DannyC_Hate_Crime_HW
2024-02-08
Dataset
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
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## ✖ 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/Danny/Downloads")
hatecrimes <- read.csv("hateCrimes2010.csv")Clearing lowercases and spaces
names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub("\\.","-",names(hatecrimes))
head(hatecrimes)## county year crime-type anti-male anti-female anti-transgender
## 1 Albany 2016 Crimes Against Persons 0 0 0
## 2 Albany 2016 Property Crimes 0 0 0
## 3 Allegany 2016 Property Crimes 0 0 0
## 4 Bronx 2016 Crimes Against Persons 0 0 4
## 5 Bronx 2016 Property Crimes 0 0 0
## 6 Broome 2016 Crimes Against Persons 0 0 0
## anti-gender-identity-expression anti-age- anti-white anti-black
## 1 0 0 0 1
## 2 0 0 0 2
## 3 0 0 0 1
## 4 0 0 1 0
## 5 0 0 1 0
## 6 0 0 0 1
## anti-american-indian-alaskan-native anti-asian
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 1
## anti-native-hawaiian-pacific-islander anti-multi-racial-groups
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## anti-other-race anti-jewish anti-catholic anti-protestant
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 1 0 0
## 6 0 0 0 0
## anti-islamic--muslim- anti-multi-religious-groups anti-atheism-agnosticism
## 1 1 0 0
## 2 0 1 0
## 3 0 0 0
## 4 6 0 0
## 5 0 0 0
## 6 0 0 0
## anti-religious-practice-generally anti-other-religion anti-buddhist
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## anti-eastern-orthodox--greek--russian--etc-- anti-hindu anti-jehovahs-witness
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## anti-mormon anti-other-christian anti-sikh anti-hispanic anti-arab
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## anti-other-ethnicity-national-origin anti-non-hispanic- anti-gay-male
## 1 0 0 1
## 2 0 0 0
## 3 0 0 0
## 4 0 0 8
## 5 0 0 0
## 6 0 0 1
## anti-gay-female anti-gay--male-and-female- anti-heterosexual anti-bisexual
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 1 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## anti-physical-disability anti-mental-disability total-incidents total-victims
## 1 0 0 3 4
## 2 0 0 3 3
## 3 0 0 1 1
## 4 0 0 20 20
## 5 0 0 2 2
## 6 0 0 3 3
## total-offenders
## 1 3
## 2 3
## 3 1
## 4 25
## 5 2
## 6 3Numeric summary of variables in hatecrimes
summary(hatecrimes)## county year crime-type 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-gender-identity-expression
## 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-american-indian-alaskan-native 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-native-hawaiian-pacific-islander anti-multi-racial-groups anti-other-race
## 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-religious-groups 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-religious-practice-generally anti-other-religion 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-eastern-orthodox--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-jehovahs-witness anti-mormon anti-other-christian 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-other-ethnicity-national-origin
## 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-gay-male anti-gay-female
## Min. :0 Min. : 0.000 Min. :0.0000
## 1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000
## Median :0 Median : 0.000 Median :0.0000
## Mean :0 Mean : 1.499 Mean :0.2411
## 3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000
## Max. :0 Max. :36.000 Max. :8.0000
## anti-gay--male-and-female- anti-heterosexual anti-bisexual
## Min. :0.0000 Min. :0.000000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0.0000 Median :0.000000 Median :0.000000
## Mean :0.1017 Mean :0.002364 Mean :0.004728
## 3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :4.0000 Max. :1.000000 Max. :1.000000
## anti-physical-disability anti-mental-disability total-incidents
## Min. :0.00000 Min. :0.000000 Min. : 1.00
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.: 1.00
## Median :0.00000 Median :0.000000 Median : 3.00
## Mean :0.01182 Mean :0.009456 Mean : 10.09
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.: 10.00
## Max. :1.00000 Max. :1.000000 Max. :101.00
## total-victims total-offenders
## Min. : 1.00 Min. : 1.00
## 1st Qu.: 1.00 1st Qu.: 1.00
## Median : 3.00 Median : 3.00
## Mean : 10.48 Mean : 11.77
## 3rd Qu.: 10.00 3rd Qu.: 11.00
## Max. :106.00 Max. :113.00If we were to look at certain criteria (in this example any variable that had over 9 or more hate crime types)
hatecrimes2 <- hatecrimes |>
select(county, year, 'anti-black', 'anti-white', 'anti-jewish', 'anti-catholic','anti-age-','anti-islamic--muslim-', `anti-multi-religious-groups`, 'anti-gay-male', 'anti-hispanic', `anti-other-ethnicity-national-origin`) |>
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> <int> <int> <int> <int> <int>
## 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-` <int>, `anti-islamic--muslim-` <int>,
## # `anti-multi-religious-groups` <int>, `anti-gay-male` <int>,
## # `anti-hispanic` <int>, `anti-other-ethnicity-national-origin` <int>How many rows and columns ther are (ie. dimensions)
dim(hatecrimes2)## [1] 423 12There is 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-religious-groups anti-gay-male 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-other-ethnicity-national-origin
## Min. : 0.0000
## 1st Qu.: 0.0000
## Median : 0.0000
## Mean : 0.2837
## 3rd Qu.: 0.0000
## Max. :19.0000Combing the columns. using pivot_longer it will combine each column from the 9 above to one column under victim_cat taking it from wide format to long format
hatelong <- hatecrimes2 |>
pivot_longer(cols = 3:12, names_to = "victim_cat", values_to = "crimecount")
hatelong## # A tibble: 4,230 × 4
## # Groups: county, year [277]
## county year victim_cat crimecount
## <chr> <int> <chr> <int>
## 1 Albany 2016 anti-black 1
## 2 Albany 2016 anti-white 0
## 3 Albany 2016 anti-jewish 0
## 4 Albany 2016 anti-catholic 0
## 5 Albany 2016 anti-age- 0
## 6 Albany 2016 anti-islamic--muslim- 1
## 7 Albany 2016 anti-multi-religious-groups 0
## 8 Albany 2016 anti-gay-male 1
## 9 Albany 2016 anti-hispanic 0
## 10 Albany 2016 anti-other-ethnicity-national-origin 0
## # ℹ 4,220 more rowsNow we create a face plot using long format
hatecrimeplot <- hatelong |>
ggplot(aes(year, crimecount)) +
geom_point()+
aes(color = victim_cat)+
facet_wrap(~victim_cat)
hatecrimeplot
The facet_wrap function showed us which of the caterogies seems to have the highest rates. fill out the top three.
hatenew <- hatelong |>
filter( victim_cat %in% c("anti-black", "anti-jewish", "anti-gay-male"))|>
group_by(year, county) |>
arrange(desc(crimecount))
hatenew## # A tibble: 1,269 × 4
## # Groups: year, county [277]
## county year victim_cat crimecount
## <chr> <int> <chr> <int>
## 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 rowsTime to plot these three hatecrimes together using a bar graph. using position=“dodge” makes these side by side bars, rather than stacked bars. stat=“identity” allows us to plot set of bars for each year from 2010-2016
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
What countries are the most effected
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
Lets look at the top 5 counties with the most number of incidents. The bar graph above does not do a good job of it because there is too many counties.
counties <- hatenew |>
group_by(year, county)|>
summarise(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
## <int> <chr> <int>
## 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 rowsThis gives us the highest total per year but what about the time period. Using the group_by tool but dropping year and only leaving county in, then using the slice_max(order_by = sum, n=5) the n5 is important as it lists the 5 counties with the highest incidents.
counties2 <- hatenew |>
group_by(county) |>
summarise(sum = sum(crimecount)) |>
slice_max(order_by = sum, n=5)
counties2## # A tibble: 5 × 2
## county sum
## <chr> <int>
## 1 Kings 713
## 2 New York 459
## 3 Suffolk 360
## 4 Nassau 298
## 5 Queens 235Another bar graph with filter 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(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")
plot4
Importing data from New York Population from 2010-2016
setwd("C:/Users/Danny/Downloads")
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.Time to remove the lowercase letter and underscores, plus changing the variable Geography to County
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 79844To focus in on one year 2012 for population by using filter
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 470978Still focusing on 2012 we now add the hate crime by using filter
counties12 <- counties |>
filter(year == 2012) |>
arrange(desc(sum))
counties12## # A tibble: 41 × 3
## # Groups: year [1]
## year county sum
## <int> <chr> <int>
## 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 rowsTieing it all together we now combine the 2012 NY population data with the hate crimes data. we do this by using the datajoin command.
datajoin <- counties12 |>
full_join(nypoplong12, by=c("county", "year"))
datajoin## # A tibble: 41 × 4
## # Groups: year [1]
## year county sum population
## <dbl> <chr> <int> <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 rowsLets calculate the rate of indicents per 100k residents. we use the mutate command and divide the total/sum by the population and then multiply it by 100000
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> <int> <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 rowsNow lets the make it in descending order
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 rowsGroup some of the catergories
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-gender-identity-expression"
## [5] "anti-age-"
## [6] "anti-white"
## [7] "anti-black"
## [8] "anti-american-indian-alaskan-native"
## [9] "anti-asian"
## [10] "anti-native-hawaiian-pacific-islander"
## [11] "anti-multi-racial-groups"
## [12] "anti-other-race"
## [13] "anti-jewish"
## [14] "anti-catholic"
## [15] "anti-protestant"
## [16] "anti-islamic--muslim-"
## [17] "anti-multi-religious-groups"
## [18] "anti-atheism-agnosticism"
## [19] "anti-religious-practice-generally"
## [20] "anti-other-religion"
## [21] "anti-buddhist"
## [22] "anti-eastern-orthodox--greek--russian--etc--"
## [23] "anti-hindu"
## [24] "anti-jehovahs-witness"
## [25] "anti-mormon"
## [26] "anti-other-christian"
## [27] "anti-sikh"
## [28] "anti-hispanic"
## [29] "anti-arab"
## [30] "anti-other-ethnicity-national-origin"
## [31] "anti-non-hispanic-"
## [32] "anti-gay-male"
## [33] "anti-gay-female"
## [34] "anti-gay--male-and-female-"
## [35] "anti-heterosexual"
## [36] "anti-bisexual"
## [37] "anti-physical-disability"
## [38] "anti-mental-disability"
## [39] "total-incidents"
## [40] "total-victims"
## [41] "total-offenders"Essay
I think this dataset was very interest. Both where it took place and how it was conduced. With that said the highlights of this dataset were just how many different demographics it covered. It went over race, religions, sexual orientation, disability, etc. Also for how long the data was collected for. It covered a 6 year period so we can different trends over time. Plus doing it statewide we could see which areas are the most affected by hate crimes in the state of New York. One of the downsides of this data was no age groups. We don’t know which age demographics are being effect for each demographic. Also why stop at 6 years it’s pretty odd. They could have stopped at 5 or have gone to 10 years it’s a weirdly specific time to stop. Plus if they are doing the state of New York and it’s counties it should have which cities commit the most hate crimes.
Something I think i would like to study would be by comparing the hate crime rate in each county and the regular crime rate of each county to see if there is any trends where high hate crime rate equals high crime rate and or vice versa. Other comparisons could done too with this data like how many hate crimes does the state of New York have compared to other states are they above or below the national average. Also I would like to study what in this dataset is biased or under reported or misclassified in terms of hate crimes. Something like social media is a influence in this as information can spread faster than ever so for example is something like hating the color green a trend on social media or is it because it hurts people’s eyes.
I would want to follow up with the victims of these hate crimes. I’m curious to see what there opinion is on the city or the state or the US or even there own personal lives as a whole after these hate crimes were committed what has changed from before and after. I would also like to follow up with the perpetrators to see what motivated them to commit these crimes and what there perspective on what happened. Plus following up with state of New York to see how the state define a hate crime it is speech or does it have to cause physical harm, etc. I think this is important because going back to misclassifcation there could be cases that don’t fit the state critetia that might have spilled through for a number of reasons. A general consensus for what is a hate crime would be needed.