time and geographical subset of netcdf raster stack or raster brick using R

After b <- brick('sst.day.mean.2016.v2.nc'), we can type b to see information of the raster brick.

b
# class       : RasterBrick 
# dimensions  : 720, 1440, 1036800, 366  (nrow, ncol, ncell, nlayers)
# resolution  : 0.25, 0.25  (x, y)
# extent      : 0, 360, -90, 90  (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
# data source : C:\Users\basaw\Downloads\sst.day.mean.2016.v2.nc 
# names       : X2016.01.01, X2016.01.02, X2016.01.03, X2016.01.04, X2016.01.05, X2016.01.06, X2016.01.07, X2016.01.08, X2016.01.09, X2016.01.10, X2016.01.11, X2016.01.12, X2016.01.13, X2016.01.14, X2016.01.15, ... 
# Date        : 2016-01-01, 2016-12-31 (min, max)
# varname     : sst 

Notice that the Date slot has information from 2016-01-01 to 2016-12-31, which means the Z values already has date information and we can use that to subset the raster brick.

We can use the getZ function to access the values stored in the Z values. Type getZ(b) we can see a series of dates.

head(getZ(b))
# [1] "2016-01-01" "2016-01-02" "2016-01-03" "2016-01-04" "2016-01-05" "2016-01-06"

class(getZ(b))
# [1] "Date"

We can thus use the following code to subset the raster brick.

b2 <- b[[which(getZ(b) >= as.Date("2016-09-01") & getZ(b) <= as.Date("2016-10-15"))]]

We can then crop the image based on the code you provided.

b3 <- crop(b2, extent(144, 146, 14, 16))

To calculate the average, just use the mean function.

b4 <- mean(b3, na.rm = TRUE)

Finally, we can plot the average.

plot(b4)

Not in R, but just to point out that the subsetting and averaging task is easy to do in CDO from the command line:

cdo timmean -sellonlatbox,lon1,lon2,lat1,lat2 -seldate,date1,date2 in.nc out.nc

where the lon1,lon2 etc define the lon-lat area to cut out and date1,date2 are the date bounds.

You can then read the resultant file into R for plotting, or take a quick look with ncview.