Assignment #2

library(readr)
library(knitr)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ purrr     1.2.2
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
── 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(dplyr)
library(ggplot2)
library(stringr)

hatecrimes = read_csv("NYPD_Hate_Crimes_19-26.csv")

Rows: 4029 Columns: 14
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): Record Create Date, Patrol Borough Name, County, Law Code Category ...
dbl (4): Full Complaint ID, Complaint Year Number, Month Number, Complaint P...
lgl (1): Arrest Date

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 × 14
  fullcomplaintid complaintyearnumber monthnumber recordcreatedate
            <dbl>               <dbl>       <dbl> <chr>           
1         2.02e14                2019           1 1/23/2019       
2         2.02e14                2019           2 2/25/2019       
3         2.02e14                2019           2 2/27/2019       
4         2.02e14                2019           4 4/16/2019       
5         2.02e14                2019           6 6/20/2019       
6         2.02e14                2019           7 7/31/2019       
# ℹ 10 more variables: complaintprecinctcode <dbl>, patrolboroughname <chr>,
#   county <chr>, lawcodecategorydescription <chr>, offensedescription <chr>,
#   pdcodedescription <chr>, biasmotivedescription <chr>,
#   offensecategory <chr>, arrestdate <lgl>, arrestid <chr>

bias_count = hatecrimes |> 
  select(biasmotivedescription) |> 
  group_by(biasmotivedescription) |> 
  count() |> 
  arrange(desc(n))
head(bias_count)

# A tibble: 6 × 2
# Groups:   biasmotivedescription [6]
  biasmotivedescription          n
  <chr>                      <int>
1 ANTI-JEWISH                 1906
2 ANTI-MALE HOMOSEXUAL (GAY)   489
3 ANTI-ASIAN                   401
4 ANTI-BLACK                   315
5 ANTI-OTHER ETHNICITY         168
6 ANTI-MUSLIM                  156

ggplot(hatecrimes, aes(x = biasmotivedescription)) + geom_bar() 

bias_count |> 
  head(10) |> 
  ggplot(aes(x = biasmotivedescription, y = n)) + geom_col() 

bias_count |> 
  head(10) |> 
  ggplot(aes(x = reorder(biasmotivedescription, n), y = n)) + geom_col() + 
  coord_flip() +
  labs(x = " ", 
       y = "Counts of hatecrime types based on motive", 
       title = "Bar Graphs of Hate Crimes from 2019 - 2026", 
       subtitle = "Counts based on the hatecrime motive", 
       caption = "Source: NY State Division of Criminal Justice Services") +
  theme_minimal() + 
  geom_text(aes(label = n), hjust = -.05, size = 3) + 
  theme(axis.text.x = element_blank())

hate_year = hatecrimes |> 
  filter(biasmotivedescription %in% c("ANTI-JEWISH", "ANTI-MALE HOMOSEXUAL (GAY)", "ANTI-ASIAN", 'ANTI-BLACK')) |> 
  group_by(complaintyearnumber) |> 
  count(biasmotivedescription) |> 
  arrange(desc(n))
hate_year

# A tibble: 28 × 3
# Groups:   complaintyearnumber [7]
   complaintyearnumber biasmotivedescription          n
                 <dbl> <chr>                      <int>
 1                2024 ANTI-JEWISH                  371
 2                2023 ANTI-JEWISH                  343
 3                2025 ANTI-JEWISH                  320
 4                2022 ANTI-JEWISH                  279
 5                2019 ANTI-JEWISH                  252
 6                2021 ANTI-JEWISH                  215
 7                2021 ANTI-ASIAN                   150
 8                2020 ANTI-JEWISH                  126
 9                2023 ANTI-MALE HOMOSEXUAL (GAY)   116
10                2022 ANTI-ASIAN                    91
# ℹ 18 more rows

hate_county = hatecrimes |> 
  filter(biasmotivedescription %in% c("ANTI-JEWISH", "ANTI-MALE HOMOSEXUAL (GAY)", "ANTI-ASIAN", 'ANTI-BLACK')) |> 
  group_by(county) |> 
  count(biasmotivedescription) |> 
  arrange(desc(n))
hate_county

# A tibble: 20 × 3
# Groups:   county [5]
   county   biasmotivedescription          n
   <chr>    <chr>                      <int>
 1 KINGS    ANTI-JEWISH                  798
 2 NEW YORK ANTI-JEWISH                  651
 3 QUEENS   ANTI-JEWISH                  289
 4 NEW YORK ANTI-MALE HOMOSEXUAL (GAY)   237
 5 NEW YORK ANTI-ASIAN                   228
 6 KINGS    ANTI-MALE HOMOSEXUAL (GAY)   120
 7 KINGS    ANTI-BLACK                    99
 8 BRONX    ANTI-JEWISH                   92
 9 QUEENS   ANTI-MALE HOMOSEXUAL (GAY)    91
10 KINGS    ANTI-ASIAN                    80
11 NEW YORK ANTI-BLACK                    79
12 QUEENS   ANTI-ASIAN                    78
13 RICHMOND ANTI-JEWISH                   76
14 QUEENS   ANTI-BLACK                    75
15 BRONX    ANTI-MALE HOMOSEXUAL (GAY)    35
16 RICHMOND ANTI-BLACK                    35
17 BRONX    ANTI-BLACK                    27
18 BRONX    ANTI-ASIAN                    10
19 RICHMOND ANTI-MALE HOMOSEXUAL (GAY)     6
20 RICHMOND ANTI-ASIAN                     5

hate2 = hatecrimes |> 
  filter(biasmotivedescription %in% c("ANTI-JEWISH", "ANTI-MALE HOMOSEXUAL (GAY)", "ANTI-ASIAN", 'ANTI-BLACK')) |> 
  group_by(complaintyearnumber, county) |> 
  count(biasmotivedescription) |> 
  arrange(desc(n))
hate2

# A tibble: 127 × 4
# Groups:   complaintyearnumber, county [35]
   complaintyearnumber county   biasmotivedescription     n
                 <dbl> <chr>    <chr>                 <int>
 1                2024 KINGS    ANTI-JEWISH             152
 2                2024 NEW YORK ANTI-JEWISH             136
 3                2025 KINGS    ANTI-JEWISH             136
 4                2019 KINGS    ANTI-JEWISH             128
 5                2023 KINGS    ANTI-JEWISH             126
 6                2022 KINGS    ANTI-JEWISH             125
 7                2023 NEW YORK ANTI-JEWISH             124
 8                2025 NEW YORK ANTI-JEWISH             110
 9                2022 NEW YORK ANTI-JEWISH             104
10                2021 NEW YORK ANTI-ASIAN               84
# ℹ 117 more rows

ggplot(data = hate2) +
  geom_bar(aes(x=complaintyearnumber, y = n, fill = biasmotivedescription), 
           position = "dodge", stat = "identity") + 
  labs(fill = "Hate Crime Type", 
       y = "Number of Hate Crimes Incidents", 
       title = "Hate Crime Type in NY Counties Between 2010-2016", 
       caption = "Source: NY State Division of Criminal Justice Services")

ggplot(data = hate2) +
  geom_bar(aes(x=county, y=n, fill = biasmotivedescription), 
           position = "dodge", stat = "identity") + 
  labs(fill = "Hate Crime Type", 
       y = "Number of Hate Crimes Incidents", 
       title = "Hate Crime Type in NY Counties Between 2010-2016", 
       caption = "Source: NY State Division of Criminal Justice Services")

ggplot(data = hate2) +
  geom_bar(aes(x=complaintyearnumber, y = n, fill = biasmotivedescription), 
           position = "dodge", stat = "identity") + 
  facet_wrap(~county) + 
  labs(fill = "Hate Crime Type", 
       y = "Number of Hate Crimes Incidents", 
       title = "Hate Crime Type in NY Counties Between 2010-2016", 
       caption = "Source: NY State Division of Criminal Justice Services")

nypop = read_csv("nyc_census_pop_2020.csv")

Rows: 62 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Area Name, Population Percent Change
num (2): 2020 Census Population, Population Change

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$'Area Name' = gsub("County", "", nypop$'Area Name')
nypop2 = nypop |> 
  rename(county = 'Area Name') |> 
  select(county, '2020 Census Population')
head(nypop2)

# A tibble: 6 × 2
  county         `2020 Census Population`
  <chr>                             <dbl>
1 "Albany "                        314848
2 "Allegany "                       46456
3 "Bronx "                        1472654
4 "Broome "                        198683
5 "Cattaraugus "                    77042
6 "Cayuga "                         76248

datajoin = left_join(hate2, nypop2, by=c("county"))
datajoin

# A tibble: 127 × 5
# Groups:   complaintyearnumber, county [35]
   complaintyearnumber county biasmotivedescription     n 2020 Census Populati…¹
                 <dbl> <chr>  <chr>                 <int>                  <dbl>
 1                2024 KINGS  ANTI-JEWISH             152                     NA
 2                2024 NEW Y… ANTI-JEWISH             136                     NA
 3                2025 KINGS  ANTI-JEWISH             136                     NA
 4                2019 KINGS  ANTI-JEWISH             128                     NA
 5                2023 KINGS  ANTI-JEWISH             126                     NA
 6                2022 KINGS  ANTI-JEWISH             125                     NA
 7                2023 NEW Y… ANTI-JEWISH             124                     NA
 8                2025 NEW Y… ANTI-JEWISH             110                     NA
 9                2022 NEW Y… ANTI-JEWISH             104                     NA
10                2021 NEW Y… ANTI-ASIAN               84                     NA
# ℹ 117 more rows
# ℹ abbreviated name: ¹​`2020 Census Population`

hate_new = hate2 |> 
  mutate(county = str_trim(str_to_lower(as.character(county))))
nypop_new = nypop2 |> 
  mutate(county = str_trim(str_to_lower(as.character(county))))
datajoin = left_join(hate_new, nypop_new, by= "county")
datajoin

# A tibble: 127 × 5
# Groups:   complaintyearnumber, county [35]
   complaintyearnumber county biasmotivedescription     n 2020 Census Populati…¹
                 <dbl> <chr>  <chr>                 <int>                  <dbl>
 1                2024 kings  ANTI-JEWISH             152                2736074
 2                2024 new y… ANTI-JEWISH             136                1694251
 3                2025 kings  ANTI-JEWISH             136                2736074
 4                2019 kings  ANTI-JEWISH             128                2736074
 5                2023 kings  ANTI-JEWISH             126                2736074
 6                2022 kings  ANTI-JEWISH             125                2736074
 7                2023 new y… ANTI-JEWISH             124                1694251
 8                2025 new y… ANTI-JEWISH             110                1694251
 9                2022 new y… ANTI-JEWISH             104                1694251
10                2021 new y… ANTI-ASIAN               84                1694251
# ℹ 117 more rows
# ℹ abbreviated name: ¹​`2020 Census Population`

Assignment #2 — Ending Essay

Data on hate crimes must be collected to this extent, as it can bring awareness to the scale to which different communities are discriminated against. The USA is commonly referred to as a “melting pot,” especially in major cities like New York, so understanding and analyzing this data is eye-opening about how much we can improve. Furthermore, this data set is massive and provides a variety of different variables to analyze. Dissecting the data allows us to better understand the whole and ensure no major bias. An obvious negative point of this data set is the fact that it exists in the first place. Although this data is important to bring awareness, it is saddening to see how many marginalized groups are persecuted simply for being different. However, the data is inconsistent, as error messages when visualizing it. For example, the counties in the data sets had capitalization differences, making it difficult to combine the necessary data.

The first path that would be interesting to study is the dates when each hate crime was reported. Looking at the dates, as well as the respective marginalized group the hate crime was against, allows us to find patterns. We can find patterns of when certain groups were targeted and use events happening at that time to better understand. A second path is looking for outliers within the data. Major outliers lead to skewed data, and with a data set as complex as this one, it can be difficult to grasp outliers immediately. Investigating outliers and working to comprehend them allows us to understand the data on a deeper level.