The goal of rangeRinPA is to reproduce most results from the paper “Protected Area Personnel and Ranger Numbers are Insufficient to Deliver Global Expectations” by Appleton et al. (2022): https://doi.org/10.1038/s41893-022-00970-0
You can install rangeRinPA as any other R package stored on GitHub using:
# install.packages("remotes")
remotes::install_github("courtiol/rangeRinPA", dependencies = TRUE, build_vignettes = TRUE)You can also install rangeRinPA by clicking on the green button above with the label “Code” and then on Download ZIP (do not unzip it!).
Then, in R type:
# install.packages("remotes")
remotes::install_local(path = file.choose(), dependencies = TRUE, build = TRUE, build_vignettes = TRUE)It will open a windows for you to select the file rangeR-master.zip
that you just have downloaded.
(Note: the same also applies to released zip files stored on Zenodo)
Follow the steps documented in the help file which is available via the following command:
?rangeRinPAThe data are embedded in the package, so you can use them after installing the package (see above) as any other data frame or tibble in R.
Here is a simple example:
library(rangeRinPA)
sum(data_rangers$staff_total, na.rm = TRUE)
#> [1] 337877Here is the information about the R session used to produce the numerical results presented in the paper:
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 11 (bullseye)
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8
[6] LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rangeRinPA_2021.12.29 testthat_3.1.1 cachem_1.0.6 memoise_2.0.1
loaded via a namespace (and not attached):
[1] rnaturalearth_0.1.0 pkgload_1.2.4 tidyr_1.1.4 spelling_2.2 R.utils_2.11.0 assertthat_0.2.1 countrycode_1.3.0
[8] sp_1.4-6 googlesheets4_1.0.0 cellranger_1.1.0 yaml_2.2.1 remotes_2.4.2 slam_0.1-49 ggrepel_0.9.1
[15] sessioninfo_1.2.2 numDeriv_2016.8-1.1 pillar_1.6.4 lattice_0.20-45 glue_1.6.0 digest_0.6.29 minqa_1.2.4
[22] colorspace_2.0-2 htmltools_0.5.2 Matrix_1.4-0 R.oo_1.24.0 spaMM_3.9.25 pkgconfig_2.0.3 devtools_2.4.3
[29] purrr_0.3.4 patchwork_1.1.1 scales_1.1.1 ranger_0.13.1 processx_3.5.2 tibble_3.1.6 proxy_0.4-26
[36] googledrive_2.0.0 generics_0.1.1 ggplot2_3.3.5 usethis_2.1.5 ellipsis_0.3.2 withr_2.4.3 pbapply_1.5-0
[43] cli_3.1.0 magrittr_2.0.1 crayon_1.4.2 evaluate_0.14 ps_1.6.0 R.methodsS3_1.8.1 fs_1.5.2
[50] fansi_0.5.0 nlme_3.1-153 MASS_7.3-54 forcats_0.5.1 xml2_1.3.3 class_7.3-19 pkgbuild_1.3.1
[57] tools_4.1.2 registry_0.5-1 hunspell_3.0.1 prettyunits_1.1.1 gargle_1.2.0 lifecycle_1.0.1 ROI_1.0-0
[64] munsell_0.5.0 ggsci_2.9 callr_3.7.0 compiler_4.1.2 e1071_1.7-9 rlang_0.4.12 classInt_0.4-3
[71] units_0.7-2 grid_4.1.2 rstudioapi_0.13 rmarkdown_2.11 boot_1.3-28 gtable_0.3.0 DBI_1.1.2
[78] R6_2.5.1 knitr_1.37 dplyr_1.0.7 fastmap_1.1.0 utf8_1.2.2 commonmark_1.7 rprojroot_2.0.2
[85] KernSmooth_2.23-20 desc_1.4.0 ape_5.6 parallel_4.1.2 Rcpp_1.0.7 vctrs_0.3.8 sf_1.0-5
[92] xfun_0.29 tidyselect_1.1.1