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---
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output: rmarkdown::github_document
editor_options:
chunk_output_type: console
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---
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```{r pkg-knitr-opts, include=FALSE}
hrbrpkghelpr::global_opts()
```
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```{r badges, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::stinking_badges()
```
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```{r description, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::yank_title_and_description()
```
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## What's Inside The Tin
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The following functions are implemented/datasets included:
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```{r ingredients, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::describe_ingredients()
```
- `statebins`: (the original sole function in the package) Create a new ggplot-based "statebin" chart for USA states/territories
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## Installation
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```{r install-ex, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::install_block()
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```
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## Usage
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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).
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```{r setup}
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library(statebins)
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library(cdcfluview)
library(hrbrthemes)
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library(tidyverse)
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# current verison
packageVersion("statebins")
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```
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### The original wapo data
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```{r original}
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()
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```
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### Continuous scale, legend on top
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```{r continuous}
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")
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```
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### Continuous scale, no legend
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```{r continuous_noleg}
statebins(adat, value_col = "avgshare08_12", palette = "Purples") +
labs(x="2008-2010") +
theme_statebins(legend_position = "none")
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```
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### Mortality data (has Puerto Rico)
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```{r mort}
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# from: http://www.cdc.gov/nchs/fastats/state-and-territorial-data.htm
dat <- read_csv(system.file("extdata", "deaths.csv", package="statebins"))
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statebins(dat, value_col = "death_rate", name="Per 100K pop") +
labs(title="Mortality Rate (2010)") +
theme_statebins()
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```
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### Fertility data
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```{r fert}
statebins(dat, value_col="fertility_rate", name="Per 100K pop", palette="PuBuGn") +
labs(title="Fertility Rate (2010)") +
theme_statebins()
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```
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### Manual - perhaps good for elections?
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```{r manual}
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election_2012 <- suppressMessages(read_csv(system.file("extdata", "election2012.csv", package="statebins")))
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mutate(election_2012, value = ifelse(is.na(Obama), "Romney", "Obama")) %>%
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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()
```
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### Rounded rects!
You can pass in a `grid::units()` call for the `radius` parameter.
Slight curves:
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```{r rounded}
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data(USArrests)
USArrests$state <- rownames(USArrests)
statebins(USArrests, value_col="Assault", name = "Assault", round=TRUE) +
theme_statebins(legend_position="right")
```
Circles!
```{r rounded2}
statebins(USArrests, value_col="Assault", name = "Assault", round=TRUE,
radius=grid::unit(16, "pt"), palette="Reds", direction=1) +
theme_statebins(legend_position="right")
```
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### Geom
```{r sb_facet, fig.width=20, fig.height=15}
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flu <- ili_weekly_activity_indicators(2017)
ggplot(flu, aes(state=statename, fill=activity_level)) +
geom_statebins() +
coord_equal() +
viridis::scale_fill_viridis(
name = "ILI Activity Level ", limits=c(0,10), breaks=0:10, option = "magma", direction = -1
) +
facet_wrap(~weekend) +
labs(title="2017-18 Flu Season ILI Activity Level") +
theme_statebins(base_family = font_ps) +
theme(plot.title=element_text(size=16, hjust=0)) +
theme(plot.margin = margin(30,30,30,30))
```
### All the "states"
`statebins` now has PR, VI & NYC (by name or abbreviation) so you can use them, too:
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```{r all}
library(statebins)
library(tidyverse)
library(viridis)
data(USArrests)
# make up some data for the example
rownames_to_column(USArrests, "state") %>%
bind_rows(
data_frame(
state = c("Virgin Islands", "Puerto Rico", "New York City"),
Murder = rep(mean(max(USArrests$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")
```