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11_Overview_map_2022.md

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206 lines (153 loc) · 5.61 KB
title Overview map
output
html_document
keep_md toc toc_depth toc_float code_folding df_print
true
true
3
true
hide
paged

Makes overview map, using the map data in NIVA's "K:/Kart"
The "second try" map is the one used

This code uses the new sf package (which supersedes sp). It works nicely together with ggplot2 (see below), but for the time being (Dec. 2018) you need the development version of ggplot2. See code below for details.

Packages

# Install
# install.packages("sf")

# Also needs the *development version* of ggplot2
# First, remove folders digest and ggplot2 in your Library folder (I needed to do that, at least)
# (Eh, I don't know where I store my packages? Run: .libPaths() )
# Then:
#   install.packages("digest")
#   devtools::install_github("tidyverse/ggplot2")


library(sf)
library(ggplot2)
library(dplyr)
library(ggrepel)

library("rnaturalearth")
library("rnaturalearthdata")
#install.packages("rgeos")
library ("rgeos")
library("ggspatial")

save_plot <- FALSE

Data

Load basemap

world <- ne_countries(scale = "medium", returnclass = "sf")
#class(world)

Stations

df_stations <- read.csv(textConnection("Station_type, Name, Lat, Long 
Hard bottom,HR104, 58.2732, 08.5372
Hard bottom,HT113, 58.5132, 08.9445
Soft bottom,BR1, 58.3253, 08.6295
Soft bottom,BT44, 58.4038, 09.0312
Hydrography/plankton, Arendal 2, 58.3870, 8.8330
River,Glomma, 59.278, 11.134
River,Drammenselva, 59.75399, 10.00903
River,Numedalslågen, 59.08627, 10.06962
River,Skienselva, 59.199, 9.611
"), stringsAsFactors = FALSE)

df_stations$Station_type <- factor(
  df_stations$Station_type,
  levels = c("River", "Hydrography/plankton", "Hard bottom", "Soft bottom"))

sf_stations <- st_as_sf(df_stations, coords = c("Long", "Lat"), crs = "+proj=longlat")
sf_stations[1,]
<script data-pagedtable-source type="application/json"> {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Station_type"],"name":[1],"type":["fct"],"align":["left"]},{"label":["Name"],"name":[2],"type":["chr"],"align":["left"]},{"label":["geometry"],"name":[3],"type":["sf_POINT"],"align":["right"]}],"data":[{"1":"Hard bottom","2":"HR104","3":"","_rn_":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} </script>

Rivers

Read data

  • takes a minute or something to read
  • downloaded from NVEs download site
file_rivers <- "Map_data/11_sf_rivers.rds"

if (!file.exists(file_rivers)){
  
  # Using st_read in package sf
  fn <- "C:/Data/Mapdata/NVE_ELVIS_elvenett/NVEData/Elv_Hovedelv.geojson"
  sf_rivers_all <- sf::st_read(fn)
  
  # str(sf_rivers_all, 1)  
  
  #### Select rivers  
  
  sel <- grepl("Glomma", sf_rivers_all$elvenavn); sum(sel)
  
  rivernames <- c("Glommavassdraget", "Drammensvassdraget", "Skiensvassdraget", "Numedalslågen")
  
  sf_rivers <- sf_rivers_all %>%
    filter(elvenavn %in% rivernames)
  
  saveRDS(sf_rivers, file_rivers)
  
} else {
  
  sf_rivers <- readRDS(file_rivers)
  
}

nrow(sf_rivers)
## [1] 4

Test plot

ggplot(data = world) +
  geom_sf() +
  geom_sf(data = sf_rivers, color = "blue", show.legend = "point") + 
    coord_sf(xlim = c(5, 13), ylim = c(57, 60))

Final plot

Make and save plot

# Select different stations for positioning labels  
leftside <- sf_stations$Name %in% c("HR104")
br1 <- sf_stations$Name %in% c("BR1")
arendal <- grepl("Arendal", sf_stations$Name)
rightside <- !leftside & !arendal & !br1

# For label with line (Arendal)
xrel <- -0.35
yrel <- 0.13
away_from_point <- 0.2   # a number 0-1, not on long or latitude scale 
away_from_text <- 0.05    # longitude scale
arendal_line <- st_coordinates(sf_stations[arendal,])
arendal_line <- rbind(arendal_line + c(X = xrel*away_from_point, Y = yrel*away_from_point), 
                      arendal_line + c(X = xrel, Y = yrel)) 
# arendal_line 

# sf_stations

p <- ggplot(data = world) +
  geom_sf(fill = "khaki") +
  geom_sf(data = sf_rivers, color = "blue") + 
  geom_sf(data = sf_stations, aes(shape = Station_type), size = 3) + 
  geom_sf_text(data = sf_stations[rightside,], aes(label = Name), 
               hjust = 0, nudge_x = 0.1) +
  geom_sf_text(data = sf_stations[leftside,], aes(label = Name), 
               hjust = 1, nudge_x = -0.1) +
  geom_sf_text(data = sf_stations[br1,], aes(label = Name), 
               hjust = 1, nudge_x = -0.1, nudge_y = 0.05) +
  geom_sf_text(data = sf_stations[arendal,], aes(label = Name), hjust = 1, 
               nudge_x = xrel - away_from_text, nudge_y = yrel) +
  geom_segment(aes(x = arendal_line[1,1], y = arendal_line[1,2],
                   xend = arendal_line[2,1], yend = arendal_line[2,2])) +
  annotation_scale(location = "bl", width_hint = 0.5) +
    annotation_north_arrow(location = "bl", which_north = "true", 
        pad_x = unit(0.75, "in"), pad_y = unit(0.5, "in"),
        style = north_arrow_fancy_orienteering) +
  coord_sf(xlim = c(5, 13), ylim = c(57, 60)) +
  theme(panel.grid = element_blank(),
        panel.background = element_rect(fill = "paleturquoise"),
        axis.title = element_blank())

p2 <- p + 
  scale_shape_manual("Station type", values = c(17, 16, 1, 2)) +
  guides(
    shape = guide_legend(
    override.aes = list(size = 3) ) 
    ) 
#  scale_color_discrete("Station Type")

# p2

ggsave("Figures_publ/11_overview_map.png", p2, width = 9, height = 11)

Show plot

p2