Hate Crime Hw Assignment

Author

Rebecca Jipdjio

Published

February 10, 2024

source: carrcenter.hks

Importing data

library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ 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     
── 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
setwd("/Users/rebeccambaho/Downloads")
hatecrimes <- read.csv("hateCrimes2010.csv")

cleaning data

library(tibble)

hatecrimes <- as_tibble(hatecrimes)
names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ", "", names(hatecrimes))
head(hatecrimes)
# A tibble: 6 × 44
  county    year crime.type             anti.male anti.female anti.transgender
  <chr>    <int> <chr>                      <int>       <int>            <int>
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
# ℹ 38 more variables: anti.gender.identity.expression <int>, anti.age. <int>,
#   anti.white <int>, anti.black <int>,
#   anti.american.indian.alaskan.native <int>, anti.asian <int>,
#   anti.native.hawaiian.pacific.islander <int>,
#   anti.multi.racial.groups <int>, anti.other.race <int>, anti.jewish <int>,
#   anti.catholic <int>, anti.protestant <int>, anti.islamic..muslim. <int>,
#   anti.multi.religious.groups <int>, anti.atheism.agnosticism <int>, …

Selecting only certain hate-crimes

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.   :113.00

I’ll focus only on hate crime types with at least 9 occurrences, prioritizing the most significant ones.

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 anti.age.
  <chr>    <int>      <int>      <int>       <int>         <int>     <int>
1 Albany    2016          1          0           0             0         0
2 Albany    2016          2          0           0             0         0
3 Allegany  2016          1          0           0             0         0
4 Bronx     2016          0          1           0             0         0
5 Bronx     2016          0          1           1             0         0
6 Broome    2016          1          0           0             0         0
# ℹ 5 more variables: anti.islamic..muslim. <int>,
#   anti.multi.religious.groups <int>, anti.gay.male <int>,
#   anti.hispanic <int>, anti.other.ethnicity.national.origin <int>

Checking the dimensions and the summary to make sure there are no missing values

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.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.   :19.0000

Converting from wide to long format

hatelong <- hatecrimes2 |> 
    pivot_longer(
        cols = 3:12,
        names_to = "victim_cat",
        values_to = "crimecount")

Using 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

Looking deeper into crimes against black people, gay males, & jews

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 rows

Plotting these three 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

Plotting by county

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), .groups = "drop") |>
  arrange(desc(sum))

summarise() has grouped output by ‘year’. You can override using the .groups argument.

counties
# A tibble: 277 × 3
    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 rows

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>    <int>
1 Kings      713
2 New York   459
3 Suffolk    360
4 Nassau     298
5 Queens     235

Barplot for the 5 countries in 2012

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

How would calculations be affected by looking at hate crimes in counties per year by population densities?

setwd("/Users/rebeccambaho/Downloads")
nypop <- read.csv("NY population.csv")

Clean the county name to match the other dataset

library(tibble)  

nypop$Geography <- gsub(" , New York", "", nypop$Geography)
nypop$Geography <- gsub("County", "", nypop$Geography)
nypoplong <- nypop %>%
  rename(county = Geography) %>%
  gather("year", "population", 2:8)
nypoplong$year <- as.integer(gsub("X", "", nypoplong$year))
as_tibble(head(nypoplong))
# A tibble: 6 × 3
  county                  year population
  <chr>                  <int>      <int>
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

Focusing on 2012

library(tibble)

nypoplong12 <- nypoplong |>
  filter(year == 2012) |>
  arrange(desc(population)) |>
  head(10)
nypoplong12$county <- gsub(" , New York", "", nypoplong12$county)
as_tibble(nypoplong12)
# A tibble: 10 × 3
   county       year population
   <chr>       <int>      <int>
 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

Filtering hate crime for 2012

counties12 <- counties |>
  filter(year == 2012) |>
  arrange(desc(sum)) 
counties12
# A tibble: 41 × 3
    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 rows

Joining the Hate Crimes data with NY population data for 2012

datajoin <- counties12 |>
  full_join(nypoplong12, by=c("county", "year"))
datajoin
# A tibble: 41 × 4
    year county        sum population
   <int> <chr>       <int>      <int>
 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

Calculating 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
    year county        sum population  rate
   <int> <chr>       <int>      <int> <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

The highest rates of hate-crimes in 2012 happened in:

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

Aggregating 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.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"                             
aggregategroups <- aggregategroups |>
  mutate(group = case_when(
    victim_cat %in% c("anti.transgender", "anti.gay.female", "anti.gender.victim.catendity.expression", "anti.gay.male", "anti.gay..male.and.female.", "anti.bisexual") ~ "anti.lgbtq",
    victim_cat %in% c("anti.multi.racial.groups", "anti.jewish", "anti.protestant", "anti.multi.religous.groups", "anti.religious.practice.generally", "anti.buddhist", "anti.hindu", "anti.mormon", "anti.sikh", "anti.catholic", "anti.islamic..muslim.", "anti.atheism.agnosticism", "anti.other.religion", "anti.eastern.orthodox..greek..russian..etc..", "anti.jehovahs.witness", "anti.other.christian") ~ "anti.religion", 
    victim_cat %in% c("anti.asian", "anti.arab", "anti.non.hispanic", "ant.white", "anti.american.indian.alaskan.native", "anti.native.hawaiian.pacific.islander", "anti.other.race", "anti.hispanic", "anti.other.ethnicity.national.origin") ~ "anti.ethnicity",
    victim_cat %in% c("anti.physical.disability", "anti.mental.disability") ~ "anti.disability",
    victim_cat %in% c("anti.female", "anti.male") ~ "anti.gender",
    TRUE ~ "others"))
aggregategroups
# A tibble: 17,343 × 6
   county  year crime.type             victim_cat               crimecount group
   <chr>  <int> <chr>                  <chr>                         <int> <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.gender.identity.ex…          0 othe…
 5 Albany  2016 Crimes Against Persons anti.age.                         0 othe…
 6 Albany  2016 Crimes Against Persons anti.white                        0 othe…
 7 Albany  2016 Crimes Against Persons anti.black                        1 othe…
 8 Albany  2016 Crimes Against Persons anti.american.indian.al…          0 anti…
 9 Albany  2016 Crimes Against Persons anti.asian                        0 anti…
10 Albany  2016 Crimes Against Persons anti.native.hawaiian.pa…          0 anti…
# ℹ 17,333 more rows

Creating subset with just black lgbtq

lgbtq <- hatecrimes |>
  pivot_longer(
      cols = 4:44,
      names_to = "victim_cat",
      values_to = "crimecount") |>
filter(victim_cat %in% c("anti.transgender", "anti.gay.female", "anti.gender.victim.catendity.expression", "anti.gay.male", "anti.gay..male.and.female", "anti.bisexual"))
lgbtq
# A tibble: 1,692 × 5
   county    year crime.type             victim_cat       crimecount
   <chr>    <int> <chr>                  <chr>                 <int>
 1 Albany    2016 Crimes Against Persons anti.transgender          0
 2 Albany    2016 Crimes Against Persons anti.gay.male             1
 3 Albany    2016 Crimes Against Persons anti.gay.female           0
 4 Albany    2016 Crimes Against Persons anti.bisexual             0
 5 Albany    2016 Property Crimes        anti.transgender          0
 6 Albany    2016 Property Crimes        anti.gay.male             0
 7 Albany    2016 Property Crimes        anti.gay.female           0
 8 Albany    2016 Property Crimes        anti.bisexual             0
 9 Allegany  2016 Property Crimes        anti.transgender          0
10 Allegany  2016 Property Crimes        anti.gay.male             0
# ℹ 1,682 more rows

Important Findings:

  1. The hate crimes dataset provides valuable insights into reported hate crimes, allowing us to grasp their frequency, trends, and specific attributes. For instance, the visualizations reveal that Jews are the most frequently targeted group according to the data. Such insights can play a crucial role in shaping policies, increasing awareness, and directing interventions aimed at combating hate-based violence. However, the dataset is not without limitations, such as instances of underreporting stemming from fear or distrust of law enforcement. Additionally, it may fail to capture the diverse experiences of marginalized communities, resulting in gaps in our comprehension. Moreover, inconsistencies in reporting standards across different regions can diminish the reliability and comparability of the data.

  2. First, we can look into the relationship between hate crimes and socioeconomic aspects by examining demographic and economic data alongside hate crime statistics. This analysis seeks to identify patterns and underlying causes of hate crimes. Second, we can investigate how hate crimes evolved. We’ll look at how frequently they occur and how they’ve changed over time. By doing this, we can see if there are times when hate crimes happen more, like during certain events or when things are tense in society.

  3. After looking at the results from the hate crimes tutorial, I would do two main things to learn more from the data. First, I would carefully check if the data is good quality by looking for any missing info or mistakes. This helps make sure the data is accurate and reliable. Additionally, I might consider conducting interviews or surveys with individuals impacted by hate crimes to gain insights into their experiences and perspectives.