plots_descriptores
ajpelu
2022-01-30
Last updated: 2022-07-11
Checks: 7 0
Knit directory: ms_mariposas_biodiversity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f4d5359 | ajpelu | 2022-07-11 | update plots with new models |
| html | a89d11b | ajpelu | 2022-01-30 | Build site. |
| Rmd | e3050d7 | ajpelu | 2022-01-30 | add plots |
Introduction
library(tidyverse)
library(readxl)
library(janitor)
library(here)
library(patchwork)
library(vegan)
library(performance)
library(kableExtra)
library(visreg)
library(MASS)- Prepara datos
diversidad_year <- read_csv(here::here("data/diversidad_by_year.csv"))
riqueza_year <- read_csv(here::here("data/riqueza_by_year.csv"))
densidad_year <- read_csv(here::here("data/densidad_by_year.csv"))
env <- read_csv(here::here("data/matrix_env_variables_selected.csv")) %>%
dplyr::select(-elev, -temp_anu, -Pp_ver, -Pp_anu)
climate_year <- read_csv(here::here("data/climate_year.csv")) %>%
dplyr::select(Id_transect, year, p_anu_year, p_ver_year, t_anu_year = t_anual)
m <- riqueza_year %>%
inner_join(diversidad_year) %>%
inner_join(densidad_year) %>%
dplyr::select(-longitud, -min_altitu, -max_altitu, -long_total, -abundancia) %>%
unite("id", c("id_transecto", "year")) %>%
inner_join(
(climate_year %>% unite("id", c("Id_transect", "year"), remove = FALSE)),
by="id") %>%
inner_join(env) %>%
rowwise() %>%
mutate(FR_ARBOL = sum(FR_CONIF, FR_QUERC)) %>%
rename(div = diversidad) %>%
relocate(transecto, id, Id_transect, site, elev,year) %>%
dplyr::select(-Transecto, -Abreviatura)Modelos
modelo_densidad <- glm(den ~ 1 + FR_MATDE + HIDRO_ITH + DS_ARBOL,
family = Gamma(link ="log"), data = m)
modelo_diversidad <- glm(div ~ 1 + FR_MATDE + HIDRO_ITH + DS_ARBOL, data = m)
modelo_riqueza <- glm(riq ~ 1 + elev + FR_MATDE + TP_RSH_V + HIDRO_ITH + TP_RSD_P + TP_EXPO, data = m)- Density
d_FR_MATDE <- visreg(modelo_densidad, ylab = "", "FR_MATDE",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
d_DS_ARBOL <- visreg(modelo_densidad, ylab = "", "DS_ARBOL",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
d_HIDRO_ITH <- visreg(modelo_densidad, ylab = "", "HIDRO_ITH",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()- Diveristy
di_FR_MATDE <- visreg(modelo_diversidad, ylab = "", "FR_MATDE",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
di_DS_ARBOL <- visreg(modelo_diversidad, ylab = "", "DS_ARBOL",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
di_HIDRO_ITH <- visreg(modelo_diversidad, ylab = "", "HIDRO_ITH",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
# di_TP_ES_OE <- visreg(modelo_diversidad, ylab = "", "TP_ES_OE",
# scale="response", partial=TRUE, gg=TRUE) + theme_bw()- Richness
r_FR_MATDE <- visreg(modelo_riqueza, ylab = "", "FR_MATDE",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
r_elev <- visreg(modelo_riqueza, ylab = "", "elev",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
r_TP_RSH_V <- visreg(modelo_riqueza, ylab = "", "TP_RSH_V",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
r_HIDRO_ITH <- visreg(modelo_riqueza, ylab = "", "HIDRO_ITH",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
r_TP_RSD_P <- visreg(modelo_riqueza, ylab = "", "TP_RSD_P",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()
#
# r_TP_ES_OE <- visreg(modelo_riqueza, ylab = "", "TP_ES_OE",
# scale="response", partial=TRUE, gg=TRUE) + theme_bw()
r_TP_EXPO <- visreg(modelo_riqueza, ylab = "", "TP_EXPO",
scale="response", partial=TRUE, gg=TRUE) + theme_bw()- All plots
plot_den <- d_FR_MATDE / (d_HIDRO_ITH + labs(y="Abundance")) / d_DS_ARBOL
plot_div <- di_FR_MATDE / (di_HIDRO_ITH + labs(y="Diversity")) / di_DS_ARBOL
plot_rich <- r_FR_MATDE / r_HIDRO_ITH / (r_elev + labs(y="Richness")) / r_TP_EXPO / r_TP_RSD_P / r_TP_RSH_V
den_div <- plot_den | plot_div ggsave(here::here("figs/predictors_den_div.pdf"),
device = "pdf",
width = 6, height = 7)
den_div
| Version | Author | Date |
|---|---|---|
| a89d11b | ajpelu | 2022-01-30 |
dev.off()null device
1 ggsave(plot= den_div,
here::here("figs/predictors_den_div.png"),
device = "png",
dpi = 300,
width = 6, height = 7)plot_rich_a <- r_FR_MATDE / (r_HIDRO_ITH + labs(y="Richness")) / r_elev
plot_rich_b <- r_TP_EXPO / r_TP_RSD_P / r_TP_RSH_V
plot_rich_ab <- plot_rich_a | plot_rich_b ggsave(here::here("figs/predictors_rich.pdf"),
device = "pdf",
width = 6, height = 7)
plot_rich_ab
| Version | Author | Date |
|---|---|---|
| a89d11b | ajpelu | 2022-01-30 |
dev.off()null device
1 ggsave(plot= plot_rich_ab,
here::here("figs/predictors_den_div.png"),
device = "png",
dpi = 300,
width = 6, height = 7)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] MASS_7.3-53 visreg_2.7.0 kableExtra_1.3.1 performance_0.8.0
[5] vegan_2.5-7 lattice_0.20-41 permute_0.9-5 patchwork_1.1.1
[9] here_1.0.1 janitor_2.1.0 readxl_1.3.1 forcats_0.5.1
[13] stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0
[17] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
[21] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] nlme_3.1-152 fs_1.5.0 lubridate_1.7.10 webshot_0.5.2
[5] insight_0.17.0 httr_1.4.2 rprojroot_2.0.2 tools_4.0.2
[9] backports_1.2.1 bslib_0.3.1 utf8_1.1.4 R6_2.5.1
[13] DBI_1.1.1 mgcv_1.8-33 colorspace_2.0-2 withr_2.4.1
[17] tidyselect_1.1.1 processx_3.5.1 compiler_4.0.2 git2r_0.28.0
[21] textshaping_0.3.2 cli_2.5.0 rvest_1.0.0 xml2_1.3.2
[25] labeling_0.4.2 sass_0.4.1 scales_1.1.1.9000 callr_3.7.0
[29] systemfonts_1.0.0 digest_0.6.27 rmarkdown_2.14 pkgconfig_2.0.3
[33] htmltools_0.5.2 highr_0.8 dbplyr_2.1.1 fastmap_1.1.0
[37] rlang_0.4.12 rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.3
[41] generics_0.1.0 jsonlite_1.7.2 magrittr_2.0.1 Matrix_1.3-2
[45] Rcpp_1.0.7 munsell_0.5.0 fansi_0.4.2 lifecycle_1.0.1
[49] stringi_1.7.4 whisker_0.4 yaml_2.2.1 snakecase_0.11.0
[53] grid_4.0.2 parallel_4.0.2 promises_1.2.0.1 crayon_1.4.1
[57] haven_2.3.1 splines_4.0.2 hms_1.0.0 knitr_1.31
[61] ps_1.5.0 pillar_1.6.1 reprex_2.0.0 glue_1.4.2
[65] evaluate_0.14 getPass_0.2-2 modelr_0.1.8 vctrs_0.3.8
[69] httpuv_1.5.5 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[73] xfun_0.30 broom_0.7.9 later_1.1.0.1 ragg_1.1.1
[77] viridisLite_0.4.0 cluster_2.1.0 ellipsis_0.3.2