Program in R to Analyse the Estimated Crimes between 1979 and 2019 Assignment Solution

Instructions

Make an R programming project to analyze the data on the estimated_crimes_1979_2019.csv file. This dataset contains estimated data at the state and national level and was derived from the Summary Reporting System (SRS). The sample of the data is shown in the screenshots below.

Assignment Solution

library(forecast)

library(ggplot2)

#preparing time series object for analysis

ts<- ts(data,start = 1985,end = 2019,frequency = 12)

seasonplot(ts, s = 12,col=rainbow(16), year.labels=TRUE,main = "Homicide rate by year and month")

ggseasonplot(ts, polar=TRUE)

autoplot.ts(ts)

ggAcf(ts)

ARIMA

fit.arima<- auto.arima(ts)

with(fit.arima,plot(fitted,residuals))

checkresiduals(fit.arima)

plot(forecast(fit.arima,h = 24))

For the Exploratory Data Analysis

library(tidyverse) ## For data wrangling and visualization

library(lubridate) ## To work with dates

library(ggpubr) ## Extra visualizations and themes

library(patchwork) ## Patch visualizations together

library(hrbrthemes)## extra themes and formatting

library(scales) ## For formatting numeric variables

library(tidytext) ## Reordering within facets in ggplot2

library(pier) ## Make interactive piecharts in R

library(ggalt) ## Extra visualizations

crimes <- read_csv(Crime.csv)

mutate(year = dmy(Date_Column))

crimes %>%

group_by(Year = floor_date(Date_Column,unit = "year")) %>%

summarise(Incidents=sum(total_incidents,na.rm = TRUE)) %>%

ungroup() %>% mutate(pct_change= (Incidents-lag(Incidents))/lag(Incidents),

pct_change=replace_na(pct_change,0)) %>%

ggplot(aes(Year,Incidents))+

geom_bar(stat="identity",fill="firebrick",color="black")+

geom_line(color="steelblue",size=1.5,linetype="dashed")+

  geom_text(aes(label=percent(pct_change)),vjust=-1,color="black",face="bold")+ geom_text(aes(label=comma(Incidents)),vjust=1,fontface="bold",color="white")+

scale_y_comma(expand = c(0,0),limits = c(0,800000))+

scale_x_date(breaks = "year",date_labels ="%Y")+

theme_classic()+

labs(title = "Total Incidents over the years")

Let's visualize a simple barplot and see total incidents

crimes %>%

group_by(state¬_name) %>%

summarise(Incidents=sum(total_incidents)) %>%

ungroup() %>%

ggplot(aes(reorder(borough,Incidents),Incidents))+

geom_bar(stat = "identity",aes(fill=borough),color="black")+

coord_flip()+

scale_y_comma()+

geom_text(aes(label=comma(Incidents)),hjust=1)+

theme_classic()+

theme(legend.position = "none")+

labs(x=" ",y=" ",title = "Total Incidents for boroughs from 2008-2016 ")