Explore: covariates association

Load packages, read data and source custom scripts

rm(list = ls())
library(dplyr)

#> 
#> Attaching package: 'dplyr'

#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag

#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(ggplot2)
library(GGally)

#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2

path_proj <- day2day::git_path()
path_data <- file.path(path_proj, "data")
path_processed <- file.path(path_data, "processed")

land_cover <- fst::read_fst(file.path(path_processed, "uk_1km_df_model.fst"))

Custom functions for ggpairs

gg_hex <- function(data, mapping, ...) {
    ggplot(data = data, mapping = mapping) +
        geom_hex(na.rm = TRUE, ...) +
        geom_smooth(aes(color = "gg_red"), data = dplyr::sample_frac(data, 0.2)) +
        colorspace::scale_fill_continuous_sequential("Viridis", trans = "log")
}
gg_hist <- function(data, mapping, ...) {
    ggplot(data = data, mapping = mapping) +
        geom_histogram(fill = "gray", bins = 100)
}

Visualise covariates association for urban variables

land_cover %>%
    dplyr::select(pop:gdhi) %>%
    ggpairs(lower = list(continuous = gg_hex), diag = list(continuous = gg_hist)) +
    theme_bw()

Visualise covariates association for environmental variables

land_cover %>%
    dplyr::select(elev:slope_nb8, gdd:sms) %>%
    ggpairs(lower = list(continuous = gg_hex), diag = list(continuous = gg_hist)) +
    theme_bw()

Time to execute the task

Only useful when executed with Rscript.

proc.time()

#>    user  system elapsed 
#> 102.068   1.254 103.271