drone_campo
2021-07-08
Last updated: 2021-07-08
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Knit directory: fire_alcontar/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0b2f433 | Antonio J Perez-Luque | 2021-07-08 | update data |
| html | 349a882 | Antonio J Perez-Luque | 2021-07-08 | Build site. |
| Rmd | 5ca67cf | Antonio J Perez-Luque | 2021-07-08 | update zoom |
| html | 74a8d6e | Antonio J Perez-Luque | 2021-07-08 | Build site. |
| Rmd | 688361e | Antonio J Perez-Luque | 2021-07-08 | add comparative |
Introduction
- Read and prepare data
library(here)
library(tidyverse)
library(readxl)
library(plotrix)
library(DT)
library(plotly)
library(ggstatsplot)
library(patchwork)
library(cowplot)
library(ggiraph)cob.raw <- read_excel(path=here::here("data/test_drone.xlsx"),
sheet = "COBERTURA")
diversidad <- read_excel(path=here::here("data/test_drone.xlsx"),
sheet = "SHANNON") %>% mutate(Shannon = abs(I_SHANNON))
df <- cob.raw %>% inner_join(diversidad)Correlation general
Method 1
g1 <- ggscatterstats(df,
title = "Método 1",
x="COB_TOTAL_M2", y = "AREA_VEG_m2",
marginal = FALSE,
ggplot.component =
list(geom_abline(slope = 1))) Method 2
g2 <- ggscatterstats(df,
title = "Método 2",
x="COB_TOTAL_M2", y = "COBERTURA",
marginal = FALSE,
ggplot.component =
list(geom_abline(slope = 1))) g1 + g2
| Version | Author | Date |
|---|---|---|
| 74a8d6e | Antonio J Perez-Luque | 2021-07-08 |
Explore by RANGO_INFOCA
pr1 <- df %>%
ggplot(aes(x=COB_TOTAL_M2, y = AREA_VEG_m2, color=as.factor(RANGO_INFOCA))) +
geom_point_interactive(aes(tooltip = QUADRAT, id=QUADRAT)) +
geom_abline(slope=1) +
facet_wrap(~RANGO_INFOCA, labeller = r2_labeller1) +
theme_bw() +
xlab("Campo (COB_TOTAL_M2)") +
ylab("Drone (AREA_VEG_m2)") +
geom_smooth(method = "lm") +
theme(
legend.position = "none",
panel.grid = element_blank(),
strip.background = element_rect(fill="white")
) + ggtitle("Método 1") pr2 <- df %>%
ggplot(aes(x=COB_TOTAL_M2, y = COBERTURA, color=as.factor(RANGO_INFOCA))) +
geom_point_interactive(aes(tooltip = QUADRAT, id=QUADRAT)) +
geom_abline(slope=1) +
facet_wrap(~RANGO_INFOCA, labeller = r2_labeller2) +
theme_bw() +
xlab("Campo (COB_TOTAL_M2)") +
ylab("Drone (AREA_VEG_m2)") +
geom_smooth(method = "lm") +
theme(
legend.position = "none",
panel.grid = element_blank(),
strip.background = element_rect(fill="white")
) + ggtitle("Método 2")# pr1 + pr2
girafe(ggobj = plot_grid(pr1, pr2),
options = list(
opts_sizing(width = .7),
opts_zoom(max = 5))
)Relation with Shannon Diversity
p1 <- df %>%
ggplot(aes(x=COB_TOTAL_M2, y = AREA_VEG_m2)) +
geom_point_interactive(aes(
size=Shannon, tooltip = QUADRAT, id=QUADRAT),
alpha = .4) +
geom_abline(slope=1) +
theme_bw() +
theme(legend.position = "bottom") + ggtitle("Método 1")p2 <- df %>%
ggplot(aes(x=COB_TOTAL_M2, y = COBERTURA)) +
geom_point_interactive(aes(
size=Shannon, tooltip = QUADRAT, id=QUADRAT),
alpha = .4) +
geom_abline(slope=1) +
theme_bw() +
theme(legend.position = "bottom") + ggtitle("Método 2")# p1 + p2
girafe(ggobj = plot_grid(p1, p2),
options = list(
opts_sizing(width = .7),
opts_zoom(max = 5))
)Notas
Intentar correlacionar los residuos del modelo (de la correlación con otras variables: ith, slope). Para ello necesito el DMT obtenido con dron o usar un dtm genérico
Aplicar análisis de clasificación (\(\kappa\) coefficient). Ver un ejemplo en Cunliffe et al. (2016).
Revisar trabajos de Cunliffe et al. (2016), Abdullah et al. (2021) y similares.
Relación de la estimación con la diversidad-abundancia (vía NMDS)
References
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] ggiraph_0.7.10 cowplot_1.1.1 patchwork_1.1.1 ggstatsplot_0.7.2
[5] plotly_4.9.3 DT_0.17 plotrix_3.8-1 readxl_1.3.1
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.3
[17] tidyverse_1.3.1 here_1.0.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] uuid_0.1-4 pairwiseComparisons_3.1.3
[3] backports_1.2.1 systemfonts_1.0.0
[5] plyr_1.8.6 lazyeval_0.2.2
[7] splines_4.0.2 gmp_0.6-2
[9] kSamples_1.2-9 ipmisc_5.0.2
[11] TH.data_1.0-10 digest_0.6.27
[13] SuppDists_1.1-9.5 htmltools_0.5.1.1
[15] fansi_0.4.2 magrittr_2.0.1
[17] memoise_2.0.0 paletteer_1.3.0
[19] modelr_0.1.8 sandwich_3.0-0
[21] colorspace_2.0-0 rvest_1.0.0
[23] ggrepel_0.9.1 haven_2.3.1
[25] xfun_0.23 crayon_1.4.1
[27] jsonlite_1.7.2 zeallot_0.1.0
[29] survival_3.2-7 zoo_1.8-8
[31] glue_1.4.2 gtable_0.3.0
[33] emmeans_1.5.4 MatrixModels_0.4-1
[35] statsExpressions_1.1.0 Rmpfr_0.8-2
[37] scales_1.1.1 mvtnorm_1.1-1
[39] DBI_1.1.1 PMCMRplus_1.9.0
[41] Rcpp_1.0.6 viridisLite_0.3.0
[43] xtable_1.8-4 performance_0.7.2
[45] htmlwidgets_1.5.3 httr_1.4.2
[47] ellipsis_0.3.2 farver_2.0.3
[49] pkgconfig_2.0.3 reshape_0.8.8
[51] multcompView_0.1-8 sass_0.3.1
[53] dbplyr_2.1.1 utf8_1.1.4
[55] labeling_0.4.2 tidyselect_1.1.0
[57] rlang_0.4.10 later_1.1.0.1
[59] ggcorrplot_0.1.3 effectsize_0.4.5
[61] munsell_0.5.0 cellranger_1.1.0
[63] tools_4.0.2 cachem_1.0.4
[65] cli_2.5.0 generics_0.1.0
[67] broom_0.7.6 evaluate_0.14
[69] fastmap_1.1.0 BWStest_0.2.2
[71] yaml_2.2.1 rematch2_2.1.2
[73] knitr_1.31 fs_1.5.0
[75] nlme_3.1-152 WRS2_1.1-1
[77] pbapply_1.4-3 whisker_0.4
[79] xml2_1.3.2 correlation_0.6.1
[81] compiler_4.0.2 rstudioapi_0.13
[83] ggsignif_0.6.0 reprex_2.0.0
[85] bslib_0.2.4 stringi_1.5.3
[87] highr_0.8 parameters_0.14.0
[89] lattice_0.20-41 Matrix_1.3-2
[91] vctrs_0.3.8 pillar_1.6.1
[93] lifecycle_1.0.0 mc2d_0.1-18
[95] jquerylib_0.1.3 estimability_1.3
[97] data.table_1.13.6 insight_0.14.1
[99] httpuv_1.5.5 R6_2.5.0
[101] promises_1.2.0.1 BayesFactor_0.9.12-4.2
[103] codetools_0.2-18 MASS_7.3-53
[105] gtools_3.8.2 assertthat_0.2.1
[107] rprojroot_2.0.2 withr_2.4.1
[109] multcomp_1.4-16 mgcv_1.8-33
[111] bayestestR_0.9.0 parallel_4.0.2
[113] hms_1.0.0 grid_4.0.2
[115] coda_0.19-4 rmarkdown_2.8
[117] git2r_0.28.0 lubridate_1.7.10