Homicides by Police in the US since 2000
Next Steps Washington
Everyone Comes Home Alive
Last data update: 2021-11-11
library(ggplot2)
library(plotly)
library(kableExtra)
library(dplyr)
library(forcats)
library(here)
library(leaflet)
library(RColorBrewer)
# For 2000-2014 data, only have in FE
load(file = here("data-outputs", "CleanData.rda"))
last.update = max(fe_clean$date)
startmo = 1 # Jan 2000
startyr = 2000
finaldata_2000 <- fe_clean %>% filter(year >= startyr)
# Lots of "Unknowns" in the FE homicide indicator
# Select homicides
# Remove suicides and some final cases that don't belong
## 25804-5 are the Tad Norman case in Lake City.
## Not victims of police violence.
## they were killed by Norman
homicides <- finaldata_2000 %>%
filter(circumstances != "Suicide") %>%
arrange(date)
# for geocoded LDs
with(homicides, write.csv(cbind(feID,latitude,longitude),
here::here("data-outputs",
"USgeocodes2000.csv")))
all.cases <- dim(finaldata_2000)[1]
last.case <- dim(homicides)[1]
last.date <- max(homicides$date)
last.name <- ifelse(homicides$lname[last.case] == "Unknown",
"(Name not released)",
paste(homicides$fname[last.case],
homicides$lname[last.case]))
last.age <- homicides$age[last.case]
last.agency <- homicides$agency[last.case]
last.cod <- homicides$cod[last.case]
tot.by.yr <- table(homicides$year)
tot.this.yr <- tot.by.yr[[length(tot.by.yr)]]
num.suffix <- ifelse(tot.this.yr == 1, "st",
ifelse(tot.this.yr == 2, "nd",
ifelse(tot.this.yr == 3, "rd", "th")))
# Indices for plotting by time
curr_mo <- lubridate::month(Sys.Date())
curr_yr = lubridate::year(Sys.Date())
endyr = curr_yr-1 # last complete year
endmo = lubridate::month(last.update) -1 # respect last data update
numyrs = endyr - startyr + 1
month = month.abb[startmo:12]
year = rep(startyr, length(month))
for(i in 1:(numyrs-1)) {
thisyr = startyr+i
year <- c(year, rep(thisyr, 12))
month <- c(month, month.abb)
}
year <- c(year, rep(curr_yr, endmo))
month <- c(month, month.abb[1:endmo])
## Month index for date plotting
index <- data.frame(year = year,
month = month,
mo.yr = paste0(month, ".", year))Introduction
This report tracks the number of persons killed by police in the US from 2000 until the last complete month of the current year.
Several states have adopted police reform legislation in the last few years. Tracking the trends in persons killed by police, before and after legal reforms, is one way to understand the the motivation for these reforms, and assess whether they are having an impact.
MOST RECENT DATA UPDATE (2021-09-30):
Total homicides by police since 2000: 27,744
Last reported case: Carlos Douglas, 38 years old, on 2021-09-30 by Florence County Sheriff’s Office
Carlos Douglas is the 1,331th person killed by police in 2020. The cause of death is reported as Vehicle.
At least one person is killed by law enforcement almost every day in the US, so the date of the last reported case is a good indication of the current lag in the Fatal Encounters database.
Where the data come from
The data in this report are updated at least once each week, pulling and merging from the Fatal Encounters project (https://fatalencounters.org/). Fatal Encounters includes all deaths during encounters with police; it does not include deaths in custody after booking.
The data in the Fatal Encounters project typically lag about two weeks behind the current date, but can sometimes fall farther behind. Since at least one person is killed by law enforcement almost every day, the date of the last reported case is a good indication of the current lag in the database.
This report is restricted to the cases that can be classified as homicides by police, it excludes cases identified in the Fatal Encounters dataset as suicides.
What is a homicide?
The deadly force incidents in this report are homicides. A homicide is simply defined as the killing of one person by another. In the context of this report it refers to any encounter with law enforcement officers that results in a fatality. Homicides normally result in a criminal investigation or inquest, but the word does not imply a crime has been committed.
The word homicide means only that the death was caused in some way by the officer.
It does not not mean the officer’s actions that led to the death were justified, or that they were unjustified.
There are many different types of homicides. In the U.S., these types and definitions vary across states, but there are some general similarities. The definitions below are taken from a useful online summary found here, based on California State laws.
Homicide
Homicide is the killing of one person by another. This is a broad term that includes both legal and illegal killings. For example, a soldier may kill another soldier in battle, but that is not a crime. The situation in which the killing happened determines whether it is a crime.
Murder is the illegal and intentional killing of another person. Under California Penal Code Section 187, for example, murder is defined as one person killing another person with malice aforethought. Malice is defined as the knowledge and intention or desire to do evil. Malice aforethought is found when one person kills another person with the intention to do so.
In California, for example, a defendant may be charged with first-degree murder, second-degree murder, or capital murder.
First-degree murder is the most serious and includes capital murder – first-degree murder with “special circumstances” that make the crime even more egregious. These cases can be punishable by life in prison without the possibility of parole, or death.
Second-degree murder is murder without premeditation, but with intent that is typically rooted in pre-existing circumstances. The penalty for second-degree murder may be up to 15 years to life in prison in California.
Felony murder is a subset of first-degree murder and is charged when a person is killed during the commission of a felony, such as a robbery or rape.
Manslaughter is the illegal killing of another person without premeditation, and in some cases without the intent to kill. These cases are treated as less severe crimes than murder. Manslaughter can also be categorized as voluntary or involuntary.
Voluntary manslaughter occurs when a person kills another without premeditation, typically in the heat of passion. The provocation must be such that a reasonable person under the same circumstances would have acted the same way. Penalties for voluntary manslaughter include up to 11 years in prison in California.
Involuntary manslaughter is when a person is killed by actions that involve a wanton disregard for life by another. Involuntary manslaughter is committed without premeditation and without the true intent to kill, but the death of another person still occurs as a result. Penalties for involuntary manslaughter include up to four years in prison in California.
Vehicular manslaughter occurs when a person dies in a car accident due to another driver’s gross negligence or even simple negligence, in certain circumstances.
Interactive Map
You can click the numbered circles to reach the individual map pointers for each person killed by police.
Hovering over the pointer brings up the name of the person killed and agency of the officer who killed them;
Clicking the pointer will bring up a url to a news article on the case (if available).
map1 <- leaflet(data = homicides, width = "100%") %>%
addTiles() %>%
addMarkers( ~ longitude,
~ latitude,
popup = ~ url_click,
label = ~ as.character(paste(name, "by", agency)),
clusterOptions = markerClusterOptions())
map1Breakdowns
Race
tab.raceOrig <- homicides %>%
group_by(raceOrig) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
bind_rows(data.frame(raceOrig="Total",
Number = sum(.$Number),
Percent = sum(.$Percent))) %>%
rename(Race = raceOrig)
tab.raceImp <- homicides %>%
group_by(raceImp) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
bind_rows(data.frame(raceImp="Total",
Number = sum(.$Number),
Percent = sum(.$Percent))) %>%
rename(Race = raceImp)
prop.missing.race.orig <- tab.raceOrig$Percent[tab.raceOrig$Race=="Unknown"]
prop.missing.race.imp <- tab.raceImp$Percent[tab.raceImp$Race=="Unknown"]
pct.imputed <- round(100*(1 -prop.missing.race.imp/prop.missing.race.orig)) Table
The race of the victim is missing in 26% of the original data. Fatal Encounters employs an algorithm to try to impute the race of these cases. They are able to impute about 80% of the missing cases with reasonable confidence, and we include these imputations in the tables and plots below. Note that after imputations, about 5% of cases are still missing race.
# first, for discussion
tab.raceImp <- homicides %>%
group_by(raceImp) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
bind_rows(data.frame(raceImp="Total",
Number = sum(.$Number),
Percent = sum(.$Percent))) %>%
rename(Race = raceImp)
# next, for display
tab <- homicides %>%
mutate(
raceImp = recode(raceImp,
"API" = "Asian/Pacific Islander",
"BAA" = "Black/African American",
"HL" = "Hispanic/Latinx",
"NAA" = "Native American/Alaskan",
"WEA" = "White/European American",
"ME" = "Other"),
raceImp = fct_relevel(raceImp, "Other", after = 5)
) %>%
group_by(raceImp) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
bind_rows(data.frame(raceImp="Total",
Number = sum(.$Number),
Percent = sum(.$Percent))) %>%
rename(Race = raceImp)
tab %>%
kable(caption = "Breakdown by Race") %>%
kable_styling(bootstrap_options = c("striped","hover")) %>%
row_spec(row=dim(tab)[1], bold = T) %>%
add_footnote(label = "Percents may not sum to 100 due to rounding",
notation = "symbol")| Race | Number | Percent |
|---|---|---|
| 4 | 0.0 | |
| Asian/Pacific Islander | 527 | 1.9 |
| Black/African American | 7895 | 28.5 |
| Hispanic/Latinx | 4789 | 17.3 |
| Native American/Alaskan | 304 | 1.1 |
| Other | 47 | 0.2 |
| White/European American | 12732 | 45.9 |
| Unknown | 1446 | 5.2 |
| Total | 27744 | 100.1 |
| * Percents may not sum to 100 due to rounding |
Plots
homicides %>%
mutate(raceImp = recode(raceImp,
"API" = "Asian",
"BAA" = "Black",
"HL" = "Latinx",
"NA" = "Native",
"WEA" = "White")
) %>%
count(raceImp) %>%
mutate(perc = n / nrow(homicides)) %>%
ggplot(aes(x=raceImp,
y = perc,
label = n)) +
# label = round(100*perc, 1))) +
geom_bar(stat="identity", fill="blue", alpha=.5) +
geom_text(aes(y = perc), size = 3, nudge_y = .025) +
labs(title = "Fatalities by Race",
caption = "the US since 2000; y-axis=pct, bar label=count") +
xlab("Reported Race") +
ylab("Percent of Total")
homicides %>%
mutate(raceb = case_when(raceImp == "WEA" ~ "White",
raceImp == "Unknown" ~ "Unknown",
TRUE ~ "BIPOC"),
raceb = fct_relevel(raceb, "Unknown",
after = Inf)) %>%
count(raceb) %>%
mutate(perc = n / nrow(homicides)) %>%
ggplot(aes(x=raceb,
y = perc,
label = n)) +
geom_text(aes(y = perc), size = 3, nudge_y = .025) +
geom_bar(stat="identity", fill="blue", alpha=.5) +
labs(title = "Fatalities by Race",
caption = "the US since 2000; y-axis=pct, bar label=count") +
xlab("Reported Race") +
ylab("Percent of Total")
Discussion
Racial disparities in the risk of being killed by police are one of the most important factors driving the public demand for police accountability and reform. For that reason it is important to understand how these numbers can, and cannot be used.
TL;DR There are many uncertainties in the data that make it difficult/impossible to estimate exact values, but there are still some conclusions we can draw with confidence.
Many case reports are missing data on race
These cases are denoted “Unknown” in the tables and plots in this report.
For the Fatal Encounters dataset, about 25% of the cases do not have information that explicitly identifies the race of the person killed. It’s worth remembering that the Fatal Encounters project relies primarily on media reports (and some public records requests) to find these cases, so they are limited by the information provided in those sources. The Fatal Encounters team uses an “imputation” model to try to predict race for the missing cases. A brief description of the methodology is online here. They are able to impute just over half of the missing cases with reasonable confidence, and we include these imputations in the breakdowns we report. After imputation, about 5% of cases are still missing race.
Bottom line: This makes it impossible to say exactly how many people killed by police are in each racial group.
We are reporting the raw counts in this report, not per capita rates
Breaking the total count down by race, the largest single group of persons killed by police are identified as White/European-American: 46%
So, does this mean that we can say: “There aren’t any racial disparities in persons killed by police?”.
No. This raw count can not be used to assess racial disparities, because the word disparity implies the risk is “disproportionate” – that is, higher (or lower) than proportional. Proportional is a comparative term: it compares the proportion of fatalities by race, to the proportion of the population by race. If the two proportions are the same, then we can say there are no disparities. In this case, if 46% of the population is White, then we can say that their risk of being killed by police is proportional to their share of the population.
The majority of the US population identifies as White – 62% in 2020 source: US Census. If we exclude Hispanics (which the Census treats as ethnicity, not race), non-Hispanic Whites comprise 58% of the US population source: ibid.
So there is a disparity in the risk of being killed by police – for non-Hispanic Whites, their share of fatalities is lower than their population share.
The “risk ratio” is a common way to combine this information into a single number that is easy to understand: the ratio of the fatality share, to the population share. When the two shares are the same, the risk ratio equals 1. When it is less than one, this means the share of fatalities is lower than the population share; they are disproportionately low. When the risk ratio is greater than one it means the share of fatalities is larger than the population share; they are disproportionately high. Then, taking 100*(risk ratio-1) tells you the percent lower (or higher) the ratio is than expected, if the risk was proportional to their share of population.
The risk ratio for non-Hispanic whites is 46% / 58% = 0.79 – their risk of being killed by police is 21% lower than expected.
By contrast, only 12% of the population identifies as Black/African American, but 28% of the persons killed by police are identified as being in this racial group. So their risk ratio is 28% / 12% = 2.37 – their risk of being killed by police is 137% higher than expected.
Note that the number of “unknown race” cases after imputation is not enough to change the direction of these disparities. Even if we assume that all of the “unknown race” cases are White, their share of incidents would still be below their share in the population.
So, even though we can’t be certain of the exact value of the risk ratio, we can say with some confidence that there are racial disparities in the risk of being killed by police, and that these disparities indicate that non-Hispanic Whites are less likely than other racial groups to be killed.
Bottom line: while the largest single group of persons killed by police is identified as White, the risk ratio for this group, which adjusts for their share of the population, shows they are disproportionately less likely to be killed than other groups.
So why don’t we calculate standardized per capita rates?
Because the classification of cases by race, in both the Fatal Encounters and WaPo datasets, is not consistent with the classification of race in population data provided by the US Census.
The calculation of per capita rates takes the ratio of the fatality count (in the numerator) to the population count (in the denominator). To break this down by race we need a consistent measure of race to use for the numerator and denominator for all groups. And we don’t have that.
About 5% of people in WA state report two or more races in the US Census. This multiple-race classification does not exist in the datasets on persons killed by police.
Hispanic/Latinx is an ethnicity that crosses several racial groups, primarily White, Black and Native American. In the US Census data, race is measured separately from ethnicity, so you can see these overlaps. But in the Fatal Encounters and WaPo datasets, “Hispanic” is coded as a racial group, rather than as a separate ethnicity classification.
For more information on how the Census codes race and ethnicity:
U.S. Decennial Census Measurement of Race and Ethnicity Across the Decades: 1790–2020
Perceived vs. self-identified race
The race classifications in all of our data (including the US Census) do not represent what the officer perceived the person’s race to be. It is likely that there is a strong correlation between these two, but we can’t use these data to answer the question of the officer’s intention, or implicit bias, with certainty.
Cause of death
Plot
homicides %>%
mutate(codb = case_when(cod == "Gunshot" ~ "Gunshot",
TRUE ~ "Other")
) %>%
count(codb) %>%
mutate(perc = n / nrow(homicides)) %>%
ggplot(aes(x=codb,
y = perc,
label = n)) +
geom_text(aes(y = perc), size = 3, nudge_y = .025) +
geom_bar(stat="identity", fill="blue", alpha=.5) +
labs(title = "Fatalities by Cause of Death",
caption = "the US since 2000; y-axis=pct, bar label=count") +
xlab("Reported Weapon Used by Police") +
ylab("Percent of Total")
Table
tab <- homicides %>%
group_by(cod) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
arrange(desc(Number)) %>%
bind_rows(data.frame(cod ="Total",
Number = sum(.$Number),
Percent = sum(.$Percent)))
tab %>%
kable(caption = "Breakdown by Cause of Death",
col.names = c("Cause of Death", "Number", "Percent")) %>%
kable_styling(bootstrap_options = c("striped","hover")) %>%
row_spec(row=dim(tab)[1], bold = T) %>%
add_footnote(label = "Percents may not sum to 100 due to rounding",
notation = "symbol")| Cause of Death | Number | Percent |
|---|---|---|
| Gunshot | 18720 | 67.5 |
| Vehicle | 6524 | 23.5 |
| Tasered | 920 | 3.3 |
| Medical emergency | 389 | 1.4 |
| Asphyxiated/Restrained | 328 | 1.2 |
| Drowned | 194 | 0.7 |
| Beaten/Bludgeoned with instrument | 177 | 0.6 |
| Drug overdose | 177 | 0.6 |
| Unknown | 102 | 0.4 |
| Fell from a height | 60 | 0.2 |
| Other | 58 | 0.2 |
| Burned/Smoke inhalation | 35 | 0.1 |
| Chemical agent/Pepper spray | 33 | 0.1 |
| Stabbed | 27 | 0.1 |
| Total | 27744 | 99.9 |
| * Percents may not sum to 100 due to rounding |
Victim armed
This information is in the process of being coded by the Fatal Encounters project. It is not available yet for 2021, or prior to 2013, so all cases in those years are coded “Unknown”.
Note that the media often rely on the law enforcement narrative when reporting this information, so the validity of these data are unknown.
Plot
homicides %>%
count(weapon) %>%
mutate(perc = n / nrow(homicides)) %>%
ggplot(aes(reorder(weapon, perc),
y = perc,
label = n)) +
geom_text(aes(y = perc), size = 3, nudge_y = .025) +
geom_bar(stat="identity", fill="blue", alpha=.5) +
labs(title = "Fatalities by Report of Victim Weapon",
caption = "the US since 2000; y-axis=pct, bar label=count") +
xlab("Reported Weapon Used by Victim") +
ylab("Percent of Total")
Table
tab <- homicides %>%
group_by(weapon) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
arrange(desc(Number)) %>%
bind_rows(data.frame(weapon ="Total",
Number = sum(.$Number),
Percent = sum(.$Percent)))
tab %>%
kable(caption = "Breakdown by Report of Victim Weapon (incomplete data)",
col.names = c("Weapon", "Number", "Percent")) %>%
kable_styling(bootstrap_options = c("striped","hover")) %>%
row_spec(row=dim(tab)[1], bold = T) %>%
add_footnote(label = "Percents may not sum to 100 due to rounding",
notation = "symbol")| Weapon | Number | Percent |
|---|---|---|
| Unknown | 15435 | 55.6 |
| Alleged firearm | 5136 | 18.5 |
| No weapon | 4933 | 17.8 |
| Alleged edged weapon | 1487 | 5.4 |
| Other | 753 | 2.7 |
| Total | 27744 | 100.0 |
| * Percents may not sum to 100 due to rounding |
State
states.obs <- unique(homicides$st)Every state has had at least one person killed by police since 2000.
Plot
homicides %>%
group_by(st) %>%
summarize(n = n(),
perc = n / nrow(homicides)) %>%
ggplot(aes(reorder(st, perc),
x = perc,
label = n)) +
geom_text(aes(x = perc), size = 3, nudge_x = .005) +
geom_bar(stat="identity", fill="blue", alpha=.5) +
labs(title = "Police-involved homicides by state",
caption = "the US since 2000; y-axis=pct, bar label=count") +
ylab("State") +
xlab("Percent of Total") #+
#theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))Table
homicides %>%
group_by(st) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
DT::datatable(rownames = F,
caption = "Breakdown by State")Type of agency involved
Law enforcement agencies exist at many different political/administrative levels: local police departments, county sheriff’s offices, state patrols, federal agencies, and a host of other units like University police departments. There are rlength(unique(homicides$agency))` different agencies with an officer-involved homicide identified in the Fatal Encounters data since 2000. We group them into types below.
homicides %>%
group_by(agency.type) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
DT::datatable(rownames = F,
caption = "Breakdown by Agency Type of Involved Officer")Online information availability
This information takes the form of a single url to a news article that is available online. There are often multiple news articles available online, and they may report the conflicting details of the event, as well as conflicting perspectives on whether the use of lethal force was justified.
The clickable urls are available in this report in the Interactive Map and Say their names sections. They should be treated as a place to start research, not as the definitive description of the event.
tab <- homicides %>%
mutate(url_info = case_when(url_info == "" ~ "No",
is.na(url_info) ~ "No",
TRUE ~ "Yes")) %>%
group_by(url_info) %>%
summarize(Number = n(),
Percent = round(100*Number/nrow(homicides), 1)
) %>%
bind_rows(data.frame(url_info ="Total",
Number = sum(.$Number),
Percent = sum(.$Percent)))
tab %>%
kable(caption = "URL for news article in Fatal Encounters",
col.names = c("Availability", "Number", "Percent")) %>%
kable_styling(bootstrap_options = c("striped","hover")) %>%
row_spec(row=dim(tab)[1], bold = T) %>%
add_footnote(label = "Percents may not sum to 100 due to rounding",
notation = "symbol")| Availability | Number | Percent |
|---|---|---|
| No | 1 | 0 |
| Yes | 27743 | 100 |
| Total | 27744 | 100 |
| * Percents may not sum to 100 due to rounding |
Date
# Pop growth
pop.growth.rate = (331449281 - 281421906)/281421906
# FE by month
dat <- homicides %>%
group_by(year, month) %>%
summarize(count = n()) %>%
left_join(index, ., by = c("year", "month")) %>%
mutate(count = tidyr::replace_na(count, 0),
seqlab = paste0(year, ".", match(month.abb, month.abb)),
sequence = 1:nrow(.),
tt.text = paste("n =", count, mo.yr))
# Projected growth in fatalities based on pop growth
# Will take average of 2000-2001 to reduce variability
mean.fe.2000 <- mean(dat$count[dat$year < 2002])
mean.fe.2020 <- mean(dat$count[dat$year==2020])
fe.growth.rate = (mean.fe.2020 - mean.fe.2000)/mean.fe.2000
pop.adj.fe.2020 <- mean.fe.2000 * (1+pop.growth.rate)
proj.fe <- seq(from=mean.fe.2000, to=pop.adj.fe.2020,
length.out=nrow(dat))
proj.df <- data.frame(sequence=dat$sequence, proj=proj.fe)Monthly Totals By Year
The points show the monthly totals, the grey line shows the smoothed trend in fatalities by month, and the grey shaded ribbon shows the 95% confidence interval around the trend line.
The blue dotted reference line shows the relative rate of US population growth from 2000 to 2020, based on Census counts. The US population grew by 18% over the two decades. During the same period, the rate of persons killed by police rose by 123% – 5.9 times faster than the population growth rate.
We only plot months once the data are complete, so the plot will lag 1-2 months behind the current date.
This is an interactive plot: You can hover over points to get the exact values for the number of cases for that month and year.
# we only plot after current month has finished
# dat table was built in the code chunk above
xaxis.ticks <- seq(0, length(dat$sequence), 12)+6 # x axis ticks
p <- ggplot(dat, aes(x = sequence,
y = count,
text = count)) + #paste0("n=", count, mo.yr))) +
geom_point(size=1, aes(color = factor(year))) +
geom_smooth(span=1, col="grey") +
geom_vline(xintercept = 12*1:numyrs+1, col="grey") +
geom_line(data=proj.df,
aes(x=sequence, y=proj, text=round(proj)),
color="blue", lty=3) +
labs(title = paste("Monthly fatalities by Year:",
month.abb[startmo], startyr, " - ",
month.abb[endmo], curr_yr),
caption = "US since 2000",
color = "Year") +
xlab("Month/Year") +
ylab("Number") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
scale_x_continuous(breaks = xaxis.ticks,
label = dat$year[xaxis.ticks]) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none")
ggplotly(p, tooltip = "text")Cumulative Totals by Month/Year
The lines show the cumulative total fatalities by month as the year progresses, for each year.
We only plot months once the data are complete, so the plot will lag 1-2 months behind the current date.
The first decade (2000-2009) is shown with blue lines, the second decade (2010-2020) with orange lines, and the current year to date is shown in black.
This is an interactive plot: You can hover over points to get the exact values for the cumulative number of cases for that month and year.
# set colors for plot
colourCount = length(unique(mtcars$hp))
getPalette = colorRampPalette(brewer.pal(9, "Set1"))
decade1 <- colorRampPalette(brewer.pal(9,"Blues"))(10)
decade2 <- colorRampPalette(brewer.pal(9,"Oranges"))(11)
mycolors <- c(decade1, decade2, "black")
df <- homicides %>%
group_by(year, month) %>%
summarize(count = n()) %>%
left_join(index, .) %>%
group_by(year) %>%
mutate(count = tidyr::replace_na(count, 0),
cumulative = cumsum(count),
month = factor(month, levels=month.abb))
df2021 <- df %>% filter(year == 2021)
p <- ggplot(data=df,
aes(x = month,
y = cumulative,
group = year,
color = as.factor(year),
text = paste('Total =', cumulative, '<br>',
month, year))) +
geom_line(size = 1.1, alpha = 0.5) +
geom_point(size=1, alpha = 0.7) +
scale_color_manual(breaks = as.character(2000:2021),
values = mycolors) +
labs(title = "US since 2000: Cumulative fatalities by month and year",
caption = "the US since 2000",
color = "Year") +
xlab("Month") +
ylab("Number")
ggplotly(p, tooltip = "text")Year to date, by Year
Here we plot the cumulative totals for year to date, using the last complete complete month of the current year, for each year.
Since we only plot months once the data are complete, the plot will lag 1-2 months behind the current date.
The bars represent totals for year-to-date for each year, divided by cause of death: gunshot vs. all other causes.
This is an interactive plot: You can hover over bar segments to get the exact values for that cause of death in that year.
p <- homicides %>%
filter(match(month, month.abb) < curr_mo) %>%
mutate(cod = ifelse(cod == "Gunshot", "shot", "other"),
cod = factor(cod, levels = c("shot", "other")),
year = as.character(year)) %>%
group_by(year, cod) %>%
summarize(n = n()) %>%
mutate(percent = round(100*n / sum(n), 1)) %>%
ggplot(aes(x = year,
y = n,
label = percent,
fill = cod,
text = paste(cod, "=", n, '<br>',
year))) +
geom_bar(stat="identity", alpha=.5,
position = position_stack(reverse=T)) +
scale_fill_manual(values = c("cadetblue", "goldenrod")) +
labs(title = paste0("Fatalities Jan-",
month.abb[endmo],
" by Year"),
caption = "US since 2000",
x = "Year",
y = "Number",
fill = "Cause of\nDeath") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
plotly::ggplotly(p, tooltip = "text")Say their names
Name known
Of the 27,744 persons killed by police, 26,901 of the victim’s names are known at this time. The table of names is unfortunately too large to be hosted online in this document. But if you are looking for a specific name, you can search the Fatal Encounters data online here: https://fatalencounters.org/view/person/
homicides %>%
filter(name != "Unknown") %>%
select(name, date, age=age, state, agency, url = url_click) %>%
arrange(desc(date)) %>%
DT::datatable(rownames = F,
caption = paste("The Names We Know: as of", scrape.date),
filter = 'top',
escape = FALSE)Name Unknown
The remaining 843 of the victim’s names are not known at this time.
homicides %>%
filter(name == "Unknown") %>%
select(name, date, age=age, state, agency, url = url_click) %>%
arrange(desc(date)) %>%
DT::datatable(rownames = F,
caption = paste("The Names We Don't Know: as of", scrape.date),
filter = 'top',
escape = FALSE)