Week 3 Homework Assignment 2

#Load Data
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── 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(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.

#Clean up data
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>

#Explore the bais motive
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

# Visualize these counts as a bar graph
ggplot(hatecrimes, aes(x = biasmotivedescription))+
  geom_bar()

# Use inlcusion/exclusion criteria to filter
bias_count |>
  head(10) |>
  ggplot(aes(x=biasmotivedescription, y= n)) +
  geom_col()

# Arrange the bars according to height and rotate

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

# Add title, caption for the data source, and x-axis label

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 hatecrime motive",
       caption = "Source: NY State Divsion of Criminal Justice Services")

# Add color and change theme
bias_count |>
  head(10) |>
  ggplot(aes(x=reorder(biasmotivedescription, n), y= n)) +
  geom_col(fill = "salmon") +
  coord_flip() +
  labs(x= "",
       y = "Counts of haatecrime 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()

# Add annotations for counts and remove the x-axis values

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())

# Look deeper into crimes against Jewish, Asian, Black people, and gay males

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: 21 × 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                2022 ANTI-ASIAN               91
10                2023 ANTI-ASIAN               60
# ℹ 11 more rows

# Check country 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

# Check information combining totals from counties and years 
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 these three types of hate crimes together

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")

# Counties 

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")

# Put it all together with years and counties using "facet"

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")

# See how calculations can be affected by looking at hate crimes in counties per year by population densities 


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.

# Clean the county name to match the other dataset

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 hate2 data with nypop

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`

# Error, the new column has NA Values

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))))

# Try joining again

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`

# Calculate the rate of incidents per 100,000. Then arrange 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>

• At the end of your markdown/Quarto file with the hatecrimes code, include an essay of about 150-200 words which that answers the following questions:

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

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

The positive and negative aspects of this hate crimes data set are that it’s in a legible order to read and understand with the rates being descending. However, just looking at the data set can be confusing to understand because if a person doesn’t know anything about the data, it’s not clear what “n” is. Additionally, I believe it would’ve been more helpful to display the final data set as a graph so it’s easier to understand. It’s not realistic for someone to look through all 127 rows of data to see a trend, so a graph would make it easier. Furthermore, just by the data it’s hard to see if there’s a trend or correlation going on. For example, I think the graphs for Hate Crime Type in NY Counties Between 2010-2016 were very informative and easy to understand. The two different paths I would like to hypothetically study about this data set are the ethnicity populations/distributions on each county to see if it correlates, and I would also like to study more about the history or previous education system of each county.