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flexdashboard exploring the WashPo fatal police shooting data set
---
title: "Fatal Police Shootings in the U.S."
output:
flexdashboard::flex_dashboard:
theme: flatly
social: menu
orientation: columns
vertical_layout: fill
---
```{r setup, include=FALSE}
library(flexdashboard)
library(lubridate)
library(dplyr)
library(ggplot2)
library(ggstance)
library(ggalt)
fatalshootings <- read.csv("./data-police-shootings/fatal-police-shootings-data.csv",
stringsAsFactors = FALSE)
fatalshootings$date <- ymd(fatalshootings$date)
fatalshootings[,c(4:5,7:8,9:14)] <- lapply(fatalshootings[,c(4:5,7:8,9:14)],
as.factor)
levels(fatalshootings$gender) <- c("Female", "Male")
levels(fatalshootings$race) <- c("Unknown", "Asian", "Black", "Hispanic",
"Unknown", "Other", "White")
fatalshootings$race <- factor(fatalshootings$race,
levels(fatalshootings$race)[c(5,2,1,4,3,6)])
levels(fatalshootings$flee) <- c("Unknown", "Car", "Foot", "Not fleeing", "Other")
fatalshootings$flee <- factor(fatalshootings$flee,
levels(fatalshootings$flee)[c(1,5,3,2,4)])
levels(fatalshootings$manner_of_death) <- c("Beaten", "Shot", "Shot/Tasered")
fatalshootings$manner_of_death <- factor(fatalshootings$manner_of_death,
levels(fatalshootings$manner_of_death)[c(1,3,2)])
```
Sidebar {.sidebar}
=====================================
The Washington Post is compiling a database of every fatal shooting in the United States by a police officer in the line of duty since January 1, 2015 and has made that database [publicly available on GitHub](https://github.com/washingtonpost/data-police-shootings). Their own visualizations and reporting are [online here](https://www.washingtonpost.com/graphics/national/police-shootings-2016/), and this dashboard presents the same data. The Washington Post's database does not include deaths of people in police custody, fatal shootings by off-duty officers, or non-shooting deaths.
This dashboard presents these fatal police shootings unadjusted for population demographics. For example, black individuals make up about 13% of the U.S. population but about 25% of these fatal police shootings. Hispanic individuals are represented at about their U.S. population level, and Asian and white people are underrepresented compared to their U.S. population levels.
Demographics {data-icon="fa-users"}
=====================================
Row
-------------------------------------
### Race/Ethnicity
```{r}
ggplot(data = fatalshootings, aes(y = race)) +
geom_barh(aes(fill = ..count..)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
### Sex/Gender
```{r}
ggplot(data = fatalshootings, aes(y = gender)) +
geom_barh(aes(fill = ..count..)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
Row
-------------------------------------
### Age
```{r}
ggplot(data = fatalshootings, aes(x = age)) +
geom_histogram(aes(fill = ..count..), bins = 20) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(x = "Age at death", y = "Number of deaths")
```
### Signs of Mental Illness
```{r}
ggplot(data = fatalshootings, aes(y = signs_of_mental_illness)) +
geom_barh(aes(fill = ..count..)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
Time {data-icon="fa-calendar"}
=====================================
Column {.tabset}
-------------------------------------
### Overall
```{r, fig.height=7, fig.width=9}
ggplot(data = fatalshootings, aes(x = date)) +
geom_histogram(aes(fill = ..count..), bins = 25) +
theme_minimal() +
theme(legend.position = "none") +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(x = NULL, y = "Number of deaths")
```
### Month
```{r, fig.height=7, fig.width=9}
ggplot(data = fatalshootings, aes(x = month(date, label = TRUE))) +
geom_bar(aes(fill = ..count..)) +
theme_minimal() +
theme(legend.position = "none") +
scale_y_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(x = NULL, y = "Number of deaths")
```
### Weekday
```{r, fig.height=7, fig.width=9}
ggplot(data = fatalshootings, aes(x = wday(date, label = TRUE))) +
geom_bar(aes(fill = ..count..)) +
theme_minimal() +
theme(legend.position = "none") +
scale_y_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(x = NULL, y = "Number of deaths")
```
Locations {data-icon="fa-map-marker"}
=====================================
### Top 15 States for Fatal Police Shootings
```{r, fig.height=9, fig.width=9}
stateinfo <- fatalshootings %>% group_by(state) %>% summarise(n = n()) %>%
arrange(desc(n)) %>% top_n(15) %>%
mutate(state = factor(state, levels = rev(unique(state))))
ggplot(stateinfo, aes(x = n, y = state)) +
geom_barh(stat="identity", aes(fill = n)) +
geom_stateface(aes(y=state, x=7, label=as.character(state)), colour="white", size=8) +
geom_text(aes(x = 17, y = state, label=as.character(state)), color="white", size=5) +
labs(y = NULL, x = "Number of deaths") +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
theme_minimal(base_size = 13) +
theme(axis.text.y=element_blank()) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0))
```
Circumstances of Death {data-icon="fa-question"}
=====================================
Column
-------------------------------------
### Was the Person Killed Armed? (top 10 reported categories)
```{r}
armedinfo <- fatalshootings %>% group_by(armed) %>% summarise(n = n()) %>%
arrange(desc(n)) %>% top_n(10) %>%
mutate(armed = factor(armed, levels = rev(unique(armed))))
ggplot(data = armedinfo, aes(x = n, y = armed)) +
geom_barh(stat="identity", aes(fill = n)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
### Was the Police Officer Wearing a Body Camera?
```{r}
ggplot(data = fatalshootings, aes(y = body_camera)) +
geom_barh(aes(fill = ..count..)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
Column
-------------------------------------
### Was the Person Killed Fleeing?
```{r}
ggplot(data = fatalshootings, aes(y = flee)) +
geom_barh(aes(fill = ..count..)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
### How Was the Person Killed?
```{r}
ggplot(data = fatalshootings, aes(y = manner_of_death)) +
geom_barh(aes(fill = ..count..)) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradient(low = "royalblue3", high = "navyblue") +
labs(y = NULL, x = "Number of deaths")
```
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