ggstates, ggfips
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().
See step-by-step:
https://evamaerey.github.io/ggplot2_grammar_guide/geom_sf.html#1
library(tidyverse)
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))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, and with easy statenames case)
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)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(maps)
MainStates <- 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 with `by = join_by(region)`## Warning in right_join(., MainStates): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
Step 1: compute function and test
library(maps)
MainStates <- map_data("state")
# AllCounty <- map_data("county")
# MainCities <- filter(us.cities, long>=-130)
MainStates %>%
rename(state = region) %>%
rename(which_polygon = group) ->
continuous_states
continuous_states %>%
count(state, which_polygon) %>%
sample_n(10)## state which_polygon n
## 1 rhode island 46 66
## 2 new york 35 290
## 3 new mexico 33 78
## 4 ohio 42 238
## 5 texas 50 1088
## 6 utah 51 59
## 7 wisconsin 62 388
## 8 delaware 7 94
## 9 new jersey 32 205
## 10 oklahoma 43 284StatePopulation <- 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(continuous_states, multiple = "all") %>%
mutate(x = long, y = lat) %>%
arrange(order) %>%
# mutate(group = paste(group, ".", which_polygon)) %>%
# group_by(which_polygon) %>%
data.frame()
}
StatePopulation %>%
rename(state = region) %>%
mutate(group = row_number()) %>%
compute_state() ->
computed## Joining with `by = join_by(state)`computed %>% sample_n(30)## state population elect_votes group long lat
## 1 new york 19746227 29 32 -76.19193 44.02608
## 2 california 38802500 55 5 -120.40134 34.46341
## 3 texas 26956958 38 43 -100.13583 28.14942
## 4 illinois 12880580 20 13 -88.45322 37.13340
## 5 texas 26956958 38 43 -97.07624 28.20671
## 6 florida 19893297 29 9 -86.37339 30.43552
## 7 texas 26956958 38 43 -96.41161 28.74529
## 8 new york 19746227 29 32 -76.97688 43.25258
## 9 west virginia 1850326 5 48 -80.62089 40.50239
## 10 minnesota 5457173 10 23 -92.68738 48.47796
## 11 new jersey 8938175 14 30 -74.11782 39.94662
## 12 tennessee 6549352 11 42 -83.42838 35.62078
## 13 west virginia 1850326 5 48 -80.55787 40.63990
## 14 new jersey 8938175 14 30 -75.39552 39.36220
## 15 south dakota 853175 3 41 -97.59190 42.84005
## 16 ohio 11594163 18 35 -81.86994 38.90384
## 17 texas 26956958 38 43 -97.59190 27.24987
## 18 illinois 12880580 20 13 -89.38715 37.08183
## 19 florida 19893297 29 9 -82.80959 29.16928
## 20 illinois 12880580 20 13 -89.42152 37.37977
## 21 wisconsin 5757564 10 49 -87.50784 44.19223
## 22 georgia 10097343 16 10 -83.27369 34.61811
## 23 maryland 5976407 10 20 -77.06283 38.99551
## 24 pennsylvania 12787209 20 38 -75.73356 39.78046
## 25 north carolina 9943964 15 33 -76.41537 35.97602
## 26 south dakota 853175 3 41 -96.83559 45.59025
## 27 wyoming 584153 3 50 -104.06059 41.39620
## 28 idaho 1634464 4 12 -111.43456 44.72509
## 29 texas 26956958 38 43 -97.76379 27.41603
## 30 west virginia 1850326 5 48 -77.75610 39.27626
## which_polygon order subregion x y
## 1 35 9173 main -76.19193 44.02608
## 2 4 839 <NA> -120.40134 34.46341
## 3 50 12822 <NA> -100.13583 28.14942
## 4 12 3075 <NA> -88.45322 37.13340
## 5 50 12546 <NA> -97.07624 28.20671
## 6 9 2195 <NA> -86.37339 30.43552
## 7 50 12463 <NA> -96.41161 28.74529
## 8 35 9132 main -76.97688 43.25258
## 9 61 15125 <NA> -80.62089 40.50239
## 10 25 7209 <NA> -92.68738 48.47796
## 11 32 8823 <NA> -74.11782 39.94662
## 12 49 12074 <NA> -83.42838 35.62078
## 13 61 15130 <NA> -80.55787 40.63990
## 14 32 8887 <NA> -75.39552 39.36220
## 15 48 11786 <NA> -97.59190 42.84005
## 16 42 10502 <NA> -81.86994 38.90384
## 17 50 12636 <NA> -97.59190 27.24987
## 18 12 3105 <NA> -89.38715 37.08183
## 19 9 1980 <NA> -82.80959 29.16928
## 20 12 3116 <NA> -89.42152 37.37977
## 21 62 15486 <NA> -87.50784 44.19223
## 22 10 2615 <NA> -83.27369 34.61811
## 23 19 5900 <NA> -77.06283 38.99551
## 24 45 11320 <NA> -75.73356 39.78046
## 25 39 9911 main -76.41537 35.97602
## 26 48 11853 <NA> -96.83559 45.59025
## 27 63 15588 <NA> -104.06059 41.39620
## 28 11 2922 <NA> -111.43456 44.72509
## 29 50 12623 <NA> -97.76379 27.41603
## 30 61 14854 <NA> -77.75610 39.27626computed %>%
ggplot() +
aes(x = x, y = y) +
geom_point() +
geom_polygon(color = "white") +
aes(group = which_polygon)
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"),
# setup_data = my_setup_data,
compute_panel = compute_state,
default_aes = aes(group = after_stat(which_polygon))
)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 %>% #count(region)
mutate(region = region %>% stringr::str_trim() %>% as.factor()) %>%
ggplot(data = .) +
aes(state = region, # state indicates position instead of x and y
fill = population) +
geom_polygon_state() +
coord_map(projection = "orthographic",
orientation = c(41, -74, 0)) +
geom_polygon_state(data = . %>%
filter(region == "new york"),
color = "red" ) +
geom_polygon_state(data = . %>%
filter(region == "colorado"),
color = "green" ) +
scale_fill_viridis_c(option = "magma", end = .9) +
ggstamp::stamp_point(y = 30.2, x = -97.7,
color = "goldenrod1",
size = 2)## Joining with `by = join_by(state)`
## Joining with `by = join_by(state)`
## Joining with `by = join_by(state)`
Step 5: What’s not working and taking a break.
- Solved! “Not sure what’s going on w/ merge so that we only get the 4 states,” now much better! there was a grouping variable in the polygons data set named ‘group’ - this really messed with things because ggplot2 uses that var internally. We renamed!
- 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.
- There are subregions. You can see the tell tail-signs. Seems like setup_data should help w/ group management
- Take a break!
0 County - ggfips?
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 = .15, alpha = .3)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
Circle pack seems similar…
https://evamaerey.github.io/mytidytuesday/2022-05-28-circle-pack/circle_pack_stipple.html