Reputation: 557
Having a difficult time conceptualization this one. I currently have a netcdf file that contains monthly climate averages from 1979-2016. I would like to make separate yearly raster stacks from this larger one (i.e., go from 1 raster stack to 38). I cannot for the life of me figure this out. Any suggestions would be great! The data can be found here: http://nimbus.cos.uidaho.edu/abatz/DATA/vpd19792016.nc
What I have started so far is as follows:
library(raster)
library(lubridate)
library(rgdal)
library(tidyverse)
library(tools)
file = "../data/raw/climate/aet_19792016.nc"
file_split <- file %>%
basename %>%
strsplit(split = "_") %>%
unlist
var <- file_split[1]
year <- substr(file_split[2], start = 1, stop = 4)
endyear <- substr(file_split[2], start = 5, stop = 8)
start_date <- as.Date(paste(year, "01", "01", sep = "-"))
end_date <- as.Date(paste(ifelse(year == endyear, year, endyear), "12", "31", sep = "-"))
date_seq <- seq(start_date, end_date, by = "1 month")
month_seq <- month(date_seq)
nc <- nc_open(file)
nc_att <- attributes(nc$var)$names
ncvar <- ncvar_get(nc, nc_att)
tvar <- aperm(ncvar, c(3,2,1))
proj <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0 "
raster <- brick(tvar, crs= proj)
extent(raster) <- c(-124.793, -67.043, 25.04186, 49.41686)
names(raster) <- paste(var, year(date_seq),
unique(month(date_seq, label = TRUE)),
sep = "_")
unstack(raster)
Thanks in advance for any suggestions!
Upvotes: 3
Views: 539
Reputation: 1704
First you have to properly name the raster stack based on Years
and months
:
library(ncdf4)
library(raster)
nc <- nc_open(file)
nc_att <- attributes(nc$var)$names
ncvar <- ncvar_get(nc, nc_att)
tvar <- aperm(ncvar, c(3,2,1))
proj <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0 "
r <- brick(tvar, crs= proj)
extent(r) <- c(-124.793, -67.043, 25.04186, 49.41686)
# name raster brick based on year and month
dates <- format(seq(as.Date(paste(1979,'/1/1',sep='')), as.Date(paste(2016,'/12/31', sep="")), by='month'), '%Y%m')
names(r) <- dates
r_sub <- subset(r, grep("X1982", names(r))) # subset based on year
Upvotes: 2