First Report- Gun and Suicide
Ali
11/17/2020
Details of the data and list of variables
# Number of participants in the first round
nrow(firstgun)## [1] 6200# Number of participants in the second round
nrow(secondgun)## [1] 3500# Number of participants in the merged data
nrow(new_data)## [1] 9700# Number of Variables in the merged data
ncol(new_data)## [1] 203# List of variables the merged data
str(new_data)## Classes 'data.table' and 'data.frame': 9700 obs. of 203 variables:
## $ new_id : chr "a[1]" "a[2]" "a[3]" "a[4]" ...
## $ gender : Factor w/ 5 levels "Male","Female",..: 1 2 1 2 1 2 2 2 1 1 ...
## $ educ : Factor w/ 6 levels "Less than High School",..: 5 4 3 2 5 1 2 4 3 4 ...
## $ relation : Factor w/ 5 levels "Unmarried and not currently in a committed relationship",..: 5 1 5 1 5 1 3 1 2 2 ...
## $ age : Factor w/ 9 levels "Under 18","18-24",..: 3 5 3 3 3 5 2 7 3 3 ...
## $ race : chr "White" "White" "Black" "Hispanic" ...
## $ sex : Factor w/ 2 levels "Male","Female": 1 2 1 2 1 2 2 2 1 1 ...
## $ income : Factor w/ 12 levels "$10,000 - $19,999",..: 3 6 5 12 9 4 5 3 4 4 ...
## $ gunhome : Factor w/ 3 levels "No","Yes","Prefer not to answer": 2 3 2 1 2 1 1 1 2 2 ...
## $ acquireplan : Factor w/ 4 levels "No","Yes","Haven't decided yet",..: 2 4 3 1 1 3 1 1 1 2 ...
## $ safety_1 : Factor w/ 5 levels "Strongly Agree",..: 1 3 2 3 4 5 3 2 2 1 ...
## $ safety_2 : Factor w/ 5 levels "Strongly Agree",..: 1 3 4 3 5 5 3 2 2 2 ...
## $ safety_3 : Factor w/ 5 levels "Strongly Agree",..: 1 4 2 5 4 5 3 3 1 1 ...
## $ ptci_1 : Factor w/ 7 levels "Totally Disagree",..: 7 7 5 1 5 6 4 3 3 5 ...
## $ ptci_2 : Factor w/ 7 levels "Totally Disagree",..: 7 7 5 7 7 6 4 3 4 7 ...
## $ ptci_3 : Factor w/ 7 levels "Totally Disagree",..: 7 7 5 7 5 6 4 2 5 6 ...
## $ si_1 : chr "I wish I could disappear or not exist" "I wish I could disappear or not exist" "0" "0" ...
## $ si_2 : chr "0" "I wish I was never born" "0" "0" ...
## $ si_3 : Factor w/ 2 levels "0","My life is not worth living": 1 2 1 1 1 1 1 1 1 1 ...
## $ si_4 : Factor w/ 2 levels "0","I wish I could go to sleep and never wake up": 1 2 1 1 1 1 1 1 1 1 ...
## $ si_5 : Factor w/ 2 levels "0","I wish I were dead": 2 2 1 1 1 1 1 1 1 2 ...
## $ si_6 : Factor w/ 2 levels "0","Maybe I should kill myself": 1 2 1 1 1 1 1 1 1 2 ...
## $ si_7 : Factor w/ 2 levels "0","I should kill myself": 1 2 1 1 1 1 1 1 1 1 ...
## $ si_8 : Factor w/ 2 levels "0","I am going to kill myself": 1 1 1 1 1 1 1 1 1 1 ...
## $ sitime_1 : Factor w/ 4 levels "0","More than 1 year ago",..: 2 4 1 1 1 1 4 1 4 3 ...
## $ sitime_2 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 4 1 1 1 1 1 1 4 1 ...
## $ sitime_3 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 4 1 1 1 1 1 1 1 1 ...
## $ sitime_4 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 4 1 1 1 1 1 1 1 1 ...
## $ sitime_5 : Factor w/ 4 levels "0","More than 1 year ago",..: 2 4 1 1 1 1 1 1 1 4 ...
## $ sitime_6 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 4 1 1 1 1 1 1 1 2 ...
## $ sitime_7 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 4 1 1 1 1 1 1 1 1 ...
## $ sitime_8 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sb_1 : Factor w/ 2 levels "0","Done something to prepare to make a suicide attempt. For example, gaining access to method, looking up a method"| __truncated__: 2 1 1 1 1 2 1 1 1 1 ...
## $ sb_2 : Factor w/ 2 levels "0","Practiced making a suicide attempt. For example, taking an overdose of an amount you knew in advance would not "| __truncated__: 2 1 1 1 1 1 1 1 1 2 ...
## $ sb_3 : Factor w/ 2 levels "0","Been very close to killing yourself but at the last minute you decided not to do it before taking any action. F"| __truncated__: 1 1 1 1 1 1 1 1 2 1 ...
## $ sb_4 : Factor w/ 2 levels "0","Been very close to killing yourself but at the last minute, someone or something else stopped you before you to"| __truncated__: 1 1 1 1 1 1 1 1 2 1 ...
## $ sb_5 : Factor w/ 2 levels "0","Started to kill yourself and then you stopped after you had already taken some action. For example, taking one "| __truncated__: 1 1 1 1 1 1 1 1 1 1 ...
## $ sb_6 : Factor w/ 2 levels "0","Started to kill yourself and then you decided to reach out for help after you had already taken some action. Fo"| __truncated__: 1 1 1 1 1 1 1 1 1 2 ...
## $ sb_7 : Factor w/ 2 levels "0","Tried to kill yourself and someone found you afterwards. For example, taking an overdose of pills and then some"| __truncated__: 1 1 1 1 1 1 1 1 1 1 ...
## $ sb_8 : Factor w/ 2 levels "0","Tried to kill yourself and no one found you afterwards. For example, taking an overdose of pills and then waking up later.": 1 1 1 1 1 1 1 1 1 1 ...
## $ sb_9 : Factor w/ 2 levels "0","Purposely hurt yourself without wanting to die. For example, cutting yourself or burning your skin to reduce em"| __truncated__: 1 1 1 1 1 1 1 1 1 1 ...
## $ sbtime_1 : Factor w/ 4 levels "0","More than 1 year ago",..: 2 1 1 1 1 2 1 1 1 1 ...
## $ sbtime_2 : Factor w/ 4 levels "0","More than 1 year ago",..: 2 1 1 1 1 1 1 1 1 4 ...
## $ sbtime_3 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 3 1 ...
## $ sbtime_4 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 3 1 ...
## $ sbtime_5 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sbtime_6 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 1 3 ...
## $ sbtime_7 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sbtime_8 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sbtime_9 : Factor w/ 4 levels "0","More than 1 year ago",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ married : Factor w/ 2 levels "No","Yes": 2 1 2 1 2 1 1 2 2 2 ...
## $ adults : Factor w/ 11 levels "0","1","10 or more",..: 4 2 4 5 4 5 4 2 10 2 ...
## $ children : Factor w/ 11 levels "0","1","10 or more",..: 2 1 2 1 4 2 2 1 5 2 ...
## $ state : Factor w/ 53 levels "Alabama","Alaska",..: 33 5 11 39 10 14 50 48 38 11 ...
## $ zip : chr "11216 "| __truncated__ "92647 "| __truncated__ "31601 "| __truncated__ "15701 "| __truncated__ ...
## $ military_1 : Factor w/ 2 levels "Army active duty",..: 1 2 1 2 2 2 2 2 1 2 ...
## $ military_2 : Factor w/ 2 levels "Army National Guard",..: 1 2 2 2 2 2 2 2 2 2 ...
## $ military_3 : Factor w/ 2 levels "Army Reserve",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_4 : Factor w/ 2 levels "Navy active duty",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_5 : Factor w/ 2 levels "Navy Reserve",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_6 : Factor w/ 2 levels "Marines active duty",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_7 : Factor w/ 2 levels "Marines Reserve",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_8 : Factor w/ 2 levels "Air Force active duty",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_9 : Factor w/ 2 levels "Air National Guard",..: 2 2 2 2 2 2 2 2 2 1 ...
## $ military_10 : Factor w/ 2 levels "Air Force Reserve",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_11 : Factor w/ 2 levels "Coast Guard active duty",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ military_12 : Factor w/ 2 levels "Coast Guard Reserve",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ vertcoll_1 : Factor w/ 7 levels "1","2","3","4",..: 6 1 5 5 7 4 7 7 5 4 ...
## $ vertcoll_2 : Factor w/ 7 levels "1","2","3","4",..: 6 1 6 4 7 5 7 4 5 3 ...
## $ vertcoll_3 : Factor w/ 7 levels "1","2","3","4",..: 7 1 6 4 6 4 7 7 5 6 ...
## $ vertcoll_4 : Factor w/ 7 levels "1","2","3","4",..: 7 1 6 4 7 4 7 7 7 4 ...
## $ vertcoll_5 : Factor w/ 7 levels "1","2","3","4",..: 5 1 6 5 6 5 7 4 6 4 ...
## $ vertcoll_6 : Factor w/ 7 levels "1","2","3","4",..: 7 4 6 5 6 5 7 7 6 6 ...
## $ crime_1 : Factor w/ 2 levels "0","Someone broke into your home or attempted to break into your home by forcing a window, pushing past someone, ji"| __truncated__: 2 1 1 1 2 1 1 2 2 1 ...
## $ crime_2 : Factor w/ 2 levels "0","Someone illegally got into or tried to get into a hotel or motel room or vacation home where you were staying.": 2 1 2 1 1 1 1 1 1 1 ...
## $ crime_3 : Factor w/ 2 levels "0","Someone illegally got into or tried to get into a garage, shed, or storage room.": 1 1 1 1 1 1 1 1 2 2 ...
## $ crime_4 : Factor w/ 2 levels "0","Someone stole or used your motor vehicle without permission": 1 1 1 1 2 1 1 2 1 2 ...
## $ crime_5 : Factor w/ 2 levels "0","Someone stole or attempted to steal parts attached to your vehicle": 1 1 2 1 1 1 1 1 1 1 ...
## $ crime_6 : Factor w/ 2 levels "0","Someone assaulted or attacked you without a weapon (for example, by hitting, slapping, kicking, or beating you up)": 1 2 1 1 1 1 1 1 1 2 ...
## $ crime_7 : Factor w/ 2 levels "0","Someone assaulted or attacked you with a weapon (for example, with a firearm, knife, baseball bat, or another item)": 1 1 2 1 1 1 1 1 1 1 ...
## $ crime_8 : Factor w/ 2 levels "0","Someone you didn't know forced or coerced you to engage in unwanted sexual activity": 1 1 1 1 1 1 1 1 1 1 ...
## $ crime_9 : Factor w/ 2 levels "0","Someone you know well forced or coerced you to engage in unwanted sexual activity": 1 1 2 1 1 1 1 1 1 2 ...
## $ crime_10 : Factor w/ 2 levels "0","A casual acquaintance forced or coerced you to engage in unwanted sexual activity": 1 1 1 1 1 1 1 1 1 1 ...
## $ defense_1 : Factor w/ 2 levels "0","Hitting, kicking, slapping, pushing, or otherwise using physical bodily force towards someone": 2 2 1 1 2 1 1 1 2 1 ...
## $ defense_2 : Factor w/ 2 levels "0","Telling someone you had a knife, baseball bat, or another non-firearm weapon": 2 1 1 1 1 1 1 1 1 2 ...
## $ defense_3 : Factor w/ 2 levels "0","Showing someone that you had a knife, baseball bat, or another non-firearm weapon in your possession": 1 1 1 1 1 1 1 1 2 1 ...
## $ defense_4 : Factor w/ 2 levels "0","Using a knife, baseball bat, or another non-firearm weapon on someone": 1 1 1 1 1 1 1 1 1 2 ...
## $ defense_5 : Factor w/ 2 levels "0","Telling someone that you had a firearm": 1 1 1 1 1 1 1 1 1 1 ...
## $ defense_6 : Factor w/ 2 levels "0","Showing someone that you had a firearm in your possession": 1 1 1 1 1 1 1 1 1 1 ...
## $ defense_7 : Factor w/ 2 levels "0","Pointing a firearm at someone": 1 1 1 1 1 1 1 1 1 2 ...
## $ defense_8 : Factor w/ 2 levels "0","Shooting or discharging a firearm in the air or another direction that would not hit or injure someone (for exa"| __truncated__: 1 1 1 1 1 1 1 1 1 1 ...
## $ defense_9 : Factor w/ 2 levels "0","Shooting or discharging a firearm at someone": 1 1 1 1 1 1 1 1 1 1 ...
## $ homereasons2 : Factor w/ 11 levels "I received it as a gift or inheritance",..: 1 NA 7 NA 1 NA NA NA 1 4 ...
## $ homesafety_1 : Factor w/ 2 levels "0","Knob locks or lever handle locks in doors": 1 2 1 2 1 2 2 1 1 2 ...
## $ homesafety_2 : Factor w/ 2 levels "0","Deadbolts, chains, or slide locks on doors": 1 2 2 2 2 2 2 2 1 1 ...
## $ homesafety_3 : Factor w/ 2 levels "0","Window locks": 1 2 2 1 2 2 2 1 2 1 ...
## $ homesafety_4 : Factor w/ 2 levels "0","Bar, rod, or other device that prevents windows/doors from sliding open": 2 2 1 1 1 1 1 1 1 1 ...
## $ homesafety_5 : Factor w/ 2 levels "0","Bars over doors and/or windows": 2 1 1 1 1 1 1 1 2 1 ...
## $ homesafety_6 : Factor w/ 2 levels "0","Alarm system": 2 1 2 1 1 1 2 1 1 2 ...
## [list output truncated]
## - attr(*, ".internal.selfref")=<externalptr>Missing in the data
## new_id gender educ relation age
## 0 0 0 0 0
## race sex income gunhome acquireplan
## 0 3 0 0 7
## safety_1 safety_2 safety_3 ptci_1 ptci_2
## 12 9 10 9 8
## ptci_3 si_1 si_2 si_3 si_4
## 9 0 0 0 0
## si_5 si_6 si_7 si_8 sitime_1
## 0 0 0 0 0
## sitime_2 sitime_3 sitime_4 sitime_5 sitime_6
## 0 0 0 0 0
## sitime_7 sitime_8 sb_1 sb_2 sb_3
## 0 0 0 0 0
## sb_4 sb_5 sb_6 sb_7 sb_8
## 0 0 0 0 0
## sb_9 sbtime_1 sbtime_2 sbtime_3 sbtime_4
## 0 0 0 0 0
## sbtime_5 sbtime_6 sbtime_7 sbtime_8 sbtime_9
## 0 0 0 0 0
## married adults children state zip
## 1 1 13 43 0
## military_1 military_2 military_3 military_4 military_5
## 0 0 0 0 0
## military_6 military_7 military_8 military_9 military_10
## 0 0 0 0 0
## military_11 military_12 vertcoll_1 vertcoll_2 vertcoll_3
## 0 0 7 10 3500
## vertcoll_4 vertcoll_5 vertcoll_6 crime_1 crime_2
## 9 10 8 0 0
## crime_3 crime_4 crime_5 crime_6 crime_7
## 0 0 0 0 0
## crime_8 crime_9 crime_10 defense_1 defense_2
## 0 0 0 0 0
## defense_3 defense_4 defense_5 defense_6 defense_7
## 0 0 0 0 0
## defense_8 defense_9 homereasons2 homesafety_1 homesafety_2
## 0 0 6380 0 0
## homesafety_3 homesafety_4 homesafety_5 homesafety_6 homesafety_7
## 0 0 0 0 0
## homesafety_8 homesafety_9 homesafety_10 homesafety_11 homesafety_12
## 0 0 0 0 0
## homesafety_13 homesafety_14 homesafety_15 homesafety_16 homesafety_17
## 0 0 0 0 0
## cos_1 cos_2 cos_3 gun1stshot gun1stacquire
## 7 8 7 3 2
## acquire gunchild acquirereason_1 acquirereason_2 acquirereason_3
## 5701 3 0 0 0
## acquirereason_4 acquirereason_5 acquirereason_6 acquirereason_7 acquiremore
## 0 0 0 0 5703
## guns_no_1 guns_no_2 guns_no_3 homereasons_1 homereasons_2
## 5713 5713 5717 0 0
## homereasons_3 homereasons_4 homereasons_5 homereasons_6 homereasons_7
## 0 0 0 0 0
## homereasons_8 homereasons_9 homereasons_10 homereasons_11 gunstorage_1
## 0 0 0 0 0
## gunstorage_2 gunstorage_3 gunstorage_4 gunstorage_5 gunstorage_6
## 0 0 0 0 0
## gunstorage_7 acquireplan2_1 acquireplan2_2 acquireplan2_3 acquireplan2_4
## 0 0 0 0 0
## acquireplan2_5 acquireplan2_6 acquireplan2_7 panas_1 panas_2
## 0 0 0 0 0
## panas_3 panas_4 panas_5 panas_6 panas_7
## 0 0 0 0 0
## panas_8 panas_9 panas_10 PurchaseWhen C_19SS_D_1
## 0 0 0 2382 8
## C_19SS_D_2 C_19SS_D_3 C_19SS_D_4 C_19SS_D_5 C_19SS_D_6
## 7 10 13 12 16
## C_19SS_SESC_1 C_19SS_SESC_2 C_19SS_SESC_3 C_19SS_SESC_4 C_19SS_SESC_5
## 12 13 13 11 12
## C_19SS_SESC_6 C_19SS_1 C_19SS_2 C_19SS_X_3 C_19SS_X_4
## 11 13 12 16 14
## C_19SS_X_5 C_19SS_X_6 C_19SS_TS_1 C_19SS_TS_2 C_19SS_TS_3
## 12 14 12 11 16
## C_19SS_TS_4 C_19SS_TS_5 C_19SS_TS_6 C_19SS_Con_1 C_19SS_Con_2
## 11 13 14 11 10
## C_19SS_Con_3 C_19SS_Con_4 C_19SS_Con_5 C_19SS_Con_6 C_19SS_Check_1
## 14 12 13 12 12
## C_19SS_Check_2 C_19SS_Check_3 C_19SS_Check_4 C_19SS_Check_5 C_19SS_Check_6
## 10 12 9 9 9
## wave count_si_1 count_si_2
## 0 0 0Plots
## [1] "crime_1" "crime_2" "crime_3" "crime_4" "crime_5" "crime_6"
## [7] "crime_7" "crime_8" "crime_9" "crime_10"
## [1] "homesafety_1" "homesafety_2" "homesafety_3" "homesafety_4"
## [5] "homesafety_5" "homesafety_6" "homesafety_7" "homesafety_8"
## [9] "homesafety_9" "homesafety_10" "homesafety_11" "homesafety_12"
## [13] "homesafety_13" "homesafety_14" "homesafety_15" "homesafety_16"
## [17] "homesafety_17"
#(n=5713 missing)
## Warning: Removed 5713 rows containing non-finite values (stat_count).
#(n=5713 missing)
## Warning: Removed 5713 rows containing non-finite values (stat_count).
#(n=5717 missing)
## Warning: Removed 5717 rows containing non-finite values (stat_count).
## [1] "homereasons_1" "homereasons_2" "homereasons_3" "homereasons_4"
## [5] "homereasons_5" "homereasons_6" "homereasons_7" "homereasons_8"
## [9] "homereasons_9" "homereasons_10" "homereasons_11"
Descriptive
# gender descriptive in the data sets
prop.table(table(new_data$gender))*100##
## Male
## 48.56701031
## Female
## 50.95876289
## Transgender
## 0.27835052
## Do not identify as male, female, or transgender
## 0.15463918
## I do not identify as male, female, or transgender
## 0.04123711# Education
firstgun %>%
count(educ)## educ n
## 1 Less than High School 121
## 2 High School Diploma or Equivalent 2418
## 3 Associate's Degree 1305
## 4 Bachelor's Degree 1460
## 5 Master's Degree 734
## 6 Doctorate's/ Professional Degree 162secondgun %>%
count(educ)## educ n
## 1 Less than High School 77
## 2 High School Diploma or Equivalent 1371
## 3 Associate's Degree 718
## 4 Bachelor's Degree 734
## 5 Master's Degree 477
## 6 Doctorate's/Professional Degree 123# Education descriptive in the
prop.table(table(firstgun$educ))*100##
## Less than High School High School Diploma or Equivalent
## 1.951613 39.000000
## Associate's Degree Bachelor's Degree
## 21.048387 23.548387
## Master's Degree Doctorate's/ Professional Degree
## 11.838710 2.612903prop.table(table(secondgun$educ))*100##
## Less than High School High School Diploma or Equivalent
## 2.200000 39.171429
## Associate's Degree Bachelor's Degree
## 20.514286 20.971429
## Master's Degree Doctorate's/Professional Degree
## 13.628571 3.514286prop.table(table(new_data$educ))*100##
## Less than High School High School Diploma or Equivalent
## 2.041237 39.061856
## Associate's Degree Bachelor's Degree
## 20.855670 22.618557
## Master's Degree Doctorate's/Professional Degree
## 12.484536 2.938144# Relationship descriptive in the data sets
firstgun %>%
count(relation)## relation
## 1 Unmarried and not currently in a committed relationship
## 2 Unmarried and currently in a committed relationship, but not living together
## 3 Unmarried and currently in a committed relationship, and living together
## 4 Married, but not living together
## 5 Married and living together
## n
## 1 1795
## 2 494
## 3 628
## 4 161
## 5 3122secondgun %>%
count(relation)## relation
## 1 Unmarried and not currently in a committed relationship
## 2 Unmarried and currently in a committed relationship, but not living together
## 3 Unmarried and currently in a committed relationship, and living together
## 4 Married, but not living together
## 5 Married and living together
## n
## 1 1190
## 2 261
## 3 316
## 4 85
## 5 1648prop.table(table(firstgun$relation))*100##
## Unmarried and not currently in a committed relationship
## 28.951613
## Unmarried and currently in a committed relationship, but not living together
## 7.967742
## Unmarried and currently in a committed relationship, and living together
## 10.129032
## Married, but not living together
## 2.596774
## Married and living together
## 50.354839prop.table(table(secondgun$relation))*100##
## Unmarried and not currently in a committed relationship
## 34.000000
## Unmarried and currently in a committed relationship, but not living together
## 7.457143
## Unmarried and currently in a committed relationship, and living together
## 9.028571
## Married, but not living together
## 2.428571
## Married and living together
## 47.085714prop.table(table(new_data$relation))*100##
## Unmarried and not currently in a committed relationship
## 30.773196
## Unmarried and currently in a committed relationship, but not living together
## 7.783505
## Unmarried and currently in a committed relationship, and living together
## 9.731959
## Married, but not living together
## 2.536082
## Married and living together
## 49.175258# age descriptive in the (age_r in the first data is categorical 4 groups, and age in the first set is 8 groups, and )
firstgun %>%
count(age)## age n
## 1 18-24 643
## 2 25-34 1217
## 3 35-44 1313
## 4 45-54 857
## 5 55-64 1136
## 6 65-74 845
## 7 75-84 181
## 8 85+ 8secondgun %>%
count(age)## age n
## 1 18-24 570
## 2 25-34 489
## 3 35-44 787
## 4 45-54 431
## 5 55-64 500
## 6 65-74 570
## 7 75-84 145
## 8 85+ 7
## 9 Under 18 1new_data %>%
count(age)## age n
## 1: Under 18 1
## 2: 18-24 1213
## 3: 25-34 1706
## 4: 35-44 2100
## 5: 45-54 1288
## 6: 55-64 1636
## 7: 65-74 1415
## 8: 75-84 326
## 9: 85+ 15prop.table(table(firstgun$age))*100##
## Under 18 18-24 25-34 35-44 45-54 55-64 65-74
## 0.0000000 10.3709677 19.6290323 21.1774194 13.8225806 18.3225806 13.6290323
## 75-84 85+
## 2.9193548 0.1290323prop.table(table(secondgun$age))*100##
## 18-24 25-34 35-44 45-54 55-64 65-74
## 16.28571429 13.97142857 22.48571429 12.31428571 14.28571429 16.28571429
## 75-84 85+ Under 18
## 4.14285714 0.20000000 0.02857143prop.table(table(new_data$age))*100##
## Under 18 18-24 25-34 35-44 45-54 55-64
## 0.01030928 12.50515464 17.58762887 21.64948454 13.27835052 16.86597938
## 65-74 75-84 85+
## 14.58762887 3.36082474 0.15463918# race
firstgun %>%
count(race)## race n
## 1 Alaska 197
## 2 Asian 818
## 3 Black 637
## 4 Hawaiian 29
## 5 Hispanic 919
## 6 Other 97
## 7 White 3503secondgun %>%
count(race)## race n
## 1 Alaska 83
## 2 Asian 340
## 3 Black 424
## 4 Hawaiian 13
## 5 Hispanic 482
## 6 Other 45
## 7 White 2113new_data %>%
count(race)## race n
## 1: Alaska 280
## 2: Asian 1158
## 3: Black 1061
## 4: Hawaiian 42
## 5: Hispanic 1401
## 6: Other 142
## 7: White 5616prop.table(table(firstgun$race))*100##
## Alaska Asian Black Hawaiian Hispanic Other White
## 3.1774194 13.1935484 10.2741935 0.4677419 14.8225806 1.5645161 56.5000000prop.table(table(secondgun$race))*100##
## Alaska Asian Black Hawaiian Hispanic Other White
## 2.3714286 9.7142857 12.1142857 0.3714286 13.7714286 1.2857143 60.3714286prop.table(table(secondgun$race))*100##
## Alaska Asian Black Hawaiian Hispanic Other White
## 2.3714286 9.7142857 12.1142857 0.3714286 13.7714286 1.2857143 60.3714286# Sex
firstgun %>%
count(sex)## sex n
## 1 Male 3038
## 2 Female 3162secondgun %>%
count(sex)## sex n
## 1 Male 1695
## 2 Female 1802
## 3 <NA> 3new_data %>%
count(sex)## sex n
## 1: Male 4733
## 2: Female 4964
## 3: <NA> 3prop.table(table(firstgun$sex))*100##
## Male Female
## 49 51prop.table(table(secondgun$sex))*100##
## Male Female
## 48.47012 51.52988prop.table(table(secondgun$sex))*100##
## Male Female
## 48.47012 51.52988# Income
firstgun %>%
count(income)## income n
## 1 $10,000 - $19,999 459
## 2 $100,000 - $149,999 1324
## 3 $150,000 or more 350
## 4 $20,000 - $29,999 561
## 5 $30,000 - $39,999 538
## 6 $40,000 - $49,999 465
## 7 $50,000 - $59,999 639
## 8 $60,000 - $69,999 410
## 9 $70,000 - $79,999 419
## 10 $80,000 - $89,999 279
## 11 $90,000 - $99,999 299
## 12 Less than $10,000 457secondgun %>%
count(income)## income n
## 1 Less than $10,000 245
## 2 $10,000 - $19,999 290
## 3 $20,000 - $29,999 349
## 4 $30,000 - $39,999 296
## 5 $40,000 - $49,999 227
## 6 $50,000 - $59,999 331
## 7 $60,000 - $69,999 244
## 8 $70,000 - $79,999 270
## 9 $80,000 - $89,999 142
## 10 $90,000 - $99,999 173
## 11 $100,000 - $149,999 560
## 12 $150,000 or more 373new_data %>%
count(income)## income n
## 1: $10,000 - $19,999 749
## 2: $100,000 - $149,999 1884
## 3: $150,000 or more 723
## 4: $20,000 - $29,999 910
## 5: $30,000 - $39,999 834
## 6: $40,000 - $49,999 692
## 7: $50,000 - $59,999 970
## 8: $60,000 - $69,999 654
## 9: $70,000 - $79,999 689
## 10: $80,000 - $89,999 421
## 11: $90,000 - $99,999 472
## 12: Less than $10,000 702prop.table(table(firstgun$income))*100##
## $10,000 - $19,999 $100,000 - $149,999 $150,000 or more $20,000 - $29,999
## 7.403226 21.354839 5.645161 9.048387
## $30,000 - $39,999 $40,000 - $49,999 $50,000 - $59,999 $60,000 - $69,999
## 8.677419 7.500000 10.306452 6.612903
## $70,000 - $79,999 $80,000 - $89,999 $90,000 - $99,999 Less than $10,000
## 6.758065 4.500000 4.822581 7.370968prop.table(table(secondgun$income))*100##
## Less than $10,000 $10,000 - $19,999 $20,000 - $29,999 $30,000 - $39,999
## 7.000000 8.285714 9.971429 8.457143
## $40,000 - $49,999 $50,000 - $59,999 $60,000 - $69,999 $70,000 - $79,999
## 6.485714 9.457143 6.971429 7.714286
## $80,000 - $89,999 $90,000 - $99,999 $100,000 - $149,999 $150,000 or more
## 4.057143 4.942857 16.000000 10.657143prop.table(table(secondgun$income))*100##
## Less than $10,000 $10,000 - $19,999 $20,000 - $29,999 $30,000 - $39,999
## 7.000000 8.285714 9.971429 8.457143
## $40,000 - $49,999 $50,000 - $59,999 $60,000 - $69,999 $70,000 - $79,999
## 6.485714 9.457143 6.971429 7.714286
## $80,000 - $89,999 $90,000 - $99,999 $100,000 - $149,999 $150,000 or more
## 4.057143 4.942857 16.000000 10.657143#Gunhome
firstgun %>%
count(gunhome)## gunhome n
## 1 No 3729
## 2 Yes 2311
## 3 Prefer not to answer 160secondgun %>%
count(gunhome)## gunhome n
## 1 No 2105
## 2 Yes 1356
## 3 Prefer not to answer 39new_data %>%
count(gunhome)## gunhome n
## 1: No 5834
## 2: Yes 3667
## 3: Prefer not to answer 199# acquireplan
firstgun %>%
count(acquireplan)## acquireplan n
## 1 No 4043
## 2 Yes 793
## 3 Haven't decided yet 1292
## 4 Prefer not to answer 72secondgun %>%
count(acquireplan)## acquireplan n
## 1 No 2365
## 2 Yes 516
## 3 Haven't decided yet 596
## 4 Prefer not to answer 16
## 5 <NA> 7new_data %>%
count(acquireplan)## acquireplan n
## 1: No 6408
## 2: Yes 1309
## 3: Haven't decided yet 1888
## 4: Prefer not to answer 88
## 5: <NA> 7# safety_1
firstgun %>%
count(safety_1)## safety_1 n
## 1 Strongly Agree 1768
## 2 Agree 2289
## 3 Neutral (neither agree not disagree) 1248
## 4 Disagree 619
## 5 Strongly Disagree 276secondgun %>%
count(safety_1)## safety_1 n
## 1 Strongly Agree 958
## 2 Agree 1347
## 3 Neutral (neither agree not disagree) 718
## 4 Disagree 325
## 5 Strongly Disagree 140
## 6 <NA> 12new_data %>%
count(safety_1)## safety_1 n
## 1: Strongly Agree 2726
## 2: Agree 3636
## 3: Neutral (neither agree not disagree) 1966
## 4: Disagree 944
## 5: Strongly Disagree 416
## 6: <NA> 12# safety_2
firstgun %>%
count(safety_2)## safety_2 n
## 1 Strongly Agree 1513
## 2 Agree 2256
## 3 Neutral (neither agree not disagree) 1310
## 4 Disagree 750
## 5 Strongly Disagree 371secondgun %>%
count(safety_2)## safety_2 n
## 1 Strongly Agree 887
## 2 Agree 1300
## 3 Neutral (neither agree not disagree) 759
## 4 Disagree 382
## 5 Strongly Disagree 163
## 6 <NA> 9new_data %>%
count(safety_2)## safety_2 n
## 1: Strongly Agree 2400
## 2: Agree 3556
## 3: Neutral (neither agree not disagree) 2069
## 4: Disagree 1132
## 5: Strongly Disagree 534
## 6: <NA> 9# safety_3
firstgun %>%
count(safety_3)## safety_3 n
## 1 Strongly Agree 1145
## 2 Agree 2146
## 3 Neutral (neither agree not disagree) 1660
## 4 Disagree 890
## 5 Strongly Disagree 359secondgun %>%
count(safety_3)## safety_3 n
## 1 Strongly Agree 672
## 2 Agree 1262
## 3 Neutral (neither agree not disagree) 935
## 4 Disagree 454
## 5 Strongly Disagree 167
## 6 <NA> 10new_data %>%
count(safety_3)## safety_3 n
## 1: Strongly Agree 1817
## 2: Agree 3408
## 3: Neutral (neither agree not disagree) 2595
## 4: Disagree 1344
## 5: Strongly Disagree 526
## 6: <NA> 10# ptci_1
firstgun %>%
count(ptci_1)## ptci_1 n
## 1 Totally Disagree 377
## 2 Disagree Very Much 682
## 3 Disagree Slightly 1017
## 4 Neutral 1437
## 5 Agree Slightly 1433
## 6 Agree Very Much 667
## 7 Totally Agree 587secondgun %>%
count(ptci_1)## ptci_1 n
## 1 Totally Disagree 207
## 2 Disagree Very Much 388
## 3 Disagree Slightly 479
## 4 Neutral 772
## 5 Agree Slightly 870
## 6 Agree Very Much 418
## 7 Totally Agree 357
## 8 <NA> 9new_data %>%
count(ptci_1)## ptci_1 n
## 1: Totally Disagree 584
## 2: Disagree Very Much 1070
## 3: Disagree Slightly 1496
## 4: Neutral 2209
## 5: Agree Slightly 2303
## 6: Agree Very Much 1085
## 7: Totally Agree 944
## 8: <NA> 9# ptci_2
firstgun %>%
count(ptci_2)## ptci_2 n
## 1 Totally Disagree 490
## 2 Disagree Very Much 744
## 3 Disagree Slightly 1099
## 4 Neutral 1259
## 5 Agree Slightly 1277
## 6 Agree Very Much 723
## 7 Totally Agree 608secondgun %>%
count(ptci_2)## ptci_2 n
## 1 Totally Disagree 261
## 2 Disagree Very Much 420
## 3 Disagree Slightly 541
## 4 Neutral 703
## 5 Agree Slightly 799
## 6 Agree Very Much 423
## 7 Totally Agree 345
## 8 <NA> 8new_data %>%
count(ptci_2)## ptci_2 n
## 1: Totally Disagree 751
## 2: Disagree Very Much 1164
## 3: Disagree Slightly 1640
## 4: Neutral 1962
## 5: Agree Slightly 2076
## 6: Agree Very Much 1146
## 7: Totally Agree 953
## 8: <NA> 8# ptci_3
firstgun %>%
count(ptci_3)## ptci_3 n
## 1 Totally Disagree 245
## 2 Disagree Very Much 454
## 3 Disagree Slightly 709
## 4 Neutral 1554
## 5 Agree Slightly 1788
## 6 Agree Very Much 813
## 7 Totally Agree 637secondgun %>%
count(ptci_3)## ptci_3 n
## 1 Totally Disagree 129
## 2 Disagree Very Much 217
## 3 Disagree Slightly 401
## 4 Neutral 808
## 5 Agree Slightly 1023
## 6 Agree Very Much 494
## 7 Totally Agree 419
## 8 <NA> 9new_data %>%
count(ptci_3)## ptci_3 n
## 1: Totally Disagree 374
## 2: Disagree Very Much 671
## 3: Disagree Slightly 1110
## 4: Neutral 2362
## 5: Agree Slightly 2811
## 6: Agree Very Much 1307
## 7: Totally Agree 1056
## 8: <NA> 9# si_1
firstgun %>%
count(si_1)## si_1 n
## 1 0 4680
## 2 I wish I could disappear or not exist 1520secondgun %>%
count(si_1)## si_1 n
## 1 0 2645
## 2 I wish I could disappear or not exist 855new_data %>%
count(si_1)## si_1 n
## 1: 0 7325
## 2: I wish I could disappear or not exist 2375# si_2
firstgun %>%
count(si_2)## si_2 n
## 1 0 5211
## 2 I wish I was never born 989secondgun %>%
count(si_2)## si_2 n
## 1 0 2902
## 2 I wish I was never born 598new_data %>%
count(si_2)## si_2 n
## 1: 0 8113
## 2: I wish I was never born 1587# si_3
firstgun %>%
count(si_3)## si_3 n
## 1 0 5123
## 2 My life is not worth living 1077secondgun %>%
count(si_3)## si_3 n
## 1 0 2867
## 2 My life is not worth living 633new_data %>%
count(si_3)## si_3 n
## 1: 0 7990
## 2: My life is not worth living 1710# si_4
firstgun %>%
count(si_4)## si_4 n
## 1 0 5006
## 2 I wish I could go to sleep and never wake up 1194secondgun %>%
count(si_4)## si_4 n
## 1 0 2735
## 2 I wish I could go to sleep and never wake up 765new_data %>%
count(si_4)## si_4 n
## 1: 0 7741
## 2: I wish I could go to sleep and never wake up 1959# si_5
firstgun %>%
count(si_5)## si_5 n
## 1 0 5348
## 2 I wish I were dead 852secondgun %>%
count(si_5)## si_5 n
## 1 0 2976
## 2 I wish I were dead 524new_data %>%
count(si_5)## si_5 n
## 1: 0 8324
## 2: I wish I were dead 1376# si_6
firstgun %>%
count(si_6)## si_6 n
## 1 0 5469
## 2 Maybe I should kill myself 731secondgun %>%
count(si_6)## si_6 n
## 1 0 3064
## 2 Maybe I should kill myself 436new_data %>%
count(si_6)## si_6 n
## 1: 0 8533
## 2: Maybe I should kill myself 1167# si_7
firstgun %>%
count(si_7)## si_7 n
## 1 0 5700
## 2 I should kill myself 500secondgun %>%
count(si_7)## si_7 n
## 1 0 3199
## 2 I should kill myself 301new_data %>%
count(si_7)## si_7 n
## 1: 0 8899
## 2: I should kill myself 801# si_8
firstgun %>%
count(si_8)## si_8 n
## 1 0 5851
## 2 I am going to kill myself 349secondgun %>%
count(si_8)## si_8 n
## 1 0 3270
## 2 I am going to kill myself 230new_data %>%
count(si_8)## si_8 n
## 1: 0 9121
## 2: I am going to kill myself 579# sb_1
firstgun %>%
count(sb_1)## sb_1
## 1 0
## 2 Done something to prepare to make a suicide attempt. For example, gaining access to method, looking up a method online, writing a suicide note, updating your will, or saying goodbye to loved ones.
## n
## 1 5522
## 2 678secondgun %>%
count(sb_1)## sb_1
## 1 Done something to prepare to make a suicide attempt. For example, gaining access to method, looking up a method online, writing a suicide note, updating your will, or saying goodbye to loved ones.
## 2 0
## n
## 1 406
## 2 3094new_data %>%
count(sb_1)## sb_1
## 1: 0
## 2: Done something to prepare to make a suicide attempt. For example, gaining access to method, looking up a method online, writing a suicide note, updating your will, or saying goodbye to loved ones.
## n
## 1: 8616
## 2: 1084# sb_2
firstgun %>%
count(sb_2)## sb_2
## 1 0
## 2 Practiced making a suicide attempt. For example, taking an overdose of an amount you knew in advance would not kill you, hanging a rope to see if it would support your body weight, or jumping from a height that you knew in advance would not kill you.
## n
## 1 5844
## 2 356secondgun %>%
count(sb_2)## sb_2
## 1 0
## 2 Practiced making a suicide attempt. For example, taking an overdose of an amount you knew in advance would not kill you, hanging a rope to see if it would support your body weight, or jumping from a height that you knew in advance would not kill you.
## n
## 1 3250
## 2 250new_data %>%
count(sb_2)## sb_2
## 1: 0
## 2: Practiced making a suicide attempt. For example, taking an overdose of an amount you knew in advance would not kill you, hanging a rope to see if it would support your body weight, or jumping from a height that you knew in advance would not kill you.
## n
## 1: 9094
## 2: 606# sb_3
firstgun %>%
count(sb_3)## sb_3
## 1 0
## 2 Been very close to killing yourself but at the last minute you decided not to do it before taking any action. For example, holding a bottle of pills in your hand but deciding not to take any, setting up a noose but then deciding not to use it, or pointing
## n
## 1 5733
## 2 467secondgun %>%
count(sb_3)## sb_3
## 1 Been very close to killing yourself but at the last minute you decided not to do it before taking any action. For example, holding a bottle of pills in your hand but deciding not to take any, setting up a noose but then deciding not to use it, or pointing
## 2 0
## n
## 1 302
## 2 3198new_data %>%
count(sb_3)## sb_3
## 1: 0
## 2: Been very close to killing yourself but at the last minute you decided not to do it before taking any action. For example, holding a bottle of pills in your hand but deciding not to take any, setting up a noose but then deciding not to use it, or pointing
## n
## 1: 8931
## 2: 769# sb_4
firstgun %>%
count(sb_4)## sb_4
## 1 0
## 2 Been very close to killing yourself but at the last minute, someone or something else stopped you before you took any action. For example, holding a bottle of pills in your hand but then someone stopped you before you took any, pointing a gun to your head
## n
## 1 5915
## 2 285secondgun %>%
count(sb_4)## sb_4
## 1 Been very close to killing yourself but at the last minute, someone or something else stopped you before you took any action. For example, holding a bottle of pills in your hand but then someone stopped you before you took any, pointing a gun to your head
## 2 0
## n
## 1 216
## 2 3284new_data %>%
count(sb_4)## sb_4
## 1: 0
## 2: Been very close to killing yourself but at the last minute, someone or something else stopped you before you took any action. For example, holding a bottle of pills in your hand but then someone stopped you before you took any, pointing a gun to your head
## n
## 1: 9199
## 2: 501# sb_5
firstgun %>%
count(sb_5)## sb_5
## 1 0
## 2 Started to kill yourself and then you stopped after you had already taken some action. For example, taking one or a few pills of an overdose but then you changed your mind and stopped yourself, or starting to hang yourself but then you changed your mind a
## n
## 1 6006
## 2 194secondgun %>%
count(sb_5)## sb_5
## 1 Started to kill yourself and then you stopped after you had already taken some action. For example, taking one or a few pills of an overdose but then you changed your mind and stopped yourself, or starting to hang yourself but then you changed your mind a
## 2 0
## n
## 1 157
## 2 3343new_data %>%
count(sb_5)## sb_5
## 1: 0
## 2: Started to kill yourself and then you stopped after you had already taken some action. For example, taking one or a few pills of an overdose but then you changed your mind and stopped yourself, or starting to hang yourself but then you changed your mind a
## n
## 1: 9349
## 2: 351# sb_6
firstgun %>%
count(sb_6)## sb_6
## 1 0
## 2 Started to kill yourself and then you decided to reach out for help after you had already taken some action. For example, taking an overdose and then calling a friend or 911 for help.
## n
## 1 6060
## 2 140secondgun %>%
count(sb_6)## sb_6
## 1 Started to kill yourself and then you decided to reach out for help after you had already taken some action. For example, taking an overdose and then calling a friend or 911 for help.
## 2 0
## n
## 1 114
## 2 3386new_data %>%
count(sb_6)## sb_6
## 1: 0
## 2: Started to kill yourself and then you decided to reach out for help after you had already taken some action. For example, taking an overdose and then calling a friend or 911 for help.
## n
## 1: 9446
## 2: 254# sb_7
firstgun %>%
count(sb_7)## sb_7
## 1 0
## 2 Tried to kill yourself and someone found you afterwards. For example, taking an overdose of pills and then someone found you unconscious and called 911, or hanging yourself and then someone found you and cut you down.
## n
## 1 6033
## 2 167secondgun %>%
count(sb_7)## sb_7
## 1 Tried to kill yourself and someone found you afterwards. For example, taking an overdose of pills and then someone found you unconscious and called 911, or hanging yourself and then someone found you and cut you down.
## 2 0
## n
## 1 119
## 2 3381new_data %>%
count(sb_7)## sb_7
## 1: 0
## 2: Tried to kill yourself and someone found you afterwards. For example, taking an overdose of pills and then someone found you unconscious and called 911, or hanging yourself and then someone found you and cut you down.
## n
## 1: 9414
## 2: 286# sb_8
firstgun %>%
count(sb_8)## sb_8
## 1 0
## 2 Tried to kill yourself and no one found you afterwards. For example, taking an overdose of pills and then waking up later.
## n
## 1 6070
## 2 130secondgun %>%
count(sb_8)## sb_8
## 1 Tried to kill yourself and no one found you afterwards. For example, taking an overdose of pills and then waking up later.
## 2 0
## n
## 1 78
## 2 3422new_data %>%
count(sb_8)## sb_8
## 1: 0
## 2: Tried to kill yourself and no one found you afterwards. For example, taking an overdose of pills and then waking up later.
## n
## 1: 9492
## 2: 208# sb_9
firstgun %>%
count(sb_9)## sb_9
## 1 0
## 2 Purposely hurt yourself without wanting to die. For example, cutting yourself or burning your skin to reduce emotional stress, hitting yourself on purpose, punching a wall or picking a fight with someone so you could feel physical pain.
## n
## 1 5492
## 2 708secondgun %>%
count(sb_9)## sb_9
## 1 Purposely hurt yourself without wanting to die. For example, cutting yourself or burning your skin to reduce emotional stress, hitting yourself on purpose, punching a wall or picking a fight with someone so you could feel physical pain.
## 2 0
## n
## 1 338
## 2 3162new_data %>%
count(sb_9)## sb_9
## 1: 0
## 2: Purposely hurt yourself without wanting to die. For example, cutting yourself or burning your skin to reduce emotional stress, hitting yourself on purpose, punching a wall or picking a fight with someone so you could feel physical pain.
## n
## 1: 8654
## 2: 1046# sbtimetime_1
firstgun %>%
count(sbtime_1)## sbtime_1 n
## 1 0 5522
## 2 More than 1 year ago 366
## 3 Within the past year 174
## 4 Within the past month 138secondgun %>%
count(sbtime_1)## sbtime_1 n
## 1 More than 1 year ago 214
## 2 Within the past year 125
## 3 Within the past month 43
## 4 0 3118new_data %>%
count(sbtime_1)## sbtime_1 n
## 1: 0 8640
## 2: More than 1 year ago 580
## 3: Within the past year 299
## 4: Within the past month 181# sbtime_2
firstgun %>%
count(sbtime_2)## sbtime_2 n
## 1 0 5844
## 2 More than 1 year ago 158
## 3 Within the past year 127
## 4 Within the past month 71secondgun %>%
count(sbtime_2)## sbtime_2 n
## 1 More than 1 year ago 107
## 2 Within the past year 99
## 3 Within the past month 32
## 4 0 3262new_data %>%
count(sbtime_2)## sbtime_2 n
## 1: 0 9106
## 2: More than 1 year ago 265
## 3: Within the past year 226
## 4: Within the past month 103# sbtime_3
firstgun %>%
count(sbtime_3)## sbtime_3 n
## 1 0 5733
## 2 More than 1 year ago 243
## 3 Within the past year 146
## 4 Within the past month 78secondgun %>%
count(sbtime_3)## sbtime_3 n
## 1 More than 1 year ago 144
## 2 Within the past year 106
## 3 Within the past month 42
## 4 0 3208new_data %>%
count(sbtime_3)## sbtime_3 n
## 1: 0 8941
## 2: More than 1 year ago 387
## 3: Within the past year 252
## 4: Within the past month 120# sbtime_4
firstgun %>%
count(sbtime_4)## sbtime_4 n
## 1 0 5915
## 2 More than 1 year ago 152
## 3 Within the past year 88
## 4 Within the past month 45secondgun %>%
count(sbtime_4)## sbtime_4 n
## 1 More than 1 year ago 99
## 2 Within the past year 80
## 3 Within the past month 28
## 4 0 3293new_data %>%
count(sbtime_4)## sbtime_4 n
## 1: 0 9208
## 2: More than 1 year ago 251
## 3: Within the past year 168
## 4: Within the past month 73# sbtime_5
firstgun %>%
count(sbtime_5)## sbtime_5 n
## 1 0 6006
## 2 More than 1 year ago 111
## 3 Within the past year 51
## 4 Within the past month 32secondgun %>%
count(sbtime_5)## sbtime_5 n
## 1 More than 1 year ago 70
## 2 Within the past year 56
## 3 Within the past month 23
## 4 0 3351new_data %>%
count(sbtime_5)## sbtime_5 n
## 1: 0 9357
## 2: More than 1 year ago 181
## 3: Within the past year 107
## 4: Within the past month 55# sbtime_6
firstgun %>%
count(sbtime_6)## sbtime_6 n
## 1 0 6060
## 2 More than 1 year ago 83
## 3 Within the past year 26
## 4 Within the past month 31secondgun %>%
count(sbtime_6)## sbtime_6 n
## 1 More than 1 year ago 48
## 2 Within the past year 48
## 3 Within the past month 17
## 4 0 3387new_data %>%
count(sbtime_6)## sbtime_6 n
## 1: 0 9447
## 2: More than 1 year ago 131
## 3: Within the past year 74
## 4: Within the past month 48# sbtime_7
firstgun %>%
count(sbtime_7)## sbtime_7 n
## 1 0 6033
## 2 More than 1 year ago 115
## 3 Within the past year 29
## 4 Within the past month 23secondgun %>%
count(sbtime_7)## sbtime_7 n
## 1 More than 1 year ago 65
## 2 Within the past year 33
## 3 Within the past month 18
## 4 0 3384new_data %>%
count(sbtime_7)## sbtime_7 n
## 1: 0 9417
## 2: More than 1 year ago 180
## 3: Within the past year 62
## 4: Within the past month 41# sbtime_8
firstgun %>%
count(sbtime_8)## sbtime_8 n
## 1 0 6070
## 2 More than 1 year ago 77
## 3 Within the past year 29
## 4 Within the past month 24secondgun %>%
count(sbtime_8)## sbtime_8 n
## 1 More than 1 year ago 44
## 2 Within the past year 23
## 3 Within the past month 10
## 4 0 3423new_data %>%
count(sbtime_8)## sbtime_8 n
## 1: 0 9493
## 2: More than 1 year ago 121
## 3: Within the past year 52
## 4: Within the past month 34# sbtime_9
firstgun %>%
count(sbtime_9)## sbtime_9 n
## 1 0 5492
## 2 More than 1 year ago 415
## 3 Within the past year 172
## 4 Within the past month 121secondgun %>%
count(sbtime_9)## sbtime_9 n
## 1 More than 1 year ago 195
## 2 Within the past year 79
## 3 Within the past month 59
## 4 0 3167new_data %>%
count(sbtime_9)## sbtime_9 n
## 1: 0 8659
## 2: More than 1 year ago 610
## 3: Within the past year 251
## 4: Within the past month 180# married
firstgun %>%
count(married)## married n
## 1 No 2013
## 2 Yes 4187firstgun %>%
summarise(weight = NROW(married))## weight
## 1 6200secondgun %>%
count(married)## married n
## 1 No 1246
## 2 Yes 2253
## 3 <NA> 1secondgun %>%
summarise(weight = NROW(married))## weight
## 1 3500new_data %>%
count(married)## married n
## 1: No 3259
## 2: Yes 6440
## 3: <NA> 1new_data %>%
summarise(weight = NROW(married))## weight
## 1 9700# adults
firstgun %>%
count(adults)## adults n
## 1 0 352
## 2 1 1155
## 3 10 or more 5
## 4 2 3289
## 5 3 868
## 6 4 374
## 7 5 110
## 8 6 33
## 9 7 11
## 10 8 1
## 11 9 2firstgun %>%
summarise(weight = NROW(adults))## weight
## 1 6200secondgun %>%
count(adults)## adults n
## 1 0 199
## 2 1 713
## 3 2 1791
## 4 3 485
## 5 4 221
## 6 5 56
## 7 6 23
## 8 7 6
## 9 8 2
## 10 9 1
## 11 10 or more 2
## 12 <NA> 1secondgun %>%
summarise(weight = NROW(adults))## weight
## 1 3500new_data %>%
count(adults)## adults n
## 1: 0 551
## 2: 1 1868
## 3: 10 or more 7
## 4: 2 5080
## 5: 3 1353
## 6: 4 595
## 7: 5 166
## 8: 6 56
## 9: 7 17
## 10: 8 3
## 11: 9 3
## 12: <NA> 1new_data %>%
summarise(weight = NROW(adults))## weight
## 1 9700# children
firstgun %>%
count(children)## children n
## 1 0 3842
## 2 1 1080
## 3 10 or more 2
## 4 2 856
## 5 3 272
## 6 4 104
## 7 5 31
## 8 6 7
## 9 7 2
## 10 8 3
## 11 9 1firstgun %>%
summarise(weight = NROW(children))## weight
## 1 6200secondgun %>%
count(children)## children n
## 1 0 2178
## 2 1 616
## 3 2 478
## 4 3 145
## 5 4 44
## 6 5 12
## 7 6 3
## 8 7 3
## 9 8 5
## 10 9 1
## 11 10 or more 2
## 12 <NA> 13secondgun %>%
summarise(weight = NROW(children))## weight
## 1 3500new_data %>%
count(children)## children n
## 1: 0 6020
## 2: 1 1696
## 3: 10 or more 4
## 4: 2 1334
## 5: 3 417
## 6: 4 148
## 7: 5 43
## 8: 6 10
## 9: 7 5
## 10: 8 8
## 11: 9 2
## 12: <NA> 13new_data %>%
summarise(weight = NROW(children))## weight
## 1 9700# state
firstgun %>%
count(state)## state n
## 1 Alabama 89
## 2 Alaska 9
## 3 Arizona 134
## 4 Arkansas 56
## 5 California 662
## 6 Colorado 90
## 7 Connecticut 69
## 8 Delaware 32
## 9 District of Columbia 18
## 10 Florida 486
## 11 Georgia 245
## 12 Hawaii 37
## 13 Idaho 19
## 14 Illinois 238
## 15 Indiana 134
## 16 Iowa 35
## 17 Kansas 61
## 18 Kentucky 88
## 19 Louisiana 67
## 20 Maine 20
## 21 Maryland 95
## 22 Massachusetts 122
## 23 Michigan 172
## 24 Minnesota 90
## 25 Mississippi 41
## 26 Missouri 110
## 27 Montana 11
## 28 Nebraska 19
## 29 Nevada 63
## 30 New Hampshire 20
## 31 New Jersey 198
## 32 New Mexico 30
## 33 New York 443
## 34 North Carolina 198
## 35 North Dakota 15
## 36 Ohio 240
## 37 Oklahoma 78
## 38 Oregon 86
## 39 Pennsylvania 267
## 40 Rhode Island 17
## 41 South Carolina 111
## 42 South Dakota 13
## 43 Tennessee 112
## 44 Texas 518
## 45 Utah 52
## 46 Vermont 9
## 47 Virginia 174
## 48 Washington 132
## 49 West Virginia 31
## 50 Wisconsin 97
## 51 Wyoming 6
## 52 <NA> 41firstgun %>%
summarise(weight = NROW(state))## weight
## 1 6200secondgun %>%
count(state)## state n
## 1 Alabama 57
## 2 Alaska 7
## 3 Arizona 68
## 4 Arkansas 25
## 5 California 360
## 6 Colorado 36
## 7 Connecticut 45
## 8 Delaware 14
## 9 District of Columbia 10
## 10 Florida 293
## 11 Georgia 136
## 12 Hawaii 22
## 13 Idaho 11
## 14 Illinois 159
## 15 Indiana 48
## 16 Iowa 15
## 17 Kansas 25
## 18 Kentucky 34
## 19 Louisiana 48
## 20 Maine 7
## 21 Maryland 76
## 22 Massachusetts 64
## 23 Michigan 83
## 24 Minnesota 39
## 25 Mississippi 29
## 26 Missouri 51
## 27 Montana 5
## 28 Nebraska 16
## 29 Nevada 40
## 30 New Hampshire 9
## 31 New Jersey 127
## 32 New Mexico 15
## 33 New York 315
## 34 North Carolina 130
## 35 North Dakota 9
## 36 Ohio 106
## 37 Oklahoma 42
## 38 Oregon 44
## 39 Pennsylvania 174
## 40 Puerto Rico 3
## 41 Rhode Island 13
## 42 South Carolina 60
## 43 South Dakota 10
## 44 Tennessee 67
## 45 Texas 296
## 46 Utah 21
## 47 Vermont 4
## 48 Virginia 84
## 49 Washington 58
## 50 West Virginia 15
## 51 Wisconsin 67
## 52 Wyoming 6
## 53 <NA> 2secondgun %>%
summarise(weight = NROW(state))## weight
## 1 3500new_data %>%
count(state)## state n
## 1: Alabama 146
## 2: Alaska 16
## 3: Arizona 202
## 4: Arkansas 81
## 5: California 1022
## 6: Colorado 126
## 7: Connecticut 114
## 8: Delaware 46
## 9: District of Columbia 28
## 10: Florida 779
## 11: Georgia 381
## 12: Hawaii 59
## 13: Idaho 30
## 14: Illinois 397
## 15: Indiana 182
## 16: Iowa 50
## 17: Kansas 86
## 18: Kentucky 122
## 19: Louisiana 115
## 20: Maine 27
## 21: Maryland 171
## 22: Massachusetts 186
## 23: Michigan 255
## 24: Minnesota 129
## 25: Mississippi 70
## 26: Missouri 161
## 27: Montana 16
## 28: Nebraska 35
## 29: Nevada 103
## 30: New Hampshire 29
## 31: New Jersey 325
## 32: New Mexico 45
## 33: New York 758
## 34: North Carolina 328
## 35: North Dakota 24
## 36: Ohio 346
## 37: Oklahoma 120
## 38: Oregon 130
## 39: Pennsylvania 441
## 40: Puerto Rico 3
## 41: Rhode Island 30
## 42: South Carolina 171
## 43: South Dakota 23
## 44: Tennessee 179
## 45: Texas 814
## 46: Utah 73
## 47: Vermont 13
## 48: Virginia 258
## 49: Washington 190
## 50: West Virginia 46
## 51: Wisconsin 164
## 52: Wyoming 12
## 53: <NA> 43
## state nnew_data %>%
summarise(weight = NROW(state))## weight
## 1 9700# zip
firstgun %>%
summarise(weight = NROW(zip))## weight
## 1 6200secondgun %>%
summarise(weight = NROW(zip))## weight
## 1 3500new_data %>%
summarise(weight = NROW(zip))## weight
## 1 9700# military_1
firstgun %>%
count(military_1)## military_1 n
## 1 Army active duty 300
## 2 0 5900secondgun %>%
count(military_1)## military_1 n
## 1 Army active duty 216
## 2 0 3284new_data %>%
count(military_1)## military_1 n
## 1: Army active duty 516
## 2: 0 9184# military_2
firstgun %>%
count(military_2)## military_2 n
## 1 Army National Guard 139
## 2 0 6061secondgun %>%
count(military_2)## military_2 n
## 1 Army National Guard 89
## 2 0 3411new_data %>%
count(military_2)## military_2 n
## 1: Army National Guard 228
## 2: 0 9472# military_3
firstgun %>%
count(military_3)## military_3 n
## 1 Army Reserve 94
## 2 0 6106secondgun %>%
count(military_3)## military_3 n
## 1 Army Reserve 58
## 2 0 3442new_data %>%
count(military_3)## military_3 n
## 1: Army Reserve 152
## 2: 0 9548# military_4
firstgun %>%
count(military_4)## military_4 n
## 1 Navy active duty 151
## 2 0 6049secondgun %>%
count(military_4)## military_4 n
## 1 Navy active duty 85
## 2 0 3415new_data %>%
count(military_4)## military_4 n
## 1: Navy active duty 236
## 2: 0 9464# military_5
firstgun %>%
count(military_5)## military_5 n
## 1 Navy Reserve 52
## 2 0 6148secondgun %>%
count(military_5)## military_5 n
## 1 Navy Reserve 37
## 2 0 3463new_data %>%
count(military_5)## military_5 n
## 1: Navy Reserve 89
## 2: 0 9611# military_6
firstgun %>%
count(military_6)## military_6 n
## 1 Marines active duty 94
## 2 0 6106secondgun %>%
count(military_6)## military_6 n
## 1 Marines active duty 41
## 2 0 3459new_data %>%
count(military_6)## military_6 n
## 1: Marines active duty 135
## 2: 0 9565# military_7
firstgun %>%
count(military_7)## military_7 n
## 1 Marines Reserve 32
## 2 0 6168secondgun %>%
count(military_7)## military_7 n
## 1 Marines Reserve 25
## 2 0 3475new_data %>%
count(military_7)## military_7 n
## 1: Marines Reserve 57
## 2: 0 9643# military_8
firstgun %>%
count(military_8)## military_8 n
## 1 Air Force active duty 141
## 2 0 6059secondgun %>%
count(military_8)## military_8 n
## 1 Air Force active duty 79
## 2 0 3421new_data %>%
count(military_8)## military_8 n
## 1: Air Force active duty 220
## 2: 0 9480# military_9
firstgun %>%
count(military_9)## military_9 n
## 1 Air National Guard 36
## 2 0 6164secondgun %>%
count(military_9)## military_9 n
## 1 Air National Guard 28
## 2 0 3472new_data %>%
count(military_9)## military_9 n
## 1: Air National Guard 64
## 2: 0 9636# military_10
firstgun %>%
count(military_10)## military_10 n
## 1 Air Force Reserve 34
## 2 0 6166secondgun %>%
count(military_10)## military_10 n
## 1 Air Force Reserve 19
## 2 0 3481new_data %>%
count(military_10)## military_10 n
## 1: Air Force Reserve 53
## 2: 0 9647# military_11
firstgun %>%
count(military_11)## military_11 n
## 1 Coast Guard active duty 16
## 2 0 6184secondgun %>%
count(military_11)## military_11 n
## 1 Coast Guard active duty 11
## 2 0 3489new_data %>%
count(military_11)## military_11 n
## 1: Coast Guard active duty 27
## 2: 0 9673# military_12
firstgun %>%
count(military_12)## military_12 n
## 1 Coast Guard Reserve 10
## 2 0 6190secondgun %>%
count(military_12)## military_12 n
## 1 Coast Guard Reserve 15
## 2 0 3485new_data %>%
count(military_12)## military_12 n
## 1: Coast Guard Reserve 25
## 2: 0 9675# vertcoll_1
firstgun %>%
count(vertcoll_1)## vertcoll_1 n
## 1 1 121
## 2 2 102
## 3 3 202
## 4 4 619
## 5 5 968
## 6 6 1397
## 7 7 2791firstgun %>%
summarise(weight = NROW(vertcoll_1))## weight
## 1 6200secondgun %>%
count(vertcoll_1)## vertcoll_1 n
## 1 1 79
## 2 2 106
## 3 3 176
## 4 4 412
## 5 5 565
## 6 6 820
## 7 7 1335
## 8 <NA> 7secondgun %>%
summarise(weight = NROW(vertcoll_1))## weight
## 1 3500new_data %>%
count(vertcoll_1)## vertcoll_1 n
## 1: 1 200
## 2: 2 208
## 3: 3 378
## 4: 4 1031
## 5: 5 1533
## 6: 6 2217
## 7: 7 4126
## 8: <NA> 7new_data %>%
summarise(weight = NROW(vertcoll_1))## weight
## 1 9700# vertcoll_2
firstgun %>%
count(vertcoll_2)## vertcoll_2 n
## 1 1 135
## 2 2 162
## 3 3 307
## 4 4 856
## 5 5 1128
## 6 6 1540
## 7 7 2072firstgun %>%
summarise(weight = NROW(vertcoll_2))## weight
## 1 6200secondgun %>%
count(vertcoll_2)## vertcoll_2 n
## 1 1 111
## 2 2 131
## 3 3 238
## 4 4 525
## 5 5 687
## 6 6 771
## 7 7 1027
## 8 <NA> 10secondgun %>%
summarise(weight = NROW(vertcoll_2))## weight
## 1 3500new_data %>%
count(vertcoll_2)## vertcoll_2 n
## 1: 1 246
## 2: 2 293
## 3: 3 545
## 4: 4 1381
## 5: 5 1815
## 6: 6 2311
## 7: 7 3099
## 8: <NA> 10new_data %>%
summarise(weight = NROW(vertcoll_2))## weight
## 1 9700# vertcoll_3
#firstgun %>%
# count(vertcoll_3)
#firstgun %>%
# summarise(weight = NROW(vertcoll_3))
#secondgun %>%
# count(vertcoll_3)
#secondgun %>%
# summarise(weight = NROW(vertcoll_3))
#new_data %>%
# count(vertcoll_3)
#new_data %>%
# summarise(weight = NROW(vertcoll_3))
# vertcoll_4
firstgun %>%
count(vertcoll_4)## vertcoll_4 n
## 1 1 80
## 2 2 76
## 3 3 162
## 4 4 684
## 5 5 1153
## 6 6 1783
## 7 7 2262firstgun %>%
summarise(weight = NROW(vertcoll_4))## weight
## 1 6200secondgun %>%
count(vertcoll_4)## vertcoll_4 n
## 1 1 80
## 2 2 105
## 3 3 248
## 4 4 706
## 5 5 840
## 6 6 789
## 7 7 723
## 8 <NA> 9secondgun %>%
summarise(weight = NROW(vertcoll_4))## weight
## 1 3500new_data %>%
count(vertcoll_4)## vertcoll_4 n
## 1: 1 160
## 2: 2 181
## 3: 3 410
## 4: 4 1390
## 5: 5 1993
## 6: 6 2572
## 7: 7 2985
## 8: <NA> 9new_data %>%
summarise(weight = NROW(vertcoll_4))## weight
## 1 9700# vertcoll_5
firstgun %>%
count(vertcoll_5)## vertcoll_5 n
## 1 1 168
## 2 2 214
## 3 3 381
## 4 4 1130
## 5 5 1366
## 6 6 1464
## 7 7 1477firstgun %>%
summarise(weight = NROW(vertcoll_5))## weight
## 1 6200secondgun %>%
count(vertcoll_5)## vertcoll_5 n
## 1 1 47
## 2 2 62
## 3 3 148
## 4 4 418
## 5 5 691
## 6 6 910
## 7 7 1214
## 8 <NA> 10secondgun %>%
summarise(weight = NROW(vertcoll_5))## weight
## 1 3500new_data %>%
count(vertcoll_5)## vertcoll_5 n
## 1: 1 215
## 2: 2 276
## 3: 3 529
## 4: 4 1548
## 5: 5 2057
## 6: 6 2374
## 7: 7 2691
## 8: <NA> 10new_data %>%
summarise(weight = NROW(vertcoll_5))## weight
## 1 9700# vertcoll_6
firstgun %>%
count(vertcoll_6)## vertcoll_6 n
## 1 1 87
## 2 2 92
## 3 3 190
## 4 4 898
## 5 5 1424
## 6 6 1763
## 7 7 1746firstgun %>%
summarise(weight = NROW(vertcoll_6))## weight
## 1 6200secondgun %>%
count(vertcoll_6)## vertcoll_6 n
## 1 1 103
## 2 2 146
## 3 3 303
## 4 4 632
## 5 5 761
## 6 6 750
## 7 7 797
## 8 <NA> 8secondgun %>%
summarise(weight = NROW(vertcoll_6))## weight
## 1 3500new_data %>%
count(vertcoll_6)## vertcoll_6 n
## 1: 1 190
## 2: 2 238
## 3: 3 493
## 4: 4 1530
## 5: 5 2185
## 6: 6 2513
## 7: 7 2543
## 8: <NA> 8new_data %>%
summarise(weight = NROW(vertcoll_6))## weight
## 1 9700# crime_1
firstgun %>%
count(crime_1)## crime_1
## 1 0
## 2 Someone broke into your home or attempted to break into your home by forcing a window, pushing past someone, jimmying a lock, cutting a screen, or entering through an open door of window.
## n
## 1 4868
## 2 1332secondgun %>%
count(crime_1)## crime_1
## 1 0
## 2 Someone broke into your home or attempted to break into your home by forcing a window, pushing past someone, jimmying a lock, cutting a screen, or entering through an open door of window.
## n
## 1 2686
## 2 814new_data %>%
count(crime_1)## crime_1
## 1: 0
## 2: Someone broke into your home or attempted to break into your home by forcing a window, pushing past someone, jimmying a lock, cutting a screen, or entering through an open door of window.
## n
## 1: 7554
## 2: 2146# crime_2
firstgun %>%
count(crime_2)## crime_2
## 1 0
## 2 Someone illegally got into or tried to get into a hotel or motel room or vacation home where you were staying.
## n
## 1 5889
## 2 311secondgun %>%
count(crime_2)## crime_2
## 1 Someone illegally got into or tried to get into a hotel or motel room or vacation home where you were staying.
## 2 0
## n
## 1 245
## 2 3255new_data %>%
count(crime_2)## crime_2
## 1: 0
## 2: Someone illegally got into or tried to get into a hotel or motel room or vacation home where you were staying.
## n
## 1: 9144
## 2: 556# crime_3
firstgun %>%
count(crime_3)## crime_3
## 1 0
## 2 Someone illegally got into or tried to get into a garage, shed, or storage room.
## n
## 1 5584
## 2 616secondgun %>%
count(crime_3)## crime_3
## 1 Someone illegally got into or tried to get into a garage, shed, or storage room.
## 2 0
## n
## 1 395
## 2 3105new_data %>%
count(crime_3)## crime_3
## 1: 0
## 2: Someone illegally got into or tried to get into a garage, shed, or storage room.
## n
## 1: 8689
## 2: 1011# crime_4
firstgun %>%
count(crime_4)## crime_4 n
## 1 0 5598
## 2 Someone stole or used your motor vehicle without permission 602secondgun %>%
count(crime_4)## crime_4 n
## 1 Someone stole or used your motor vehicle without permission 406
## 2 0 3094new_data %>%
count(crime_4)## crime_4 n
## 1: 0 8692
## 2: Someone stole or used your motor vehicle without permission 1008# crime_5
firstgun %>%
count(crime_5)## crime_5 n
## 1 0 5477
## 2 Someone stole or attempted to steal parts attached to your vehicle 723secondgun %>%
count(crime_5)## crime_5 n
## 1 Someone stole or attempted to steal parts attached to your vehicle 448
## 2 0 3052new_data %>%
count(crime_5)## crime_5 n
## 1: 0 8529
## 2: Someone stole or attempted to steal parts attached to your vehicle 1171# crime_6
firstgun %>%
count(crime_6)## crime_6
## 1 0
## 2 Someone assaulted or attacked you without a weapon (for example, by hitting, slapping, kicking, or beating you up)
## n
## 1 5362
## 2 838secondgun %>%
count(crime_6)## crime_6
## 1 Someone assaulted or attacked you without a weapon (for example, by hitting, slapping, kicking, or beating you up)
## 2 0
## n
## 1 588
## 2 2912new_data %>%
count(crime_6)## crime_6
## 1: 0
## 2: Someone assaulted or attacked you without a weapon (for example, by hitting, slapping, kicking, or beating you up)
## n
## 1: 8274
## 2: 1426# crime_7
firstgun %>%
count(crime_7)## crime_7
## 1 0
## 2 Someone assaulted or attacked you with a weapon (for example, with a firearm, knife, baseball bat, or another item)
## n
## 1 5807
## 2 393secondgun %>%
count(crime_7)## crime_7
## 1 Someone assaulted or attacked you with a weapon (for example, with a firearm, knife, baseball bat, or another item)
## 2 0
## n
## 1 289
## 2 3211new_data %>%
count(crime_7)## crime_7
## 1: 0
## 2: Someone assaulted or attacked you with a weapon (for example, with a firearm, knife, baseball bat, or another item)
## n
## 1: 9018
## 2: 682# crime_8
firstgun %>%
count(crime_8)## crime_8
## 1 0
## 2 Someone you didn't know forced or coerced you to engage in unwanted sexual activity
## n
## 1 5907
## 2 293secondgun %>%
count(crime_8)## crime_8
## 1 Someone you didn't know forced or coerced you to engage in unwanted sexual activity
## 2 0
## n
## 1 203
## 2 3297new_data %>%
count(crime_8)## crime_8
## 1: 0
## 2: Someone you didn't know forced or coerced you to engage in unwanted sexual activity
## n
## 1: 9204
## 2: 496# crime_9
firstgun %>%
count(crime_9)## crime_9
## 1 0
## 2 Someone you know well forced or coerced you to engage in unwanted sexual activity
## n
## 1 5674
## 2 526secondgun %>%
count(crime_9)## crime_9
## 1 Someone you know well forced or coerced you to engage in unwanted sexual activity
## 2 0
## n
## 1 331
## 2 3169new_data %>%
count(crime_9)## crime_9
## 1: 0
## 2: Someone you know well forced or coerced you to engage in unwanted sexual activity
## n
## 1: 8843
## 2: 857# crime_10
firstgun %>%
count(crime_10)## crime_10
## 1 0
## 2 A casual acquaintance forced or coerced you to engage in unwanted sexual activity
## n
## 1 5799
## 2 401secondgun %>%
count(crime_10)## crime_10
## 1 A casual acquaintance forced or coerced you to engage in unwanted sexual activity
## 2 0
## n
## 1 208
## 2 3292new_data %>%
count(crime_10)## crime_10
## 1: 0
## 2: A casual acquaintance forced or coerced you to engage in unwanted sexual activity
## n
## 1: 9091
## 2: 609# defense_1
firstgun %>%
count(defense_1)## defense_1
## 1 0
## 2 Hitting, kicking, slapping, pushing, or otherwise using physical bodily force towards someone
## n
## 1 4493
## 2 1707secondgun %>%
count(defense_1)## defense_1
## 1 Hitting, kicking, slapping, pushing, or otherwise using physical bodily force towards someone
## 2 0
## n
## 1 979
## 2 2521new_data %>%
count(defense_1)## defense_1
## 1: 0
## 2: Hitting, kicking, slapping, pushing, or otherwise using physical bodily force towards someone
## n
## 1: 7014
## 2: 2686# defense_2
firstgun %>%
count(defense_2)## defense_2
## 1 0
## 2 Telling someone you had a knife, baseball bat, or another non-firearm weapon
## n
## 1 5638
## 2 562secondgun %>%
count(defense_2)## defense_2
## 1 Telling someone you had a knife, baseball bat, or another non-firearm weapon
## 2 0
## n
## 1 334
## 2 3166new_data %>%
count(defense_2)## defense_2
## 1: 0
## 2: Telling someone you had a knife, baseball bat, or another non-firearm weapon
## n
## 1: 8804
## 2: 896# defense_3
firstgun %>%
count(defense_3)## defense_3
## 1 0
## 2 Showing someone that you had a knife, baseball bat, or another non-firearm weapon in your possession
## n
## 1 5647
## 2 553secondgun %>%
count(defense_3)## defense_3
## 1 Showing someone that you had a knife, baseball bat, or another non-firearm weapon in your possession
## 2 0
## n
## 1 343
## 2 3157new_data %>%
count(defense_3)## defense_3
## 1: 0
## 2: Showing someone that you had a knife, baseball bat, or another non-firearm weapon in your possession
## n
## 1: 8804
## 2: 896# defense_4
firstgun %>%
count(defense_4)## defense_4 n
## 1 0 5917
## 2 Using a knife, baseball bat, or another non-firearm weapon on someone 283secondgun %>%
count(defense_4)## defense_4 n
## 1 Using a knife, baseball bat, or another non-firearm weapon on someone 242
## 2 0 3258new_data %>%
count(defense_4)## defense_4 n
## 1: 0 9175
## 2: Using a knife, baseball bat, or another non-firearm weapon on someone 525# defense_5
firstgun %>%
count(defense_5)## defense_5 n
## 1 0 5729
## 2 Telling someone that you had a firearm 471secondgun %>%
count(defense_5)## defense_5 n
## 1 Telling someone that you had a firearm 315
## 2 0 3185new_data %>%
count(defense_5)## defense_5 n
## 1: 0 8914
## 2: Telling someone that you had a firearm 786# defense_6
firstgun %>%
count(defense_6)## defense_6 n
## 1 0 5882
## 2 Showing someone that you had a firearm in your possession 318secondgun %>%
count(defense_6)## defense_6 n
## 1 Showing someone that you had a firearm in your possession 224
## 2 0 3276new_data %>%
count(defense_6)## defense_6 n
## 1: 0 9158
## 2: Showing someone that you had a firearm in your possession 542# defense_7
firstgun %>%
count(defense_7)## defense_7 n
## 1 0 5963
## 2 Pointing a firearm at someone 237secondgun %>%
count(defense_7)## defense_7 n
## 1 Pointing a firearm at someone 194
## 2 0 3306new_data %>%
count(defense_7)## defense_7 n
## 1: 0 9269
## 2: Pointing a firearm at someone 431# defense_8
firstgun %>%
count(defense_8)## defense_8
## 1 0
## 2 Shooting or discharging a firearm in the air or another direction that would not hit or injure someone (for example, a warning shot)
## n
## 1 6034
## 2 166secondgun %>%
count(defense_8)## defense_8
## 1 Shooting or discharging a firearm in the air or another direction that would not hit or injure someone (for example, a warning shot)
## 2 0
## n
## 1 128
## 2 3372new_data %>%
count(defense_8)## defense_8
## 1: 0
## 2: Shooting or discharging a firearm in the air or another direction that would not hit or injure someone (for example, a warning shot)
## n
## 1: 9406
## 2: 294# defense_9
firstgun %>%
count(defense_9)## defense_9 n
## 1 0 6095
## 2 Shooting or discharging a firearm at someone 105secondgun %>%
count(defense_9)## defense_9 n
## 1 Shooting or discharging a firearm at someone 75
## 2 0 3425new_data %>%
count(defense_9)## defense_9 n
## 1: 0 9520
## 2: Shooting or discharging a firearm at someone 180# homereasons2
firstgun %>%
count(homereasons2)## homereasons2 n
## 1 I received it as a gift or inheritance 290
## 2 Family heirloom 153
## 3 Personal safety or protection at home 1105
## 4 Personal safety or protection away from home 148
## 5 For competition 30
## 6 For hunting 234
## 7 For other recreational reasons 79
## 8 To express my freedom 36
## 9 The firearm(s) belongs to someone else who lives with me 32
## 10 I don't know how or where to get rid of my firearm(s) 17
## 11 Other 35
## 12 <NA> 4041secondgun %>%
count(homereasons2)## homereasons2 n
## 1 I received it as a gift or inheritance 173
## 2 Family heirloom 80
## 3 Personal safety or protection at home 581
## 4 Personal safety or protection away from home 96
## 5 For competition 20
## 6 For hunting 98
## 7 For other recreational reasons 40
## 8 To express my freedom 22
## 9 The firearm(s) belongs to someone else who lives with me 22
## 10 I don't know how or where to get rid of my firearm(s) 15
## 11 Other 14
## 12 <NA> 2339new_data %>%
count(homereasons2)## homereasons2 n
## 1: I received it as a gift or inheritance 463
## 2: Family heirloom 233
## 3: Personal safety or protection at home 1686
## 4: Personal safety or protection away from home 244
## 5: For competition 50
## 6: For hunting 332
## 7: For other recreational reasons 119
## 8: To express my freedom 58
## 9: The firearm(s) belongs to someone else who lives with me 54
## 10: I don't know how or where to get rid of my firearm(s) 32
## 11: Other 49
## 12: <NA> 6380# homesafety_1
firstgun %>%
count(homesafety_1)## homesafety_1 n
## 1 0 2884
## 2 Knob locks or lever handle locks in doors 3316secondgun %>%
count(homesafety_1)## homesafety_1 n
## 1 Knob locks or lever handle locks in doors 1764
## 2 0 1736new_data %>%
count(homesafety_1)## homesafety_1 n
## 1: 0 4620
## 2: Knob locks or lever handle locks in doors 5080# homesafety_2
firstgun %>%
count(homesafety_2)## homesafety_2 n
## 1 0 2588
## 2 Deadbolts, chains, or slide locks on doors 3612secondgun %>%
count(homesafety_2)## homesafety_2 n
## 1 Deadbolts, chains, or slide locks on doors 1856
## 2 0 1644new_data %>%
count(homesafety_2)## homesafety_2 n
## 1: 0 4232
## 2: Deadbolts, chains, or slide locks on doors 5468# homesafety_3
firstgun %>%
count(homesafety_3)## homesafety_3 n
## 1 0 2322
## 2 Window locks 3878firstgun %>%
summarise(weight = NROW(homesafety_3))## weight
## 1 6200secondgun %>%
count(homesafety_3)## homesafety_3 n
## 1 Window locks 2051
## 2 0 1449secondgun %>%
summarise(weight = NROW(homesafety_3))## weight
## 1 3500new_data %>%
count(homesafety_3)## homesafety_3 n
## 1: 0 3771
## 2: Window locks 5929new_data %>%
summarise(weight = NROW(homesafety_3))## weight
## 1 9700# homesafety_4
firstgun %>%
count(homesafety_4)## homesafety_4 n
## 1 0 4990
## 2 Bar, rod, or other device that prevents windows/doors from sliding open 1210secondgun %>%
count(homesafety_4)## homesafety_4 n
## 1 Bar, rod, or other device that prevents windows/doors from sliding open 673
## 2 0 2827new_data %>%
count(homesafety_4)## homesafety_4 n
## 1: 0 7817
## 2: Bar, rod, or other device that prevents windows/doors from sliding open 1883# homesafety_5
firstgun %>%
count(homesafety_5)## homesafety_5 n
## 1 0 5873
## 2 Bars over doors and/or windows 327secondgun %>%
count(homesafety_5)## homesafety_5 n
## 1 Bars over doors and/or windows 234
## 2 0 3266new_data %>%
count(homesafety_5)## homesafety_5 n
## 1: 0 9139
## 2: Bars over doors and/or windows 561# homesafety_6
firstgun %>%
count(homesafety_6)## homesafety_6 n
## 1 0 4422
## 2 Alarm system 1778secondgun %>%
count(homesafety_6)## homesafety_6 n
## 1 Alarm system 964
## 2 0 2536new_data %>%
count(homesafety_6)## homesafety_6 n
## 1: 0 6958
## 2: Alarm system 2742# homesafety_7
firstgun %>%
count(homesafety_7)## homesafety_7 n
## 1 0 4615
## 2 Security cameras or video devices 1585secondgun %>%
count(homesafety_7)## homesafety_7 n
## 1 Security cameras or video devices 895
## 2 0 2605new_data %>%
count(homesafety_7)## homesafety_7 n
## 1: 0 7220
## 2: Security cameras or video devices 2480# homesafety_8
firstgun %>%
count(homesafety_8)## homesafety_8 n
## 1 0 5174
## 2 Signs or stickers indicating the use of an alarm system 1026secondgun %>%
count(homesafety_8)## homesafety_8 n
## 1 Signs or stickers indicating the use of an alarm system 526
## 2 0 2974new_data %>%
count(homesafety_8)## homesafety_8 n
## 1: 0 8148
## 2: Signs or stickers indicating the use of an alarm system 1552# homesafety_9
firstgun %>%
count(homesafety_9)## homesafety_9 n
## 1 0 5500
## 2 "Beware of dog" signs 700secondgun %>%
count(homesafety_9)## homesafety_9 n
## 1 "Beware of dog" signs 404
## 2 0 3096new_data %>%
count(homesafety_9)## homesafety_9 n
## 1: 0 8596
## 2: "Beware of dog" signs 1104# homesafety_9
firstgun %>%
count(homesafety_9)## homesafety_9 n
## 1 0 5500
## 2 "Beware of dog" signs 700secondgun %>%
count(homesafety_9)## homesafety_9 n
## 1 "Beware of dog" signs 404
## 2 0 3096new_data %>%
count(homesafety_9)## homesafety_9 n
## 1: 0 8596
## 2: "Beware of dog" signs 1104# homesafety_10
firstgun %>%
count(homesafety_10)## homesafety_10 n
## 1 0 5724
## 2 "Do not trespass" signs 476secondgun %>%
count(homesafety_10)## homesafety_10 n
## 1 "Do not trespass" signs 298
## 2 0 3202new_data %>%
count(homesafety_10)## homesafety_10 n
## 1: 0 8926
## 2: "Do not trespass" signs 774# homesafety_11
firstgun %>%
count(homesafety_11)## homesafety_11 n
## 1 0 4276
## 2 Dogs or other pets 1924secondgun %>%
count(homesafety_11)## homesafety_11 n
## 1 Dogs or other pets 920
## 2 0 2580new_data %>%
count(homesafety_11)## homesafety_11 n
## 1: 0 6856
## 2: Dogs or other pets 2844# homesafety_12
firstgun %>%
count(homesafety_12)## homesafety_12 n
## 1 0 4626
## 2 Fencing 1574secondgun %>%
summarise(weight = NROW(homesafety_12))## weight
## 1 3500# homesafety_13
firstgun %>%
count(homesafety_13)## homesafety_13 n
## 1 0 4955
## 2 Blunt objects like baseball bats, hockey sticks, etc. 1245secondgun %>%
count(homesafety_13)## homesafety_13 n
## 1 Blunt objects like baseball bats, hockey sticks, etc. 641
## 2 0 2859new_data %>%
count(homesafety_13)## homesafety_13 n
## 1: 0 7814
## 2: Blunt objects like baseball bats, hockey sticks, etc. 1886# homesafety_14
firstgun %>%
count(homesafety_14)## homesafety_14 n
## 1 0 4592
## 2 Firearms 1608secondgun %>%
count(homesafety_14)## homesafety_14 n
## 1 Firearms 773
## 2 0 2727new_data %>%
count(homesafety_14)## homesafety_14 n
## 1: 0 7319
## 2: Firearms 2381# homesafety_15
firstgun %>%
count(homesafety_15)## homesafety_15 n
## 1 0 5680
## 2 Martial arts 520secondgun %>%
count(homesafety_15)## homesafety_15 n
## 1 Martial arts 245
## 2 0 3255new_data %>%
count(homesafety_15)## homesafety_15 n
## 1: 0 8935
## 2: Martial arts 765# homesafety_16
firstgun %>%
count(homesafety_16)## homesafety_16 n
## 1 0 4632
## 2 Sharp objects like knives 1568secondgun %>%
count(homesafety_16)## homesafety_16 n
## 1 Sharp objects like knives 786
## 2 0 2714new_data %>%
count(homesafety_16)## homesafety_16 n
## 1: 0 7346
## 2: Sharp objects like knives 2354# homesafety_17
firstgun %>%
count(homesafety_17)## homesafety_17 n
## 1 0 6078
## 2 Other method 122secondgun %>%
count(homesafety_17)## homesafety_17 n
## 1 Other method 42
## 2 0 3458new_data %>%
count(homesafety_17)## homesafety_17 n
## 1: 0 9536
## 2: Other method 164# cos_1
firstgun %>%
count(cos_1)## cos_1 n
## 1 1 2133
## 2 2 1456
## 3 3 769
## 4 4 691
## 5 5 452
## 6 6 354
## 7 7 345secondgun %>%
count(cos_1)## cos_1 n
## 1 1 1133
## 2 2 672
## 3 3 428
## 4 4 450
## 5 5 314
## 6 6 260
## 7 7 236
## 8 <NA> 7new_data %>%
count(cos_1)## cos_1 n
## 1: 1 3266
## 2: 2 2128
## 3: 3 1197
## 4: 4 1141
## 5: 5 766
## 6: 6 614
## 7: 7 581
## 8: <NA> 7# cos_2
firstgun %>%
count(cos_2)## cos_2 n
## 1 1 134
## 2 2 180
## 3 3 319
## 4 4 837
## 5 5 1298
## 6 6 1560
## 7 7 1872secondgun %>%
count(cos_2)## cos_2 n
## 1 1 90
## 2 2 118
## 3 3 222
## 4 4 510
## 5 5 761
## 6 6 771
## 7 7 1020
## 8 <NA> 8new_data %>%
count(cos_2)## cos_2 n
## 1: 1 224
## 2: 2 298
## 3: 3 541
## 4: 4 1347
## 5: 5 2059
## 6: 6 2331
## 7: 7 2892
## 8: <NA> 8# cos_3
firstgun %>%
count(cos_3)## cos_3 n
## 1 1 1868
## 2 2 1312
## 3 3 794
## 4 4 762
## 5 5 573
## 6 6 449
## 7 7 442secondgun %>%
count(cos_3)## cos_3 n
## 1 1 984
## 2 2 621
## 3 3 458
## 4 4 462
## 5 5 404
## 6 6 264
## 7 7 300
## 8 <NA> 7new_data %>%
count(cos_3)## cos_3 n
## 1: 1 2852
## 2: 2 1933
## 3: 3 1252
## 4: 4 1224
## 5: 5 977
## 6: 6 713
## 7: 7 742
## 8: <NA> 7# gun1stshot
firstgun %>%
count(gun1stshot)## gun1stshot n
## 1 I have never shot a firearm 2273
## 2 Under 10 years old 742
## 3 11-15 years old 1314
## 4 16-25 years old 1336
## 5 26-30 years old 272
## 6 Over 30 years old 263secondgun %>%
count(gun1stshot)## gun1stshot n
## 1 I have never shot a firearm 1385
## 2 Under 10 years old 435
## 3 11-15 years old 695
## 4 16-25 years old 695
## 5 26-30 years old 148
## 6 Over 30 years old 139
## 7 <NA> 3new_data %>%
count(gun1stshot)## gun1stshot n
## 1: I have never shot a firearm 3658
## 2: Under 10 years old 1177
## 3: 11-15 years old 2009
## 4: 16-25 years old 2031
## 5: 26-30 years old 420
## 6: Over 30 years old 402
## 7: <NA> 3# gun1stacquire
firstgun %>%
count(gun1stacquire)## gun1stacquire n
## 1 I have never acquired a firearm 3626
## 2 Under 10 years old 172
## 3 11-15 years old 456
## 4 16-25 years old 1111
## 5 26-30 years old 382
## 6 Over 30 years old 453secondgun %>%
count(gun1stacquire)## gun1stacquire n
## 1 I have never acquired a firearm 2072
## 2 Under 10 years old 149
## 3 11-15 years old 239
## 4 16-25 years old 596
## 5 26-30 years old 222
## 6 Over 30 years old 220
## 7 <NA> 2new_data %>%
count(gun1stacquire)## gun1stacquire n
## 1: I have never acquired a firearm 5698
## 2: Under 10 years old 321
## 3: 11-15 years old 695
## 4: 16-25 years old 1707
## 5: 26-30 years old 604
## 6: Over 30 years old 673
## 7: <NA> 2# acquire
firstgun %>%
count(acquire)## acquire n
## 1 I received it from someone else as a gift 862
## 2 I purchased it myself 1254
## 3 I found it 102
## 4 I inherited it 272
## 5 Prefer not to answer 84
## 6 <NA> 3626secondgun %>%
count(acquire)## acquire n
## 1 I received it from someone else as a gift 502
## 2 I purchased it myself 676
## 3 I found it 76
## 4 I inherited it 145
## 5 Prefer not to answer 26
## 6 <NA> 2075new_data %>%
count(acquire)## acquire n
## 1: I received it from someone else as a gift 1364
## 2: I purchased it myself 1930
## 3: I found it 178
## 4: I inherited it 417
## 5: Prefer not to answer 110
## 6: <NA> 5701# gunchild
firstgun %>%
count(gunchild)## gunchild n
## 1 No 3318
## 2 Yes 2372
## 3 I don't know 436
## 4 Prefer not to answer 74secondgun %>%
count(gunchild)## gunchild n
## 1 No 1901
## 2 Yes 1339
## 3 I don't know 247
## 4 Prefer not to answer 10
## 5 <NA> 3new_data %>%
count(gunchild)## gunchild n
## 1: No 5219
## 2: Yes 3711
## 3: I don't know 683
## 4: Prefer not to answer 84
## 5: <NA> 3# acquirereason_1
firstgun %>%
count(acquirereason_1)## acquirereason_1 n
## 1 Personal safety or protection at home 1058
## 2 0 5142secondgun %>%
count(acquirereason_1)## acquirereason_1 n
## 1 Personal safety or protection at home 633
## 2 0 2867new_data %>%
count(acquirereason_1)## acquirereason_1 n
## 1: Personal safety or protection at home 1691
## 2: 0 8009# acquirereason_2
firstgun %>%
count(acquirereason_2)## acquirereason_2 n
## 1 Personal safety or protection away from home 605
## 2 0 5595secondgun %>%
count(acquirereason_2)## acquirereason_2 n
## 1 Personal safety or protection away from home 277
## 2 0 3223new_data %>%
count(acquirereason_2)## acquirereason_2 n
## 1: Personal safety or protection away from home 882
## 2: 0 8818# acquirereason_3
firstgun %>%
count(acquirereason_3)## acquirereason_3 n
## 1 For competition 181
## 2 0 6019secondgun %>%
count(acquirereason_3)## acquirereason_3 n
## 1 For competition 96
## 2 0 3404new_data %>%
count(acquirereason_3)## acquirereason_3 n
## 1: For competition 277
## 2: 0 9423# acquirereason_4
firstgun %>%
count(acquirereason_4)## acquirereason_4 n
## 1 For hunting 729
## 2 0 5471secondgun %>%
count(acquirereason_4)## acquirereason_4 n
## 1 For hunting 346
## 2 0 3154new_data %>%
count(acquirereason_4)## acquirereason_4 n
## 1: For hunting 1075
## 2: 0 8625# acquirereason_5
firstgun %>%
count(acquirereason_5)## acquirereason_5 n
## 1 For other recreational reasons 456
## 2 0 5744secondgun %>%
count(acquirereason_5)## acquirereason_5 n
## 1 For other recreational reasons 188
## 2 0 3312new_data %>%
count(acquirereason_5)## acquirereason_5 n
## 1: For other recreational reasons 644
## 2: 0 9056# acquirereason_6
firstgun %>%
count(acquirereason_6)## acquirereason_6 n
## 1 To express my freedom 178
## 2 0 6022secondgun %>%
count(acquirereason_6)## acquirereason_6 n
## 1 To express my freedom 58
## 2 0 3442new_data %>%
count(acquirereason_6)## acquirereason_6 n
## 1: To express my freedom 236
## 2: 0 9464# acquirereason_7
firstgun %>%
count(acquirereason_7)## acquirereason_7 n
## 1 Other 96
## 2 0 6104secondgun %>%
count(acquirereason_7)## acquirereason_7 n
## 1 Other 56
## 2 0 3444new_data %>%
count(acquirereason_7)## acquirereason_7 n
## 1: Other 152
## 2: 0 9548# acquiremore (missing is high-more than 50%)
firstgun %>%
count(acquiremore)## acquiremore n
## 1 No 1251
## 2 Yes 1267
## 3 Prefer not to answer 56
## 4 <NA> 3626secondgun %>%
count(acquiremore)## acquiremore n
## 1 No 669
## 2 Yes 723
## 3 Prefer not to answer 31
## 4 <NA> 2077new_data %>%
count(acquiremore)## acquiremore n
## 1: No 1920
## 2: Yes 1990
## 3: Prefer not to answer 87
## 4: <NA> 5703# guns_no_1
firstgun %>%
count(guns_no_1)## guns_no_1 n
## 1 0 729
## 2 1 1102
## 3 2 387
## 4 3 160
## 5 4 81
## 6 5 37
## 7 6 23
## 8 7 8
## 9 8 10
## 10 9 8
## 11 10 or more 29
## 12 <NA> 3626secondgun %>%
count(guns_no_1)## guns_no_1 n
## 1 0 399
## 2 1 631
## 3 2 193
## 4 3 83
## 5 4 38
## 6 5 22
## 7 6 12
## 8 7 9
## 9 8 5
## 10 10 or more 21
## 11 <NA> 2087new_data %>%
count(guns_no_1)## guns_no_1 n
## 1: 0 1128
## 2: 1 1733
## 3: 2 580
## 4: 3 243
## 5: 4 119
## 6: 5 59
## 7: 6 35
## 8: 7 17
## 9: 8 15
## 10: 9 8
## 11: 10 or more 50
## 12: <NA> 5713# guns_no_2
firstgun %>%
count(guns_no_2)## guns_no_2 n
## 1 0 1417
## 2 1 678
## 3 2 260
## 4 3 82
## 5 4 60
## 6 5 28
## 7 6 19
## 8 7 4
## 9 8 7
## 10 9 2
## 11 10 or more 17
## 12 <NA> 3626secondgun %>%
count(guns_no_2)## guns_no_2 n
## 1 0 765
## 2 1 378
## 3 2 129
## 4 3 63
## 5 4 27
## 6 5 19
## 7 6 8
## 8 7 5
## 9 8 2
## 10 9 1
## 11 10 or more 16
## 12 <NA> 2087new_data %>%
count(guns_no_2)## guns_no_2 n
## 1: 0 2182
## 2: 1 1056
## 3: 2 389
## 4: 3 145
## 5: 4 87
## 6: 5 47
## 7: 6 27
## 8: 7 9
## 9: 8 9
## 10: 9 3
## 11: 10 or more 33
## 12: <NA> 5713# guns_no_3
firstgun %>%
count(guns_no_3)## guns_no_3 n
## 1 0 1382
## 2 1 640
## 3 2 237
## 4 3 110
## 5 4 63
## 6 5 50
## 7 6 29
## 8 7 17
## 9 8 10
## 10 9 7
## 11 10 or more 29
## 12 <NA> 3626secondgun %>%
count(guns_no_3)## guns_no_3 n
## 1 0 784
## 2 1 333
## 3 2 109
## 4 3 67
## 5 4 36
## 6 5 18
## 7 6 16
## 8 7 8
## 9 8 10
## 10 9 3
## 11 10 or more 25
## 12 <NA> 2091new_data %>%
count(guns_no_3)## guns_no_3 n
## 1: 0 2166
## 2: 1 973
## 3: 2 346
## 4: 3 177
## 5: 4 99
## 6: 5 68
## 7: 6 45
## 8: 7 25
## 9: 8 20
## 10: 9 10
## 11: 10 or more 54
## 12: <NA> 5717# homereasons_1
firstgun %>%
count(homereasons_1)## homereasons_1 n
## 1 0 5542
## 2 I received it as a gift or inheritance 658secondgun %>%
count(homereasons_1)## homereasons_1 n
## 1 I received it as a gift or inheritance 351
## 2 0 3149new_data %>%
count(homereasons_1)## homereasons_1 n
## 1: 0 8691
## 2: I received it as a gift or inheritance 1009# homereasons_2
firstgun %>%
count(homereasons_2)## homereasons_2 n
## 1 0 5751
## 2 Family heirloom 449secondgun %>%
count(homereasons_2)## homereasons_2 n
## 1 Family heirloom 239
## 2 0 3261new_data %>%
count(homereasons_2)## homereasons_2 n
## 1: 0 9012
## 2: Family heirloom 688# homereasons_3
firstgun %>%
count(homereasons_3)## homereasons_3 n
## 1 0 4678
## 2 Personal safety or protection at home 1522secondgun %>%
count(homereasons_3)## homereasons_3 n
## 1 Personal safety or protection at home 773
## 2 0 2727new_data %>%
count(homereasons_3)## homereasons_3 n
## 1: 0 7405
## 2: Personal safety or protection at home 2295# homereasons_4
firstgun %>%
count(homereasons_4)## homereasons_4 n
## 1 0 5514
## 2 Personal safety or protection away from home 686secondgun %>%
count(homereasons_4)## homereasons_4 n
## 1 Personal safety or protection away from home 343
## 2 0 3157new_data %>%
count(homereasons_4)## homereasons_4 n
## 1: 0 8671
## 2: Personal safety or protection away from home 1029# homereasons_5
firstgun %>%
count(homereasons_5)## homereasons_5 n
## 1 0 6046
## 2 For competition 154secondgun %>%
count(homereasons_5)## homereasons_5 n
## 1 For competition 95
## 2 0 3405new_data %>%
count(homereasons_5)## homereasons_5 n
## 1: 0 9451
## 2: For competition 249# homereasons_6
firstgun %>%
count(homereasons_6)## homereasons_6 n
## 1 0 5590
## 2 For hunting 610secondgun %>%
count(homereasons_6)## homereasons_6 n
## 1 For hunting 287
## 2 0 3213new_data %>%
count(homereasons_6)## homereasons_6 n
## 1: 0 8803
## 2: For hunting 897# homereasons_7
firstgun %>%
count(homereasons_7)## homereasons_7 n
## 1 0 5718
## 2 For other recreational reasons 482secondgun %>%
count(homereasons_7)## homereasons_7 n
## 1 For other recreational reasons 198
## 2 0 3302new_data %>%
count(homereasons_7)## homereasons_7 n
## 1: 0 9020
## 2: For other recreational reasons 680# homereasons_8
firstgun %>%
count(homereasons_8)## homereasons_8 n
## 1 0 5909
## 2 To express my freedom 291secondgun %>%
count(homereasons_8)## homereasons_8 n
## 1 To express my freedom 146
## 2 0 3354new_data %>%
count(homereasons_8)## homereasons_8 n
## 1: 0 9263
## 2: To express my freedom 437# homereasons_9
firstgun %>%
count(homereasons_9)## homereasons_9 n
## 1 0 6029
## 2 The firearm(s) belongs to someone else who lives with me 171secondgun %>%
count(homereasons_9)## homereasons_9 n
## 1 The firearm(s) belongs to someone else who lives with me 78
## 2 0 3422new_data %>%
count(homereasons_9)## homereasons_9 n
## 1: 0 9451
## 2: The firearm(s) belongs to someone else who lives with me 249# homereasons_10
firstgun %>%
count(homereasons_10)## homereasons_10 n
## 1 0 6166
## 2 I don't know how or where to get rid of my firearm(s) 34secondgun %>%
count(homereasons_10)## homereasons_10 n
## 1 I don't know how or where to get rid of my firearm(s) 19
## 2 0 3481new_data %>%
count(homereasons_10)## homereasons_10 n
## 1: 0 9647
## 2: I don't know how or where to get rid of my firearm(s) 53# homereasons_11
firstgun %>%
count(homereasons_11)## homereasons_11 n
## 1 0 6145
## 2 Other 55secondgun %>%
count(homereasons_11)## homereasons_11 n
## 1 Other 25
## 2 0 3475new_data %>%
count(homereasons_11)## homereasons_11 n
## 1: 0 9620
## 2: Other 80# gunstorage_1
firstgun %>%
count(gunstorage_1)## gunstorage_1 n
## 1 Gun safe 1092
## 2 0 5108secondgun %>%
count(gunstorage_1)## gunstorage_1 n
## 1 Gun safe 601
## 2 0 2899new_data %>%
count(gunstorage_1)## gunstorage_1 n
## 1: Gun safe 1693
## 2: 0 8007# gunstorage_2
firstgun %>%
count(gunstorage_2)## gunstorage_2 n
## 1 Gun cabinet 440
## 2 0 5760secondgun %>%
count(gunstorage_2)## gunstorage_2 n
## 1 Gun cabinet 256
## 2 0 3244new_data %>%
count(gunstorage_2)## gunstorage_2 n
## 1: Gun cabinet 696
## 2: 0 9004# gunstorage_3
firstgun %>%
count(gunstorage_3)## gunstorage_3 n
## 1 Locking device (e.g., trigger lock, cable lock) 533
## 2 0 5667secondgun %>%
count(gunstorage_3)## gunstorage_3 n
## 1 Locking device (e.g., trigger lock, cable lock) 366
## 2 0 3134new_data %>%
count(gunstorage_3)## gunstorage_3 n
## 1: Locking device (e.g., trigger lock, cable lock) 899
## 2: 0 8801# gunstorage_4
firstgun %>%
count(gunstorage_4)## gunstorage_4 n
## 1 Hard cases (e.g., Pelican cases) 457
## 2 0 5743secondgun %>%
count(gunstorage_4)## gunstorage_4 n
## 1 Hard cases (e.g., Pelican cases) 243
## 2 0 3257new_data %>%
count(gunstorage_4)## gunstorage_4 n
## 1: Hard cases (e.g., Pelican cases) 700
## 2: 0 9000# gunstorage_5
firstgun %>%
count(gunstorage_5)## gunstorage_5 n
## 1 Hide in closet or drawer, unloaded 515
## 2 0 5685secondgun %>%
count(gunstorage_5)## gunstorage_5 n
## 1 Hide in closet or drawer, unloaded 241
## 2 0 3259new_data %>%
count(gunstorage_5)## gunstorage_5 n
## 1: Hide in closet or drawer, unloaded 756
## 2: 0 8944# gunstorage_6
firstgun %>%
count(gunstorage_6)## gunstorage_6 n
## 1 Hide in closet or drawer, loaded 344
## 2 0 5856secondgun %>%
count(gunstorage_6)## gunstorage_6 n
## 1 Hide in closet or drawer, loaded 195
## 2 0 3305new_data %>%
count(gunstorage_6)## gunstorage_6 n
## 1: Hide in closet or drawer, loaded 539
## 2: 0 9161# gunstorage_7
firstgun %>%
count(gunstorage_7)## gunstorage_7 n
## 1 Other safety procedure 120
## 2 0 6080secondgun %>%
count(gunstorage_7)## gunstorage_7 n
## 1 Other safety procedure 42
## 2 0 3458new_data %>%
count(gunstorage_7)## gunstorage_7 n
## 1: Other safety procedure 162
## 2: 0 9538# acquireplan
firstgun %>%
count(acquireplan)## acquireplan n
## 1 No 4043
## 2 Yes 793
## 3 Haven't decided yet 1292
## 4 Prefer not to answer 72secondgun %>%
count(acquireplan)## acquireplan n
## 1 No 2365
## 2 Yes 516
## 3 Haven't decided yet 596
## 4 Prefer not to answer 16
## 5 <NA> 7new_data %>%
count(acquireplan)## acquireplan n
## 1: No 6408
## 2: Yes 1309
## 3: Haven't decided yet 1888
## 4: Prefer not to answer 88
## 5: <NA> 7# acquireplan2_1
firstgun %>%
count(acquireplan2_1)## acquireplan2_1 n
## 1 Personal safety or protection at home 1477
## 2 0 4723secondgun %>%
count(acquireplan2_1)## acquireplan2_1 n
## 1 Personal safety or protection at home 779
## 2 0 2721new_data %>%
count(acquireplan2_1)## acquireplan2_1 n
## 1: Personal safety or protection at home 2256
## 2: 0 7444# acquireplan2_2
firstgun %>%
count(acquireplan2_2)## acquireplan2_2 n
## 1 Personal safety or protection away from home 818
## 2 0 5382secondgun %>%
count(acquireplan2_2)## acquireplan2_2 n
## 1 Personal safety or protection away from home 426
## 2 0 3074new_data %>%
count(acquireplan2_2)## acquireplan2_2 n
## 1: Personal safety or protection away from home 1244
## 2: 0 8456# acquireplan2_3
firstgun %>%
count(acquireplan2_3)## acquireplan2_3 n
## 1 For competition 155
## 2 0 6045secondgun %>%
count(acquireplan2_3)## acquireplan2_3 n
## 1 For competition 100
## 2 0 3400new_data %>%
count(acquireplan2_3)## acquireplan2_3 n
## 1: For competition 255
## 2: 0 9445# acquireplan2_4
firstgun %>%
count(acquireplan2_4)## acquireplan2_4 n
## 1 For hunting 363
## 2 0 5837secondgun %>%
count(acquireplan2_4)## acquireplan2_4 n
## 1 For hunting 150
## 2 0 3350new_data %>%
count(acquireplan2_4)## acquireplan2_4 n
## 1: For hunting 513
## 2: 0 9187# acquireplan2_5
firstgun %>%
count(acquireplan2_5)## acquireplan2_5 n
## 1 For other recreational reasons 423
## 2 0 5777secondgun %>%
count(acquireplan2_5)## acquireplan2_5 n
## 1 For other recreational reasons 132
## 2 0 3368new_data %>%
count(acquireplan2_5)## acquireplan2_5 n
## 1: For other recreational reasons 555
## 2: 0 9145# acquireplan2_6
firstgun %>%
count(acquireplan2_6)## acquireplan2_6 n
## 1 To express my freedom 312
## 2 0 5888secondgun %>%
count(acquireplan2_6)## acquireplan2_6 n
## 1 To express my freedom 97
## 2 0 3403new_data %>%
count(acquireplan2_6)## acquireplan2_6 n
## 1: To express my freedom 409
## 2: 0 9291# acquireplan2_7
firstgun %>%
count(acquireplan2_7)## acquireplan2_7 n
## 1 Other 83
## 2 0 6117secondgun %>%
count(acquireplan2_7)## acquireplan2_7 n
## 1 Other 38
## 2 0 3462new_data %>%
count(acquireplan2_7)## acquireplan2_7 n
## 1: Other 121
## 2: 0 9579# panas_1
firstgun %>%
count(panas_1)## panas_1 n
## 1 Very slightly or not at all 431
## 2 A little 876
## 3 Moderately 1821
## 4 Quite a bit 1882
## 5 Extremely 1190secondgun %>%
count(panas_1)## panas_1 n
## 1 empty 3500new_data %>%
count(panas_1)## panas_1 n
## 1: Very slightly or not at all 431
## 2: A little 876
## 3: Moderately 1821
## 4: Quite a bit 1882
## 5: Extremely 1190
## 6: empty 3500# panas_2
firstgun %>%
count(panas_2)## panas_2 n
## 1 Very slightly or not at all 455
## 2 A little 749
## 3 Moderately 1864
## 4 Quite a bit 2143
## 5 Extremely 989secondgun %>%
count(panas_2)## panas_2 n
## 1 empty 3500new_data %>%
count(panas_2)## panas_2 n
## 1: Very slightly or not at all 455
## 2: A little 749
## 3: Moderately 1864
## 4: Quite a bit 2143
## 5: Extremely 989
## 6: empty 3500# panas_3
firstgun %>%
count(panas_3)## panas_3 n
## 1 Very slightly or not at all 481
## 2 A little 775
## 3 Moderately 1740
## 4 Quite a bit 2018
## 5 Extremely 1186secondgun %>%
count(panas_3)## panas_3 n
## 1 empty 3500new_data %>%
count(panas_3)## panas_3 n
## 1: Very slightly or not at all 481
## 2: A little 775
## 3: Moderately 1740
## 4: Quite a bit 2018
## 5: Extremely 1186
## 6: empty 3500# panas_4
firstgun %>%
count(panas_4)## panas_4 n
## 1 Very slightly or not at all 747
## 2 A little 1210
## 3 Moderately 1957
## 4 Quite a bit 1444
## 5 Extremely 842secondgun %>%
count(panas_4)## panas_4 n
## 1 empty 3500new_data %>%
count(panas_4)## panas_4 n
## 1: Very slightly or not at all 747
## 2: A little 1210
## 3: Moderately 1957
## 4: Quite a bit 1444
## 5: Extremely 842
## 6: empty 3500# panas_5
firstgun %>%
count(panas_5)## panas_5 n
## 1 Very slightly or not at all 509
## 2 A little 1004
## 3 Moderately 1898
## 4 Quite a bit 1750
## 5 Extremely 1039secondgun %>%
count(panas_5)## panas_5 n
## 1 empty 3500new_data %>%
count(panas_5)## panas_5 n
## 1: Very slightly or not at all 509
## 2: A little 1004
## 3: Moderately 1898
## 4: Quite a bit 1750
## 5: Extremely 1039
## 6: empty 3500# panas_6
firstgun %>%
count(panas_6)## panas_6 n
## 1 Very slightly or not at all 3247
## 2 A little 1362
## 3 Moderately 835
## 4 Quite a bit 483
## 5 Extremely 273secondgun %>%
count(panas_6)## panas_6 n
## 1 empty 3500new_data %>%
count(panas_6)## panas_6 n
## 1: Very slightly or not at all 3247
## 2: A little 1362
## 3: Moderately 835
## 4: Quite a bit 483
## 5: Extremely 273
## 6: empty 3500# panas_7
firstgun %>%
count(panas_7)## panas_7 n
## 1 Very slightly or not at all 2144
## 2 A little 1711
## 3 Moderately 1120
## 4 Quite a bit 785
## 5 Extremely 440secondgun %>%
count(panas_7)## panas_7 n
## 1 empty 3500new_data %>%
count(panas_7)## panas_7 n
## 1: Very slightly or not at all 2144
## 2: A little 1711
## 3: Moderately 1120
## 4: Quite a bit 785
## 5: Extremely 440
## 6: empty 3500# panas_8
firstgun %>%
count(panas_8)## panas_8 n
## 1 Very slightly or not at all 1952
## 2 A little 1938
## 3 Moderately 1161
## 4 Quite a bit 708
## 5 Extremely 441secondgun %>%
count(panas_8)## panas_8 n
## 1 empty 3500new_data %>%
count(panas_8)## panas_8 n
## 1: Very slightly or not at all 1952
## 2: A little 1938
## 3: Moderately 1161
## 4: Quite a bit 708
## 5: Extremely 441
## 6: empty 3500# panas_9
firstgun %>%
count(panas_9)## panas_9 n
## 1 Very slightly or not at all 3689
## 2 A little 1115
## 3 Moderately 734
## 4 Quite a bit 404
## 5 Extremely 258secondgun %>%
count(panas_9)## panas_9 n
## 1 empty 3500new_data %>%
count(panas_9)## panas_9 n
## 1: Very slightly or not at all 3689
## 2: A little 1115
## 3: Moderately 734
## 4: Quite a bit 404
## 5: Extremely 258
## 6: empty 3500# panas_10
firstgun %>%
count(panas_10)## panas_10 n
## 1 Very slightly or not at all 3657
## 2 A little 1118
## 3 Moderately 836
## 4 Quite a bit 379
## 5 Extremely 210secondgun %>%
count(panas_10)## panas_10 n
## 1 empty 3500new_data %>%
count(panas_10)## panas_10 n
## 1: Very slightly or not at all 3657
## 2: A little 1118
## 3: Moderately 836
## 4: Quite a bit 379
## 5: Extremely 210
## 6: empty 3500# PurchaseWhen (NA ???)
firstgun %>%
count(PurchaseWhen)## PurchaseWhen n
## 1 empty 6200secondgun %>%
count(PurchaseWhen)## PurchaseWhen n
## 1 Next week 38
## 2 1 month from now 90
## 3 3 months from now 161
## 4 6 months from now 227
## 5 9 months from now 95
## 6 12 months from now 507
## 7 <NA> 2382new_data %>%
count(PurchaseWhen)## PurchaseWhen n
## 1: empty 6200
## 2: Next week 38
## 3: 1 month from now 90
## 4: 3 months from now 161
## 5: 6 months from now 227
## 6: 9 months from now 95
## 7: 12 months from now 507
## 8: <NA> 2382# C_19SS_D_1
firstgun %>%
count(C_19SS_D_1)## C_19SS_D_1 n
## 1 empty 6200secondgun %>%
count(C_19SS_D_1)## C_19SS_D_1 n
## 1 Not at all 466
## 2 Slightly 786
## 3 Moderately 952
## 4 Very 638
## 5 Extremely 650
## 6 <NA> 8new_data %>%
count(C_19SS_D_1)## C_19SS_D_1 n
## 1: empty 6200
## 2: Not at all 466
## 3: Slightly 786
## 4: Moderately 952
## 5: Very 638
## 6: Extremely 650
## 7: <NA> 8# C_19SS_D_2
firstgun %>%
count(C_19SS_D_2)## C_19SS_D_2 n
## 1 empty 6200secondgun %>%
count(C_19SS_D_2)## C_19SS_D_2 n
## 1 Not at all 663
## 2 Slightly 784
## 3 Moderately 960
## 4 Very 633
## 5 Extremely 453
## 6 <NA> 7new_data %>%
count(C_19SS_D_2)## C_19SS_D_2 n
## 1: empty 6200
## 2: Not at all 663
## 3: Slightly 784
## 4: Moderately 960
## 5: Very 633
## 6: Extremely 453
## 7: <NA> 7# C_19SS_D_3
firstgun %>%
count(C_19SS_D_3)## C_19SS_D_3 n
## 1 empty 6200secondgun %>%
count(C_19SS_D_3)## C_19SS_D_3 n
## 1 Not at all 588
## 2 Slightly 708
## 3 Moderately 914
## 4 Very 686
## 5 Extremely 594
## 6 <NA> 10new_data %>%
count(C_19SS_D_3)## C_19SS_D_3 n
## 1: empty 6200
## 2: Not at all 588
## 3: Slightly 708
## 4: Moderately 914
## 5: Very 686
## 6: Extremely 594
## 7: <NA> 10# C_19SS_D_4
firstgun %>%
count(C_19SS_D_4)## C_19SS_D_4 n
## 1 empty 6200secondgun %>%
count(C_19SS_D_4)## C_19SS_D_4 n
## 1 Not at all 544
## 2 Slightly 705
## 3 Moderately 899
## 4 Very 730
## 5 Extremely 609
## 6 <NA> 13new_data %>%
count(C_19SS_D_4)## C_19SS_D_4 n
## 1: empty 6200
## 2: Not at all 544
## 3: Slightly 705
## 4: Moderately 899
## 5: Very 730
## 6: Extremely 609
## 7: <NA> 13# C_19SS_D_5
firstgun %>%
count(C_19SS_D_5)## C_19SS_D_5 n
## 1 empty 6200secondgun %>%
count(C_19SS_D_5)## C_19SS_D_5 n
## 1 Not at all 563
## 2 Slightly 650
## 3 Moderately 887
## 4 Very 752
## 5 Extremely 636
## 6 <NA> 12new_data %>%
count(C_19SS_D_5)## C_19SS_D_5 n
## 1: empty 6200
## 2: Not at all 563
## 3: Slightly 650
## 4: Moderately 887
## 5: Very 752
## 6: Extremely 636
## 7: <NA> 12# C_19SS_D_6
firstgun %>%
count(C_19SS_D_6)## C_19SS_D_6 n
## 1 empty 6200secondgun %>%
count(C_19SS_D_6)## C_19SS_D_6 n
## 1 Not at all 584
## 2 Slightly 757
## 3 Moderately 905
## 4 Very 688
## 5 Extremely 550
## 6 <NA> 16new_data %>%
count(C_19SS_D_6)## C_19SS_D_6 n
## 1: empty 6200
## 2: Not at all 584
## 3: Slightly 757
## 4: Moderately 905
## 5: Very 688
## 6: Extremely 550
## 7: <NA> 16# C_19SS_SESC_1
firstgun %>%
count(C_19SS_SESC_1)## C_19SS_SESC_1 n
## 1 empty 6200secondgun %>%
count(C_19SS_SESC_1)## C_19SS_SESC_1 n
## 1 Not at all 1238
## 2 Slightly 773
## 3 Moderately 728
## 4 Very 408
## 5 Extremely 341
## 6 <NA> 12new_data %>%
count(C_19SS_SESC_1)## C_19SS_SESC_1 n
## 1: empty 6200
## 2: Not at all 1238
## 3: Slightly 773
## 4: Moderately 728
## 5: Very 408
## 6: Extremely 341
## 7: <NA> 12# C_19SS_SESC_2
firstgun %>%
count(C_19SS_SESC_2)## C_19SS_SESC_2 n
## 1 empty 6200secondgun %>%
count(C_19SS_SESC_2)## C_19SS_SESC_2 n
## 1 Not at all 1303
## 2 Slightly 734
## 3 Moderately 687
## 4 Very 457
## 5 Extremely 306
## 6 <NA> 13new_data %>%
count(C_19SS_SESC_2)## C_19SS_SESC_2 n
## 1: empty 6200
## 2: Not at all 1303
## 3: Slightly 734
## 4: Moderately 687
## 5: Very 457
## 6: Extremely 306
## 7: <NA> 13# C_19SS_SESC_3
firstgun %>%
count(C_19SS_SESC_3)## C_19SS_SESC_3 n
## 1 empty 6200secondgun %>%
count(C_19SS_SESC_3)## C_19SS_SESC_3 n
## 1 Not at all 1229
## 2 Slightly 738
## 3 Moderately 736
## 4 Very 437
## 5 Extremely 347
## 6 <NA> 13new_data %>%
count(C_19SS_SESC_3)## C_19SS_SESC_3 n
## 1: empty 6200
## 2: Not at all 1229
## 3: Slightly 738
## 4: Moderately 736
## 5: Very 437
## 6: Extremely 347
## 7: <NA> 13# C_19SS_SESC_4
firstgun %>%
count(C_19SS_SESC_4)## C_19SS_SESC_4 n
## 1 empty 6200secondgun %>%
count(C_19SS_SESC_4)## C_19SS_SESC_4 n
## 1 Not at all 1444
## 2 Slightly 634
## 3 Moderately 616
## 4 Very 457
## 5 Extremely 338
## 6 <NA> 11new_data %>%
count(C_19SS_SESC_4)## C_19SS_SESC_4 n
## 1: empty 6200
## 2: Not at all 1444
## 3: Slightly 634
## 4: Moderately 616
## 5: Very 457
## 6: Extremely 338
## 7: <NA> 11# C_19SS_SESC_5
firstgun %>%
count(C_19SS_SESC_5)## C_19SS_SESC_5 n
## 1 empty 6200secondgun %>%
count(C_19SS_SESC_5)## C_19SS_SESC_5 n
## 1 Not at all 857
## 2 Slightly 766
## 3 Moderately 751
## 4 Very 651
## 5 Extremely 463
## 6 <NA> 12new_data %>%
count(C_19SS_SESC_5)## C_19SS_SESC_5 n
## 1: empty 6200
## 2: Not at all 857
## 3: Slightly 766
## 4: Moderately 751
## 5: Very 651
## 6: Extremely 463
## 7: <NA> 12# C_19SS_SESC_6
firstgun %>%
count(C_19SS_SESC_6)## C_19SS_SESC_6 n
## 1 empty 6200secondgun %>%
count(C_19SS_SESC_6)## C_19SS_SESC_6 n
## 1 Not at all 1404
## 2 Slightly 630
## 3 Moderately 661
## 4 Very 434
## 5 Extremely 360
## 6 <NA> 11new_data %>%
count(C_19SS_SESC_6)## C_19SS_SESC_6 n
## 1: empty 6200
## 2: Not at all 1404
## 3: Slightly 630
## 4: Moderately 661
## 5: Very 434
## 6: Extremely 360
## 7: <NA> 11# C_19SS_1
firstgun %>%
count(C_19SS_1)## C_19SS_1 n
## 1 empty 6200secondgun %>%
count(C_19SS_1)## C_19SS_1 n
## 1 Not at all 1248
## 2 Slightly 737
## 3 Moderately 700
## 4 Very 418
## 5 Extremely 384
## 6 <NA> 13new_data %>%
count(C_19SS_1)## C_19SS_1 n
## 1: empty 6200
## 2: Not at all 1248
## 3: Slightly 737
## 4: Moderately 700
## 5: Very 418
## 6: Extremely 384
## 7: <NA> 13# C_19SS_2
firstgun %>%
count(C_19SS_2)## C_19SS_2 n
## 1 empty 6200secondgun %>%
count(C_19SS_2)## C_19SS_2 n
## 1 Not at all 1213
## 2 Slightly 819
## 3 Moderately 694
## 4 Very 459
## 5 Extremely 303
## 6 <NA> 12new_data %>%
count(C_19SS_2)## C_19SS_2 n
## 1: empty 6200
## 2: Not at all 1213
## 3: Slightly 819
## 4: Moderately 694
## 5: Very 459
## 6: Extremely 303
## 7: <NA> 12# C_19SS_X_3
firstgun %>%
count(C_19SS_X_3)## C_19SS_X_3 n
## 1 empty 6200secondgun %>%
count(C_19SS_X_3)## C_19SS_X_3 n
## 1 Not at all 1319
## 2 Slightly 747
## 3 Moderately 679
## 4 Very 435
## 5 Extremely 304
## 6 <NA> 16new_data %>%
count(C_19SS_X_3)## C_19SS_X_3 n
## 1: empty 6200
## 2: Not at all 1319
## 3: Slightly 747
## 4: Moderately 679
## 5: Very 435
## 6: Extremely 304
## 7: <NA> 16# C_19SS_X_4
firstgun %>%
count(C_19SS_X_4)## C_19SS_X_4 n
## 1 empty 6200secondgun %>%
count(C_19SS_X_4)## C_19SS_X_4 n
## 1 Not at all 1771
## 2 Slightly 530
## 3 Moderately 530
## 4 Very 387
## 5 Extremely 268
## 6 <NA> 14new_data %>%
count(C_19SS_X_4)## C_19SS_X_4 n
## 1: empty 6200
## 2: Not at all 1771
## 3: Slightly 530
## 4: Moderately 530
## 5: Very 387
## 6: Extremely 268
## 7: <NA> 14# C_19SS_X_5
firstgun %>%
count(C_19SS_X_5)## C_19SS_X_5 n
## 1 empty 6200secondgun %>%
count(C_19SS_X_5)## C_19SS_X_5 n
## 1 Not at all 1667
## 2 Slightly 573
## 3 Moderately 584
## 4 Very 387
## 5 Extremely 277
## 6 <NA> 12new_data %>%
count(C_19SS_X_5)## C_19SS_X_5 n
## 1: empty 6200
## 2: Not at all 1667
## 3: Slightly 573
## 4: Moderately 584
## 5: Very 387
## 6: Extremely 277
## 7: <NA> 12# C_19SS_X_6
firstgun %>%
count(C_19SS_X_6)## C_19SS_X_6 n
## 1 empty 6200secondgun %>%
count(C_19SS_X_6)## C_19SS_X_6 n
## 1 Not at all 1192
## 2 Slightly 855
## 3 Moderately 658
## 4 Very 438
## 5 Extremely 343
## 6 <NA> 14new_data %>%
count(C_19SS_X_6)## C_19SS_X_6 n
## 1: empty 6200
## 2: Not at all 1192
## 3: Slightly 855
## 4: Moderately 658
## 5: Very 438
## 6: Extremely 343
## 7: <NA> 14# C_19SS_Con_1
firstgun %>%
count(C_19SS_Con_1)## C_19SS_Con_1 n
## 1 empty 6200secondgun %>%
count(C_19SS_Con_1)## C_19SS_Con_1 n
## 1 Not at all 643
## 2 Slightly 1006
## 3 Moderately 890
## 4 Very 539
## 5 Extremely 411
## 6 <NA> 11new_data %>%
count(C_19SS_Con_1)## C_19SS_Con_1 n
## 1: empty 6200
## 2: Not at all 643
## 3: Slightly 1006
## 4: Moderately 890
## 5: Very 539
## 6: Extremely 411
## 7: <NA> 11# C_19SS_Con_2
firstgun %>%
count(C_19SS_Con_2)## C_19SS_Con_2 n
## 1 empty 6200secondgun %>%
count(C_19SS_Con_2)## C_19SS_Con_2 n
## 1 Not at all 635
## 2 Slightly 988
## 3 Moderately 882
## 4 Very 564
## 5 Extremely 421
## 6 <NA> 10new_data %>%
count(C_19SS_Con_2)## C_19SS_Con_2 n
## 1: empty 6200
## 2: Not at all 635
## 3: Slightly 988
## 4: Moderately 882
## 5: Very 564
## 6: Extremely 421
## 7: <NA> 10# C_19SS_Con_3
firstgun %>%
count(C_19SS_Con_3)## C_19SS_Con_3 n
## 1 empty 6200secondgun %>%
count(C_19SS_Con_3)## C_19SS_Con_3 n
## 1 Not at all 419
## 2 Slightly 861
## 3 Moderately 888
## 4 Very 740
## 5 Extremely 578
## 6 <NA> 14new_data %>%
count(C_19SS_Con_3)## C_19SS_Con_3 n
## 1: empty 6200
## 2: Not at all 419
## 3: Slightly 861
## 4: Moderately 888
## 5: Very 740
## 6: Extremely 578
## 7: <NA> 14# C_19SS_Con_4
firstgun %>%
count(C_19SS_Con_4)## C_19SS_Con_4 n
## 1 empty 6200secondgun %>%
count(C_19SS_Con_4)## C_19SS_Con_4 n
## 1 Not at all 818
## 2 Slightly 913
## 3 Moderately 812
## 4 Very 559
## 5 Extremely 386
## 6 <NA> 12new_data %>%
count(C_19SS_Con_4)## C_19SS_Con_4 n
## 1: empty 6200
## 2: Not at all 818
## 3: Slightly 913
## 4: Moderately 812
## 5: Very 559
## 6: Extremely 386
## 7: <NA> 12# C_19SS_Con_5
firstgun %>%
count(C_19SS_Con_5)## C_19SS_Con_5 n
## 1 empty 6200secondgun %>%
count(C_19SS_Con_5)## C_19SS_Con_5 n
## 1 Not at all 934
## 2 Slightly 829
## 3 Moderately 782
## 4 Very 551
## 5 Extremely 391
## 6 <NA> 13new_data %>%
count(C_19SS_Con_5)## C_19SS_Con_5 n
## 1: empty 6200
## 2: Not at all 934
## 3: Slightly 829
## 4: Moderately 782
## 5: Very 551
## 6: Extremely 391
## 7: <NA> 13# C_19SS_Con_6
firstgun %>%
count(C_19SS_Con_6)## C_19SS_Con_6 n
## 1 empty 6200secondgun %>%
count(C_19SS_Con_6)## C_19SS_Con_6 n
## 1 Not at all 1226
## 2 Slightly 776
## 3 Moderately 694
## 4 Very 466
## 5 Extremely 326
## 6 <NA> 12new_data %>%
count(C_19SS_Con_6)## C_19SS_Con_6 n
## 1: empty 6200
## 2: Not at all 1226
## 3: Slightly 776
## 4: Moderately 694
## 5: Very 466
## 6: Extremely 326
## 7: <NA> 12# C_19SS_TS_1
firstgun %>%
count(C_19SS_TS_1)## C_19SS_TS_1 n
## 1 empty 6200secondgun %>%
count(C_19SS_TS_1)## C_19SS_TS_1 n
## 1 Never 1634
## 2 Rarely 690
## 3 Sometimes 641
## 4 Often 287
## 5 Almost Always 236
## 6 <NA> 12new_data %>%
count(C_19SS_TS_1)## C_19SS_TS_1 n
## 1: empty 6200
## 2: Never 1634
## 3: Rarely 690
## 4: Sometimes 641
## 5: Often 287
## 6: Almost Always 236
## 7: <NA> 12# C_19SS_TS_2
firstgun %>%
count(C_19SS_TS_2)## C_19SS_TS_2 n
## 1 empty 6200secondgun %>%
count(C_19SS_TS_2)## C_19SS_TS_2 n
## 1 Never 2007
## 2 Rarely 472
## 3 Sometimes 483
## 4 Often 328
## 5 Almost Always 199
## 6 <NA> 11new_data %>%
count(C_19SS_TS_2)## C_19SS_TS_2 n
## 1: empty 6200
## 2: Never 2007
## 3: Rarely 472
## 4: Sometimes 483
## 5: Often 328
## 6: Almost Always 199
## 7: <NA> 11# C_19SS_TS_3
firstgun %>%
count(C_19SS_TS_3)## C_19SS_TS_3 n
## 1 empty 6200secondgun %>%
count(C_19SS_TS_3)## C_19SS_TS_3 n
## 1 Never 1342
## 2 Rarely 709
## 3 Sometimes 786
## 4 Often 406
## 5 Almost Always 241
## 6 <NA> 16new_data %>%
count(C_19SS_TS_3)## C_19SS_TS_3 n
## 1: empty 6200
## 2: Never 1342
## 3: Rarely 709
## 4: Sometimes 786
## 5: Often 406
## 6: Almost Always 241
## 7: <NA> 16# C_19SS_TS_4
firstgun %>%
count(C_19SS_TS_4)## C_19SS_TS_4 n
## 1 empty 6200secondgun %>%
count(C_19SS_TS_4)## C_19SS_TS_4 n
## 1 Never 1877
## 2 Rarely 544
## 3 Sometimes 545
## 4 Often 311
## 5 Almost Always 212
## 6 <NA> 11new_data %>%
count(C_19SS_TS_4)## C_19SS_TS_4 n
## 1: empty 6200
## 2: Never 1877
## 3: Rarely 544
## 4: Sometimes 545
## 5: Often 311
## 6: Almost Always 212
## 7: <NA> 11# C_19SS_TS_5
firstgun %>%
count(C_19SS_TS_5)## C_19SS_TS_5 n
## 1 empty 6200secondgun %>%
count(C_19SS_TS_5)## C_19SS_TS_5 n
## 1 Never 1786
## 2 Rarely 596
## 3 Sometimes 580
## 4 Often 332
## 5 Almost Always 193
## 6 <NA> 13new_data %>%
count(C_19SS_TS_5)## C_19SS_TS_5 n
## 1: empty 6200
## 2: Never 1786
## 3: Rarely 596
## 4: Sometimes 580
## 5: Often 332
## 6: Almost Always 193
## 7: <NA> 13# C_19SS_TS_6
firstgun %>%
count(C_19SS_TS_6)## C_19SS_TS_6 n
## 1 empty 6200secondgun %>%
count(C_19SS_TS_6)## C_19SS_TS_6 n
## 1 Never 2021
## 2 Rarely 477
## 3 Sometimes 498
## 4 Often 308
## 5 Almost Always 182
## 6 <NA> 14new_data %>%
count(C_19SS_TS_6)## C_19SS_TS_6 n
## 1: empty 6200
## 2: Never 2021
## 3: Rarely 477
## 4: Sometimes 498
## 5: Often 308
## 6: Almost Always 182
## 7: <NA> 14# C_19SS_Check_1
firstgun %>%
count(C_19SS_Check_1)## C_19SS_Check_1 n
## 1 empty 6200secondgun %>%
count(C_19SS_Check_1)## C_19SS_Check_1 n
## 1 Never 1026
## 2 Rarely 612
## 3 Sometimes 943
## 4 Often 569
## 5 Almost Always 338
## 6 <NA> 12new_data %>%
count(C_19SS_Check_1)## C_19SS_Check_1 n
## 1: empty 6200
## 2: Never 1026
## 3: Rarely 612
## 4: Sometimes 943
## 5: Often 569
## 6: Almost Always 338
## 7: <NA> 12# C_19SS_Check_2
firstgun %>%
count(C_19SS_Check_2)## C_19SS_Check_2 n
## 1 empty 6200secondgun %>%
count(C_19SS_Check_2)## C_19SS_Check_2 n
## 1 Never 1604
## 2 Rarely 534
## 3 Sometimes 671
## 4 Often 423
## 5 Almost Always 258
## 6 <NA> 10new_data %>%
count(C_19SS_Check_2)## C_19SS_Check_2 n
## 1: empty 6200
## 2: Never 1604
## 3: Rarely 534
## 4: Sometimes 671
## 5: Often 423
## 6: Almost Always 258
## 7: <NA> 10# C_19SS_Check_3
firstgun %>%
count(C_19SS_Check_3)## C_19SS_Check_3 n
## 1 empty 6200secondgun %>%
count(C_19SS_Check_3)## C_19SS_Check_3 n
## 1 Never 1268
## 2 Rarely 667
## 3 Sometimes 823
## 4 Often 465
## 5 Almost Always 265
## 6 <NA> 12new_data %>%
count(C_19SS_Check_3)## C_19SS_Check_3 n
## 1: empty 6200
## 2: Never 1268
## 3: Rarely 667
## 4: Sometimes 823
## 5: Often 465
## 6: Almost Always 265
## 7: <NA> 12# C_19SS_Check_4
firstgun %>%
count(C_19SS_Check_4)## C_19SS_Check_4 n
## 1 empty 6200secondgun %>%
count(C_19SS_Check_4)## C_19SS_Check_4 n
## 1 Never 1087
## 2 Rarely 653
## 3 Sometimes 882
## 4 Often 574
## 5 Almost Always 295
## 6 <NA> 9new_data %>%
count(C_19SS_Check_4)## C_19SS_Check_4 n
## 1: empty 6200
## 2: Never 1087
## 3: Rarely 653
## 4: Sometimes 882
## 5: Often 574
## 6: Almost Always 295
## 7: <NA> 9# C_19SS_Check_5
firstgun %>%
count(C_19SS_Check_5)## C_19SS_Check_5 n
## 1 empty 6200secondgun %>%
count(C_19SS_Check_5)## C_19SS_Check_5 n
## 1 Never 1592
## 2 Rarely 587
## 3 Sometimes 700
## 4 Often 381
## 5 Almost Always 231
## 6 <NA> 9new_data %>%
count(C_19SS_Check_5)## C_19SS_Check_5 n
## 1: empty 6200
## 2: Never 1592
## 3: Rarely 587
## 4: Sometimes 700
## 5: Often 381
## 6: Almost Always 231
## 7: <NA> 9# C_19SS_Check_6
firstgun %>%
count(C_19SS_Check_6)## C_19SS_Check_6 n
## 1 empty 6200secondgun %>%
count(C_19SS_Check_6)## C_19SS_Check_6 n
## 1 Never 1508
## 2 Rarely 599
## 3 Sometimes 657
## 4 Often 467
## 5 Almost Always 260
## 6 <NA> 9new_data %>%
count(C_19SS_Check_6)## C_19SS_Check_6 n
## 1: empty 6200
## 2: Never 1508
## 3: Rarely 599
## 4: Sometimes 657
## 5: Often 467
## 6: Almost Always 260
## 7: <NA> 9```