mapview for Quick Maps (01_03)

Hi Perry! In this course I chose to cover only one option for static and interactive maps. I prefer {ggplot2} because it's more widely used than {tmap}, and it also has an extensive collection of packages that extend its capabilities. Here are some useful extensions when it comes to maps:

• {gghighlight} makes it easy to quickly highlight areas in a choropleth
• {geomtextpath} is designed to label lines, and can be used to label rivers
• {ggtext} does lots, including making it easy to include color in titles. This is an alternative to providing legends and can be very powerful.

On top of this, the {cowplot} and {patchwork} packages are both designed for assembling multiple {ggplot2} charts together - which includes maps built using geom_sf().

However, I do recognise that {tmap} is very powerful and can be used to create beautiful maps. Because it is designed specifically with mapping in mind it often takes less code to do things in {tmap} vs {ggplot2}.

Hi Irantzu, sorry this is happening. Could you tell me a little bit about your set up:

• What OS are you running
• Could you run `version` in the R console and paste what appears - I've given an example below
• Could you get the current version of RStudio by going to Help > About RStudio

Example R version output

version _
platform x86_64-apple-darwin17.0
arch x86_64
os darwin17.0
system x86_64, darwin17.0
status
major 4
minor 1.1
year 2021
month 08
day 10
svn rev 80725
language R
version.string R version 4.1.1 (2021-08-10) nickname Kick Things

Hello Latif. I'm sorry I didn't quite understand your question, could you help me? Is the problem that the mapview maps are not showing at all for you? Are the maps showing in a separate window or your browser instead? Thanks, Charlotte.

It appears that the package has been archived. We're looking into this and will get back to you!

Hello Jeremy,

The good news is that you're not making any mistakes. But, unfortunately the bottom is falling out of {raster} due to the retirement of {rgdal} in 2023. This means there will be unexpected behaviours as progress forwards in time. I need to think about how to update this course around those issues, as they're coming in more quickly than I expected.

My recommended workflow is to move to {terra} which I demonstrate in this 4min video. Which using this code:

library(rnaturalearthdata)
# remotes::install_github("hrbrmstr/albersusa")
library(albersusa)
library(sf)
library(raster)
library(mapview)

alaska_landcover <- raster("data/alaska_landcover.img")

alaska_landcover %>% 
  mapview()


# terra -------------------------------------------------------------------

library(terra)


alaska_terra <- rast("data/alaska_landcover.img")

alaska_terra %>% 
  mapview()

# tidyterra ---------------------------------------------------------------

library(tidyverse)
library(tidyterra)

alaska_recoded <- alaska_terra %>% 
  mutate(red_recoded = case_when(Red == 75 ~ NA,
                                 TRUE ~ Red))

ggplot() +
  geom_spatraster(data = alaska_recoded,
                  aes(fill = red_recoded))