DATA110_Fernandez_Maira_Assignment3_NYPD_Hate_Crimes

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)
setwd("C:/Users/MFERN123/DATA_110")
hatecrimes<-read.csv("NYPD_Hate_Crimes_19-26.csv")

names(hatecrimes)<-tolower(names(hatecrimes))
names(hatecrimes)<-gsub(" ","",names(hatecrimes))
head(hatecrimes)

  full.complaint.id complaint.year.number month.number record.create.date
1       2.01906e+14                  2019            1          1/23/2019
2       2.01906e+14                  2019            2          2/25/2019
3       2.01906e+14                  2019            2          2/27/2019
4       2.01906e+14                  2019            4          4/16/2019
5       2.01906e+14                  2019            6          6/20/2019
6       2.01906e+14                  2019            7          7/31/2019
  complaint.precinct.code     patrol.borough.name county
1                      60 PATROL BORO BKLYN SOUTH  KINGS
2                      60 PATROL BORO BKLYN SOUTH  KINGS
3                      60 PATROL BORO BKLYN SOUTH  KINGS
4                      60 PATROL BORO BKLYN SOUTH  KINGS
5                      60 PATROL BORO BKLYN SOUTH  KINGS
6                      60 PATROL BORO BKLYN SOUTH  KINGS
  law.code.category.description     offense.description     pd.code.description
1                        FELONY MISCELLANEOUS PENAL LAW AGGRAVATED HARASSMENT 1
2                        FELONY MISCELLANEOUS PENAL LAW AGGRAVATED HARASSMENT 1
3                        FELONY MISCELLANEOUS PENAL LAW AGGRAVATED HARASSMENT 1
4                        FELONY MISCELLANEOUS PENAL LAW AGGRAVATED HARASSMENT 1
5                        FELONY MISCELLANEOUS PENAL LAW AGGRAVATED HARASSMENT 1
6                        FELONY MISCELLANEOUS PENAL LAW AGGRAVATED HARASSMENT 1
  bias.motive.description            offense.category arrest.date arrest.id
1             ANTI-JEWISH Religion/Religious Practice          NA          
2             ANTI-JEWISH Religion/Religious Practice          NA          
3             ANTI-JEWISH Religion/Religious Practice          NA          
4             ANTI-JEWISH Religion/Religious Practice          NA          
5             ANTI-JEWISH Religion/Religious Practice          NA          
6             ANTI-JEWISH Religion/Religious Practice          NA          

summary(hatecrimes)

 full.complaint.id   complaint.year.number  month.number    record.create.date
 Min.   :2.019e+14   Min.   :2019          Min.   : 1.000   Length   :4029    
 1st Qu.:2.021e+14   1st Qu.:2021          1st Qu.: 4.000   N.unique :1783    
 Median :2.023e+14   Median :2023          Median : 6.000   N.blank  :   0    
 Mean   :2.022e+14   Mean   :2022          Mean   : 6.456   Min.nchar:   8    
 3rd Qu.:2.024e+14   3rd Qu.:2024          3rd Qu.: 9.000   Max.nchar:  10    
 Max.   :2.025e+14   Max.   :2025          Max.   :12.000                     
 complaint.precinct.code patrol.borough.name       county    
 Min.   :  1.00          Length   :4029      Length   :4029  
 1st Qu.: 19.00          N.unique :   8      N.unique :   5  
 Median : 66.00          N.blank  :   0      N.blank  :   0  
 Mean   : 59.41          Min.nchar:  17      Min.nchar:   5  
 3rd Qu.: 90.00          Max.nchar:  25      Max.nchar:   8  
 Max.   :123.00                                              
 law.code.category.description offense.description pd.code.description
 Length   :4029                Length   :4029      Length   :4029     
 N.unique :   4                N.unique :  24      N.unique :  87     
 N.blank  :   0                N.blank  :   0      N.blank  :   0     
 Min.nchar:   6                Min.nchar:   4      Min.nchar:   6     
 Max.nchar:  13                Max.nchar:  30      Max.nchar:  60     
                                                                      
 bias.motive.description  offense.category arrest.date        arrest.id   
 Length   :4029          Length   :4029    Mode:logical   Length   :4029  
 N.unique :  29          N.unique :   7    NAs :4029      N.unique :1726  
 N.blank  :   0          N.blank  :   0                   N.blank  :2282  
 Min.nchar:   9          Min.nchar:   3                   Min.nchar:   0  
 Max.nchar:  33          Max.nchar:  34                   Max.nchar:   9  
                                                                          

bias_count<-hatecrimes |>
  select(bias.motive.description) |>
  group_by(bias.motive.description) |>
  count() |>
  arrange(desc(n))
head(bias_count)

# A tibble: 6 × 2
# Groups:   bias.motive.description [6]
  bias.motive.description        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=bias.motive.description))+geom_bar()

bias_count |>
  head(10) |>
  ggplot(aes(x=bias.motive.description,y=n))+
  geom_col()

bias_count |>
  head(10) |>
  ggplot(aes(x=reorder(bias.motive.description,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 hatecrimes motive",
       caption = "Source: NY State Division of Criminal Justice Services")

bias_count |>
  head(10) |>
  ggplot(aes(x=reorder(bias.motive.description,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 hatecrimes motive",
       caption = "Source: NY State Division of Criminal Justice Services")

theme_minimal()

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  [list output truncated]
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bias_count |>
  head(10) |>
  ggplot(aes(x=reorder(bias.motive.description,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 hatecrimes motive",
       caption = "Source: NY State Division of Criminal Justice Services")+
  geom_text(aes(label=n),hjust = -.05,size = 3)+
  theme(axis.text.x = element_blank())

theme_minimal()

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  [list output truncated]
 @ complete: logi TRUE
 @ validate: logi TRUE

hate_year<- hatecrimes |>
  filter(bias.motive.description%in% c("ANTI-JEWISH","ANTI-MALE HOMOSEXUAL (GAY)","ANTI-ASIAN","ANTI-BLACK"))|>
  group_by(complaint.year.number) |>
  count(bias.motive.description) |>
  arrange(desc(n))
hate_year

# A tibble: 28 × 3
# Groups:   complaint.year.number [7]
   complaint.year.number bias.motive.description        n
                   <int> <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(bias.motive.description%in% c("ANTI-JEWISH","ANTI-MALE HOMOSEXUAL (GAY)","ANTI-ASIAN","ANTI-BLACK"))|>
  group_by(county) |>
  count(bias.motive.description) |>
  arrange(desc(n))
hate_county

# A tibble: 20 × 3
# Groups:   county [5]
   county   bias.motive.description        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(bias.motive.description%in% c("ANTI-JEWISH","ANTI-MALE HOMOSEXUAL (GAY)","ANTI-ASIAN","ANTI-BLACK"))|>
  group_by(complaint.year.number,county) |>
  count(bias.motive.description) |>
  arrange(desc(n))
hate2

# A tibble: 127 × 4
# Groups:   complaint.year.number, county [35]
   complaint.year.number county   bias.motive.description     n
                   <int> <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=complaint.year.number, y=n, fill = bias.motive.description),
           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")

ggplot(data = hate2) +
  geom_bar(aes(x=county, y=n, fill = bias.motive.description),
           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")

ggplot(data = hate2) +
  geom_bar(aes(x=complaint.year.number, y=n, fill = bias.motive.description),
           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")

setwd("C:/Users/MFERN123/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.

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:   complaint.year.number, county [35]
   complaint.year.number county   bias.motive.description     n
                   <int> <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
# ℹ 1 more variable: `2020 Census Population` <dbl>

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))))

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

# A tibble: 127 × 5
# Groups:   complaint.year.number, county [35]
   complaint.year.number county   bias.motive.description     n
                   <int> <fct>    <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
# ℹ 1 more variable: `2020 Census Population` <dbl>

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

# A tibble: 127 × 6
# Groups:   complaint.year.number, county [35]
   complaint.year.number county   bias.motive.description     n
                   <int> <fct>    <chr>                   <int>
 1                  2024 new york ANTI-JEWISH               136
 2                  2023 new york ANTI-JEWISH               124
 3                  2025 new york ANTI-JEWISH               110
 4                  2022 new york ANTI-JEWISH               104
 5                  2024 kings    ANTI-JEWISH               152
 6                  2025 kings    ANTI-JEWISH               136
 7                  2021 new york ANTI-ASIAN                 84
 8                  2021 new york ANTI-JEWISH                84
 9                  2019 kings    ANTI-JEWISH               128
10                  2023 kings    ANTI-JEWISH               126
# ℹ 117 more rows
# ℹ 2 more variables: `2020 Census Population` <dbl>, rate <dbl>

One positive aspect of this dataset is the wide available information which students can work with. It has a large and complete amount of information about male categories of hate crimes. A negative aspect is the fact that this information is only from New York and not related with Maryland, also I think the dataset does not show information about hate crimes for women, which can be interesting to analyze.

One hypothetical path to work with would be if we could analyze data from the same source but from different years. For example, analyze the same factors before the pandemic, and try to guess if the pandemic and lockdown have any influence in hate crimes. Another hypothetical path to work would be if we could analyze data from the same source and same factors but for hate crimes against women. It can be interesting analyze also, if the rates per year are the same for all the states in United States, and if there is any factor that influence in those changes.