "113020.53599999999", "114020.7151", "110686.77619999999",
"111853.64969999999", "115354.2831", "115854.379", "116687.86380000001",
"117021.25260000001", "115187.58869999999", "114354.10400000001",
"113520.6192", "109853.2914", "109186.50109999999", "107686.226",
"107352.83719999999", "106852.74129999999", "116687.86380000001",
"NA", "NA", "75451.061100000006", "70753.717799999999", "72662.012000000002",
"77799.73", "79414.439100000003", "81469.528600000005", "81029.153900000005",
"82203.488299999997", "90183.046499999997", "91850.015899999999",
"92516.806299999997", "94350.470199999996", "91683.321500000005",
"95017.260500000004", "97184.319600000003", "97684.409100000004",
"98184.505099999995", "99017.989799999996", "100851.6537",
"99684.773799999995", "99518.079400000002", "100351.5641",
"99684.773799999995", "99518.079400000002", "100018.1689",
"NA", "NA")), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-81L), .Names = c("Firm", "Date", "Week", "TotalSales", "AP",
"BG", "CK"))
它具有每个公司重复 5 个日期值的每周销售值。新数据框应包含 Sample2 中的所有其他列。
还建议当数据框（示例 2）的每五个日期值与数据框（示例 3）的每周销售值匹配时如何制作数据框。数据框很大，所以我提供了所需的链接。 请提出没有 Data.table 包的解决方案。非常感谢。
请不要在评论前忽略问题的面板设置。
我们可以使用基础 R merge() 函数，传递适当的 "all" 参数（all.x with Sample2 as x）来保留键无法加入 Sample3。
然后，我们可以通过将 NA 显式分配给您要在合并后取消的单元格来实现您的特殊要求。
res <- merge(Sample2,Sample3,all.x=T);
res[is.na(res$TotalSales),c('Week','AP','BG','CK')] <- NA;
res;
## Firm Week Date AP BG CK TotalSales
## 1 AUS 1 2016-01-06 82203.49 72662.01 100518.26 140
## 2 AUS 1 2016-01-07 90183.05 77799.73 98017.80 140
## 3 AUS 1 2016-01-05 81029.15 70753.72 106019.26 140
## 4 AUS 1 2016-01-08 91850.02 79414.44 108686.41 140
## 5 AUS 1 2016-01-04 81469.53 75451.06 104852.38 140
## 6 AUS 2 2016-01-13 100518.26 82203.49 117354.65 145
## 7 AUS 2 2016-01-11 104852.38 81469.53 114854.20 145
## 8 AUS 2 2016-01-14 98017.80 90183.05 113020.54 145
## 9 AUS 2 2016-01-15 108686.41 91850.02 114020.72 145
## 10 AUS 2 2016-01-12 106019.26 81029.15 116021.07 145
## 11 AUS 3 2016-01-19 81029.15 94350.47 111853.65 150
## 12 AUS 3 2016-01-22 91850.02 97184.32 116687.86 150
## 13 AUS 3 2016-01-20 82203.49 91683.32 115354.28 150
## 14 AUS 3 2016-01-18 81469.53 92516.81 110686.78 150
## 15 AUS 3 2016-01-21 90183.05 95017.26 115854.38 150
## 16 AUS 4 2016-01-27 111853.65 98184.51 115187.59 155
## 17 AUS 4 2016-01-28 115354.28 99017.99 114354.10 155
## 18 AUS 4 2016-01-29 115854.38 100851.65 113520.62 155
## 19 AUS 4 2016-01-25 110686.78 97684.41 117021.25 155
## 20 AUS 4 2016-02-01 116687.86 99684.77 109853.29 155
## 21 AUS 5 2016-02-02 104852.38 99518.08 109186.50 160
## 22 AUS 5 2016-02-08 81469.53 100018.17 116687.86 160
## 23 AUS 5 2016-02-03 106019.26 100351.56 107686.23 160
## 24 AUS 5 2016-02-04 100518.26 99684.77 107352.84 160
## 25 AUS 5 2016-02-05 98017.80 99518.08 106852.74 160
## 26 AUS NA 2016-02-10 NA NA NA NA
## 27 AUS NA 2016-02-09 NA NA NA NA
## 28 ENG 1 2016-01-07 77799.73 98017.80 90183.05 200
## 29 ENG 1 2016-01-06 72662.01 100518.26 82203.49 200
## 30 ENG 1 2016-01-08 79414.44 108686.41 91850.02 200
## 31 ENG 1 2016-01-04 75451.06 104852.38 81469.53 200
## 32 ENG 1 2016-01-05 70753.72 106019.26 81029.15 200
## 33 ENG 2 2016-01-11 81469.53 114854.20 104852.38 155
## 34 ENG 2 2016-01-14 90183.05 113020.54 98017.80 155
## 35 ENG 2 2016-01-15 91850.02 114020.72 108686.41 155
## 36 ENG 2 2016-01-12 81029.15 116021.07 106019.26 155
## 37 ENG 2 2016-01-13 82203.49 117354.65 100518.26 155
## 38 ENG 3 2016-01-22 97184.32 116687.86 91850.02 195
## 39 ENG 3 2016-01-20 91683.32 115354.28 82203.49 195
## 40 ENG 3 2016-01-18 92516.81 110686.78 81469.53 195
## 41 ENG 3 2016-01-19 94350.47 111853.65 81029.15 195
## 42 ENG 3 2016-01-21 95017.26 115854.38 90183.05 195
## 43 ENG 4 2016-01-27 98184.51 115187.59 111853.65 150
## 44 ENG 4 2016-01-28 99017.99 114354.10 115354.28 150
## 45 ENG 4 2016-01-29 100851.65 113520.62 115854.38 150
## 46 ENG 4 2016-01-25 97684.41 117021.25 110686.78 150
## 47 ENG 4 2016-02-01 99684.77 109853.29 116687.86 150
## 48 ENG 5 2016-02-03 100351.56 107686.23 106019.26 155
## 49 ENG 5 2016-02-04 99684.77 107352.84 100518.26 155
## 50 ENG 5 2016-02-02 99518.08 109186.50 104852.38 155
## 51 ENG 5 2016-02-05 99518.08 106852.74 98017.80 155
## 52 ENG 5 2016-02-08 100018.17 116687.86 81469.53 155
## 53 ENG NA 2016-02-10 NA NA NA NA
## 54 ENG NA 2016-02-09 NA NA NA NA
## 55 NZ 1 2016-01-05 70753.72 81029.15 70753.72 120
## 56 NZ 1 2016-01-06 72662.01 82203.49 72662.01 120
## 57 NZ 1 2016-01-07 77799.73 90183.05 77799.73 120
## 58 NZ 1 2016-01-04 75451.06 81469.53 75451.06 120
## 59 NZ 1 2016-01-08 79414.44 91850.02 79414.44 120
## 60 NZ 2 2016-01-12 81029.15 106019.26 81029.15 125
## 61 NZ 2 2016-01-13 82203.49 100518.26 82203.49 125
## 62 NZ 2 2016-01-11 81469.53 104852.38 81469.53 125
## 63 NZ 2 2016-01-14 90183.05 98017.80 90183.05 125
## 64 NZ 2 2016-01-15 91850.02 108686.41 91850.02 125
## 65 NZ 3 2016-01-18 92516.81 81469.53 92516.81 130
## 66 NZ 3 2016-01-19 94350.47 81029.15 94350.47 130
## 67 NZ 3 2016-01-22 97184.32 91850.02 97184.32 130
## 68 NZ 3 2016-01-20 91683.32 82203.49 91683.32 130
## 69 NZ 3 2016-01-21 95017.26 90183.05 95017.26 130
## 70 NZ 4 2016-01-25 97684.41 110686.78 97684.41 135
## 71 NZ 4 2016-01-27 98184.51 111853.65 98184.51 135
## 72 NZ 4 2016-02-01 99684.77 116687.86 99684.77 135
## 73 NZ 4 2016-01-28 99017.99 115354.28 99017.99 135
## 74 NZ 4 2016-01-29 100851.65 115854.38 100851.65 135
## 75 NZ 5 2016-02-02 99518.08 104852.38 99518.08 140
## 76 NZ 5 2016-02-05 99518.08 98017.80 99518.08 140
## 77 NZ 5 2016-02-08 100018.17 81469.53 100018.17 140
## 78 NZ 5 2016-02-03 100351.56 106019.26 100351.56 140
## 79 NZ 5 2016-02-04 99684.77 100518.26 99684.77 140
## 80 NZ NA 2016-02-10 NA NA NA NA
## 81 NZ NA 2016-02-09 NA NA NA NA
之后我们可以手动修复行顺序，如果需要，也可以修复行名：
res <- res[order(match(res$Firm,unique(Sample3$Firm)),res$Date),];
rownames(res) <- NULL;
res;
## Firm Week Date AP BG CK TotalSales
## 1 ENG 1 2016-01-04 75451.06 104852.38 81469.53 200
## 2 ENG 1 2016-01-05 70753.72 106019.26 81029.15 200
## 3 ENG 1 2016-01-06 72662.01 100518.26 82203.49 200
## 4 ENG 1 2016-01-07 77799.73 98017.80 90183.05 200
## 5 ENG 1 2016-01-08 79414.44 108686.41 91850.02 200
## 6 ENG 2 2016-01-11 81469.53 114854.20 104852.38 155
## 7 ENG 2 2016-01-12 81029.15 116021.07 106019.26 155
## 8 ENG 2 2016-01-13 82203.49 117354.65 100518.26 155
## 9 ENG 2 2016-01-14 90183.05 113020.54 98017.80 155
## 10 ENG 2 2016-01-15 91850.02 114020.72 108686.41 155
## 11 ENG 3 2016-01-18 92516.81 110686.78 81469.53 195
## 12 ENG 3 2016-01-19 94350.47 111853.65 81029.15 195
## 13 ENG 3 2016-01-20 91683.32 115354.28 82203.49 195
## 14 ENG 3 2016-01-21 95017.26 115854.38 90183.05 195
## 15 ENG 3 2016-01-22 97184.32 116687.86 91850.02 195
## 16 ENG 4 2016-01-25 97684.41 117021.25 110686.78 150
## 17 ENG 4 2016-01-27 98184.51 115187.59 111853.65 150
## 18 ENG 4 2016-01-28 99017.99 114354.10 115354.28 150
## 19 ENG 4 2016-01-29 100851.65 113520.62 115854.38 150
## 20 ENG 4 2016-02-01 99684.77 109853.29 116687.86 150
## 21 ENG 5 2016-02-02 99518.08 109186.50 104852.38 155
## 22 ENG 5 2016-02-03 100351.56 107686.23 106019.26 155
## 23 ENG 5 2016-02-04 99684.77 107352.84 100518.26 155
## 24 ENG 5 2016-02-05 99518.08 106852.74 98017.80 155
## 25 ENG 5 2016-02-08 100018.17 116687.86 81469.53 155
## 26 ENG NA 2016-02-09 NA NA NA NA
## 27 ENG NA 2016-02-10 NA NA NA NA
## 28 AUS 1 2016-01-04 81469.53 75451.06 104852.38 140
## 29 AUS 1 2016-01-05 81029.15 70753.72 106019.26 140
## 30 AUS 1 2016-01-06 82203.49 72662.01 100518.26 140
## 31 AUS 1 2016-01-07 90183.05 77799.73 98017.80 140
## 32 AUS 1 2016-01-08 91850.02 79414.44 108686.41 140
## 33 AUS 2 2016-01-11 104852.38 81469.53 114854.20 145
## 34 AUS 2 2016-01-12 106019.26 81029.15 116021.07 145
## 35 AUS 2 2016-01-13 100518.26 82203.49 117354.65 145
## 36 AUS 2 2016-01-14 98017.80 90183.05 113020.54 145
## 37 AUS 2 2016-01-15 108686.41 91850.02 114020.72 145
## 38 AUS 3 2016-01-18 81469.53 92516.81 110686.78 150
## 39 AUS 3 2016-01-19 81029.15 94350.47 111853.65 150
## 40 AUS 3 2016-01-20 82203.49 91683.32 115354.28 150
## 41 AUS 3 2016-01-21 90183.05 95017.26 115854.38 150
## 42 AUS 3 2016-01-22 91850.02 97184.32 116687.86 150
## 43 AUS 4 2016-01-25 110686.78 97684.41 117021.25 155
## 44 AUS 4 2016-01-27 111853.65 98184.51 115187.59 155
## 45 AUS 4 2016-01-28 115354.28 99017.99 114354.10 155
## 46 AUS 4 2016-01-29 115854.38 100851.65 113520.62 155
## 47 AUS 4 2016-02-01 116687.86 99684.77 109853.29 155
## 48 AUS 5 2016-02-02 104852.38 99518.08 109186.50 160
## 49 AUS 5 2016-02-03 106019.26 100351.56 107686.23 160
## 50 AUS 5 2016-02-04 100518.26 99684.77 107352.84 160
## 51 AUS 5 2016-02-05 98017.80 99518.08 106852.74 160
## 52 AUS 5 2016-02-08 81469.53 100018.17 116687.86 160
## 53 AUS NA 2016-02-09 NA NA NA NA
## 54 AUS NA 2016-02-10 NA NA NA NA
## 55 NZ 1 2016-01-04 75451.06 81469.53 75451.06 120
## 56 NZ 1 2016-01-05 70753.72 81029.15 70753.72 120
## 57 NZ 1 2016-01-06 72662.01 82203.49 72662.01 120
## 58 NZ 1 2016-01-07 77799.73 90183.05 77799.73 120
## 59 NZ 1 2016-01-08 79414.44 91850.02 79414.44 120
## 60 NZ 2 2016-01-11 81469.53 104852.38 81469.53 125
## 61 NZ 2 2016-01-12 81029.15 106019.26 81029.15 125
## 62 NZ 2 2016-01-13 82203.49 100518.26 82203.49 125
## 63 NZ 2 2016-01-14 90183.05 98017.80 90183.05 125
## 64 NZ 2 2016-01-15 91850.02 108686.41 91850.02 125
## 65 NZ 3 2016-01-18 92516.81 81469.53 92516.81 130
## 66 NZ 3 2016-01-19 94350.47 81029.15 94350.47 130
## 67 NZ 3 2016-01-20 91683.32 82203.49 91683.32 130
## 68 NZ 3 2016-01-21 95017.26 90183.05 95017.26 130
## 69 NZ 3 2016-01-22 97184.32 91850.02 97184.32 130
## 70 NZ 4 2016-01-25 97684.41 110686.78 97684.41 135
## 71 NZ 4 2016-01-27 98184.51 111853.65 98184.51 135
## 72 NZ 4 2016-01-28 99017.99 115354.28 99017.99 135
## 73 NZ 4 2016-01-29 100851.65 115854.38 100851.65 135
## 74 NZ 4 2016-02-01 99684.77 116687.86 99684.77 135
## 75 NZ 5 2016-02-02 99518.08 104852.38 99518.08 140
## 76 NZ 5 2016-02-03 100351.56 106019.26 100351.56 140
## 77 NZ 5 2016-02-04 99684.77 100518.26 99684.77 140
## 78 NZ 5 2016-02-05 99518.08 98017.80 99518.08 140
## 79 NZ 5 2016-02-08 100018.17 81469.53 100018.17 140
## 80 NZ NA 2016-02-09 NA NA NA NA
## 81 NZ NA 2016-02-10 NA NA NA NA
等价性证明：
identical(res,transform(as.data.frame(SampleFinal),Week=as.double(replace(Week,Week=='NA',NA)),TotalSales=as.double(replace(TotalSales,TotalSales=='NA',NA)),AP=as.double(replace(AP,AP=='NA',NA)),BG=as.double(replace(BG,BG=='NA',NA)),CK=as.double(replace(CK,CK=='NA',NA)))[,match(names(res),names(SampleFinal))]);
## [1] TRUE