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在R的“冬季”中进行过滤和汇总

[英]filtering and summarizing over 'winter' months in R

 原文  2018-04-10 20:22:19  8   3    r/ datetime/ tidyverse/ lubridate

问题描述

I would like to extract mean temperature (tmax) for different nest sites during the preceding 'winter' months (in this case, winter would be considered November, December, January, and February preceding the nest date). 我想提取之前的“冬季”月份中不同巢穴的平均温度（tmax）（在这种情况下，冬季应视为巢穴日期之前的11月，12月，1月和2月）。

So for example, with the data set below I'd like get the mean winter temperature (tmax) for a nest that is initiated on April 16th, 1981, meaning that I would need to average tmax values over November 1980 - February 1981. How would I go about averaging over these different years using lubridate/tidyverse functions? 因此，例如，使用下面的数据集，我想获得1981年4月16日启动的燕窝的平均冬季温度（tmax），这意味着我需要对1980年11月至1981年2月之间的tmax值进行平均。我将如何使用lubridate / tidyverse函数对这些不同年份进行平均？ 

temp_data<-dput(data)
structure(list(site = c("1", "1", "1", "1", "1", "1", "1", "1", 
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34, 32.5, 33.5, 30.5, 27, 29.5, 29, 29.5, 32, 31.5, 31.5, 34.5, 
34.5, 32.5, 31.5, 29.5, 28.5, 29.5, 28, 22.5, 27, 30.5, 29.5, 
29.5, 31.5, 31, 30, 28.5, 24.5, 23.5, 22.5, 23, 25.5, 25, 22.5, 
27.5, 33, 33.5, 31, 28, 28.5, 26.5, 22.5, 25, 23, 21, 23, 24.5, 
27.5, 26.5, 21, 19.5, 15.5, 18, 20.5, 22.5, 23.5, 25, 28.5, 27, 
26, 26, 25, 25.5, 26.5, 27.5, 27.5, 28, 28.5, 28, 25, 23, 25.5, 
21.5, 18, 14.5, 11.5, 12.5, 14, 17, 18.5, 19.5, 18, 15, 14, 15, 
13.5, 11.5, 12.5, 12.5, 13.5, 14, 16.5, 16.5, 17, 18, 20, 20.5, 
18, 18, 15, 13, 11, 7.5, 7, 6.5, 6.5, 6, 6, 7.5, 8.5, 7, 7.5, 
8, 8.5, 5.5, 3, 2.5, 2, 2, 4, 9.5, 9, 5.5, 9, 11.5, 12, 7, 3.5, 
0.5, 0, 0, -0.5, 1.5, 2, -1.5, -2, 2, 5, 2.5, 0.5, -0.5, 1, 6, 
9, 9, 6, 9.5, 12.5, 11.5, 9.5, 9, 7.5, 3.5, 2, 1.5, 2, 2, 3, 
3.5, 2, 2.5, 3, 2, 1, -1, -1.5, -1.5, 1.5, 7, 8.5, 8.5, 10, 8.5, 
9, 9.5, 7, 5, 4, 5, 9, 8.5, 5, 3.5, 4.5, 4.5, 3.5, 4, 3.5, 3, 
5, 5.5, 7.5, 3.5, -1, 5, 10, 12.5, 10.5, 10, 11, 11.5, 15, 10.5, 
8, 11.5, 14, 16, 11.5, 9, 11, 13, 13.5, 14, 14.5, 15.5, 11, 9.5, 
12.5, 14, 14.5, 15, 16.5, 17.5, 17, 16.5, 18, 15.5, 9.5, 10, 
10.5, 8.5, 10.5, 14, 14, 15, 14, 11, 13, 15, 15.5, 11.5, 11.5, 
12.5, 10, 10, 11, 17.5, 16, 10, 12, 12.5, 13.5, 15, 14.5, 14, 
19, 24.5, 22, 22, 23, 22, 16.5, 14.5, 18, 23, 22, 16.5, 16.5, 
16.5, 20.5, 23, 26.5, 28, 22, 16.5, 16, 15.5, 15, 14.5, 16.5, 
21, 18.5, 16.5, 18, 20.5, 19, 16, 17, 20, 19, 16.5, 17.5, 17.5, 
18.5, 23, 23, 22, 23, 24.5, 26, 28, 28, 22.5, 26.5, 24.5, 23.5, 
26, 30.5, 28, 23.5, 18.5, 19.5, 23.5, 24.5, 20, 15.5, 17.5, 23.5, 
25, 20.5, 24.5, 25.5, 26, 26.5, 31, 29, 30, 33.5, 33.5, 30.5, 
27, 30, 35, 32.5, 30.5, 34.5, 38.5, 37.5, 33.5, 25.5, 25.5, 31.5, 
31, 29.5, 32.5, 29.5, 28, 31.5, 34, 35, 33.5, 32, 32.5, 32, 32, 
32, 32.5, 31.5, 30, 32, 35, 33, 30, 32.5, 34.5, 32.5, 31, 31.5, 
33.5, 34.5, 35, 36, 35, 34, 35, 35.5, 37.5, 36.5, 35.5, 36, 36, 
35.5, 35, 32.5, 31, 32, 36, 36, 35, 35, 33, 33.5, 33.5, 29, 27, 
29.5, 27, 29, 28.5, 27, 28, 32, 32.5, 33.5, 30, 31, 32.5, 33, 
32, 32.5, 34, 35.5, 36, 29.5, 22.5, 21, 20, 19, 18, 15.5, 16, 
25, 21.5, 18.5, 18.5, 21, 21, 16, 14.5, 18, 22, 21.5, 14.5, 16.5, 
15, 12, 9.5, 12, 13.5, 14, 17.5, 20, 21, 21, 20, 16.5, 14.5, 
15.5, 17, 19.5, 19.5, 19, 16.5, 12, 11, 15, 17.5, 18, 18, 15.5, 
14.5, 14, 15, 16, 15.5, 16, 17.5, 13.5, 15, 15, 11.5, 15, 15.5, 
11, 9.5, 8, 12, 10.5, 8, 9, 6, 4.5, 2.5, 3, 0, 4, 5, 10, 6.5, 
5.5, 10, 13, 12, 12.5, 13, 12.5, 7, 2.5, 5, 6, 9.5, 8.5, 7, 5.5, 
9.5, 9, 7.5, 3.5, 1.5, 0.5, 3, 1, 2, 0, 0, -1, -1, 0, -1.5, 1, 
4.5, 1, -5.5, -9, -6.5, -5.5, -5.5, -5.5, -1, 3, 3.5, 5, 4.5, 
6, 5, 3, 1, -1.5, -1, 4, 8, 6.5, 8, 6.5, 3, 3, 3, 4, 3, 2.5, 
2, -2, -7.5, -7.5, -6.5, -6, -5.5, -4.5, -4.5, 0, 3.5, 7, 8, 
11.5, 10.5, 9, 11, 12, 12.5, 10, 5, 5, 7.5, 9, 10, 12, 14, 11.5, 
8.5, 7.5, 8, 10, 12.5, 11.5, 13, 14.5, 15.5, 10.5, 11.5, 11, 
8, 7.5, 9, 9, 6.5, 6.5, 8.5, 9.5, 12, 14, 15.5, 16, 15, 13.5, 
10, 7.5, 11, 9, 7.5, 10, 8, 8, 8, 7.5, 9, 11.5, 16, 18.5, 17, 
16.5, 15.5, 11.5, 11, 14.5, 13, 9, 11, 15.5, 20.5, 23.5, 22.5, 
20, 20, 21.5, 21, 16.5, 21.5, 25.5, 26, 23.5, 17.5, 14.5, 19, 
21, 18.5, 14, 13, 15.5, 18.5, 21, 23, 22, 23, 24, 20, 17.5, 21.5, 
25.5, 26, 24.5, 25.5, 28.5, 27.5, 20, 16.5, 16.5, 19, 24, 24, 
22.5, 22.5, 20.5, 17.5, 18, 18.5, 20, 24, 27.5, 30.5, 28, 24.5, 
26.5, 29.5, 31.5, 31.5, 31.5, 33, 34, 33, 30, 30, 30.5, 28.5, 
31.5, 32.5, 30, 24.5, 26, 25, 24, 24.5, 23, 21.5, 25.5, 26, 23, 
26.5, 30, 33, 33.5, 34, 31.5, 26, 24.5, 26.5, 31.5, 33.5, 35, 
35, 33.5, 31, 30.5, 33.5, 35.5, 34, 30.5, 31, 35.5, 36, 34, 31, 
28.5, 29.5, 32.5, 34.5, 36, 35.5, 34, 33.5, 31, 27.5, 31.5, 32.5, 
31, 32.5, 33, 32, 32.5, 35, 35.5, 35, 33.5, 32, 33.5, 34, 33.5, 
32, 30, 28, 26.5, 29, 31.5, 31.5, 28.5, 27, 28.5, 30, 32, 33, 
28.5, 19, 18.5, 19.5, 18, 14.5, 17, 24, 28, 25.5, 22, 23, 25, 
27.5, 26, 24.5, 23, 18, 16, 14, 15, 16.5, 20, 19.5, 16, 14.5, 
18, 17, 12.5, 13.5, 16, 18, 21, 21, 22.5, 23.5, 23.5, 20, 16, 
13, 15, 19, 22.5, 20, 20.5, 18.5, 15, 11, 11.5, 10.5, 9, 10, 
11, 10, 11, 12, 13, 11, 8, 8.5, 7, 4.5, 5.5, 6.5, 5.5, 6, 7, 
7.5, 8.5, 10, 9, 7, 4, 3.5, 1.5, 1, 1, 2.5, 4, 7.5, 8.5, 6.5, 
6, 6, 6.5, 9, 8, 7.5, 5, 2.5, 2, 2.5, 3.5, 4.5, 4, 3.5, 5.5, 
9, 11, 7, 7, 8.5, 9.5, 9, 4, 1.5, -1, -1, -2, -4, -6.5, -7.5, 
-7), nest_date = structure(c(3747, 3747, 3747, 3747, 3747, 3747, 
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4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 
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4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 
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4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496, 4496
), class = "Date")), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-1095L), .Names = c("site", "date", "tmax", "nest_date"))

3 个解决方案

解决方案1
 4 已采纳  2018-04-10 20:42:55

library(lubridate)
library(tidyverse)

out <- temp_data %>%
  mutate(
    stat_year = ifelse(month(date) %in% c(11:12), year(date) + 1, year(date))
    ) %>%
  filter(month(date) %in% c(1:2, 11:12)) %>%
  group_by(site, stat_year) %>%
  summarise(
    tmax_mean = mean(tmax)
    )

解决方案2
 0  2018-04-10 20:37:20

library(lubridate)
library(dplyr)

temp_data$shifted_date <- temp_data$date - 60
temp_data$year <- year(temp_data$shifted_date) # The year of the "shifted" date
temp_data$month <- month(temp_data$date) # The actual month
temp_data$winter <- ifelse(temp_data$month %in% c(11, 12, 1, 2), 1, 0)

outdf <- temp_data %>%
  group_by(year, site) %>%
  summarize(mean_winter = mean(tmax[winter == 1])) %>%
  data.frame

head(outdf)
# year site mean_winter
# 1 1979    1    5.608333
# 2 1980    1    6.777311
# 3 1981    1    5.712500
# 4 1982    1    5.278689

解决方案3
 0  2018-04-10 21:40:24

Effectively lubridate::ceiling_date and lubridate::floor_date functions can be used derive a winter date-range based on nest_date of a row. 有效地使用lubridate::ceiling_date和lubridate::floor_date函数可以基于行的nest_date导出冬季日期范围。 The rows meeting with criteria will be filtered and group_by can be applied to calculate the mean. 符合条件的行将被过滤， group_by可用于计算平均值。 

library(lubridate)
library(dplyr)
temp_data %>% filter(date <= (ceiling_date(nest_date - months(2), "months")-days(1)) &
                       date >= floor_date(nest_date - months(5), "months")) %>%
  group_by(site, nest_date) %>%
  summarise(tmax_mean = mean(tmax))

# # A tibble: 3 x 3
# # Groups: site [?]
# site  nest_date  tmax_mean
# <chr> <date>         <dbl>
# 1 1     1980-04-05      5.61
# 2 1     1981-04-16      6.22
# 3 1     1982-04-24      2.51

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