Plot correlations between variables
Antonio J. Pérez-Luque
2021-12-29
Last updated: 2022-09-16
Checks: 7 0
Knit directory: ms_mariposas_biodiversity/
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Prepare data
library(tidyverse)
library(raster)
library(fasterize)
library(rgdal)
library(ggpubr)
library(ggstatsplot)Warning: package 'ggstatsplot' was built under R version 4.0.5Warning: replacing previous import 'dplyr::collapse' by 'glue::collapse' when
loading 'statsExpressions'library(here)
library(statsExpressions)Warning: package 'statsExpressions' was built under R version 4.0.5den <- raster::raster(here::here("data/density.ovr"))
riq <- raster::raster(here::here("data/richness.ovr"))
div <- raster::raster(here::here("data/diversity_v2.ovr"))
s <- stack(den, riq, div)
df <- as.data.frame(s, xy=TRUE) %>%
filter(!is.na(density))Non-parametric
cor_den_rich_np <- ggscatterstats(df,
x = density, y = richness,
marginal = FALSE,
type = "parametric",
point.args = list(fill="gray", alpha = 0.8),
smooth.line.args =
list(method = "gam", formula = y ~ s(x, bs = "cs"),
color = "blue"))
cor_den_rich_np
cor_den_div_np <- ggscatterstats(df,
x = density, y = diversity_v2,
marginal = FALSE,
type = "nonparametric",
point.args = list(fill="gray", alpha = 0.2),
smooth.line.args =
list(method = "gam", formula = y ~ s(x, bs = "cs"),
color = "blue"))
cor_den_div_np
cor_div_ric_np <- ggscatterstats(df,
y = richness, x = diversity_v2,
marginal = FALSE,
type = "nonparametric",
point.args = list(fill="gray", alpha = 0.2),
smooth.line.args =
list(method = "gam", formula = y ~ s(x, bs = "cs"),
color = "blue"))
cor_div_ric_np
ggsave(
filename = "figs/cor_den_rich_np.png",
plot = cor_den_rich_np,
width = 8,
height = 8,
device = "png"
)
ggsave(
filename = "figs/cor_den_div_np.png",
plot = cor_den_div_np,
width = 8,
height = 8,
device = "png"
)
ggsave(
filename = "figs/cor_div_ric_np.png",
plot = cor_div_ric_np,
width = 8,
height = 8,
device = "png"
)Parametric
mylabel <- c(
den = "Abundance (n ind / 100 m)",
div = "Diversity (Shannon index)",
riq = "Richness (species number)")ct <- corr_test(df, x = density, y = richness, type = "parametric")
cor_den_rich_p <- ggplot(df, aes(x=density, y=richness)) +
geom_point(col="gray", size=.3, alpha=.2) +
theme_minimal() +
geom_smooth(method = "gam",
formula = y ~ s(x, bs = "cs"),
color = "blue",
size =.5) +
labs(subtitle = ggplot2::expr(paste(
widehat(italic("r"))["Pearson"], "=0.727, ", italic("p"), "<", "0.001")),
x = mylabel["den"],
y = mylabel["riq"])
cor_den_rich_p
ct <- corr_test(df, x = density, y = diversity_v2, type = "parametric")
cor_den_div_p <- ggplot(df, aes(x = density, y = diversity_v2)) +
geom_point(col="gray", size=.3, alpha=0.2) +
theme_minimal() +
geom_smooth(method = "gam",
formula = y ~ s(x, bs = "cs"),
color = "blue",
size =.5) +
labs(subtitle = ggplot2::expr(paste(
widehat(italic("r"))["Pearson"], "=0.773, ", italic("p"), "<", "0.001")),
x = mylabel["den"],
y = mylabel["div"])
cor_den_div_p
ct <- corr_test(df, x = diversity_v2, y = richness, type = "parametric")
cor_div_ric_p <- ggplot(df, aes(x = diversity_v2, y = richness)) +
geom_point(col="gray", size=.3, alpha=0.2) +
theme_minimal() +
geom_smooth(method = "gam",
formula = y ~ s(x, bs = "cs"),
color = "blue",
size =.5) +
labs(subtitle = ggplot2::expr(paste(
widehat(italic("r"))["Pearson"], "=0.673, ", italic("p"), "<", "0.001")),
x = mylabel["div"],
y = mylabel["riq"])
cor_div_ric_p
ggsave(
filename = "figs/cor_den_rich_p.png",
plot = cor_den_rich_p,
width = 8,
height = 8,
device = "png"
)
ggsave(
filename = "figs/cor_den_div_p.png",
plot = cor_den_div_p,
width = 8,
height = 8,
device = "png"
)
ggsave(
filename = "figs/cor_div_ric_p.png",
plot = cor_div_ric_p,
width = 8,
height = 8,
device = "png"
)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 10.16
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] statsExpressions_1.3.1 here_1.0.1 ggstatsplot_0.9.1
[4] ggpubr_0.4.0 rgdal_1.5-23 fasterize_1.0.3
[7] raster_3.4-5 sp_1.4-5 forcats_0.5.1
[10] 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
[16] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] TH.data_1.0-10 colorspace_2.0-2 ggsignif_0.6.3
[4] ellipsis_0.3.2 rio_0.5.16 rprojroot_2.0.2
[7] estimability_1.3 parameters_0.17.0 fs_1.5.0
[10] mc2d_0.1-18 rstudioapi_0.13 farver_2.1.0
[13] MatrixModels_0.4-1 fansi_0.4.2 mvtnorm_1.1-1
[16] lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18
[19] splines_4.0.2 knitr_1.31 zeallot_0.1.0
[22] jsonlite_1.7.2 broom_0.7.9 dbplyr_2.1.1
[25] effectsize_0.6.0.1 compiler_4.0.2 httr_1.4.2
[28] emmeans_1.5.4 backports_1.2.1 assertthat_0.2.1
[31] Matrix_1.3-2 fastmap_1.1.0 cli_2.5.0
[34] later_1.1.0.1 htmltools_0.5.2 tools_4.0.2
[37] coda_0.19-4 gtable_0.3.0 glue_1.4.2
[40] Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
[43] jquerylib_0.1.3 vctrs_0.3.8 nlme_3.1-152
[46] insight_0.17.0 xfun_0.30 ps_1.5.0
[49] openxlsx_4.2.3 rvest_1.0.0 lifecycle_1.0.1
[52] gtools_3.8.2 rstatix_0.6.0 getPass_0.2-2
[55] MASS_7.3-53 zoo_1.8-8 scales_1.1.1.9000
[58] BayesFactor_0.9.12-4.3 ragg_1.1.1 hms_1.0.0
[61] promises_1.2.0.1 parallel_4.0.2 sandwich_3.0-0
[64] rematch2_2.1.2 yaml_2.2.1 curl_4.3.2
[67] pbapply_1.4-3 sass_0.4.1 reshape_0.8.8
[70] stringi_1.7.4 highr_0.8 paletteer_1.3.0
[73] bayestestR_0.11.5 zip_2.1.1 systemfonts_1.0.0
[76] rlang_0.4.12 pkgconfig_2.0.3 evaluate_0.14
[79] lattice_0.20-41 labeling_0.4.2 patchwork_1.1.1
[82] processx_3.5.1 tidyselect_1.1.1 plyr_1.8.6
[85] magrittr_2.0.1 R6_2.5.1 generics_0.1.0
[88] multcomp_1.4-16 DBI_1.1.1 mgcv_1.8-33
[91] pillar_1.6.1 haven_2.3.1 whisker_0.4
[94] foreign_0.8-81 withr_2.5.0 survival_3.2-7
[97] datawizard_0.4.0 abind_1.4-5 performance_0.8.0
[100] WRS2_1.1-3 modelr_0.1.8 crayon_1.4.1
[103] car_3.0-10 utf8_1.1.4 correlation_0.8.0
[106] rmarkdown_2.14 grid_4.0.2 readxl_1.3.1
[109] data.table_1.14.0 callr_3.7.0 git2r_0.28.0
[112] reprex_2.0.0 digest_0.6.27 xtable_1.8-4
[115] httpuv_1.5.5 textshaping_0.3.2 munsell_0.5.0
[118] bslib_0.3.1