I want to aggregate zoo data in R by two, four or six months periods. There are only two avaliable options for this type of date processing, using:
a) as.yearmon => process daily data grouped by each month
b) as.yearqtr => process daily data grouped by fixed groups of 3 months periods (jan-mar, apr-jun, jul-set and oct-dec).
library(zoo)
# creating a vector of Dates
dt = as.Date(c("2001-01-01","2001-01-02","2001-04-01","2001-05-01","2001-07-01","2001-10-01"),
"%Y-%m-%d")
# the original dates
dt
[1] "2001-01-01" "2001-01-02" "2001-04-01" "2001-05-01" "2001-07-01" "2001-10-01"
# conversion to monthly data
as.yearmon(dt)
[1] "jan 2001" "jan 2001" "abr 2001" "mai 2001" "jul 2001" "out 2001"
# conversion to quarterly data
as.yearqtr(dt)
[1] "2001 Q1" "2001 Q1" "2001 Q2" "2001 Q2" "2001 Q3" "2001 Q4"
set.seed(0)
# irregular time series
daily_db = zoo(matrix(rnorm(3 * length(dt)),
nrow = length(dt),
ncol = 3),
order.by = dt)
daily_db
2001-01-01 1.2629543 -0.928567035 -1.1476570
2001-01-02 -0.3262334 -0.294720447 -0.2894616
2001-04-01 1.3297993 -0.005767173 -0.2992151
2001-05-01 1.2724293 2.404653389 -0.4115108
2001-07-01 0.4146414 0.763593461 0.2522234
2001-10-01 -1.5399500 -0.799009249 -0.8919211
# data aggregated by month
aggregate(daily_db,as.yearmon,sum)
V1 V2 V3
jan 2001 0.9367209 -1.223287482 -1.4371186
abr 2001 1.3297993 -0.005767173 -0.2992151
mai 2001 1.2724293 2.404653389 -0.4115108
jul 2001 0.4146414 0.763593461 0.2522234
out 2001 -1.5399500 -0.799009249 -0.8919211
# data aggregated by quarter
aggregate(daily_db,as.yearqtr,sum)
V1 V2 V3
2001 Q1 0.9367209 -1.2232875 -1.4371186
2001 Q2 2.6022286 2.3988862 -0.7107260
2001 Q3 0.4146414 0.7635935 0.2522234
2001 Q4 -1.5399500 -0.7990092 -0.8919211
I want to define a function like:
as.yearperiod = function(x, period = 6) {...} # convert dates in semesters
To use this way:
# data aggregated by semester
aggregate(base_dados_diaria, as.yearperiod, period = 6, sum)
I expect an result like this one:
V1 V2 V3
2001 S1 3.538950 1.175599 -2.147845
2001 S2 -1.125309 -0.035416 -0.639698
Sir, I suggest you to use lubridate package, to deal with custom date intervals. Your task could be easy accomplished applying floor_date, as below:
six_m_interval <- lubridate::floor_date( dt , "6 months" )
# [1] "2001-01-01" "2001-01-01" "2001-01-01" "2001-01-01" "2001-07-01" "2001-07-01"
aggregate( daily_db , six_m_interval , sum )
# V1 V2 V3
# 2001-01-01 3.538950 1.17559873 -2.1478445
# 2001-07-01 -1.125309 -0.03541579 -0.6396977