Assignment_5
Hina Ahmed
2024-10-18
R Markdown
The following bar graphs highlight the count of police murders across race and gender, where male murders seem to in the highest amount, across all race types.
The point maps highlight the distribution of police murders across the country, in terms of gender and age. It seems the results across the country are consistent, where male murders in the age bracket of 30-35 are the most frequent across almost all of the US states.
library (tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.3## Warning: package 'ggplot2' was built under R version 4.3.3## ── 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 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(plotly)## Warning: package 'plotly' was built under R version 4.3.3##
## Attaching package: 'plotly'
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## The following object is masked from 'package:ggplot2':
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## last_plot
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## The following object is masked from 'package:stats':
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## filter
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## The following object is masked from 'package:graphics':
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## layoutfatal<- read.csv("C:/Users/Hina/Desktop/FATAL_ENCOUNTERS.csv")
library(stringr)
fatal %>%
group_by(Gender, Race) %>% # Group by Gender and Race
tally(name = "n") %>% # Count rows and name the column 'n'
mutate(p = n / sum(n)) %>% # Calculate percentage for each group
arrange(desc(Race)) %>% # Arrange data by Race
ggplot(aes(x = Gender, y = p, fill = Race)) + # Create the ggplot
geom_bar(stat = "identity", position = "stack") + # Create stacked bar chart
geom_text(aes(label = str_c(round(p * 100, 1), "%")), # Add text labels
color = "white",
position = position_stack(vjust = 0.3) ) + # Position labels inside the bars
labs(y = "Percentage", x = "Gender") + # Labels for axes
theme_minimal() 
ggplot(data = fatal) +
geom_bar(mapping = aes(x=Gender, fill=Race), position = "stack")
library(viridis)## Warning: package 'viridis' was built under R version 4.3.3## Loading required package: viridisLitelibrary(sf)## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUEclass(fatal)## [1] "data.frame"sum(is.na(fatal$Longitude))## [1] 1sum(is.na(fatal$Latitude))## [1] 0fatal[is.na(fatal$Longitude) | is.na(fatal$Latitude), ]## Unique.ID Name Age Gender Race
## 31498 NA This is a spacer for Fatal Encounters use.
## Race.with.imputations Imputation.probability
## 31498
## URL.of.image..PLS.NO.HOTLINKS.
## 31498
## Date.of.injury.resulting.in.death..month.day.year.
## 31498 12/31/1999
## Location.of.injury..address. Location.of.death..city.
## 31498 This row is a spacer for Fatal Encounters use.
## State Location.of.death..zip.code. Location.of.death..county.
## 31498 NA
## Full.Address Latitude Longitude Agency.or.agencies.involved
## 31498 NA
## Highest.level.of.force UID.Temporary Name.Temporary Armed.Unarmed
## 31498 NA
## Alleged.weapon Aggressive.physical.movement Fleeing.Not.fleeing
## 31498
## Description.Temp URL.Temp Brief.description
## 31498
## Dispositions.Exclusions.INTERNAL.USE..NOT.FOR.ANALYSIS
## 31498
## Intended.use.of.force..Developing. Supporting.document.link X X.1
## 31498 NA NA
## Unique.ID.formula Unique.identifier..redundant.
## 31498 31497 NAfatal <- fatal[!is.na(fatal$Longitude) & !is.na(fatal$Latitude), ]
fatal$Longitude <- as.numeric(as.character(fatal$Longitude))
fatal$Latitude <- as.numeric(as.character(fatal$Latitude))## Warning: NAs introduced by coercionfatal[!is.finite(fatal$Longitude), ]## [1] Unique.ID
## [2] Name
## [3] Age
## [4] Gender
## [5] Race
## [6] Race.with.imputations
## [7] Imputation.probability
## [8] URL.of.image..PLS.NO.HOTLINKS.
## [9] Date.of.injury.resulting.in.death..month.day.year.
## [10] Location.of.injury..address.
## [11] Location.of.death..city.
## [12] State
## [13] Location.of.death..zip.code.
## [14] Location.of.death..county.
## [15] Full.Address
## [16] Latitude
## [17] Longitude
## [18] Agency.or.agencies.involved
## [19] Highest.level.of.force
## [20] UID.Temporary
## [21] Name.Temporary
## [22] Armed.Unarmed
## [23] Alleged.weapon
## [24] Aggressive.physical.movement
## [25] Fleeing.Not.fleeing
## [26] Description.Temp
## [27] URL.Temp
## [28] Brief.description
## [29] Dispositions.Exclusions.INTERNAL.USE..NOT.FOR.ANALYSIS
## [30] Intended.use.of.force..Developing.
## [31] Supporting.document.link
## [32] X
## [33] X.1
## [34] Unique.ID.formula
## [35] Unique.identifier..redundant.
## <0 rows> (or 0-length row.names)fatal[!is.finite(fatal$Latitude), ]## Unique.ID Name Age Gender Race
## 2514 28891 Wildan Tmusic 22 Male European-American/White
## Race.with.imputations Imputation.probability
## 2514 European-American/White Not imputed
## URL.of.image..PLS.NO.HOTLINKS.
## 2514 https://fatalencounters.org/wp-content/uploads/2020/10/Wildan-Tmusic.jpg
## Date.of.injury.resulting.in.death..month.day.year.
## 2514 10/03/2020
## Location.of.injury..address. Location.of.death..city. State
## 2514 I-95 Norwood MA
## Location.of.death..zip.code. Location.of.death..county.
## 2514 2021 Norfolk
## Full.Address Latitude Longitude
## 2514 I-95 Norwood MA 2021 Norfolk NA -71.17373
## Agency.or.agencies.involved Highest.level.of.force UID.Temporary
## 2514 Massachusetts State Police Vehicle 28891
## Name.Temporary Armed.Unarmed Alleged.weapon Aggressive.physical.movement
## 2514 Wildan Tmusic Unarmed None None
## Fleeing.Not.fleeing Description.Temp URL.Temp
## 2514 Fleeing/Vehicle
## Brief.description
## 2514 A trooper was attempting to stop a driver, Wildan Tmusic, for speeding just after 2:30 a.m. when the driver refused to stop and fled. The trooper was given the go-ahead to pursue the vehicle moments before it crashed.Tmusic was killed and passenger Adam J. Rodriguez later succumbed to his injuries in the hospital.
## Dispositions.Exclusions.INTERNAL.USE..NOT.FOR.ANALYSIS
## 2514 Pending investigation
## Intended.use.of.force..Developing.
## 2514 Pursuit
## Supporting.document.link
## 2514 https://www.wcvb.com/article/second-victim-of-interstate-95-crash-in-norwood-conencted-to-pursuits-dies-massachusetts-state-police-say/34278934#
## X X.1 Unique.ID.formula Unique.identifier..redundant.
## 2514 NA NA NA 28891fatal <- fatal[is.finite(fatal$Longitude) & is.finite(fatal$Latitude), ]
fatal_sf <- st_as_sf(fatal, coords = c("Longitude", "Latitude"), crs = 4326)
ggplot(data = fatal_sf) +
geom_point(aes(x = st_coordinates(geometry)[, 1],
y = st_coordinates(geometry)[, 2],
color = (Gender))) +
labs(x = "Longitude", y = "Latitude", color = "Gender") +
theme_minimal()
ggplot(data = fatal_sf) +
geom_point(aes(x = st_coordinates(geometry)[, 1],
y = st_coordinates(geometry)[, 2],
color = (Age))) +
labs(x = "Longitude", y = "Latitude", color = "Age") +
theme_minimal()
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.