[英]Extrapolating data using interp linear interpolation for water quality data visualizations
背景：我正在从水质数据中创建数据可视化。 我的代码在大多数情况下都很好用，但有时如果数据太不稳定而无法获得好的数据，我们必须从表面删除数据，所以我们最终会得到从 2m（有时 3m）深度开始的数据。 当我们删除这些值时，插值会在表面返回 NA 值，从而使可视化无法用于我们的目的。 有关示例，请参见图像： 良好可视化示例 使用表面 NA 进行可视化示例
示例数据片段和代码下方的进一步说明
akima::interp function 在使用线性插值时不允许外推，并且非线性选项不适合我们的数据。
我正在为此寻找解决方法。 我的想法是：
一行代码执行如下操作：“如果站的最小深度大于 1，则将数据从最小深度复制到 1 到最小值之间的所有深度”（免责声明：我知道这不适合分析，但对于我们的目的是创建可视化来显示数据趋势，我们可以接受）
我在 function 中缺少的东西将允许外推（或使用不同的线性插值函数）
这是我到目前为止的代码（这是从一个更大的函数中获取的）。
library(akima); library(dplyr)
library("data.table");library(tidyverse);library(naniar)
library(magrittr);library(janitor);library(lubridate);library(wql)
##Example data import
example.data<-structure(list(Station = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12,
12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16,
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25,
25, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27,
27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29,
29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29.5, 29.5, 29.5, 29.5,
29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,
32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33,
33, 33, 34, 34, 649, 649, 649, 649, 649, 649, 649, 649, 649,
649, 657, 657, 657, 657, 657, 657, 657, 657, 657, 657), Distance.from.36 = c(127.7053,
127.7053, 127.7053, 127.7053, 127.7053, 127.7053, 127.7053, 127.7053,
127.7053, 127.7053, 125.3243, 125.3243, 125.3243, 125.3243, 125.3243,
125.3243, 125.3243, 125.3243, 125.3243, 125.3243, 119.8957, 119.8957,
119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957,
119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957,
115.6252, 115.6252, 115.6252, 115.6252, 115.6252, 115.6252, 115.6252,
115.6252, 115.6252, 115.6252, 115.6252, 115.6252, 110.8977, 110.8977,
110.8977, 110.8977, 110.8977, 110.8977, 110.8977, 110.8977, 110.8977,
110.8977, 104.981, 104.981, 104.981, 104.981, 104.981, 104.981,
104.981, 104.981, 104.981, 104.981, 104.981, 104.981, 104.981,
99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699,
99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699,
99.7699, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889,
96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889,
96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889,
96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 93.5951, 93.5951,
93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951,
93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951,
93.5951, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672,
89.7672, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672,
84.4458, 84.4458, 84.4458, 84.4458, 84.4458, 84.4458, 84.4458,
84.4458, 78.5444, 78.5444, 78.5444, 78.5444, 78.5444, 78.5444,
78.5444, 78.5444, 78.5444, 74.4288, 74.4288, 74.4288, 74.4288,
74.4288, 74.4288, 74.4288, 74.4288, 74.4288, 74.4288, 74.4288,
74.4288, 74.4288, 74.4288, 69.9895, 69.9895, 69.9895, 69.9895,
69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895,
69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895,
69.9895, 69.9895, 69.9895, 69.9895, 63.0794, 63.0794, 63.0794,
63.0794, 63.0794, 63.0794, 63.0794, 63.0794, 63.0794, 63.0794,
63.0794, 58.8909, 58.8909, 58.8909, 58.8909, 58.8909, 58.8909,
58.8909, 58.8909, 58.8909, 58.8909, 58.8909, 58.8909, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 51.4501, 51.4501, 51.4501, 51.4501,
51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501,
51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501,
51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 46.6266,
46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266,
46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266,
46.6266, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921,
43.921, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921,
43.921, 43.921, 39.6557, 39.6557, 39.6557, 39.6557, 39.6557,
39.6557, 39.6557, 39.6557, 39.6557, 39.6557, 39.6557, 39.6557,
39.6557, 37.0911, 37.0911, 37.0911, 37.0911, 37.0911, 37.0911,
37.0911, 37.0911, 32.8382, 32.8382, 32.8382, 32.8382, 32.8382,
32.8382, 28.756, 28.756, 28.756, 28.756, 28.756, 28.756, 28.756,
28.756, 26.1872, 26.1872, 26.1872, 26.1872, 26.1872, 26.1872,
26.1872, 26.1872, 26.1872, 26.1872, 23.5695, 23.5695, 23.5695,
23.5695, 23.5695, 23.5695, 23.5695, 23.5695, 23.5695, 23.5695,
23.5695, 23.5695, 20.2526, 20.2526, 20.2526, 20.2526, 20.2526,
20.2526, 20.2526, 20.2526, 20.2526, 20.2526, 20.2526, 17.564,
17.564, 17.564, 17.564, 17.564, 17.564, 17.564, 17.564, 17.564,
17.564, 17.564, 17.564, 14.7543, 14.7543, 14.7543, 14.7543, 14.7543,
14.7543, 14.7543, 14.7543, 14.7543, 14.7543, 10.6065, 10.6065,
10.6065, 10.6065, 10.6065, 10.6065, 10.6065, 10.6065, 10.6065,
10.6065, 8.1507, 8.1507, 8.1507, 8.1507, 8.1507, 8.1507, 8.1507,
8.1507, 6.8085, 6.8085, 6.8085, 6.8085, 6.8085, 6.8085, 6.8085,
6.8085, 6.8085, 6.8085, 3.529, 3.529, 132.9388, 132.9388, 132.9388,
132.9388, 132.9388, 132.9388, 132.9388, 132.9388, 132.9388, 132.9388,
147.2548, 147.2548, 147.2548, 147.2548, 147.2548, 147.2548, 147.2548,
147.2548, 147.2548, 147.2548), Depth = c(2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
3L, 4L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L), Calc.Chl = c(3.3, 3.2, 3.2, 3.2,
3.2, 3.2, 3.1, 3.1, 3.2, 3.2, 3.4, 3.4, 3.6, 3.6, 3.6, 3.7, 3.5,
3.4, 3.6, 3.6, 3.7, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.5, 3.4,
3.5, 3.5, 3.5, 3.5, 3.5, 3.4, 3.9, 3.9, 3.8, 3.9, 3.8, 3.8, 3.8,
3.8, 3.7, 3.7, 3.7, 3.6, 4.4, 4.4, 4.3, 4.4, 4.3, 4.1, 4.1, 4.1,
4, 4.1, 3.3, 3, 2.9, 2.9, 2.9, 2.8, 2.6, 2.8, 2.8, 2.7, 2.7,
2.7, 2.8, 3.1, 3.1, 2.8, 2.5, 2.5, 2.4, 2.2, 2.2, 2.1, 2, 1.7,
1.9, 2, 2.1, 2.1, 2.2, 2.2, 2, 2, 2, 1.9, 1.9, 1.9, 1.8, 1.7,
1.6, 1.6, 1.6, 1.8, 1.9, 1.9, 1.9, 1.9, 2, 1.9, 2, 2, 2, 2, 2,
1.2, 1.9, 1.9, 1.8, 1.7, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.7,
1.4, 1.3, 1.8, 2, 1.2, 1.7, 1.7, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5,
1.7, 1.7, 1.7, 1.7, 2.9, 2.8, 1.8, 1.8, 1.8, 2.2, 2.1, 1.7, 4.5,
3.8, 2.8, 2.3, 2.8, 2.9, 2.6, 2.4, 2.9, 3.8, 3.6, 3.1, 3, 3.1,
3, 2.9, 2.6, 3, 3.1, 2.8, 2.6, 2.8, 2.3, 4, 3.8, 3.2, 2.8, 2.6,
2.5, 2.7, 2.6, 2.5, 2.5, 2.5, 2.6, 2.7, 2.7, 2.6, 2.7, 3, 3,
2.8, 2.8, 2.8, 2.8, 4.1, 4.3, 4.4, 4.3, 4.3, 4.4, 4.6, 4.8, 4.9,
4.8, 4.8, 3.6, 4, 4.2, 4.2, 4, 4.3, 4.9, 5.1, 5.5, 5.5, 5.3,
5.7, 4.1, 5, 4.7, 4.9, 4.9, 5.2, 5.2, 5.2, 5.2, 5, 4.9, 4.9,
5.1, 5.1, 5.3, 5.2, 5.5, 5.6, 5.7, 5.7, 5.3, 5.2, 5.2, 5.4, 5.6,
5.8, 5.8, 5.8, 5.8, 6, 6.1, 6, 6, 6, 5.9, 6, 5.8, 6.1, 6, 4.5,
5, 5.1, 4.5, 4.6, 4.5, 4.5, 4.4, 4.4, 4.5, 4.5, 4.5, 4.4, 4.6,
4.6, 4.7, 4.8, 4.7, 4.9, 4.8, 4.5, 4.9, 5.3, 5.6, 4.1, 4.1, 4.1,
4.2, 4.2, 4.4, 4.4, 4.3, 4.4, 4.4, 4.5, 4.8, 4.7, 4.7, 4.9, 5.1,
4.1, 3.9, 3.9, 3.9, 3.8, 3.8, 3.9, 4, 4.2, 4.3, 4.5, 4.6, 5,
5.1, 5.2, 5.3, 5.2, 6.2, 5.7, 4.5, 4.1, 4.1, 4.2, 4.2, 4.3, 4.3,
4.4, 4.4, 4.4, 4.6, 4.3, 4.2, 4.1, 4.2, 4.2, 4.2, 4.3, 4.3, 4.7,
4.6, 4.6, 4.6, 4.6, 4.6, 4.2, 4.2, 4.2, 4.1, 4.1, 4.3, 4.3, 4.3,
4.8, 4.6, 4.6, 4.6, 4.6, 4.5, 4.4, 4.3, 4.3, 4.4, 4.7, 4.6, 4.7,
4.6, 4.5, 4.4, 4.3, 4.2, 4.3, 4.3, 4.4, 4.5, 5.5, 5.4, 5.3, 5.1,
5, 4.9, 4.9, 5, 5, 5.1, 5, 7.1, 6.9, 6.9, 6.9, 6.8, 6.8, 6.7,
6.5, 6.4, 6.1, 5.5, 5.3, 7.3, 7.2, 7.3, 7.4, 7.1, 6.8, 6.6, 6.4,
6.2, 6, 7.3, 8.2, 8.8, 9.6, 10.4, 10.5, 10.8, 11.1, 11.3, 11.4,
6.7, 6.6, 6.8, 6.9, 6.9, 7.3, 7.6, 7.8, 8.2, 8.2, 8.2, 8.1, 8.1,
8.1, 8.1, 8.1, 8.1, 8.5, 8.3, 8.1, 3.1, 3.1, 3.1, 3.1, 3.2, 3.1,
3.1, 3.2, 3.2, 3.2, 2.1, 2.2, 2.2, 2.3, 2.4, 2.4, 2.2, 2.3, 2.2,
2.1), DO = c(91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L,
91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L,
91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 92L, 91L, 91L, 91L,
91L, 91L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 91L, 90L, 90L, 89L,
89L, 89L, 89L, 88L, 88L, 88L, 88L, 88L, 88L, 88L, 88L, 89L, 89L,
89L, 88L, 88L, 88L, 88L, 88L, 88L, 87L, 87L, 87L, 87L, 87L, 87L,
87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 90L, 90L, 89L,
88L, 88L, 88L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L,
87L, 88L, 88L, 88L, 87L, 87L, 87L, 87L, 86L, 86L, 86L, 86L, 86L,
86L, 89L, 87L, 86L, 86L, 86L, 86L, 86L, 85L, 89L, 87L, 86L, 86L,
86L, 86L, 86L, 86L, 85L, 87L, 87L, 86L, 86L, 86L, 86L, 86L, 86L,
85L, 85L, 85L, 85L, 85L, 85L, 88L, 87L, 87L, 86L, 86L, 86L, 86L,
85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L,
85L, 85L, 87L, 87L, 86L, 86L, 86L, 85L, 85L, 85L, 85L, 85L, 85L,
84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 86L,
86L, 86L, 86L, 86L, 86L, 86L, 86L, 86L, 86L, 85L, 85L, 85L, 84L,
84L, 84L, 84L, 83L, 83L, 83L, 83L, 83L, 82L, 82L, 82L, 82L, 82L,
82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 85L,
84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L,
84L, 84L, 84L, 84L, 84L, 83L, 83L, 83L, 83L, 83L, 85L, 85L, 84L,
84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L,
86L, 86L, 85L, 85L, 85L, 85L, 85L, 84L, 84L, 84L, 84L, 84L, 84L,
84L, 84L, 84L, 84L, 92L, 90L, 89L, 88L, 88L, 87L, 87L, 87L, 87L,
87L, 87L, 87L, 87L, 90L, 90L, 89L, 89L, 89L, 89L, 89L, 90L, 92L,
92L, 92L, 92L, 92L, 91L, 91L, 91L, 91L, 91L, 90L, 90L, 90L, 90L,
92L, 92L, 92L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 90L,
90L, 91L, 91L, 91L, 91L, 91L, 92L, 92L, 92L, 91L, 90L, 90L, 89L,
89L, 89L, 89L, 88L, 88L, 88L, 88L, 91L, 91L, 92L, 91L, 91L, 91L,
91L, 90L, 89L, 88L, 87L, 87L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 89L, 89L, 89L, 89L, 89L, 89L, 89L, 89L, 89L, 89L,
85L, 85L, 85L, 85L, 85L, 85L, 86L, 86L, 82L, 83L, 83L, 84L, 84L,
85L, 85L, 86L, 86L, 86L, 74L, 74L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L), Calc.SPM = c(35L, 38L, 37L, 39L, 42L, 42L, 45L, 48L, 46L,
46L, 46L, 44L, 46L, 52L, 50L, 53L, 59L, 67L, 65L, 65L, 41L, 41L,
41L, 40L, 42L, 41L, 41L, 45L, 46L, 47L, 46L, 47L, 47L, 48L, 48L,
49L, 47L, 47L, 46L, 48L, 48L, 51L, 55L, 55L, 65L, 66L, 70L, 72L,
55L, 55L, 60L, 60L, 62L, 68L, 71L, 69L, 77L, 72L, 47L, 54L, 60L,
63L, 68L, 74L, 94L, 94L, 106L, 123L, 120L, 127L, 130L, 36L, 33L,
31L, 34L, 34L, 41L, 55L, 68L, 87L, 114L, 168L, 204L, 226L, 240L,
262L, 42L, 44L, 54L, 58L, 52L, 52L, 51L, 50L, 58L, 68L, 90L,
118L, 156L, 174L, 180L, 187L, 180L, 190L, 189L, 187L, 187L, 185L,
184L, 183L, 192L, 28L, 28L, 28L, 29L, 30L, 31L, 31L, 35L, 40L,
41L, 45L, 48L, 53L, 78L, 169L, 186L, 195L, 25L, 25L, 25L, 27L,
29L, 32L, 32L, 41L, 65L, 70L, 67L, 66L, 69L, 27L, 29L, 74L, 102L,
148L, 141L, 149L, 217L, 24L, 25L, 33L, 88L, 118L, 133L, 170L,
223L, 225L, 27L, 32L, 36L, 41L, 45L, 52L, 66L, 106L, 110L, 106L,
113L, 136L, 149L, 185L, 23L, 23L, 23L, 23L, 24L, 28L, 33L, 40L,
60L, 72L, 76L, 84L, 90L, 92L, 91L, 91L, 90L, 89L, 86L, 85L, 84L,
86L, 15L, 14L, 15L, 14L, 20L, 29L, 32L, 34L, 34L, 37L, 45L, 14L,
14L, 15L, 16L, 17L, 40L, 47L, 53L, 67L, 70L, 85L, 100L, 11L,
11L, 11L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
13L, 14L, 16L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 18L, 19L,
18L, 20L, 22L, 23L, 24L, 27L, 30L, 28L, 26L, 25L, 24L, 25L, 13L,
14L, 16L, 16L, 17L, 17L, 19L, 20L, 21L, 21L, 21L, 22L, 23L, 23L,
23L, 23L, 21L, 20L, 23L, 22L, 25L, 36L, 39L, 41L, 30L, 34L, 40L,
47L, 53L, 56L, 62L, 64L, 67L, 70L, 88L, 93L, 87L, 89L, 98L, 109L,
17L, 19L, 19L, 21L, 22L, 23L, 28L, 38L, 50L, 63L, 70L, 100L,
118L, 117L, 128L, 135L, 124L, 22L, 19L, 18L, 26L, 42L, 51L, 63L,
65L, 70L, 68L, 70L, 79L, 92L, 31L, 31L, 30L, 31L, 35L, 38L, 42L,
44L, 47L, 45L, 48L, 50L, 51L, 50L, 22L, 23L, 23L, 23L, 31L, 54L,
58L, 56L, 25L, 25L, 25L, 27L, 27L, 25L, 24L, 26L, 30L, 34L, 23L,
23L, 23L, 24L, 24L, 22L, 21L, 20L, 20L, 20L, 21L, 24L, 23L, 25L,
26L, 26L, 26L, 28L, 31L, 36L, 34L, 36L, 34L, 24L, 26L, 28L, 28L,
32L, 32L, 32L, 32L, 31L, 32L, 34L, 37L, 27L, 27L, 29L, 29L, 27L,
27L, 27L, 25L, 25L, 23L, 35L, 35L, 35L, 35L, 39L, 40L, 40L, 43L,
45L, 43L, 67L, 72L, 75L, 82L, 96L, 137L, 142L, 105L, 245L, 264L,
267L, 279L, 248L, 224L, 180L, 137L, 104L, 98L, 293L, 294L, 33L,
35L, 36L, 36L, 35L, 34L, 38L, 38L, 38L, 39L, 27L, 27L, 27L, 28L,
28L, 28L, 28L, 28L, 28L, 29L), Salinity = c(0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.66, 0.65, 0.66, 0.66, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65,
2.97, 3.12, 3.15, 3.16, 3.17, 3.17, 3.16, 3.16, 3.2, 3.23, 3.23,
3.24, 3.24, 3.86, 4.2, 4.57, 4.68, 4.7, 5, 5.26, 5.5, 5.84, 6.05,
6.12, 6.13, 6.12, 6.12, 6.12, 6.01, 6.23, 6.78, 7.03, 7.16, 7.25,
7.35, 7.34, 7.74, 8.05, 8.25, 8.38, 8.54, 8.59, 8.59, 8.6, 8.58,
8.61, 8.61, 8.63, 8.63, 8.64, 8.63, 8.64, 8.64, 6.87, 6.88, 6.93,
7.37, 7.67, 7.89, 8.09, 8.34, 8.5, 8.54, 8.57, 8.67, 8.81, 9.28,
10.01, 10.07, 10.09, 9.45, 9.5, 9.53, 9.74, 9.92, 10.11, 10.23,
10.94, 11.77, 11.87, 11.79, 11.77, 11.85, 13.1, 13.71, 14.52,
14.63, 14.98, 15.06, 15.06, 15.05, 16.77, 17.64, 18.82, 20.03,
20.27, 20.33, 20.48, 20.56, 20.57, 20.6, 21.19, 21.56, 21.66,
21.73, 21.78, 21.83, 21.88, 21.87, 21.87, 21.88, 21.89, 21.89,
21.89, 20.12, 20.13, 20.23, 20.31, 20.65, 21.24, 21.62, 21.87,
22.12, 22.18, 22.24, 22.34, 22.45, 22.53, 22.55, 22.53, 22.53,
22.52, 22.56, 22.57, 22.57, 22.56, 23.69, 25.05, 25.27, 25.53,
26.04, 26.32, 26.36, 26.38, 26.41, 26.46, 26.46, 25.11, 25.72,
25.97, 26.19, 26.59, 26.97, 27.05, 27.09, 27.15, 27.15, 27.22,
27.22, 27.36, 27.43, 27.48, 27.44, 27.33, 27.26, 27.27, 27.33,
27.37, 27.57, 27.68, 27.72, 27.79, 28.01, 28.19, 28.38, 28.47,
28.48, 28.48, 28.57, 28.66, 28.68, 28.8, 28.85, 28.92, 28.93,
28.95, 28.95, 28.95, 28.93, 28.93, 28.93, 28.91, 28.9, 28.91,
28.91, 28.92, 28.92, 28.92, 26.77, 26.95, 27.12, 27.26, 27.35,
27.33, 27.39, 27.41, 27.44, 27.46, 27.46, 27.47, 27.51, 27.58,
27.61, 27.62, 27.66, 27.71, 27.82, 27.73, 27.87, 27.94, 27.94,
27.94, 26.28, 26.32, 26.42, 26.48, 26.55, 26.59, 26.62, 26.62,
26.64, 26.65, 26.65, 26.65, 26.66, 26.66, 26.65, 26.65, 25.74,
25.9, 25.91, 26.16, 26.23, 26.25, 26.37, 26.45, 26.48, 26.49,
26.5, 26.52, 26.53, 26.53, 26.53, 26.53, 26.53, 24.72, 25, 25.13,
25.3, 25.43, 25.46, 25.49, 25.49, 25.5, 25.49, 25.49, 25.5, 25.5,
25.36, 25.36, 25.36, 25.36, 25.35, 25.35, 25.34, 25.34, 24.45,
24.45, 24.45, 24.45, 24.44, 24.44, 23.78, 23.78, 23.78, 23.78,
23.8, 23.81, 23.81, 23.81, 23.56, 23.56, 23.56, 23.58, 23.63,
23.68, 23.72, 23.73, 23.74, 23.74, 23.31, 23.31, 23.31, 23.33,
23.37, 23.47, 23.51, 23.58, 23.61, 23.61, 23.66, 23.74, 23.12,
23.16, 23.22, 23.26, 23.27, 23.27, 23.29, 23.33, 23.36, 23.38,
23.39, 23.05, 23.05, 23.06, 23.07, 23.08, 23.08, 23.09, 23.1,
23.12, 23.18, 23.25, 23.27, 22.63, 22.63, 22.7, 22.87, 22.89,
22.9, 22.93, 22.95, 22.98, 23.01, 22.33, 22.47, 22.51, 22.55,
22.58, 22.6, 22.62, 22.64, 22.68, 22.72, 21.29, 21.67, 21.67,
21.66, 21.68, 21.74, 21.9, 22.15, 20.73, 20.83, 20.91, 21.02,
21.1, 21.25, 21.39, 21.5, 21.68, 21.83, 20.46, 20.51, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06), Temperature = c(19.14,
19.12, 19.11, 19.09, 19.08, 19.08, 19.06, 19.05, 19.06, 19.06,
19.1, 19.11, 19.09, 19.07, 19.07, 19.06, 19.06, 19.05, 19.05,
19.05, 19.32, 19.32, 19.33, 19.32, 19.32, 19.32, 19.32, 19.31,
19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.29, 19.71, 19.71,
19.72, 19.7, 19.7, 19.68, 19.65, 19.66, 19.62, 19.62, 19.62,
19.62, 19.78, 19.79, 19.76, 19.76, 19.74, 19.72, 19.72, 19.72,
19.71, 19.71, 19.63, 19.54, 19.5, 19.49, 19.48, 19.45, 19.39,
19.39, 19.35, 19.32, 19.33, 19.31, 19.3, 19.47, 19.21, 18.91,
18.86, 18.86, 18.81, 18.76, 18.72, 18.67, 18.64, 18.63, 18.63,
18.63, 18.63, 18.63, 18.93, 18.85, 18.7, 18.65, 18.64, 18.63,
18.62, 18.62, 18.56, 18.51, 18.48, 18.45, 18.42, 18.41, 18.41,
18.41, 18.41, 18.4, 18.41, 18.41, 18.41, 18.41, 18.41, 18.41,
18.41, 19.24, 19.21, 19.09, 18.62, 18.56, 18.5, 18.47, 18.45,
18.43, 18.44, 18.44, 18.44, 18.44, 18.41, 18.34, 18.34, 18.33,
18.72, 18.66, 18.65, 18.5, 18.39, 18.3, 18.28, 18.21, 18.13,
18.12, 18.13, 18.13, 18.12, 19.18, 18.47, 18.05, 18.02, 17.96,
17.95, 17.95, 17.95, 18.36, 17.55, 17.21, 16.85, 16.78, 16.76,
16.71, 16.69, 16.69, 16.84, 16.61, 16.51, 16.45, 16.42, 16.39,
16.37, 16.34, 16.34, 16.34, 16.34, 16.33, 16.33, 16.32, 17.11,
17.05, 16.88, 16.81, 16.69, 16.52, 16.4, 16.31, 16.21, 16.19,
16.17, 16.14, 16.12, 16.1, 16.1, 16.1, 16.1, 16.11, 16.11, 16.11,
16.11, 16.12, 16.32, 15.69, 15.54, 15.46, 15.2, 15.05, 15.02,
15.01, 14.99, 14.96, 14.96, 15.28, 15.16, 15.09, 15.04, 14.93,
14.77, 14.74, 14.72, 14.69, 14.69, 14.66, 14.65, 14.77, 14.74,
14.71, 14.73, 14.8, 14.83, 14.83, 14.8, 14.78, 14.66, 14.59,
14.57, 14.52, 14.39, 14.27, 14.15, 14.09, 14.09, 14.09, 14.03,
13.97, 13.96, 13.89, 13.86, 13.8, 13.79, 13.77, 13.77, 13.77,
13.78, 13.78, 13.78, 13.78, 13.78, 13.78, 13.78, 13.77, 13.77,
13.77, 14.97, 14.87, 14.77, 14.7, 14.65, 14.67, 14.65, 14.64,
14.63, 14.62, 14.62, 14.61, 14.59, 14.54, 14.52, 14.5, 14.49,
14.47, 14.4, 14.45, 14.38, 14.34, 14.34, 14.34, 15.6, 15.58,
15.53, 15.49, 15.46, 15.44, 15.43, 15.43, 15.41, 15.39, 15.38,
15.38, 15.38, 15.38, 15.39, 15.4, 15.69, 15.63, 15.63, 15.58,
15.58, 15.59, 15.57, 15.53, 15.5, 15.49, 15.48, 15.46, 15.45,
15.45, 15.44, 15.44, 15.45, 17.95, 17.59, 17.42, 17.15, 16.93,
16.87, 16.83, 16.81, 16.8, 16.81, 16.8, 16.79, 16.79, 17.2, 17.2,
17.2, 17.2, 17.21, 17.21, 17.22, 17.22, 18.16, 18.16, 18.16,
18.16, 18.16, 18.16, 18.88, 18.88, 18.88, 18.88, 18.87, 18.86,
18.86, 18.86, 19.34, 19.35, 19.34, 19.33, 19.26, 19.13, 19, 18.97,
18.96, 18.96, 19.76, 19.76, 19.77, 19.75, 19.68, 19.46, 19.37,
19.2, 19.14, 19.12, 19.03, 18.88, 20.13, 20.1, 19.98, 19.88,
19.86, 19.85, 19.81, 19.77, 19.73, 19.7, 19.68, 20.28, 20.27,
20.26, 20.25, 20.24, 20.23, 20.21, 20.18, 20.13, 19.96, 19.78,
19.76, 20.48, 20.48, 20.46, 20.31, 20.27, 20.25, 20.23, 20.2,
20.19, 20.17, 20.38, 20.36, 20.37, 20.36, 20.33, 20.31, 20.31,
20.31, 20.29, 20.26, 20.51, 20.43, 20.39, 20.37, 20.34, 20.22,
20.06, 19.91, 20.53, 20.49, 20.48, 20.46, 20.47, 20.48, 20.48,
20.48, 20.49, 20.48, 19.47, 19.43, 19.11, 19.1, 19.08, 19.09,
19.09, 19.09, 19.09, 19.1, 19.1, 19.11, 18.22, 18.22, 18.23,
18.22, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2)), class = "data.frame", row.names = c(NA,
-453L))
##Setting up data
example.data$Date<-'06/10/2005'
example.data$Date<-as.Date(example.data$Date,"%m/%d/%Y")
d_date<-example.data[example.data$Date=='2005-06-10',]
d_date<-drop_na(d_date,c(Calc.Chl,DO,Calc.SPM,Salinity,Temperature))
d_date$logDepth<-log10(d_date$Depth)
##add a line, if minimum depth @station > 1, copy minimum as all depths between 1 and min
#Interpolations
interp.chl <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Calc.Chl, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.chl <- interp.chl %>% interp2xyz() %>% as.data.frame()
names(interp.df.chl) <- c("x", "y", "Chl")
interp.do <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$DO, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.do <- interp.do %>% interp2xyz() %>% as.data.frame()
names(interp.df.do) <- c("x", "y", "Oxy")
interp.spm <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Calc.SPM, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.spm <- interp.spm %>% interp2xyz() %>% as.data.frame()
names(interp.df.spm) <- c("x", "y", "SPM")
interp.sal <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Salinity, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.sal <- interp.sal %>% interp2xyz() %>% as.data.frame()
names(interp.df.sal) <- c("x", "y", "Sal")
interp.temp <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Temperature, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.temp <- interp.temp %>% interp2xyz() %>% as.data.frame()
names(interp.df.temp) <- c("x", "y", "Temp")
就上下文而言，我们的数据按日期/站点/深度组织，其中每个日期在许多位置（站点）采样，深度以 1m 为增量。 如果我们丢弃地表数据，则没有 1m 深度，第一个深度是 2。这是一个日期内两个站点的数据片段，一个最小深度是 1m，第二个是 2m。
蒂亚！
使用Akima软件包线性插值：对非常不规则的网格进行插值
[英]Using Akima package linear interpolation: interp for a very irregular grid
通过使用查找表和线性插值添加data.frame列
[英]add data.frame column by using a look up table and linear interpolation
在 R 中使用线性插值计算 data.frame 每一行的值？
[英]Computing values of each row of a data.frame using linear interpolation in R?
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