我想从一组嵌套数据中获取前x个结果。在管道函数的末尾添加map(data, slice(seq(n()*0.2)))会给我错误。
因此，我尝试获取每个tbl_df的前20％并返回它们。
第二，如何基于日期列嵌套数据？我还尝试根据t年7月至t-1年6月之间的所有观察结果嵌套数据。我不想基于Yrs嵌套它们，而是基于date列中的特定数据范围嵌套它们。
数据看起来像（我将所有年份更改为2007/2008）：
id Yrs date var NESTID
1 1144215 2006_2007 2007-02-23 0.7897917 1
2 352510 2011_2012 2007-02-27 0.9626791 1
3 793952 2008_2009 2007-11-27 0.8186645 2
4 1368457 2014_2015 2007-03-02 0.9151516 1
5 319201 2012_2013 2007-07-20 0.9816049 2
6 882095 2017_2018 2008-03-19 0.9924851 2
所以所有1都在一个巢中，而所有2都在另一个巢中，因为最终直到7月tan少的2008阻塞与2一起在同一巢中。
代码：
nested_result <- x %>%
as_tibble() %>%
group_by(Yrs) %>%
nest(.key = "data")
数据：
x <- structure(list(id = c(1144215L, 352510L, 793952L, 1368457L, 319201L,
882095L, 28412L, 1048911L, 109198L, 42293L, 97854L, 764180L,
53117L, 1038357L, 310158L, 1324404L, 1133421L, 1646383L, 1095073L,
1166691L, 1133421L, 721083L, 28412L, 1171825L, 764622L, 40493L,
27904L, 109198L, 1039684L, 916457L, 72741L, 800240L, 1168054L,
16918L, 4447L, 885639L, 1066806L, 1156039L, 732485L, 63754L,
1310067L, 720005L, 1168054L, 92122L, 310158L, 1601712L, 42582L,
70858L, 789388L, 21344L, 310764L, 835910L, 1065696L, 835541L,
863157L, 51253L, 1137774L, 1018963L, 217346L, 945436L, 1058290L,
84792L, 50104L, 8868L, 200406L, 929887L, 200406L, 64803L, 709804L,
41077L, 785161L, 1585689L, 1037868L, 769397L, 63754L, 720005L,
63541L, 33213L, 723527L, 1021860L, 4904L, 832428L, 915389L, 356028L,
1361658L, 929887L, 40987L, 352541L, 1518832L, 100893L, 1004440L,
1633917L, 14693L, 783325L, 92380L, 1043277L, 797468L, 922224L,
1018963L, 205520L), Yrs = c("2006_2007", "2011_2012", "2008_2009",
"2014_2015", "2012_2013", "2017_2018", "2008_2009", "2010_2011",
"2018_2019", "2016_2017", "2011_2012", "2018_2019", "2017_2018",
"2017_2018", "2013_2014", "2013_2014", "2014_2015", "2018_2019",
"2008_2009", "2011_2012", "2012_2013", "2010_2011", "2017_2018",
"2006_2007", "2013_2014", "2008_2009", "2015_2016", "2006_2007",
"2006_2007", "2010_2011", "2015_2016", "2012_2013", "2017_2018",
"2011_2012", "2011_2012", "2010_2011", "2006_2007", "2008_2009",
"2007_2008", "2011_2012", "2016_2017", "2008_2009", "2008_2009",
"2013_2014", "2008_2009", "2008_2009", "2006_2007", "2013_2014",
"2008_2009", "2013_2014", "2008_2009", "2009_2010", "2013_2014",
"2017_2018", "2016_2017", "2006_2007", "2011_2012", "2018_2019",
"2013_2014", "2012_2013", "2012_2013", "2011_2012", "2013_2014",
"2017_2018", "2010_2011", "2017_2018", "2018_2019", "2009_2010",
"2017_2018", "2007_2008", "2012_2013", "2008_2009", "2009_2010",
"2006_2007", "2009_2010", "2011_2012", "2015_2016", "2007_2008",
"2007_2008", "2010_2011", "2018_2019", "2007_2008", "2012_2013",
"2011_2012", "2015_2016", "2006_2007", "2007_2008", "2014_2015",
"2013_2014", "2009_2010", "2011_2012", "2016_2017", "2011_2012",
"2007_2008", "2008_2009", "2017_2018", "2014_2015", "2017_2018",
"2014_2015", "2008_2009"), date = c("2007-02-23", "2012-02-27",
"2009-11-27", "2015-03-02", "2013-02-20", "2018-03-19", "2009-02-20",
"2011-03-01", "2019-02-15", "2017-07-24", "2012-02-29", "2019-02-26",
"2018-03-20", "2018-02-21", "2014-02-27", "2014-02-06", "2015-02-26",
"2019-02-21", "2009-02-27", "2012-02-28", "2013-02-27", "2011-03-01",
"2018-02-16", "2007-02-27", "2014-12-10", "2009-08-26", "2016-08-30",
"2007-02-28", "2007-03-01", "2011-02-17", "2016-02-26", "2013-06-17",
"2018-02-23", "2012-11-16", "2012-02-24", "2011-02-24", "2007-02-28",
"2009-10-27", "2008-02-22", "2012-05-21", "2017-03-21", "2009-02-17",
"2009-03-02", "2014-02-27", "2009-02-27", "2009-02-26", "2007-02-16",
"2014-02-26", "2009-02-19", "2014-02-27", "2009-12-14", "2010-12-20",
"2014-02-07", "2018-02-22", "2017-03-24", "2007-02-15", "2012-02-15",
"2019-02-25", "2014-02-27", "2013-05-30", "2013-02-25", "2012-06-27",
"2014-02-27", "2018-02-07", "2011-02-25", "2018-02-23", "2019-02-25",
"2010-02-26", "2018-05-02", "2008-03-14", "2013-02-19", "2009-02-25",
"2010-02-19", "2007-03-01", "2010-05-24", "2012-02-21", "2016-12-20",
"2008-02-27", "2008-02-28", "2011-03-11", "2019-02-21", "2008-02-28",
"2013-02-26", "2012-02-28", "2016-02-26", "2007-02-28", "2008-07-10",
"2015-03-02", "2014-02-24", "2010-05-07", "2012-02-23", "2017-03-16",
"2012-10-31", "2008-06-27", "2009-02-11", "2018-02-27", "2015-02-13",
"2018-02-15", "2015-02-23", "2009-02-25"), var = c(0.789791703652461,
0.962679090148091, 0.818664455488741, 0.915151626033204, 0.981604875838189,
0.992485078188146, 0.919096450897814, 1, 1, 0.999495268313767,
0.971549791181422, 0.142410091613585, 0.854300303926079, 0.963969335350853,
0.100915282090841, 0.788888929835236, 0.998838037754812, 1, 0.585900427740673,
0.99197864817115, 0.963194355499937, 0.665254900669739, 0.989803980097533,
0.989481867437919, 0.50494546710205, 1, 0.955496106519928, 0.926277449400448,
0.848103309375422, 1, 0.643463529314152, 0.480751853712661, 0.912805193910848,
0.980011721979559, 0.850678323003415, 0.97329136045642, 0.9463752199707,
0.984883553015867, 0.950093429532104, 0.694897812240191, 0.988148152699705,
0.909736787656526, 0.882419459468187, 1, 1, 0.602296327788106,
0.982390900591669, 0.832481527160082, 0.970062225639918, 0.93938848599177,
0.420104624462152, 0.822785559294837, 0.922609495465592, 1, 0.847618582834581,
1, 0.998884518357301, 0.856694145446766, 0.954934410586873, 0.999483529190324,
0.964333698862739, 0.000025317480951105, 1, 1, 0.988227892981814,
0.976453170572977, 0.897281824659027, 1, 0.991392575240258, 0.918719921619656,
0.308549664065154, 0.989794242194186, 0.984928826473983, 0.921184863501297,
0.993424278980621, 0.981441305437729, 1, 1, 0.945774347345707,
0.9593304680852, 0.944686323046023, 0.954719328141632, 0.996087437964504,
0.00723186083983232, 0.998107962441803, 1, 0.999992291359637,
0.954625273413497, 0.937925176467551, 0.840391718964617, 0.976419264342728,
0.994956214638493, 0.993807958583037, 0.929808089723338, 0.497183583710065,
0.826207496536073, 1, 1, 0.827230630432659, 0.714753109251633
)), row.names = c(NA, -100L), class = "data.frame")
答案 0 :(得分：1)
我使用year(as.Date(date) %m+% months(6)在7月或更晚的时间将任何内容移至下一年，然后使用paste(mod_year-1,mod_year,sep = "_")创建该年的标签。我使用group_by()在嵌套之前对它们进行排名，以避免使用循环。
library(tidyverse)
library(lubridate)
x %>%
as_tibble() %>%
mutate(mod_year = year(as.Date(date) %m+% months(6)),
mod_year = paste(mod_year-1,mod_year,sep = "_")) %>%
group_by(mod_year) %>%
filter(percent_rank(var) >= .8) %>%
nest(.key = "data")
# A tibble: 13 x 2
mod_year data
<chr> <list>
1 2010_2011 <tibble [2 × 4]>
2 2018_2019 <tibble [2 × 4]>
3 2014_2015 <tibble [2 × 4]>
4 2011_2012 <tibble [2 × 4]>
5 2009_2010 <tibble [2 × 4]>
6 2013_2014 <tibble [2 × 4]>
7 2008_2009 <tibble [3 × 4]>
8 2017_2018 <tibble [3 × 4]>
9 2006_2007 <tibble [2 × 4]>
10 2012_2013 <tibble [2 × 4]>
11 2016_2017 <tibble [1 × 4]>
12 2007_2008 <tibble [2 × 4]>
13 2015_2016 <tibble [1 × 4]>
更新
这是一个更详细的示例，显示了如何对日期进行分组：
data.frame(date = seq.Date(from = as.Date("2017-01-01"),by = "months",length.out = 24)) %>%
as_tibble() %>%
mutate(mod_year = year(as.Date(date) %m+% months(6)),
mod_year = paste(mod_year-1,mod_year,sep = "_")) %>%
as.data.frame()
date mod_year
1 2017-01-01 2016_2017
2 2017-02-01 2016_2017
3 2017-03-01 2016_2017
4 2017-04-01 2016_2017
5 2017-05-01 2016_2017
6 2017-06-01 2016_2017
7 2017-07-01 2017_2018
8 2017-08-01 2017_2018
9 2017-09-01 2017_2018
10 2017-10-01 2017_2018
11 2017-11-01 2017_2018
12 2017-12-01 2017_2018
13 2018-01-01 2017_2018
14 2018-02-01 2017_2018
15 2018-03-01 2017_2018
16 2018-04-01 2017_2018
17 2018-05-01 2017_2018
18 2018-06-01 2017_2018
19 2018-07-01 2018_2019
20 2018-08-01 2018_2019
21 2018-09-01 2018_2019
22 2018-10-01 2018_2019
23 2018-11-01 2018_2019
24 2018-12-01 2018_2019
答案 1 :(得分：0)
您的问题有点令人困惑，但这是我的看法：
library(tidyverse)
library(lubridate)
df %>%
mutate(date=as_date(date)) %>% # change date column to date format
filter(date<='2019-02-15' & date>='2011-03-01') %>% # choose range of dates
top_n(n=length(df$var)*0.20, wt=var) %>% # choose the top 20% of 'var'
arrange(desc(var), date) # arrange dataframe by descending 'var'
这将提供以下输出：
id Yrs date var
1 1048911 2010_2011 2011-03-01 1.0000000
2 92122 2013_2014 2014-02-27 1.0000000
3 50104 2013_2014 2014-02-27 1.0000000
4 797468 2014_2015 2015-02-13 1.0000000
5 63541 2015_2016 2016-12-20 1.0000000
6 8868 2017_2018 2018-02-07 1.0000000
7 922224 2017_2018 2018-02-15 1.0000000
8 835541 2017_2018 2018-02-22 1.0000000
9 109198 2018_2019 2019-02-15 1.0000000
10 42293 2016_2017 2017-07-24 0.9994953
11 945436 2012_2013 2013-05-30 0.9994835
12 1137774 2011_2012 2012-02-15 0.9988845
13 1133421 2014_2015 2015-02-26 0.9988380
14 1361658 2015_2016 2016-02-26 0.9981080
15 915389 2012_2013 2013-02-26 0.9960874
16 1633917 2016_2017 2017-03-16 0.9949562
17 14693 2011_2012 2012-10-31 0.9938080
18 882095 2017_2018 2018-03-19 0.9924851
19 1166691 2011_2012 2012-02-28 0.9919786
20 709804 2017_2018 2018-05-02 0.9913926