library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ 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)
hatecrimes <- read_csv("data/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))
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>, …
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>
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
hatelong <- hatecrimes2 |>
pivot_longer(
cols = 3:12,
names_to = "victim_cat",
values_to = "crimecount")
hatecrimplot <-hatelong |>
ggplot(aes(year, crimecount))+
geom_point()+
aes(color = victim_cat)+
facet_wrap(~victim_cat)
hatecrimplot
hatecrimplot <-hatelong |>
ggplot(aes(year, crimecount))+
geom_boxplot()+
aes(color = victim_cat)+
facet_wrap(~victim_cat)
hatecrimplot
#2. List 2 different paths you would like to (hypothetically) study about this dataset.
#Path1
# Summing up hate crimes by county and type
county_data <- hatelong %>%
group_by(county, victim_cat) %>%
summarise(total_crimes = sum(crimecount, na.rm = TRUE))
## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.
# Creating a heatmap (this is a simplified example)
ggplot(county_data, aes(x = county, y = victim_cat)) +
geom_tile(aes(fill = total_crimes), color = "white") +
scale_fill_gradient(low = "blue", high = "red") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Heatmap of Hate Crimes by County and Type") +
xlab("County") +
ylab("Type of Hate Crime")
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
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
#path2
# Summing up each type of hate crime for all counties for each year
annual_trends <- hatelong %>%
group_by(year, victim_cat) %>%
summarise(total_crimes = sum(crimecount, na.rm = TRUE))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
# Plotting the trends over time
ggplot(annual_trends, aes(x = year, y = total_crimes, color = victim_cat)) +
geom_line() +
geom_point() +
ggtitle("Annual Trends of Different Types of Hate Crimes") +
xlab("Year") +
ylab("Total Incidents")
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
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
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
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
nypop <- read_csv("data/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 79844
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
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
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
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
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
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
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
#1. Write about the positive and negative aspects of this hate crimes dataset.
##Positive aspects
###Given that the data are from 2010, it is possible to conduct a concentrated study of hate crimes that occurred during that time period. This analysis can be useful for making historical comparisons or analyzing the effects of events that occurred just in that year.
###When paired with data from prior years, a 2010 dataset may form the basis for longitudinal analyses that would enable researchers to monitor trends over time.
### Policymakers' decisions on legislation, law enforcement procedures, and community outreach initiatives can be affected by data that is reliable and complete about the state of hate crimes in a given year.
##negative aspects
###The data is from 2010, thus it could not represent current social trends or conditions. Therefore, it might be inaccurate to use it to inform present policy.
###Hate crimes are frequently underreported, and different jurisdictions may use different data collection techniques. This can lead to an inaccurate or skewed depiction of the actual number of incidences.
###In order to comprehend the underlying reasons of hate crimes, the efficacy of remedies, or the lived experiences of victims, data alone may not be sufficient.
# 3. Describe 2 things you would do to follow up after seeing the output from the hate crimes tutorial.
##I'm curious to see how hate crimes are spread among various racial and ethnic groupings. Specifically:
### Do particular racial or ethnic groups face hate crimes more frequently than others?
### What age range is most frequently involved or targeted?
### Do victims and offenders differ in terms of gender?
### Are there specific cities, states, or regions where hate crimes are more prevalent?
### Are urban areas more affected than rural areas or vice versa?
### Look for links between different forms of hate crimes. Are counties with a high rate of one form of hate crime more likely to have a high rate of another?