Merge pull request #13 from slu-openGIS/cran-release

Cran release

CRAN-RELEASE

@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2019-03-22.
+Once it is accepted, delete this file and tag the release (commit e1edbe830f).

DESCRIPTION

@@ -1,23 +1,23 @@
 Package: compstatr
 Type: Package
 Title: Tools for St. Louis Crime Data
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: c(
     person("Christopher", "Prener", ,"chris.prener@slu.edu", c("aut", "cre")),
     person("Cree", "Foeller", ,"cree.foeller@slu.edu", c("aut")),
     person("Taylor", "Braswell", , , c("com"))
     )
-Description: 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. We therefore provide
-    a set of tools for accessing and editing St. Louis Metropolitan Police 
-    Department (SLMPD) crime data, which are available on their website in csv form. 
-    The categorization tools that are provided will work with any police department 
-    that uses 5 and 6 digit numeric codes to identify specific crimes.
+Description: Provides a set of tools for creating yearly data sets of St. Louis 
+    Metropolitan Police Department (SLMPD) crime data, which are available from
+    January 2008 onward as monthly CSV releases on their website 
+    (<http:www.slmpd.org/Crimereports.shtml>). Once data are validated and created 
+    (monthly data releases have varying numbers of columns 
+    as well as different column names and formats), 'compstatr' also provides 
+    functions for categorizing and mapping crimes in St. Louis. The categorization 
+    tools that are provided will also work with any police department that uses 5 
+    and 6 digit numeric codes to identify specific crimes. These data provide researchers
+    and policy makers detailed data for St. Louis, which in the last several years 
+    has had some of the highest or the highest violent crime rates in the United States.
 Depends: R (>= 3.3)
 License: GPL-3
 URL: https://github.com/slu-openGIS/compstatr

NAMESPACE

@@ -1,9 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
+export(cs_address)
 export(cs_collapse)
 export(cs_combine)
 export(cs_crime)
 export(cs_crime_cat)
+export(cs_example)
 export(cs_extract_month)
 export(cs_filter_count)
 export(cs_filter_crime)
@@ -28,6 +30,7 @@ importFrom(dplyr,is.tbl)
 importFrom(dplyr,mutate)
 importFrom(dplyr,rename)
 importFrom(dplyr,select)
+importFrom(fs,file_copy)
 importFrom(fs,file_move)
 importFrom(lubridate,date)
 importFrom(lubridate,parse_date_time)
@@ -51,4 +54,5 @@ importFrom(stringr,str_c)
 importFrom(stringr,str_detect)
 importFrom(stringr,str_replace)
 importFrom(stringr,str_sub)
+importFrom(stringr,str_trim)
 importFrom(tidyr,separate)

NEWS.md

@@ -1,3 +1,18 @@
+# compstatr 0.1.1
+
+* **initial CRAN submission**
+* add Zenodo release badge to `README`
+* update installation instructions on `README` and on the `pkgdown` site
+* add `cran-comments.md`
+* in response to initial feedback from Matthias Sterrer (CRAN):
+    * add `cs_example()` function for creating a sample year worth of `.csv` files 
+    * add executable examples for `cs_prep_year()`, `cs_load_year()`, and `cs_projectXY`
+    * clarify `DESCRIPTION` for package
+* in response to additional feedback from Swetlana Herbrandt (CRAN):
+    * added a link to the `DESCRIPTION` file to the SLMPD data source
+    * ensured that the examples in `example.R` and `create.R` read from and write to a temporary directory
+* add a function `cs_address()` to facilitate concatenation of street addresses prior to geocoding
+
 # compstatr 0.1.0
 
 * Added a `NEWS.md` file to track changes to the package.

R/address.R

@@ -0,0 +1,93 @@
+#' Create a Single Address Field
+#'
+#' @description The street address data in SLMPD releases (either \code{ILEADSAddress} and
+#'     \code{ILEADSStreet} or \code{CADAddress} and \code{CADStreet}) are stored in separate
+#'     columns. In order to faciliate geocoding, this function combines the fields and
+#'     removes inappropriate characters in the address fields.
+#'
+#' @usage cs_address(.data, address, street, newVar)
+#'
+#' @param .data A tibble or data frame
+#' @param address Name of address number variable (typically either \code{ILEADSAddress}
+#'     or \code{CADAddress})
+#' @param street Name of street name variable (typically either \code{ILEADSStreet}
+#'     or \code{CADStreet})
+#' @param newVar Name of new variable to store concatenated address
+#'
+#' @return A copy of the object with a character vector that contains the concatenated
+#'     street address data.
+#'
+#' @examples
+#' # load example data
+#' testData <- january2018
+#'
+#' # add concatenated address variable
+#' testData <- cs_address(testData, address = ILEADSAddress, street = ILEADSStreet, newVar = address)
+#'
+#' @importFrom dplyr mutate
+#' @importFrom rlang :=
+#' @importFrom rlang quo
+#' @importFrom rlang enquo
+#' @importFrom rlang sym
+#' @importFrom stringr str_c
+#' @importFrom stringr str_detect
+#' @importFrom stringr str_replace
+#' @importFrom stringr str_trim
+#'
+#' @export
+cs_address <- function(.data, address, street, newVar){
+
+  # check for missing parameters
+  if (missing(.data)) {
+    stop("An existing data frame with crime data must be specified for '.data'.")
+  }
+
+  if (missing(address)) {
+    stop("The column containing address numbers must be specified for 'address'.")
+  }
+
+  if (missing(street)) {
+    stop("The column containing street names must be specified for 'street'.")
+  }
+
+  if (missing(newVar)) {
+    stop("A new variable name must be specified for 'newVar'.")
+  }
+
+  #save parameters to list
+  paramList <- as.list(match.call())
+
+  #quote input variables
+  if (!is.character(paramList$address)) {
+    addressQ <- rlang::enquo(address)
+  } else if (is.character(paramList$address)) {
+    addressQ <- rlang::quo(!! rlang::sym(address))
+  }
+
+  if (!is.character(paramList$street)) {
+    streetQ <- rlang::enquo(street)
+  } else if (is.character(paramList$street)) {
+    streetQ <- rlang::quo(!! rlang::sym(street))
+  }
+
+  if (!is.character(paramList$newVar)) {
+    newVarQ <- rlang::enquo(newVar)
+  } else if (is.character(paramList$newVar)) {
+    newVarQ <- rlang::quo(!! rlang::sym(newVar))
+  }
+
+  newVarQN <- rlang::quo_name(rlang::enquo(newVar))
+
+  # concatenate address
+  .data <- dplyr::mutate(.data, !!newVarQN := stringr::str_c(!!addressQ, !!streetQ, sep = " "))
+
+  # clean address
+  .data <- dplyr::mutate(.data, !!newVarQN := ifelse(stringr::str_detect(string = !!newVarQ, pattern = "^[0\\b]") == TRUE,
+                                   stringr::str_replace(string = !!newVarQ, pattern = "^[0\\b]", replacement = ""),
+                                   address))
+  .data <- dplyr::mutate(.data, !!newVarQN := stringr::str_trim(!!newVarQ))
+
+  # return output
+  return(.data)
+
+}

R/categorize.R

@@ -71,7 +71,7 @@
 #' @param newVar Name of output variable to be created with logical data
 #' @param crime A string describing the crime type to be identified
 #'
-#' @return A cope of the object with a logical vector that is \code{TRUE} if the given crime matches
+#' @return A copy of the object with a logical vector that is \code{TRUE} if the given crime matches
 #'     the category given in the function.
 #'
 #' @examples

R/create.R

@@ -18,9 +18,36 @@
 #'     specified path.
 #'
 #' @examples
-#' \dontrun{
-#' cs_prep_year(path = "data/raw/2008")
-#' }
+#' # create temporary directory
+#' tmpdir <- tempdir()
+#' fs::dir_create(paste0(tmpdir,"/data/"))
+#'
+#' # load sample files into temporary directory
+#' cs_example(path = paste0(tmpdir,"/data/"))
+#'
+#' # list files
+#' list.files(paste0(tmpdir,"/data/"))
+#'
+#' # prep sample files
+#' cs_prep_year(path = paste0(tmpdir,"/data/"))
+#'
+#' # list files again
+#' list.files(paste0(tmpdir,"/data/"))
+#'
+#' # delete data
+#' fs::dir_delete(paste0(tmpdir,"/data/"))
+#'
+#' # create temporary directory
+#' fs::dir_create(paste0(tmpdir,"/data/"))
+#'
+#' # load sample files into temporary directory
+#' cs_example(path = paste0(tmpdir,"/data/"))
+#'
+#' # prep sample files
+#' cs_prep_year(path = paste0(tmpdir,"/data/"), verbose = TRUE)
+#'
+#' # delete data again
+#' fs::dir_delete(paste0(tmpdir,"/data/"))
 #'
 #' @importFrom dplyr as_tibble
 #' @importFrom dplyr filter
@@ -141,9 +168,24 @@ cs_edit_filename <- function(path, file){
 #'    of crime data stored within a list.
 #'
 #' @examples
-#' \dontrun{
-#' yearList08 <- cs_load_year(path = "data/raw/2008")
-#' }
+#' # create temporary directory
+#' tmpdir <- tempdir()
+#' fs::dir_create(paste0(tmpdir,"/data/"))
+#'
+#' # load sample files into temporary directory
+#' cs_example(path = paste0(tmpdir,"/data/"))
+#'
+#' # prep sample files
+#' cs_prep_year(path = paste0(tmpdir,"/data/"))
+#'
+#' # load sample files
+#' yearList17 <- cs_load_year(path = paste0(tmpdir,"/data/"))
+#'
+#' # delete data
+#' fs::dir_delete(paste0(tmpdir,"/data/"))
+#'
+#' # print year-list object
+#' yearList17
 #'
 #' @importFrom dplyr %>%
 #' @importFrom purrr map

R/example.R

@@ -0,0 +1,79 @@
+#' Load Example Files
+#'
+#' @description Adds a sample set of twelve files, one for each month of 2017,
+#'     to the specified path. These are not full data files; each file has twenty
+#'     observations. They can be used to practice preparing, loading, standardizing,
+#'     and collapsing data.
+#'
+#' @param path File path where example data should be placed
+#' @param overwrite Overwrite files if they exist. If this is FALSE and the file exists
+#'     an error will be thrown.
+#'
+#' @examples
+#' # create temporary directory
+#' tmpdir <- tempdir()
+#' fs::dir_create(paste0(tmpdir,"/data/"))
+#'
+#' # load sample files into temporary directory
+#' cs_example(path = paste0(tmpdir,"/data/"))
+#'
+#' # list files
+#' list.files(paste0(tmpdir,"/data/"))
+#'
+#' # delete data
+#' fs::dir_delete(paste0(tmpdir,"/data/"))
+#'
+#' @importFrom fs file_copy
+#'
+#' @export
+cs_example <- function(path, overwrite = FALSE){
+
+  # copy file, january
+  fs::file_copy(system.file("extdata", "January2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/January2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, february
+  fs::file_copy(system.file("extdata", "February2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/February2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, march
+  fs::file_copy(system.file("extdata", "March2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/March2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, april
+  fs::file_copy(system.file("extdata", "April2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/April2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, may
+  fs::file_copy(system.file("extdata", "May2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/May2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, june
+  fs::file_copy(system.file("extdata", "June2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/June2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, july
+  fs::file_copy(system.file("extdata", "July2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/July2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, august
+  fs::file_copy(system.file("extdata", "August2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/August2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, september
+  fs::file_copy(system.file("extdata", "September2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/September2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, october
+  fs::file_copy(system.file("extdata", "October2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/October2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, november
+  fs::file_copy(system.file("extdata", "November2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/November2017.CSV.html"), overwrite = overwrite)
+
+  # copy file, december
+  fs::file_copy(system.file("extdata", "December2017.csv.html", package = "compstatr", mustWork = TRUE),
+                paste0(path,"/December2017.CSV.html"), overwrite = overwrite)
+
+}

R/spatial.R

@@ -145,6 +145,7 @@ cs_missingXY <- function(.data, varX, varY, newVar){
 #'
 #' @return A \code{sf} object with the crime data projected for mapping.
 #'
+#' @examples
 #' # load example data
 #' testData <- january2018
 #'

README.Rmd

@@ -30,9 +30,15 @@ The City's crime data, however, are difficult to work with and present a number
 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
-You can install compstatr from Github with `remotes`:
+The easiest way to get `compstatr` is to install it from CRAN:
 
-```{r gh-installation, eval = FALSE}
+``` 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")
 ```

_pkgdown.yml

@@ -34,11 +34,13 @@ reference:
   - title: "Spatial Data"
     desc: "Wrangle and Project Crimes"
     contents:
+    - cs_address
     - cs_replace0
     - cs_missingXY
     - cs_projectXY
 
   - title: "Sample Data"
     desc: "Example Data for Practicing Wrangling and Mapping"
     contents:
+    - cs_example
     - january2018