@ercbk.bsky.social

@ercbk.bsky.social

97 Followers 166 Following

Github: https://github.com/ercbk Website: https://ericbook.netlify.app/ Mastodon: @erc_bk@fosstodon.org

full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spat-temp.html#sec-geo-sptemp-krig-ex Only showing code for the catboost row. Both are similar. p_cat_pred <- ggplot() + stars::geom_stars( data = star_preds_cat, aes(x = x, y = y, fill = predicted) ) + geom_sf( data = sf_germany_line, color = "grey40", linewidth = 0.3 ) + geom_sf( data = sf_obs_sample, color = "white", size = 0.8, alpha = 0.7 ) + geom_point( data = sf_obs_sample_nl |> st_drop_geometry(), aes(x = x, y = y, fill = pm10), shape = 21, size = 3, color = "white", stroke = 0.1 ) + facet_wrap(~as.Date(time), nrow = 1) + scico::scale_fill_scico( palette = "glasgow", limits = legend_limits, direction = -1, name = "PM10\n(µg/m³)", na.value = "transparent" ) + coord_sf(lims_method = "geometry_bbox") + labs(title = "Catboost") + theme_charcoal_map() theme_patch <- theme( plot.title = element_text( family = "Anybody", size = 25, color = "white", face = "bold" ), plot.subtitle = element_text( family = "Anybody", size = 16, color = "white" ), plot.background = element_rect(fill = "#191a1a", color = NA) ) p_krige_pred / p_cat_pred + plot_layout(guides = "collect") + plot_annotation( title = "PM10 Predictions", subtitle = "Station location color represents observed PM10 level", theme = theme_patch )
A map of Germany that looks like a paint brush was dipped in orange pain and lightly run across vertically and horizontally over a light blue surface.
source - https://cran.r-project.org/web/packages/gstat/vignettes/st.pdf full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spat-temp.html#sec-geo-sptemp-krig-var-ex plot(vario_emp, best_mod_fits_all, map = FALSE, wireframe = TRUE, all = TRUE)
source - https://cran.r-project.org/web/packages/gstat/vignettes/st.pdf full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spat-temp.html#sec-geo-sptemp-krig-var-ex plot(vario_emp, best_mod_fits_all, map = TRUE, all = TRUE)
source - https://cran.r-project.org/web/packages/gstat/vignettes/st.pdf full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spat-temp.html#sec-geo-sptemp-krig-var-ex best_mod_fits_all <- list(fit_vario_sm, best_ssm) |> append(best_mod_fits$fit_stv) plot(vario_emp, best_mod_fits_all, map = FALSE, all = TRUE)
source - https://r-spatial.org/book/15-Measures.html#local-gearys-c_i full code - https://ercbk.github.io/Data-Science-Notebook/qmd/association-spatial.html#sec-assoc-spat-local-ex tmap_options_reset() tmap_theme_notebook() notebook_colors <- unname(swatches::read_ase(here::here("palettes/Forest Floor.ase"))) tm_shape(clust_mcg_iturn_pres15) + tm_polygons(col = "#4A3728") + tm_fill( fill = "clust_type", fill.scale = tm_scale( values = c(notebook_colors[[5]], notebook_colors[[6]], notebook_colors[[7]]), value.na = notebook_colors[[2]], label.na = "Not Determined" ), fill.legend = tm_legend( "Hotspot Type", frame = FALSE, position = tm_pos_on_top( pos.v = 0.4, pos.h = 0.66 ) ) ) + tm_facets_wrap("method_type", ncol = 2) + tm_title( "High and Low Voter Turnout Clusters in Poland (2015)", size = 2, fontface = "bold", # get title flush with left side to line up with subtitle # every number except 0.05 is default padding = c(0.25, 0.15, 0.25, 0.25) ) + tm_title( "Comparing Local Moran's I, Local Getis-Ord G, and Local Geary's C methods", size = 1.25 ) + tm_layout( panel.labels = c( "MoranI-Permutation", "Getis-OrdG-Permutation", "GearyC-Permutation" ) )
source - https://r-spatial.org/book/15-Measures.html#local-morans-i_i full code - https://ercbk.github.io/Data-Science-Notebook/qmd/association-spatial.html#sec-assoc-spat-local-ex pv_brks <- c(0, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1) tmap_options_reset() tmap_theme_charcoal() tm_shape(pv_iturn_pres15) + tm_polygons(col = "gray40") + tm_fill( fill = "p_value", fill.scale = tm_scale(breaks = pv_brks, values = "scico.davos"), fill.legend = tm_legend( "Adjusted P-Values (BH)", frame = FALSE, position = tm_pos_on_top( pos.v = 0.4, pos.h = 0.66 ) ) ) + tm_facets_wrap("pval_type", ncol = 2) + tm_title( "Local Moran's I P-Values", size = 2, fontface = "bold" ) + tm_title("With regards to Poland's voter turnout (2015)", size = 1.25) + tm_layout( panel.labels = c( "Analytical-Conditional", "Permutation-Std.Deviate", "Permutation-Rank" ) )
source - https://r-spatial.org/book/15-Measures.html#local-measures full code - https://ercbk.github.io/Data-Science-Notebook/qmd/association-spatial.html#sec-assoc-spat-global-ex data(pol_pres15, package = "spDataLarge") pol_pres15 <- pol_pres15 |> janitor::clean_names() iturnout <- pol_pres15 |> st_drop_geometry() |> pull(i_turnout) lw_w_pol <- nb_poly_pol |> nb2listw(style = "W") infl_turnout_pol <- moran.plot( iturnout, listw = lw_w_pol, labels = pol_pres15$TERYT, cex = 1, pch = ".", xlab = "I round turnout", ylab = "lagged turnout" )
source - https://r-spatial.github.io/spdep/articles/nb.html#higher-order-neighbours full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spatial-weights.html#sec-geo-swgt-diag dat_nb |> tidyplot(x = tot_neigh, y = order, color = tot_nodes, width = 125, height = 125) |> add_heatmap(rotate_labels = 0) |> adjust_colors(colors_continuous_cividis) |> adjust_legend_title("Total Census Tracts") |> adjust_y_axis(title = "Order") |> adjust_x_axis(title = "Total Neighbors", position = "top") + theme_notebook( legend.title = element_text(hjust = 0.5), plot.margin = margin(25, 25, 50, 25) )
source - https://r-spatial.github.io/spdep/articles/nb_sf.html#k-nearest-neighbours full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spatial-weights.html#sec-geo-swgt-nbalg-distb NY88_nb_sf <- knn2nb(knearneigh( NY8_ct_sf, # centroids k = 4 )) # plot boundaries plot(st_geometry(NY8_sf), border="gray") # plot edges and nodes plot( NY88_nb_sf, coords = coords_ct, add = TRUE ) title(main="K nearest neighbours, k = 4")
library(ggdag); library(ggplot2) notebook_colors <- unname(swatches::read_ase(here::here("palettes/Forest Floor.ase"))) da_dag <- dagify( Y ~ A + B + C, D ~ A + B, C ~ D, outcome = "Y", exposure = "A" ) |> tidy_dagitty(layout = "sugiyama") da_dag |> ggplot(aes( x = -.data$y, y = -.data$x, xend = -.data$yend, yend = -.data$xend) ) + geom_dag_node(color = notebook_colors[[6]]) + geom_dag_edges() + geom_dag_text() + theme_dag( panel.background = element_rect(fill='#FFFDF9FF', color = NA), panel.border = element_blank(), panel.grid.minor = element_blank(), plot.background = element_rect(fill='#FFFDF9FF', color = NA), legend.background = element_rect(color = NA, fill='#FFFDF9FF'), legend.box.background = element_rect(fill='#FFFDF9FF', color = NA) )
library(ggdag); library(ggplot2) notebook_colors <- unname(swatches::read_ase(here::here("palettes/Forest Floor.ase"))) da_dag <- dagify( Y ~ A + B + C, D ~ A + B, C ~ D, outcome = "Y", exposure = "A" ) |> tidy_dagitty(layout = "sugiyama") da_dag |> ggplot(aes( x = .data$x, y = .data$y, xend = .data$xend, yend = .data$yend) ) + geom_dag_node(color = notebook_colors[[6]]) + geom_dag_edges() + geom_dag_text() + theme_dag( panel.background = element_rect(fill='#FFFDF9FF', color = NA), panel.border = element_blank(), panel.grid.minor = element_blank(), plot.background = element_rect(fill='#FFFDF9FF', color = NA), legend.background = element_rect(color = NA, fill='#FFFDF9FF'), legend.box.background = element_rect(fill='#FFFDF9FF', color = NA) )
DuckDB UI showing a table with bar and density plots in a side panel