Week 3 Homework Assignment

Author

Jaiden Soto

Initial Data:

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── 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

library(knitr)
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.

Cleaning the Data:

Making headers lowercase and removing spaces

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>

Exposing bigotry with the data

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

Visualizing bigoted motives

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

Filtering the top 10 groups that face the most bigotry

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

Reorganizing the data vertically

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

Adding labels

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 Graph of Hate Crimes from 2019-2026",
       subtitle = "Counts based on the hatecrime motive",
       caption = "Source: NY State Division of Criminal Justice Services")

Adding color and switching the theme

bias_count |>
  head(10) |>
  ggplot(aes(x=reorder(biasmotivedescription, n), y = n)) +
  geom_col(fill = "salmon") +
  coord_flip()+
  labs(x = "",
       y = "Counts of hatecrime types based on motive",
       title = "Bar Graph of Hate Crimes from 2019-2026",
       subtitle = "Counts based on the hatecrime motive",
       caption = "Source: NY State Division of Criminal Justice Services") +
  theme_minimal()

Reorganizing the data by removing x-values, adding direct count

bias_count |>
  head(10) |>
  ggplot(aes(x=reorder(biasmotivedescription, n), y = n)) +
  geom_col(fill = "salmon") +
  coord_flip()+
  labs(x = "",
       y = "Counts of hatecrime types based on motive",
       title = "Bar Graph 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())

Researching specific hate crime data on oppressed groups:

Year Totals

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

County Totals

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

Merge year totals and county totals onto one table

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

Plot yearly data with the, “dodge”, “identity” functions

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 Crime Incidents",
       title = "Hate Crime Type in NY Counties Between 2010-2016",
       caption = "Source: NY State Division of Criminal Justice Services")

Plot county data with the “dodge”, and, “identity” function

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 Crime Incidents",
       title = "Hate Crime Type in NY Counties Between 2010-2016",
       caption = "Source: NY State Division of Criminal Justice Services")

Merge the data onto one chart with the “facet” function

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 Crime Incidents",
       title = "Hate Crime Type in NY Counties Between 2010-2016",
       caption = "Source: NY State Division of Criminal Justice Services")

Incorporate NYC census data for further understanding

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.

Rename one of the census variables, geography, to county to match with county data set

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

Join the county hate data with the NYC census data

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`

Fix the error, correct for values that are shown as N/A

hate_new <- hate2 |>
  mutate(county = as_factor(str_to_lower(as.character(county))))
nypop_new <- nypop2 |>
  mutate(county = as_factor(str_to_lower(as.character(county))))

Fixed code

datajoin <- left_join(hate_new, nypop_new, by=c("county"))
datajoin

# A tibble: 127 × 5
# Groups:   complaintyearnumber, county [35]
   complaintyearnumber county biasmotivedescription     n 2020 Census Populati…¹
                 <dbl> <fct>  <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`

Find the rate of incidents per 100,000 people in descending order

datajoinrate <- datajoin |>
  mutate(rate = n/`2020 Census Population`* 100000) |>
  arrange(desc(rate))
datajoinrate

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

Essay:

When it comes to the hate crime data set, there are definitely positives when it comes to the raw information. The variables available in the data set are diverse, with the location, date, victim of the hate crime, and the case information available. This is, however, the limit when it comes to the positives. The negatives quickly show when we want to connect the variables into a larger context. The data isn’t organized in a way to find patterns in the data. It’s individually organized on a case-by-case basis, making it hard to make connections between the cases to gather more information. When it comes to this individual hate crime data set, there are more possibilities that add greater context available. I would think about incorporating the type of crime, specifically physical, verbal, or other. This could give a greater context when it comes to the individual incidents, and give a greater context about what we need to work on as a society. Another path would be if there was a conviction or not. This could show us if the justice system is properly incorporating the correct ways of handling these incidents.