compstatr

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The goal of compstatr is to provide a suite of tools for working with crime data made public by the City of St. Louis’ Metropolitan Police Department.

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 (SLMPD) crime data, which are available on their website 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.

What’s New?

Version v0.2.0 is here! This updated release of compstatr introduces three new functions that enable users to access data directly from SLMPD’s website without having to download individual data files first:

  1. cs_last_update() - returns the date of the last available data release on SLMPD’s website
  2. cs_create_index() - creates an index of data sets available on SLMPD’s website for scraping
  3. cs_get_data() - scrapes either a single month or a full year worth of data from SLMPD’s website

In order to enable them, there is one breaking change for users - all variable names are standardized into snake_case at import. This will impact code that has been written using a prior version of compstatr.

Installation

The easiest way to get compstatr is to install it from CRAN:

install.packages("compstatr")

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

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

Usage

We’ll start with loading the compstatr package:

> library(compstatr)

Data Access - 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 and imported directly as objects in R’s global environment. To identify the last available month:

> 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:

> # create index
> i <- cs_create_index()

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:

> # 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.

Data Access - Use Tables Downloaded Manually

While scraping is now an option, St. Louis data can still be downloaded month-by-month from SLMPD. 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.

The function cs_prep_data() can be used to rename files, which may be downloaded with the wrong file extension (January2018.csv.html). Once downloaded you can load them into what we call year-list objects:

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

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

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():

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

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

> 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:

> # 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:

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

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

> 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():

> # 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:

> # 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")

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

> # 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). Finally, we can project and map our data:

> # project data
> janHomicides_sf <- cs_projectXY(janHomicides, varX = XCoord, varY = YCoord)
> 
> # preview data
> mapview(janHomicides_sf)

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

> library(ggplot2)
> ggplot() + 
+   geom_sf(data = janHomicides_sf, color = "red", fill = NA, size = .5)

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)).

Acknowledgements

We wish to thank Taylor Braswell for his significant efforts compiling Stata code early in this project. Taylor’s code was used as a reference when developing this package, and many of the functions reflect issues that he worked to identify.

Contributor Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.