library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✔ ggplot2 3.4.0 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()#tinytex::install_tinytex()
library(tinytex)setwd("/Users/Linh/Desktop/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.names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","",names(hatecrimes))
str(hatecrimes)spc_tbl_ [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)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-…¹ anti-…² anti-…³ anti-…⁴ anti-…⁵ anti-…⁶ anti-…⁷ anti-…⁸
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Albany 2016 1 0 0 0 0 1 1 0
2 Albany 2016 2 0 0 0 0 0 0 0
3 Allegany 2016 1 0 0 0 0 0 0 0
4 Bronx 2016 0 1 0 0 0 6 8 0
5 Bronx 2016 0 1 1 0 0 0 0 0
6 Broome 2016 1 0 0 0 0 0 1 0
# … with abbreviated variable names ¹`anti-black`, ²`anti-white`,
# ³`anti-jewish`, ⁴`anti-catholic`, ⁵`anti-age*`, ⁶`anti-islamic(muslim)`,
# ⁷`anti-gaymale`, ⁸`anti-hispanic`dim(hatecrimes2)[1] 423 10# There 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. :17.0000 hatecrimeslong <- hatecrimes2 %>%
tidyr::gather("id", "crimecount", 3:10)
hatecrimesplot <-hatecrimeslong %>%
ggplot(., aes(year, crimecount))+
geom_point()+
aes(color = id)+
facet_wrap(~id)
hatecrimesplot
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 rowsplot2 <- 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
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
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 rowsplot4 <- 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
setwd("/Users/Linh/Desktop/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.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 79844nypoplong12 <- 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 470978Kings 713
New York 459
Suffolk 360
Nassau 298
Queens 235
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 rowsdatajoin <- 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 rowsdatajoinrate <- 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 rowsdt <- 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 rowsaggregategroups <- 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" 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 rowslgbtq <- 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 rowsRegardless of the flawed dataset, it has given us a general idea of how hate crime has been. I have never known that there are so many different types of hate crime. And because of its specification, we can distinguish each category. The upward trend reflects reality and helps to bring attention to the public and law makers.
There is a lot of value 0, which makes it a bit hard to read without generating charts. There are “Property Crimes” and ” Crimes Against Persons” but not every county has those two categories, which causes the difficulty in navigating the information.
Two different paths you would like to study about this dataset:
I would generate charts to group areas that have hate crime upward trends. Doing so might help legislatures work on specific areas to find reasons, then come up with effective solutions.
Furthermore, I would study the dataset of Property Crimes and Crimes Against Persons in NY counties separately and then, compare those two categories based on NY population dataset.
Two things I would do to follow up the hate crimes dataset:
First of all, I would like to gather data of ethnicity predominance in each county to find the correlations in hate crimes.
Secondly, police force plays such an important role in hate crime control. Thus, I would like to study the number of police stations in each county in New York that stay active and responsive to crimes.