In ropenscilabs/cmipr: Client for Coupled Model Intercomparison Project (CMIP) Data

knitr::opts_chunk$set(
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cmipr introduction

cmipr - Client to work with CMIP data - downscaled climate and hydrology projections. Package lists avail. files, downloads and caches, and reads into raster objects.

Install

Dev version

devtools::install_github("ropenscilabs/cmipr")

library("cmipr")

cmip_cache$delete_all()

List files

List files in their FTP directory

cmip_list_files()

cmip_list_files('cmip5/bcsd')

Fetch data

Define a path to a file.

key <- "bcsd/yearly/cnrm_cm3.1/cnrm_cm3.1.sresa1b.monthly.Prcp.2034.nc"

Fetch the file

(res <- cmip_fetch(key))

Caching

When requesting data, we first check if you already have that data cached. You'll know when this happens as the request will finish much faster when you have data cached already.

We use the package hoardr to manage cached files.

On package load, a hoardr object is created, it's an R6 object.

cmip_cache

See ?cmip_cache for all cache management help.

The cmip_cache object has variables and functions on it. For example, we can get the cache path

cmip_cache$cache_path_get()

Or list all files in the cache

cmip_cache$list()

Or delete all files (won't run this here though)

cmip_cache$delete_all()

Read data

After fetching data, you need to read the data into a RasterLayer or RasterBrick object

Get some data first

keys <- c(
  "bcsd/yearly/cnrm_cm3.1/cnrm_cm3.1.sresa1b.monthly.Prcp.2039.nc",
  "bcsd/yearly/gfdl_cm2_1.1/gfdl_cm2_1.1.sresa1b.monthly.Prcp.2033.nc"
)
res <- lapply(keys, cmip_fetch)

You can read a single file

cmip_read(res[[1]])

many files

cmip_read(unlist(res))

Visualize

library("raster")
plot(cmip_read(res[[1]]))

ropenscilabs/cmipr documentation built on May 18, 2022, 7:35 p.m.