R如何根据数据库中的其他测量值填写缺失的日期答案

【问题标题】：R how to fill in missing dates based on other measurements within a databaseR如何根据数据库中的其他测量值填写缺失的日期
【发布时间】：2020-12-29 13:09:27
【问题描述】：

这里我有一个数据库，它是一个更大数据库的摘要，我在该数据库中对威基基或恐龙湾的所有实验单元（模块#）的珊瑚鱼进行了普查。对于任何给定的采样周期（TimeStep），给定站点的所有模块都在同一日期进行了普查。我使用 complete() 来填充缺少普查数据的缺失值（例如，在模块普查期间没有观察到鱼）。我正在尝试根据 Site (Site_long) 和 TimeStep 为我的数据库填写日期。

数据头

数据库

data <- structure(list(Date = structure(c(18244, 18244, 17503, 17503, 
17503, 17503, 17873, 17873, 18309, 18309, 18314, 17977, 17977, 
17977, 17671, 17671, 17671, 17671, 17311, 17311, 17311, 18411, 
18411, 18050, 17775, 17775, 17775, 18154, 18154, 18154, 17416, 
17416, 17416, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), class = "Date"), Year = c(2019, 2019, 2017, 2017, 
2017, 2017, 2018, 2018, 2020, 2020, 2020, 2019, 2019, 2019, 2018, 
2018, 2018, 2018, 2017, 2017, 2017, 2020, 2020, 2019, 2018, 2018, 
2018, 2019, 2019, 2019, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020), `Module #` = c(111, 
113, 111, 113, 115, 116, 115, 116, 111, 113, 115, 112, 114, 115, 
113, 114, 115, 116, 111, 113, 115, 112, 115, 115, 112, 113, 115, 
111, 113, 116, 111, 113, 115, 111, 111, 111, 111, 111, 111, 111, 
111, 111, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 
112, 113, 113, 113, 113, 113, 113, 113, 113, 113, 114, 114, 114, 
114, 114, 114, 114, 114, 114, 114, 114, 114, 115, 115, 115, 115, 
115, 115, 115, 115, 115, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 
111, 111, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 
113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 114, 114, 114, 
114, 114, 114, 114, 114, 114, 114, 114, 115, 115, 115, 115, 115, 
115, 115, 115, 115, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 112, 112, 
112, 112, 112, 112, 112, 112, 112, 112, 112, 113, 113, 113, 113, 
113, 113, 113, 113, 113, 113, 114, 114, 114, 114, 114, 114, 114, 
114, 114, 114, 114, 115, 115, 115, 115, 115, 115, 115, 115, 115, 
115, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 111, 
111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 112, 112, 112, 
112, 112, 112, 112, 112, 112, 112, 112, 113, 113, 113, 113, 113, 
113, 113, 113, 113, 113, 113, 114, 114, 114, 114, 114, 114, 114, 
114, 114, 114, 114, 114, 115, 115, 115, 115, 115, 115, 115, 115, 
115, 115, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116), Site_long = c("Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
"Waikiki", "Waikiki"), Shelter = c("High", "High", "High", "High", 
"High", "Low", "High", "Low", "High", "High", "High", "Low", 
"Low", "High", "High", "Low", "High", "Low", "High", "High", 
"High", "Low", "High", "High", "Low", "High", "High", "High", 
"High", "Low", "High", "High", "High", "High", "High", "High", 
"High", "High", "High", "High", "High", "High", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "High", "High", "High", "High", "High", "High", "High", 
"High", "High", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "High", "High", "High", "High", 
"High", "High", "High", "High", "High", "Low", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "High", 
"High", "High", "High", "High", "High", "High", "High", "High", 
"High", "High", "High", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "High", "High", "High", "High", 
"High", "High", "High", "High", "High", "High", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"High", "High", "High", "High", "High", "High", "High", "High", 
"High", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "High", "High", "High", "High", "High", "High", 
"High", "High", "High", "High", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "High", "High", "High", 
"High", "High", "High", "High", "High", "High", "High", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "High", "High", "High", "High", "High", "High", "High", 
"High", "High", "High", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "High", "High", "High", "High", 
"High", "High", "High", "High", "High", "High", "High", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "High", "High", "High", "High", "High", "High", "High", 
"High", "High", "High", "High", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "High", "High", 
"High", "High", "High", "High", "High", "High", "High", "High", 
"Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", "Low", 
"Low", "Low", "Low"), TimeStep = c("11", "11", "3", "3", "3", 
"3", "7", "7", "12", "12", "12", "8", "8", "8", "5", "5", "5", 
"5", "1", "1", "1", "13", "13", "9", "6", "6", "6", "10", "10", 
"10", "2", "2", "2", "10", "11", "12", "13", "5", "6", "7", "8", 
"9", "1", "10", "11", "12", "13", "2", "3", "5", "6", "7", "8", 
"9", "10", "11", "12", "13", "5", "6", "7", "8", "9", "1", "10", 
"11", "12", "13", "2", "3", "5", "6", "7", "8", "9", "10", "11", 
"12", "13", "5", "6", "7", "8", "9", "1", "10", "11", "12", "13", 
"2", "5", "6", "7", "8", "9", "1", "10", "11", "12", "13", "2", 
"3", "5", "6", "7", "8", "9", "1", "10", "11", "12", "13", "2", 
"3", "5", "7", "8", "9", "1", "10", "11", "12", "13", "2", "3", 
"7", "8", "9", "1", "10", "11", "12", "13", "2", "3", "6", "7", 
"8", "9", "1", "10", "11", "12", "13", "2", "3", "8", "9", "1", 
"10", "11", "12", "13", "2", "3", "6", "8", "9", "1", "12", "13", 
"2", "3", "5", "6", "7", "8", "9", "1", "10", "11", "12", "13", 
"2", "3", "5", "6", "7", "9", "1", "12", "13", "2", "3", "5", 
"6", "7", "8", "9", "1", "10", "11", "12", "13", "2", "3", "5", 
"6", "7", "9", "1", "10", "11", "12", "13", "2", "3", "5", "6", 
"7", "1", "11", "12", "13", "2", "3", "5", "6", "7", "8", "9", 
"1", "10", "11", "13", "2", "3", "5", "6", "7", "8", "9", "1", 
"10", "11", "12", "2", "3", "5", "6", "7", "8", "9", "1", "10", 
"11", "13", "2", "3", "5", "6", "7", "8", "9", "1", "10", "11", 
"12", "13", "2", "3", "5", "6", "7", "8", "9", "1", "10", "11", 
"2", "3", "5", "6", "7", "8", "9", "1", "10", "11", "12", "13", 
"2", "3", "5", "6", "7", "8", "9"), total_biomass = c(0.0347972963845844, 
0.0491864247516633, 0.0337429360353172, 0.0491864247516633, 0.0676700712806197, 
0.0176129136061979, 0.0463414029816723, 0.0438269494805073, 0.0540876987656689, 
0.0540876987656689, 0.013587464291258, 0.00803709822823084, 0.00467403151010407, 
0.0409256138571204, 0.0620895115023818, 0.0209695276260751, 0.0204081680175056, 
0.00206199419933497, 0.01080234898264, 0.0316349973376856, 0.00612831747253596, 
0.0025587897405708, 0.00969619960291588, 0.124762345913799, 0.00202327772014947, 
0.00403651893214743, 0.0316209605244676, 0.016930666455176, 0.0219387977347698, 
0.00121768478272671, 0.0361091366626131, 0.0122566349450719, 
0.00969619960291588, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Season = c("winter", "winter", 
"winter", "winter", "winter", "winter", "winter", "winter", "spring", 
"spring", "spring", "spring", "spring", "spring", "summer", "summer", 
"summer", "summer", "summer", "summer", "summer", "summer", "summer", 
"summer", "fall", "fall", "fall", "fall", "fall", "fall", "fall", 
"fall", "fall", "fall", "winter", "spring", "summer", "summer", 
"fall", "winter", "spring", "summer", "summer", "fall", "winter", 
"spring", "summer", "fall", "winter", "summer", "fall", "winter", 
"spring", "summer", "fall", "winter", "spring", "summer", "summer", 
"fall", "winter", "spring", "summer", "summer", "fall", "winter", 
"spring", "summer", "fall", "winter", "summer", "fall", "winter", 
"spring", "summer", "fall", "winter", "spring", "summer", "summer", 
"fall", "winter", "spring", "summer", "summer", "fall", "winter", 
"spring", "summer", "fall", "summer", "fall", "winter", "spring", 
"summer", "summer", "fall", "winter", "spring", "summer", "fall", 
"winter", "summer", "fall", "winter", "spring", "summer", "summer", 
"fall", "winter", "spring", "summer", "fall", "winter", "summer", 
"winter", "spring", "summer", "summer", "fall", "winter", "spring", 
"summer", "fall", "winter", "winter", "spring", "summer", "summer", 
"fall", "winter", "spring", "summer", "fall", "winter", "fall", 
"winter", "spring", "summer", "summer", "fall", "winter", "spring", 
"summer", "fall", "winter", "spring", "summer", "summer", "fall", 
"winter", "spring", "summer", "fall", "winter", "fall", "spring", 
"summer", "summer", "spring", "summer", "fall", "winter", "summer", 
"fall", "winter", "spring", "summer", "summer", "fall", "winter", 
"spring", "summer", "fall", "winter", "summer", "fall", "winter", 
"summer", "summer", "spring", "summer", "fall", "winter", "summer", 
"fall", "winter", "spring", "summer", "summer", "fall", "winter", 
"spring", "summer", "fall", "winter", "summer", "fall", "winter", 
"summer", "summer", "fall", "winter", "spring", "summer", "fall", 
"winter", "summer", "fall", "winter", "summer", "winter", "spring", 
"summer", "fall", "winter", "summer", "fall", "winter", "spring", 
"summer", "summer", "fall", "winter", "summer", "fall", "winter", 
"summer", "fall", "winter", "spring", "summer", "summer", "fall", 
"winter", "spring", "fall", "winter", "summer", "fall", "winter", 
"spring", "summer", "summer", "fall", "winter", "summer", "fall", 
"winter", "summer", "fall", "winter", "spring", "summer", "summer", 
"fall", "winter", "spring", "summer", "fall", "winter", "summer", 
"fall", "winter", "spring", "summer", "summer", "fall", "winter", 
"fall", "winter", "summer", "fall", "winter", "spring", "summer", 
"summer", "fall", "winter", "spring", "summer", "fall", "winter", 
"summer", "fall", "winter", "spring", "summer")), row.names = c(NA, 
-288L), class = c("tbl_df", "tbl", "data.frame"))

为了根据来自同一站点和 TimeStep 的其他条目填充日期，我尝试使用 complete() 函数。

尝试的代码

data <- data %>% 
  complete(Date, nesting(Site_long, TimeStep))

所需的输出应该包含所有包含日期的行。应填写日期，以便如果缺少，新的日期值应与对给定站点和时间步长进行观察的日期相对应。

例如，在标题数据中，您可以看到缺少日期的第一行是在 TimeStep 10 期间在威基基的第 111 模块。应填写日期，使其与在TimeStep 10 (2019-09-15) 期间的威基基。提前感谢您的意见！

【问题讨论】：

标签： r date missing-data

【解决方案1】：

您可以通过Module #、Site_long 和TimeStep 将那些具有非缺失Date 的行合并到原始数据中。

library(dplyr)

data %>%
  filter(!is.na(Date)) %>%
  select(Date, Site_long, TimeStep) %>%
  distinct(Site_long, TimeStep, .keep_all = TRUE) %>% 
  left_join(data, ., by = c("Site_long", "TimeStep")) %>%
  mutate(Date = coalesce(Date.x, Date.y), .keep = "unused", .before = Year)

【讨论】：

Darren Tsai 您的代码出现以下错误：“选择错误（., Date, Module #, Site_long, TimeStep）：未使用的参数（Date, Module #, Site_long, TimeStep）”。我完全不明白为什么或如何发生这种情况
此外，我将代码分成 3 行单独的行，以防出现问题，即使它运行没有错误，我得到的数据库仍然有 0 个生物量条目，没有对应的日期 [r] data_dates <- data %>% filter(!is.na(Date)) data <- left_join(data, data_dates, by = c("Module #", "Site_long", "TimeStep")) data <- data %>% mutate(Date = coalesce(Date.x, Date.y), .keep = "unused", .before = Year.x)
@EricDilley 我使用您在帖子中提供的数据，并通过我的代码获得出色的输出！可能是因为dplyr的包版本。更新您的dplyr 并再试一次怎么样！
我尝试只加载 tidyverse，然后我没有收到错误。但是，有些日期没有填写，并且日期仍然有 NA。我已将生成的数据库与您的代码一起放入问题中。您的结果是否填写了整个日期列？
我无法发布数据库，因为它超出了字符数限制。但正如我所说，日期列仍然有 NA 条目。