This repository includes the data and R scripts to reproduce analyses and figures found in the article Long-term impact of a major ice storm on tree mortality in an old-growth forest by Deschênes, Brice and Brisson published in Forest Ecology and Management.
The analyses were carried out with R version 3.5.1 (a free software environment for statistical computing and graphics) and require the installation of a recent version of it.
Analyses were reproduced in the MacOSX Mojave environment:
Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
locale: [1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] units_0.6-1 sf_0.7-1 graphicsutils_1.2-1 scales_1.0.0
[5] colourlovers_0.2.2 RColorBrewer_1.1-2 rms_5.1-3 SparseM_1.77
[9] Hmisc_4.2-0 Formula_1.2-3 lattice_0.20-35 sjPlot_2.6.1
[13] survminer_0.4.3 ggpubr_0.2 magrittr_1.5 ggplot2_3.1.0
[17] survival_2.42-3 dplyr_0.7.8 gtools_3.8.1
loaded via a namespace (and not attached):
[1] TH.data_1.0-9 minqa_1.2.4 colorspace_1.4-0 class_7.3-14
[5] modeltools_0.2-22 ggridges_0.5.1 sjlabelled_1.0.14 estimability_1.3
[9] snakecase_0.9.2 htmlTable_1.13.1 base64enc_0.1-3 rstudioapi_0.9.0
[13] glmmTMB_0.2.2.0 MatrixModels_0.4-1 mvtnorm_1.0-8 coin_1.2-2
[17] codetools_0.2-15 splines_3.5.1 mnormt_1.5-5 knitr_1.21
[21] sjmisc_2.7.6 bayesplot_1.6.0 jsonlite_1.6 nloptr_1.2.1
[25] ggeffects_0.7.0 broom_0.5.0 km.ci_0.5-2 cluster_2.0.7-1
[29] png_0.1-7 compiler_3.5.1 sjstats_0.17.2 emmeans_1.3.0
[33] backports_1.1.3 assertthat_0.2.0 Matrix_1.2-14 lazyeval_0.2.1
[37] acepack_1.4.1 htmltools_0.3.6 quantreg_5.36 tools_3.5.1
[41] bindrcpp_0.2.2 coda_0.19-2 gtable_0.2.0 glue_1.3.0
[45] Rcpp_1.0.0 nlme_3.1-137 psych_1.8.10 xfun_0.4
[49] stringr_1.3.1 lme4_1.1-19 XML_3.98-1.16 stringdist_0.9.5.1
[53] polspline_1.1.13 MASS_7.3-50 zoo_1.8-4 hms_0.4.2
[57] parallel_3.5.1 sandwich_2.5-0 pwr_1.2-2 TMB_1.7.15
[61] yaml_2.2.0 gridExtra_2.3 KMsurv_0.1-5 rpart_4.1-13
[65] latticeExtra_0.6-28 stringi_1.2.4 e1071_1.7-0 checkmate_1.9.1
[69] spData_0.2.9.4 rlang_0.3.1 pkgconfig_2.0.2 purrr_0.2.5
[73] prediction_0.3.6 bindr_0.1.1 htmlwidgets_1.3 cmprsk_2.2-7
[77] tidyselect_0.2.5 plyr_1.8.4 R6_2.3.0 multcomp_1.4-8
[81] DBI_1.0.0 pillar_1.3.1 haven_1.1.2 foreign_0.8-70
[85] withr_2.1.2 nnet_7.3-12 tibble_2.0.1 modelr_0.1.2
[89] crayon_1.3.4 survMisc_0.5.5 grid_3.5.1 data.table_1.12.0
[93] forcats_0.3.0 classInt_0.2-3 digest_0.6.18 xtable_1.8-3
[97] tidyr_0.8.2 stats4_3.5.1 munsell_0.5.0
- survival
- survminer
- dplyr
- sf
- units
- gtools
- rms
- sjPlot
- RColorBrewer
- colourlovers
- scales
- graphicsutils (not on CRAN)
Below are the R commands to install them all:
install.packages(
c("survival", "survminer", "dplyr", "sf", "units", "gtools", "rms", "sjPlot",
"RColorBrewer", "colourlovers", "scales", "remotes")
)
remotes::install_github("inSileco/graphicsutils")To reproduce the entire analysis including data cleaning, analyses and figures, run:
source("scripts/1_dataFormatting.R")
source("scripts/2_coxph.R")All data used for the analyses can be found in the data folder.
Script #1, scripts/1_dataFormatting.R, prepares and formats the data for the analyses. The cleaned data are also included in the data folder, hence the first script (scripts/1_dataFormatting.R) can be skipped.
Script #2, scripts/2_coxph.R, performed all the analyses and produced the figures.
The figures and tables produced are saved in the results folder.