diff --git a/.Rbuildignore b/.Rbuildignore
index f2ca8ef..5539a12 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -2,3 +2,4 @@
 ^\.Rproj\.user$
 ^README\.Rmd$
 ^\.github$
+^LICENSE\.md$
diff --git a/.Rhistory b/.Rhistory
index 46f0bdf..af94a48 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,327 +1,6 @@
-results,
-function(x) x$`Correlation Matrix`[1, 2]
-),
-server = if (type == "split") names(results) else rep("combine", length(results)),
-row.names = NULL
-)
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-source("dsExampleClient/R/ds.groupcor.R")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-results_data %>% head()
-if(type == 'split' && length(names_region) != length(stdnames)){
-names_region <- rep(names_region, times = length(stdnames))
-}
-shape_list <- lapply(names_region, function(region) {
-boundr::bounds(
-"lsoa",
-within_level = "lad",
-within_names = region,
-lookup_year = 2011,
-opts = boundr::boundr_options(resolution = "BFC")
-) |>
-dplyr::select(lsoa11cd, geometry)
-})
-head(shape_list[[1]])
-shape_list[[1]] %>% head()
-shape_list[[2]] %>% head()
-shape_list[[3]] %>% head()
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-shape_list %>% names()
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-names(shape_list)
-shape_list[[1]]
-shape_list[[1]] %>% head()
-shape_list[[2]] %>% head()
-stdnames
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-head(results_data)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-correlation_data %>% head()
-correlation_data %>% tail()
-shape_list[[1]] %>% head()
-server_name
-shape_list[[server_name]] %>% head()
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-head(results_data)
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-head(results_data)
-levels(factor(results_data$server))
-ggplot2::ggplot(results_data) +
-ggplot2::geom_sf(ggplot2::aes(fill = correlation.coef)) +
-ggplot2::scale_fill_gradientn(
-colours = grDevices::colorRampPalette(c(
-"#440154",
-"#414487",
-"#2A788E",
-"#22A884",
-"#7AD151",
-"#FDE725"
-))(100),
-na.value = "grey90"
-) +
-ggplot2::labs(fill = "Correlation Coefficient") +
-ggplot2::facet_wrap(~server) +
-ggplot2::theme_minimal()
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "split")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-shape_list[[1]]
-shape_list[[2]]
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/groupcorDS.R")
-source("dsExample/R/groupcorDS.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-names(result)
-source("dsExample/R/groupcorDS.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExample/R/groupcorDS.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-result[["Nfilter.tab"]]
-names(result) %>% tail()
-source("dsExample/R/groupcorDS.R")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-names(output)
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-!is.null(common_lsoas)
-is.null(common_lsoas)
-length(common_lsoas)
-length(unique(common_lsoas))
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-pwd
-getwd()
-pdf("Personal/images/combined_correlation_map.pdf", width = 8, height = 6)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-png("Personal/images/combined_correlation_map.png", width = 8, height = 6)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-png("Personal/images/combined_correlation_map.png", width = 8, height = 6)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-is.null(common_lsoas) || length(common_lsoas) < output[[1]][["Nfilter.tab"]]
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-reference <- output[[1]][[lsoa]]
-reference
-combined.sums.of.products <- matrix(
-0,
-nrow = nrow(reference[[1]]),
-ncol = ncol(reference[[1]]),
-dimnames = dimnames(reference[[1]])
-)
-combined.sums.of.products
-combined.sums.of.products <- matrix(
-0,
-nrow = nrow(reference[[1]]),
-ncol = ncol(reference[[1]]),
-dimnames = dimnames(reference[[1]])
-)
-combined.sums <- matrix(
-0,
-nrow = nrow(reference[[2]]),
-ncol = ncol(reference[[2]]),
-dimnames = dimnames(reference[[2]])
-)
-combined.complete.cases <- matrix(
-0,
-nrow = nrow(reference[[3]]),
-ncol = ncol(reference[[3]]),
-dimnames = dimnames(reference[[3]])
-)
-combined.missing.cases.vector <- matrix(
-0,
-nrow = nrow(reference[[4]][[1]]),
-ncol = ncol(reference[[4]][[1]]),
-dimnames = dimnames(reference[[4]][[1]])
-)
-combined.missing.cases.matrix <- matrix(
-0,
-nrow = nrow(reference[[4]][[2]]),
-ncol = ncol(reference[[4]][[2]]),
-dimnames = dimnames(reference[[4]][[2]])
-)
-combined.sums.of.squares <- matrix(
-0,
-nrow = nrow(reference[[5]]),
-ncol = ncol(reference[[5]]),
-dimnames = dimnames(reference[[5]])
-)
-reference[[4]][[2]]
-dimnames(reference[[4]][[2]])
-nrow(reference[[4]][[2]])
-ncol(reference[[4]][[2]])
-reference[[6]]
-reference[[5]]
-tail(common_lsoas)
-names(output[[1]]) %>% tail()
-common_lsoas <- sort(Reduce(intersect, lapply(head(output, -1), names)))
-tail(common_lsoas)
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-png("Personal/images/combined_correlation_map.png", width = 8, height = 6)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-dev.off()
-source("dsExampleClient/R/ds.groupcor.R")
-png("Personal/images/combined_correlation_map.png", width = 8, height = 6)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-dev.off()
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-png("Personal/images/combined_correlation_map.png", width = 12, height = 10)
-ds.groupcor("D$has_asthma", "D$distance_local_greenspace", "D$lsoa11cd", datasources = conns, type = "combine")
-dev.off()
-png(
-filename = "Personal/images/combined_correlation_map.png",
-width = 12,
-height = 10,
-units = "in",
-res = 300
-)
-p <- ds.groupcor(
-"D$has_asthma",
-"D$distance_local_greenspace",
-"D$lsoa11cd",
-datasources = conns,
-type = "combine"
-)
-print(p)
-dev.off()
-png(
-filename = "Personal/images/split_correlation_map.png",
-width = 12,
-height = 10,
-units = "in",
-res = 300
-)
-p <- ds.groupcor(
-"D$has_asthma",
-"D$distance_local_greenspace",
-"D$lsoa11cd",
-datasources = conns,
-type = "split"
-)
-source("dsExampleClient/R/ds.groupcor.R")
-source("dsExampleClient/R/ds.groupcor.R")
-png(
-filename = "Personal/images/split_correlation_map.png",
-width = 12,
-height = 10,
-units = "in",
-res = 300
-)
-p <- ds.groupcor(
-"D$has_asthma",
-"D$distance_local_greenspace",
-"D$lsoa11cd",
-datasources = conns,
-type = "split"
-)
-source("dsExampleClient/R/ds.groupcor.R")
-png(
-filename = "Personal/images/split_correlation_map.png",
-width = 12,
-height = 10,
-units = "in",
-res = 300
-)
-p <- ds.groupcor(
-"D$has_asthma",
-"D$distance_local_greenspace",
-"D$lsoa11cd",
-datasources = conns,
-type = "split"
-)
-source("dsExampleClient/R/ds.groupcor.R")
-ds.groupcor(
-"D$has_asthma",
-"D$distance_local_greenspace",
-"D$lsoa11cd",
-datasources = conns,
-type = "split"
-)
-length(output$server_1)
-output[[1]][["Nfilter.tab"]]
-nfilter.tab <- output[[1]][["Nfilter.tab"]]
-output <- lapply(output, function(x) {
-x[-length(x)]
-})
-length(output)
-length(output[[1]])
-length(output[[2]])
-source("dsExampleClient/R/ds.groupcor.R")
-png(
-filename = "Personal/images/split_correlation_map.png",
-width = 12,
-height = 10,
-units = "in",
-res = 300
-)
-p <- ds.groupcor(
-"D$has_asthma",
-"D$distance_local_greenspace",
-"D$lsoa11cd",
-datasources = conns,
-type = "split"
-)
-print(p)
-dev.off()
-getwd()
-dat <- dd
-getwd()
-setwd("dsGeospatial/")
-usethis::create_package(".", open = FALSE)
-usethis::use_roxygen_md()
-usethis::use_readme_rmd()
-usethis::use_news_md()
-usethis::use_vignette()
-usethis::use_vignette("getting_started")
-usethis::use_package_doc()
-usethis::use_spell_check()
-getwd()
-library(devtools)
-devtools::document()
-devtools::document()
-?dsGeospatial
-rm(list = ls())
-knitr::opts_chunk$set(echo = TRUE)
-# retrieve LSOA boundaries for Cheshire and Merseyside
-# to reduce coverage, remove names from the `within_names`
+library(opalr)
+library(readr)
+library(magrittr)
 cm_lsoa_sf <- boundr::bounds(
 "lsoa",
 within_level = "lad",
@@ -336,177 +15,309 @@ within_names = c(
 "Warrington",
 "Wirral"
 ),
-lookup_year = 2011, # can be either 2011 or 2021
+lookup_year = 2021,
+# can be either 2011 or 2021
 opts = boundr::boundr_options(resolution = "BFC")
 )
+# Get Liverpool LSOAs
 liverpool_sf <- cm_lsoa_sf |>
-dplyr::filter(lad22nm %in% "Liverpool")
-uprn_gs_sf <- "https://pldr.org/download/2k6r3/n12/UPRN_2_1_greenspace_distances_with_coords.csv" |>
+dplyr::filter(lad25nm == "Liverpool")
+satellite_sf <- "https://pldr.org/download/2o64n/jqv/UPRN_5_2_satellite_measures_cm_multiple_years_with_coords.csv" |>
 readr::read_csv() |>
-# convert to spatial object
+sf::st_as_sf(
+coords = c("longitude", "latitude"),
+crs = 4326,
+remove = FALSE
+)
+# Match CRS with Liverpool boundary
+satellite_sf <- st_transform(satellite_sf, st_crs(liverpool_sf))
+library(sf)
+satellite_sf <- st_transform(satellite_sf, st_crs(liverpool_sf))
+satellite_liverpool_sf <- satellite_sf[liverpool_sf, ]
+# convert sf to dataframe
+satellite_liverpool <- satellite_liverpool_sf |>
+st_drop_geometry()
+ggplot() +
+geom_sf(data = satellite_liverpool_sf,
+fill = "lightgrey",
+colour = "white",
+linewidth = 0.1) +
+geom_sf(data = pts_sf,
+colour = "red",
+size = 0.3) +
+theme_minimal()
+library(ggplot2)
+ggplot() +
+geom_sf(data = satellite_liverpool_sf,
+fill = "lightgrey",
+colour = "white",
+linewidth = 0.1) +
+geom_sf(data = pts_sf,
+colour = "red",
+size = 0.3) +
+theme_minimal()
+ggplot() +
+geom_sf(data = cm_lsoa_sf,
+fill = "lightgrey",
+colour = "white",
+linewidth = 0.1) +
+geom_sf(data = satellite_liverpool_sf,
+colour = "red",
+size = 0.3) +
+theme_minimal()
+o <- opalr::opal.login(username = "administrator",
+password = "password",
+url = "https://opal-demo.obiba.org")
+?opal.project_create
+opal.project_create(o, "UPRN", database = NULL, title = NULL)
+opal.table_save(
+o,
+satellite_liverpool,
+"UPRN",
+"satellite_metrics",
+overwrite = TRUE,
+force = TRUE,
+id.name = "UPRN"
+)
+opal.project_create(o, "UPRN", database = "DataSHIELD", title = NULL)
+opal.project_delete(o, "UPRN")
+opal.project_create(o, "UPRN", database = "DataSHIELD", title = NULL)
+opal.project_create(o, "UPRN", database = "mongodb", title = NULL)
+opal.table_save(
+o,
+satellite_liverpool,
+"UPRN",
+"satellite_metrics",
+overwrite = TRUE,
+force = TRUE,
+id.name = "UPRN"
+)
+uprn_dist_gs <- "https://pldr.org/download/2k6r3/n12/UPRN_2_1_greenspace_distances_with_coords.csv" |>
+readr::read_csv()
+uprn_dist_bs <- "https://pldr.org/download/2g6k0/ksp/UPRN_4_1_bluespace_distances_with_coords.csv" |>
+readr::read_csv()
+uprn_evi <- "https://pldr.org/download/2o64n/0l9/UPRN_5_2_EVI_multiple_years_recategorised_with_coords.csv"|>
+readr::read_csv()
+uprn_330300 <- "https://pldr.org/download/2z71q/1me/UPRN_6_1_3_30_300_measures_cm_with_coords.csv" |>
+readr::read_csv() |>
+dplyr::select(UPRN, composite_3_30_300)
+head(uprn_dist_gs)
+head(satellite_sf)
+uprn_dist_gs |>
+dplyr::select(UPRN, distance_local_greenspace, latitude, longitude) |> head()
+uprn_dist_gs_v2 <- uprn_dist_gs |>
+dplyr::select(UPRN, distance_local_greenspace, latitude, longitude) |>
+dplyr::mutate(
+distance_local_greenspace = round(distance_local_greenspace / 5) * 5, # round to nearest 5th
+distance_local_greenspace = ifelse(distance_local_greenspace > 1500, 1500, distance_local_greenspace),
+distance_local_greenspace_100cat = distance_local_greenspace |>
+cut(breaks = c(-Inf, seq(0, 1500, 100)))
+)
+uprn_dist_gs_v2 <- uprn_dist_gs |>
+dplyr::select(UPRN, distance_local_greenspace, latitude, longitude) |>
+dplyr::mutate(
+distance_local_greenspace = round(distance_local_greenspace / 5) * 5, # round to nearest 5th
+distance_local_greenspace = ifelse(distance_local_greenspace > 1500, 1500, distance_local_greenspace),
+distance_local_greenspace_100cat = distance_local_greenspace |>
+cut(breaks = c(-Inf, seq(0, 1500, 100)))
+)
+head(uprn_dist_gs_v2)
+levels(factor(uprn_dist_gs_v2$distance_local_greenspace_100cat))
+uprn_dist_gs_v2[which(uprn_dist_gs_v2$distance_local_greenspace == "(-Inf,0]"),]
+uprn_dist_gs_v2[which(uprn_dist_gs_v2$distance_local_greenspace == "(600,700]"),]
+uprn_dist_gs_v2[which(uprn_dist_gs_v2$distance_local_greenspace == "(100,200]"),]
+uprn_dist_gs_v2[which(uprn_dist_gs_v2$distance_local_greenspace_100cat == "(100,200]"),]
+uprn_dist_gs_v2[which(uprn_dist_gs_v2$distance_local_greenspace_100cat == "(-Inf,0]"),]
+uprn_dist_bs_v2 <- uprn_dist_bs |>
+dplyr::filter(!is.na(distance_any_bluespace)) |>
+dplyr::mutate(
+dist_bs_100cat = distance_any_bluespace |>
+cut(breaks = c(-Inf, seq(0, 4200, 100), Inf)) |>
+as.factor()
+) |>
+dplyr::arrange(UPRN)
+uprn_dist_bs_sf <- uprn_dist_bs_v2 |>
 sf::st_as_sf(coords = c("longitude", "latitude"), crs = 4326) |>
-# add LSOA details
+# attach LSOA information
 sf::st_join(cm_lsoa_sf)
-qof_asthma_tbl <- "https://pldr.org/download/e6nzv/ng1/QOF_4_03_Asthma_LSOA.csv" |>
+head(uprn_evi)
+head(uprn_330300)
+uprn_dist_bs |>
+dplyr::filter(!is.na(distance_any_bluespace)) |> head()
+dist_bs_evi_330300_dist_gs_v2 %>% head()
+uprn_dist_gs <- "https://pldr.org/download/2k6r3/n12/UPRN_2_1_greenspace_distances_with_coords.csv" |>
+readr::read_csv()
+uprn_dist_bs <- "https://pldr.org/download/2g6k0/ksp/UPRN_4_1_bluespace_distances_with_coords.csv" |>
+readr::read_csv()
+uprn_evi <- "https://pldr.org/download/2o64n/0l9/UPRN_5_2_EVI_multiple_years_recategorised_with_coords.csv"|>
+readr::read_csv()
+uprn_330300 <- "https://pldr.org/download/2z71q/1me/UPRN_6_1_3_30_300_measures_cm_with_coords.csv" |>
 readr::read_csv() |>
-# filter out CM LSOAs
-dplyr::filter(lsoa11 %in% cm_lsoa_sf$lsoa11cd) |>
-# filter out last year
-dplyr::filter(year == 2024)
-# set seed for reproducibility
-set.seed(20250603)
-qof_asthma_cohort_tbl <- qof_asthma_tbl |>
-purrr::pmap(function(lsoa11, den, num, ...) {
-# recalculate pop to be a multiple of 2
-pop <- ifelse(den %% 2 != 0, den + 1, den)
-# extract UPRNs for this LSOA
-uprn_tbl <- uprn_gs_sf |>
-sf::st_drop_geometry() |>
-dplyr::filter(lsoa11cd == lsoa11) |>
+dplyr::select(UPRN, composite_3_30_300)
+# re-categorise the distance to local greenspace
+uprn_dist_gs_v2 <- uprn_dist_gs |>
+dplyr::select(UPRN, distance_local_greenspace, latitude, longitude) |>
 dplyr::mutate(
-dist_gs_dec = dplyr::ntile(distance_local_greenspace, 10)
+distance_local_greenspace = round(distance_local_greenspace / 5) * 5, # round to nearest 5th
+distance_local_greenspace = ifelse(distance_local_greenspace > 1500, 1500, distance_local_greenspace),
+distance_local_greenspace_100cat = distance_local_greenspace |>
+cut(breaks = c(-Inf, seq(0, 1500, 100)))
+)
+# re-categorise the distance to bluespace
+uprn_dist_bs_v2 <- uprn_dist_bs |>
+dplyr::filter(!is.na(distance_any_bluespace)) |>
+dplyr::mutate(
+dist_bs_100cat = distance_any_bluespace |>
+cut(breaks = c(-Inf, seq(0, 4200, 100), Inf)) |>
+as.factor()
+) |>
+dplyr::arrange(UPRN)
+# WRANGLING ---
+# create spatial version of the UPRN metric
+uprn_dist_bs_sf <- uprn_dist_bs_v2 |>
+sf::st_as_sf(coords = c("longitude", "latitude"), crs = 4326) |>
+# attach LSOA information
+sf::st_join(cm_lsoa_sf)
+# COMBINING DATASETS ----
+# inspect that there are not categories in an LSOA for which the counts are smaller than 10
+# combining all 4 datasets
+dist_bs_evi_330300_dist_gs <- uprn_dist_bs_sf |>
+dplyr::inner_join(uprn_evi, by = "UPRN") |>
+dplyr::inner_join(uprn_330300, by = "UPRN") |>
+dplyr::inner_join(uprn_dist_gs_v2, by = "UPRN")
+distance_bs_100_levels <-
+c("<0m", "0_100m", "101_200m", "201_300m", "301_400m","401_500m", "501_600m",
+"601_700m", "701_800m", "801_900m", "901_1000m","1km_1.1km", "1.1km_1.2km",
+"1.2km_1.3km","1.3km_1.4km", "1.4km_1.5km", "1.5km_1.6km", "1.6km_1.7km",
+"1.7km_1.8km", "1.8km_1.9km", "1.9km_2km", "2km_2.1km", "2.1km_2.2km",
+"2.2km_2.3km", "2.3km_2.4km", "2.4km_2.5km", "2.5km_2.6km", "2.6km_2.7km",
+"2.7km_2.8km", "2.8km_2.9km","2.9km_3km", "3km_3.1km", "3.1km_3.2km",
+"3.2km_3.3km", "3.3km_3.4km", "3.4km_3.5km","3.5km_3.6km", "3.6km_3.7km",
+"3.7km_3.8km", "3.8km_3.9km", "3.9km_4km","4km_4.1km", "4.1km_4.2km",
+">4.2km"
+)
+head(dist_bs_evi_330300_dist_gs_v2 )
+head(dist_bs_evi_330300_dist_gs)
+dist_bs_evi_330300_dist_gs %>% dplyr::select("UPRN","distance_any_bluespace", "dist_bs_100cat", "distance_local_greenspace", "distance_local_greenspace_100cat")
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
 ) |>
-dplyr::arrange(dist_gs_dec)
-# create cohort table
-cohort_tbl <- tibble::tibble(
-lsoa11cd = lsoa11,
-sex = rep(c(0, 1), each = pop / 2),
-has_asthma = FALSE,
-uprn = sample(uprn_tbl$UPRN, size = pop, replace = TRUE)
+dplyr::select(-lad22cd, -distance_any_bluespace,
+-latitude.x, -longitude.x,-latitude.y, -longitude.y,
+-lad22nm, -distance_local_greenspace) |>
+dplyr::relocate(lsoa11cd, lsoa11nm, UPRN, distance_local_greenspace_100cat, .before = 1)
+dist_bs_evi_330300_dist_gs <- uprn_dist_bs_sf |>
+dplyr::inner_join(uprn_evi, by = "UPRN") |>
+dplyr::inner_join(uprn_330300, by = "UPRN") |>
+dplyr::inner_join(uprn_dist_gs_v2, by = "UPRN")
+distance_bs_100_levels <-
+c("<0m", "0_100m", "101_200m", "201_300m", "301_400m","401_500m", "501_600m",
+"601_700m", "701_800m", "801_900m", "901_1000m","1km_1.1km", "1.1km_1.2km",
+"1.2km_1.3km","1.3km_1.4km", "1.4km_1.5km", "1.5km_1.6km", "1.6km_1.7km",
+"1.7km_1.8km", "1.8km_1.9km", "1.9km_2km", "2km_2.1km", "2.1km_2.2km",
+"2.2km_2.3km", "2.3km_2.4km", "2.4km_2.5km", "2.5km_2.6km", "2.6km_2.7km",
+"2.7km_2.8km", "2.8km_2.9km","2.9km_3km", "3km_3.1km", "3.1km_3.2km",
+"3.2km_3.3km", "3.3km_3.4km", "3.4km_3.5km","3.5km_3.6km", "3.6km_3.7km",
+"3.7km_3.8km", "3.8km_3.9km", "3.9km_4km","4km_4.1km", "4.1km_4.2km",
+">4.2km"
+)
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
+) |>
+dplyr::select(-lad22cd, -distance_any_bluespace,
+-latitude.x, -longitude.x,-latitude.y, -longitude.y,
+-lad22nm, -distance_local_greenspace) |>
+dplyr::relocate(lsoa11cd, lsoa11nm, UPRN, distance_local_greenspace_100cat, .before = 1)
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
 )
-# randomly assigned the outcome to `num` patients
-idx <- sample(seq_len(pop), ceiling(num), replace = FALSE)
-cohort_tbl$has_asthma[idx] <- TRUE
-# randomly re-assigned UPRNs with higher distances to patients with outcome
-## create vector of probabilities, so further distances are favoured
-p <- c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.2, 0.2, 0.2)
-idx_uprn_tile <- sample(1:10, ceiling(num), replace = TRUE, prob = p)
-## subset UPRNs with match tile as per `idx_uprn_tile`
-cohort_tbl$uprn[idx] <- purrr::map(idx_uprn_tile, function(x) {
-# subset based on tile decile
-aux <- dplyr::filter(uprn_tbl, dist_gs_dec == x)
-# extract UPRN
-aux$UPRN[sample(seq_len(nrow(aux)), 1)]
-}) |>
-purrr::list_c()
-# return final cohort table
-return(cohort_tbl)
-}) |>
-purrr::list_c()
-qof_asthma_cohort_dist_gs_sf <- qof_asthma_cohort_tbl |>
-dplyr::left_join(uprn_gs_sf, by = c("uprn" = "UPRN", "lsoa11cd")) |>
-dplyr::arrange(lsoa11cd, uprn) |>
-sf::st_as_sf()
-library(dsGeospatial)
-knitr::opts_chunk$set(echo = TRUE)
-library(DSLite)
-library(devtools)
+colnames(dist_bs_evi_330300_dist_gs_v2)
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
+) |>
+dplyr::select(-lad25cd, -distance_any_bluespace,
+-latitude.x, -longitude.x,-latitude.y, -longitude.y,
+-lad25nm, -distance_local_greenspace) |>
+dplyr::relocate(lsoa11cd, lsoa11nm, UPRN, distance_local_greenspace_100cat, .before = 1)
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
+) |>
+dplyr::select(-lad25cd, -distance_any_bluespace,
+-latitude.x, -longitude.x,-latitude.y, -longitude.y,
+-lad25nm, -distance_local_greenspace) |>
+dplyr::relocate(lsoa21cd, lsoa21nm, UPRN, distance_local_greenspace_100cat, .before = 1)
+head(dist_bs_evi_330300_dist_gs_v2)
+?dplyr::relocate
+df <- tibble(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
+library(dplyr)
+df <- tibble(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
+df |> relocate(f)
+df |> relocate(a, .after = c)
+df |> relocate(f, .before = b)
+df |> relocate(f, .before = 1)
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
+) |>
+dplyr::select(-lad25cd, -distance_any_bluespace,
+-latitude.x, -longitude.x,-latitude.y, -longitude.y,
+-lad25nm, -distance_local_greenspace)
+head(dist_bs_evi_330300_dist_gs_v2)
+dist_bs_evi_330300_dist_gs_v2 <- dist_bs_evi_330300_dist_gs |>
+dplyr::mutate(
+distance_bluespace_description =
+ifelse(is.na(distance_any_bluespace), NA, distance_bs_100_levels[dist_bs_100cat])
+) |>
+dplyr::select(-lad25cd, -distance_any_bluespace,
+-latitude.x, -longitude.x,-latitude.y, -longitude.y,
+-lad25nm, -distance_local_greenspace) |>
+dplyr::relocate(lsoa21cd, lsoa21nm, UPRN, distance_local_greenspace_100cat, .before = 1)
+colna,
+head(dist_bs_evi_330300_dist_gs_v2 )
+levels(factor(dist_bs_evi_330300_dist_gs_v2$distance_bluespace_description))
+levels(factor(dist_bs_evi_330300_dist_gs_v2$distance_bluespace_description))[1:11]
+levels(factor(dist_bs_evi_330300_dist_gs_v2$dist_bs_100cat))
+getwd()
+setwd("/Users/tkariya/Documents/DataSHIELD/dsGeospatialClient/")
+library(opalr)
+# creating and uploading table
+# creating project
+o <- opalr::opal.login(username = "administrator",
+password = "password",
+url = "http://localhost:8880")
+library(DSOpal)
 library(dsBase)
 library(dsBaseClient)
-library(dsGeospatial)
-dat <- qof_asthma_cohort_dist_gs_sf |>
-dplyr::select("lsoa11cd", "has_asthma","uprn","distance_local_greenspace") |>
-sf::st_drop_geometry()
-devtools::load_all("/Users/tkariya/Documents/DataSHIELD/dsExample")
-#devtools::load_all("/Users/tkariya/Documents/DataSHIELD/dsExampleClient")
-dslite.server1 <- newDSLiteServer(
-tables = list(
-uprn_asthma_green1 = dat
-)
-)
-dslite.server2 <- newDSLiteServer(
-tables = list(
-uprn_asthma_green2 = dat
-)
-)
-dslite.server1$config(defaultDSConfiguration(include=c("dsBase", "dsExample")))
-dslite.server1$aggregateMethod("MeanSdGpDS", "MeanSdGpDS")
-dslite.server2$config(defaultDSConfiguration(include=c("dsBase", "dsExample")))
-dslite.server2$aggregateMethod("MeanSdGpDS", "MeanSdGpDS")
-options(
-datashield.privacyControlLevel = "banana",
-nfilter.tab = 3,
-nfilter.subset = 3,
-nfilter.glm = 0.33,
-nfilter.string = 80,
-nfilter.stringShort = 20,
-nfilter.kNN = 3,
-nfilter.levels.density = 0.33,
-nfilter.levels.max = 40,
-nfilter.noise = 0.25,
-nfilter.privacy.old = 5
-)
 builder <- DSI::newDSLoginBuilder()
-builder$append(
-server = "server_1",
-url = "dslite.server1",
-table = "uprn_asthma_green1",
-driver = "DSLiteDriver"
-)
-builder$append(
-server = "server_2",
-url = "dslite.server2",
-table = "uprn_asthma_green2",
-driver = "DSLiteDriver"
-)
+builder$append(server = "study1",
+url = "http://localhost:8880",
+user = "administrator",
+password = "password",
+table = "UPRN.greenspace_distance_metrics",
+profile = "ds-geospatial")
+builder$append(server = "study2",
+url = "http://localhost:8880",
+user = "administrator",
+password = "password",
+table = "UPRN.greenspace_distance_metrics",
+profile = "ds-geospatial")
 logindata <- builder$build()
-conns <- DSI::datashield.login(logins = logindata, assign = FALSE)
-datashield.assign.table(conns, "D", logindata)
-nfilter.tab  <- 3
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns)
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns,
-type='split')
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns)
-p <- ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns)
-print(p)
-source("R/ds.geoheatmapPlot.R")
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns)
-source("R/ds.geoheatmapPlot.R")
-dev.off()
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns)
-library(devtools)
-devtools::document()
-library(dsGeospatial)
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns)
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns,
-type='split')
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns,
-type='split', names_region = c("Liverpool", "Sefton"))
-ds.geoheatmapPlot(x="D$distance_local_greenspace", y = "D$lsoa11cd",
-datasources = conns)
-ds.geoheatmapPlot(x="D$has_asthma", y = "D$lsoa11cd", datasources = conns,
-type = 'split', names_region = c("Liverpool", "Sefton"))
-install.packages("dsGeospatial")
-rm(list = ls())
-pak::pak("FederatedMethods/dsGeospatial")
-pak::pak("git::git@github.com:FederatedMethods/dsGeospatial.git")
-pak::pak("git::git@github.com:FederatedMethods/dsGeospatial.git")
-pak::pak(
-"git::ssh://git@github.com/FederatedMethods/dsGeospatial.git"
-)
-pak::pkg_install(
-"git::ssh://git@github.com/FederatedMethods/dsGeospatial.git"
-)
-pak::local_install(
-"/Users/tkariya/Documents/DataSHIELD/dsGeospatial/"
-)
-pak::local_install(
-"/Users/tkariya/Documents/DataSHIELD/dsGeospatial/"
-)
-pak::local_install(
-"/Users/tkariya/Documents/DataSHIELD/dsGeospatial/"
-)
-pak::local_install(
-"/Users/tkariya/Documents/DataSHIELD/dsGeospatial/"
-)
-getwd()
-setwd("/Users/tkariya/Documents/DataSHIELD/")
-setwd("/Users/tkariya/Documents/DataSHIELD/dsGeospatialClient/")
-usethis::create_package()
-usethis::create_package(".", open = FALSE)
-usethis::use_roxygen_md()
-usethis::use_readme_rmd()
-getwd()
-usethis::use_readme_rmd()
-usethis::use_news_md()
-usethis::use_vignette("getting-started")
+conns <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")
+ds.colnames("D", conns)
+library(opalr)
+# creating and uploading table
+# creating project
+o <- opalr::opal.login(username = "administrator",
+password = "password",
+url = "http://localhost:8880")
diff --git a/.github/.gitignore b/.github/.gitignore
index cc7b82f..926e968 100644
--- a/.github/.gitignore
+++ b/.github/.gitignore
@@ -1,3 +1,4 @@
 .Rproj.user
 inst/doc
 *.html
+.Rhistory
\ No newline at end of file
diff --git a/DESCRIPTION b/DESCRIPTION
index 00de032..33601b8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -9,10 +9,9 @@ Description: Provides privacy-preserving methods for the visualisation and
     remain within the data-holding organisations, while only disclosure-
     controlled aggregate results are returned to the analyst. The package is
     being developed as part of the GROVE project within the DARE UK programme.
-License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
-    license
-Depends:R (>= 4.0.0)
-Imports:cli,
+License: MIT + file LICENSE
+Depends: R (>= 4.0.0)
+Imports: cli,
         methods
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..d8f6df1
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,2 @@
+YEAR: 2026
+COPYRIGHT HOLDER: University of Liverpool
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000..632fa02
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,21 @@
+# MIT License
+
+Copyright (c) 2026 University of Liverpool
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/R/ds.localmoran.R b/R/ds.localmoran.R
new file mode 100644
index 0000000..7653466
--- /dev/null
+++ b/R/ds.localmoran.R
@@ -0,0 +1,138 @@
+#' Calculate Local Moran's I
+#'
+#' Calculates Local Moran's I for a numeric variable using a spatial weights
+#' object across one or more DataSHIELD studies.
+#'
+#' @param x Character string specifying the name of the numeric vector on the
+#'   server to be analysed.
+#' @param listw Character string specifying the name of the spatial weights
+#'   object of class \code{listw} on the server.
+#' @param type Character string specifying the output format. Currently only
+#'   \code{"split"} is supported.
+#' @param vars Optional variable specification. Currently unused.
+#' @param do.checks Logical. If \code{TRUE}, checks that the input objects are
+#'   defined and have consistent classes across the studies.
+#' @param nsim Numeric value specifying the number of simulations used for
+#'   calculating the Monte Carlo p-value. Default is 199.
+#' @param datasources A list of \code{DSConnection} objects. If \code{NULL},
+#'   active DataSHIELD connections are used.
+#'
+#' @return A named list with one element per study. Each study contains a
+#'   named numeric vector with:
+#'   \itemize{
+#'     \item \code{moran_I}: Moran's I statistic.
+#'     \item \code{z}: standardised Moran's I statistic.
+#'   }
+#'
+#' @examples
+#' \dontrun{
+#' DSOpal::Opal()
+#' builder <- DSI::newDSLoginBuilder()
+#' builder$append(
+#'   server = "mersey-demo",
+#'   url = "http://localhost:8880",
+#'   user = "administrator",
+#'   password = "password",
+#'   driver = "OpalDriver",
+#'   profile = "ds-geospatial"
+#' )
+#' 
+#' logindata <- builder$build()
+#' conns <- DSI::datashield.login(logins = logindata)
+#' DSI::datashield.assign.resource(
+#' conns,
+#' symbol = "cells",
+#' resource = paste0(project, ".", "cells")
+#' )
+#' DSI::datashield.assign.resource(
+#' conns,
+#' symbol = "W",
+#' resource = paste0(project, ".", "W_D_5km")
+#' )
+#' ## 2.8 Materialise arbitrary R objects ----
+#' DSI::datashield.assign.resource(
+#'   conns,
+#'   symbol = "cells_resource",
+#'   resource = paste0(project, ".cells")
+#' )
+#' 
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "cells_object",
+#'   expr = quote(as.resource.object(cells_resource))
+#' )
+#' ## 2.9 Map specific columns ----
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "obesity_qof_2024_25",
+#'   expr = quote(cells_object$obesity_qof_2024_25)
+#' )
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "W_object",
+#'   expr = quote(as.resource.object(W))
+#' )
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "W_band800",
+#'   expr = quote(W_object$band800)
+#' )
+#' 
+#' ds.moransI(
+#'   x = "obesity_qof_2024_25",
+#'   listw = "W_band800",
+#'   nsim = 199,
+#'   datasources = conns
+#' )
+#' 
+#'
+#' @export
+
+ds.localmoran <- function(x = NULL, listw = NULL, type='split',
+                       vars = NULL, do.checks = TRUE,
+                       nsim = 199, datasources=NULL){
+  
+  # look for DS connections
+  if(is.null(datasources)){
+    datasources <- datashield.connections_find()
+  }
+  
+  # ensure datasources is a list of DSConnection-class
+  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
+    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
+  }
+  
+  if(is.null(x)){
+    stop("Please provide the input vector 'x', a numeric!", call.=FALSE)
+  }
+  
+  if(is.null(listw)){
+    stop("Please provide the input vector 'listw'!, a list object", call.=FALSE)
+  }
+  
+  if(do.checks){
+    
+    # check if the input objects are defined in all the studies
+    isDefined(datasources, x)
+    isDefined(datasources, listw)
+    
+    # call the internal function that checks the input object is of the same class in all studies.
+    typ1 <- checkClass(datasources, x)
+    typ2 <- checkClass(datasources, listw)
+  }
+  
+  if(!is.numeric(nsim) & length(nsim != 1)){
+    stop("nsim should be numeric of length 1")
+  }
+  calltext <- paste0("Localmoran(", x, ", ", listw, ")")
+
+  output <- DSI::datashield.aggregate(datasources, as.symbol(calltext))
+
+  ## Only type split supported now 
+  if(type == "split"){
+    return(output)
+  }
+  
+}
+
+
diff --git a/R/ds.moransI.R b/R/ds.moransI.R
new file mode 100644
index 0000000..0306257
--- /dev/null
+++ b/R/ds.moransI.R
@@ -0,0 +1,140 @@
+#' Calculate Moran's I
+#'
+#' Calculates Moran's I for a numeric variable using a spatial weights
+#' object across one or more DataSHIELD studies.
+#'
+#' @param x Character string specifying the name of the numeric vector on the
+#'   server to be analysed.
+#' @param listw Character string specifying the name of the spatial weights
+#'   object of class \code{listw} on the server.
+#' @param type Character string specifying the output format. Currently only
+#'   \code{"split"} is supported.
+#' @param vars Optional variable specification. Currently unused.
+#' @param do.checks Logical. If \code{TRUE}, checks that the input objects are
+#'   defined and have consistent classes across the studies.
+#' @param nsim Numeric value specifying the number of simulations used for
+#'   calculating the Monte Carlo p-value. Default is 199.
+#' @param datasources A list of \code{DSConnection} objects. If \code{NULL},
+#'   active DataSHIELD connections are used.
+#'
+#' @return A named list with one element per study. Each study contains a
+#'   named numeric vector with:
+#'   \itemize{
+#'     \item \code{moran_I}: Moran's I statistic.
+#'     \item \code{z}: standardised Moran's I statistic.
+#'     \item \code{p_mc}: Monte Carlo p-value.
+#'   }
+#'
+#' @examples
+#' \dontrun{
+#' DSOpal::Opal()
+#' builder <- DSI::newDSLoginBuilder()
+#' builder$append(
+#'   server = "mersey-demo",
+#'   url = "http://localhost:8880",
+#'   user = "administrator",
+#'   password = "password",
+#'   driver = "OpalDriver",
+#'   profile = "ds-geospatial"
+#' )
+#' 
+#' logindata <- builder$build()
+#' conns <- DSI::datashield.login(logins = logindata)
+#' DSI::datashield.assign.resource(
+#' conns,
+#' symbol = "cells",
+#' resource = paste0(project, ".", "cells")
+#' )
+#' DSI::datashield.assign.resource(
+#' conns,
+#' symbol = "W",
+#' resource = paste0(project, ".", "W_D_5km")
+#' )
+#' ## 2.8 Materialise arbitrary R objects ----
+#' DSI::datashield.assign.resource(
+#'   conns,
+#'   symbol = "cells_resource",
+#'   resource = paste0(project, ".cells")
+#' )
+#' 
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "cells_object",
+#'   expr = quote(as.resource.object(cells_resource))
+#' )
+#' ## 2.9 Map specific columns ----
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "obesity_qof_2024_25",
+#'   expr = quote(cells_object$obesity_qof_2024_25)
+#' )
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "W_object",
+#'   expr = quote(as.resource.object(W))
+#' )
+#' DSI::datashield.assign.expr(
+#'   conns,
+#'   symbol = "W_band800",
+#'   expr = quote(W_object$band800)
+#' )
+#' 
+#' ds.moransI(
+#'   x = "obesity_qof_2024_25",
+#'   listw = "W_band800",
+#'   nsim = 199,
+#'   datasources = conns
+#' )
+#' 
+#'
+#' @export
+
+ds.moransI <- function(x = NULL, listw = NULL, type='split',
+                       vars = NULL, do.checks = TRUE,
+                       nsim = 199, datasources=NULL){
+  
+  # look for DS connections
+  if(is.null(datasources)){
+    datasources <- datashield.connections_find()
+  }
+  
+  # ensure datasources is a list of DSConnection-class
+  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
+    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
+  }
+  
+  if(is.null(x)){
+    stop("Please provide the input vector 'x', a numeric!", call.=FALSE)
+  }
+  
+  if(is.null(listw)){
+    stop("Please provide the input vector 'listw'!, a list object", call.=FALSE)
+  }
+
+  if(do.checks){
+    
+    # check if the input objects are defined in all the studies
+    isDefined(datasources, x)
+    isDefined(datasources, listw)
+    
+    # call the internal function that checks the input object is of the same class in all studies.
+    typ1 <- checkClass(datasources, x)
+    typ2 <- checkClass(datasources, listw)
+  }
+  
+  if(!is.numeric(nsim) & length(nsim != 1)){
+    stop("nsim should be numeric of length 1")
+  }
+  calltext <- paste0("MoransI(", x, ", ", listw, ", ", nsim, ")")
+
+  output <- DSI::datashield.aggregate(datasources, as.symbol(calltext))
+  
+  ## Only type split supported now 
+  if(type == "split"){
+  
+    result <- lapply(output, function(x) unlist(x))
+  return(result)
+  }
+}
+
+
diff --git a/R/ds.summarystat.R b/R/ds.summarystat.R
index c1e0380..c70df63 100644
--- a/R/ds.summarystat.R
+++ b/R/ds.summarystat.R
@@ -198,18 +198,71 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
   plot_matrix <- list()
   split <- type == 'split'
   
-  if(!is.null(names_region) && all(is.character(names_region))){
+  ## names_region check 
+  # check if list and all elements are character 
+
+  if (is.list(names_region) &&
+      length(names_region) > 0 &&
+      all(is.character(unlist(names_region)))) {
     names_region.valid = TRUE
     
-    if(!split && length(names_region) > 1){
-      warning(paste0("more than one region selected for type = ", type,
-                     ", using only first names_region to return"))
-      names_region = names_region[1]
-    }
     
-    if(split && length(names_region) != numsources){
-      names_region <- rep(names_region, times = numsources)
-    }
+    names_region <- lapply(names_region, function(i){
+      if("CheshireMercyside" %in% i){
+        indx <- which(unlist(i) == "CheshireMercyside")
+        i <- i[-indx]
+        i <- c(i, "Cheshire East","Cheshire West and Chester",
+               "Halton","Knowsley","Liverpool","Sefton", 
+               "St. Helens","Warrington","Wirral")
+      }else{
+        i <- i
+      }
+    })
+    
+    # names_region is valid check type combination
+    
+    if (split) {
+      if (numsources == 1) {
+        warning(
+          "type 'split', expects length of datasources to be more than 1,
+              defaulting to type 'combine'"
+        )
+        split = FALSE
+      } else{
+        # check list length = numsources
+        # check is all entries match when length is >1
+        if (length(names_region) != numsources) {
+          warning(
+            paste0(
+              "length of names_region should match length of datasources,
+                       using only first elements in names_region to return"
+            )
+          )
+          names_region <- lapply(1:numsources, function(i) {
+            names_region[[i]] <- names_region[[1]]
+          })
+          names_region <- lapply(names_region, unique)
+        } else{
+          names_region <- lapply(names_region, unique)
+        }
+      }
+      } else if(!split) {
+      # when combine, list must have length 1
+      # ifnot repeat list[[1]] for all numsources
+      if (length(names_region) != 1) {
+        warning(
+          "length of names_region must be 1 for type 'combine',
+                 using only first elements in names_region to return"
+        )
+        names_region = list(names_region[[1]])
+        names_region <- lapply(names_region, unique)
+        
+      } else{
+        names_region = list(names_region[[1]])
+        names_region <- lapply(names_region, unique)
+      }
+      }
+    
     
     shape_list <- lapply(names_region, function(region) {
       boundr::bounds(
@@ -221,16 +274,20 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       ) |>
         dplyr::select(lsoa11cd, geometry)
     })
+    q.names <- c("5th_Quantile", "10th_Quantile", "25th_Quantile", "50th_Quantile",
+                 "75th_Quantile", "90th_Quantile", "95th_Quantile")
     
-    if(is.null(metric) || !metric %in% c("Mean", "SD","SEM")){
+    if(is.null(metric) || !metric %in% c("Mean", "SD","SEM", q.names)){
       warning("Invalid metric type, returning available metric table")
       draw.plot = FALSE
       names_region.valid =FALSE
     } else {
+      
       if(!split){
       metric_map <- list("Mean" = "mean.gp", 
                          "SD" = "SD.gp", 
                          "SEM" = "SEM.gp")
+      
       } else {
       metric_map <- list("Mean" = "mean.gp.study", 
                          "SD" = "SD.gp.study", 
@@ -239,11 +296,11 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
     }
     
   }else{
-    warning("invalid region names, returning the whole table")
+    warning("invalid region names/format, returning the whole table")
     draw.plot = FALSE
     names_region.valid = FALSE
   }
-  
+ 
 
 ##########
   
@@ -253,6 +310,7 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       numsources <- length(output)
       mean.matrix <- NULL
       sd.matrix <- NULL
+      qq.matrix <- NULL
       n.matrix <- NULL
       Nvalid <- 0
       Nmissing <- 0
@@ -261,10 +319,11 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       for(j in 1:numsources){
         mean.matrix <- rbind(mean.matrix,as.numeric(unlist(output[[j]][2])))
         sd.matrix <- rbind(sd.matrix,as.numeric(unlist(output[[j]][3])))
-        n.matrix <- rbind(n.matrix,as.numeric(unlist(output[[j]][4])))
-        Nvalid <- Nvalid+as.numeric(unlist(output[[j]][5]))
-        Nmissing <- Nmissing+as.numeric(unlist(output[[j]][6]))
-        Ntotal <- Ntotal+as.numeric(unlist(output[[j]][7]))
+        qq.matrix <- rbind(qq.matrix,as.numeric(unlist(output[[j]][4])))
+        n.matrix <- rbind(n.matrix,as.numeric(unlist(output[[j]][5])))
+        Nvalid <- Nvalid+as.numeric(unlist(output[[j]][6]))
+        Nmissing <- Nmissing+as.numeric(unlist(output[[j]][7]))
+        Ntotal <- Ntotal+as.numeric(unlist(output[[j]][8]))
         
 
       }
@@ -274,6 +333,29 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       # Calculate weighted means across studies in each group
       mean.gp <- (diag(t(mean.matrix)%*%n.matrix))/(t(n.matrix)%*%nsum.vector)
       
+      # Calculate weighted quantiles across studies in each group
+      # Repeat each LSOA sample size for its 7 quantiles
+      qq.n.matrix <- t(
+        apply(n.matrix, 1, function(x) rep(x, each = 7))
+      )
+      
+      # Weighted quantiles across studies
+      qq.gp <- (diag(t(qq.matrix) %*% qq.n.matrix)) /
+        (t(qq.n.matrix) %*% nsum.vector)
+      
+      qq.gp <- matrix(
+        qq.gp,
+        ncol = 7,
+        byrow = TRUE
+      )
+      
+      # Add names
+      rownames(qq.gp) <- names(output[[1]][[4]])
+      
+      colnames(qq.gp) <- c("5%_gp", "10%_gp", "25%_gp", "50%_gp", "75%_gp", "90%_gp", "95%_gp")
+      
+      qq.gp <- as.data.frame(qq.gp)
+      
       # Calculate weighted SDs across studies in each group
       var.gp <- (diag(t(var.matrix)%*%n.matrix))/(t(n.matrix)%*%nsum.vector)
       SD.gp <- sqrt(var.gp)
@@ -292,7 +374,6 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       dimnames(N.gp) <- c(list(names.gp),list("Nvalid_gp"))
       dimnames(SEM.gp) <- c(list(names.gp),list("SEM_gp"))
       
-     
     }
     
     # SPLIT 
@@ -300,6 +381,7 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       numsources <- length(output)
       mean.matrix <- NULL
       sd.matrix <- NULL
+      qq.matrix <- list()
       n.matrix <- NULL
       Nvalid <- 0
       Nmissing <- 0
@@ -308,23 +390,32 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       for(j in 1:numsources){
         mean.matrix <- rbind(mean.matrix,as.numeric(unlist(output[[j]][2])))
         sd.matrix <- rbind(sd.matrix,as.numeric(unlist(output[[j]][3])))
-        n.matrix <- rbind(n.matrix,as.numeric(unlist(output[[j]][4])))
-        Nvalid <- Nvalid+as.numeric(unlist(output[[j]][5]))
-        Nmissing <- Nmissing+as.numeric(unlist(output[[j]][6]))
-        Ntotal <- Ntotal+as.numeric(unlist(output[[j]][7]))
+        qq.matrix[[j]] <- rbind(qq.matrix,as.numeric(unlist(output[[j]][4])))
+        n.matrix <- rbind(n.matrix,as.numeric(unlist(output[[j]][5])))
+        Nvalid <- Nvalid+as.numeric(unlist(output[[j]][6]))
+        Nmissing <- Nmissing+as.numeric(unlist(output[[j]][7]))
+        Ntotal <- Ntotal+as.numeric(unlist(output[[j]][8]))
         
 
       }
       var.matrix <- sd.matrix^2
-      
       mean.gp.study <- t(mean.matrix)
       SD.gp.study <- t(sd.matrix)
       N.gp.study <- t(n.matrix)
       SEM.gp.study <- SD.gp.study/sqrt(N.gp.study)
       
-
+      qq.gp.study <- lapply(qq.matrix, function(i){
+                              matrix(i, ncol = 7, 
+                                     byrow = TRUE)})
       
-      # create names
+      qq.gp.study <- lapply(qq.gp.study, function(i) {
+        colnames(i) <- c("5%_gp", "10%_gp", "25%_gp", "50%_gp",
+                         "75%_gp", "90%_gp", "95%_gp")
+        i
+      })
+      
+      
+     # create names
       names.gp <- rep(NA,dim(mean.gp.study)[1])
       
       for(k in 1:dim(mean.gp.study)[1]){
@@ -338,7 +429,6 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       dimnames(N.gp.study) <- c(list(names.gp),list(names.study))
       dimnames(SEM.gp.study) <- c(list(names.gp),list(names.study))
       
-
     }
     
     if(type=="combine"){
@@ -360,20 +450,28 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       if(draw.plot){
         
       ## If plot is TRUE please select which metric to plot else table is returned
-      sel.metric <- get(metric_map[[metric]])
       
-      sel.metric.data <- data.frame(lsoa11cd = lsoanames,
+      if(metric %in% q.names){
+        
+        sel.metric.data <- data.frame(lsoa11cd = rownames(qq.gp))
+        sel.metric.data$value <- qq.gp[ , which(metric == q.names)]
+        sel.metric.data$server <- 'combine'
+       
+      } else {
+        sel.metric <- get(metric_map[[metric]])
+        
+        sel.metric.data <- data.frame(lsoa11cd = lsoanames,
                               value = as.numeric(sel.metric),
                               server = 'combine')
-      
+      }
       shape_sf <- shape_list[[1]] |>
         dplyr::left_join(sel.metric.data, by = "lsoa11cd") |>
         sf::st_as_sf()
       
 
       } else {
-        result <- list(mean.gp,SD.gp,N.gp,SEM.gp,Nvalid,Nmissing,Ntotal, lsoa_names[[1]])
-        names(result) <- list("Mean_gp","StDev_gp","Nvalid_gp","SEM_gp","Total_Nvalid",
+        result <- list(mean.gp,SD.gp,N.gp,SEM.gp,qq.gp,Nvalid,Nmissing,Ntotal, lsoa_names[[1]])
+        names(result) <- list("Mean_gp","StDev_gp","Nvalid_gp","SEM_gp", "Q_gp","Total_Nvalid",
                               "Total_Nmissing","Total_Ntotal", "LSOAnames")
         return(result)
       }
@@ -383,6 +481,20 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
     if(type=="split"){
     
     if(draw.plot){
+      browser()
+      if(metric %in% q.names){
+        
+        sel.metric.data <- 
+          lapply(1:length(qq.gp.study), function(i) {
+            data.frame(
+              server = names(datasources)[i],
+              value = qq.gp.study[[i]][ ,which(metric == q.names)],
+              lsoa11cd = lsoa_names[[i]],
+              row.names = NULL
+            )
+          })
+        
+      }else{
     sel.metric <- get(metric_map[[metric]])
     
     sel.metric.data <- 
@@ -394,7 +506,7 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
             row.names = NULL
           )
         })
-      
+      }
     shape_sf <- do.call(
       rbind,
       lapply(1:numsources, function(i){
@@ -409,10 +521,10 @@ ds.summarystat <- function(x=NULL, y=NULL, type='combine', do.checks=FALSE,
       
       lsoanames <- matrix(unlist(lsoa_names), ncol = numsources)
       dimnames(lsoanames) <- dimnames(mean.gp.study) 
-      result <- list(mean.gp.study,SD.gp.study,N.gp.study,SEM.gp.study,Nvalid,
+      result <- list(mean.gp.study,SD.gp.study,N.gp.study,SEM.gp.study,qq.gp.study,Nvalid,
                      Nmissing,Ntotal, lsoanames)
       names(result) <- list("Mean_gp_study","StDev_gp_study","Nvalid_gp_study",
-                            "SEM_gp_study","Total_Nvalid","Total_Nmissing",
+                            "SEM_gp_study","Q_gp","Total_Nvalid","Total_Nmissing",
                             "Total_Ntotal", "LSOAnames")
       return(result)
     }
diff --git a/vignettes/Demin's_work_functions.Rmd b/vignettes/Demin's_work_functions.Rmd
new file mode 100644
index 0000000..e7f2d9f
--- /dev/null
+++ b/vignettes/Demin's_work_functions.Rmd
@@ -0,0 +1,115 @@
+---
+title: "R Notebook"
+output: html_notebook
+---
+
+
+```{r}
+DSOpal::Opal()
+builder <- DSI::newDSLoginBuilder()
+builder$append(
+  server = "mersey-demo",
+  url = "http://localhost:8880",
+  user = "administrator",
+  password = "password",
+  driver = "OpalDriver",
+  profile = "ds-geospatial"
+)
+```
+
+```{r}
+logindata <- builder$build()
+conns <- DSI::datashield.login(logins = logindata)
+
+```
+
+
+```{r}
+DSI::datashield.assign.resource(
+conns,
+symbol = "cells",
+resource = paste0(project, ".", "cells")
+)
+
+DSI::datashield.assign.resource(
+conns,
+symbol = "W",
+resource = paste0(project, ".", "W_D_5km")
+)
+```
+
+## Materialise arbitrary R objects ----
+
+
+```{r}
+
+DSI::datashield.assign.resource(
+  conns,
+  symbol = "cells_resource",
+  resource = paste0(project, ".cells")
+)
+
+DSI::datashield.assign.expr(
+  conns,
+  symbol = "cells_object",
+  expr = quote(as.resource.object(cells_resource))
+)
+
+```
+
+## Map specific columns ----
+
+
+```{r}
+DSI::datashield.assign.expr(
+  conns,
+  symbol = "obesity_qof_2024_25",
+  expr = quote(cells_object$obesity_qof_2024_25)
+)
+DSI::datashield.assign.expr(
+  conns,
+  symbol = "W_object",
+  expr = quote(as.resource.object(W))
+)
+DSI::datashield.assign.expr(
+  conns,
+  symbol = "W_band800",
+  expr = quote(W_object$band800)
+)
+
+```
+
+
+
+```{r}
+ds.moransI(
+  x = "obesity_qof_2024_25",
+  listw = "W_band800",
+  nsim = 199,
+  datasources = conns
+)
+
+```
+
+```{r}
+out <- ds.localmoran( 
+  x = "obesity_qof_2024_25",
+  listw = "W_band800",
+  datasources = conns
+)
+
+quadr_mean <- out$`mersey-demo`$quadr$mean
+
+counts <- table(quadr_mean)
+
+barplot(
+  counts,
+  xlab = "Local Moran's I quadrant",
+  ylab = "Count",
+  main = "Local Moran's I Quadrants"
+)
+
+
+```
+
+
diff --git a/vignettes/Opal_functioncheck.Rmd b/vignettes/Opal_functioncheck.Rmd
index bcb8269..533984e 100644
--- a/vignettes/Opal_functioncheck.Rmd
+++ b/vignettes/Opal_functioncheck.Rmd
@@ -110,9 +110,11 @@ ds.colnames("D", conns)
 
 ```{r}
 library(dsGeospatialClient)
+names_region <- list(c("Liverpool", "Sefton"), c("Liverpool"))
+
 ds.summarystat(x = "D$has_asthma", y = "D$lsoa11cd", 
-               type='combine', do.checks=FALSE, 
-               draw.plot = TRUE, names_region = "Liverpool", 
+               type='combe', do.checks=FALSE, 
+               draw.plot = TRUE, names_region = names_region, 
                metric = "Mean", datasources=conns)
 ```