filtering and summarizing over 'winter' months in R

0
votes

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).

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?

temp_data<-dput(data)
structure(list(site = c("1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
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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, 
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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, 
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32, 30, 28, 26.5, 29, 31.5, 31.5, 28.5, 27, 28.5, 30, 32, 33, 
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13, 15, 19, 22.5, 20, 20.5, 18.5, 15, 11, 11.5, 10.5, 9, 10, 
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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, 
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-1095L), .Names = c("site", "date", "tmax", "nest_date"))
3
rdatetimetidyverselubridate
it doesn't help you given that your 'winter' consists of November, which is typically bucketed into 'fall', but for others who are interested... lubridate has a season parameter for ceiling_date or floor_date. See temp_data$season <- floor_date(temp_data$date, 'season'). – D.sen

3 Answers

4
votes
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)
    )
0
votes
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
0
votes

Effectively lubridate::ceiling_date and lubridate::floor_date functions can be used derive a winter date-range based on nest_date of a row. The rows meeting with criteria will be filtered and group_by can be applied to calculate the mean. 

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