Hate Crimes Dataset
This dataset looks at all types of hate crimes in New York counties
by the type of hate crime from 2010 to 2016.
My caveat:
Flawed hate crime data collection - we should know how the data was
collected
(Nathan Yau of Flowing Data, Dec 5, 2017)
Data can provvictim_cate you with important information, but when
the collection process is flawed, there’s not much you can do. Ken
Schwencke, reporting for ProPublica, researched the tiered system that
the FBI relies on to gather hate crime data for the United States:
“Under a federal law passed in 1990, the FBI is required to track
and tabulate crimes in which there was ‘manifest evvictim_catence of
prejudice’ against a host of protected groups, regardless of differences
in how state laws define who’s protected. The FBI, in turn, relies on
local law enforcement agencies to collect and submit this data, but
can’t compel them to do so.”
So now we know that there is possible bias in the dataset, what can
we do with it?
library(tidyverse)
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## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#tinytex::install_tinytex()
#library(tinytex)
setwd("C:/Users/yosep/Downloads/Datasets")
hatecrimes <- read_csv("hateCrimes2010.csv")
## Rows: 423 Columns: 44
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): County, Crime Type
## dbl (42): Year, Anti-Male, Anti-Female, Anti-Transgender, Anti-Gender Identi...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Clean up the data:
Make all headers lowercase and remove spaces
After cleaning up the variable names, look at the structure of the
data. Since there are 44 variables in this dataset, you can use
“summary” to decide which hate crimes to focus on. In the output of
“summary”, look at the min/max values. Some have a max-vale of 1.
names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","",names(hatecrimes))
head(hatecrimes)
## # A tibble: 6 × 44
## county year crimetype `anti-male` `anti-female` `anti-transgender`
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Albany 2016 Crimes Against Pe… 0 0 0
## 2 Albany 2016 Property Crimes 0 0 0
## 3 Allegany 2016 Property Crimes 0 0 0
## 4 Bronx 2016 Crimes Against Pe… 0 0 4
## 5 Bronx 2016 Property Crimes 0 0 0
## 6 Broome 2016 Crimes Against Pe… 0 0 0
## # ℹ 38 more variables: `anti-genderidentityexpression` <dbl>,
## # `anti-age*` <dbl>, `anti-white` <dbl>, `anti-black` <dbl>,
## # `anti-americanindian/alaskannative` <dbl>, `anti-asian` <dbl>,
## # `anti-nativehawaiian/pacificislander` <dbl>,
## # `anti-multi-racialgroups` <dbl>, `anti-otherrace` <dbl>,
## # `anti-jewish` <dbl>, `anti-catholic` <dbl>, `anti-protestant` <dbl>,
## # `anti-islamic(muslim)` <dbl>, `anti-multi-religiousgroups` <dbl>, …
Select only certain hate-crimes
summary(hatecrimes)
## county year crimetype anti-male
## Length:423 Min. :2010 Length:423 Min. :0.000000
## Class :character 1st Qu.:2011 Class :character 1st Qu.:0.000000
## Mode :character Median :2013 Mode :character Median :0.000000
## Mean :2013 Mean :0.007092
## 3rd Qu.:2015 3rd Qu.:0.000000
## Max. :2016 Max. :1.000000
## anti-female anti-transgender anti-genderidentityexpression
## Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.01655 Mean :0.04728 Mean :0.05674
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :5.00000 Max. :3.00000
## anti-age* anti-white anti-black
## Min. :0.00000 Min. : 0.0000 Min. : 0.000
## 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.000
## Median :0.00000 Median : 0.0000 Median : 1.000
## Mean :0.05201 Mean : 0.3357 Mean : 1.761
## 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 2.000
## Max. :9.00000 Max. :11.0000 Max. :18.000
## anti-americanindian/alaskannative anti-asian
## Min. :0.000000 Min. :0.0000
## 1st Qu.:0.000000 1st Qu.:0.0000
## Median :0.000000 Median :0.0000
## Mean :0.007092 Mean :0.1773
## 3rd Qu.:0.000000 3rd Qu.:0.0000
## Max. :1.000000 Max. :8.0000
## anti-nativehawaiian/pacificislander anti-multi-racialgroups anti-otherrace
## Min. :0 Min. :0.00000 Min. :0
## 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
## Median :0 Median :0.00000 Median :0
## Mean :0 Mean :0.08511 Mean :0
## 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
## Max. :0 Max. :3.00000 Max. :0
## anti-jewish anti-catholic anti-protestant anti-islamic(muslim)
## Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
## Mean : 3.981 Mean : 0.2695 Mean :0.02364 Mean : 0.4704
## 3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :82.000 Max. :12.0000 Max. :1.00000 Max. :10.0000
## anti-multi-religiousgroups anti-atheism/agnosticism
## Min. : 0.00000 Min. :0
## 1st Qu.: 0.00000 1st Qu.:0
## Median : 0.00000 Median :0
## Mean : 0.07565 Mean :0
## 3rd Qu.: 0.00000 3rd Qu.:0
## Max. :10.00000 Max. :0
## anti-religiouspracticegenerally anti-otherreligion anti-buddhist
## Min. :0.000000 Min. :0.000 Min. :0
## 1st Qu.:0.000000 1st Qu.:0.000 1st Qu.:0
## Median :0.000000 Median :0.000 Median :0
## Mean :0.007092 Mean :0.104 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:0.000 3rd Qu.:0
## Max. :2.000000 Max. :4.000 Max. :0
## anti-easternorthodox(greek,russian,etc.) anti-hindu
## Min. :0.000000 Min. :0.000000
## 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0.000000 Median :0.000000
## Mean :0.002364 Mean :0.002364
## 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :1.000000 Max. :1.000000
## anti-jehovahswitness anti-mormon anti-otherchristian anti-sikh
## Min. :0 Min. :0 Min. :0.00000 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
## Median :0 Median :0 Median :0.00000 Median :0
## Mean :0 Mean :0 Mean :0.01655 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
## Max. :0 Max. :0 Max. :3.00000 Max. :0
## anti-hispanic anti-arab anti-otherethnicity/nationalorigin
## Min. : 0.0000 Min. :0.00000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median : 0.0000 Median :0.00000 Median : 0.0000
## Mean : 0.3735 Mean :0.06619 Mean : 0.2837
## 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :17.0000 Max. :2.00000 Max. :19.0000
## anti-non-hispanic* anti-gaymale anti-gayfemale anti-gay(maleandfemale)
## Min. :0 Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0 Median : 0.000 Median :0.0000 Median :0.0000
## Mean :0 Mean : 1.499 Mean :0.2411 Mean :0.1017
## 3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :0 Max. :36.000 Max. :8.0000 Max. :4.0000
## anti-heterosexual anti-bisexual anti-physicaldisability
## Min. :0.000000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.000000 Median :0.000000 Median :0.00000
## Mean :0.002364 Mean :0.004728 Mean :0.01182
## 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :1.000000 Max. :1.000000 Max. :1.00000
## anti-mentaldisability totalincidents totalvictims totaloffenders
## Min. :0.000000 Min. : 1.00 Min. : 1.00 Min. : 1.00
## 1st Qu.:0.000000 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
## Median :0.000000 Median : 3.00 Median : 3.00 Median : 3.00
## Mean :0.009456 Mean : 10.09 Mean : 10.48 Mean : 11.77
## 3rd Qu.:0.000000 3rd Qu.: 10.00 3rd Qu.: 10.00 3rd Qu.: 11.00
## Max. :1.000000 Max. :101.00 Max. :106.00 Max. :113.00
I decided I would only look at the hate-crime types with a max
number of 9 or more. That way I can focus on the most prominent types of
hate-crimes.
hatecrimes2 <- hatecrimes |>
select(county, year, 'anti-black', 'anti-white', 'anti-jewish', 'anti-catholic','anti-age*','anti-islamic(muslim)', `anti-multi-religiousgroups`, 'anti-gaymale', 'anti-hispanic', `anti-otherethnicity/nationalorigin`) |>
group_by(county, year)
head(hatecrimes2)
## # A tibble: 6 × 12
## # Groups: county, year [4]
## county year `anti-black` `anti-white` `anti-jewish` `anti-catholic`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Albany 2016 1 0 0 0
## 2 Albany 2016 2 0 0 0
## 3 Allegany 2016 1 0 0 0
## 4 Bronx 2016 0 1 0 0
## 5 Bronx 2016 0 1 1 0
## 6 Broome 2016 1 0 0 0
## # ℹ 6 more variables: `anti-age*` <dbl>, `anti-islamic(muslim)` <dbl>,
## # `anti-multi-religiousgroups` <dbl>, `anti-gaymale` <dbl>,
## # `anti-hispanic` <dbl>, `anti-otherethnicity/nationalorigin` <dbl>
Check the dimensions and the summary to make sure no missing
values
Also check the dimensions to count how many variables remain
dim(hatecrimes2)
## [1] 423 12
# There are currently 12 variables with 423 rows.
summary(hatecrimes2)
## county year anti-black anti-white
## Length:423 Min. :2010 Min. : 0.000 Min. : 0.0000
## Class :character 1st Qu.:2011 1st Qu.: 0.000 1st Qu.: 0.0000
## Mode :character Median :2013 Median : 1.000 Median : 0.0000
## Mean :2013 Mean : 1.761 Mean : 0.3357
## 3rd Qu.:2015 3rd Qu.: 2.000 3rd Qu.: 0.0000
## Max. :2016 Max. :18.000 Max. :11.0000
## anti-jewish anti-catholic anti-age* anti-islamic(muslim)
## Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
## Mean : 3.981 Mean : 0.2695 Mean :0.05201 Mean : 0.4704
## 3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :82.000 Max. :12.0000 Max. :9.00000 Max. :10.0000
## anti-multi-religiousgroups anti-gaymale anti-hispanic
## Min. : 0.00000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 0.00000 Median : 0.000 Median : 0.0000
## Mean : 0.07565 Mean : 1.499 Mean : 0.3735
## 3rd Qu.: 0.00000 3rd Qu.: 1.000 3rd Qu.: 0.0000
## Max. :10.00000 Max. :36.000 Max. :17.0000
## anti-otherethnicity/nationalorigin
## Min. : 0.0000
## 1st Qu.: 0.0000
## Median : 0.0000
## Mean : 0.2837
## 3rd Qu.: 0.0000
## Max. :19.0000
Look deeper into crimes against blacks, gay males, and jews
From the facet_wrap plot above, anti-black, anti-gay males, and
anti-jewish categories seem to have highest rates of offenses reported.
Filter out just for those 3 crimes.
hatenew <- hatelong |>
filter( victim_cat %in% c("anti-black", "anti-jewish", "anti-gaymale"))|>
group_by(year, county) |>
arrange(desc(crimecount))
hatenew
## # A tibble: 1,269 × 4
## # Groups: year, county [277]
## county year victim_cat crimecount
## <chr> <dbl> <chr> <dbl>
## 1 Kings 2012 anti-jewish 82
## 2 Kings 2016 anti-jewish 51
## 3 Suffolk 2014 anti-jewish 48
## 4 Suffolk 2012 anti-jewish 48
## 5 Kings 2011 anti-jewish 44
## 6 Kings 2013 anti-jewish 41
## 7 Kings 2010 anti-jewish 39
## 8 Nassau 2011 anti-jewish 38
## 9 Suffolk 2013 anti-jewish 37
## 10 Nassau 2016 anti-jewish 36
## # ℹ 1,259 more rows
Plot these three types of hate crimes together
Use the following commands to finalize your barplot: - position =
“dodge” makes side-by-side bars, rather than stacked bars - stat =
“identity” allows you to plot each set of bars for each year between
2010 and 2016 - ggtitle gives the plot a title - labs gives a title to
the legend
plot2 <- hatenew |>
ggplot() +
geom_bar(aes(x=year, y=crimecount, fill = victim_cat),
position = "dodge", stat = "identity") +
labs(fill = "Hate Crime Type",
y = "Number of Hate Crime Incidents",
title = "Hate Crime Type in NY Counties Between 2010-2016",
caption = "Source: NY State Division of Criminal Justice Services")
plot2
We can see that hate crimes against jews spiked in 2012. All other
years were relatively consistent with a slight upward trend. There was
also an upward trend in hate crimes against gay males. Finally, there
appears to be a downward trend in hate crimes against blacks during this
period.
What about the counties?
I have not dealt with the counties, but I think that is the next
place to explore. I can make bar graphs by county instead of by
year.
plot3 <- hatenew |>
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = victim_cat),
position = "dodge", stat = "identity") +
labs(fill = "Hate Crime Type",
y = "Number of Hate Crime Incidents",
title = "Hate Crime Type in NY Counties Between 2010-2016",
caption = "Source: NY State Division of Criminal Justice Services")
plot3
So many counties
There are too many counties for this plot to make sense, but maybe
we can just look at the 5 counties with the highest number of incidents.
- use “group_by” to group each row by counties - use summarize to get
the total sum of incidents by county - use arrange(desc) to arrange
those sums of total incidents by counties in descending order.
counties <- hatenew |>
group_by(year, county)|>
summarize(sum = sum(crimecount)) |>
arrange(desc(sum))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
counties
## # A tibble: 277 × 3
## # Groups: year [7]
## year county sum
## <dbl> <chr> <dbl>
## 1 2012 Kings 136
## 2 2010 Kings 110
## 3 2016 Kings 101
## 4 2013 Kings 96
## 5 2014 Kings 94
## 6 2015 Kings 90
## 7 2011 Kings 86
## 8 2016 New York 86
## 9 2012 Suffolk 83
## 10 2013 New York 75
## # ℹ 267 more rows
Top 5
To list the 5 counties with the highest total incidents, change
group_by to: group_by(county), then use slice_max(order_by = sum, n=5)
to list the 5 counties with highest total incidents
counties2 <- hatenew |>
group_by(county)|>
summarize(sum = sum(crimecount)) |>
slice_max(order_by = sum, n=5)
counties2
## # A tibble: 5 × 2
## county sum
## <chr> <dbl>
## 1 Kings 713
## 2 New York 459
## 3 Suffolk 360
## 4 Nassau 298
## 5 Queens 235
Finally, create the barplot above, but only for the 5 counties in
2012 with the highest incidents of hate-crimes. The command “labs” is
nice, because you can get a title, subtitle, y-axis label, and legend
title, all in one command.
plot4 <- hatenew |>
filter(county %in% c("Kings", "New York", "Suffolk", "Nassau", "Queens")) |>
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = victim_cat),
position = "dodge", stat = "identity") +
labs(y = "Number of Hate Crime Incidents",
title = "5 Counties in NY with Highest Incidents of Hate Crimes",
subtitle = "Between 2010-2016",
fill = "Hate Crime Type",
caption = "Source: NY State Division of Criminal Justice Services")
plot4
How would calculations be affected by looking at hate crimes in
counties per year by population densities?
Bring in census data for populations of New York counties. These are
estimates from the 2010 census.
setwd("C:/Users/yosep/Downloads/Datasets")
nypop <- read_csv("newyorkpopulation.csv")
## Rows: 62 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Geography
## dbl (7): 2010, 2011, 2012, 2013, 2014, 2015, 2016
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Clean the county name to match the other dataset
Rename the variable “Geography” as “county” so that it matches in
the other dataset.
nypop$Geography <- gsub(" , New York", "", nypop$Geography)
nypop$Geography <- gsub("County", "", nypop$Geography)
nypoplong <- nypop |>
rename(county = Geography) |>
gather("year", "population", 2:8)
nypoplong$year <- as.double(nypoplong$year)
head(nypoplong)
## # A tibble: 6 × 3
## county year population
## <chr> <dbl> <dbl>
## 1 Albany , New York 2010 304078
## 2 Allegany , New York 2010 48949
## 3 Bronx , New York 2010 1388240
## 4 Broome , New York 2010 200469
## 5 Cattaraugus , New York 2010 80249
## 6 Cayuga , New York 2010 79844
Focus on 2012
Since 2012 had the highest counts of hate crimes, let’s look at the
populations of the counties in 2012.
Clean the nypoplong12 variable, county, so that matches the
counties12 variable by Cutting off the “, New York” portion of the
county listing
nypoplong12 <- nypoplong |>
filter(year == 2012) |>
arrange(desc(population)) |>
head(10)
nypoplong12$county<-gsub(" , New York","",nypoplong12$county)
nypoplong12
## # A tibble: 10 × 3
## county year population
## <chr> <dbl> <dbl>
## 1 Kings 2012 2572282
## 2 Queens 2012 2278024
## 3 New York 2012 1625121
## 4 Suffolk 2012 1499382
## 5 Bronx 2012 1414774
## 6 Nassau 2012 1350748
## 7 Westchester 2012 961073
## 8 Erie 2012 920792
## 9 Monroe 2012 748947
## 10 Richmond 2012 470978
Not surprisingly, 4/5 of the counties with the highest populations
also were listed in the counties with the highest number of hate crimes.
Only the Bronx, which has the fifth highest population is not in the
list with the highest number of total hate crimes over the period from
2010 to 2016.
Recall the total hate crime counts:
Kings 713 New York 459 Suffolk 360 Nassau 298 Queens 235
Filter hate crimes just for 2012 as well
counties12 <- counties |>
filter(year == 2012) |>
arrange(desc(sum))
counties12
## # A tibble: 41 × 3
## # Groups: year [1]
## year county sum
## <dbl> <chr> <dbl>
## 1 2012 Kings 136
## 2 2012 Suffolk 83
## 3 2012 New York 71
## 4 2012 Nassau 48
## 5 2012 Queens 48
## 6 2012 Erie 28
## 7 2012 Bronx 23
## 8 2012 Richmond 18
## 9 2012 Multiple 14
## 10 2012 Westchester 13
## # ℹ 31 more rows
Join the Hate Crimes data with NY population data for 2012
datajoin <- counties12 |>
full_join(nypoplong12, by=c("county", "year"))
datajoin
## # A tibble: 41 × 4
## # Groups: year [1]
## year county sum population
## <dbl> <chr> <dbl> <dbl>
## 1 2012 Kings 136 2572282
## 2 2012 Suffolk 83 1499382
## 3 2012 New York 71 1625121
## 4 2012 Nassau 48 1350748
## 5 2012 Queens 48 2278024
## 6 2012 Erie 28 920792
## 7 2012 Bronx 23 1414774
## 8 2012 Richmond 18 470978
## 9 2012 Multiple 14 NA
## 10 2012 Westchester 13 961073
## # ℹ 31 more rows
Calculate the rate of incidents per 100,000. Then arrange in
descending order
datajoinrate <- datajoin |>
mutate(rate = sum/population*100000) |>
arrange(desc(rate))
datajoinrate
## # A tibble: 41 × 5
## # Groups: year [1]
## year county sum population rate
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 2012 Suffolk 83 1499382 5.54
## 2 2012 Kings 136 2572282 5.29
## 3 2012 New York 71 1625121 4.37
## 4 2012 Richmond 18 470978 3.82
## 5 2012 Nassau 48 1350748 3.55
## 6 2012 Erie 28 920792 3.04
## 7 2012 Queens 48 2278024 2.11
## 8 2012 Bronx 23 1414774 1.63
## 9 2012 Westchester 13 961073 1.35
## 10 2012 Monroe 5 748947 0.668
## # ℹ 31 more rows
Notice that the highest rates of hate crimes in 2012 happened
in:
dt <- datajoinrate[,c("county","rate")]
dt
## # A tibble: 41 × 2
## county rate
## <chr> <dbl>
## 1 Suffolk 5.54
## 2 Kings 5.29
## 3 New York 4.37
## 4 Richmond 3.82
## 5 Nassau 3.55
## 6 Erie 3.04
## 7 Queens 2.11
## 8 Bronx 1.63
## 9 Westchester 1.35
## 10 Monroe 0.668
## # ℹ 31 more rows
But the highest populated counties were: Kings (Brooklyn), Queens,
New York, Suffolk (Long Island), Bronx, and Nassau. They do not
correspond directly, though they are similar, to the counties with
highest rates of hate crimes.
Follow Up
Aggregating some of the categories
aggregategroups <- hatecrimes |>
pivot_longer(
cols = 4:44,
names_to = "victim_cat",
values_to = "crimecount"
)
unique(aggregategroups$victim_cat)
## [1] "anti-male"
## [2] "anti-female"
## [3] "anti-transgender"
## [4] "anti-genderidentityexpression"
## [5] "anti-age*"
## [6] "anti-white"
## [7] "anti-black"
## [8] "anti-americanindian/alaskannative"
## [9] "anti-asian"
## [10] "anti-nativehawaiian/pacificislander"
## [11] "anti-multi-racialgroups"
## [12] "anti-otherrace"
## [13] "anti-jewish"
## [14] "anti-catholic"
## [15] "anti-protestant"
## [16] "anti-islamic(muslim)"
## [17] "anti-multi-religiousgroups"
## [18] "anti-atheism/agnosticism"
## [19] "anti-religiouspracticegenerally"
## [20] "anti-otherreligion"
## [21] "anti-buddhist"
## [22] "anti-easternorthodox(greek,russian,etc.)"
## [23] "anti-hindu"
## [24] "anti-jehovahswitness"
## [25] "anti-mormon"
## [26] "anti-otherchristian"
## [27] "anti-sikh"
## [28] "anti-hispanic"
## [29] "anti-arab"
## [30] "anti-otherethnicity/nationalorigin"
## [31] "anti-non-hispanic*"
## [32] "anti-gaymale"
## [33] "anti-gayfemale"
## [34] "anti-gay(maleandfemale)"
## [35] "anti-heterosexual"
## [36] "anti-bisexual"
## [37] "anti-physicaldisability"
## [38] "anti-mentaldisability"
## [39] "totalincidents"
## [40] "totalvictims"
## [41] "totaloffenders"
aggregategroups <- aggregategroups |>
mutate(group = case_when(
victim_cat %in% c("anti-transgender", "anti-gayfemale", "anti-gendervictim_catendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual") ~ "anti-lgbtq",
victim_cat %in% c("anti-multi-racialgroups", "anti-jewish", "anti-protestant", "anti-multi-religousgroups", "anti-religiouspracticegenerally", "anti-buddhist", "anti-hindu", "anti-mormon", "anti-sikh", "anti-catholic", "anti-islamic(muslim)", "anti-atheism/agnosticism", "anti-otherreligion", "anti-easternorthodox(greek,russian,etc.)", "anti-jehovahswitness", "anti-otherchristian") ~ "anti-religion",
victim_cat %in% c("anti-asian", "anti-arab", "anti-non-hispanic", "anti-white", "anti-americanindian/alaskannative", "anti-nativehawaiian/pacificislander", "anti-otherrace", "anti-hispanic", "anti-otherethnicity/nationalorigin") ~ "anti-ethnicity",
victim_cat %in% c("anti-physicaldisability", "anti-mentaldisability") ~ "anti-disability",
victim_cat %in% c("anti-female", "anti-male") ~ "anti-gender",
TRUE ~ "others"))
aggregategroups
## # A tibble: 17,343 × 6
## county year crimetype victim_cat crimecount group
## <chr> <dbl> <chr> <chr> <dbl> <chr>
## 1 Albany 2016 Crimes Against Persons anti-male 0 anti…
## 2 Albany 2016 Crimes Against Persons anti-female 0 anti…
## 3 Albany 2016 Crimes Against Persons anti-transgender 0 anti…
## 4 Albany 2016 Crimes Against Persons anti-genderidentityexpr… 0 othe…
## 5 Albany 2016 Crimes Against Persons anti-age* 0 othe…
## 6 Albany 2016 Crimes Against Persons anti-white 0 anti…
## 7 Albany 2016 Crimes Against Persons anti-black 1 othe…
## 8 Albany 2016 Crimes Against Persons anti-americanindian/ala… 0 anti…
## 9 Albany 2016 Crimes Against Persons anti-asian 0 anti…
## 10 Albany 2016 Crimes Against Persons anti-nativehawaiian/pac… 0 anti…
## # ℹ 17,333 more rows
or create subset with just lgbtq
lgbtq <- hatecrimes |>
pivot_longer(
cols = 4:44,
names_to = "victim_cat",
values_to = "crimecount") |>
filter(victim_cat %in% c("anti-transgender", "anti-gayfemale", "anti-gendervictim_catendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual"))
lgbtq
## # A tibble: 1,692 × 5
## county year crimetype victim_cat crimecount
## <chr> <dbl> <chr> <chr> <dbl>
## 1 Albany 2016 Crimes Against Persons anti-transgender 0
## 2 Albany 2016 Crimes Against Persons anti-gaymale 1
## 3 Albany 2016 Crimes Against Persons anti-gayfemale 0
## 4 Albany 2016 Crimes Against Persons anti-bisexual 0
## 5 Albany 2016 Property Crimes anti-transgender 0
## 6 Albany 2016 Property Crimes anti-gaymale 0
## 7 Albany 2016 Property Crimes anti-gayfemale 0
## 8 Albany 2016 Property Crimes anti-bisexual 0
## 9 Allegany 2016 Property Crimes anti-transgender 0
## 10 Allegany 2016 Property Crimes anti-gaymale 0
## # ℹ 1,682 more rows
Essay: One positive aspect of this dataset is that it contains
information for a wide range of variables, including year, county, type
of hate crime, and targets of hate crime (race, age, sexual orientation,
and several other factors were observed). Something this dataset could
use is the addition of caveats so that users can better interpret the
collected data and understand the limitations of the dataset. Two paths
I would further study about this dataset are crimetypes (Crimes Against
Persons and Property Crimes) most common in hate crimes across the
different categories of targeted groups, as well as the number of
anti-race hate crimes across all the counties duing that 2010-2016
period. After seeing the output from the hatecrimes tutorial, I would do
more research on the events in 2012 that caused a spike in anti-Jewish
hate crimes in New York. I would also look more into the demographics of
the Bronx to explain why it has lower incidents of hate crimes relative
to its population density compared to other NY counties.
