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Crimes_Sweden.rmd
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---
title: "What's Wrong With Sweden These Days!!!"
author: "Pranav Pandya | [email protected]"
date: "13th March 2017"
output: html_document
---
-----------------------------------------------------------
<p>A new ranking from U.S. News & World Report (https://www.usnews.com/news/best-countries/women-full-list, dated 8th March 2017) suggests that Sweden is #1 in Best Countries for Women. Gender equality is one of the "cornerstones" of society in Sweden, making it the best country for women to live in. Nearly two-thirds of university degrees are awarded to women, and around three months of leave are given to each parent during parental leave. <br>
But there is a dark side....(which we will explore)</p>
<br>
<h4>
Let's do exploratory data analysis of reported crimes in Sweden from 1950 to 2015 and see if it's really as shocking as heard in media. I am going to use Highcharter alongwith plotly for interactive visualization. </h4>
```{r, message=FALSE, warning=FALSE}
library(dplyr)
library(tidyr)
library(reshape2)
library(DT)
library(highcharter)
library(ggplot2)
library(plotly)
library(gridExtra)
#dfC <- read.csv("../input/reported.csv")
dfC <- read.csv("reported.csv")
```
####First, let's compare total number of crimes with total number of reported offenses in some categories.
```{r, message=FALSE, warning=FALSE, fig.width= 10, fig.height= 6}
dfC %>%
select(Year, fraud, crimes.total, stealing.general, other.penal.crimes) %>%
plot_ly(x = ~Year, y = ~crimes.total, name = 'Total Crimes', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~stealing.general, name = 'Stealing (gen)', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~fraud, name = 'Fraud', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~other.penal.crimes, name = 'Penal Crimes (other)', type = 'scatter', mode = 'lines') %>%
layout(legend = list(orientation = 'h'))
```
####Sweden has always been in the news recently due to growing sexual offences. Let's check the same with historical data.
```{r, message=FALSE, warning=FALSE}
dfC %>%
select(Year, sexual.offenses) %>%
hchart("scatter", hcaes(Year, sexual.offenses, color = -(sexual.offenses), size = sexual.offenses),
dataLabels = list(enabled = TRUE, format = "{point.label}")) %>%
hc_title(text = "By Total Number of Sexual Offenses") %>%
hc_tooltip(useHTML = TRUE,
headerFormat = "<table>",
pointFormat = paste("<tr><th colspan=\"1\"><b>{point.label}</b></th></tr>",
"<tr><th>Year :</th<td> {point.x} </td></tr>",
"<tr><th>Sexual offenses :</th<td> {point.y} </td></tr>",
"<tr><th></th><td>{point.valuecolor} </td></tr>"),
footerFormat = "</table>") %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_chart(zoomType = "xy") %>%
hc_credits(enabled = TRUE, text = "Sources: Swedish crime statistics | https://www.bra.se/",
style = list(fontSize = "12px"))
```
<br>
<h4> Although we don't have figures for 2016 but checking news for past year suggests that the rate is still growing!
```{r, message=FALSE, warning=FALSE}
dfC %>%
select(Year, rape) %>%
hchart("scatter", hcaes(Year, rape, color = -(rape), size = rape),
dataLabels = list(enabled = TRUE, format = "{point.label}")) %>%
hc_title(text = "By Total Number of Rape Crimes") %>%
hc_tooltip(useHTML = TRUE,
headerFormat = "<table>",
pointFormat = paste("<tr><th colspan=\"1\"><b>{point.label}</b></th></tr>",
"<tr><th>Year :</th<td> {point.x} </td></tr>",
"<tr><th>Rapes :</th<td>{point.y} </td></tr>",
"<tr><th></th><td>{point.valuecolor} </td></tr>"),
footerFormat = "</table>") %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_chart(zoomType = "xy") %>%
hc_credits(enabled = TRUE, text = "Sources: Swedish crime statistics | https://www.bra.se/",
style = list(fontSize = "12px"))
```
<h4>This is really shocking! It's growing exponentially after 2004.</h4>
<p>Following the incidents took place in Feb and March 2016, Police in Östersund, with a population of 45,000, say they have never seen anything like this before. The streets are deserted after dark in a sleepy Swedish town which is a now no-go zone for women after eight sex attacks in three weeks.
Women have been warned by police not to walk alone at night after the once peaceful town of Östersund was shaken to its core by a string of vicious attacks on females and even children by groups of young men of 'foreign origin'.
</p>
```{r, message=FALSE, warning=FALSE, fig.width= 10, fig.height= 6.5}
by_sexOff_rape_murder <- dfC %>%
select(Year, rape, sexual.offenses) %>%
melt(id = "Year") %>%
ggplot(aes(Year, value, fill=variable)) +
geom_bar(stat='identity', position='dodge') +
ggtitle("Shocking Increase in Sexual Offenses and Rapes in Sweden")+
labs(x="Year", y="Total Number of Crimes") +
scale_x_continuous(limit = c(1949, 2016), breaks=seq(1950, 2016, by=5)) +
scale_y_continuous(limit = c(0, 220), breaks=seq(0, 220, by=20))
ggplotly(by_sexOff_rape_murder) %>% layout(legend = list(orientation = 'h'))
```
###Let's compare Sexual Offense rate with Rape and Murder Rate
```{r, message=FALSE, warning=FALSE, fig.width= 10, fig.height= 6}
dfC %>%
select(Year, sexual.offenses, rape, murder) %>%
plot_ly(x = ~Year, y = ~sexual.offenses, name = 'Sexual Offenses', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~rape, name = 'Rape', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~murder, name = 'Murder', type = 'scatter', mode = 'lines') %>% layout(legend = list(orientation = 'h'))
```
###Now, let's see how other crime categories looks like over the years.
```{r, message=FALSE, warning=FALSE}
dfC %>%
select(Year, assault) %>%
hchart("scatter", hcaes(Year, assault, color = -(assault), size = assault),
dataLabels = list(enabled = TRUE, format = "{point.label}")) %>%
hc_title(text = "By Total Number of Assaults") %>%
hc_tooltip(useHTML = TRUE,
headerFormat = "<table>",
pointFormat = paste("<tr><th colspan=\"1\"><b>{point.label}</b></th></tr>",
"<tr><th>Year :</th><td>{point.x}</td></tr>",
"<tr><th>Assaults :</th<td>{point.y}</td></tr>",
"<tr><th></th><td>{point.valuecolor}</td></tr>"),
footerFormat = "</table>") %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_chart(zoomType = "xy") %>%
hc_credits(enabled = TRUE, text = "Sources: Swedish crime statistics | https://www.bra.se/",
style = list(fontSize = "12px"))
dfC %>%
select(Year, fraud) %>%
hchart("scatter", hcaes(Year, fraud, color = -(fraud), size = fraud),
dataLabels = list(enabled = TRUE, format = "{point.label}")) %>%
hc_title(text = "By Total Number of Frauds") %>%
hc_tooltip(useHTML = TRUE,
headerFormat = "<table>",
pointFormat = paste("<tr><th colspan=\"1\"><b>{point.label}</b></th></tr>",
"<tr><th>Year :</th<td>{point.x} </td></tr>",
"<tr><th>Fraud :</th<td>{point.y} </td></tr>",
"<tr><th></th><td>{point.valuecolor} </td></tr>"),
footerFormat = "</table>") %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_chart(zoomType = "xy") %>%
hc_credits(enabled = TRUE, text = "Sources: Swedish crime statistics | https://www.bra.se/",
style = list(fontSize = "12px"))
dfC %>%
select(Year, criminal.damage) %>%
hchart("scatter", hcaes(Year, criminal.damage, color = -(criminal.damage), size = criminal.damage),
dataLabels = list(enabled = TRUE, format = "{point.label}")) %>%
hc_title(text = "By Total Number of Criminal Damages") %>%
hc_tooltip(useHTML = TRUE,
headerFormat = "<table>",
pointFormat = paste("<tr><th colspan=\"1\"><b>{point.label}</b></th></tr>",
"<tr><th>Year :</th<td>{point.x}</td></tr>",
"<tr><th>Criminal damage :</th<td>{point.y}</td></tr>",
"<tr><th></th><td>{point.valuecolor} </td></tr>"),
footerFormat = "</table>") %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_chart(zoomType = "xy") %>%
hc_credits(enabled = TRUE, text = "Sources: Swedish crime statistics | https://www.bra.se/",
style = list(fontSize = "12px"))
```
###Let's also compare crime rate in Stealing and Robbery category
```{r, message=FALSE, warning=FALSE, fig.width= 10, fig.height= 7}
theft_robery <- dfC %>%
select(Year, burglary, house.theft, vehicle.theft,
out.of.vehicle.theft, shop.theft, robbery) %>%
plot_ly(x = ~Year, y = ~burglary, name = 'Burglary', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~out.of.vehicle.theft, name = 'Theft from vehicle', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~vehicle.theft, name = 'Vehicle theft', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~shop.theft, name = 'Shop theft', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~house.theft, name = 'House theft', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~robbery, name = 'Roberry', type = 'scatter', mode = 'lines')
theft_robery %>% layout(legend = list(orientation = 'h'))
```
####Searchable dataset
```{r, message=FALSE, warning=FALSE}
dfC %>% select(Year, population, crimes.total, sexual.offenses, rape, murder, assault ) %>%
arrange(desc(Year)) %>%
datatable(options = list(columnDefs = list(list(className = 'dt-center', targets = 7)),
pageLength = 5,lengthMenu = c(5, 10, 25, 50, 100)))
```
###Let's get an overview of crime severity by comparing them
```{r, fig.width= 15, fig.height= 9}
gs <- ggplot(dfC, aes(Year, stealing.general)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total General Stealing", y = "Year", title = "General Stealing by Year")
burglary <- ggplot(dfC, aes(Year, burglary)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total Burglary", y = "Year", title = "Burglary by Year")
ht <- ggplot(dfC, aes(Year, house.theft)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total Thefts from House", y = "Year", title = "Thefts from House by Year")
vt <- ggplot(dfC, aes(Year, vehicle.theft)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total Vehicle Theft", y = "Year", title = "Vehicle Theft by Year")
ovt <- ggplot(dfC, aes(Year, out.of.vehicle.theft)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total Out of Vehicle Theft", y = "Year", title = "Out of Vehicle Theft by Year")
st <- ggplot(dfC, aes(Year, shop.theft)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total Shop Theft", y = "Year", title = "Shop Theft by Year")
robbery <- ggplot(dfC, aes(Year, robbery)) + geom_line(color = "gold3", size = 1) +
labs(x = "Total Robbery", y = "Year", title = "Robbery by Year")
```
###Crimes under control
```{r, fig.width= 15, fig.height= 9}
grid.arrange(gs, burglary, ht, vt, ovt, st, robbery, ncol = 4)
```
```{r, fig.width= 15, fig.height= 9}
Population <- ggplot(dfC, aes(Year, population)) + geom_line(color = "dodgerblue2", size = 1) +
labs(x = "Year", y = "Population", title = "Population by Year")
tc <- ggplot(dfC, aes(Year, crimes.total)) + geom_line(color = "dodgerblue2", size = 1) +
labs(x = "Total Crimes", y = "Year", title = "Total Crimes by Year")
tcpc <- ggplot(dfC, aes(Year, crimes.penal.code)) + geom_line(color = "dodgerblue2", size = 1) +
labs(x = "Total Crimes by Penal Code", y = "Year", title = "Total Crimes for Penal Code by Year")
tcp <- ggplot(dfC, aes(Year, crimes.person)) + geom_line(color = "dodgerblue2", size = 1) +
labs(x = "Total Crimes Against Person", y = "Year", title = "Crimes Against Person by Year")
murder <- ggplot(dfC, aes(Year, murder)) + geom_line(color = "dodgerblue2", size = 1) +
labs(x = "Total Murder", y = "Year", title = "Murder by Year")
assault <- ggplot(dfC, aes(Year, assault)) + geom_line(color = "dodgerblue2", size = 1) +
labs(x = "Total Assaults", y = "Year", title = "Assaults by Year")
```
###Crimes that constantly growing
```{r, fig.width= 15, fig.height= 9}
grid.arrange(Population, tc, tcpc, tcp, murder, assault, ncol = 3)
```
```{r, fig.width= 15, fig.height= 9}
so <- ggplot(dfC, aes(Year, sexual.offenses)) + geom_line(color = "red", size = 1) +
labs(x = "Total Sexual Offenses", y = "Year", title = "Sexual Offenses by Year")
fraud <- ggplot(dfC, aes(Year, fraud), fraud) + geom_line(color = "red", size = 1) +
labs(x = "Total Fraud", y = "Year", title = "Fraud by Year")
cd <- ggplot(dfC, aes(Year, criminal.damage)) + geom_line(color = "red", size = 1) +
labs(x = "Total Criminal Damage", y = "Year", title = "Criminal Damage by Year")
opc <- ggplot(dfC, aes(Year, other.penal.crimes)) + geom_line(color = "red", size = 1) +
labs(x = "Total Penal Crimes (other)", y = "Year", title = "Other Penal Crimes by Year")
narcotics <- ggplot(dfC, aes(Year, narcotics)) + geom_line(color = "red", size = 1) +
labs(x = "Total Narcotics", y = "Year", title = "Narcotics by Year")
dd <- ggplot(dfC, aes(Year, drunk.driving)) + geom_line(color = "red", size = 1) +
labs(x = "Total Drunk Driving", y = "Year", title = "Drunk Driving by Year")
```
###Crimes that are shockingly growing
```{r, fig.width= 15, fig.height= 9}
grid.arrange(so, fraud, cd, opc, narcotics, dd, ncol = 3)
```
<br>
<p>
According to a 2014 study published by the European Union Agency for Fundamental Rights (FRA), approximately one third of all women in the EU were said to have suffered physical and/or sexual abuse. At the top end was Denmark (52%), Finland (47%) and Sweden (46%).
Every second woman in the EU has experienced sexual harassment at least once since the age of 15. In Sweden that figure was 81 percent, closely followed by Denmark (80%) and France (75%). Included in the definition of "sexual harassment" was - among other things - inappropriate staring or leering and cyber harassment. The report concluded that there's a strong correlation between higher levels of gender equality and disclosure of sexual violence.
</p>
<br>
References:
* https://en.wikipedia.org/wiki/Crime_in_Sweden#Sex_crimes
* https://www.usnews.com/news/best-countries/women-full-list