brain
Evangeline Reynolds
11/4/2022
Problem: positioning with SF feels spooky and data prep can feel effortful
W/ ggplot2, we are able to say ‘where’ with x and y. These are positional aesthetics. With SF the ‘where’ is implied with the geometry column. This feels non-declarative and spooky to me.
Also, it often requires pre-processing to work with data you’d like to represent.
When we ask the question ‘where’ in a geographical context, a human being will respond with the name of a region; not coordinates and not a set of coordinates that define a region. Is it possible to create an interface where you define ‘where’ with geographical names or other ids? For example: aes(country = my_countries_variable) could be used with a geom/stat like geom_polygon_countries().
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## Warning: package 'ggplot2' was built under R version 3.6.2## Warning: package 'tibble' was built under R version 3.6.2## Warning: package 'purrr' was built under R version 3.6.2## Warning: package 'dplyr' was built under R version 3.6.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()nato_names <- c("Albania", "Belgium", "Bulgaria", "Canada", "Croatia", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary",
"Iceland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Turkey", "United Kingdom", "United States")
library(gapminder)
gapminder %>%
filter(year == 2002) %>%
rename(name = country) ->
gapminder_2002_prepped
rnaturalearth::ne_countries(scale = "medium", returnclass = "sf") %>%
select(name, pop_est, gdp_md_est,
continent, geometry) %>%
filter(name %in% nato_names) %>%
left_join(gapminder_2002_prepped, by = "name") ->
nato_countries
ggplot(data = nato_countries) +
# This part feels spooky to me
ggplot2::geom_sf(data = nato_countries) +
aes(fill = lifeExp) +
scale_fill_viridis_c(option = "magma", direction = -1) +
ggplot2::coord_sf(xlim = c(-175, 47.5),
ylim = c(23, 85),
expand = FALSE) +
labs(title = "Life Expectancy, 2002",
subtitle = "NATO Member States",
fill = "Share of GDP (%)") +
theme_bw() +
theme(plot.title =
element_text(hjust = 0.5)) +
theme(plot.subtitle =
element_text(hjust = 0.5))
See step-by-step: https://evamaerey.github.io/ggplot2_grammar_guide/geom_sf.html#1
Wait, why is this project called ‘brain’?
One inspiration for this is ggseg too. Very cool project that uses sf spatial approach. I wonder if there’d be a version where regions of the brain could be simply named, and other aesthetics defined. Motivated by how I’d like to learn more about the regions of the brain! :-)
A different API?
Here is what I think might be an easy to use API.
ggworld(data = gapminder_2002, projection = 'a good one') +
aes(country = country, # positioning aesthetic
fill = lifeExp) +
geom_polygon_country()Note: country names are really hard. Other codes could be used. One could try to use the wonderful countrycodes as well when
Proof of concept? (without SF)
Because of my limited knowledge of SF, let’s start w/ the x and y inputs approach to mapping with ggplot2 and see if we can cook something up.
Step 00: See how someone gets it done
I was here: https://remiller1450.github.io/s230s19/Intro_maps.html
Step 0: get it done w/ ggplot2
Look at mapping w/ ggplot2, no sf, we use geom_polygon and the x, y and group aesthetics). It is not very efficient (lots of redundant info in the ), but we won’t worry about that for this demonstration. A little rewrite to get inspired.
library(ggplot2)
library(dplyr)
library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:purrr':
##
## mapMainStates <- map_data("state")
StatePopulation <- read.csv("https://raw.githubusercontent.com/ds4stats/r-tutorials/master/intro-maps/data/StatePopulation.csv", as.is = TRUE)
MergedStates <- inner_join(MainStates, StatePopulation, by = "region")
StatePopulation %>% # becomes ggus()
right_join(MainStates) %>% # becomes ggus()
ggplot() + # becomes ggus()
geom_polygon() + # becomes geom_polygon state
aes(x = long, # becomes state = region
y = lat, # becomes state = region
group = group, # becomes state = region
fill = population/1000000) +
coord_map(projection = "orthographic",
orientation = c(41, -74, 0))## Joining, by = "region"
Step 1: compute function and test
library(maps)
MainStates <- map_data("state")
# AllCounty <- map_data("county")
# MainCities <- filter(us.cities, long>=-130)
StatePopulation <- read.csv("https://raw.githubusercontent.com/ds4stats/r-tutorials/master/intro-maps/data/StatePopulation.csv", as.is = TRUE)
compute_state <- function(data, scales){
data %>%
inner_join(map_data("state") %>% rename(state = region)) %>%
mutate(x = long, y = lat) %>%
mutate(group = group)
}
StatePopulation %>%
rename(state = region) %>%
compute_state() ->
computed## Joining, by = "state"str(computed)## 'data.frame': 15527 obs. of 10 variables:
## $ state : chr "alabama" "alabama" "alabama" "alabama" ...
## $ population : int 4849377 4849377 4849377 4849377 4849377 4849377 4849377 4849377 4849377 4849377 ...
## $ elect_votes: int 9 9 9 9 9 9 9 9 9 9 ...
## $ long : num -87.5 -87.5 -87.5 -87.5 -87.6 ...
## $ lat : num 30.4 30.4 30.4 30.3 30.3 ...
## $ group : num 1 1 1 1 1 1 1 1 1 1 ...
## $ order : int 1 2 3 4 5 6 7 8 9 10 ...
## $ subregion : chr NA NA NA NA ...
## $ x : num -87.5 -87.5 -87.5 -87.5 -87.6 ...
## $ y : num 30.4 30.4 30.4 30.3 30.3 ...computed %>%
ggplot() +
aes(x = x, y = y) +
geom_point()
my_setup_data <- function(data, params){
if(data$group[1] == -1){
nrows <- nrow(data)
data$group <- seq_len(nrows)
}
data
}Step 2: pass to ggproto
StatPolygonstate <- ggplot2::ggproto(`_class` = "StatPolygonstate",
`_inherit` = ggplot2::Stat,
required_aes = c("state"),
compute_panel = compute_state#,
# setup_data = my_setup_data
# default_aes = aes(group = after_stat(state))
)Step 3: write geom_* function
geom_polygon_state <- function(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE, ...) {
ggplot2::layer(
stat = StatPolygonstate, # proto object from step 2
geom = ggplot2::GeomPolygon, # inherit other behavior
data = data,
mapping = mapping,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}step 4: test geom_* function, celebrate!
StatePopulation %>%
mutate(region = as.factor(region)) %>%
ggplot(data = .) +
aes(state = region, # state indicates position instead of x and y
fill = population) +
geom_polygon_state() +
coord_map() +
geom_polygon_state(data = . %>%
filter(region == "alabama"),
fill = "darkred" ) +
coord_map(projection = "orthographic",
orientation = c(41, -74, 0))## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## Joining, by = c("state", "group")
## Joining, by = c("state", "group")
Step 5: What’s not working and taking a break.
- Not sure what’s going on w/ merge so that we only get the 4 states.
- state = var, var must be factor (not character). I’ve seen this before and I’d like that not to be the case, but fine for now.
- We are celebrating in spite of 1 and 2! And putting aside.
StatePopulation %>% distinct(region) %>% pull()## [1] "alabama" "alaska" "arizona" "arkansas"
## [5] "california" "colorado" "connecticut" "delaware"
## [9] "florida" "georgia" "hawaii" "idaho"
## [13] "illinois" "indiana" "iowa" "kansas"
## [17] "kentucky" "louisiana" "maine" "maryland"
## [21] "massachusetts" "michigan" "minnesota" "mississippi"
## [25] "missouri" "montana" "nebraska" "nevada"
## [29] "new hampshire" "new jersey" "new mexico" "new york"
## [33] "north carolina" "north dakota" "ohio" "oklahoma"
## [37] "oregon" "pennsylvania" "rhode island" "south carolina"
## [41] "south dakota" "tennessee" "texas" "utah"
## [45] "vermont" "virginia" "washington" "west virginia"
## [49] "wisconsin" "wyoming"0 County
AllCounty <- map_data("county")
ggplot() + geom_polygon( data=AllCounty, aes(x=long, y=lat, group=group),
color="darkblue", fill="lightblue", size = .1 ) +
geom_polygon( data=MainStates, aes(x=long, y=lat, group=group),
color="black", fill="lightblue", size = 1, alpha = .3)
Circle pack seems similar…
https://evamaerey.github.io/mytidytuesday/2022-05-28-circle-pack/circle_pack_stipple.html