0

我有一个数据框，其中包含以下格式的观察结果：

（我的实际数据有比这更多的列，但为了清楚起见，这些是重要的）

head(sampleDF, 20)
               Timestamp TimeIntoSession                      CorrelationGuid Position_x Position_z
1  11/22/2017 11:12:30 AM        1234.331 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.166      4.947
2  11/22/2017 11:12:30 AM        1234.397 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.155      4.902
3  11/22/2017 11:12:30 AM        1234.464 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.144      4.858
4  11/22/2017 11:12:30 AM        1234.547 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.133      4.810
5  11/22/2017 11:12:30 AM        1234.614 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.125      4.777
6  11/22/2017 11:12:30 AM        1234.697 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.118      4.741
7  11/22/2017 11:12:30 AM        1234.764 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.114      4.714
8  11/22/2017 11:12:30 AM        1234.847 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.114      4.683
9  11/22/2017 11:12:30 AM        1234.914 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.119      4.661
10 11/22/2017 11:12:30 AM        1234.997 714e8a89-91a5-415b-b102-6ed5c0cf9f44      5.128      4.639
11 11/22/2017 11:12:30 AM         327.341 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.105      4.099
12 11/22/2017 11:12:30 AM         327.480 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.104      4.100
13 11/22/2017 11:12:30 AM         327.557 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.103      4.100
14 11/22/2017 11:12:30 AM         327.640 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.103      4.099
15 11/22/2017 11:12:30 AM         327.723 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.103      4.099
16 11/22/2017 11:12:30 AM         327.807 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.103      4.098
17 11/22/2017 11:12:30 AM         327.890 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.102      4.097
18 11/22/2017 11:12:30 AM         327.957 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.101      4.096
19 11/22/2017 11:12:30 AM         328.040 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.099      4.095
20 11/22/2017 11:12:30 AM         328.123 22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6      3.096      4.094

对于特定 CorrelationGuid 中的每一行，我想找到由当前行中的 X 和 Z 值定义的位置的欧几里德范数与前一行的欧几里得范数之间的差异。

我可以像这样对整个数据框执行此操作：

norm_vec <- function(x,y) sqrt(x^2 + y^2)
sampleMag<- mutate(sampleDF, sqMag = norm_vec(Position_x, Position_z) - norm_vec(lag(Position_x, default = 0), lag(Position_z, default = 0)))

但这会给每一行带来差异；我想在每个 CorrelationGuid 中执行此操作；也就是说，我不希望新的 CorrelationGuid 的第一行在进行计算时查看之前的 CorrelationGuid 的最后一行。

我可以像这样尝试一个 CorrelationGuid：

sampleMag<- mutate(sampleDF, sqMag = ifelse(CorrelationGuid == "714e8a89-91a5-415b-b102-6ed5c0cf9f44", 
                                        (norm_vec(Position_x, Position_z) - norm_vec(lag(Position_x, default = 0), lag(Position_z, default = 0))), NA))

但这并不是我真正想要的。我想为每个CorrelationGuid 执行此操作，并且除了一个之外没有其他的 NA。

我可以使用 unique() 或 distinct() 轻松生成唯一 CorrelationGuid 值的列表，但是为每个唯一 CorrelationGuid 运行一次上述逻辑的最佳方法是什么？

我可以找到每个 CorrelationGuid 的第一个和最后一个实例，然后循环遍历它，但是这里的 for 循环会非常慢，尤其是在大型数据集上执行此操作时。

apply 似乎很合适，但我不确定如何在这里最好地使用它。

4

1 回答 1

0

只是为了对评论中的所有讨论表示敬意，我相信您正在寻找@jazzurro 提供的内容......

sampleDF <- sampleDF %>% group_by(CorrelationGuid) %>% 
    mutate(sqMag = norm_vec(Position_x, Position_z) - norm_vec(lag(Position_x, default = 0), lag(Position_z, default = 0)))
sampleDF

使用来自 dput 的以下内容来重现数据

structure(list(CorrelationGuid = c("714e8a89-91a5-415b-b102-6ed5c0cf9f44", 
"714e8a89-91a5-415b-b102-6ed5c0cf9f44", "714e8a89-91a5-415b-b102-6ed5c0cf9f44", 
"714e8a89-91a5-415b-b102-6ed5c0cf9f44", "714e8a89-91a5-415b-b102-6ed5c0cf9f44", 
"714e8a89-91a5-415b-b102-6ed5c0cf9f44", "714e8a89-91a5-415b-b102-6ed5c0cf9f44", 
"714e8a89-91a5-415b-b102-6ed5c0cf9f44", "714e8a89-91a5-415b-b102-6ed5c0cf9f44", 
"714e8a89-91a5-415b-b102-6ed5c0cf9f44", "22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", 
"22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", "22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", 
"22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", "22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", 
"22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", "22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", 
"22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", "22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", 
"22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6"), Position_x = c(5.166, 
5.155, 5.144, 5.133, 5.125, 5.118, 5.114, 5.114, 5.119, 5.128, 
3.105, 3.104, 3.103, 3.103, 3.103, 3.103, 3.102, 3.101, 3.099, 
3.096), Position_z = c(4.947, 4.902, 4.858, 4.81, 4.777, 4.741, 
4.714, 4.683, 4.661, 4.639, 4.099, 4.1, 4.1, 4.099, 4.099, 4.098, 
4.097, 4.096, 4.095, 4.094), sqMag = c(7.15264741197272, -0.0390246359183104, 
-0.0382499943559047, -0.0409013019070033, -0.0283774181905558, 
-0.0296332826672581, -0.0212618601391981, -0.0209732951217187, 
-0.0111423510261082, -0.00812087217042912, 5.1422588421821, 0.000193491092365328, 
-0.000603541254226236, -0.000797343143268847, 0, -0.000797272282198946, 
-0.00140089984128977, -0.00140089254591658, -0.0020043998541075, 
-0.00260744523674017)), .Names = c("CorrelationGuid", "Position_x", 
"Position_z", "sqMag"), class = c("grouped_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -20L), vars = "CorrelationGuid", labels = structure(list(
    CorrelationGuid = c("22f2f3bd-0750-4ccb-a5fc-e8f8a83d06f6", 
    "714e8a89-91a5-415b-b102-6ed5c0cf9f44")), class = "data.frame", row.names = c(NA, 
-2L), vars = "CorrelationGuid", drop = TRUE, .Names = "CorrelationGuid"), indices = list(
    10:19, 0:9), drop = TRUE, group_sizes = c(10L, 10L), biggest_group_size = 10L)
于 2018-01-18T01:50:11.183 回答