Version 1.16.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: njoaguof
 Title: NJ OAG Use of Force data package
-Version: 1.15.0
+Version: 1.16.0
 Authors@R: 
     person("Tor", "Gunston", , "tor.gunston@protonmail.com", role = c("aut", "cre"))
 Description: This is cleaned-up version of the NJ OAG Use of Force database
@@ -10,7 +10,7 @@ URL: https://github.com/tor-gu/njoaguof
 BugReports: https://github.com/tor-gu/njoaguof/issues
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Imports: 
     magrittr
 Suggests: 

NEWS.md

@@ -1,3 +1,21 @@
+# njoaguof 1.16.0
+* Update to most recent dataset from NJ OAG:
+  "NJOAG Use of Force Data Dashboard 10-01-20 to 09-30-24.dta"
+
+* Change to `incident`:
+  - New variable `other_officer_involved`
+  - New variable `officer_in_uniform`
+
+* Change to `subject`:
+  - New variable `injured`
+  - New variable `injured_prior`
+
+* Change to `incident_contact_origin`:
+  - New level `Officer Dispatched`
+
+* Change to `incident_planned_contact`:
+  - New level `No Knock Warrant`
+
 # njoaguof 1.15.0
 * Update to most recent dataset from NJ OAG:
   "NJOAG Use of Force Data Dashboard 10-01-20 to 05-31-23.dta"

README.Rmd

@@ -74,21 +74,21 @@ incident %>%
 
 The raw data from the NJ OAG is available in table ```use_of_force_raw```, which has one row for each use of force incident.  Fields with multiple values are recorded as comma separated lists. For example:
 ```{r}
-use_of_force_raw %>% count(SubjectsGender) %>% head(5)
+use_of_force_raw %>% count(SubjectGender) %>% head(5)
 ```
 These fields are broken out into new data tables in the following categories.
 
 #### Subject Fields
 Several fields contain one value for each subject. We presume that the order is preserved, so that we may create one row for each subject in the ```subject``` table.
 ```{r}
-use_of_force_raw %>% filter(form_id == 16301) %>%
+use_of_force_raw %>% filter(FormID == 16301) %>%
   select(
-    form_id,
-    SubjectsArrested,
-    subject_type,
-    SubectsAge,
-    SubjectRace,
-    SubjectsGender
+    FormID,
+    SubjectArrested,
+    SubjectType,
+    SubjectAge,
+    SubjectRaceEthnicity,
+    SubjectGender
   )
 
 subject %>% filter(form_id == 16301)
@@ -98,8 +98,8 @@ subject %>% filter(form_id == 16301)
 Some fields contain multiple values which apply to the entire incident. For each such column, we create a separate table expressing this many-to-one relationship. For example, this row in the source data has three values for ```incident_type```, and this results in three rows in the ```incident_type``` table.
 ```{r}
 library(tidyverse)
-use_of_force_raw %>% filter(form_id == 16301) %>%
-  select(incident_type)
+use_of_force_raw %>% filter(FormID == 16301) %>%
+  select(IncidentType)
 incident_type %>% filter(form_id == 16301)
 ```
 
@@ -108,8 +108,8 @@ incident_type %>% filter(form_id == 16301)
 Some fields contain multiple values which apply to individual subjects, but there is no reliable way to assign the values to subjects. For example, in this row of the raw data, there are two subjects and three values in the ```SubResist``` field. In this case, we create three rows in the ```incident_subject_resistance``` table, indicating the position of each item in the list with the ```index``` value.  
 ```{r}
 use_of_force_raw %>% 
-  filter(form_id == 19542) %>% 
-  select(subject_type,SubResist)
+  filter(FormID == 19542) %>% 
+  select(SubjectType,SubjectResistance)
 incident_subject_resistance %>% filter(form_id == 19542)
 ```
 Note that it is not clear in the source data if "Aggressive resistance" should be associated to the first subject or to the second subject.

README.md

@@ -59,20 +59,20 @@ incident %>%
   select(form_id, agency_county) %>%
   right_join(incident_video_type, by = "form_id") %>%
   count(agency_county, video_type)
-#> # A tibble: 179 × 3
+#> # A tibble: 187 × 3
 #>    agency_county   video_type              n
 #>    <fct>           <fct>               <int>
-#>  1 Atlantic County Body Worn            1026
-#>  2 Atlantic County CED Camera              7
-#>  3 Atlantic County Cell Phone              8
-#>  4 Atlantic County Commercial Building    57
-#>  5 Atlantic County Motor Vehicle         235
-#>  6 Atlantic County Residential/Home        2
-#>  7 Atlantic County Station House          55
+#>  1 Atlantic County Body Worn            2269
+#>  2 Atlantic County CED Camera             14
+#>  3 Atlantic County Cell Phone             11
+#>  4 Atlantic County Commercial Building   100
+#>  5 Atlantic County Motor Vehicle         322
+#>  6 Atlantic County Residential/Home       13
+#>  7 Atlantic County Station House          97
 #>  8 Atlantic County Other                  31
-#>  9 Bergen County   Body Worn            1295
-#> 10 Bergen County   CED Camera             61
-#> # ℹ 169 more rows
+#>  9 Bergen County   Body Worn            2855
+#> 10 Bergen County   CED Camera             72
+#> # ℹ 177 more rows
 ```
 
 ``` r
@@ -83,27 +83,25 @@ incident %>%
   select(form_id, officer_gender) %>% 
   right_join(subject, by="form_id") %>%
   count(officer_gender, subject_gender=gender)
-#> # A tibble: 18 × 3
-#>    officer_gender          subject_gender              n
-#>    <fct>                   <fct>                   <int>
-#>  1 Male                    Male                    26177
-#>  2 Male                    Female                   7109
-#>  3 Male                    Gender Non-Conforming/X    48
-#>  4 Male                    <NA>                      254
-#>  5 Female                  Male                     1274
-#>  6 Female                  Female                    779
-#>  7 Female                  Gender Non-Conforming/X     4
-#>  8 Female                  <NA>                       11
-#>  9 Gender Non-Conforming/X Male                       17
-#> 10 Gender Non-Conforming/X Female                      8
-#> 11 Gender Non-Conforming/X Gender Non-Conforming/X     2
-#> 12 Other                   Male                     3075
-#> 13 Other                   Female                    811
-#> 14 Other                   Gender Non-Conforming/X     4
-#> 15 Other                   <NA>                       48
-#> 16 <NA>                    Male                      634
-#> 17 <NA>                    Female                    172
-#> 18 <NA>                    <NA>                       48
+#> # A tibble: 16 × 3
+#>    officer_gender subject_gender     n
+#>    <fct>          <fct>          <int>
+#>  1 Male           Male           19940
+#>  2 Male           Female          5313
+#>  3 Male           Non-Binary/X      36
+#>  4 Male           <NA>             874
+#>  5 Female         Male            1139
+#>  6 Female         Female           694
+#>  7 Female         Non-Binary/X       2
+#>  8 Female         <NA>              76
+#>  9 Other          Male           33845
+#> 10 Other          Female          9740
+#> 11 Other          Non-Binary/X      42
+#> 12 Other          <NA>            1305
+#> 13 <NA>           Male              27
+#> 14 <NA>           Female             9
+#> 15 <NA>           Non-Binary/X       4
+#> 16 <NA>           <NA>               7
 ```
 
 ## Notes
@@ -113,15 +111,15 @@ which has one row for each use of force incident. Fields with multiple
 values are recorded as comma separated lists. For example:
 
 ``` r
-use_of_force_raw %>% count(SubjectsGender) %>% head(5)
+use_of_force_raw %>% count(SubjectGender) %>% head(5)
 #> # A tibble: 5 × 2
-#>   SubjectsGender                    n
-#>   <chr>                         <int>
-#> 1 ""                                1
-#> 2 "Female,"                      8517
-#> 3 "Female,Female"                  73
-#> 4 "Female,Female,Female,"           4
-#> 5 "Female,Female,Female,Female"     4
+#>   SubjectGender                              n
+#>   <chr>                                  <int>
+#> 1 Female                                 15359
+#> 2 Female, Female                           114
+#> 3 Female, Female, Female                    12
+#> 4 Female, Female, Female, Female             4
+#> 5 Female, Female, Female, Female, Female     1
 ```
 
 These fields are broken out into new data tables in the following
@@ -134,26 +132,27 @@ order is preserved, so that we may create one row for each subject in
 the `subject` table.
 
 ``` r
-use_of_force_raw %>% filter(form_id == 16301) %>%
+use_of_force_raw %>% filter(FormID == 16301) %>%
   select(
-    form_id,
-    SubjectsArrested,
-    subject_type,
-    SubectsAge,
-    SubjectRace,
-    SubjectsGender
+    FormID,
+    SubjectArrested,
+    SubjectType,
+    SubjectAge,
+    SubjectRaceEthnicity,
+    SubjectGender
   )
 #> # A tibble: 1 × 6
-#>   form_id SubjectsArrested subject_type  SubectsAge SubjectRace   SubjectsGender
-#>     <dbl> <chr>            <chr>         <chr>      <chr>         <chr>         
-#> 1   16301 True,False       Person,Person 26,23      Hispanic,Bla… Male,Female
+#>   FormID SubjectArrested SubjectType    SubjectAge SubjectRaceEthnicity         
+#>    <dbl> <chr>           <chr>          <chr>      <chr>                        
+#> 1  16301 False, True     Person, Person 23, 26     Black or African American, H…
+#> # ℹ 1 more variable: SubjectGender <chr>
 
 subject %>% filter(form_id == 16301)
-#> # A tibble: 2 × 8
-#>   form_id index arrested type     age juvenile race     gender
-#>     <dbl> <int> <lgl>    <fct>  <int> <lgl>    <fct>    <fct> 
-#> 1   16301     1 TRUE     Person    26 FALSE    Hispanic Male  
-#> 2   16301     2 FALSE    Person    23 FALSE    Black    Female
+#> # A tibble: 2 × 10
+#>   form_id index arrested type    age juvenile race  gender injured injured_prior
+#>     <dbl> <int> <lgl>    <fct> <int> <lgl>    <fct> <fct>  <lgl>   <lgl>        
+#> 1   16301     1 FALSE    Pers…    23 FALSE    Black Female FALSE   FALSE        
+#> 2   16301     2 NA       <NA>     26 FALSE    <NA>  <NA>   TRUE    FALSE
 ```
 
 #### Multi-value incident fields
@@ -166,10 +165,10 @@ three values for `incident_type`, and this results in three rows in the
 
 ``` r
 library(tidyverse)
-use_of_force_raw %>% filter(form_id == 16301) %>%
-  select(incident_type)
+use_of_force_raw %>% filter(FormID == 16301) %>%
+  select(IncidentType)
 #> # A tibble: 1 × 1
-#>   incident_type                                                                 
+#>   IncidentType                                                                  
 #>   <chr>                                                                         
 #> 1 Potential Mental Health Incident, Suspicious person, Disturbance (drinking, f…
 incident_type %>% filter(form_id == 16301)
@@ -192,19 +191,19 @@ item in the list with the `index` value.
 
 ``` r
 use_of_force_raw %>% 
-  filter(form_id == 19542) %>% 
-  select(subject_type,SubResist)
+  filter(FormID == 19542) %>% 
+  select(SubjectType,SubjectResistance)
 #> # A tibble: 1 × 2
-#>   subject_type  SubResist                                                      
-#>   <chr>         <chr>                                                          
-#> 1 Person,Person Verbal,Aggressive resistance(attempt to attack or harm),Verbal,
+#>   SubjectType    SubjectResistance                                              
+#>   <chr>          <chr>                                                          
+#> 1 Person, Person Verbal, Verbal, Aggressive resistance (attempt to attack or ha…
 incident_subject_resistance %>% filter(form_id == 19542)
 #> # A tibble: 3 × 3
-#>   form_id index subject_resistance                              
-#>     <dbl> <int> <fct>                                           
-#> 1   19542     1 Verbal                                          
-#> 2   19542     2 Aggressive resistance(attempt to attack or harm)
-#> 3   19542     3 Verbal
+#>   form_id index subject_resistance                               
+#>     <dbl> <int> <fct>                                            
+#> 1   19542     1 Verbal                                           
+#> 2   19542     2 Verbal                                           
+#> 3   19542     3 Aggressive resistance (attempt to attack or harm)
 ```
 
 Note that it is not clear in the source data if “Aggressive resistance”