library(tidyverse)
setwd("~/DATA 110")
hatecrimes <- read_csv("hateCrimes2010.csv")Hatecrimes HW
##So now we know that there is possible bias in the dataset, what can we do with it?
##Make all headers lowercase and remove spaces
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 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
dim(hatecrimes2)[1] 423 12summary(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 ##Convert from wide to long format
hatelong <- hatecrimes2 |>
pivot_longer(
cols = 3:12,
names_to = "victim_cat",
values_to = "crimecount")##Now use the long format to create a facet plot
hatecrimplot <-hatelong |>
ggplot(aes(year, crimecount))+
geom_point()+
aes(color = victim_cat)+
facet_wrap(~victim_cat)
hatecrimplot
##Look deeper into crimes against blacks, gay males, and jews
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
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 about the counties?
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
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.##Top 5
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 235plot4 <- 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?
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
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 79844Focus on 2012
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 470978Filter 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 rowsAggregating 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 rowsSUMMARY: A few positive things about this dataset is that it gives great details. It puts the hate crimes into categories like; country, race, religion, year, and even national origin. That’s helpful because it gets to show what groups and regions are affected the most, or were. It also tells us what kinds of hate crimes are most prevalent. The dataset also has a couple of negative aspects. I think the worst for me was the amount of detail, which was also one of my positives. They give a lot of detail and even though this helps explain the data, it could also confuse the viewer. Another negative is the fact that these are cases reported to the police, and labeled hate crimes. There could be a lot more crimes out there, unreported. It’s also from 2010-2016, pretty dated. I would try to look into hate crimes based on social groups like immigrants, or immigrants of a very specific type, to see how the scale of the crimes differs based on each. Since the dataset shows crimes from 2010 to 2016, I’d also create a dataset from recent data, recent years- and compare it to that of 2010-2016 to see if the hate crime rates in those years continued on to later years, increased or reduced.