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社区首页 >问答首页 >如何在以前大于特定值的时间序列中筛选值

问如何在以前大于特定值的时间序列中筛选值
EN

Stack Overflow用户
提问于 2020-03-12 02:15:05
回答 1查看 99关注 0票数 0

我试图在一个时间序列中过滤珊瑚的人口数据。我有一组珊瑚，每三个月测量一次。我想做的是在某一点的最大直径为9、10或11 mm和b.)清除之前在珊瑚普查中大于9，10或11毫米的珊瑚。值得注意的是，我也想过滤珊瑚的大小范围和在下一个TimeStep，没有离开9-11毫米的范围，因为这构成了0增长，我也想包括这些珊瑚。

我已经创建了一个要使用的示例数据库。菌落#1是一个珊瑚的例子，生长超过大小范围(9-11毫米)，然后缩小到9个，第1号，我想从数据库中完全删除。

菌落#2开始超过预期的大小范围(9-11毫米)，然后缩小到范围内以后。我也希望这个珊瑚被移除，因为我需要确保在这个范围内的珊瑚不会收缩，而是生长到它。

第三群是一个珊瑚生长到大小范围(9-11毫米)而不收缩的例子，这是我想保留的珊瑚，因为它长到了大小范围。

第四群是珊瑚的一个例子，它开始于大小范围以上，因此需要移除。

第五蜂群是一个珊瑚的例子，它开始在山脉下面生长，后来又缩小到这个范围。对于这种情况，我只想包括第一次直径下降到范围内，而不是第二次。这是因为第一次是自然生长，而第二次是收缩及其结果的恢复(我想排除或过滤掉)。

菌落#6是珊瑚的一个例子，它从TimeStep 1的大小范围开始，然后在下一个TimeStep中长大，然后继续生长。我希望维护这个实例中的第一个TimeStep的所有度量，以便计算TimeStep 1和2之间的增长。

殖民地#7是珊瑚的一个例子，它从TimeStep 1的大小范围开始，然后保持在TimeStep 2的范围内。在这种情况下(假设珊瑚没有缩小到以后的大小范围)，我想保持所有的测量结果都是TimeStep 1和2。在这种情况下，珊瑚从第一次出现在这个范围内时增长为0，我想将这些珊瑚包括在这个数据库中进行分析。

8号珊瑚是一个例子，它生长到TimeStep 3的大小范围，在TimeStep 4中保持在10 => 9的范围内，然后缩小到预期的范围以下，然后TimeStep 6生长回到这个范围。对于这个蜂群，我希望将TimeStep 4包含在这个珊瑚中，因为珊瑚在TimeStep 3和4之间被认为是相同的大小(因为大小仍然在测量误差范围内)。

9号珊瑚是一个例子，它生长到TimeStep 3的大小范围，停留在TimeStep 4 (10 => 9)中，然后生长在TimeStep 5和TimeStep 6的范围之上。因此，这种珊瑚应该包含在数据库中的所有测量值(TimeStep 1-6)，因为这种珊瑚从不收缩。

10号珊瑚是一个例子，它生长到了TimeStep 4的大小范围，然后缩小到TimeStep 5的范围以下，然后在TimeStep 6中超过了它。在这种情况下，我想包括TimeStep 5，因为我想从尺寸范围中得到收缩的程度。因此，只有TimeStep 6应该过滤掉，因为珊瑚缩小到小于大小范围(9-11毫米)。

总之，我想要的代码过滤这个数据库，如果某个点的珊瑚直径为9-11毫米，但以前大于这个范围，从未在这个范围内或低于这个范围，或者从这个范围以下开始，并且从来不属于这个范围，它们就会被完全从数据库中移除。此外，我希望保持任何珊瑚生长到范围，然后缩小到它在数据库中，同时删除第二次落在范围内。我正在寻找一个通用的代码形式，以便能够过滤掉这些情况，使数据库中的所有珊瑚都开始在9-11毫米以下，然后增长到这个范围。谢谢您抽时间见我!

数据库

代码语言：javascript
复制
Data <- structure(list(Site = c("WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI"
), `Module #` = c(116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116), 
    Side = c("N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
    "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
    "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
    "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
    "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
    "N", "N"), TimeStep = c(1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 
    6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 
    1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 
    2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6), Settlement_Area = c(0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
    0.75902336, 0.75902336, 0.75902336, 0.75902336), `Colony #` = c(1, 
    1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 
    4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 
    7, 7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 
    10, 10, 10), Location = c("C1", "C1", "C1", "C1", "C1", "C1", 
    "B1", "B1", "B1", "B1", "B1", "B1", "A1", "A1", "A1", "A1", 
    "A1", "A1", "D1", "D1", "D1", "D1", "D1", "D1", "D1", "D1", 
    "D1", "D1", "D1", "D1", "A2", "A2", "A2", "A2", "A2", "A2", 
    "A4", "A4", "A4", "A4", "A4", "A4", "B3", "B3", "B3", "B3", 
    "B3", "B3", "C2", "C2", "C2", "C2", "C2", "C2", "B4", "B4", 
    "B4", "B4", "B4", "B4"), `Taxonomic Code` = c("PC", "PC", 
    "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
    "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
    "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
    "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
    "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
    "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC"), `Cover Code` = c(1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1), `Max Diameter (cm)` = c(5, 8, 12, 15, 9, 16, 15, 13, 
    11, 15, 17, 20, 3, 6, 9, 12, 15, 20, 13, 16, 24, 22, 28, 
    30, 6, 9, 14, 9, 15, 19, 11, 14, 17, 17, 21, 24, 9, 11, 14, 
    16, 20, 22, 3, 6, 10, 9, 7, 10, 5, 7, 10, 9, 13, 16, 5, 7, 
    9, 10, 8, 13)), class = c("spec_tbl_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -60L), spec = structure(list(
    cols = list(Site = structure(list(), class = c("collector_character", 
    "collector")), `Module #` = structure(list(), class = c("collector_double", 
    "collector")), Side = structure(list(), class = c("collector_character", 
    "collector")), TimeStep = structure(list(), class = c("collector_double", 
    "collector")), Settlement_Area = structure(list(), class = c("collector_double", 
    "collector")), `Colony #` = structure(list(), class = c("collector_double", 
    "collector")), Location = structure(list(), class = c("collector_character", 
    "collector")), `Taxonomic Code` = structure(list(), class = c("collector_character", 
    "collector")), `Cover Code` = structure(list(), class = c("collector_double", 
    "collector")), `Max Diameter (cm)` = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1), class = "col_spec"))

所需数据库(过滤)

代码语言：javascript
复制
Data_2 <- structure(list(Site = c("WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI", 
"WAI", "WAI", "WAI", "WAI", "WAI", "WAI", "WAI"), `Module #` = c(116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 
116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116
), Side = c("N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N"), TimeStep = c(1, 2, 3, 4, 1, 2, 3, 4, 5, 
6, 1, 2, 3, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 1, 
2, 3, 4, 5, 6, 1, 2, 3, 4, 5), Settlement_Area = c(0.75902336, 
0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 0.75902336, 
0.75902336, 0.75902336, 0.75902336), `Colony #` = c(1, 1, 1, 
1, 3, 3, 3, 3, 3, 3, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 
7, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10), Location = c("C1", 
"C1", "C1", "C1", "A1", "A1", "A1", "A1", "A1", "A1", "D1", "D1", 
"D1", "A2", "A2", "A2", "A2", "A2", "A2", "A4", "A4", "A4", "A4", 
"A4", "A4", "B3", "B3", "B3", "B3", "C2", "C2", "C2", "C2", "C2", 
"C2", "B4", "B4", "B4", "B4", "B4"), `Taxonomic Code` = c("PC", 
"PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
"PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
"PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", 
"PC", "PC", "PC", "PC", "PC", "PC"), `Cover Code` = c(1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `Max Diameter (cm)` = c(5, 
8, 12, 15, 3, 6, 9, 12, 15, 20, 6, 9, 14, 11, 14, 17, 17, 21, 
24, 9, 11, 14, 16, 20, 22, 3, 6, 10, 9, 5, 7, 10, 9, 13, 16, 
5, 7, 9, 10, 8)), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -40L), spec = structure(list(cols = list(
    Site = structure(list(), class = c("collector_character", 
    "collector")), `Module #` = structure(list(), class = c("collector_double", 
    "collector")), Side = structure(list(), class = c("collector_character", 
    "collector")), TimeStep = structure(list(), class = c("collector_double", 
    "collector")), Settlement_Area = structure(list(), class = c("collector_double", 
    "collector")), `Colony #` = structure(list(), class = c("collector_double", 
    "collector")), Location = structure(list(), class = c("collector_character", 
    "collector")), `Taxonomic Code` = structure(list(), class = c("collector_character", 
    "collector")), `Cover Code` = structure(list(), class = c("collector_double", 
    "collector")), `Max Diameter (cm)` = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"))
filter
dplyr
r
EN

回答 1

Stack Overflow用户

发布于 2020-03-12 13:41:52

编辑#2:只是为了总结我所理解的过滤器标准：

  1. 过滤掉每一个直径大于11厘米的珊瑚，因为你不知道它是长了还是缩小到了预期的范围(珊瑚#2和#4)
  2. 过滤出的珊瑚甚至从来没有达到你想要的范围(在你的示例数据集中没有例子)
  3. 在珊瑚开始再次收缩之后的所有时间步骤都过滤掉了(比如珊瑚#1，时间步骤5)

。

当你把珊瑚#1保持在你想要的输出中时，我想这并不重要，无论是珊瑚生长到你想要的范围，还是直接跳过它。对吗？

代码：

代码语言：javascript
复制
Data_filtered <- Data %>%
  group_by(`Colony #`) %>%
  filter(any((TimeStep == 1 & `Max Diameter (cm)` < 12)), # criterion 1
         !all(`Max Diameter (cm)` < 9), # criterion 2
         row_number() + 1 <= min(which(lag(`Max Diameter (cm)`) > `Max Diameter (cm)`))) # criterion 3

# test whether the filtering worked ok
all_equal(Data_filtered, Data_2)
[1] TRUE

这将导致与所需的输出数据框架相同的过滤数据框架(感谢这一点--它使事情变得更容易了！)

票数 1
EN
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://stackoverflow.com/questions/60646724

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