Hate Crime in NY from 2010-2916
Bai Sesay
2022-06-13
HAte Crime Dataset
This dataset looks at all types of hate crime in NY counties by the type of hate from 2010-2016
Flawed hate crime data collection- we should know how the data was colleected
Data can provide 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 evidence 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.”
This is a link to the ProPublica Article: https://www.propublica.org/article/why-america-fails-at-gathering-hate-crime-statistics
Here is a data visualization of where hate crimes do NOT get reported around the country (Ken Schwencke, 2017): https://projects.propublica.org/graphics/hatecrime-map
So now we know that there is possible bias in the dataset, what can we do with it?
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#tinytex: :install_tinytex()
library(tinytex)find data from my computer
setwd("C:/Users/baise/OneDrive/Desktop/Baidata110summer")
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.load the data form computer
clean up data
names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","",names(hatecrimes))
str(hatecrimes)## spec_tbl_df [423 × 44] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ county : chr [1:423] "Albany" "Albany" "Allegany" "Bronx" ...
## $ year : num [1:423] 2016 2016 2016 2016 2016 ...
## $ crimetype : chr [1:423] "Crimes Against Persons" "Property Crimes" "Property Crimes" "Crimes Against Persons" ...
## $ anti-male : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-female : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-transgender : num [1:423] 0 0 0 4 0 0 0 0 0 0 ...
## $ anti-genderidentityexpression : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-age* : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-white : num [1:423] 0 0 0 1 1 0 0 0 0 0 ...
## $ anti-black : num [1:423] 1 2 1 0 0 1 0 1 0 2 ...
## $ anti-americanindian/alaskannative : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-asian : num [1:423] 0 0 0 0 0 1 0 0 0 0 ...
## $ anti-nativehawaiian/pacificislander : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-multi-racialgroups : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherrace : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-jewish : num [1:423] 0 0 0 0 1 0 1 0 0 0 ...
## $ anti-catholic : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-protestant : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-islamic(muslim) : num [1:423] 1 0 0 6 0 0 0 0 1 0 ...
## $ anti-multi-religiousgroups : num [1:423] 0 1 0 0 0 0 0 0 0 0 ...
## $ anti-atheism/agnosticism : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-religiouspracticegenerally : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherreligion : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-buddhist : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-easternorthodox(greek,russian,etc.): num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-hindu : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-jehovahswitness : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-mormon : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherchristian : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-sikh : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-hispanic : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-arab : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherethnicity/nationalorigin : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-non-hispanic* : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-gaymale : num [1:423] 1 0 0 8 0 1 0 0 0 0 ...
## $ anti-gayfemale : num [1:423] 0 0 0 1 0 0 0 0 0 0 ...
## $ anti-gay(maleandfemale) : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-heterosexual : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-bisexual : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-physicaldisability : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-mentaldisability : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
## $ totalincidents : num [1:423] 3 3 1 20 2 3 1 1 1 2 ...
## $ totalvictims : num [1:423] 4 3 1 20 2 3 1 1 1 2 ...
## $ totaloffenders : num [1:423] 3 3 1 25 2 3 1 1 1 2 ...
## - attr(*, "spec")=
## .. cols(
## .. County = col_character(),
## .. Year = col_double(),
## .. `Crime Type` = col_character(),
## .. `Anti-Male` = col_double(),
## .. `Anti-Female` = col_double(),
## .. `Anti-Transgender` = col_double(),
## .. `Anti-Gender Identity Expression` = col_double(),
## .. `Anti-Age*` = col_double(),
## .. `Anti-White` = col_double(),
## .. `Anti-Black` = col_double(),
## .. `Anti-American Indian/Alaskan Native` = col_double(),
## .. `Anti-Asian` = col_double(),
## .. `Anti-Native Hawaiian/Pacific Islander` = col_double(),
## .. `Anti-Multi-Racial Groups` = col_double(),
## .. `Anti-Other Race` = col_double(),
## .. `Anti-Jewish` = col_double(),
## .. `Anti-Catholic` = col_double(),
## .. `Anti-Protestant` = col_double(),
## .. `Anti-Islamic (Muslim)` = col_double(),
## .. `Anti-Multi-Religious Groups` = col_double(),
## .. `Anti-Atheism/Agnosticism` = col_double(),
## .. `Anti-Religious Practice Generally` = col_double(),
## .. `Anti-Other Religion` = col_double(),
## .. `Anti-Buddhist` = col_double(),
## .. `Anti-Eastern Orthodox (Greek, Russian, etc.)` = col_double(),
## .. `Anti-Hindu` = col_double(),
## .. `Anti-Jehovahs Witness` = col_double(),
## .. `Anti-Mormon` = col_double(),
## .. `Anti-Other Christian` = col_double(),
## .. `Anti-Sikh` = col_double(),
## .. `Anti-Hispanic` = col_double(),
## .. `Anti-Arab` = col_double(),
## .. `Anti-Other Ethnicity/National Origin` = col_double(),
## .. `Anti-Non-Hispanic*` = col_double(),
## .. `Anti-Gay Male` = col_double(),
## .. `Anti-Gay Female` = col_double(),
## .. `Anti-Gay (Male and Female)` = col_double(),
## .. `Anti-Heterosexual` = col_double(),
## .. `Anti-Bisexual` = col_double(),
## .. `Anti-Physical Disability` = col_double(),
## .. `Anti-Mental Disability` = col_double(),
## .. `Total Incidents` = col_double(),
## .. `Total Victims` = col_double(),
## .. `Total Offenders` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>#summary(hatecrimes)seleted hate crime only
will only select and look at the hate-crime types with a max number or 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-gaymale', 'anti-hispanic') %>%
group_by(county, year)
head(hatecrimes2)## # A tibble: 6 × 10
## # 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
## # … with 4 more variables: `anti-age*` <dbl>, `anti-islamic(muslim)` <dbl>,
## # `anti-gaymale` <dbl>, `anti-hispanic` <dbl>##Checking the dimensions and the summary to make sure no missing values
According to the chart, there are no missing variables in all counties. there are 13 variable remain in the chart.
dim(hatecrimes2)## [1] 423 10There are currently 13 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-gaymale anti-hispanic
## Min. : 0.000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0000
## Mean : 1.499 Mean : 0.3735
## 3rd Qu.: 1.000 3rd Qu.: 0.0000
## Max. :36.000 Max. :17.0000use the face_Wrap
hatecrimeslong <- hatecrimes2 %>%
tidyr::gather("id", "crimecount", 3:10)
hatecrimesplot <-hatecrimeslong %>%
ggplot(., aes(year, crimecount))+
geom_point()+
aes(color = id)+
facet_wrap(~id)
hatecrimesplot
Analysing crimes against blacks, gay males, and jews
Theses list suprised me. According to the chart, crime against anti jews is way more higher than all the category in on the list. follows by anti gay male,and then black. crime against black back in 2010-2106 was below the number compare to now. I was pretty suprised by the chart. I thought hate crime against black would have been higher than all from the list.
hatenew <- hatecrimeslong %>%
filter( id== "anti-black" | id == "anti-jewish" | id == "anti-gaymale")%>%
group_by(year, county) %>%
arrange(desc(crimecount))
hatenew## # A tibble: 1,269 × 4
## # Groups: year, county [277]
## county year id 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
## # … with 1,259 more rowsPloting the graph all these three types of hate crimes together
#plot two
plot2 <- hatenew %>%
ggplot() +
geom_bar(aes(x=year, y=crimecount, fill = id),
position = "dodge", stat = "identity") +
ggtitle("Hate Crime Type in NY Counties Between 2010-2016") +
ylab("Number of Hate Crime Incidents") +
labs(fill = "Hate Crime Type")
plot2
we can see that hate cime against anti-jewish spike drastically in 2012,
followed by anti-gay and then anti-jewish.
counties chart
plot3 <- hatenew %>%
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = id),
position = "dodge", stat = "identity") +
ggtitle("Hate Crime Type in NY Counties Between 2010-2016") +
ylab("Number of Hate Crime Incidents") +
labs(fill = "Hate Crime Type")
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 - use top_n to list the 5 counties with highest total
incidents
counties <- hatenew %>%
group_by(county, year)%>%
summarize(sum = sum(crimecount)) %>%
arrange(desc(sum)) ## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.counties## # A tibble: 277 × 3
## # Groups: county [60]
## county year sum
## <chr> <dbl> <dbl>
## 1 Kings 2012 136
## 2 Kings 2010 110
## 3 Kings 2016 101
## 4 Kings 2013 96
## 5 Kings 2014 94
## 6 Kings 2015 90
## 7 Kings 2011 86
## 8 New York 2016 86
## 9 Suffolk 2012 83
## 10 New York 2013 75
## # … with 267 more rowsFinally, 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. Professor, I am saving this project for future reference that is why am coping everything. ## Plot four
plot4 <- hatenew %>%
filter(county =="Kings" | county =="New York" | county == "Suffolk" | county == "Nassau" | county == "Queens") %>%
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = id),
position = "dodge", stat = "identity") +
labs(ylab = "Number of Hate Crime Incidents",
title = "5 Counties in NY with Highest Incidents of Hate Crimes",
subtitle = "Between 2010-2016",
fill = "Hate Crime Type")
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.Again, I want to explore this data in the future
setwd("C:/Users/baise/OneDrive/Desktop/Baidata110summer")
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.Professor, please this is for future refrence. I will copied everything on this tutorial for future references
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 470978Not 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: county [41]
## county year sum
## <chr> <dbl> <dbl>
## 1 Kings 2012 136
## 2 Suffolk 2012 83
## 3 New York 2012 71
## 4 Nassau 2012 48
## 5 Queens 2012 48
## 6 Erie 2012 28
## 7 Bronx 2012 23
## 8 Richmond 2012 18
## 9 Multiple 2012 14
## 10 Westchester 2012 13
## # … with 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: county [41]
## county year sum population
## <chr> <dbl> <dbl> <dbl>
## 1 Kings 2012 136 2572282
## 2 Suffolk 2012 83 1499382
## 3 New York 2012 71 1625121
## 4 Nassau 2012 48 1350748
## 5 Queens 2012 48 2278024
## 6 Erie 2012 28 920792
## 7 Bronx 2012 23 1414774
## 8 Richmond 2012 18 470978
## 9 Multiple 2012 14 NA
## 10 Westchester 2012 13 961073
## # … with 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: county [41]
## county year sum population rate
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Suffolk 2012 83 1499382 5.54
## 2 Kings 2012 136 2572282 5.29
## 3 New York 2012 71 1625121 4.37
## 4 Richmond 2012 18 470978 3.82
## 5 Nassau 2012 48 1350748 3.55
## 6 Erie 2012 28 920792 3.04
## 7 Queens 2012 48 2278024 2.11
## 8 Bronx 2012 23 1414774 1.63
## 9 Westchester 2012 13 961073 1.35
## 10 Monroe 2012 5 748947 0.668
## # … with 31 more rowsNotice that the highest rates of hate crimes in 2012 happened in:
dt <- datajoinrate[,c("county","rate")]
dt## # A tibble: 41 × 2
## # Groups: county [41]
## 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
## # … with 31 more rows##Follow up Aggregating some of the categories
aggregategroups <- hatecrimes %>%
tidyr::gather("id", "crimecount", 4:44)
unique(aggregategroups$id)## [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"other data
aggregategroups <- aggregategroups %>%
mutate(group = case_when(
id %in% c("anti-transgender", "anti-gayfemale", "anti-genderidendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual") ~ "anti-lgbtq",
id %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",
id %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",
id %in% c("anti-physicaldisability", "anti-mentaldisability") ~ "anti-disability",
id %in% c("anti-female", "anti-male") ~ "anti-gender",
TRUE ~ "others"))
aggregategroups## # A tibble: 17,343 × 6
## county year crimetype id crimecount group
## <chr> <dbl> <chr> <chr> <dbl> <chr>
## 1 Albany 2016 Crimes Against Persons anti-male 0 anti-gender
## 2 Albany 2016 Property Crimes anti-male 0 anti-gender
## 3 Allegany 2016 Property Crimes anti-male 0 anti-gender
## 4 Bronx 2016 Crimes Against Persons anti-male 0 anti-gender
## 5 Bronx 2016 Property Crimes anti-male 0 anti-gender
## 6 Broome 2016 Crimes Against Persons anti-male 0 anti-gender
## 7 Cayuga 2016 Property Crimes anti-male 0 anti-gender
## 8 Chemung 2016 Crimes Against Persons anti-male 0 anti-gender
## 9 Chemung 2016 Property Crimes anti-male 0 anti-gender
## 10 Chenango 2016 Crimes Against Persons anti-male 0 anti-gender
## # … with 17,333 more rows#or create subset with just lgbtq
lgbtq <- hatecrimes %>%
tidyr::gather("id", "crimecount", 4:44) %>%
filter(id %in% c("anti-transgender", "anti-gayfemale", "anti-genderidendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual"))
lgbtq## # A tibble: 1,692 × 5
## county year crimetype id crimecount
## <chr> <dbl> <chr> <chr> <dbl>
## 1 Albany 2016 Crimes Against Persons anti-transgender 0
## 2 Albany 2016 Property Crimes anti-transgender 0
## 3 Allegany 2016 Property Crimes anti-transgender 0
## 4 Bronx 2016 Crimes Against Persons anti-transgender 4
## 5 Bronx 2016 Property Crimes anti-transgender 0
## 6 Broome 2016 Crimes Against Persons anti-transgender 0
## 7 Cayuga 2016 Property Crimes anti-transgender 0
## 8 Chemung 2016 Crimes Against Persons anti-transgender 0
## 9 Chemung 2016 Property Crimes anti-transgender 0
## 10 Chenango 2016 Crimes Against Persons anti-transgender 0
## # … with 1,682 more rowsThis statistic is very interesting. If there was a universal accepted required type of data collection for this finding, there would have been different chart that will have to be consider in the united states instead of concentrating crime hates in New York.During my research, the hate crime on anti-Jewish is higher than all the categories in different statistical bureau. The incident on hate crime is very alarming in the U.S. I have done a lot of finding regarding hate crime incident in general, and still the figures of anti-Jews spike drastically high.Which really surprised my research. When it comes to religious and ethnicity, I thought anti-Muslim and anti-black would have been top off the list, but even when I look up hate crime in race religious, ethnicity, sexual orientation, gender identity etc.. on the FBI bureau in recent statistics(2019,2020 and 2021), Anti-Jews was still higher. So now I have dig in to find a data collection of hate crime in religious, race, ethnicity,gender Identity, disability and gender for the total number of single bias incidents in 2019 ,2020 and 2021 just to compared hate crime in other categories. Here are links I would like to share during my research. https://www.justice.gov/crs/highlights/2020-hate-crimes-statistics https://www.justice.gov/crs/highlights/2020-hate-crimes-statistics.
Reflecting back to the questions, according to my finding and analysis, one positive aspect of data set is what data do you use to find your findings and how it has been analysis. Based on some trend data set and trend, you will have the opportunity to asked yourself and see how the data have been presented and analysed. Then you may predict, analysed or solve the problem. Since data is accessible to the public, one negative aspect may be security problem. Data especially big data can be tempered and abused when falling to the wrong person specially scam and hacker which will probably lead to misleading or disinformation sources.The two path I might hypothetically study this data would have been why the researchers only based their studies in New York. Anti-Jews, anti-black, and religious especially anti-Muslim would have also be something to consider in my research.The two things I would have done or I have already done is to explore more data in hate crime in different type of categories such as race, religion, ethnicity, gender, sexual orientation etc… just to explored different hate crimes in the united states.