Hate Crime HW

Author

Arif Jadji

The Hate Crimes Dataset

This dataset looks at all types of hate crimes in New York counties by the type of hate crime from 2010 to 2016.

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   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── 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)

setwd("/Users/arifjadji/Desktop/DATA110/DATA110 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.

Cleaning up the data:

Make all headers lowercase and remove spaces

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)

Select only certain hate-crimes

Only looking at the hate-crime types with a max number or 9 or more.

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
# ℹ 4 more variables: `anti-age*` <dbl>, `anti-islamic(muslim)` <dbl>,
#   `anti-gaymale` <dbl>, `anti-hispanic` <dbl>

Check the dimensions and the summary to make sure no missing values

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

Use Facet_Wrap

Look at each set of hate-crimes for each type for each year. Use the package “tidyr” to convert the dataset from wide to long with the command “gather”. It will take each column’s hate-crime type combine them all into one column called “id”. Then each cell count will go into the new column, “crimecount”. Finally, we are only doing this for the quantitiative variables, which are in columns 3 - 10. Note the command facet_wrap requires (~) before “id”.

hatecrimeslong <- hatecrimes2 %>% 
  tidyr::gather("id", "crimecount", 3:10) 

hatecrimesplot <-hatecrimeslong %>% 
  ggplot(., aes(year, crimecount))+
  geom_point()+
  aes(color = id)+
  facet_wrap(~id)
hatecrimesplot

Looking deeper into crimes blacks, gay males, and jews

From the facet_wrap plot above, anti-black, anti-gay males, and anti-jewish categories seem to have highest rates of offenses reported. Filter out just for those 3 crimes.

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
# ℹ 1,259 more rows

Plot this three types of hate crimes together

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

What about the counties?

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

Looking at 5 counties with the highest number of 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
# ℹ 267 more rows

A barplot for the 5 counties in 2012 with the highest incidents of hate-crimes.

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 the 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.

setwd("/Users/arifjadji/Desktop/DATA110/DATA110 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.

Clean the county name to match the other dataset

Rename the variable “Geography” as “county” so that it matches in 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      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     470978

Recall the total hate crime counts:

Kings 713 New York 459 Suffolk 360 Nassau 298 Queens 235

Filter hate crimes just for 2012

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
# 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:   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
# 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:   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
# ℹ 31 more rows

Notice 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
# ℹ 31 more rows

But the highest populated counties were: Kings (Brooklyn), Queens, New York, Suffolk (Long Island), Bronx, and Nassau. They do not correspond directly, though they are similar, to the counties with highest rates of hate crimes. # 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"

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
# ℹ 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
# ℹ 1,682 more rows

My Thoughts:

The positive aspects of this dataset is that it raised awareness of hate crimes. The negative aspects of this dataset is that there may be less or over reporting of hate crimes which can lead to misrepresentation and flawed dataset. I would like to study hate crimes among white, and islamic religion. After seeing these results, I would follow up by asking the local law enforcement agencies how they collect hate crime data. Second, publish the data with visualitzation to the public to raise discussions.