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.
CRAN version
install.packages("ccafs")
Dev version
devtools::install_github("ropensci/ccafs")
library("ccafs")
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] 106
res[1:5]
#> [1] "http://cgiardata.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://cgiardata.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://cgiardata.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://cgiardata.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://cgiardata.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"
Keys are basically paths to files on Amazon S3's store of CCAFS files
cc_list_keys()
#> # A tibble: 1,000 × 5
#> Key
#> <chr>
#> 1 ccafs/
#> 2 ccafs/2014-05-24-01-19-33-3A0DFF1F86F3E7F7.txt
#> 3 ccafs/amzn.csv
#> 4 ccafs/ccafs-climate/
#> 5 ccafs/ccafs-climate/data/
#> 6 ccafs/ccafs-climate/data/eta/
#> 7 ccafs/ccafs-climate/data/eta/eta_south_america/
#> 8 ccafs/ccafs-climate/data/eta/eta_south_america/baseline/
#> 9 ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/
#> 10 ccafs/ccafs-climate/data/eta/eta_south_america/baseline/1970s/hadcm_cntrl/
#> # ... with 990 more rows, and 4 more variables: LastModified <chr>,
#> # ETag <chr>, Size <chr>, StorageClass <chr>
Max keys
cc_list_keys(max = 3)
#> # A tibble: 3 × 5
#> Key LastModified
#> <chr> <chr>
#> 1 ccafs/ 2014-02-28T15:15:45.000Z
#> 2 ccafs/2014-05-24-01-19-33-3A0DFF1F86F3E7F7.txt 2014-07-01T02:15:51.000Z
#> 3 ccafs/amzn.csv 2014-02-28T15:21:32.000Z
#> # ... with 3 more variables: ETag <chr>, Size <chr>, StorageClass <chr>
Key prefix
cc_list_keys(prefix = "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/")
#> # A tibble: 1,000 × 5
#> Key
#> <chr>
#> 1 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/
#> 2 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/
#> 3 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/
#> 4 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/
#> 5 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/1
#> 6 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/1
#> 7 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/1
#> 8 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/1
#> 9 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/1
#> 10 ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1/1
#> # ... with 990 more rows, and 4 more variables: LastModified <chr>,
#> # ETag <chr>, Size <chr>, StorageClass <chr>
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.
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)
#> coord. ref. : NA
#> data source : /Users/sacmac/Library/Caches/ccafs/hadcm_cntrl_baseline_1970s_prec_20min_sa_eta_asc/prec_1.asc
#> names : prec_1
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/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc"
#> [2] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc.zip"
#> [3] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_1.asc"
#> [4] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_10.asc"
#> [5] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_11.asc"
#> [6] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_12.asc"
#> [7] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_13.asc"
#> [8] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_14.asc"
#> [9] "/Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_bio_10min_r1i1p1_no_tile_asc/bio_15.asc"
#> [10] "/Users/sacmac/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.
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)
#> coord. ref. : NA
#> data source : /Users/sacmac/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)
#> coord. ref. : 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)
#> coord. ref. : 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
library("raster")
plot(cc_data_read(res[1:6]))
For more control over vizualizations of raster data, check out the rasterVis package.