Body Armour Female Police Survey: England and Wales - 2016
Patrick Ford
2024-08-11
The data was collated from over 2000 female police officers in relation to the wearing of body armour, bra type and size, discomfort and ability to perform simple tasks. The data relates to officers from the police forces of England and Wales only, collected in 2016.
###Original Survey
cat(" Original Survey
I confirm that I have been informed about the aim and objectives of this research project and agree to give my inputs.
- I understand that all information that I provide will be treated with the strictest confidence and my name will not be used in any report publication or presentation.
- I understand that the information I provide will be used by Cranfield University for the purpose of research only. The data will be stored on a secure network accessed only by authorised users in accordance with the Data Protection Act (1998).
- I understand that the results of the research may be published in scientific journals and an anonymised version of the data may be published in support of these results.
- I understand that I am not required to answer a question if I prefer not to provide a response.
- I understand that I am free to withdraw from this survey at any stage.
By ticking this box I confirm that I am happy to participate. [ ]
Q1: What is your primary role?
- Routine Patrol
- Firearms
- Public Order
- Dog Handler
- Mounted Branch
- Traffic
- Other (please specify)
Q2: How many years have you been wearing body armour?
- < 2
- 2 to 5
- 6 to 10
- 11 to 15
- 16 to 20
- >20
Q3: What size bra do you normally wear?
Q4: What type of bra do you normally wear under your body armour?
- No bra
- Sports bra
- Underwired bra
- Padded/push up bra
- Other (please specify)
Q5: When standing wearing your body armour how comfortable is it?
- Very Comfortable
- Comfortable
- Uncomfortable
- Very Uncomfortable
Q6: On the images below please indicate any areas that are uncomfortable or rub when wearing your body armour.
Q7: Whilst wearing your body armour please rate the following actions (if applicable):
- Sitting in a car (passenger)
- Driving a car
- Walking
- Running
- Self defence techniques
- Using a pistol/TASER
- Using a carbine
- Riding (horse)
- Riding (pedal cycle)
- Riding (motorbike)
Q8: Please indicate which items of equipment you routinely carry on either your body armour or on a belt:
- Body Armour
- Belt
- Not applicable
- Handcuffs
- Irritant spray
- TASER
- Police radio
- Police mobile phone
- Personal mobile phone
- Notebook
- Baton
- Body worn video
- Other (please specify)
")## Original Survey
## I confirm that I have been informed about the aim and objectives of this research project and agree to give my inputs.
## - I understand that all information that I provide will be treated with the strictest confidence and my name will not be used in any report publication or presentation.
## - I understand that the information I provide will be used by Cranfield University for the purpose of research only. The data will be stored on a secure network accessed only by authorised users in accordance with the Data Protection Act (1998).
## - I understand that the results of the research may be published in scientific journals and an anonymised version of the data may be published in support of these results.
## - I understand that I am not required to answer a question if I prefer not to provide a response.
## - I understand that I am free to withdraw from this survey at any stage.
## By ticking this box I confirm that I am happy to participate. [ ]
##
## Q1: What is your primary role?
## - Routine Patrol
## - Firearms
## - Public Order
## - Dog Handler
## - Mounted Branch
## - Traffic
## - Other (please specify)
##
## Q2: How many years have you been wearing body armour?
## - < 2
## - 2 to 5
## - 6 to 10
## - 11 to 15
## - 16 to 20
## - >20
##
## Q3: What size bra do you normally wear?
##
## Q4: What type of bra do you normally wear under your body armour?
## - No bra
## - Sports bra
## - Underwired bra
## - Padded/push up bra
## - Other (please specify)
##
## Q5: When standing wearing your body armour how comfortable is it?
## - Very Comfortable
## - Comfortable
## - Uncomfortable
## - Very Uncomfortable
##
## Q6: On the images below please indicate any areas that are uncomfortable or rub when wearing your body armour.
##
## Q7: Whilst wearing your body armour please rate the following actions (if applicable):
## - Sitting in a car (passenger)
## - Driving a car
## - Walking
## - Running
## - Self defence techniques
## - Using a pistol/TASER
## - Using a carbine
## - Riding (horse)
## - Riding (pedal cycle)
## - Riding (motorbike)
##
## Q8: Please indicate which items of equipment you routinely carry on either your body armour or on a belt:
## - Body Armour
## - Belt
## - Not applicable
## - Handcuffs
## - Irritant spray
## - TASER
## - Police radio
## - Police mobile phone
## - Personal mobile phone
## - Notebook
## - Baton
## - Body worn video
## - Other (please specify)# Load necessary libraries
pacman::p_load(pacman, Amelia, corrplot, tidyverse, ggplot2, GGally, reshape2, scales, ggbiplot)
# Load the data
file_path <- "Survey_data_UK_female_police-2016.csv"
data <- read.csv(file_path)
# Replace '#NULL!' with NA
data[data == "#NULL!"] <- NA
# Convert relevant columns to factors
categorical_cols <- c("Role", "Years_wearing", "Bra_size", "Bra_type", "Comfort", "sit_car",
"Drive_car", "Walking", "Running", "SDAR", "Pistol_TASER", "Carbine",
"Horse", "Bike", "Motorbike", "Handcuffs", "spray", "TASER", "Radio",
"Mobile_police", "Mobile_personal", "Notebook", "Baton", "BWV", "Other")
data[categorical_cols] <- lapply(data[categorical_cols], as.factor)
# Convert columns to numeric, removing non-numeric characters where applicable
numeric_cols <- c("Front_RH_Breast", "Front_LH_breast", "LHS_breast", "Back_breast",
"RHS_Abdo", "Front_Abdo", "LHS_Abdo", "Back_abdo", "RHS_shoulder",
"Front_LH_shoulder", "Front_RH_shoulder", "LHS_shoulder",
"Rear_LHS_shoulder", "Rear_RHS_Shoulder", "Total_discomfort", "other_count")
data[numeric_cols] <- lapply(data[numeric_cols], function(x) {
x <- gsub("[^0-9.]", "", x) # Remove non-numeric characters
as.numeric(x)
})
# Recheck the structure after conversion
str(data)## 'data.frame': 2634 obs. of 44 variables:
## $ ID : int 1 2 3 4 5 6 8 9 10 11 ...
## $ Role : Factor w/ 9 levels "Custody / Offender management",..: 2 8 8 8 8 2 2 4 8 8 ...
## $ Years_wearing : Factor w/ 6 levels "< 2","> 20","11 to 15",..: 3 6 4 3 4 3 4 6 5 5 ...
## $ Bra_size : Factor w/ 107 levels "","23B","23F",..: 29 61 47 77 46 46 62 60 77 24 ...
## $ Bra_type : Factor w/ 5 levels "No bra","Other",..: 5 5 5 5 5 5 5 5 2 3 ...
## $ Comfort : Factor w/ 4 levels "Comfortable",..: 3 2 2 1 1 2 2 2 1 2 ...
## $ RHS_breast : chr NA NA NA NA ...
## $ Front_RH_Breast : num NA NA 1 1 NA 1 NA 2 NA NA ...
## $ Front_LH_breast : num NA NA 1 1 NA 1 NA 3 NA NA ...
## $ LHS_breast : num NA NA 1 1 1 NA NA NA NA NA ...
## $ Back_breast : num NA NA NA NA NA NA NA NA NA 2 ...
## $ RHS_Abdo : num NA NA NA 1 NA NA 1 NA 1 1 ...
## $ Front_Abdo : num NA NA 1 NA NA NA 2 NA 2 NA ...
## $ LHS_Abdo : num NA NA 1 NA NA NA 1 NA 1 1 ...
## $ Back_abdo : num NA 2 3 NA NA 2 NA NA 4 5 ...
## $ RHS_shoulder : num NA NA NA NA 1 NA 1 NA NA NA ...
## $ Front_LH_shoulder: num NA 1 1 NA NA NA 1 NA NA NA ...
## $ Front_RH_shoulder: num NA 1 NA 1 NA NA 1 NA NA NA ...
## $ LHS_shoulder : num NA NA NA NA NA NA 1 NA NA NA ...
## $ Rear_LHS_shoulder: num NA NA 1 NA NA 1 1 3 NA NA ...
## $ Rear_RHS_Shoulder: num NA NA NA 1 NA 1 1 1 NA NA ...
## $ Total_discomfort : num NA 4 1 6 2 6 1 9 8 9 ...
## $ sit_car : Factor w/ 5 levels "Difficult","Easy",..: 1 1 1 1 1 1 4 4 2 2 ...
## $ Drive_car : Factor w/ 5 levels "Difficult","Easy",..: 1 1 1 1 1 1 4 4 2 1 ...
## $ Walking : Factor w/ 5 levels "Difficult","Easy",..: 2 2 2 2 2 1 1 1 2 2 ...
## $ Running : Factor w/ 5 levels "Difficult","Easy",..: 4 1 4 2 1 4 4 4 4 4 ...
## $ SDAR : Factor w/ 5 levels "Difficult","Easy",..: 1 1 2 2 1 1 4 1 2 4 ...
## $ Pistol_TASER : Factor w/ 5 levels "Difficult","Easy",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Carbine : Factor w/ 5 levels "Difficult","Easy",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Horse : Factor w/ 4 levels "Difficult","Easy",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Bike : Factor w/ 5 levels "Difficult","Easy",..: 3 3 3 3 3 3 3 3 3 4 ...
## $ Motorbike : Factor w/ 3 levels "Difficult","Not applicable",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Handcuffs : Factor w/ 3 levels "Body armour",..: 3 3 1 3 NA 3 1 1 3 3 ...
## $ spray : Factor w/ 3 levels "Body armour",..: 3 3 1 3 NA 3 1 1 3 3 ...
## $ TASER : Factor w/ 3 levels "Body armour",..: 2 2 NA 2 NA 2 NA 2 2 2 ...
## $ Radio : Factor w/ 3 levels "Body armour",..: 1 1 1 3 NA 1 1 1 1 1 ...
## $ Mobile_police : Factor w/ 3 levels "Body armour",..: NA 1 1 2 NA 2 NA 1 1 1 ...
## $ Mobile_personal : Factor w/ 3 levels "Body armour",..: 1 1 1 2 NA 2 1 1 1 1 ...
## $ Notebook : Factor w/ 3 levels "Body armour",..: 1 1 1 1 NA 2 1 1 1 1 ...
## $ Baton : Factor w/ 3 levels "Body armour",..: 3 3 1 3 NA 3 1 1 3 3 ...
## $ BWV : Factor w/ 3 levels "Body armour",..: 2 2 NA 2 NA 2 NA 2 1 1 ...
## $ Other : Factor w/ 3 levels "Body armour",..: NA 3 NA 2 NA NA NA NA NA 1 ...
## $ Q8_10_TEXT : chr "" "First aid pouch" "" "" ...
## $ other_count : num NA 1 NA NA NA NA NA NA NA 2 ...# Summary of the cleaned dataset
summary(data)## ID Role Years_wearing
## Min. : 1.0 Routine Patrol :2013 < 2 :180
## 1st Qu.: 706.2 Detective : 311 > 20 :101
## Median :1571.5 Other : 83 11 to 15:788
## Mean :1575.3 Traffic : 66 16 to 20:266
## 3rd Qu.:2446.8 Custody / Offender management: 59 2 to 5 :382
## Max. :3211.0 (Other) : 101 6 to 10 :915
## NA's : 1 NA's : 2
## Bra_size Bra_type Comfort
## 34B : 223 No bra : 12 Comfortable : 817
## 36C : 212 Other : 87 Uncomfortable :1378
## 34C : 208 Padded / push up bra: 221 Very comfortable : 37
## 34D : 152 Sports bra : 445 Very uncomfortable: 381
## 36B : 148 Underwired bra :1858 NA's : 21
## 34DD : 145 NA's : 11
## (Other):1546
## RHS_breast Front_RH_Breast Front_LH_breast LHS_breast
## Length:2634 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Mode :character Median :1.000 Median :1.000 Median :1.000
## Mean :1.282 Mean :1.332 Mean :1.218
## 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :6.000 Max. :7.000 Max. :4.000
## NA's :1526 NA's :1471 NA's :1698
## Back_breast RHS_Abdo Front_Abdo LHS_Abdo
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2.00 Median :1.000
## Mean :1.915 Mean :1.163 Mean :1.99 Mean :1.151
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.00 3rd Qu.:1.000
## Max. :6.000 Max. :3.000 Max. :8.00 Max. :4.000
## NA's :2117 NA's :2259 NA's :2019 NA's :2284
## Back_abdo RHS_shoulder Front_LH_shoulder Front_RH_shoulder
## Min. : 1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median : 2.000 Median :1.000 Median :1.000 Median :1.000
## Mean : 1.946 Mean :1.058 Mean :1.113 Mean :1.096
## 3rd Qu.: 2.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :10.000 Max. :3.000 Max. :5.000 Max. :5.000
## NA's :1826 NA's :2360 NA's :1988 NA's :2001
## LHS_shoulder Rear_LHS_shoulder Rear_RHS_Shoulder Total_discomfort
## Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 2.000
## Median :1.000 Median :1.000 Median :1.000 Median : 4.000
## Mean :1.098 Mean :1.218 Mean :1.205 Mean : 4.395
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 6.000
## Max. :3.000 Max. :5.000 Max. :4.000 Max. :13.000
## NA's :2358 NA's :2019 NA's :2043 NA's :297
## sit_car Drive_car Walking
## Difficult :1200 Difficult :1302 Difficult : 556
## Easy : 928 Easy : 633 Easy :1785
## Not applicable: 11 Not applicable: 43 Not applicable: 3
## Very difficult: 330 Very difficult: 506 Very difficult: 64
## Very easy : 44 Very easy : 31 Very easy : 82
## NA's : 121 NA's : 119 NA's : 144
##
## Running SDAR Pistol_TASER
## Difficult :1127 Difficult :1392 Difficult : 76
## Easy : 186 Easy : 563 Easy : 101
## Not applicable: 12 Not applicable: 61 Not applicable:2222
## Very difficult:1177 Very difficult: 443 Very difficult: 30
## Very easy : 12 Very easy : 14 Very easy : 7
## NA's : 120 NA's : 161 NA's : 198
##
## Carbine Horse Bike
## Difficult : 25 Difficult : 5 Difficult : 192
## Easy : 30 Easy : 8 Easy : 46
## Not applicable:2342 Not applicable:2395 Not applicable:1981
## Very difficult: 12 Very difficult: 8 Very difficult: 218
## Very easy : 1 NA's : 218 Very easy : 3
## NA's : 224 NA's : 194
##
## Motorbike Handcuffs spray
## Difficult : 2 Body armour : 785 Body armour : 772
## Not applicable:2399 Not applicable: 117 Not applicable: 134
## Very difficult: 8 Utility belt :1575 Utility belt :1564
## NA's : 225 NA's : 157 NA's : 164
##
##
##
## TASER Radio Mobile_police
## Body armour : 123 Body armour :2340 Body armour : 942
## Not applicable:2003 Not applicable: 33 Not applicable:1228
## Utility belt : 64 Utility belt : 105 Utility belt : 118
## NA's : 444 NA's : 156 NA's : 346
##
##
##
## Mobile_personal Notebook Baton
## Body armour :1326 Body armour :1832 Body armour : 751
## Not applicable: 861 Not applicable: 327 Not applicable: 127
## Utility belt : 115 Utility belt : 226 Utility belt :1571
## NA's : 332 NA's : 249 NA's : 185
##
##
##
## BWV Other Q8_10_TEXT other_count
## Body armour : 984 Body armour : 307 Length:2634 Min. :1.000
## Not applicable:1275 Not applicable: 394 Class :character 1st Qu.:1.000
## Utility belt : 23 Utility belt : 153 Mode :character Median :1.000
## NA's : 352 NA's :1780 Mean :1.969
## 3rd Qu.:3.000
## Max. :8.000
## NA's :2115# Check for missing values
missing_values <- colSums(is.na(data))
print(missing_values)## ID Role Years_wearing Bra_size
## 0 1 2 0
## Bra_type Comfort RHS_breast Front_RH_Breast
## 11 21 1732 1526
## Front_LH_breast LHS_breast Back_breast RHS_Abdo
## 1471 1698 2117 2259
## Front_Abdo LHS_Abdo Back_abdo RHS_shoulder
## 2019 2284 1826 2360
## Front_LH_shoulder Front_RH_shoulder LHS_shoulder Rear_LHS_shoulder
## 1988 2001 2358 2019
## Rear_RHS_Shoulder Total_discomfort sit_car Drive_car
## 2043 297 121 119
## Walking Running SDAR Pistol_TASER
## 144 120 161 198
## Carbine Horse Bike Motorbike
## 224 218 194 225
## Handcuffs spray TASER Radio
## 157 164 444 156
## Mobile_police Mobile_personal Notebook Baton
## 346 332 249 185
## BWV Other Q8_10_TEXT other_count
## 352 1780 0 2115# Visualise missing values
missmap(data, main = "Missing values in the dataset after cleaning", col = c("yellow", "black"), legend = FALSE)
# Exploratory Data Analysis (EDA)
# Distribution of categorical variables
for (col in categorical_cols) {
print(
ggplot(data, aes(x = !!sym(col))) +
geom_bar(fill = "steelblue", na.rm = TRUE) +
theme_minimal() +
labs(title = paste("Distribution of", col)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels
)
}
# Distribution of numerical variables
for (col in numeric_cols) {
print(
ggplot(data, aes(x = !!sym(col))) +
geom_histogram(fill = "lightgreen", color = "black", bins = 30, na.rm = TRUE) +
theme_minimal() +
labs(title = paste("Distribution of", col))
)
}
# Bra Size Distribution
ggplot(data, aes(x = Bra_size)) +
geom_bar(fill = "steelblue", na.rm = TRUE) +
theme_minimal() +
labs(title = "Distribution of Bra Size") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) # Rotate x-axis labels
# Breast Size vs. Discomfort (Total Discomfort Score)
ggplot(data, aes(x = Bra_size, y = Total_discomfort, fill = Bra_size)) +
geom_boxplot(na.rm = TRUE) +
theme_minimal() +
labs(title = "Total Discomfort by Bra Size", x = "Bra Size", y = "Total Discomfort Score") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) # Rotate x-axis labels
# Correlation Between Discomfort and Equipment Used
equipment_cols <- c("Pistol_TASER", "Carbine", "Radio", "Mobile_police", "Mobile_personal",
"Notebook", "Baton", "TASER", "spray", "BWV", "Other")
for (col in equipment_cols) {
print(
ggplot(data, aes_string(x = col, y = "Total_discomfort", fill = col)) +
geom_boxplot(na.rm = TRUE) +
theme_minimal() +
labs(title = paste("Total Discomfort by", col), x = col, y = "Total Discomfort Score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels
)
}## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Comparison of Discomfort Across Roles
ggplot(data, aes(x = Role, y = Total_discomfort, fill = Role)) +
geom_boxplot(na.rm = TRUE) +
theme_minimal() +
labs(title = "Total Discomfort by Role", x = "Role", y = "Total Discomfort Score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels
# Bra Type and Discomfort
ggplot(data, aes(x = Bra_type, y = Total_discomfort, fill = Bra_type)) +
geom_boxplot(na.rm = TRUE) +
theme_minimal() +
labs(title = "Total Discomfort by Bra Type", x = "Bra Type", y = "Total Discomfort Score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels
# Handling missing values before correlation analysis
# Removing columns with too many NAs
numeric_cols_clean <- numeric_cols[colSums(is.na(data[, numeric_cols])) < 0.5 * nrow(data)]
# Correlation matrix of numerical variables
if (length(numeric_cols_clean) > 1) {
corr_matrix <- cor(data[, numeric_cols_clean], use = "complete.obs")
corrplot(corr_matrix, method = "circle", type = "lower", tl.cex = 0.7)
} else {
print("Not enough data to compute a correlation matrix.")
}## [1] "Not enough data to compute a correlation matrix."