Plotting x and y values using data from two separate netCDF files in R
I would try for a single point: x <- raster::brick(ncfname, varname = "cum_co2_emi-CanESM2") y <- raster::brick(ncfname1, varname = "onedaymax") pt <- cbind(30, -5) to_plot <- cbind(raster::extract(x, pt), raster::extract(y, pt)) plot(to_plot) Is that close? You might be better off using raster::as.data.frame(x, xy = TRUE, long = TRUE) if you want all locations at their actual centre. See if the times of the 3rd axis are valid (and the same) in getZ(x) and getZ(y). There's rarely a need to use ncdf4 directly, though that's important sometimes, more so for grids that raster's regular-affine referencing model doesn't support. cheers, Mike On Wed, 27 Mar 2019 at 05:29 rain1290--- via R-sig-Geo <
r-sig-geo at r-project.org> wrote:
Hi there,
I am currently trying to plot precipitation data (y-axis values) with
cumulative emissions data (x-axis) using R. Both of these data are found on
two separate netCDF files that I have already read into R. Ultimately, What
I would like to do is plot precipitation as a function of cumulative
emissions for a selected location (as shown below in the following code). I
have, so far, used the following code (with "#" to highlight each step):
library(raster)
library(ncdf4)
library(maps)
library(maptools)
library(rasterVis)
library(ggplot2)
library(rgdal)
library(sp) #Geting cumulative emissions data for x-axis
ncfname<-"cumulative_emissions_1pctCO2.nc"
Model1<-nc_open(ncfname)
print(Model1)
get<-ncvar_get(Model1, "cum_co2_emi-CanESM2") #units of terratones
of
carbon (TtC) for x-axis (140 values)
print(get)
Year<-ncvar_get(Model1, "time") #140 years
#Getting Model data for extreme precipitation (units of millimeters/day)
for y-axis ncfname1<-"MaxPrecCCCMACanESM21pctCO2.nc"
Model2<-nc_open(ncfname1)
print(Model2)
get1<-ncvar_get(Model2, "onedaymax") #units of millimeters/day
print(get1)
#Reading in latitude, longitude and time from this file:
latitude<-ncvar_get(Model2, "lat") #64 degrees latitude
longitude<-ncvar_get(Model2, "lon") #128 degrees longitude
Year1<-ncvar_get(Model2, "Year") #140 years
#Plotting attempt randompointlon<-30 #selecting a longitude
randompointlat<--5 #selecting a latitude
Hope<-extract(r_brick,
SpatialPoints(cbind(randompointlon,randompointlat)),method='simple')
df<-data.frame(cumulativeemissions=seq(from=1, to=140, by=1),
Precipitation=t(Hope))
ggplot(data=df, aes(x=get, y=Precipitation,
group=1))+geom_line()+ggtitle("One-day maximum precipitation
(mm/day)
for random location for CanESM2 1pctCO2 as a function of cumulative
emissions")
print(Model1) yields the following (I read in variable #2 for now):
File cumulative_emissions_1pctCO2.nc (NC_FORMAT_NETCDF4):
14 variables (excluding dimension variables):
float cum_co2_emi-BNU-ESM[time] (Contiguous storage)
long_name: Cumulative carbon emissions for BNU-ESM
units: Tt C
float cum_co2_emi-CanESM2[time] (Contiguous storage)
long_name: Cumulative carbon emissions for CanESM2
units: Tt C
float cum_co2_emi-CESM1-BGC[time] (Contiguous storage)
long_name: Cumulative carbon emissions for CESM1-BGC
units: Tt C
float cum_co2_emi-HadGEM2-ES[time] (Contiguous storage)
long_name: Cumulative carbon emissions for HadGEM2-ES
units: Tt C
float cum_co2_emi-inmcm4[time] (Contiguous storage)
long_name: Cumulative carbon emissions for inmcm4
units: Tt C
float cum_co2_emi-IPSL-CM5A-LR[time] (Contiguous storage)
long_name: Cumulative carbon emissions for IPSL-CM5A-LR
units: Tt C
float cum_co2_emi-IPSL-CM5A-MR[time] (Contiguous storage)
long_name: Cumulative carbon emissions for IPSL-CM5A-MR
units: Tt C
float cum_co2_emi-IPSL-CM5B-LR[time] (Contiguous storage)
long_name: Cumulative carbon emissions for IPSL-CM5B-LR
units: Tt C
float cum_co2_emi-MIROC-ESM[time] (Contiguous storage)
long_name: Cumulative carbon emissions for MIROC-ESM
units: Tt C
float cum_co2_emi-MPI-ESM-LR[time] (Contiguous storage)
long_name: Cumulative carbon emissions for MPI-ESM-LR
units: Tt C
float cum_co2_emi-MPI-ESM-MR[time] (Contiguous storage)
long_name: Cumulative carbon emissions for MPI-ESM-MR
units: Tt C
float cum_co2_emi-NorESM1-ME[time] (Contiguous storage)
long_name: Cumulative carbon emissions for NorESM1-ME
units: Tt C
float cum_co2_emi-GFDL-ESM2G[time] (Contiguous storage)
long_name: Cumulative carbon emissions for GFDL-ESM2G
units: Tt C
float cum_co2_emi-GFDL-ESM2M[time] (Contiguous storage)
long_name: Cumulative carbon emissions for GFDL-ESM2M
units: Tt C 1 dimensions:
time Size:140
units: years since 0-1-1 0:0:0
long_name: time
standard_name: time
calender: noleap 4 global attributes:
description: Cumulative carbon emissions for the 1pctCO2 scenario
from the CMIP5 dataset.
history: Created Fri Jul 21 14:50:39 2017
source: CMIP5 archieve
print(Model2) yields the following:File MaxPrecCCCMACanESM21pctCO2.nc
(NC_FORMAT_NETCDF4): 3 variables (excluding dimension variables):
double onedaymax[lon,lat,time] (Contiguous storage)
units: mm/day
double fivedaymax[lon,lat,time] (Contiguous storage)
units: mm/day
short Year[time] (Contiguous storage) 3 dimensions:
time Size:140
lat Size:64
units: degree North
lon Size:128
units: degree East 3 global attributes:
description: Annual global maximum precipitation from the CanESM2
1pctCO2 scenario
history: Created Mon Jun 4 11:24:02 2018
contact: rain1290 at aim.com
So, in general, this is what I am trying to achieve, but I am not sure if
what I am doing in the ggplot function is the right approach for this.
Any assistance with this would be greatly appreciated!
Thanks,
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