我正在使用列表（测量深度）列表（2014-2018年每年的数据框架）的嵌套列表（地面传感器）。 'SE'表示传感器及其编号，'d'表示传感器在土壤中放置的深度。看起来像这样：
str(GRP3_OUT_gwFERN)
List of 9
$ SE10:List of 3
..$ d20:List of 5
.. ..$ 2014:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2014-01-01" "2014-01-01" "2014-01-01" ...
.. .. ..$ SWC : logi [1:8760] NA NA NA NA NA NA ...
.. ..$ 2015:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2015-01-01" "2015-01-01" "2015-01-01" ...
.. .. ..$ SWC : logi [1:8760] NA NA NA NA NA NA ...
.. ..$ 2016:'data.frame': 8784 obs. of 2 variables:
.. .. ..$ Date: Date[1:8784], format: "2016-01-01" "2016-01-01" "2016-01-01" ...
.. .. ..$ SWC : logi [1:8784] NA NA NA NA NA NA ...
.. ..$ 2017:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2017-01-01" "2017-01-01" "2017-01-01" ...
.. .. ..$ SWC : num [1:8760] NA NA NA NA NA NA NA NA NA NA ...
.. ..$ 2018:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2018-01-01" "2018-01-01" "2018-01-01" ...
.. .. ..$ SWC : logi [1:8760] NA NA NA NA NA NA ...
..$ d50:List of 5
.. ..$ 2014:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2014-01-01" "2014-01-01" "2014-01-01" ...
.. .. ..$ SWC : num [1:8760] 39.8 39.7 39.8 39.7 39.7 ...
.. ..$ 2015:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2015-01-01" "2015-01-01" "2015-01-01" ...
.. .. ..$ SWC : num [1:8760] 39.7 39.7 39.7 39.7 39.7 ...
.. ..$ 2016:'data.frame': 8784 obs. of 2 variables:
.. .. ..$ Date: Date[1:8784], format: "2016-01-01" "2016-01-01" "2016-01-01" ...
.. .. ..$ SWC : num [1:8784] 39 39.1 39.1 39 39 ...
.. ..$ 2017:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2017-01-01" "2017-01-01" "2017-01-01" ...
.. .. ..$ SWC : num [1:8760] 37.9 38 37.9 37.9 37.9 ...
.. ..$ 2018:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2018-01-01" "2018-01-01" "2018-01-01" ...
.. .. ..$ SWC : num [1:8760] 39.1 39 39.1 39 39 ...
..$ d5 :List of 5
.. ..$ 2014:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2014-01-01" "2014-01-01" "2014-01-01" ...
.. .. ..$ SWC : num [1:8760] 41 41 40.9 41 40.9 ...
.. ..$ 2015:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2015-01-01" "2015-01-01" "2015-01-01" ...
.. .. ..$ SWC : num [1:8760] 42 42.1 42.1 42 42.1 ...
.. ..$ 2016:'data.frame': 8784 obs. of 2 variables:
.. .. ..$ Date: Date[1:8784], format: "2016-01-01" "2016-01-01" "2016-01-01" ...
.. .. ..$ SWC : num [1:8784] 43.3 43.4 43.4 43.3 43.3 ...
.. ..$ 2017:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2017-01-01" "2017-01-01" "2017-01-01" ...
.. .. ..$ SWC : num [1:8760] 42.1 42.1 42.2 42.1 42.1 ...
.. ..$ 2018:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2018-01-01" "2018-01-01" "2018-01-01" ...
.. .. ..$ SWC : num [1:8760] 44 44.1 44.1 44.1 44.1 ...
$ SE11:List of 3
..$ d20:List of 5
.. ..$ 2014:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2014-01-01" "2014-01-01" "2014-01-01" ...
.. .. ..$ SWC : num [1:8760] 46.6 46.5 46.4 46.4 46.4 ...
.. ..$ 2015:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2015-01-01" "2015-01-01" "2015-01-01" ...
.. .. ..$ SWC : num [1:8760] 46.6 46.5 46.6 46.6 46.6 ...
.. ..$ 2016:'data.frame': 8784 obs. of 2 variables:
.. .. ..$ Date: Date[1:8784], format: "2016-01-01" "2016-01-01" "2016-01-01" ...
.. .. ..$ SWC : num [1:8784] 45.1 45.1 45.1 45.1 45.1 ...
.. ..$ 2017:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2017-01-01" "2017-01-01" "2017-01-01" ...
.. .. ..$ SWC : num [1:8760] 40.2 40.2 40.2 40.2 40.2 ...
.. ..$ 2018:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2018-01-01" "2018-01-01" "2018-01-01" ...
.. .. ..$ SWC : num [1:8760] 49.1 49.2 49.3 49.2 49.3 ...
..$ d50:List of 5
.. ..$ 2014:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2014-01-01" "2014-01-01" "2014-01-01" ...
.. .. ..$ SWC : num [1:8760] 34.1 34 34.1 34 34 ...
.. ..$ 2015:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2015-01-01" "2015-01-01" "2015-01-01" ...
.. .. ..$ SWC : num [1:8760] 32.8 32.8 32.8 32.7 32.7 ...
.. ..$ 2016:'data.frame': 8784 obs. of 2 variables:
.. .. ..$ Date: Date[1:8784], format: "2016-01-01" "2016-01-01" "2016-01-01" ...
.. .. ..$ SWC : logi [1:8784] NA NA NA NA NA NA ...
.. ..$ 2017:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2017-01-01" "2017-01-01" "2017-01-01" ...
.. .. ..$ SWC : logi [1:8760] NA NA NA NA NA NA ...
.. ..$ 2018:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2018-01-01" "2018-01-01" "2018-01-01" ...
.. .. ..$ SWC : logi [1:8760] NA NA NA NA NA NA ...
..$ d5 :List of 5
.. ..$ 2014:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2014-01-01" "2014-01-01" "2014-01-01" ...
.. .. ..$ SWC : num [1:8760] 33.8 33.8 33.8 33.8 33.7 ...
.. ..$ 2015:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2015-01-01" "2015-01-01" "2015-01-01" ...
.. .. ..$ SWC : num [1:8760] 35.7 35.7 35.7 35.7 35.7 ...
.. ..$ 2016:'data.frame': 8784 obs. of 2 variables:
.. .. ..$ Date: Date[1:8784], format: "2016-01-01" "2016-01-01" "2016-01-01" ...
.. .. ..$ SWC : num [1:8784] 31.5 31.5 31.5 31.5 31.5 ...
.. ..$ 2017:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2017-01-01" "2017-01-01" "2017-01-01" ...
.. .. ..$ SWC : num [1:8760] 28.3 28.3 28.3 28.2 28.2 ...
.. ..$ 2018:'data.frame': 8760 obs. of 2 variables:
.. .. ..$ Date: Date[1:8760], format: "2018-01-01" "2018-01-01" "2018-01-01" ...
.. .. ..$ SWC : num [1:8760] 35.4 35.5 35.6 35.5 35.4 ..
由于所有数据框都包含NA值，因此我想执行线性回归以用NA值填补空白。我想这样做是为了[[我需要以某种方式重组清单，以获取2014年的数据帧，深度20和列表中相同年份和深度的所有传感器。然后在下一个数据帧中，2015年是相同的，在下一个数据帧中是2016年，依此类推... ...> 我为什么要这样做？因为为了执行线性回归，我想在SE11的新列中创建一个指标变量（例如），并用相关系数最高的另一个传感器的值填充NA间隙 SE11_d20_2014_SWC SE12_d20_2014_SWC SE_44_d20_2014_SWC
2 NA 4
3 2 6
NA 3 NA
6 NA 3
SE11_d50_2014_SWC SE12_d50_2014_SWC SE_44_d50_2014_SWC
2 NA 4
3 2 6
4 5 4
NA 3 NA
6 NA 3
SE11_d5_2014_SWC SE12_d5_2014_SWC SE_44_d5_2014_SWC
2 NA 4
3 2 6
4 5 4
NA 3 NA
6 NA 3
我已经进行了一些研究，并寻找了重组清单，但是很遗憾，我找不到任何可以帮助我的东西。有人可以帮忙吗？
菲尔
我正在使用列表（测量深度）列表（2014-2018年每年的数据框架）的嵌套列表（地面传感器）。 “ SE”表示传感器及其编号，“ d”表示深度...
list转换为data.frame并保留所有嵌套信息即可。然后，您可以根据需要对其进行过滤。