R代码按组获取时间序列数据的最大计数 [英] R code to get max count of time series data by group

问题描述

我想获得时间序列数据的摘要，其中组是Flare"，FlareLength 的最大值是该组感兴趣的数据.

如果我有一个数据框，就像这样:

<预><代码>日期耀斑耀斑长度1 2015-12-01 0 12 2015-12-02 0 23 2015-12-03 0 34 2015-12-04 0 45 2015-12-05 0 56 2015-12-06 0 67 2015-12-07 1 18 2015-12-08 1 29 2015-12-09 1 310 2015-12-10 1 411 2015-12-11 0 112 2015-12-12 0 213 2015-12-13 0 314 2015-12-14 0 415 2015-12-15 0 516 2015-12-16 0 617 2015-12-17 0 718 2015-12-18 0 819 2015-12-19 0 920 2015-12-20 0 1021 2015-12-21 0 1122 2016-01-11 1 123 2016-01-12 1 224 2016-01-13 1 325 2016-01-14 1 426 2016-01-15 1 527 2016-01-16 1 628 2016-01-17 1 729 2016-01-18 1 8

我希望输出如下:

 Date Flare FlareLength1 2015-12-06 0 62 2015-12-10 1 43 2015-12-21 0 114 2016-01-18 1 8

我尝试过各种聚合形式，但我对时间序列皱纹不是很熟悉.

解决方案

使用 dplyr，我们可以通过将 FlareLength 与之前的 进行比较来创建分组变量FlareLength 值并选择组中具有 maximum FlareLength 的行.

库(dplyr)df%＞%group_by(gr = cumsum(FlareLength < lag(FlareLength,默认值 = first(FlareLength)))) %>%切片(which.max(FlareLength))％>％取消分组()%>%选择(-gr)# 小费:4 x 3# 日期耀斑 FlareLength# <fct><int><int>#1 2015-12-06 0 6#2 2015-12-10 1 4#3 2015-12-21 0 11#4 2016-01-18 1 8

在带有 ave 的基础 R 中，我们可以执行与

相同的操作

subset(df, FlareLength == ave(FlareLength, cumsum(c(TRUE, diff(FlareLength) <0)),乐趣 = 最大))

I'd like to get a summary of time series data where group is "Flare" and the max value of the FlareLength is the data of interest for that group.

If I have a dataframe, like this:

   Date           Flare       FlareLength
1  2015-12-01     0           1
2  2015-12-02     0           2
3  2015-12-03     0           3
4  2015-12-04     0           4
5  2015-12-05     0           5
6  2015-12-06     0           6
7  2015-12-07     1           1
8  2015-12-08     1           2
9  2015-12-09     1           3
10 2015-12-10     1           4
11 2015-12-11     0           1
12 2015-12-12     0           2
13 2015-12-13     0           3
14 2015-12-14     0           4
15 2015-12-15     0           5
16 2015-12-16     0           6
17 2015-12-17     0           7
18 2015-12-18     0           8
19 2015-12-19     0           9
20 2015-12-20     0          10
21 2015-12-21     0          11
22 2016-01-11     1           1
23 2016-01-12     1           2
24 2016-01-13     1           3
25 2016-01-14     1           4
26 2016-01-15     1           5
27 2016-01-16     1           6
28 2016-01-17     1           7
29 2016-01-18     1           8

I'd like output like:

  Date           Flare       FlareLength
1 2015-12-06     0           6
2 2015-12-10     1           4
3 2015-12-21     0          11
4 2016-01-18     1           8

I have tried various aggregate forms but I'm not very familiar with the time series wrinkle.

解决方案

Using dplyr, we can create a grouping variable by comparing the FlareLength with the previous FlareLength value and select the row with maximum FlareLength in the group.

library(dplyr)

df %>%
  group_by(gr = cumsum(FlareLength < lag(FlareLength, 
                       default = first(FlareLength)))) %>%
  slice(which.max(FlareLength)) %>%
  ungroup() %>%
  select(-gr)

# A tibble: 4 x 3
#  Date       Flare FlareLength
#  <fct>      <int>       <int>
#1 2015-12-06     0           6
#2 2015-12-10     1           4
#3 2015-12-21     0          11
#4 2016-01-18     1           8

In base R with ave we can do the same as

subset(df, FlareLength == ave(FlareLength, cumsum(c(TRUE, diff(FlareLength) < 0)), 
           FUN = max))

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