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✔ ggplot2 4.0.1 ✔ tibble 3.3.0
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.1.0
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✖ dplyr::filter() masks stats::filter()
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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.
# 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>
# 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
# 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
# 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
# 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
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.
# 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
# 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`
# 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`
# 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>
1a. The dataset
2a. I would like to study
