How to offset points when moveCAN is set to TRUE about mapspain

[pedrotercero3]

Okay, I just discovered a super hacky way to solve it: I saved an object with the Canary Islands in their correct geographical position (moveCAN = FALSE) and another one with their shifted position (moveCAN = TRUE). I then extracted the polygon coordinates for both of them (for a random island) and subtracted the corresponding longitudes and latitudes, getting a shifting factor of ~7 units for the latitude and of ~4.94 units for the longitude.

I now just wonder if there's a cleaner way of getting the original offset used in the code.

[dieghernan]

Hi! The default offset is offset <- c(550000, 920000) in EPSG:3857 (so 550km North 920 km West in Mercator). I am not able right now to provide a full example on how to do it (away of my PC) but see how this is done internally.

Then, the object (your points) is converted to EPSG:3857, the offset is applied to the geometry column and after that the resulting object is transformed back to its initial CRS.

I would provide a neat example when possible

mapSpain/R/esp_get_nuts.R

Lines 261 to 291 in fdab71f

	if (length(grep("ES7", data_sf$NUTS_ID)) > 0) {
	offset <- c(550000, 920000)
	if (length(moveCAN) > 1) {
	coords <- sf::st_point(moveCAN)
	coords <- sf::st_sfc(coords, crs = sf::st_crs(4326))
	coords <- sf::st_transform(coords, 3857)
	coords <- sf::st_coordinates(coords)
	offset <- offset + as.double(coords)
	}
	data_sf <- sf::st_transform(data_sf, 3857)
	penin <- data_sf[-grep("ES7", data_sf$NUTS_ID), ]
	can <- data_sf[grep("ES7", data_sf$NUTS_ID), ]
	# Move can
	can <- sf::st_sf(
	sf::st_drop_geometry(can),
	geometry = sf::st_geometry(can) + offset,
	crs = sf::st_crs(can)
	)
	# Regenerate
	if (nrow(penin) > 0) {
	data_sf <- rbind(penin, can)
	} else {
	data_sf <- can
	}
	}
	}
	data_sf <- sf::st_transform(data_sf, as.double(init_epsg))

[pedrotercero3]

Thanks for the quick reply, Diego! I actually managed to find that part in the code, but apparently I can't seem to use it correctly. Here's what I've tried so far:

I've uploaded a sample CSV here. It's just a list of AEMET's solar radiation measurement stations and their coordinates in decimal format.

Here's the R code where I try to offset the coordinates of the stations located in the Canary Islands (warning: it's a mess. I know next to nothing about both geography and sf objects):

read_csv("https://gist.githubusercontent.com/pedrotercero3/7d789d11c5b22374c5ad13a47d828526/raw/53bf0bed9ef3decc997a95ffc8178f6ac8b806f1/solar_rad_stations_esp.csv") %>% 
  mutate(shifted_coords = if_else(
    nombre_estacion %in% c("Sta. Cruz de Tenerife", "Izaña", "Maspalomas"),
    map2(
      lat_dec, long_dec,
      ~ {
        c(.x, .y) %>% 
          st_point() %>% 
          st_sfc(crs = sf::st_crs(4326)) %>% 
          st_transform(3857) %>% 
          st_coordinates() %>% 
          as.double() + c(550000, 920000)
      }
    ),
    map2(
      lat_dec, long_dec,
      ~ {
        c(.x, .y) %>% 
          st_point() %>% 
          st_sfc(crs = sf::st_crs(4326)) %>% 
          st_transform(3857) %>% 
          st_coordinates() %>% 
          as.double()
      }
    )),
    shifted_coords = map(shifted_coords, ~ {
      st_point(.x) %>% 
        st_sfc(crs = sf::st_crs(3857)) %>% 
        st_transform(4326) %>% 
        st_coordinates() %>% 
        as.double()
    })) %>% 
  unnest_wider(shifted_coords, names_sep = "_") %>% 
  rename(lat_dec_moveCAN = shifted_coords_1,
         long_dec_moveCAN = shifted_coords_2)

Basically in the beginning it's all wrapped in an if_else(). If the station's in the islands, I convert the coordinates with st_point(), set the reference CRS, transform them to the 3857 reference, and add the offset. If the station's not in the islands I apply the same functions but without the offset. After the if_else() I just convert the coordinates back to the decimal system and format them into individual columns again (unnest_wider()) so I can plot them with geom_point().

However, this is the plot that I get after all of this!

But if use the values I got with the hacky approach I mentioned in my earlier comment, the locations are more correct:

All of this is to say that I solved my initial problem (which, I want to emphasize, is not at all a problem related to the package, which by the way is amazing; it has the best ggplot2 map of Spain I've seen to date), but I'm just curious about how to solve it using the same approach you used in the original code. If you have time in the future to look into it that'd be cool, but I don't want you to waste any more of your time on this so please feel free to close the issue!

[dieghernan]

Hi, for completion I applied the same code of the package to your dataset (thanks for providing sample data btw), I think it works fine:

library(mapSpain)
library(tidyverse)
library(sf)
#> Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE

# Points as sf
points <- read_csv("https://gist.githubusercontent.com/pedrotercero3/7d789d11c5b22374c5ad13a47d828526/raw/53bf0bed9ef3decc997a95ffc8178f6ac8b806f1/solar_rad_stations_esp.csv") %>%
  st_as_sf(
    coords = c("long_dec", "lat_dec"),
    crs = 4326
  )
#> Rows: 26 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): nombre_estacion
#> dbl (2): lat_dec, long_dec
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Map

provs <- esp_get_prov(epsg = 4326)


ggplot(provs) +
  geom_sf() +
  geom_sf(data = points)

# ---- Move points on Canary Islands ---- 

# Split dataset
data_sf <- st_transform(points, 3857)
penin <- data_sf %>% filter(!nombre_estacion %in% c(
  "Sta. Cruz de Tenerife",
  "Izaña", "Maspalomas"
))

can <- data_sf %>% filter(nombre_estacion %in% c(
  "Sta. Cruz de Tenerife",
  "Izaña", "Maspalomas"
))

# Move Canary points
offset <- c(550000, 920000)

can <- st_sf(
  st_drop_geometry(can),
  geometry = st_geometry(can) + offset,
  crs = st_crs(can)
)

# Regenerate and project back to the initial CRS

data_sf <- rbind(penin, can) %>%
  st_transform(st_crs(points))

# ---- End---- #


ggplot(provs) +
  geom_sf() +
  geom_sf(data = data_sf)

^{Created on 2022-08-21 with reprex v2.0.2}

[pedrotercero3]

Thank you very much! My problem was knowing the order in which to apply the necessary st functions.

[dieghernan]

Hi,

Just to let you know that in mapspain v0.9.0 I introduced a new function esp_move_can() that displaces arbitray objects with the same offset than moveCAN = TRUE.

Now it is as easy as:

library(mapSpain)
library(tidyverse)
library(sf)
#> Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE

# Points as sf
points <- read_csv("https://gist.githubusercontent.com/pedrotercero3/7d789d11c5b22374c5ad13a47d828526/raw/53bf0bed9ef3decc997a95ffc8178f6ac8b806f1/solar_rad_stations_esp.csv",
  show_col_types = FALSE
) %>%
  st_as_sf(
    coords = c("long_dec", "lat_dec"),
    crs = 4326
  )


# Split points
penin <- points %>% filter(!nombre_estacion %in% c(
  "Sta. Cruz de Tenerife",
  "Izaña", "Maspalomas"
))

# Move this points with esp_move_can()
can <- points %>% filter(nombre_estacion %in% c(
  "Sta. Cruz de Tenerife",
  "Izaña", "Maspalomas"
)) %>%
  # Use esp_move_can
  esp_move_can()


# Regenrate but canary points moved

points_disp <- penin %>% bind_rows(can)

# Map
provs <- esp_get_prov(epsg = 4326)
bb <- esp_get_can_box()

ggplot(provs) +
  geom_sf() +
  geom_sf(data = bb) +
  geom_sf(data = points_disp)

^{Created on 2024-01-30 with reprex v2.1.0}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.2 (2023-10-31 ucrt)
#>  os       Windows 11 x64 (build 22631)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  Spanish_Spain.utf8
#>  ctype    Spanish_Spain.utf8
#>  tz       Europe/Madrid
#>  date     2024-01-30
#>  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
#>  bit           4.0.5   2022-11-15 [1] CRAN (R 4.3.0)
#>  bit64         4.0.5   2020-08-30 [1] CRAN (R 4.3.0)
#>  class         7.3-22  2023-05-03 [1] CRAN (R 4.3.1)
#>  classInt      0.4-10  2023-09-05 [1] CRAN (R 4.3.1)
#>  cli           3.6.2   2023-12-11 [1] CRAN (R 4.3.2)
#>  colorspace    2.1-0   2023-01-23 [1] CRAN (R 4.3.0)
#>  countrycode   1.5.0   2023-05-30 [1] CRAN (R 4.3.0)
#>  crayon        1.5.2   2022-09-29 [1] CRAN (R 4.3.0)
#>  curl          5.2.0   2023-12-08 [1] CRAN (R 4.3.2)
#>  DBI           1.2.1   2024-01-12 [1] CRAN (R 4.3.2)
#>  digest        0.6.34  2024-01-11 [1] CRAN (R 4.3.2)
#>  dplyr       * 1.1.4   2023-11-17 [1] CRAN (R 4.3.2)
#>  e1071         1.7-14  2023-12-06 [1] CRAN (R 4.3.2)
#>  evaluate      0.23    2023-11-01 [1] CRAN (R 4.3.2)
#>  fansi         1.0.6   2023-12-08 [1] CRAN (R 4.3.2)
#>  farver        2.1.1   2022-07-06 [1] CRAN (R 4.3.0)
#>  fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
#>  forcats     * 1.0.0   2023-01-29 [1] CRAN (R 4.3.0)
#>  fs            1.6.3   2023-07-20 [1] CRAN (R 4.3.1)
#>  generics      0.1.3   2022-07-05 [1] CRAN (R 4.3.0)
#>  ggplot2     * 3.4.4   2023-10-12 [1] CRAN (R 4.3.2)
#>  glue          1.7.0   2024-01-09 [1] CRAN (R 4.3.2)
#>  gtable        0.3.4   2023-08-21 [1] CRAN (R 4.3.1)
#>  highr         0.10    2022-12-22 [1] CRAN (R 4.3.0)
#>  hms           1.1.3   2023-03-21 [1] CRAN (R 4.3.0)
#>  htmltools     0.5.7   2023-11-03 [1] CRAN (R 4.3.2)
#>  KernSmooth    2.23-22 2023-07-10 [1] CRAN (R 4.3.1)
#>  knitr         1.45    2023-10-30 [1] CRAN (R 4.3.2)
#>  lifecycle     1.0.4   2023-11-07 [1] CRAN (R 4.3.2)
#>  lubridate   * 1.9.3   2023-09-27 [1] CRAN (R 4.3.1)
#>  magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
#>  mapSpain    * 0.9.0   2024-01-23 [1] CRAN (R 4.3.2)
#>  munsell       0.5.0   2018-06-12 [1] CRAN (R 4.3.0)
#>  pillar        1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
#>  pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
#>  proxy         0.4-27  2022-06-09 [1] CRAN (R 4.3.0)
#>  purrr       * 1.0.2   2023-08-10 [1] CRAN (R 4.3.1)
#>  R.cache       0.16.0  2022-07-21 [1] CRAN (R 4.3.0)
#>  R.methodsS3   1.8.2   2022-06-13 [1] CRAN (R 4.3.0)
#>  R.oo          1.26.0  2024-01-24 [1] CRAN (R 4.3.2)
#>  R.utils       2.12.3  2023-11-18 [1] CRAN (R 4.3.2)
#>  R6            2.5.1   2021-08-19 [1] CRAN (R 4.3.0)
#>  Rcpp          1.0.12  2024-01-09 [1] CRAN (R 4.3.2)
#>  readr       * 2.1.5   2024-01-10 [1] CRAN (R 4.3.2)
#>  reprex        2.1.0   2024-01-11 [1] CRAN (R 4.3.2)
#>  rlang         1.1.3   2024-01-10 [1] CRAN (R 4.3.2)
#>  rmarkdown     2.25    2023-09-18 [1] CRAN (R 4.3.1)
#>  rstudioapi    0.15.0  2023-07-07 [1] CRAN (R 4.3.1)
#>  scales        1.3.0   2023-11-28 [1] CRAN (R 4.3.2)
#>  sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
#>  sf          * 1.0-15  2023-12-18 [1] CRAN (R 4.3.2)
#>  stringi       1.8.3   2023-12-11 [1] CRAN (R 4.3.2)
#>  stringr     * 1.5.1   2023-11-14 [1] CRAN (R 4.3.2)
#>  styler        1.10.2  2023-08-29 [1] CRAN (R 4.3.1)
#>  tibble      * 3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
#>  tidyr       * 1.3.1   2024-01-24 [1] CRAN (R 4.3.2)
#>  tidyselect    1.2.0   2022-10-10 [1] CRAN (R 4.3.0)
#>  tidyverse   * 2.0.0   2023-02-22 [1] CRAN (R 4.3.0)
#>  timechange    0.3.0   2024-01-18 [1] CRAN (R 4.3.2)
#>  tzdb          0.4.0   2023-05-12 [1] CRAN (R 4.3.0)
#>  units         0.8-5   2023-11-28 [1] CRAN (R 4.3.2)
#>  utf8          1.2.4   2023-10-22 [1] CRAN (R 4.3.2)
#>  vctrs         0.6.5   2023-12-01 [1] CRAN (R 4.3.2)
#>  vroom         1.6.5   2023-12-05 [1] CRAN (R 4.3.2)
#>  withr         3.0.0   2024-01-16 [1] CRAN (R 4.3.2)
#>  xfun          0.41    2023-11-01 [1] CRAN (R 4.3.2)
#>  xml2          1.3.6   2023-12-04 [1] CRAN (R 4.3.2)
#>  yaml          2.3.8   2023-12-11 [1] CRAN (R 4.3.2)
#> 
#>  [1] C:/Users/XXX/AppData/Local/R/win-library/4.3
#>  [2] C:/Program Files/R/R-4.3.2/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────