Foreigner’s and Crime

Introduction

An illustration of R Markdown….

What’s the link between foreigners and crime?

Illustration

To illustrate your story you can include images (even of thugs who attack foreigners)

OK some more serious work now

Now let’s load some data. To do that you can include chunks of are code like this:

ff=read.csv("https://www.dropbox.com/s/g1w75gkw7g91zef/foreigners.csv?dl=1")  

This loads the local authority dataset we have seen before. Note that you can include inline are code as well. For instance: the dataset has 348 observations and contains 5 variables.

As before we can summarise the data:

summary(ff)

##        X             crimes11          b_migr11          pop11        
##  Min.   :  1.00   Min.   :   1134   Min.   : 2.241   Min.   :   2203  
##  1st Qu.: 87.75   1st Qu.: 107618   1st Qu.: 4.899   1st Qu.:  94263  
##  Median :174.50   Median : 160556   Median : 7.603   Median : 125746  
##  Mean   :174.50   Mean   : 236988   Mean   :11.226   Mean   : 161434  
##  3rd Qu.:261.25   3rd Qu.: 309377   3rd Qu.:12.382   3rd Qu.: 200247  
##  Max.   :348.00   Max.   :1714295   Max.   :55.161   Max.   :1072372  
##                   NA's   :24        NA's   :9        NA's   :9        
##            area    
##              :  9  
##  Adur        :  1  
##  Allerdale   :  1  
##  Amber Valley:  1  
##  Arun        :  1  
##  Ashfield    :  1  
##  (Other)     :334

Note, you might want to see the output of a command in your final document, but you might not want to see the command. Just do it like this:

##        X             crimes11          b_migr11          pop11        
##  Min.   :  1.00   Min.   :   1134   Min.   : 2.241   Min.   :   2203  
##  1st Qu.: 87.75   1st Qu.: 107618   1st Qu.: 4.899   1st Qu.:  94263  
##  Median :174.50   Median : 160556   Median : 7.603   Median : 125746  
##  Mean   :174.50   Mean   : 236988   Mean   :11.226   Mean   : 161434  
##  3rd Qu.:261.25   3rd Qu.: 309377   3rd Qu.:12.382   3rd Qu.: 200247  
##  Max.   :348.00   Max.   :1714295   Max.   :55.161   Max.   :1072372  
##                   NA's   :24        NA's   :9        NA's   :9        
##            area    
##              :  9  
##  Adur        :  1  
##  Allerdale   :  1  
##  Amber Valley:  1  
##  Arun        :  1  
##  Ashfield    :  1  
##  (Other)     :334

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## Warning: Removed 24 rows containing missing values (geom_point).

Let’s get rid of outliers… …and do some other stuff to make it look nicer..