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VT_ORG.R
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###_____________________________________________________________________________
### I show an example of how an organization
### can apply the Vicarious Trauma Organizational Readiness Guide
### from the assessment provided by the Office of Justice Programs.
###_____________________________________________________________________________
### The assessment can be found here:
### https://ovc.ojp.gov/sites/g/files/xyckuh226/files/media/document/os_vt-org_victim_services-508.pdf
###_____________________________________________________________________________
### The Office of Justice Programs provides a website full of resources that help
### interpret the VT-ORG measure and use it to improve an organization.
### An explanation of vicarious trauma:
### https://ovc.ojp.gov/program/vtt/what-is-vicarious-trauma
###_____________________________________________________________________________
### The website for the VT-ORG tool and plenty of resoures on how to use it!
### https://ovc.ojp.gov/program/vtt/vt-org-and-compendium
###_____________________________________________________________________________
### In my following script, I create a dataset from real responses
### on the survey and show my analyses and visualizations of the raw data.
### Feel free to give me feedback and enjoy!
###_____________________________________________________________________________
###_____________________________________________________________________________
# Install and load appropriate packages.
###_____________________________________________________________________________
install.packages(c("tidyverse", "assertive"))
library(tidyverse)
library(readxl)
library(assertive)
library(scales)
library(magrittr)
library(ggthemes)
#__________________________________________________
### Create functions to make analyses faster!
#__________________________________________________
# Function to read sheets.
read_my_sheets <- function(sheet) {
assert_is_character(sheet)
path <- file.path("C:/Users/ADDS 5/Desktop/R Projects/VT-ORG/vt_org.xlsx") # you can change the file.path call to use your own working directory
read_excel(path, sheet = sheet) # I provided the file in the Github repo for this code.
}
# Function to take data.frame and pivot longer for plotting and further analyses.
pivot_my_df <- function(df_name, first_col, last_col, header) {
new_df_pivot <- df_name %>%
mutate(Respondent = respondents, .before = first_col) %>%
pivot_longer(cols = first_col:last_col,
names_to = "question",
values_to = "value") %>%
mutate(question = factor(question,
levels = names(header)
)
)
}
# Function to get contingency table.
my_cont_tbl <- function(df) {
assert_is_data.frame(df)
new_cont_tbl <- table(df$question, df$value)
}
# Function to make plotable prop.table.
prop_plotable <- function(cont_tbl) {
assert_is_table(cont_tbl)
new_prop_tbl <- cont_tbl %>%
prop.table(1) %>%
multiply_by(100) %>%
as.data.frame()
}
# Function to summarize to get average scores.
pivot_summary <- function(pivot_df) {
assert_is_data.frame(pivot_df)
new_df_summary <- pivot_df %>%
group_by(question) %>%
summarize(Average = mean(value, na.rm = TRUE))
}
# Function to plot averages in colored bar charts.
plot_summary_df <- function(summary_df, subtitle, range_of_questions, plot_name, width = 7, height = 5) {
assert_is_data.frame(summary_df)
assert_is_numeric(range_of_questions)
path <- file.path("C:/Users/ADDS 5/Desktop/R Projects/VT-ORG/")
numbers <- range_of_questions
labels = paste0("Q", numbers)
new_colored_bar <-
ggplot(summary_df, aes(question, Average, fill = question)) +
geom_col(position = position_dodge(width = 0.5),
color = "black",
width = 0.75
) +
labs(x = "Question",
y = "Average Score",
title = "Vicarious Trauma - Organizational Readiness Guide for Victim Services",
subtitle = subtitle,
caption = "***Total of 8 surveys collected",
fill = "Question") +
scale_x_discrete(labels = labels)
ggsave(filename = plot_name, plot = last_plot(), path = path, width = width, height = height, units = "in")
return(new_colored_bar)
}
# Function for getting the average score across each dataset,
# turn into data.frame, useful for plotting.
get_averages <- function(data1, data2, data3, data4, data5) {
mean1 <- mean(colMeans(data1, na.rm = TRUE))
mean2 <- mean(colMeans(data2, na.rm = TRUE))
mean3 <- mean(colMeans(data3, na.rm = TRUE))
mean4 <- mean(colMeans(data4, na.rm = TRUE))
mean5 <- mean(colMeans(data5, na.rm = TRUE))
fac <- c("Leadership and Mission",
"Management and Supervision",
"Employee Empowerment and Work Environment",
"Training and Professional Development",
"Staff Health and Wellness"
)
average_df <- data.frame(Data_Sets = factor(fac, levels = fac),
Averages = c(mean1, mean2, mean3, mean4, mean5)
)
}
# Function to plot histogram to visualize distribution of scores for Leadership/Mission Section.
vtorg_histogram <- function(pivot_tbl, subtitle, fill = fill, plot_name, width = 7, height = 5) {
assert_is_data.frame(pivot_tbl)
assert_is_character(subtitle)
assert_is_character(fill)
assert_is_character(plot_name)
path <- file.path("C:/Users/ADDS 5/Desktop/R Projects/VT-ORG/")
new_plot <-
ggplot(pivot_tbl, aes(value)) +
geom_histogram(fill = fill,
color = "black") +
facet_wrap(~ question) +
labs(title = "Vicarious Trauma - Organizational Readiness Guide for Victim Services",
subtitle = subtitle,
x = "Value",
y = "Count",
caption = paste0("***Total of 8 surveys collected\n", "***", sum(is.na(pivot_tbl)), " NA values detected")
) +
theme(plot.caption = element_text(hjust = 0))
ggsave(filename = plot_name, plot = last_plot(), path = path, width = width, height = height, units = "in")
return(new_plot)
}
# Stacked bar chart to show proportions for each section.
vtorg_stacked_bar <- function(prop_tbl, pivot_tbl, subtitle, range_of_questions, palette, plot_name, width = 7, height = 5) {
assert_is_data.frame(prop_tbl)
assert_is_data.frame(pivot_tbl)
assert_is_character(subtitle)
assert_is_numeric(range_of_questions)
assert_is_character(palette)
assert_is_character(plot_name)
numbers <- range_of_questions
labels <- paste0("Q", numbers)
path <- file.path("C:/Users/ADDS 5/Desktop/R Projects/VT-ORG/")
new_stacked_bar <-
ggplot(prop_tbl, aes(x = Var1, y = Freq, fill = Var2, palette = palette)) +
geom_col(position = "fill",
color = "black") +
labs(x = "Survey Section",
y = "Proportion",
title = "Vicarious Trauma - Organizational Readiness Guide for Victim Services",
subtitle = subtitle,
caption = paste0("***Total of 8 surveys collected\n","***", sum(is.na(pivot_tbl)), " NA values detected"),
fill = "Score") +
scale_fill_brewer(palette = palette) +
theme(plot.caption = element_text(hjust = 0)
) +
scale_x_discrete(labels = labels) +
theme_clean()
ggsave(filename = plot_name, plot = last_plot(), path = path, width = width, height = height, units = "in")
return(new_stacked_bar)
}
#__________________________________________________
### Get file headers.
### I took the section names of the assessment
### itself and made each section a column
### on an excel file to save time later.
### I shortened the names, so please refer to the
### assessment itself if the names are not clear.
#__________________________________________________
# There are 8 respondents to the survey.
respondents <- c(1:8)
# Get sheets and use the columns for new file headers.
leadership_mission_header <- read_my_sheets("leadership_mission")
management_supervision_header <- read_my_sheets("management_supervision")
employee_empowerment_work_env_header <- read_my_sheets("employee_empowerment_work_env")
training_professional_develpmnt_header <- read_my_sheets("training_professional_develpmnt")
staff_health_wellness_header <- read_my_sheets("staff_health_wellness")
#____________________________________________________________
### Set values for these datasets to set up analyses.
#____________________________________________________________
#____________________________________________________________
#Leadership / Mission section.
#____________________________________________________________
leadership_mission <- data.frame(one = c(4,5,5,4,4,4,5,5),
two = c(4,5,4,4,4,4,4,5),
three = c(4,5,4,3,5,4,4,5),
four = c(4,4,5,3,2,3,4,3),
five = c(3,5,5,4,5,4,5,5),
six = c(4,4,5,3,3,3,3,3),
seven = c(3,5,5,5,5,5,5,4),
eight= c(3,5,5,5,5,4,5,4),
nine = c(4,5,5,5,2,4,5,4),
ten = c(3,5,5,5,3,4,5,4)
)
# Better names.
names(leadership_mission) <- names(leadership_mission_header)
# Create a longer data.frame to perform calculations.
leadership_mission_pivot <-
pivot_my_df(leadership_mission,
"clear_decisive_vision",
"no_tolerance_discrim",
leadership_mission_header)
# Contingency table for leadership / mission.
leadership_mission_cont_tbl <- my_cont_tbl(leadership_mission_pivot)
# Plotable prop.table.
leadership_mission_prop_tbl <- prop_plotable(leadership_mission_cont_tbl)
# Summarize to get average scores.
leadership_mission_summary <- pivot_summary(leadership_mission_pivot)
#Plot averages across scores in dataset.
plot_summary_df(leadership_mission_summary,
subtitle = "Leadership Mission and Mission Results",
range_of_questions = 1:10,
plot_name = "leadership_mission_averages.png")
# Plot histogram to visualize distribution of scores for Leadership/Mission Section.
vtorg_histogram(leadership_mission_pivot,
subtitle = "Leadership and Mission Results",
fill = "chartreuse",
plot_name = "leadership_mission_hist.png")
# Stacked bar chart to show proportions for each section.
vtorg_stacked_bar(leadership_mission_prop_tbl,
leadership_mission_pivot,
subtitle = "Leadership and Mission Results",
range_of_questions = 1:10,
palette = "PuOr",
plot_name = "leadership_mission_stacked_bar.png")
#____________________________________________________________
# Management / supervision data.frame.
#____________________________________________________________
management_supervision <- data.frame(one = c(4,5,5,2,4,3,4,3),
two = c(NA,5,5,2,4,3,4,3),
three = c(4,4,5,2,3,3,2,3),
four = c(4,4,5,2,4,3,4,3),
five = c(4,NA,5,3,5,5,5,4),
six = c(5,5,5,NA,4,4,2,5),
seven = c(5,5,5,1,3,4,2,5),
eight = c(4,5,5,2,3,3,2,5),
nine = c(5,5,5,3,4,5,3,5),
ten = c(5,5,5,2,4,5,3,4),
eleven = c(4,4,5,3,3,4,4,5),
twelve = c(4,4,5,2,3,3,2,4),
thirteen = c(5,5,NA,3,4,4,3,4),
fourteen = c(2,5,3,3,3,3,2,4),
fifteen = c(4,5,5,4,5,5,5,5),
sixteen = c(5,5,5,2,4,5,4,5),
seventeen = c(5,NA,5,2,4,NA,2,4),
eighteen = c(5,NA,5,2,4,5,2,4)
)
# Better names.
names(management_supervision) <- names(management_supervision_header)
# Create a longer data.frame to perform calculations.
management_supervision_pivot <-
pivot_my_df(management_supervision,
"protocol__client_trauma",
"perf_eval_pos_contrb",
management_supervision_header)
# Contingency table for management / supervision.
management_supervision_cont_tbl <- my_cont_tbl(management_supervision_pivot)
# Plotable prop.table.
management_supervision_prop_tbl <- prop_plotable(management_supervision_cont_tbl)
# Summarize to get average scores.
management_supervision_summary <- pivot_summary(management_supervision_pivot)
#Plot averages across scores in dataset.
plot_summary_df(management_supervision_summary,
subtitle = "Management and Supervision Results",
range_of_questions = 1:18,
width = 8.5,
height = 6.5,
plot_name = "management_supervision_averages.png"
)
# Plot histogram to visualize distribution of scores for Leadership/Mission Section.
vtorg_histogram(management_supervision_pivot,
subtitle = "Management and Supervision Results",
fill = "midnightblue",
plot_name = "management_supervisoin_hist.png",
width = 10,
height = 6.5
)
# Stacked bar chart to show proportions for each section.
vtorg_stacked_bar(management_supervision_prop_tbl,
management_supervision_pivot,
subtitle = "Management and Supervision Results",
range_of_questions = 1:18,
palette = "PuOr",
plot_name = "management_supervision_stacked_bar.png",
width = 8.5,
height = 6.5
)
#____________________________________________________________
# Employee Empowerment and Work Environment data.frame.
#____________________________________________________________
employee_empowerment_work_env <- data.frame(one = c(4,4,3,4,3,4,3,5),
two = c(4,4,3,4,3,4,3,5),
three = c(5,4,4,3,4,5,3,5),
four = c(4,3,3,4,3,4,3,5),
five = c(5,4,4,4,5,4,4,5),
six = c(5,4,4,4,5,5,3,5),
seven = c(5,5,4,4,3,5,3,5),
eight = c(5,5,4,NA,5,4,3,4),
nine = c(5,4,4,4,5,4,4,4),
ten = c(4,3,4,4,3,4,4,4),
eleven = c(5,3,3,NA,3,4,4,4),
twelve = c(5,3,2,NA,2,3,4,4),
thirteen = c(5,4,3,4,5,4,4,5),
fourteen = c(5,4,4,5,4,4,4,5),
fifteen = c(5,4,3,5,5,4,4,4)
)
# Better names.
names(employee_empowerment_work_env) <- names(employee_empowerment_work_env_header)
# Create a longer data.frame to perform calculations.
employee_empowerment_work_env_pivot <- pivot_my_df(employee_empowerment_work_env,
"staff_input_dev_policy",
"org_provid_diverse_tasks",
employee_empowerment_work_env_header)
# Contingency table for employee empowerment and work environment.
employee_empowerment_work_env_cont_tbl <- my_cont_tbl(employee_empowerment_work_env_pivot)
# Plotable prop.table.
employee_empowerment_work_env_prop_tbl <- prop_plotable(employee_empowerment_work_env_cont_tbl)
# Summarize to get average scores.
employee_empowerment_work_env_summary <- pivot_summary(employee_empowerment_work_env_pivot)
#Plot averages across scores in dataset.
plot_summary_df(employee_empowerment_work_env_summary,
subtitle = "Employee Empowerment and Work Environment Results",
range_of_questions = 1:15,
plot_name = "employee_empowerment_work_env_averages.png",
width = 8,
height = 6
)
# Plot histogram to visualize distribution of scores for Leadership/Mission Section.
vtorg_histogram(employee_empowerment_work_env_pivot,
subtitle = "Employee Empowerment and Work Environment Results",
fill = "firebrick",
plot_name = "employee_empowerment_work_env_hist.png",
width = 8,
height = 6)
# Stacked bar chart to show proportions for each section.
vtorg_stacked_bar(employee_empowerment_work_env_prop_tbl,
employee_empowerment_work_env_pivot,
subtitle = "Employee Empowerment and Work Environment Results",
range_of_questions = 1:15,
palette = "PuOr",
plot_name = "employee_empowerment_work_env_stacked_bar.png",
width = 8,
height = 6
)
#____________________________________________________________
# Training and Professional Development data.frame.
#____________________________________________________________
training_professional_develpmnt <- data.frame(one = c(3,5,5,5,5,5,4,5),
two = c(4,4,3,3,5,4,4,4),
three = c(4,3,3,3,5,4,4,4),
four = c(5,4,4,5,4,4,4,5),
five = c(4,3,4,5,4,4,4,5),
six = c(4,4,3,5,4,3,5,5),
seven = c(5,4,4,5,5,4,3,5),
eight = c(5,3,3,5,4,4,4,5)
)
# Better names.
names(training_professional_develpmnt) <- names(training_professional_develpmnt_header)
# Create a longer data.frame to perform calculations.
training_professional_develpmnt_pivot <- pivot_my_df(training_professional_develpmnt,
"org_orient_new_staff_to_job",
"encourage_networking_collab",
training_professional_develpmnt_header)
# Contingency table for training and professional development.
training_professional_develpmnt_cont_tbl <- my_cont_tbl(training_professional_develpmnt_pivot)
# Plotable prop.table.
training_professional_develpmnt_prop_tbl <- prop_plotable(training_professional_develpmnt_cont_tbl)
# Summarize to get average scores.
training_professional_develpmnt_summary <- pivot_summary(training_professional_develpmnt_pivot)
#Plot averages across scores in dataset.
plot_summary_df(training_professional_develpmnt_summary,
subtitle = "Training and Professional Development Results",
range_of_questions = 1:8,
plot_name = "training_professional_develpmnt_averages.png"
)
# Plot histogram to visualize distribution of scores for Leadership/Mission Section.
vtorg_histogram(training_professional_develpmnt_pivot,
subtitle = "Training and Professional Develoment Results",
fill = "forestgreen",
plot_name = "training_professional_develpmnt_hist.png"
)
# Stacked bar chart to show proportions for each section.
vtorg_stacked_bar(training_professional_develpmnt_prop_tbl,
training_professional_develpmnt_pivot,
subtitle = "Training and Professional Development Results",
range_of_questions = 1:8,
palette = "PuOr",
plot_name = "training_professional_develpmnt_stacked_bar.png",
width = 7.5
)
#____________________________________________________________
# Staff Health and Wellness data.frame.
#____________________________________________________________
staff_health_wellness <- data.frame(one = c(4,4,3,4,2,4,4,NA),
two = c(4,3,2,4,3,3,4,4),
three = c(4,5,4,4,5,5,4,5),
four = c(5,4,4,4,5,5,4,5),
five = c(5,2,4,NA,5,NA,NA,5),
six = c(5,3,3,3,5,4,3,5),
seven = c(5,4,4,4,4,4,4,5),
eight = c(5,3,4,4,4,4,4,5),
nine = c(2,NA,2,NA,1,1,2,4),
ten = c(3,2,4,4,5,1,3,4),
eleven = c(5,4,2,5,2,5,4,4),
twelve = c(5,2,2,NA,4,NA,3,3)
)
# Better names.
names(staff_health_wellness) <- names(staff_health_wellness_header)
# Create a longer data.frame to perform calculations.
staff_health_wellness_pivot <- pivot_my_df(staff_health_wellness,
"ask_job_applicants_coping",
"org_plcy_support_worker_fam",
staff_health_wellness_header)
# Contingency table for staff health and wellness.
staff_health_wellness_cont_tbl <- my_cont_tbl(staff_health_wellness_pivot)
# Plotable prop.table.
staff_health_wellness_prop_tbl <- prop_plotable(staff_health_wellness_cont_tbl)
# Summarize to get average scores.
staff_health_wellness_summary <- pivot_summary(staff_health_wellness_pivot)
#Plot averages across scores in dataset.
plot_summary_df(staff_health_wellness_summary,
subtitle = "Staff Health and Wellness Results",
range_of_questions = 1:12,
plot_name = "staff_health_wellness_averages.png",
width = 8,
height = 6)
# Plot histogram to visualize distribution of scores for Leadership/Mission Section.
vtorg_histogram(staff_health_wellness_pivot,
subtitle = "Staff Health and Wellness Results",
fill = "deeppink",
plot_name = "staff_health_wellness_hist.png",
width = 8,
height = 6)
# Stacked bar chart to show proportions for each section.
vtorg_stacked_bar(staff_health_wellness_prop_tbl,
staff_health_wellness_pivot,
subtitle = "Staff Health and Wellness Results",
range_of_questions = 1:12,
palette = "PuOr",
plot_name = "staff_health_wellness_stacked_bar.png",
width = 8,
height = 6
)
#____________________________________________________________
### Get dataset averages to understand where to best
### expand leadership team energy.
#____________________________________________________________
# Use custom function to create a data.frame of means to plot.
VT_ORG_means <-
get_averages(leadership_mission,
management_supervision,
employee_empowerment_work_env,
training_professional_develpmnt,
staff_health_wellness
)
# Plot the means data.frame.
ggplot(VT_ORG_means, aes(x = factor(Data_Sets, levels = rev(levels(Data_Sets))),
y = round(Averages, digits = 1),
fill = Data_Sets,
group = Data_Sets,
label = round(Averages, digits = 1)
)
) +
geom_col(color = "black",
position = position_dodge(width = 0.5),
width = 0.5
) +
geom_text(aes(x = factor(Data_Sets, levels = rev(levels(Data_Sets))),
y = Averages),
color = "white",
size = 5,
fontface = "bold",
hjust = c(-0.15,-0.15,-0.15,-0.25,-0.15)
) +
coord_flip() +
labs(x = NULL,
y = "Average Scores of Each Data Set",
title = "Average Scores Across VT-ORG Data Sets",
subtitle = "Summary Scores of 5 Data Sets Containing 8 Responses Each",
caption = paste0("***Detection of a total of ", sum(sum(is.na(leadership_mission)),
sum(is.na(management_supervision)),
sum(is.na(employee_empowerment_work_env)),
sum(is.na(training_professional_develpmnt)),
sum(is.na(staff_health_wellness))
),
" NA values across all data sets."
),
fill = "Data Set"
) +
theme_dark() +
scale_fill_brewer(palette = "RdYlBu")
ggsave(filename = "vt_org_means.png", plot = last_plot(), width = 15, height = 6, unit = "in")