Last updated: 2021-09-15
Checks: 7 0
Knit directory: mapme.protectedareas/
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.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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(20210305) 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 2fc3683. 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: .RData
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data-raw/addons/docs/rest/
Ignored: data-raw/addons/etc/
Ignored: data-raw/addons/scripts/
Untracked files:
Untracked: .tmp/
Untracked: analysis/figure/
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/about.Rmd) and HTML (docs/about.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 |
|---|---|---|---|---|
| html | 37b7e5f | Johannes Schielein | 2021-09-15 | Build site. |
| html | d1cfe2d | Johannes Schielein | 2021-09-15 | initial comit with sampling code |
| html | fa42f34 | Johannes Schielein | 2021-07-01 | Host with GitHub. |
| html | 8bd1321 | Johannes Schielein | 2021-06-30 | Host with GitLab. |
| html | 3a39ee3 | Johannes Schielein | 2021-06-30 | Host with GitHub. |
| html | ae67dca | Johannes Schielein | 2021-06-30 | Host with GitLab. |
| Rmd | e9e6d98 | Johannes Schielein | 2021-03-12 | updated the website to be rendered. |
| html | e9e6d98 | Johannes Schielein | 2021-03-12 | updated the website to be rendered. |
| html | 86caefa | Johannes Schielein | 2021-03-11 | small changes to link the readme html (only test) |
| html | 516d799 | Johannes Schielein | 2021-03-05 | create workflow r website |
| Rmd | e410b62 | Johannes Schielein | 2021-03-05 | new init commit with new datastructure for improved reproduciblity |
This file contains information on the general goals of the project. For details on the repository, open-source code and information on how to contribute please have a look at our contribution description (currently in contributions.md). If you want to use the source code of this repository to create your own analysis, please have a look at our documentation pages (currently in contributions.md). If you are interested in the results of our portfolio analysis and impact evaluation please visit our reporting sites (insert link here later).
Protected Areas (short PAs) are essential to conserve endangered natural habitats with high biodiversity and ecosystem functions. KfW assists other countries with financial cooperation projects to establish new PAs and increase conservation efficiency in existing PA networks worldwide.
The overarching goal of this repository is to create reproducible processing and analysis routines with open source tools such as R (and/or other languages, if necessary) that can serve as a software stack to enables us (and third parties) to leverage the potential of open (geo-) data to assist in planning monitoring and evaluation of projects to support PAs worldwide.
Variables to be processed and analyzed can be grouped into five broad categories:
We rely heavily on external pre-processed datasources to derive these variables. Furthermore, our analysis is based on the IUCN/WDPA database, i.e. KfW portfolio was matched with WDPA to identify the relevant areas.
As such this repository has two broader spheres of action 1. Create reproducible pre-processing routines to derive the relevant geodatabase(s) for analysis 2. Create reproducible analysis routines consisting in a) a simple portfolio analysis and b) a quasi-experimental impact evaluations (IE) for the KfW portfolio.
The specific outputs that should be produced by this repository regarding the geodatabase(s) consists in: 1. A database for the portfolio analysis (vector data): This database will use PAs as units of observations. The output is expected to be a Geopackage (gpkg) with PAs as rows and all processed variables for the portfolio analysis as columns. 2. A database for the quasi-experimental IE (raster data): this database will create rasterstacks for all relevant variables regarding the IE. We need raster stacks for this exercise in order to be able to analyze also what is happening outside of protected areas.
The specific outputs that should be produced by this repository regarding the analysis consist:
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 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=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.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_1.0.7 whisker_0.4 knitr_1.30 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_3.6.3 xfun_0.24 utf8_1.2.1
[13] git2r_0.28.0 htmltools_0.5.1.1 ellipsis_0.3.2 rprojroot_2.0.2
[17] yaml_2.2.1 digest_0.6.27 tibble_3.1.1 lifecycle_1.0.0
[21] crayon_1.4.1 later_1.2.0 vctrs_0.3.8 fs_1.5.0
[25] promises_1.2.0.1 glue_1.4.2 evaluate_0.14 rmarkdown_2.6
[29] stringi_1.6.2 compiler_3.6.3 pillar_1.6.0 httpuv_1.6.1
[33] pkgconfig_2.0.3