Aggregated Data Products for FACE-IT
Robert Schlegel & Jean-Pierre Gattuso
2021-10-04
Last updated: 2021-09-28
Checks: 6 1
Knit directory: WP1/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20210216) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version a010b46. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: code/ny-alesund_cc_pf_rws.R
Ignored: data/model/
Ignored: data/pg_data/
Ignored: metadata/pangaea_parameters.tab
Unstaged changes:
Modified: analysis/2021_analysis.Rmd
Modified: code/functions.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/2021_analysis.Rmd) and HTML (docs/2021_analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a010b46 | Robert | 2021-09-28 | Workflowr issues |
| html | a010b46 | Robert | 2021-09-28 | Workflowr issues |
| Rmd | 31b16a4 | Robert | 2021-09-28 | Rearrange talks to now be part of the WP1 website. Full version of summary talk complete. |
| html | 31b16a4 | Robert | 2021-09-28 | Rearrange talks to now be part of the WP1 website. Full version of summary talk complete. |
Isfjorden data
Figure 1: High level overview of the data available for Isfjorden. The acronyms for the variable groups seen throughout the figure are: bio = biology, chem = chemistry, cryo = cryosphere, phys = physical, soc = social (currently there are no social data for Isfjorden). A) Metadata showing the range of values available within the data. B) Spatial summary of data available per ~1 km grouping. Note that there are some important moorings outside of this bounding box that are included in the data counts. C) Temporal summary of available data. D) Summmary of data available by depth. Note that all of the data summaries are log10 transformed. For C) and D) the log10 transformation is applied before the data are stacked by category, which gives the impression that there are much more data are than there are."
Anthropogenic impacts in Isfjorden
- Keep in mind that this analysis is just an example to stimulate thought about real analyses
- These then are to be used for the review paper
- Have a specific focus on social science data because little effort has been done for social science so far
- For the analysis:
- Get the number of inhabitants of Longyearbyen over time and relate that to nutrient concentration
- Also want to look at relationships with ice cover and any sort of physical phenology
Summary
The aggregated data products showcased in this talk are intended to be used by other FACE-IT members for their research as well as to be used for a review of change in the FACE-IT study sites.
R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 whisker_0.4 knitr_1.33 magrittr_2.0.1
[5] workflowr_1.6.2 R6_2.5.0 rlang_0.4.11 fansi_0.5.0
[9] stringr_1.4.0 tools_4.1.1 xfun_0.23 utf8_1.2.1
[13] git2r_0.28.0 jquerylib_0.1.4 htmltools_0.5.1.1 ellipsis_0.3.2
[17] rprojroot_2.0.2 yaml_2.2.1 digest_0.6.27 tibble_3.1.2
[21] lifecycle_1.0.0 crayon_1.4.1 later_1.2.0 sass_0.4.0
[25] vctrs_0.3.8 promises_1.2.0.1 fs_1.5.0 glue_1.4.2
[29] evaluate_0.14 rmarkdown_2.8 stringi_1.6.2 compiler_4.1.1
[33] bslib_0.2.5.1 pillar_1.6.1 jsonlite_1.7.2 httpuv_1.6.1
[37] pkgconfig_2.0.3