---
title: "Wrangling Crime Data with R"
author: "Christopher Prener, Ph.D."
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{compstatr}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Overview
The goal of `compstatr` is to provide a suite of tools for working with crime data made public by the City of St. Louis's [Metropolitan Police Department](http://www.slmpd.org).

### Motivation
Among cities in the United States, St. Louis has the distinction of having the highest or one of the highest violent crime and homicide rates since 2010. It therefore presents an important site for social scientists, public health researchers, and health care providers as well as policy makers to understand the effects of violent crime on urban communities. 

The City's crime data, however, are difficult to work with and present a number of challenges for researchers. These data are inconsistently organized, with all data before 2013 and some months of 2013 itself having eighteen variables. Beginning during 2013, most (but not all) months have twenty variables, many of which are named differently from their pre-2014 counterparts. These inconsistencies, and the fact that working with their data requires managing over 120 spreadsheets that each download with with a `.html` file extension, are the motivating force behind `compstatr`.

We therefore provide a set of tools for accessing, preparing, editing, and mapping St. Louis [Metropolitan Police Department](http://www.slmpd.org) (SLMPD) crime data, which are available [on their website](http://www.slmpd.org/Crimereports.shtml) as `.csv` files. The categorization tools that are provided will work with any police department that uses 5 and 6 digit numeric codes to identify specific crimes.

### Installation
We recommend that users install `sf` before proceeding with the installation of `compstatr`. Windows users should be able to install `sf` without significant issues, but macOS and Linux users will need to install several open source spatial libraries to get `sf` itself up and running. The easiest approach for macOS users is to install the GDAL 2.0 Complete framework from [Kyng Chaos](https://www.kyngchaos.com/software/frameworks/). 

For Linux users, steps will vary based on the flavor being used. Our [configuration file](https://github.com/slu-openGIS/compstatr/blob/master/.travis.yml) for Travis CI and its associated [bash script](https://github.com/slu-openGIS/areal/blob/master/.travis/install.sh) should be useful in determining the necessary components to install.

Once `sf` is installed, the easiest way to get `compstatr` is to install it from CRAN:

``` r
install.packages("compstatr")
```

The development version of `compstatr` can be accessed from GitHub with `remotes`:

```r
# install.packages("remotes")
remotes::install_github("slu-openGIS/compstatr")
```

### Tips

* All functions support non-standard evaluation, meaning you can use either quoted or unquoted inputs for arguments.
* Use RStudio for its helpful auto completion ability. Type `cs_` to see all of the functions available in this package.
* All functions for data creation and preparation return data as a tibble, which is essentially identical to a data.frame, except for its print behavior in the console and in notebooks.

## Accessing SLMPD Data
SLMPD data can be accessed two ways - you can either read tables from the website directly into `R` using `compstatr`'s web scraping tools, or you can manually download the individual monthly tables and store them on disk before reading them in. 

Both techniques produce identical "year-list" objects that are the core data type used for validation and standardization. The only difference between the two approaches is that the scraping method produces a year-list object that is ordered in reverse chronological order by month, whereas the manual download approach results in a year-list object ordered alphabetically by month.

In either case, you'll want to start by loading the `compstatr` package:

```r
> library(compstatr)
```

### Read Tables Directly into R
As of version `v0.2.0`, data tables can be scraped and read directly into `R` without manually downloading them first. They are read from the St. Louis Metropolitan Police Department's [website](http://www.slmpd.org/Crimereports.shtml) and imported directly as objects in `R`'s global environment. To identify the last available month: 

```r
> cs_last_update()
[1] "May 2019"
```

To enable scraping, an index of the available data needs to be created. Doing this is optional but highly recommended to improve performance:

```r
> # create index
> i <- cs_create_index()
>
> # preview index
> i
# A tibble: 137 x 6
    page row   value  year month date          
   <int> <chr> <chr> <dbl> <int> <chr>         
 1     1 02    1 02   2019     5 May 2019      
 2     1 03    1 03   2019     4 April 2019    
 3     1 04    1 04   2019     3 March 2019    
 4     1 05    1 05   2019     2 February 2019 
 5     1 06    1 06   2019     1 January 2019  
 6     1 07    1 07   2018    12 December 2018 
 7     1 08    1 08   2018    11 November 2018 
 8     1 09    1 09   2018    10 October 2018  
 9     1 10    1 10   2018     9 September 2018
10     1 11    1 11   2018     8 August 2018   
# … with 127 more rows
```

The `page`, `row`, and `value` columns reflect the organizational characteristics of the `iframe` on SLMPD's website. This index is used by `cs_get_data()` to find the requested table or tables, post a request via the SLMPD website's form system, and then download your data:

```r
> # download single month
> may17 <- cs_get_data(year = 2017, month = "May", index = i)
>
> # download full year
> yearList17 <- cs_get_data(year = 2017, index = i)
```

Once data are downloaded, they need to be validated and standardized before proceeding with analysis.

### Use Tables Downloaded Manually
While scraping is now an option, St. Louis data can still be downloaded month-by-month from [SLMPD](http://www.slmpd.org/Crimereports.shtml). `compstatr` assumes that only one year of crime data (or less) is included in specific folders within your project. These next examples assume you have downloaded all of the data for 2017 and 2018, and saved them respectively in `data/raw/2017` and `data/raw/2018`. 

You should be aware that `compstatr` uses an opinionated approach for data organization. Data should be downloaded from [SLMPD](http://www.slmpd.org/Crimereports.shtml) into a clear folder hierarchy that approximates this:

```r
|- data/
|--- 2017/
|------ January2017.csv.html
|------ February2017.csv.html
|------ March2017.csv.html
|------ April2017.csv.html
|------ May2017.csv.html
|------ June2017.csv.html
|------ July2017.csv.html
|------ August2017.csv.html
|------ September2017.csv.html
|------ October2017.csv.html
|------ Novemeber2017.csv.html
|------ December2017.csv.html
|--- 2018/
|------ January2018.csv.html
|------ February2018.csv.html
|------ March2018.csv.html
|------ April2018.csv.html
|------ May2018.csv.html
|------ June2018.csv.html
|------ July2018.csv.html
|------ August2018.csv.html
|------ September2018.csv.html
|------ October2018.csv.html
|------ Novemeber2018.csv.html
|------ December2018.csv.html
```

The initial functions `cs_prep_data()` and `cs_load_data()` (both of which are described below) assume that there are no more than 12 files per folder containing raw data. The function `cs_prep_data()` can be used to rename files, which will be downloaded with the wrong file extension (`January2018.csv.html`). Once downloaded you can load them into what we call year-list objects:

```r
> cs_prep_year(path = "data/2017")
>
> yearList17 <- cs_load_year(path = "data/2017")
```

Once data are downloaded, they need to be validated and standardized before proceeding with analysis.

## Usage
Regardless of how you create your year-list object, you will need to follow the same workflow below.

### Data Preparation
Both the data downloaded manually as well as the tables scraped from SLMPD's website are inconsistently organized. Problems that need to be addressed prior to collapsing a year-list into a single object can be identified with `cs_validate()`:

```r
> cs_validate(yearList17, year = 2017)
[1] FALSE
```

If a `FALSE` value is returned, the `vebose = TRUE` argument provides additional detail:

```r
> cs_validate(yearList17, year = 2017, verbose = TRUE)
# A tibble: 12 x 8
   namedMonth codedMonth valMonth codedYear valYear oneMonth varCount valVars
   <chr>      <chr>      <lgl>        <int> <lgl>   <lgl>    <lgl>    <lgl>  
 1 January    January    TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 2 February   February   TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 3 March      March      TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 4 April      April      TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 5 May        May        TRUE          2017 TRUE    TRUE     FALSE    NA     
 6 June       June       TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 7 July       July       TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 8 August     August     TRUE          2017 TRUE    TRUE     TRUE     TRUE   
 9 September  September  TRUE          2017 TRUE    TRUE     TRUE     TRUE   
10 October    October    TRUE          2017 TRUE    TRUE     TRUE     TRUE   
11 November   November   TRUE          2017 TRUE    TRUE     TRUE     TRUE   
12 December   December   TRUE          2017 TRUE    TRUE     TRUE     TRUE   
```

In this case, we have the wrong number of variables for the month of May (in this case there are 26). We can fix this by using `cs_standardize()` to create the correct number of columns (20) and name them appropriately:

```r
> # standardize 
yearList17 <- cs_standardize(yearList17, month = "May", config = 26)
>
> # confirm data are now valid
> cs_validate(yearList17, year = 2017)
[1] TRUE
```

For 2013 and prior years, there will be only 18 variables. The 2013 data need to be fixed month by month because there are some correct months, but years 2008 through 2012 can be fixed en masse:

```r
> yearList08 <- cs_standardize(yearList08, config = 18, month = "all")
```

If there are more significant problems with a given month, `cs_extract_month()` can be used to pull a single month out of a year-list object. It can then be edited using `dplyr` or similar tools, and then replaced into the year-list object with `cs_replace_month()`. As of March 2019, there are no months of released data that require this. These functions are therefore provided as cautionary tools.

Once the data have been standardized, we can collapse them into a single object with `cs_collapse()`:

```r
> reports17 <- cs_collapse(yearList17)
```

This gives us all of the crimes reported in 2017. However, there will be crimes that were reported that year that occurred in prior years, and there may also be crimes reported in 2018 that took place in our year of interest. We can address both issues (assuming we have the next year's data) with `cs_combine()`:

```r
> # load and standardize 2018 data
> cs_prep_year(path = "data/raw/2018")
> yearList18 <- cs_load_year(path = "data/raw/2018")
> cs_validate(yearList18, year = 2018)
[1] TRUE
> reports18 <- cs_collapse(yearList18)
>
> # combine 2017 and 2018 data
crimes17 <- cs_combine(type = "year", date = 2017, reports17, reports18)
```

We now have a tibble containing all of the known crimes that occurred in 2017 (including those reported in 2018).

### Data Wrangling and Mapping
Once we have the data prepared, we can easily pull out a specific set of crimes to inspect further. For example, we could identify homicides. In the next few examples, we'll use the `january2018` example data that comes with the package. We'll start by using `cs_filter_crimes()` to select only homicides as well as `cs_filter_count()` to remove any unfounded incidents:

```r
> # load dependencies
> library(compstatr)
> library(ggplot2)
> library(magrittr)
> library(mapview)
> 
> # subset homicides and removed unfounded incidents
> janHomicides <- january2018 %>%
+   cs_filter_count(var = Count) %>%
+   cs_filter_crime(var = Crime, crime = "homicide")
```

There are also options for categorizing crimes, including `cs_crime_cat()`, which creates a numeric, string, or factor variable that summarizes each individual crime by UCR code. Likewise, there is a `cs_crime()` function that creates logical vectors that are `TRUE` if a given observation fits into the specified UCR code or family of UCR codes (like `Part 1`, `violent`, or `property` crimes).

Next, we'll check for missing spatial data with `cs_missingXY()`:

```r
> # identify missing spatial data
> janHomicides <- cs_missingXY(janHomicides, varX = XCoord, varY = YCoord, newVar = missing)
> 
> # check for any TRUE values
> table(janHomicides$missing)
```

We don't have any missing spatial data in this example, but if we did we would need to remove those observations with `dplyr::filter()` (or another subsetting tool). The `compstatr` package also comes with a function, `cs_replace0()`, that can be used to properly convert coordinates that are given as `0` to `NA`.

Finally, we can project and map our data:

```r
> # project data
> janHomicides_sf <- cs_projectXY(janHomicides, varX = XCoord, varY = YCoord)
> 
> # preview data
> mapview(janHomicides_sf)
```
```{r exampleMap1, echo=FALSE, out.width = '100%'}
knitr::include_graphics("../man/figures/homicide_mapview.png")
```

These data can also be mapped using `ggplot2` once they have been projected:

```r
> library(ggplot2)
> ggplot() + 
+   geom_sf(data = janHomicides_sf, color = "red", fill = NA, size = .5)
```
```{r exampleMap2, echo=FALSE, out.width = '40%'}
knitr::include_graphics("../man/figures/homicide_map.png")
```

## Non St. Louis Data
If you work with data from other police departments, the `cs_crime()`, `cs_crime_cat()`, and `cs_filter_crime()` functions may be useful for identifying, grouping, and subsetting by crime so long as they use a standard set of 5 and 6 digit codes based on the UCR system (e.g. `31111` (robbery with a firearm) or `142320` (malicious destruction of property)).

## Getting Help
* If you are new to `R` itself, welcome! Hadley Wickham's [*R for Data Science*](http://r4ds.had.co.nz) is an excellent way to get started with data manipulation in the tidyverse, which `compstatr` is designed to integrate seamlessly with.
* If you are new to spatial analysis in `R`, we strongly encourage you check out the excellent new [*Geocomputation in R*](https://geocompr.robinlovelace.net) by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow.
* If you have questions about using `compstatr`, you are encouraged to use the [RStudio Community forums](https://community.rstudio.com). Please create a [`reprex`](http://reprex.tidyverse.org/) before posting. Feel free to tag Chris (`@chris.prener`) in any posts about `compstatr`.
* If you think you've found a bug, please create a [`reprex`](http://reprex.tidyverse.org/) and then open an issue on [GitHub](https://github.com/slu-openGIS/compstatr/issues/new/choose).
