Wheeler County

Setup

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.3     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(janitor)

Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test

library(readxl)
library(lubridate)

Import

Same process as before

wheeler <- read_excel("data-raw/WheelerCounty.xlsx")

wheeler |> glimpse()

Rows: 116
Columns: 22
$ inc_index     <dbl> 1953118, 1931092, 1907492, 1894481, 1899162, 1890396, 18…
$ incident_no   <chr> "22-12-466C", "22-11-435C", "22-10-384C", "22-09-330C", …
$ other_no      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ event_no      <chr> "2212250001", "2211290022", "2210090012", "2209030002", …
$ date_arrest   <chr> "25-Dec-22 00:00:00", "29-Nov-22 00:00:00", "09-Oct-22 0…
$ time_arrest   <chr> "00:23", "23:21", "11:23", "01:43", "18:48", "22:26", "1…
$ offense       <chr> "481.121(b)(3)", "481.121(b)(3)", "481.121(b)(1)", "481.…
$ date_1        <chr> "25-Dec-22 00:00:00", "29-Nov-22 00:00:00", "09-Oct-22 0…
$ agency_arrest <chr> "WSO", "WSO", "WSO", "WSO", "WSO", "WSO", "WSO", "WSO", …
$ officer_id    <chr> "ET0910", "908", "908", "PYLE", "ET0910", "909TJK", "908…
$ dispo         <chr> "31ST", "31ST", "CO", "BOTH", "BOTH", "CO", "CO", "31ST"…
$ streetnbr     <chr> NA, NA, NA, "176 E", NA, NA, NA, NA, NA, "161", "120", N…
$ street_prfx   <chr> NA, "E", NA, NA, NA, NA, "E", NA, "E", "E", "S", NA, NA,…
$ street        <chr> "CEFCO PARKING LT", "ROUTE 66", "NORTH MAIN STREET 83", …
$ street_type   <chr> NA, NA, NA, NA, NA, NA, NA, NA, "ST", NA, NA, NA, NA, "S…
$ suffix        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ city          <chr> "SHAMROCK", "SHAMROCK", "SHAMROCK", "WHEELER", "SHAMROCK…
$ state         <chr> "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "T…
$ zip_code      <chr> "79079", "79096", "79096", "79096", "79096", "79079", "7…
$ geox          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ geoy          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ charges       <chr> "POSS MARIJ >4OZ<=5LBS", "POSS MARIJ >4OZ<=5LBS", "POSS …

Clean

wheeler_clean <- wheeler |> mutate(
  date_arrest = dmy_hms(date_arrest),
  address_arrest = paste(streetnbr, street_prfx, street, street_type)
) |> select(
  -inc_index,
  -incident_no,
  -other_no,
  -event_no,
  -time_arrest,
  -offense,
  -date_1,
  -suffix,
  -geox,
  -geoy,
  -officer_id,
  -dispo,
  -streetnbr,
  -street_prfx,
  -street,
  -street_type,
  -city,
  -zip_code,
  -state
) |> cbind(datetime_arrest = NA, age = NA, race = NA, sex = NA, ethnicity = NA, name = NA)

wheeler_clean |> glimpse()

Rows: 116
Columns: 10
$ date_arrest     <dttm> 2022-12-25, 2022-11-29, 2022-10-09, 2022-09-03, 2022-…
$ agency_arrest   <chr> "WSO", "WSO", "WSO", "WSO", "WSO", "WSO", "WSO", "WSO"…
$ charges         <chr> "POSS MARIJ >4OZ<=5LBS", "POSS MARIJ >4OZ<=5LBS", "POS…
$ address_arrest  <chr> "NA NA CEFCO PARKING LT NA", "NA E ROUTE 66 NA", "NA N…
$ datetime_arrest <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ age             <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ race            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ sex             <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ ethnicity       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ name            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

Export

wheeler_clean |> write_csv("data-processed/Wheeler-County.csv")