Following this tutorial, I am trying to plot a map with the ggplot2 and sf packages. Just as in the tutorial, my code is:
library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(rgeos)
world <- ne_countries(scale = "medium", returnclass = "sf")
class(world)
ggplot(data = world) + geom_sf()
However, when I run this code on the Linux machine I must work on, I get the following error and warning (repeated many times):
Error: node stack overflow
In addition: There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In CPL_crs_from_input(x) :
GDAL Error 1: PROJ: proj_create_from_database: Open of /home/<redacted>/miniconda3/envs/r_env2/share/proj failed
However, on my home computer, I get the expected output:
Here is some possibly relevant information:
Work computer:
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux
Matrix products: default
BLAS/LAPACK: /home/<redacted>/miniconda3/envs/r_env2/lib/libopenblasp-r0.3.6.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rgeos_0.5-3 sp_1.4-2 rnaturalearthdata_0.1.0
[4] rnaturalearth_0.1.0 sf_0.9-4 ggplot2_3.1.1
> system('free -m')
total used free shared buff/cache available
Mem: 257665 42503 73665 298 141496 177005
Swap: 80595 4577 76018
Home computer:
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rgeos_0.5-3 sp_1.4-2 rnaturalearthdata_0.1.0
[4] rnaturalearth_0.1.0 sf_0.9-4 ggplot2_3.3.2
> memory.limit()
[1] 1.759219e+13
Unfortunately, I cannot do this work on my home computer. The datasets I need to interact with the map are simply too large.
Thank you very much!