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25_cov_expl_analysis_cont_cat.Rmd
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25_cov_expl_analysis_cont_cat.Rmd
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---
title: "Exploratory Analysis of Covariates"
subtitle: "Continuous vs. categorical covariates"
author: "Anatol Helfenstein"
date: "2021-02-01 (updated)"
output:
html_document:
toc: yes
toc_float: yes
'': default
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = FALSE)
options(width = 100) # sets width of R code output (not images)
```
```{r load required pkgs, include = FALSE}
# load packages
pkgs <- c("tidyverse", "raster", "rgdal", "sf", "rasterVis", "viridis", "foreach",
"RColorBrewer", "corrplot")
lapply(pkgs, library, character.only = TRUE)
```
```{r list of covariates, include = FALSE}
# locate rasters for stack
v_cov_names <- dir("out/data/covariates/final_stack",
pattern = "\\.grd$", recursive = TRUE)
# read in prepared rasters ready for model calibration
ls_r_cov <- foreach(cov = 1:length(v_cov_names)) %do%
raster(paste0("out/data/covariates/final_stack/", v_cov_names[[cov]]))
# read in prepared covariate stack
r_stack_cov <- stack(ls_r_cov)
# read in covariate metadata
tbl_cov_meta <- read_csv("data/covariates/covariates_metadata.csv") %>%
# only interested in covariates we use in model
filter(name %in% names(r_stack_cov)) %>%
arrange(name)
```
## Continuous covariates
```{r continuous covariates overview, echo=TRUE, warning=FALSE}
# stack of continuous covariates
r_stack_cov_cont <- r_stack_cov[[tbl_cov_meta %>%
filter(values_type %in% "continuous") %>%
.$name]]
# All continuous covariates at 25m resolution
dim(r_stack_cov_cont)
# Names of all continuous covariates
names(r_stack_cov_cont)
```
### Univariate exploratory analysis
#### Histograms
```{r continuous covariates histograms, echo=FALSE, warning=FALSE, message=FALSE, results='hide', out.height='100%', out.width='100%'}
# set plotting layout
par(mfrow = c(2, 3))
# plot histograms
raster::hist(r_stack_cov_cont[[1]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[1]])))
raster::hist(r_stack_cov_cont[[2]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[2]])))
raster::hist(r_stack_cov_cont[[3]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[3]])))
raster::hist(r_stack_cov_cont[[4]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[4]])))
raster::hist(r_stack_cov_cont[[5]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[5]])))
raster::hist(r_stack_cov_cont[[6]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[6]])))
# set plotting layout
par(mfrow = c(2, 3))
# plot histograms
raster::hist(r_stack_cov_cont[[7]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[7]])))
raster::hist(r_stack_cov_cont[[8]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[8]])))
raster::hist(r_stack_cov_cont[[9]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[9]])))
raster::hist(r_stack_cov_cont[[10]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[10]])))
raster::hist(r_stack_cov_cont[[11]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[11]])))
raster::hist(r_stack_cov_cont[[12]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[12]])))
# set plotting layout
par(mfrow = c(2, 3))
# plot histograms
raster::hist(r_stack_cov_cont[[13]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[13]])))
raster::hist(r_stack_cov_cont[[14]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[14]])))
raster::hist(r_stack_cov_cont[[15]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[15]])))
raster::hist(r_stack_cov_cont[[16]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[16]])))
raster::hist(r_stack_cov_cont[[17]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[17]])))
raster::hist(r_stack_cov_cont[[18]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[18]])))
# set plotting layout
par(mfrow = c(2, 3))
# plot histograms
raster::hist(r_stack_cov_cont[[19]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[19]])))
raster::hist(r_stack_cov_cont[[20]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[20]])))
raster::hist(r_stack_cov_cont[[21]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[21]])))
raster::hist(r_stack_cov_cont[[22]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[22]])))
raster::hist(r_stack_cov_cont[[23]], main = NULL, plot = TRUE,
xlab = paste0(names(r_stack_cov_cont[[23]])))
# list of histograms of continuous covariates
# foreach(cont = 1:length(names(r_stack_cov_cont))) %dopar% {
# raster::hist(r_stack_cov_cont[[cont]],
# main = NULL,
# plot = TRUE,
# xlab = paste0(names(r_stack_cov_cont[[cont]])))
# } # time elapse: 1.7 min
```
### Multivariate exploratory analysis: correlations
```{r continuous covariates correlations, echo=FALSE, warning=FALSE, message=FALSE, results='hide', out.height='100%', out.width='100%'}
# calculate correlation matrix
# system.time(
# ls_corr <- layerStats(r_stack_cov_cont, "pearson", na.rm = TRUE)
# )
# # time elapse sequential: had to stop at about 2 hours
#
# # make correlation plot
# corrplot(corr = ls_corr$`pearson correlation coefficient`,
# is.corr = FALSE,
# method = "square",
# type = "upper")
```
### Maps
```{r continuous covariates maps, echo=FALSE, warning=FALSE, message=FALSE, results='hide', out.height='200%', out.width='200%'}
# list of color schemes for all continuous covariates
ls_colors_cont <- foreach(cont = 1:length(names(r_stack_cov_cont))) %do% {
if (grepl("ahn", names(r_stack_cov_cont))[[cont]]) {
terrain.colors(1000)
} else {
viridis::viridis(100)
}
}
# set up parallel backend to use multiple cores
cores <- parallel::detectCores()
cl <- parallel::makeCluster(cores - 2) # to not overload memory
doParallel::registerDoParallel(cl)
# list of plots of continuous covariates with designated description and color scheme
foreach(cont = 1:length(names(r_stack_cov_cont))) %dopar% {
rasterVis::levelplot(r_stack_cov_cont[[cont]],
main = paste0(names(r_stack_cov_cont[[cont]])),
margin = list(FUN = 'median'),
par.settings = list(axis.line = list(col = "transparent")),
scales = list(draw = FALSE),
col.regions = ls_colors_cont[[cont]])
} # time elapse: 1.5 min
# stop parallel backend
parallel::stopCluster(cl)
```
## Categorical covariates
```{r categorical covariates overview, echo=TRUE, warning=FALSE}
# stack of categorical covariates
r_stack_cov_cat <- r_stack_cov[[tbl_cov_meta %>%
filter(values_type %in% "categorical") %>%
.$name]]
# All categorical covariates at 25m resolution
dim(r_stack_cov_cat)
# Names of all categorical covariates
names(r_stack_cov_cat)
# description of classes in RAT attribute table, e.g. LGN8:
r_stack_cov_cat$lgn8_25m@data@attributes[[1]]
```
### Univariate exploratory analysis: categories and maps
```{r categorical covariates detailed, echo=FALSE, warning=FALSE, message=FALSE, results='hide', out.height='100%', out.width='100%'}
# use RColorBrewer colors https://colorbrewer2.org/?type=qualitative&scheme=Paired&n=12#type=qualitative&scheme=Paired&n=12
# if > 12 classes, create an assortment of categorical colors
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
v_colors = unlist(mapply(brewer.pal,
qual_col_pals$maxcolors,
rownames(qual_col_pals)))
# list of color schemes for all categorical covariates
# if <= 12 classes, use "Paired" scheme,
ls_colors_cat <- foreach(cat = 1:length(names(r_stack_cov_cat))) %do% {
if (nrow(levels(r_stack_cov_cat[[cat]])[[1]]) <= 12) {
brewer.pal(n = nrow(levels(r_stack_cov_cat[[cat]])[[1]]), name = "Paired")
} else {
if (nrow(levels(r_stack_cov_cat[[cat]])[[1]]) <= length(v_colors)) {
v_colors[1:nrow(levels(r_stack_cov_cat[[cat]])[[1]])]
} else {
sample(v_colors,
nrow(levels(r_stack_cov_cat[[cat]])[[1]]),
replace = TRUE)
}}}
# list of pie charts for every factor
ls_pie_cat <- foreach(cat = 1:length(names(r_stack_cov_cat))) %do% {
ggplot(r_stack_cov_cat[[cat]]@data@attributes[[1]],
aes(x = "", y = COUNT, fill = reorder(description, ID))) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
plot.background = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
scale_fill_manual(values = ls_colors_cat[[cat]]) +
labs(fill = paste(names(r_stack_cov_cat[[cat]])))
}
# set up parallel backend to use multiple cores
cores <- parallel::detectCores()
cl <- parallel::makeCluster(cores - 10) # to not overload memory
doParallel::registerDoParallel(cl)
# list of plots of categorical covariates with designated description and color scheme
system.time(
ls_plots_cat <- foreach(cat = 1:length(names(r_stack_cov_cat))) %dopar% {
rasterVis::levelplot(r_stack_cov_cat[[cat]],
att = "description",
main = paste0(names(r_stack_cov_cat[[cat]])),
par.settings = list(axis.line = list(col = "transparent")),
scales = list(draw = FALSE),
col.regions = ls_colors_cat[[cat]],
# since we already have legend from pie chart
colorkey = FALSE)
}
) # time elapsed: 3.2 min
# combine pie charts and maps alternating the combinations to get one variable after another
ls_pie_plots_cat <- c(rbind(ls_pie_cat, ls_plots_cat))
# print plots
ls_pie_plots_cat
# stop parallel backend
parallel::stopCluster(cl)
#if(.Platform$OS.type == "windows"){
# plan(multisession)
#} else {
# plan(multicore)
# }
# future_map(as.list(r_stack_cov_cat)[1:5],
# ~levelplot(.x,
# att = "description",
# main = paste0(names(.x)),
# par.settings = list(axis.line = list(col = "transparent")),
# scales = list(draw = FALSE)))
# #col.regions = n_color))
```