[](https://www.repostatus.org/#active)
[](https://keybase.io/hrbrmstr)
[](https://travis-ci.org/hrbrmstr/statebins)
[](https://ci.appveyor.com/project/hrbrmstr/statebins)
[](https://codecov.io/gh/hrbrmstr/statebins)
[](https://cranchecks.info/pkgs/statebins)
[](https://www.r-pkg.org/pkg/statebins)
# statebins
Create United States Uniform Cartogram Heatmaps
## Description
The cartogram heatmaps generated by the included methods are an
alternative to choropleth maps for the United States and are based on
work by the Washington Post graphics department in their report on “The
states most threatened by trade”
().
“State bins” preserve as much of the geographic placement of the states
as possible but have the look and feel of a traditional heatmap.
Functions are provided that allow for use of a binned, discrete scale, a
continuous scale or manually specified colors depending on what is
needed for the underlying data.
## What’s Inside The Tin
The following functions are implemented/datasets included:
- `geom_statebins`: A statebins Geom
- `state_tbl`: “State” abbreviation to name data frame
- `theme_statebins`: Base statebins theme
- `statebins`: (the original sole function in the package) Create a
new ggplot-based “statebin” chart for USA states/territories
## Installation
``` r
install.packages("statebins) # NOTE: CRAN version is 1.2.2
# or
install.packages("statebins", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/"))
# or
remotes::install_git("https://git.rud.is/hrbrmstr/statebins.git")
# or
remotes::install_git("https://git.sr.ht/~hrbrmstr/statebins")
# or
remotes::install_gitlab("hrbrmstr/statebins")
# or
remotes::install_bitbucket("hrbrmstr/statebins")
# or
remotes::install_github("hrbrmstr/statebins")
```
NOTE: To use the ‘remotes’ install options you will need to have the
[{remotes} package](https://github.com/r-lib/remotes) installed.
## Usage
All of the following examples use the [WaPo
data](http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/states.csv?cache=1).
It looks like the columns they use are scaled data and I didn’t take the
time to figure out what they did, so the final figure just mimics their
output (including the non-annotated legend).
``` r
library(statebins)
library(cdcfluview)
library(hrbrthemes)
library(tidyverse)
# current verison
packageVersion("statebins")
## [1] '1.4.0'
```
### The original wapo data
``` r
adat <- read_csv(system.file("extdata", "wapostates.csv", package="statebins"))
mutate(
adat,
share = cut(avgshare94_00, breaks = 4, labels = c("0-1", "1-2", "2-3", "3-4"))
) %>%
statebins(
value_col = "share",
ggplot2_scale_function = scale_fill_brewer,
name = "Share of workforce with jobs lost or threatened by trade"
) +
labs(title = "1994-2000") +
theme_statebins()
```
### Continuous scale, legend on top
``` r
statebins(
adat,
value_col = "avgshare01_07",
name = "Share of workforce with jobs lost or threatened by trade",
palette = "OrRd",
direction = 1
) +
labs(x="2001-2007") +
theme_statebins(legend_position="top")
```
### Continuous scale, no legend
``` r
statebins(adat, value_col = "avgshare08_12", palette = "Purples") +
labs(x="2008-2010") +
theme_statebins(legend_position = "none")
```
### Mortality data (has Puerto Rico)
``` r
# from: http://www.cdc.gov/nchs/fastats/state-and-territorial-data.htm
dat <- read_csv(system.file("extdata", "deaths.csv", package="statebins"))
statebins(dat, value_col = "death_rate", name="Per 100K pop") +
labs(title="Mortality Rate (2010)") +
theme_statebins()
```
### Fertility data
``` r
statebins(dat, value_col="fertility_rate", name="Per 100K pop", palette="PuBuGn") +
labs(title="Fertility Rate (2010)") +
theme_statebins()
```
### Manual - perhaps good for elections?
``` r
election_2012 <- suppressMessages(read_csv(system.file("extdata", "election2012.csv", package="statebins")))
mutate(election_2012, value = ifelse(is.na(Obama), "Romney", "Obama")) %>%
statebins(
font_size=4, dark_label = "white", light_label = "white",
ggplot2_scale_function = scale_fill_manual,
name = "Winner",
values = c(Romney = "#2166ac", Obama = "#b2182b")
) +
theme_statebins()
```
### Rounded rects\!
You can pass in a `grid::units()` call for the `radius` parameter.
Slight curves:
``` r
data(USArrests)
USArrests$state <- rownames(USArrests)
statebins(USArrests, value_col="Assault", name = "Assault", round=TRUE) +
theme_statebins(legend_position="right")
```
Circles\!
``` r
statebins(USArrests, value_col="Assault", name = "Assault", round=TRUE,
radius=grid::unit(16, "pt"), palette="Reds", direction=1) +
theme_statebins(legend_position="right")
```
### Geom
``` r
flu <- ili_weekly_activity_indicators(2017)
ggplot(flu, aes(state=statename, fill=activity_level)) +
geom_statebins() +
coord_equal() +
viridis::scale_fill_viridis(
name = "ILI Activity Level ", limits=c(0,10), breaks=0:10, option = "magma", direction = -1
) +
facet_wrap(~weekend) +
labs(title="2017-18 Flu Season ILI Activity Level") +
theme_statebins(base_family = font_ps) +
theme(plot.title=element_text(size=16, hjust=0)) +
theme(plot.margin = margin(30,30,30,30))
```
### All the “states”
`statebins` now has PR, VI & NYC (by name or abbreviation) so you can
use them, too:
``` r
library(statebins)
library(tidyverse)
library(viridis)
data(USArrests)
# make up some data for the example
rownames_to_column(USArrests, "state") %>%
bind_rows(
data_frame(
state = c("Virgin Islands", "Puerto Rico", "New York City"),
Murder = rep(mean(max(USArrests$Murder),3)),
Assault = rep(mean(max(USArrests$Assault),3)),
Rape = rep(mean(max(USArrests$Rape),3)),
UrbanPop = c(93, 95, 100)
)
) -> us_arrests
statebins(us_arrests, value_col="Assault",
ggplot2_scale_function = viridis::scale_fill_viridis) +
labs(title="USArrests + made up data") +
theme_statebins("right")
```
