Hate Crimes in NY from 2010-2016

Author

Wilfried Bilong

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.

My caveat:

Flawed hate crime data collection - we should know how the data was collected

(Nathan Yau of Flowing Data, Dec 5, 2017)

Data can provide you with important information, but when the collection process is flawed, there’s not much you can do. Ken Schwencke, reporting for ProPublica, researched the tiered system that the FBI relies on to gather hate crime data for the United States:

“Under a federal law passed in 1990, the FBI is required to track and tabulate crimes in which there was “manifest evidence of prejudice” against a host of protected groups, including homosexuals, regardless of differences in how state laws define who’s protected. The FBI, in turn, relies on local law enforcement agencies to collect and submit this data, but can’t compel them to do so.”

This is a link to the ProPublica Article: https://www.propublica.org/article/why-america-fails-at-gathering-hate-crime-statistics

Here is a data visualization of where hate crimes do NOT get reported around the country (Ken Schwencke, 2017): https://projects.propublica.org/graphics/hatecrime-map

So now we know that there is possible bias in the dataset, what can we do with it?

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
library(tinytex)
setwd("/Users/wyembilong/Desktop/Data 110") 
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.

Clean up the data:

Make all headers lowercase and remove spaces

After cleaning up the variable names, look at the structure of the data. Since there are 44 variables in this dataset, you can use “summary” to decide which hate crimes to focus on. In the output of “summary”, look at the min/max values. Some have a max-vale of 1.

names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub("anti-black ","anti-asian",names(hatecrimes))
head(hatecrimes)
# A tibble: 6 × 44
  county    year `crime type`       `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-gender identity expression` <dbl>,
#   `anti-age*` <dbl>, `anti-white` <dbl>, `anti-black` <dbl>,
#   `anti-american indian/alaskan native` <dbl>, `anti-asian` <dbl>,
#   `anti-native hawaiian/pacific islander` <dbl>,
#   `anti-multi-racial groups` <dbl>, `anti-other race` <dbl>,
#   `anti-jewish` <dbl>, `anti-catholic` <dbl>, `anti-protestant` <dbl>,
#   `anti-islamic (muslim)` <dbl>, `anti-multi-religious groups` <dbl>, …

Select 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 decided I would only look at the hate-crime types with a max number of 9 or more. That way I can focus on the most prominent types of hate-crimes.

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`
  <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-religious groups` <dbl>, `anti-gay male` <dbl>,
#   `anti-hispanic` <dbl>, `anti-other ethnicity/national origin` <dbl>

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

Also check the dimensions to count how many variables remain

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

Convert from wide to long format

Look at each set of hate-crimes for each type for each year. Convert the dataset from wide to long with the pivot_longer function. It will take each column’s hate-crime type combine them all into one column called “victim_cat”. Then each cell count will go into the new column, “crimecount”.

Finally, we are only doing this for the quantitative variables, which are in columns 3 - 10. Note the command facet_wrap requires (~) before “victim_cat”.

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

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 <- hatelong |>
  filter( victim_cat %in% c("anti-black", "anti-jewish", "anti-gaymale"))|>
  group_by(year, county) |>
  arrange(desc(crimecount))
hatenew
# A tibble: 846 × 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 836 more rows

Plot these three types of hate crimes together

Use the following commands to finalize your barplot: - position = “dodge” makes side-by-side bars, rather than stacked bars - stat = “identity” allows you to plot each set of bars for each year between 2010 and 2016 - ggtitle gives the plot a title - labs gives a title to the legend

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

We can see that hate crimes against Jews spiked in 2012. All other years were relatively consistent with a slight upward trend. There was also an upward trend in hate crimes against gay males. Finally, there appears to be a downward trend in hate crimes against blacks during this period.

What about the counties?

I have not dealt with the counties, but I think that is the next place to explore. I can make bar graphs by county instead of by year.

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

There are too many counties for this plot to make sense, but maybe we can just look at the 5 counties with the highest number of incidents. - use “group_by” to group each row by counties - use summarize to get the total sum of incidents by county - use arrange(desc) to arrange those sums of total incidents by counties in descending order.

counties <- hatenew |>
  group_by(year, county)|>
  summarise(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     124
 2  2010 Kings      89
 3  2016 Kings      83
 4  2012 Suffolk    80
 5  2014 Kings      69
 6  2015 Kings      69
 7  2011 Kings      68
 8  2013 Kings      68
 9  2014 Suffolk    65
10  2013 Suffolk    64
# ℹ 267 more rows

Top 5

To list the 5 counties with the highest total incidents, change group_by to: group_by(county), then use slice_max(order_by = sum, n=5) to list the 5 counties with highest total incidents

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      570
2 Suffolk    317
3 Nassau     283
4 New York   266
5 Queens     175

Finally, create the barplot above, but only for the 5 counties in 2012 with the highest incidents of hate-crimes. The command “labs” is nice, because you can get a title, subtitle, y-axis label, and legend title, all in one command.

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?

Bring in census data for populations of New York counties. These are estimates from the 2010 census.

setwd("/Users/wyembilong/Desktop/Data 110")
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 it 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$county<-gsub(" , ","",nypoplong12$county)
nypoplong12
# A tibble: 10 × 3
   county               year population
   <chr>               <dbl>      <dbl>
 1 KingsNew York        2012    2572282
 2 QueensNew York       2012    2278024
 3 New YorkNew York     2012    1625121
 4 SuffolkNew York      2012    1499382
 5 BronxNew York        2012    1414774
 6 NassauNew York       2012    1350748
 7 WestchesterNew York  2012     961073
 8 ErieNew York         2012     920792
 9 MonroeNew York       2012     748947
10 RichmondNew York     2012     470978

Not surprisingly, 4/5 of the counties with the highest populations also were listed in the counties with the highest number of hate crimes. Only the Bronx, which has the fifth highest population is not in the list with the highest number of total hate crimes over the period from 2010 to 2016.

Recall the total hate crime counts:

Kings 713 New York 459 Suffolk 360 Nassau 298 Queens 235

Filter 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      124
 2  2012 Suffolk     80
 3  2012 New York    54
 4  2012 Nassau      48
 5  2012 Queens      38
 6  2012 Erie        20
 7  2012 Bronx       19
 8  2012 Richmond    16
 9  2012 Multiple    14
10  2012 Dutchess     9
# 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: 51 × 4
# Groups:   year [1]
    year county     sum population
   <dbl> <chr>    <dbl>      <dbl>
 1  2012 Kings      124         NA
 2  2012 Suffolk     80         NA
 3  2012 New York    54         NA
 4  2012 Nassau      48         NA
 5  2012 Queens      38         NA
 6  2012 Erie        20         NA
 7  2012 Bronx       19         NA
 8  2012 Richmond    16         NA
 9  2012 Multiple    14         NA
10  2012 Dutchess     9         NA
# ℹ 41 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: 51 × 5
# Groups:   year [1]
    year county     sum population  rate
   <dbl> <chr>    <dbl>      <dbl> <dbl>
 1  2012 Kings      124         NA    NA
 2  2012 Suffolk     80         NA    NA
 3  2012 New York    54         NA    NA
 4  2012 Nassau      48         NA    NA
 5  2012 Queens      38         NA    NA
 6  2012 Erie        20         NA    NA
 7  2012 Bronx       19         NA    NA
 8  2012 Richmond    16         NA    NA
 9  2012 Multiple    14         NA    NA
10  2012 Dutchess     9         NA    NA
# ℹ 41 more rows

Notice that the highest rates of hate crimes in 2012 happened in Suffolk

dt <- datajoinrate[,c("county","rate")]
dt
# A tibble: 51 × 2
   county    rate
   <chr>    <dbl>
 1 Kings       NA
 2 Suffolk     NA
 3 New York    NA
 4 Nassau      NA
 5 Queens      NA
 6 Erie        NA
 7 Bronx       NA
 8 Richmond    NA
 9 Multiple    NA
10 Dutchess    NA
# ℹ 41 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 |>
  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-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 `crime type`           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-gender identity ex…          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-american indian/al…          0 othe…
 9 Albany  2016 Crimes Against Persons anti-asian                        0 anti…
10 Albany  2016 Crimes Against Persons anti-native hawaiian/pa…          0 othe…
# 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: 846 × 5
   county    year `crime type`           victim_cat       crimecount
   <chr>    <dbl> <chr>                  <chr>                 <dbl>
 1 Albany    2016 Crimes Against Persons anti-transgender          0
 2 Albany    2016 Crimes Against Persons anti-bisexual             0
 3 Albany    2016 Property Crimes        anti-transgender          0
 4 Albany    2016 Property Crimes        anti-bisexual             0
 5 Allegany  2016 Property Crimes        anti-transgender          0
 6 Allegany  2016 Property Crimes        anti-bisexual             0
 7 Bronx     2016 Crimes Against Persons anti-transgender          4
 8 Bronx     2016 Crimes Against Persons anti-bisexual             0
 9 Bronx     2016 Property Crimes        anti-transgender          0
10 Bronx     2016 Property Crimes        anti-bisexual             0
# ℹ 836 more rows

So what does all of this mean?

Important Findings:

  1. I wonder what the data would look like if there was a universally accepted requirement for this type of data collection.

  2. The Bronx appears to have much lower than expected victims of hate crimes relative to its population density in comparison to other NY counties.

  3. In Kings County, NY (which is home to Brooklyn; according to Wikipedia, it is New York’s most populous borough and the second most densly populated county in the US) in 2012, there was a spike in hate crimes against Jews.

  4. All of these findings are corroborated in Hate Crime in New York State 2012 Annual Report: https://www.criminaljustice.ny.gov/crimnet/ojsa/hate-crime-in-nys-2012-annual-report.pdf

Essay:

  1. Write about the positive and negative aspects of this hate crimes dataset. 

  2. List 2 different paths you would like to (hypothetically) study about this dataset.

  3. Describe 2 things you would do to follow up after seeing the output from the hate crimes tutorial. 

I would say the positive aspects of this data is how much there is to work with. The more data the better because it allows us to have a good idea of what is going on at the time. One of the main negative aspects would simply be the time. This is data coming from New York from 2010 - 2016, it has been 7 years since then and so much has happened from that point. Simply put the data is outdated and likely no longer as reliable as it once was. To start I’d simply like to look at the progression of hate crimes as a whole. Going back to my original point the data is outdated by now but we can look at it to find trends over the years. Another thing that can be looked at is correlation between those possible trends. It was noted that when certain types of hate crimes rose others seemed to fall. Based on what we can look at from the context of the years when that happened we might be able to see if there’s a connection between certain hate crimes happening at certain times. Following the data that I’ve seen from this tutorial I’d like to see if I could find anything closer to date. The goal would be to see how reliable the data is compared to any new data and see if anything can be gleaned from this old data set. Drawing from my other point I would use the data to create graphs (line graphs and scatter plots) possibly showing any correlation between a rise in Jewish Hate crimes happening around or near a fall in black hate crimes for example.