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ccafs introduction
In ccafs: Client for 'CCAFS' 'GCM' Data
ccafs provides access to Climate Change, Agriculture, and Food Security (CCAFS)
General Circulation Models (GCM) data. Data is stored in Amazon S3, from
which we provide functions to fetch data.
Potential users are probably anyone from scientists asking questions about
climate change, to scientists or companies projecting crop yields in the future.
Install
CRAN version
install.packages("ccafs")
Dev version
remotes::install_github("ropensci/ccafs")
library("ccafs")
Search
Search CCAFS
You can search by the numbers representing each possible value for
each parameter. See the ?'ccafs-search' for help on that.
(res <- cc_search(file_set = 4, scenario = 6, model = 2, extent = "global",
format = "ascii", period = 5, variable = 2, resolution = 3))
#> [1] "http://gisweb.ciat.cgiar.org/ccafs_climate/files/data/ipcc_4ar_ciat/sres_b1/2040s/bccr_bcm2_0/5min/bccr_bcm2_0_sres_b1_2040s_prec_5min_no_tile_asc.zip"
Some saerches, like that above, result in one URL, while others, like that
below, result in many URLs.
res <- cc_search(file_set = 12, extent = "global", format = "ascii",
period = 4, variable = 1, resolution = 4)
length(res)
#> [1] 110
res[1:5]
#> [1] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/10min/bcc_csm1_1_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
#> [2] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1_m/10min/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
#> [3] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bnu_esm/10min/bnu_esm_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
#> [4] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/cccma_canesm2/10min/cccma_canesm2_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
#> [5] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/cesm1_cam5/10min/cesm1_cam5_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
One we have some URLs, we can fetch the files at the URLs
One at a time
cc_data_fetch(res[1], progress = FALSE)
#>
#> <CCAFS GCM files>
#> 19 files
#> Base dir: /bcc_csm1_1_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc
#> File types (count):
#> - .asc: 19
Or many at once
lapply(res[1:3], cc_data_fetch, progress = FALSE)
#> [[1]]
#>
#> <CCAFS GCM files>
#> 19 files
#> Base dir: /bcc_csm1_1_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc
#> File types (count):
#> - .asc: 19
#>
#> [[2]]
#>
#> <CCAFS GCM files>
#> 19 files
#> Base dir: /bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc
#> File types (count):
#> - .asc: 19
#>
#> [[3]]
#>
#> <CCAFS GCM files>
#> 19 files
#> Base dir: /bnu_esm_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc
#> File types (count):
#> - .asc: 19
Alternatively, you can use the helper list where you can reference options
by name; the downside is that this leads to very verbose code.
(res <- cc_search(file_set = cc_params$file_set$`Delta method IPCC AR4`,
scenario = cc_params$scenario$`SRES B1`,
model = cc_params$model$bccr_bcm2_0,
extent = cc_params$extent$global,
format = cc_params$format$ascii,
period = cc_params$period$`2040s`,
variable = cc_params$variable$Precipitation,
resolution = cc_params$resolution$`5 minutes`))
#> [1] "http://gisweb.ciat.cgiar.org/ccafs_climate/files/data/ipcc_4ar_ciat/sres_b1/2040s/bccr_bcm2_0/5min/bccr_bcm2_0_sres_b1_2040s_prec_5min_no_tile_asc.zip"
List keys
Keys are basically paths to files on Amazon S3's store of CCAFS files
cc_list_keys()
#> # A tibble: 1,000 x 7
#> Key LastModified ETag Size Owner.ID Owner.DisplayNa… StorageClass
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ccafs/ 2014-02-28T1… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 2 ccafs/20… 2014-07-01T0… "\"d7… 1171 b1e110da4… amazonec2public… STANDARD
#> 3 ccafs/am… 2014-02-28T1… "\"1d… 1108… b1e110da4… amazonec2public… STANDARD
#> 4 ccafs/cc… 2014-07-15T1… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 5 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 6 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 7 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 8 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 9 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 10 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> # … with 990 more rows
Max keys
cc_list_keys(max = 3)
#> # A tibble: 3 x 7
#> Key LastModified ETag Size Owner.ID Owner.DisplayNa… StorageClass
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ccafs/ 2014-02-28T1… "\"d41… 0 b1e110da4d… amazonec2public… STANDARD
#> 2 ccafs/2… 2014-07-01T0… "\"d72… 1171 b1e110da4d… amazonec2public… STANDARD
#> 3 ccafs/a… 2014-02-28T1… "\"1db… 1108… b1e110da4d… amazonec2public… STANDARD
Key prefix
cc_list_keys(prefix = "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/")
#> # A tibble: 1,000 x 7
#> Key LastModified ETag Size Owner.ID Owner.DisplayNa… StorageClass
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 2 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 3 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 4 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 5 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD
#> 6 ccafs/cc… 2014-11-10T2… "\"6d… 1113… b1e110da4… amazonec2public… STANDARD
#> 7 ccafs/cc… 2014-11-10T2… "\"cd… 9684… b1e110da4… amazonec2public… STANDARD
#> 8 ccafs/cc… 2014-11-10T2… "\"d2… 1125… b1e110da4… amazonec2public… STANDARD
#> 9 ccafs/cc… 2014-11-10T2… "\"8a… 90200 b1e110da4… amazonec2public… STANDARD
#> 10 ccafs/cc… 2014-11-10T2… "\"e3… 5411… b1e110da4… amazonec2public… STANDARD
#> # … with 990 more rows
You can find certain files easily. Here, we'll find zip files
res <- cc_list_keys()
zips <- grep("\\.zip", res$Key, value = TRUE)
zips[1:5]
#> [1] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_prec_20min_sa_eta_asc.zip"
#> [2] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_tmax_20min_sa_eta_asc.zip"
#> [3] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_tmean_20min_sa_eta_asc.zip"
#> [4] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_tmin_20min_sa_eta_asc.zip"
#> [5] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_high/20min/hadcm_high_baseline_1970s_prec_20min_sa_eta_asc.zip"
Which you can use below to fetch data from.
Fetch some data
key <- "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/
2030s/bcc_csm1_1_m/10min/
bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc.zip"
#>
#> <CCAFS GCM files>
#> 12 files
#> Base dir: /bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc
#> File types (count):
#> - .asc: 12
(res <- cc_data_fetch(key = key, progress = FALSE))
Using the above set of zips, fetch some data:
x <- cc_data_fetch(zips[1], progress = FALSE)
cc_data_read(x[1])
#> class : RasterLayer
#> dimensions : 151, 141, 21291 (nrow, ncol, ncell)
#> resolution : 0.400002, 0.400002 (x, y)
#> extent : -81.99999, -25.59971, -49.20001, 11.2003 (xmin, xmax, ymin, ymax)
#> crs : NA
#> source : /Users/sckott/Library/Caches/ccafs/hadcm_cntrl_baseline_1970s_prec_20min_sa_eta_asc/prec_1.asc
#> names : prec_1
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.
You can list cached files
cc_cache_list()[1:10]
#> [1] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc"
#> [2] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
#> [3] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_1.asc"
#> [4] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_10.asc"
#> [5] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_11.asc"
#> [6] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_12.asc"
#> [7] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_13.asc"
#> [8] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_14.asc"
#> [9] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_15.asc"
#> [10] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_16.asc"
See ?cc_cache for all the cache management functions.
Read data
After fetching data, you need to read the data into a RasterLayer or
RasterBrick object
You can read a single file
cc_data_read(res[1])
#> class : RasterLayer
#> dimensions : 900, 2160, 1944000 (nrow, ncol, ncell)
#> resolution : 0.1666667, 0.1666667 (x, y)
#> extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
#> crs : NA
#> source : /Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc/prec_1.asc
#> names : prec_1
#> values : -2147483648, 2147483647 (min, max)
many files
cc_data_read(res[1:2])
#> class : RasterStack
#> dimensions : 900, 2160, 1944000, 2 (nrow, ncol, ncell, nlayers)
#> resolution : 0.1666667, 0.1666667 (x, y)
#> extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
#> crs : NA
#> names : prec_1, prec_10
#> min values : -2147483648, -2147483648
#> max values : 2147483647, 2147483647
or all files
cc_data_read(res)
#> class : RasterStack
#> dimensions : 900, 2160, 1944000, 12 (nrow, ncol, ncell, nlayers)
#> resolution : 0.1666667, 0.1666667 (x, y)
#> extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
#> crs : NA
#> names : prec_1, prec_10, prec_11, prec_12, prec_2, prec_3, prec_4, prec_5, prec_6, prec_7, prec_8, prec_9
#> min values : -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648
#> max values : 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647
Visualize
library("raster")
plot(cc_data_read(res[1:6]))
For more control over vizualizations of raster data, check out the
rasterVis package.
Try the ccafs package in your browser
Any scripts or data that you put into this service are public.
ccafs documentation built on Aug. 26, 2020, 5:15 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
ccafs provides access to Climate Change, Agriculture, and Food Security (CCAFS)
General Circulation Models (GCM) data. Data is stored in Amazon S3, from
which we provide functions to fetch data.
Potential users are probably anyone from scientists asking questions about climate change, to scientists or companies projecting crop yields in the future.
Install
CRAN version
install.packages("ccafs")
Dev version
remotes::install_github("ropensci/ccafs")
library("ccafs")
Search
Search CCAFS
You can search by the numbers representing each possible value for
each parameter. See the ?'ccafs-search' for help on that.
(res <- cc_search(file_set = 4, scenario = 6, model = 2, extent = "global", format = "ascii", period = 5, variable = 2, resolution = 3)) #> [1] "http://gisweb.ciat.cgiar.org/ccafs_climate/files/data/ipcc_4ar_ciat/sres_b1/2040s/bccr_bcm2_0/5min/bccr_bcm2_0_sres_b1_2040s_prec_5min_no_tile_asc.zip"
Some saerches, like that above, result in one URL, while others, like that below, result in many URLs.
res <- cc_search(file_set = 12, extent = "global", format = "ascii", period = 4, variable = 1, resolution = 4) length(res) #> [1] 110 res[1:5] #> [1] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/10min/bcc_csm1_1_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip" #> [2] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1_m/10min/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip" #> [3] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bnu_esm/10min/bnu_esm_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip" #> [4] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/cccma_canesm2/10min/cccma_canesm2_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip" #> [5] "http://datacgiar.s3.amazonaws.com/ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/cesm1_cam5/10min/cesm1_cam5_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
One we have some URLs, we can fetch the files at the URLs
One at a time
cc_data_fetch(res[1], progress = FALSE) #> #> <CCAFS GCM files> #> 19 files #> Base dir: /bcc_csm1_1_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc #> File types (count): #> - .asc: 19
Or many at once
lapply(res[1:3], cc_data_fetch, progress = FALSE) #> [[1]] #> #> <CCAFS GCM files> #> 19 files #> Base dir: /bcc_csm1_1_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc #> File types (count): #> - .asc: 19 #> #> [[2]] #> #> <CCAFS GCM files> #> 19 files #> Base dir: /bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc #> File types (count): #> - .asc: 19 #> #> [[3]] #> #> <CCAFS GCM files> #> 19 files #> Base dir: /bnu_esm_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc #> File types (count): #> - .asc: 19
Alternatively, you can use the helper list where you can reference options by name; the downside is that this leads to very verbose code.
(res <- cc_search(file_set = cc_params$file_set$`Delta method IPCC AR4`, scenario = cc_params$scenario$`SRES B1`, model = cc_params$model$bccr_bcm2_0, extent = cc_params$extent$global, format = cc_params$format$ascii, period = cc_params$period$`2040s`, variable = cc_params$variable$Precipitation, resolution = cc_params$resolution$`5 minutes`)) #> [1] "http://gisweb.ciat.cgiar.org/ccafs_climate/files/data/ipcc_4ar_ciat/sres_b1/2040s/bccr_bcm2_0/5min/bccr_bcm2_0_sres_b1_2040s_prec_5min_no_tile_asc.zip"
List keys
Keys are basically paths to files on Amazon S3's store of CCAFS files
cc_list_keys() #> # A tibble: 1,000 x 7 #> Key LastModified ETag Size Owner.ID Owner.DisplayNa… StorageClass #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 ccafs/ 2014-02-28T1… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 2 ccafs/20… 2014-07-01T0… "\"d7… 1171 b1e110da4… amazonec2public… STANDARD #> 3 ccafs/am… 2014-02-28T1… "\"1d… 1108… b1e110da4… amazonec2public… STANDARD #> 4 ccafs/cc… 2014-07-15T1… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 5 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 6 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 7 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 8 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 9 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 10 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> # … with 990 more rows
Max keys
cc_list_keys(max = 3) #> # A tibble: 3 x 7 #> Key LastModified ETag Size Owner.ID Owner.DisplayNa… StorageClass #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 ccafs/ 2014-02-28T1… "\"d41… 0 b1e110da4d… amazonec2public… STANDARD #> 2 ccafs/2… 2014-07-01T0… "\"d72… 1171 b1e110da4d… amazonec2public… STANDARD #> 3 ccafs/a… 2014-02-28T1… "\"1db… 1108… b1e110da4d… amazonec2public… STANDARD
Key prefix
cc_list_keys(prefix = "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/") #> # A tibble: 1,000 x 7 #> Key LastModified ETag Size Owner.ID Owner.DisplayNa… StorageClass #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 2 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 3 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 4 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 5 ccafs/cc… 2014-11-10T2… "\"d4… 0 b1e110da4… amazonec2public… STANDARD #> 6 ccafs/cc… 2014-11-10T2… "\"6d… 1113… b1e110da4… amazonec2public… STANDARD #> 7 ccafs/cc… 2014-11-10T2… "\"cd… 9684… b1e110da4… amazonec2public… STANDARD #> 8 ccafs/cc… 2014-11-10T2… "\"d2… 1125… b1e110da4… amazonec2public… STANDARD #> 9 ccafs/cc… 2014-11-10T2… "\"8a… 90200 b1e110da4… amazonec2public… STANDARD #> 10 ccafs/cc… 2014-11-10T2… "\"e3… 5411… b1e110da4… amazonec2public… STANDARD #> # … with 990 more rows
You can find certain files easily. Here, we'll find zip files
res <- cc_list_keys() zips <- grep("\\.zip", res$Key, value = TRUE) zips[1:5] #> [1] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_prec_20min_sa_eta_asc.zip" #> [2] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_tmax_20min_sa_eta_asc.zip" #> [3] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_tmean_20min_sa_eta_asc.zip" #> [4] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/20min/hadcm_cntrl_baseline_1970s_tmin_20min_sa_eta_asc.zip" #> [5] "ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_high/20min/hadcm_high_baseline_1970s_prec_20min_sa_eta_asc.zip"
Which you can use below to fetch data from.
Fetch some data
key <- "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/ 2030s/bcc_csm1_1_m/10min/ bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc.zip"
#> #> <CCAFS GCM files> #> 12 files #> Base dir: /bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc #> File types (count): #> - .asc: 12
(res <- cc_data_fetch(key = key, progress = FALSE))
Using the above set of zips, fetch some data:
x <- cc_data_fetch(zips[1], progress = FALSE) cc_data_read(x[1]) #> class : RasterLayer #> dimensions : 151, 141, 21291 (nrow, ncol, ncell) #> resolution : 0.400002, 0.400002 (x, y) #> extent : -81.99999, -25.59971, -49.20001, 11.2003 (xmin, xmax, ymin, ymax) #> crs : NA #> source : /Users/sckott/Library/Caches/ccafs/hadcm_cntrl_baseline_1970s_prec_20min_sa_eta_asc/prec_1.asc #> names : prec_1
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.
You can list cached files
cc_cache_list()[1:10] #> [1] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc" #> [2] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip" #> [3] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_1.asc" #> [4] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_10.asc" #> [5] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_11.asc" #> [6] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_12.asc" #> [7] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_13.asc" #> [8] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_14.asc" #> [9] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_15.asc" #> [10] "/Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_16.asc"
See ?cc_cache for all the cache management functions.
Read data
After fetching data, you need to read the data into a RasterLayer or
RasterBrick object
You can read a single file
cc_data_read(res[1]) #> class : RasterLayer #> dimensions : 900, 2160, 1944000 (nrow, ncol, ncell) #> resolution : 0.1666667, 0.1666667 (x, y) #> extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) #> crs : NA #> source : /Users/sckott/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc/prec_1.asc #> names : prec_1 #> values : -2147483648, 2147483647 (min, max)
many files
cc_data_read(res[1:2]) #> class : RasterStack #> dimensions : 900, 2160, 1944000, 2 (nrow, ncol, ncell, nlayers) #> resolution : 0.1666667, 0.1666667 (x, y) #> extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) #> crs : NA #> names : prec_1, prec_10 #> min values : -2147483648, -2147483648 #> max values : 2147483647, 2147483647
or all files
cc_data_read(res) #> class : RasterStack #> dimensions : 900, 2160, 1944000, 12 (nrow, ncol, ncell, nlayers) #> resolution : 0.1666667, 0.1666667 (x, y) #> extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) #> crs : NA #> names : prec_1, prec_10, prec_11, prec_12, prec_2, prec_3, prec_4, prec_5, prec_6, prec_7, prec_8, prec_9 #> min values : -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648, -2147483648 #> max values : 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647
Visualize
library("raster") plot(cc_data_read(res[1:6]))
For more control over vizualizations of raster data, check out the rasterVis package.
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