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library(zoo)##
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## as.Date, as.Date.numericsetwd("~/Desktop/Project 1 DATA101")
df <- read_csv("Police_Department_Investigated_Hate_Crimes.csv")## Rows: 1790 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): record_id, suspects_race_as_a_group, most_serious_ucr, most_serio...
## dbl (8): ncic, total_number_of_victims, total_number_of_individual_victims...
## lgl (1): is_multiple_bias
## date (1): occurence_month
##
## 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.What is a hate crime? A hate crime is a crime against an individual’s real or perceived race, color, religion, ethnicity, sexual orientation, gender identity, or disability. The data set represents hate crimes reported by the SFPD (San Francisco Police Department) to the California Department of Justice from 2001 to September 2025. This data set was derived from data.gov.
This data set consists of 1790 observations across 23 variables, including suspect_race_as_a_group, offensive_Act, most_serious_ucr, and most_serious_bias. This provides valuable insights into the details of the hate crime reports, such as when the crime occurred, where it occurred, and the type of bias. The variables I will be using in this project are most_serious_bias and occurrence_month. With these variables, I plan on comparing the types of hate crime bias reported the most for each year between 2016 and 2025.
In this data analysis, I will obtain information on the types of hate crime bias reported for each year, as well as the type of bias that is reported the most. I will first create a data frame that only contains data from 2016 to 2025. Once I have this data frame, I can then begin the process of extracting the number of reports on each type of bias between the years 2016 and 2025. The following objective is to find the maximum bias for every year. I first created a data frame for each year by passing the data frame, which contains the years between 2016 and 2025-to my new data frame. Then I grouped ‘most_serious_bias’ and summarized the count to extract the numbers of each bias for that specific year. Once I have the amount of each bias, I can then compare the values to find the maximum number and output the type of bias with the maximum count of reports.
str(df)## spc_tbl_ [1,790 × 23] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ record_id : chr [1:1790] "CA01-0000001892" "CA01-0000001899" "CA01-0000001906" "CA01-0000001913" ...
## $ occurence_month : Date[1:1790], format: "2001-12-01" "2001-01-01" ...
## $ ncic : num [1:1790] 3801 3801 3801 3801 NA ...
## $ total_number_of_victims : num [1:1790] 1 1 1 1 1 1 1 1 1 1 ...
## $ total_number_of_individual_victims : num [1:1790] 1 1 1 1 1 1 1 0 0 2 ...
## $ suspects_race_as_a_group : chr [1:1790] "White" "Unknown" "White" "White" ...
## $ total_number_of_suspects : num [1:1790] 1 0 2 1 2 1 5 1 0 1 ...
## $ most_serious_ucr : chr [1:1790] "Intimidation" "Intimidation" "Intimidation" "Simple Assault" ...
## $ most_serious_ucr_type : chr [1:1790] "Violent Crimes" "Violent Crimes" "Violent Crimes" "Violent Crimes" ...
## $ most_serious_location : chr [1:1790] "Highway/Road/Alley/Street" "Commercial/Office Building" "Highway/Road/Alley/Street" "Parking Lot/Garage" ...
## $ most_serious_bias : chr [1:1790] "Anti-Transgender" "Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group)" "Anti-Black or African American" "Anti-Other Race/Ethnicity/Ancestry" ...
## $ most_serious_bias_type : chr [1:1790] "Gender Nonconforming" "Sexual Orientation" "Race/Ethnicity/Ancestry" "Race/Ethnicity/Ancestry" ...
## $ most_serious_victim_type : chr [1:1790] "Person" "Person" "Person" "Person" ...
## $ weapon_type : chr [1:1790] NA NA NA "Other (bottle, rocks, spitting)" ...
## $ offensive_act : chr [1:1790] "Verbal slurs" "Threatening letters/flyers/email" "Verbal slurs" "Verbal slurs" ...
## $ is_multiple_bias : logi [1:1790] NA NA NA NA NA NA ...
## $ total_number_of_individual_victims_adult : num [1:1790] NA NA NA NA 1 1 1 0 0 2 ...
## $ total_number_of_individual_victims_juvenile: num [1:1790] NA NA NA NA 0 0 0 0 0 0 ...
## $ total_number_of_suspects_adult : num [1:1790] NA NA NA NA 2 0 0 1 0 1 ...
## $ total_number_of_suspects_juvenile : num [1:1790] NA NA NA NA 0 0 0 0 0 0 ...
## $ suspects_ethnicity_as_a_group : chr [1:1790] NA NA NA NA ...
## $ data_as_of : chr [1:1790] "2025/09/18 12:00:00 AM" "2025/09/18 12:00:00 AM" "2025/09/18 12:00:00 AM" "2025/09/18 12:00:00 AM" ...
## $ data_loaded_at : chr [1:1790] "2025/09/20 11:03:55 AM" "2025/09/20 11:03:55 AM" "2025/09/20 11:03:55 AM" "2025/09/20 11:03:55 AM" ...
## - attr(*, "spec")=
## .. cols(
## .. record_id = col_character(),
## .. occurence_month = col_date(format = ""),
## .. ncic = col_double(),
## .. total_number_of_victims = col_double(),
## .. total_number_of_individual_victims = col_double(),
## .. suspects_race_as_a_group = col_character(),
## .. total_number_of_suspects = col_double(),
## .. most_serious_ucr = col_character(),
## .. most_serious_ucr_type = col_character(),
## .. most_serious_location = col_character(),
## .. most_serious_bias = col_character(),
## .. most_serious_bias_type = col_character(),
## .. most_serious_victim_type = col_character(),
## .. weapon_type = col_character(),
## .. offensive_act = col_character(),
## .. is_multiple_bias = col_logical(),
## .. total_number_of_individual_victims_adult = col_double(),
## .. total_number_of_individual_victims_juvenile = col_double(),
## .. total_number_of_suspects_adult = col_double(),
## .. total_number_of_suspects_juvenile = col_double(),
## .. suspects_ethnicity_as_a_group = col_character(),
## .. data_as_of = col_character(),
## .. data_loaded_at = col_character()
## .. )
## - attr(*, "problems")=<externalptr>head(df)## # A tibble: 6 × 23
## record_id occurence_month ncic total_number_of_vict…¹ total_number_of_indi…²
## <chr> <date> <dbl> <dbl> <dbl>
## 1 CA01-0000… 2001-12-01 3801 1 1
## 2 CA01-0000… 2001-01-01 3801 1 1
## 3 CA01-0000… 2001-03-01 3801 1 1
## 4 CA01-0000… 2001-01-01 3801 1 1
## 5 220015368 2022-01-01 NA 1 1
## 6 220078413 2022-02-01 NA 1 1
## # ℹ abbreviated names: ¹total_number_of_victims,
## # ²total_number_of_individual_victims
## # ℹ 18 more variables: suspects_race_as_a_group <chr>,
## # total_number_of_suspects <dbl>, most_serious_ucr <chr>,
## # most_serious_ucr_type <chr>, most_serious_location <chr>,
## # most_serious_bias <chr>, most_serious_bias_type <chr>,
## # most_serious_victim_type <chr>, weapon_type <chr>, offensive_act <chr>, …colSums(is.na(df))## record_id
## 0
## occurence_month
## 0
## ncic
## 182
## total_number_of_victims
## 0
## total_number_of_individual_victims
## 0
## suspects_race_as_a_group
## 0
## total_number_of_suspects
## 0
## most_serious_ucr
## 0
## most_serious_ucr_type
## 182
## most_serious_location
## 0
## most_serious_bias
## 0
## most_serious_bias_type
## 0
## most_serious_victim_type
## 0
## weapon_type
## 774
## offensive_act
## 47
## is_multiple_bias
## 1789
## total_number_of_individual_victims_adult
## 1221
## total_number_of_individual_victims_juvenile
## 1221
## total_number_of_suspects_adult
## 1221
## total_number_of_suspects_juvenile
## 1221
## suspects_ethnicity_as_a_group
## 1422
## data_as_of
## 0
## data_loaded_at
## 0#no NA's present in the columns I will be using#Frequency table
table(df$most_serious_bias)##
## Anti-Arab
## 42
## Anti-Asian
## 210
## Anti-Bisexual
## 2
## Anti-Black or African American
## 229
## Anti-Catholic
## 16
## Anti-Citizenship Status
## 2
## Anti-Female
## 4
## Anti-Gay (Male)
## 427
## Anti-Gender Non-Conforming
## 1
## Anti-Hindu
## 1
## Anti-Hispanic or Latino
## 96
## Anti-Islamic (Muslim)
## 47
## Anti-Jewish
## 187
## Anti-Lesbian
## 62
## Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group)
## 54
## Anti-Lesbian/Gay/Bisexual/Transgender
## 10
## Anti-Male
## 1
## Anti-Mental Disability
## 1
## Anti-Multiple Races (Group)
## 36
## Anti-Multiple Religions (Group)
## 4
## Anti-Other Christian
## 5
## Anti-Other Race/Ethnicity/Ancestry
## 141
## Anti-Other Religion
## 13
## Anti-Physical Disability
## 4
## Anti-Protestant
## 1
## Anti-Transgender
## 85
## Anti-White
## 109df <- df |>
mutate(year = year(occurence_month)) #extract the year from the date the crime occurred
head(df)## # A tibble: 6 × 24
## record_id occurence_month ncic total_number_of_vict…¹ total_number_of_indi…²
## <chr> <date> <dbl> <dbl> <dbl>
## 1 CA01-0000… 2001-12-01 3801 1 1
## 2 CA01-0000… 2001-01-01 3801 1 1
## 3 CA01-0000… 2001-03-01 3801 1 1
## 4 CA01-0000… 2001-01-01 3801 1 1
## 5 220015368 2022-01-01 NA 1 1
## 6 220078413 2022-02-01 NA 1 1
## # ℹ abbreviated names: ¹total_number_of_victims,
## # ²total_number_of_individual_victims
## # ℹ 19 more variables: suspects_race_as_a_group <chr>,
## # total_number_of_suspects <dbl>, most_serious_ucr <chr>,
## # most_serious_ucr_type <chr>, most_serious_location <chr>,
## # most_serious_bias <chr>, most_serious_bias_type <chr>,
## # most_serious_victim_type <chr>, weapon_type <chr>, offensive_act <chr>, …df_post2016 <- df |>
filter(year %in% c(2016:2025)) |>
arrange(year)
print(df_post2016)## # A tibble: 560 × 24
## record_id occurence_month ncic total_number_of_vict…¹ total_number_of_indi…²
## <chr> <date> <dbl> <dbl> <dbl>
## 1 CA00-000… 2016-03-01 3801 1 1
## 2 CA00-000… 2016-06-01 3801 1 1
## 3 CA00-000… 2016-06-01 3801 2 2
## 4 CA00-000… 2016-08-01 3801 1 1
## 5 CA00-000… 2016-10-01 3801 1 1
## 6 CA00-000… 2016-09-01 3801 1 1
## 7 CA00-000… 2016-02-01 3801 1 1
## 8 CA00-000… 2016-04-01 3801 2 2
## 9 CA00-000… 2016-06-01 3801 1 1
## 10 CA00-000… 2016-08-01 3801 1 1
## # ℹ 550 more rows
## # ℹ abbreviated names: ¹total_number_of_victims,
## # ²total_number_of_individual_victims
## # ℹ 19 more variables: suspects_race_as_a_group <chr>,
## # total_number_of_suspects <dbl>, most_serious_ucr <chr>,
## # most_serious_ucr_type <chr>, most_serious_location <chr>,
## # most_serious_bias <chr>, most_serious_bias_type <chr>, …table(df_post2016$most_serious_bias)##
## Anti-Arab
## 11
## Anti-Asian
## 118
## Anti-Bisexual
## 1
## Anti-Black or African American
## 77
## Anti-Catholic
## 2
## Anti-Citizenship Status
## 2
## Anti-Female
## 2
## Anti-Gay (Male)
## 97
## Anti-Gender Non-Conforming
## 1
## Anti-Hindu
## 1
## Anti-Hispanic or Latino
## 53
## Anti-Islamic (Muslim)
## 17
## Anti-Jewish
## 71
## Anti-Lesbian
## 8
## Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group)
## 6
## Anti-Lesbian/Gay/Bisexual/Transgender
## 10
## Anti-Male
## 1
## Anti-Mental Disability
## 1
## Anti-Multiple Races (Group)
## 6
## Anti-Other Christian
## 5
## Anti-Other Race/Ethnicity/Ancestry
## 15
## Anti-Other Religion
## 2
## Anti-Physical Disability
## 1
## Anti-Protestant
## 1
## Anti-Transgender
## 28
## Anti-White
## 23#According to the table Anti-Asian is the most prominent hate crime in San Francisco between the years 2016-2025year_2016 <- df_post2016 |>
filter(year == 2016) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2016)## # A tibble: 13 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 1
## 2 Anti-Asian 3
## 3 Anti-Black or African American 3
## 4 Anti-Female 1
## 5 Anti-Gay (Male) 11
## 6 Anti-Hindu 1
## 7 Anti-Jewish 4
## 8 Anti-Lesbian 2
## 9 Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group) 1
## 10 Anti-Other Religion 1
## 11 Anti-Protestant 1
## 12 Anti-Transgender 4
## 13 Anti-White 3most_bias_2016 <- year_2016$most_serious_bias[which.max(year_2016$Count)]
print(most_bias_2016) ## [1] "Anti-Gay (Male)"year_2017 <- df_post2016 |>
filter(year == 2017) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2017)## # A tibble: 12 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Asian 7
## 2 Anti-Bisexual 1
## 3 Anti-Black or African American 5
## 4 Anti-Catholic 1
## 5 Anti-Gay (Male) 9
## 6 Anti-Hispanic or Latino 5
## 7 Anti-Islamic (Muslim) 3
## 8 Anti-Jewish 3
## 9 Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group) 1
## 10 Anti-Other Religion 1
## 11 Anti-Transgender 4
## 12 Anti-White 2most_bias_2017 <- year_2017$most_serious_bias[which.max(year_2017$Count)]
print(most_bias_2017) ## [1] "Anti-Gay (Male)"year_2018 <- df_post2016 |>
filter(year == 2018) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2018)## # A tibble: 15 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 1
## 2 Anti-Asian 4
## 3 Anti-Black or African American 12
## 4 Anti-Citizenship Status 1
## 5 Anti-Gay (Male) 13
## 6 Anti-Hispanic or Latino 14
## 7 Anti-Islamic (Muslim) 3
## 8 Anti-Jewish 4
## 9 Anti-Lesbian 1
## 10 Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group) 3
## 11 Anti-Multiple Races (Group) 1
## 12 Anti-Other Christian 1
## 13 Anti-Other Race/Ethnicity/Ancestry 4
## 14 Anti-Transgender 2
## 15 Anti-White 4most_bias_2018 <- year_2018$most_serious_bias[which.max(year_2018$Count)]
print(most_bias_2018) ## [1] "Anti-Hispanic or Latino"year_2019 <- df_post2016 |>
filter(year == 2019) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2019)## # A tibble: 12 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 4
## 2 Anti-Asian 6
## 3 Anti-Black or African American 11
## 4 Anti-Gay (Male) 21
## 5 Anti-Hispanic or Latino 6
## 6 Anti-Islamic (Muslim) 3
## 7 Anti-Jewish 2
## 8 Anti-Lesbian 1
## 9 Anti-Multiple Races (Group) 3
## 10 Anti-Other Race/Ethnicity/Ancestry 1
## 11 Anti-Transgender 2
## 12 Anti-White 4most_bias_2019 <- year_2019$most_serious_bias[which.max(year_2019$Count)]
print(most_bias_2019) ## [1] "Anti-Gay (Male)"year_2020 <- df_post2016 |>
filter(year == 2020) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2020)## # A tibble: 14 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 1
## 2 Anti-Asian 9
## 3 Anti-Black or African American 10
## 4 Anti-Catholic 1
## 5 Anti-Citizenship Status 1
## 6 Anti-Gay (Male) 5
## 7 Anti-Hispanic or Latino 9
## 8 Anti-Islamic (Muslim) 2
## 9 Anti-Jewish 4
## 10 Anti-Lesbian 2
## 11 Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group) 1
## 12 Anti-Other Race/Ethnicity/Ancestry 2
## 13 Anti-Transgender 2
## 14 Anti-White 5most_bias_2020 <- year_2020$most_serious_bias[which.max(year_2020$Count)]
print(most_bias_2020) ## [1] "Anti-Black or African American"year_2021 <- df_post2016 |>
filter(year == 2021) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2021)## # A tibble: 10 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Asian 60
## 2 Anti-Black or African American 14
## 3 Anti-Gay (Male) 15
## 4 Anti-Hispanic or Latino 5
## 5 Anti-Islamic (Muslim) 2
## 6 Anti-Jewish 8
## 7 Anti-Multiple Races (Group) 1
## 8 Anti-Other Race/Ethnicity/Ancestry 6
## 9 Anti-Transgender 1
## 10 Anti-White 2most_bias_2021 <- year_2021$most_serious_bias[which.max(year_2021$Count)]
print(most_bias_2021) ## [1] "Anti-Asian"year_2022 <- df_post2016 |>
filter(year == 2022) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2022)## # A tibble: 13 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 1
## 2 Anti-Asian 6
## 3 Anti-Black or African American 3
## 4 Anti-Female 1
## 5 Anti-Gay (Male) 6
## 6 Anti-Gender Non-Conforming 1
## 7 Anti-Hispanic or Latino 3
## 8 Anti-Jewish 5
## 9 Anti-Lesbian/Gay/Bisexual/Transgender 2
## 10 Anti-Male 1
## 11 Anti-Other Christian 3
## 12 Anti-Transgender 2
## 13 Anti-White 2most_bias_2022 <- year_2022$most_serious_bias[which.max(year_2022$Count)]
print(most_bias_2022) ## [1] "Anti-Asian"year_2023 <- df_post2016 |>
filter(year == 2023) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2023)## # A tibble: 11 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 2
## 2 Anti-Asian 13
## 3 Anti-Black or African American 5
## 4 Anti-Gay (Male) 4
## 5 Anti-Hispanic or Latino 3
## 6 Anti-Islamic (Muslim) 1
## 7 Anti-Jewish 23
## 8 Anti-Lesbian 2
## 9 Anti-Lesbian/Gay/Bisexual/Transgender 3
## 10 Anti-Other Christian 1
## 11 Anti-Transgender 6most_bias_2023 <- year_2023$most_serious_bias[which.max(year_2023$Count)]
print(most_bias_2023) ## [1] "Anti-Jewish"year_2024 <- df_post2016 |>
filter(year == 2024) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2024)## # A tibble: 11 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Arab 1
## 2 Anti-Asian 5
## 3 Anti-Black or African American 13
## 4 Anti-Gay (Male) 9
## 5 Anti-Hispanic or Latino 2
## 6 Anti-Jewish 13
## 7 Anti-Lesbian/Gay/Bisexual/Transgender 3
## 8 Anti-Mental Disability 1
## 9 Anti-Other Race/Ethnicity/Ancestry 2
## 10 Anti-Physical Disability 1
## 11 Anti-Transgender 2most_bias_2024 <- year_2024$most_serious_bias[which(year_2024$Count == max(year_2024$Count) )]
print(most_bias_2024) #There's 2 max## [1] "Anti-Black or African American" "Anti-Jewish"year_2025 <- df_post2016 |>
filter(year == 2025) |>
group_by(most_serious_bias) |>
summarize(Count = n())
print(year_2025)## # A tibble: 10 × 2
## most_serious_bias Count
## <chr> <int>
## 1 Anti-Asian 5
## 2 Anti-Black or African American 1
## 3 Anti-Gay (Male) 4
## 4 Anti-Hispanic or Latino 6
## 5 Anti-Islamic (Muslim) 3
## 6 Anti-Jewish 5
## 7 Anti-Lesbian/Gay/Bisexual/Transgender 2
## 8 Anti-Multiple Races (Group) 1
## 9 Anti-Transgender 3
## 10 Anti-White 1most_bias_2025 <- year_2025$most_serious_bias[which.max(year_2025$Count)]
print(most_bias_2025) ## [1] "Anti-Hispanic or Latino"df_most_bias <- df_post2016 |>
group_by(year, most_serious_bias) |>
summarize(Count = n())## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.most_bias <- df_most_bias$most_serious_bias[which.max(df_most_bias$Count)]
print(df_most_bias)## # A tibble: 121 × 3
## # Groups: year [10]
## year most_serious_bias Count
## <dbl> <chr> <int>
## 1 2016 Anti-Arab 1
## 2 2016 Anti-Asian 3
## 3 2016 Anti-Black or African American 3
## 4 2016 Anti-Female 1
## 5 2016 Anti-Gay (Male) 11
## 6 2016 Anti-Hindu 1
## 7 2016 Anti-Jewish 4
## 8 2016 Anti-Lesbian 2
## 9 2016 Anti-Lesbian/Gay/Bisexual or Transgender (Mixed Group) 1
## 10 2016 Anti-Other Religion 1
## # ℹ 111 more rowsprint(most_bias) #Between 2016 and 2025 Anti-Asian is the most prevalent hate crime## [1] "Anti-Asian"The analysis of hate crime reports in SF from 2016 to 2025 reveals an annual fluctuation of hate crime biases. Throughout the years, the most reported hate crime biases have shifted notably. For instance, Anti-Gay bias was the most prevalent hate crime bias in 2016, 2017, and 2019. In 2018 and 2025, anti-Hispanic or Latino bias was commonly reported. In 2020 and 2024, Anti-Black or African American bias was reported the most. Anti-Asian hate crime was prevalent in 2021 and 2022, and in 2023, anti-Jewish bias reached its peak. The variance of hate crime bias potentially suggests a relationship between incidents and the political timeline. By connecting these data points with historical and political context, we can better understand which communities are impacted the most and explore the underlying reasons for the rise in hate crimes against certain groups. For example, the analysis shows an increase in anti-Hispanic or Latino bias for the year 2025. I can infer this increase is related to the rise of anti-immigration sentiment. This exploratory data analysis provides a foundation for research on how and why these types of hate crime bias fluctuate throughout the years. In addition, this hate crime bias analysis is crucial for understanding reports, identifying trends, and relationships. Thus, allowing us to form preventative measures on hate crimes, and to create new hypotheses to further the analysis.
Morey, Brittany N. “Mechanisms by Which Anti-Immigrant Stigma Exacerbates Racial/Ethnic Health Disparities.” American journal of public health vol. 108,4 (2018): 460-463. doi:10.2105/AJPH.2017.304266
“Data.gov.” Data.gov, 2025, catalog.data.gov/dataset/police-department-investigated-hate-crimes.