Simplified geometries

.gitignore

@@ -19,3 +19,4 @@ data-raw/translation.Rmd
 dev/translation.html
 dev/data-docker/
 bdd_connect.sh
+/data-raw/*.html

DESCRIPTION

@@ -50,7 +50,6 @@ Imports:
     pkgload (>= 1.2.2),
     promises,
     purrr (>= 0.3.4),
-    rmapshaper (>= 0.4.5),
     RPostgres (>= 1.3.3),
     sf (>= 1.0.2),
     shinipsum (>= 0.1.0),

R/app_ui.R

@@ -21,7 +21,7 @@ app_ui <- function(request) {
         welcomemodal = modal(
           inputId = "welcome",
           title = tagList(
-            tags$h2("Welcome on DiadES Atlas!") %>% with_i18("welcome")
+            tags$h2("Welcome to DiadES Atlas!") %>% with_i18("welcome")
           ),
           body = tagList(
             tags$div(

R/mod_a_first_fct_map.R

@@ -44,7 +44,8 @@ tm_ocean <- function(dataOcean,
         "[7, 9]",
         "[10, 12]",
         "[13, 15]"
-      )
+      ),
+      popup.vars = c("prevalence" = "nb_occurence")
     )
 }
 
@@ -152,7 +153,7 @@ tm_draw <- function(species_latin_name,
   # ------------------------------------------ display the map
   tm_graticules() +
     tm_ocean +
-    tm_frontiers +
+    # tm_frontiers +
     tm_catchmment +
     tm_layout(
       main.title.fontface = 3,

R/utils_helpers.R

@@ -208,8 +208,9 @@ generate_datasets <- function(con) {
   catchment_geom <- sf::st_read(
     con,
     query =   "SELECT * FROM diadesatlas.v_basin vb"
-  ) %>%
-    rmapshaper::ms_simplify()
+  ) #%>%
+    #mutate(geom = st_make_valid(geom))
+    # rmapshaper::ms_simplify(keep = .05)
 
   dataALL <- DBI::dbGetQuery(
     con,
@@ -223,8 +224,8 @@ generate_datasets <- function(con) {
     query = "SELECT * FROM diadesatlas.v_ices_geom;"
   ) %>%
     # sf::st_transform("+proj=eqearth +wktext") %>%
-    sf::st_transform("+proj=wintri") %>%
-    rmapshaper::ms_simplify()
+    sf::st_transform("+proj=wintri") # %>%
+    # rmapshaper::ms_simplify()
 
   species_order <- c(
     "Alosa alosa",

data-raw/aa-a-exploration_data.Rmd

@@ -7,6 +7,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
+TUTORIAL: The aims of this vignette is to learn how to request tables with dplyr package.
+
 ## Installation
 
 Install the present package and use its core functions

data-raw/aa-data-exploration-and-preparation.Rmd

@@ -7,42 +7,16 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
+This vignette explores the tables in the database and ensures that R has access to the tables.
+It also transforms the data in json.
+
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
   eval = TRUE # Set to TRUE when data OK for test, but not on GitHub
 )
-# To compile manually (run in the console directly)
-if (FALSE) {
-  vignette_name <- "aa-data-exploration-and-preparation"
-  vignette_file <- paste0(vignette_name, ".Rmd")
-
-  rmarkdown::render(
-    input = here::here(file.path("data-raw", vignette_file)),
-    output_format = "rmarkdown::html_vignette",
-    output_options = list(toc = TRUE),
-    output_file = here::here(file.path("vignettes", vignette_file))
-  )
-
-  # Add header for title
-  lines <- readLines(here::here(file.path("vignettes", vignette_file)))
-
-  cat(
-    glue::glue('---
-title: ".{vignette_name}."
-output: rmarkdown::html_vignette
-vignette: >
-  %\\VignetteIndexEntry{.{vignette_name}.}
-  %\\VignetteEngine{knitr::rmarkdown}
-  %\\VignetteEncoding{UTF-8}
----
-', .open = ".{", .close = "}."),
-    lines,
-    sep = "\n",
-    file = here::here(file.path("vignettes", vignette_file))
-  )
-}
+
 ```
 
 ```{r setup}
@@ -52,6 +26,7 @@ library(diades.atlas)
 library(dplyr)
 library(leaflet)
 library(ggplot2)
+library(sf)
 ```
 
 ## Data exploration
@@ -60,23 +35,12 @@ library(ggplot2)
 
 Do not forget to set environment variables in .Renviron
 ```{r}
-postgis_host <- Sys.getenv("POSTGRES_HOST")
-postgis_user <- Sys.getenv("POSTGRES_USER")
-postgis_password <- Sys.getenv("POSTGRES_PASSWORD")
-
-con <- DBI::dbConnect(
-  RPostgres::Postgres(),
-  host = Sys.getenv("POSTGRES_HOST", diades.atlas:::get_golem_config("POSTGRES_HOST")),
-  dbname = Sys.getenv("POSTGRES_DBNAME", diades.atlas:::get_golem_config("POSTGRES_DBNAME")),
-  port = Sys.getenv("POSTGRES_PORT", diades.atlas:::get_golem_config("POSTGRES_PORT")),
-  user = Sys.getenv("POSTGRES_USER", "diadesatlas_r"),
-  password = Sys.getenv("POSTGRES_PASS", "diadesPassword"),
-  options = "-c search_path=diadesatlas"
-)
-# Listtables
-# DBI::dbListTables(con)
-# DBI::dbListObjects(con)
+session <- shiny::MockShinySession$new() #new.env()
+connect(session)
+con <- get_con(session)
+```
 
+```{r, eval=FALSE}
 # Get the names of all the tables in the schema
 all_schemas <- DBI::dbGetQuery(con, "SELECT table_name FROM information_schema.tables WHERE table_schema='diadesatlas'")
 all_schemas
@@ -90,23 +54,15 @@ casestudy <- tbl(con, dbplyr::in_schema("diadesatlas", "casestudy"))
 casestudy
 ```
 
-### Create connection
-
-```{r, eval=FALSE}
-my_dm <- dm_from_src(con, schema = "diadesatlas", learn_keys = TRUE)
-my_dm
-```
-
 ### Function to collect data
 ```{r, eval=FALSE}
 # empty
 # dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "cices51")) %>% collect()
 # services ?
 # dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "cices")) %>% collect()
-# services new
-dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "es_diades")) %>% collect()
+
 # geometry of all basin
-dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "basin_outlet")) %>% collect()
+dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "basin_location")) %>% collect()
 # Model results over the years
 dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "hybrid_model_result")) %>% collect()
 # Abundance of species in a given basin ove the years
@@ -131,24 +87,41 @@ dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "species")) %>% collect()
 ### Prepare json files
 Create all json data used on the home page of the app
 ```{r, eval=FALSE}
-# List of case studies
-casestudy <- dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "casestudy")) %>% collect()
-# List of the corresponding basins
-casestudy_basin <- dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "casestudy_basin")) %>% collect()
-# Put them two together
-cstd <- full_join(casestudy, casestudy_basin) %>% filter(publishable)
+# # List of case studies
+# casestudy <- dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "casestudy")) %>% collect()
+# # List of the corresponding basins
+# casestudy_basin <- dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "casestudy_basin")) %>% collect()
+# # Put them two together
+# cstd <- full_join(casestudy, casestudy_basin) %>% filter(publishable) %>%  collect()
 
-# Get the features for drawing the polygons on the map
-qry <- "SELECT ST_Transform(diadesatlas.basin_outlet.simplified_geom, 4326) as geom, basin_id as basin_id FROM diadesatlas.basin_outlet"
 
-# i then read as an sf object
-pols <- sf::st_read(con, query = qry, geom = "geom") %>%
-  filter(basin_id %in% casestudy_basin$basin_id) %>%
-  left_join(cstd)
-pols %>%
+casestudy_new <- sf::st_read(con, 
+                             query = "SELECT casestudy_id, casestudy_name, geom FROM diadesatlas.casestudy", geom = "geom") %>% 
+  # fake basin_id to be compatabible with mod_b_second.R
+  mutate(basin_id = 1:n()) %>% 
+  st_transform(crs = 4326)
+
+casestudy_new %>%
   geojson::as.geojson() %>%
   geojson::geo_write(here::here("inst/casestudy.json"))
 
+
+# Get the features for drawing the polygons on the map
+# qry <- "SELECT ST_Transform(diadesatlas.basin_outlet.simplified_geom, 4326) as geom, basin_id as basin_id FROM diadesatlas.basin_outlet"
+
+# qry <- "SELECT diadesatlas.basin_outlet.simplified_geom as geom, basin_id as basin_id FROM diadesatlas.basin_outlet"
+# 
+# pols <- sf::st_read(con, query = qry, geom = "geom") %>% 
+#   st_transform(crs = 4326) 
+# 
+# # i then read as an sf object
+# pols %>%
+#   filter(basin_id %in% casestudy_basin$basin_id) %>%
+#   left_join(cstd)
+# pols %>%
+#   geojson::as.geojson() %>%
+#   geojson::geo_write(here::here("inst/casestudy.json"))
+
 # List of ecosystem services
 ecosystems <- dplyr::tbl(con, dbplyr::in_schema("diadesatlas", "ecosystem_service")) %>% collect()
 ecosystems %>%
@@ -179,37 +152,25 @@ species %>%
   jsonlite::write_json(here::here("inst/species.json"))
 ```
 
-### Prepare and test leaflet map
-```{r, eval=FALSE}
-# set query and reproject to EPSG:4326 for GeoJSON creation
-# (the real query is more involved with a bounding box etc)
-qry <- "SELECT ST_Transform(diadesatlas.basin_outlet.simplified_geom, 4326) as geom FROM diadesatlas.basin_outlet"
-
-# i then read as an sf object
-pols <- sf::st_read(con, query = qry, geom = "geom")
-
-# and convert to GeoJSON
-pols.js <- geojsonio::geojson_json(pols)
-# pols %>% geojson::as.geojson() %>% geojson::geo_write("plop.json")
-
-# options(readr.default_locale=readr::locale(tz="Europe/Berlin"))
-# df60 <- get_eurostat_geospatial(resolution = 60)
-#
-# CE.sf <- df60 %>%
-#   dplyr::filter(LEVL_CODE == 2 & CNTR_CODE %in% c("AT","CZ","DE","HU","PL","SK")) %>%
-#   dplyr::select(NUTS_ID)
-
-plot(pols)
-
-pols %>%
-  ggplot() +
-  geom_sf(color = "black", size = 0.4)
-
-# CE = sf::as_Spatial(pols)
-
-leaflet() %>%
-  addProviderTiles("CartoDB.Positron") %>%
-  addPolygons(data = pols, color = "green")
+### Explore ecosystem services table
+```{r}
+r <- list(lg = 'en')
+datasets <- generate_datasets(con = con)
+.globals <- getFromNamespace('.globals', 'shiny')
+
+# simulate a species_list
+session$options$golem_options$species_list <- datasets$species_list
+.globals$domain <- session
+
+golem::get_golem_options("species_list")
+
+# debugonce(ecosystem_table)
+ecosystem_table(species = "Alosa alosa", 
+                case_study = 5, 
+                ecosystem = '1_4',
+                r = r,
+                session = session) %>% 
+DT::datatable()
 ```