I have a time Series DataFrame:

[https://www.dropbox.com/s/elaxfuvqyip1eq8/SampleDF.csv?dl=0][1]

My intention is to divide this DataFrame into different seasons according to:

1. winter: Dec Jan Feb
2. Pre-monsoon: Mar Apr May Jun15 (i.e. till 15th of June)
3. Monsoon: 15Jun Jul Aug Sep (i.e. from 15th of June)
4. Post-monsoon: Oct Nov.

I tried using openair package function

selectByDate()

But no luck yet. Being novice in R. Any help would be highly appreciated.

Thanks!

CodePudding user response：

Please see the lubridate package which makes working with date/time a bit easier.

For your problem, I guess you can use sapply:

df["season"] = sapply(df["date"], assign_season)

where, assign_season:

assign_season <- function(date){
    # return a season based on date 

}

once you have seasons, then you can divide the dataframe easily:

winter = subset(df, season == "winter")
# and so on

Sorry, I have to rush now, but can come back and finish this, if someone else hasn't answered already.

EDIT:

So, R does have a built in function cut, that can work on dates and split a vector based on date ranges.

For your data, I did this like so:

library(lubridate)
library(dplyr)

df = read.csv('SampleDF.csv')

## reformat date into POSIXct
df <- df %>%
         mutate(date_reformat = as.POSIXct(date(mdy_hm(date))))

## define breaks & labels
breaks = c("2014-12-01", "2015-03-01", "2015-06-15", "2015-10-01", "2015-12-01", "2016-03-01", "2016-06-15", "2016-10-01", "2016-12-01", "2017-03-01")
labels = c("winter", "pre_monsoon", "monsoon", "post_monsoon", "winter", "pre_monsoon", "monsoon", "post_monsoon", "winter")
df["season"] = cut(df$date_reformat, breaks=as.POSIXct(breaks), labels=labels)

splits = list()

for (s in c("winter", "pre_monsoon", "monsoon", "post_monsoon")){
  splits[[s]] = subset(df, season == s)[c("date", "value")]
}

Now, the splits list should have all the data you need