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chirps

A quasi-global high-resolution rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
https://github.com/ropensci/chirps

Category: Climate Change
Sub Category: Climate Data Access and Visualization

Keywords

chirps climatology precipitation-data rstats

Keywords from Contributors

routing climate-data geocoding

Last synced: about 19 hours ago
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Repository metadata

API Client for CHIRPS

README.md

peer-review
status
CRAN status
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chirps: API Client for CHIRPS and CHIRTS

Overview

chirps provides the API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery
and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit its official home page https://www.chc.ucsb.edu/data.

Quick start

From CRAN

The stable version is available through CRAN.

install.packages("chirps")

From GitHub

A development version that may have new features or bug fixes is available through GitHub.

library("remotes")

install_github("ropensci/chirps", build_vignettes = TRUE)

Example

Fetch CHIRPS data from three points across the Tapajós National Forest (Brazil) from in Jan-2017. The default procedure will download the COG files from the CHIRPS server and handle it internally using the package terra. This is more interesting when dealing with hundreds of points and days. Data can be returned as a matrix using the argument as.matrix = TRUE.

library("chirps")

lonlat <- data.frame(lon = c(-55.0281,-54.9857, -55.0714),
                     lat = c(-2.8094, -2.8756, -3.5279))

dates <- c("2017-01-01", "2017-01-31")

dat <- get_chirps(lonlat, dates, server = "CHC", as.matrix = FALSE)

For a faster download of few datapoints (~ 10 datapoints), the argument server = "ClimateSERV" can be used

library("chirps")

lonlat <- data.frame(lon = c(-55.0281,-54.9857, -55.0714),
                     lat = c(-2.8094, -2.8756, -3.5279))

dates <- c("2017-01-01", "2017-01-31")

dat <- get_chirps(lonlat, dates, server = "ClimateSERV", as.matrix = FALSE)

Going further

The full functionality of chirps is illustrated in the package vignette. The vignette can be found on the package website or from within R once the package has been installed, e.g. via:

vignette("Overview", package = "chirps")

Use of CHIRPS data

While chirps does not redistribute the data or provide it in any way, we encourage users to cite Funk et al. (2015) when using CHIRPS and Funk et al. (2019) when using CHIRTS

Funk C., Peterson P., Landsfeld M., … Michaelsen J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66

Funk, C., Peterson, P., Peterson, S., … Mata, N. (2019). A high-resolution 1983–2016 TMAX climate data record based on infrared temperatures and stations by the climate hazard center. Journal of Climate, 32(17), 5639–5658. https://doi.org/10.1175/JCLI-D-18-0698.1

Meta

  • Please report any issues or bugs.

  • License: MIT.

  • Get citation information for chirps in R by typing citation(package = "chirps").

  • You are welcome to contribute to the chirps project. Please read our contribution guidelines.

  • Please note that the chirps project is released with a a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.


Owner metadata


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Committers metadata

Last synced: 6 days ago

Total Commits: 320
Total Committers: 4
Avg Commits per committer: 80.0
Development Distribution Score (DDS): 0.366

Commits in past year: 22
Committers in past year: 2
Avg Commits per committer in past year: 11.0
Development Distribution Score (DDS) in past year: 0.091

Name Email Commits
Adam H. Sparks a****s@g****m 203
kauedesousa k****a@i****o 115
Patrick Hausmann p****n@c****e 1
Maëlle Salmon m****n@y****e 1

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Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 48
Total pull requests: 5
Average time to close issues: 3 months
Average time to close pull requests: 2 days
Total issue authors: 20
Total pull request authors: 4
Average comments per issue: 3.58
Average comments per pull request: 0.4
Merged pull request: 5
Bot issues: 0
Bot pull requests: 0

Past year issues: 2
Past year pull requests: 1
Past year average time to close issues: 3 months
Past year average time to close pull requests: 10 days
Past year issue authors: 2
Past year pull request authors: 1
Past year average comments per issue: 1.0
Past year average comments per pull request: 0.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/ropensci/chirps

Top Issue Authors

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  • kauedesousa (11)
  • hydroversebr (1)
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Top Pull Request Authors

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Top Issue Labels

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  • bug (8)
  • documentation (4)
  • help wanted (1)
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Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • methods * depends
  • httr * imports
  • jsonlite * imports
  • sf * imports
  • stats * imports
  • terra >= 1.2 imports
  • climatrends * suggests
  • knitr * suggests
  • markdown * suggests
  • rmarkdown * suggests
  • testthat >= 2.1.0 suggests
  • vcr >= 0.5 suggests
.github/workflows/tic.yml actions
  • actions/checkout v3 composite
  • actions/upload-artifact v2 composite
  • pat-s/always-upload-cache v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-tinytex v2 composite
.github/workflows/update-tic.yml actions
  • actions/checkout v2 composite
  • peter-evans/create-pull-request v3 composite
  • r-lib/actions/setup-r master composite

Score: 5.147494476813453