README.md

In paleolimbot/argodata: Locate, Download, and Read Argo Ocean Float Data

argodata

The goal of argodata is to provide a data frame-based interface to data generated by the Argo floats program (doi:10.17882/42182). This package provides low-level access to all variables contained within Argo NetCDF files; for a higher-level interface with built-in visualization and quality control, see the argoFloats package.

Installation

You can install the development version from GitHub using:

# install.packages("remotes")
remotes::install_github("ArgoCanada/argodata")

The argodata package downloads files from the FTP and HTTPS mirrors, caches them, and loads them into R. You can set the mirror using argo_set_mirror() and the cache directory using argo_set_cache_dir():

argo_set_mirror("https://data-argo.ifremer.fr/")
argo_set_cache_dir("my/argo/cache")

Optionally, you can set options(argodata.cache_dir = "my/argo/cache") in your .Rprofile to persist this value between R sessions (see usethis::edit_r_profile()). Cached files are used indefinitely by default because of the considerable time it takes to refresh them. If you do use a persistent cache, you should update the index files regularly using argo_update_global() (data files are also updated occasionally; update these using argo_update_data()).

Example

The main workflow supported by argodata is:

• Start with argo_global_prof(), argo_global_traj(), argo_global_meta(), argo_global_bio_prof(), argo_global_bio_prof(), or argo_global_synthetic_prof(). These functions return data frames that contain meta information about each data file in Argo.
• Use argo_filter_radius(), other argo_filter_*() functions, or dplyr::filter() to subset the index using search criteria.
• Use extractor functions like argo_prof_levels(), argo_traj_measurement(), argo_meta_config_param(), and argo_tech_tech_param() to read information for all files in the index subset.

library(tidyverse)
library(argodata)

# filter profile index using search criteria
prof_lab_may_2020 <- argo_global_prof() %>%
  argo_filter_rect(50, 60, -60, -50) %>% 
  filter(
    lubridate::year(date) == 2020, 
    lubridate::month(date) == 5
  )

# download, cache, and load NetCDF files
levels_lab_may_2020 <- prof_lab_may_2020 %>% 
  argo_prof_levels()
#> Downloading 55 files from 'https://data-argo.ifremer.fr'
#> Extracting from 55 files
# plot!
levels_lab_may_2020 %>% 
  filter(psal_qc == 1) %>% 
  ggplot(aes(x = psal, y = pres, col = temp)) +
  geom_point() +
  scale_y_reverse() +
  theme_bw()

See the reference for argo_prof_levels() for more ways to load Argo profiles from argo_global_prof(), argo_global_bio_prof() and argo_global_synthetic_prof(); see argo_traj_measurement() for ways to load Argo trajectories from argo_global_traj() or argo_global_bio_traj(); see argo_meta_missions() for ways to load float meta from argo_global_meta(); see argo_tech_tech_param() for ways to load float technical information from argo_global_tech(); and see argo_info() and argo_vars() for ways to load global metadata from Argo NetCDF files.

Advanced

The argodata package also exports the low-level readers it uses to produce tables from Argo NetCDF files. You can access these using argo_read_*() functions.

prof_file <- system.file(
  "cache-test/dac/csio/2900313/profiles/D2900313_000.nc",
  package = "argodata"
)

argo_read_prof_levels(prof_file)
#> # A tibble: 70 × 17
#>    N_LEVELS N_PROF   PRES PRES_QC PRES_ADJUSTED PRES_ADJUSTED_QC
#>  *    <int>  <int>  <dbl> <chr>           <dbl> <chr>           
#>  1        1      1   9.80 1                9.80 1               
#>  2        2      1  20.1  1               20.1  1               
#>  3        3      1  29.9  1               29.9  1               
#>  4        4      1  39.7  1               39.7  1               
#>  5        5      1  49.9  1               49.9  1               
#>  6        6      1  60.3  1               60.3  1               
#>  7        7      1  69.7  1               69.7  1               
#>  8        8      1  80.3  1               80.3  1               
#>  9        9      1  90.2  1               90.2  1               
#> 10       10      1 100.   1              100.   1               
#> # … with 60 more rows, and 11 more variables: PRES_ADJUSTED_ERROR <dbl>,
#> #   TEMP <dbl>, TEMP_QC <chr>, TEMP_ADJUSTED <dbl>, TEMP_ADJUSTED_QC <chr>,
#> #   TEMP_ADJUSTED_ERROR <dbl>, PSAL <dbl>, PSAL_QC <chr>, PSAL_ADJUSTED <dbl>,
#> #   PSAL_ADJUSTED_QC <chr>, PSAL_ADJUSTED_ERROR <dbl>

Code of Conduct

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

paleolimbot/argodata documentation built on Dec. 13, 2021, 8:19 a.m.