Assignment 3 Mantilla

Author

Javier Mantilla

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

library(tidyverse)
Warning: package 'ggplot2' was built under R version 4.5.2
── 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   4.0.2     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── 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)
setwd("~/Documents/Data 110")
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 the data:

Make all headers lowercase and remove 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>

Explore the bias motive (biasmotivedescription)

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 inclusion/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 the hatecrime motive",
       caption = "Source: NY State Division of Criminal Justice Services")

#Finally add color and change 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()

#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

First Check The 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

Then check the 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

#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")

#What about the 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")

#How would calculations be affected by looking at hate crimes in counties per year by population densities?

setwd("~/Documents/Data 110")
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 the 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`

It didn’t work - 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>

Your turn!

Once you complete this tutorial, 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 could hypothetically like to study about this dataset at some future point.

  3. Some positive things about the hatecrimes dataset is the broadness of the variables. They go in many different directions from the offense category, like religion and religious practice all the way to the complaint year. This setup of the dataset makes it very easy to show correlation between variables and it makes it a well-rounded dataset. Some negatives about the hatecrimes dataset are the two variables at the end. Arrestdate and arrestid are filled all the way down with N/A values, which makes me question the reason why those variables are included in the first place. Also, the vertical barplots x-axis was very messy, making it very hard to follow along and understand those visualizations.

  4. One thing that I would like to study about this dataset in the future is the correlation between the population density of each county ( Bronx, Kings, Queens, New York and Richmond) and the actual counts of the hate crimes. This would see if the increasing rates of the hate crimes would be caused by the bigger population size or the actual increase of hate crime incidents. Another thing that I would like to study about this dataset in the future is the is if the hate crimes increase when major social or political events happen. For example, if more anti-semetic crimes happen during the Palestine and Israel conflict.