---
title: "HCAMP Paper Version 2"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: scroll
theme: united
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(here)
library(janitor)
library(rio)
library(colorblindr)
library(gghighlight)
library(forcats)
library(ggrepel)
library(knitr)
library(kableExtra)
library(reactable)
library(plotly)
library(glue)
library(fs)
library(rstatix)
library(ggpubr)
library(writexl)
library(remotes)
library(profvis)
# theme_fivethirtyeight <- function(base_size = 15, base_family = "") {
# theme_grey(base_size = base_size, base_family = base_family) %+replace%
# theme(
#
# # Base elements which are not used directly but inherited by others
# line = element_line(colour = '#DADADA', size = 0.75,
# linetype = 1, lineend = "butt"),
# rect = element_rect(fill = "#F0F0F0", colour = "#F0F0F0",
# size = 0.5, linetype = 1),
# text = element_text(family = base_family, face = "plain",
# colour = "#656565", size = base_size,
# hjust = 0.5, vjust = 0.5, angle = 0,
# lineheight = 0.9),
#
# # Modified inheritance structure of text element
# plot.title = element_text(size = rel(1.5), family = '' ,
# face = 'bold', hjust = -0.05,
# vjust = 1.5, colour = '#3B3B3B'),
# axis.title.x = element_text(),
# axis.title.y = element_text(),
# axis.text = element_text(),
#
# # Modified inheritance structure of line element
# axis.ticks = element_line(),
# panel.grid.major = element_line(),
# panel.grid.minor = element_blank(),
#
# # Modified inheritance structure of rect element
# plot.background = element_rect(),
# panel.background = element_rect(),
# legend.key = element_rect(colour = '#DADADA'),
#
# # Modifiying legend.position
# legend.position = 'none',
#
# complete = TRUE
# )
# }
#
#
# theme_set(theme_fivethirtyeight())
theme_set(theme_minimal(15) +
theme(legend.position = "bottom",
panel.grid.major.x = element_line(color = "gray60"),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank())
)
```
```{r global, include=FALSE}
#all clean sims data
sims_concussion_data <- read_csv(here("data", "sims_concussion_data.csv"))
sims_concussion_data <- sims_concussion_data %>%
mutate(age = as.factor(age))
simsimp <- read_csv(here("data", "clean_impact_sims_data.csv"))
mod_data <- read_csv(here("data", "clean_impact_sims_data.csv"))
mod_datav2 <- read_csv(here("data", "clean_impact_sims_data.csv"))
str(simsimp)
simsimp <- simsimp %>%
mutate(dataset = as.factor(dataset),
school_year = as.factor(school_year),
school = as.factor(school),
league = as.factor(league),
gender = as.factor(gender),
age = as.factor(age),
sport = as.factor(sport),
injury = as.factor(injury)) %>%
mutate_if(is.numeric, round, digits = 3)
mod_data <- mod_data %>%
mutate(dataset = as.factor(dataset),
school_year = as.factor(school_year),
school = as.factor(school),
league = as.factor(league),
gender = as.factor(gender),
sport = as.factor(sport),
injury = as.factor(injury)) %>%
mutate_if(is.numeric, round, digits = 3)
str(mod_data)
mod_datav2 <- mod_datav2 %>%
mutate(dataset = as.factor(dataset),
school_year = as.factor(school_year),
school = as.factor(school),
league = as.factor(league),
gender = as.factor(gender),
sport = as.factor(sport),
injury = as.factor(injury)) %>%
mutate_if(is.numeric, round, digits = 3)
str(mod_datav2)
```
```{r, include=FALSE}
# creating new data set for machine learning models
names(simsimp)
str(simsimp)
names(mod_data)
mod_data[mod_data$age <= 16, "age_group"] <- "13-16"
mod_data[mod_data$age >=17, "age_group"] <- "17-18"
names(mod_data)
mod_data %>%
count(age_group)
str(mod_data)
mod_data <- mod_data %>%
mutate(age_group = as.factor(age_group))
str(mod_data)
mod_data[mod_data$dys_btwn_onset_rtp_3 <= 6, "rtl_group"] <- "0-6"
mod_data[mod_data$dys_btwn_onset_rtp_3 >= 7 & mod_data$dys_btwn_onset_rtp_3 <= 12,
"rtl_group"] <- "7-12"
mod_data[mod_data$dys_btwn_onset_rtp_3 >= 13 & mod_data$dys_btwn_onset_rtp_3 <= 18,
"rtl_group"] <- "13-18"
mod_data[mod_data$dys_btwn_onset_rtp_3 >= 19 & mod_data$dys_btwn_onset_rtp_3 <= 24,
"rtl_group"] <- "19-24"
mod_data[mod_data$dys_btwn_onset_rtp_3 >= 25 & mod_data$dys_btwn_onset_rtp_3 <= 30,
"rtl_group"] <- "25-30"
str(mod_data)
mod_data <- mod_data %>%
mutate(rtl_group = as.factor(rtl_group))
mod_data %>%
count(rtl_group)
mod_data_2 <- mod_data
mod_data_2[mod_data_2$total_symptom_score_post_injury_1 <= 16,
"test_1_pcss_group"] <- "0-16"
mod_data_2[mod_data_2$total_symptom_score_post_injury_1 >= 17 &
mod_data_2$total_symptom_score_post_injury_1 <= 32,
"test_1_pcss_group"] <- "17-32"
mod_data_2[mod_data_2$total_symptom_score_post_injury_1 >= 33 &
mod_data_2$total_symptom_score_post_injury_1 <= 48,
"test_1_pcss_group"] <- "33-48"
mod_data_2[mod_data_2$total_symptom_score_post_injury_1 >= 49 &
mod_data_2$total_symptom_score_post_injury_1 <= 64,
"test_1_pcss_group"] <- "49-64"
mod_data_2[mod_data_2$total_symptom_score_post_injury_1 >= 65 &
mod_data_2$total_symptom_score_post_injury_1 <= 80,
"test_1_pcss_group"] <- "65-80"
mod_data_2[mod_data_2$total_symptom_score_post_injury_1 >= 81,
"test_1_pcss_group"] <- "81 or higher"
str(mod_data_2)
mod_data_2 <- mod_data_2 %>%
mutate(test_1_pcss_group = as.factor(test_1_pcss_group))
str(mod_data_2)
mod_data_2 %>%
count(test_1_pcss_group)
# per Tom's suggestion, divding RTL group into two levels - splitting around 12, which is the mean
mod_datav2[mod_datav2$age <= 16, "age_group"] <- "13-16"
mod_datav2[mod_datav2$age >=17, "age_group"] <- "17-18"
mod_datav2[mod_datav2$dys_btwn_onset_rtp_3 <= 12, "rtl_group"] <- "0-12"
mod_datav2[mod_datav2$dys_btwn_onset_rtp_3 >= 13, "rtl_group"] <- "13-30"
mod_datav2[mod_datav2$total_symptom_score_post_injury_1 <= 16,
"test_1_pcss_group"] <- "0-16"
mod_datav2[mod_datav2$total_symptom_score_post_injury_1 >= 17 &
mod_datav2$total_symptom_score_post_injury_1 <= 32,
"test_1_pcss_group"] <- "17-32"
mod_datav2[mod_datav2$total_symptom_score_post_injury_1 >= 33 &
mod_datav2$total_symptom_score_post_injury_1 <= 48,
"test_1_pcss_group"] <- "33-48"
mod_datav2[mod_datav2$total_symptom_score_post_injury_1 >= 49 &
mod_datav2$total_symptom_score_post_injury_1 <= 64,
"test_1_pcss_group"] <- "49-64"
mod_datav2[mod_datav2$total_symptom_score_post_injury_1 >= 65 &
mod_datav2$total_symptom_score_post_injury_1 <= 80,
"test_1_pcss_group"] <- "65-80"
mod_datav2[mod_datav2$total_symptom_score_post_injury_1 >= 81,
"test_1_pcss_group"] <- "81 or higher"
str(mod_datav2)
mod_datav2 <- mod_datav2 %>%
mutate(rtl_group = as.factor(rtl_group),
test_1_pcss_group = as.factor(test_1_pcss_group),
age_group = as.factor(age_group))
str(mod_datav2)
mod_datav2 %>%
count(rtl_group)
```
```{r, include=FALSE}
#helpful functions
mean_2 <- function(x) {
z <- na.omit(x)
sum(z) / length(z)
}
my_mean <- function(x) {
mean(x[x >= 0], na.rm = TRUE)
}
create_react_time <- function(df, var) {
df %>%
summarize(Mean = mean({{var}}),
SD = sd({{var}}),
Min = min({{var}}),
Max = max({{var}}),
Total = length({{var}})) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(columns = list(
Mean = colDef(format = colFormat(separators = TRUE, suffix = " days")),
SD = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Min = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Max = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
))
}
create_react_time2 <- function(df, x, var) {
df %>%
group_by({{x}}) %>%
summarize(Mean = mean({{var}}),
SD = sd({{var}}),
Min = min({{var}}),
Max = max({{var}}),
Total = length({{var}})) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(columns = list(
Mean = colDef(format = colFormat(separators = TRUE, suffix = " days")),
SD = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Min = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Max = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
))
}
create_react <- function(df, var) {
df %>%
summarize(Mean = mean({{var}}),
SD = sd({{var}}),
Min = min({{var}}),
Max = max({{var}}),
Total = length({{var}})) %>%
mutate_if(is.numeric, round, 3) %>%
reactable(columns = list(
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
))
}
create_react_age <- function(df, var) {
df %>%
group_by(age) %>%
summarize(Mean = mean({{var}}),
SD = sd({{var}}),
Min = min({{var}}),
Max = max({{var}}),
Total = length({{var}})) %>%
mutate_if(is.numeric, round, 3) %>%
reactable(columns = list(
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
))
}
create_react_gender <- function(df, var) {
df %>%
group_by(gender) %>%
summarize(Mean = mean({{var}}),
SD = sd({{var}}),
Min = min({{var}}),
Max = max({{var}}),
Total = length({{var}})) %>%
mutate_if(is.numeric, round, 3) %>%
reactable(columns = list(
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
))
}
my_mean(simsimp$dys_btwn_onset_test_4)
```
```{r, include=FALSE}
simsimp %>%
count(student_id)
length(unique(simsimp$student_id))
length(unique(simsimp$gender))
simsimp %>%
group_by(row, gender) %>%
count()
```
# Demographics
Sidebar {.sidebar}
------------
The **Sex** table displays the total number of injuries by sex used in the data set. The total number of injuries is 755 that can be utilized for analysis. Like the previous iteration of the paper, some individuals sustained multiple injuries that are tracked individually. This is a characteristic that one of the reviewers specified we describe more to better explain the sample. The tables displayed present data representing the total number of _injuries_, which include instances of repeat injuries. Data on the number of unique individuals is outlined here:
* **Number of females:** 271
* **Number of males:** 460
* 260 females sustained one tracked injury
* 447 males sustained one tracked injury
* 10 females sustained two tracked injuries
* 12 males sustained two tracked injuries
* 1 female sustained three tracked injuries
* 1 male sustained three tracked injuries
Row {.tabset}
-----------------------------------------------------------------------
### Sex
```{r, include=TRUE}
simsimp %>%
group_by(gender) %>%
summarize(total = n()) %>%
arrange(desc(total)) %>%
reactable(
columns = list(
gender = colDef(name = "Sex",
align = "center"),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
```{r, include=FALSE}
sims_sex <- simsimp %>%
group_by(gender) %>%
summarize(total = n()) %>%
arrange(desc(total))
sims_sex_plot <- ggplot(sims_sex, aes(fct_reorder(gender, total), total)) +
geom_col(fill = "blue",
alpha = 0.7) +
scale_y_continuous(limits = c(0, 600),
breaks = c(0, 200, 400, 600)) +
coord_flip() +
labs(x = "",
y = "Total")
```
```{r, include=FALSE}
ggplotly(sims_sex_plot)
```
### Age
```{r, include=TRUE}
simsimp %>%
group_by(age) %>%
summarize(total = n()) %>%
reactable(
columns = list(
age = colDef(name = "Age",
align = "center"),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
```{r, include=FALSE}
sims_age <- simsimp %>%
mutate(age = as.factor(age)) %>%
group_by(age) %>%
summarize(total = n()) %>%
arrange(desc(total))
sims_age_plot <- ggplot(sims_age, aes(fct_reorder(age, total), total)) +
geom_col(fill = "blue",
alpha = 0.7) +
coord_flip() +
labs(x = "Age",
y = "Total")
```
```{r, include=FALSE}
ggplotly(sims_age_plot)
```
### League
```{r, include=TRUE}
simsimp %>%
group_by(league) %>%
summarize(total = n()) %>%
arrange(desc(total)) %>%
reactable(
columns = list(
league = colDef(name = "League",
align = "center"),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
### School
```{r}
simsimp %>%
group_by(school) %>%
summarize(total = n()) %>%
arrange(desc(total)) %>%
reactable(
columns = list(
school = colDef(name = "School",
align = "center"),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
```
### Sport
```{r}
simsimp %>%
group_by(sport) %>%
summarize(total = n()) %>%
arrange(desc(total)) %>%
reactable(
columns = list(
sport = colDef(name = "Sport",
align = "center"),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
```
### Sport Level
```{r, include=FALSE}
simsimp %>%
group_by(level) %>%
summarize(total = n()) %>%
arrange(desc(total)) %>%
reactable(
columns = list(
level = colDef(name = "Level",
align = "center"),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
Row {.tabset}
-----------------------------------------------------------------------
### RTL Summary
```{r, include=TRUE}
create_react_time(simsimp, dys_btwn_onset_rtp_3)
```
### RTL Sex
```{r, include=TRUE}
create_react_time2(simsimp, gender, dys_btwn_onset_rtp_3)
```
### RTL Age
```{r, include=TRUE}
create_react_time2(simsimp, age, dys_btwn_onset_rtp_3)
```
### RTL League
```{r, include=TRUE}
create_react_time2(simsimp, league, dys_btwn_onset_rtp_3)
```
### RTL School
```{r, include=TRUE}
create_react_time2(simsimp, school, dys_btwn_onset_rtp_3)
```
### RTL Sport
```{r, include=TRUE}
create_react_time2(simsimp, sport, dys_btwn_onset_rtp_3)
```
```{r, include=FALSE}
rtl_smry_plot <- ggplot(simsimp, aes(dys_btwn_onset_rtp_3)) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 10) +
labs(x = "Days to Complete RTL",
y = "Number of Injuries")
rtp_smry_plot <- ggplot(simsimp, aes(dys_btwn_onset_rtp_7)) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 10) +
labs(x = "Days to Complete RTP",
y = "Number of Injuries")
rtl_smry_plot2 <- function(df, x, y) {
p <- ggplot(df, aes({{x}})) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 10)
p + facet_wrap(vars({{y}})) +
labs(x = "Days to Complete RTL",
y = "Number of Injuries")
}
rtp_smry_plot2 <- function(df, x, y) {
p <- ggplot(df, aes({{x}})) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 10)
p + facet_wrap(vars({{y}})) +
labs(x = "Days to Complete RTP",
y = "Number of Injuries")
}
rtl_smry_plot2(simsimp, dys_btwn_onset_rtp_3, gender)
```
### RTL Total
```{r, include=TRUE}
ggplotly(rtl_smry_plot)
```
### Sex
```{r, include=TRUE}
ggplotly(rtl_smry_plot2(simsimp, dys_btwn_onset_rtp_3, gender))
```
### Age
```{r, include=TRUE}
ggplotly(rtl_smry_plot2(simsimp, dys_btwn_onset_rtp_3, age))
```
### League
```{r, include=TRUE}
ggplotly(rtl_smry_plot2(simsimp, dys_btwn_onset_rtp_3, league))
```
### RTL Group Summary
```{r, include=TRUE}
mod_data %>%
group_by(gender, age_group, rtl_group) %>%
summarize(total = n()) %>%
reactable(
columns = list(
gender = colDef(name = "Sex",
align = "center"),
age_group = colDef(name = "Age Group",
align = "center"),
rtl_group = colDef(name = "RTL Group",
align = "center",
format = colFormat(suffix = " days")),
total = colDef(name = "Total",
align = "center",
format = colFormat(suffix = " injuries"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
Row {.tabset}
-----------------------------------------------------------------------
### RTP Summary
```{r, include=TRUE}
create_react_time(simsimp, dys_btwn_onset_rtp_7)
```
### RTP Sex
```{r, include=TRUE}
create_react_time2(simsimp, gender, dys_btwn_onset_rtp_7)
```
### RTP Age
```{r, include=TRUE}
create_react_time2(simsimp, age, dys_btwn_onset_rtp_7)
```
### RTP League
```{r, include=TRUE}
create_react_time2(simsimp, league, dys_btwn_onset_rtp_7)
```
### RTP School
```{r, include=TRUE}
create_react_time2(simsimp, school, dys_btwn_onset_rtp_7)
```
### RTP Sport
```{r, include=TRUE}
create_react_time2(simsimp, sport, dys_btwn_onset_rtp_7)
```
### RTL Total
```{r, include=TRUE}
ggplotly(rtp_smry_plot)
```
### Sex
```{r, include=TRUE}
ggplotly(rtp_smry_plot2(simsimp, dys_btwn_onset_rtp_7, gender))
```
### Age
```{r, include=TRUE}
ggplotly(rtp_smry_plot2(simsimp, dys_btwn_onset_rtp_7, age))
```
### League
```{r, include=TRUE}
ggplotly(rtp_smry_plot2(simsimp, dys_btwn_onset_rtp_7, league))
```
# Test One PCSS Summary Scores
Row {.tabset}
-----------------------------------------------------------------------
```{r, include=FALSE}
score_hist <- function(df, x) {
ggplot(df, aes({{x}})) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 25) +
labs(x = "Symptom Severity",
y = "Number of Injuries")
}
gender_hist <- function(df, x) {
ggplot(df, aes({{x}})) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 25) +
facet_wrap(~gender) +
labs(x = "Symptom Severity",
y = "Number of Injuries")
}
age_hist <- function(df, x) {
ggplot(df, aes({{x}})) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 25) +
facet_wrap(~age) +
labs(x = "Symptom Severity",
y = "Number of Injuries")
}
names(simsimp)
```
### Total Symptom Score
```{r, include=TRUE}
ggplotly(score_hist(simsimp, total_symptom_score_post_injury_1))
```
### Total Symptom Score Summary
```{r, include=TRUE}
create_react(simsimp, total_symptom_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, total_symptom_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, total_symptom_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, total_symptom_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, total_symptom_score_post_injury_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Headache-Migraine
```{r, include=TRUE}
ggplotly(score_hist(simsimp, headache_migraine_cluster_score_post_injury_1))
```
### Headache-Migraine Summary
```{r, include=TRUE}
create_react(simsimp, headache_migraine_cluster_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, headache_migraine_cluster_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, headache_migraine_cluster_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, headache_migraine_cluster_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, headache_migraine_cluster_score_post_injury_1)
```
### Headache-Migraine Normalized
```{r, include=TRUE}
ggplotly(score_hist(simsimp, headache_migraine_test_1))
```
### Headache-Migraine Summary Normalized
```{r, include=TRUE}
create_react(simsimp, headache_migraine_test_1)
```
### Sex Normalized
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, headache_migraine_test_1))
```
### Sex Summary Normalized
```{r, include=TRUE}
create_react_gender(simsimp, headache_migraine_test_1)
```
### Age Normalized
```{r, include=TRUE}
ggplotly(age_hist(simsimp, headache_migraine_test_1))
```
### Age Summary Normalized
```{r, include=TRUE}
create_react_age(simsimp, headache_migraine_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Cognitive
```{r, include=TRUE}
ggplotly(score_hist(simsimp, cognitive_cluster_score_post_injury_1))
```
### Cognitive Summary
```{r, include=TRUE}
create_react(simsimp, cognitive_cluster_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, cognitive_cluster_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, cognitive_cluster_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, cognitive_cluster_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, cognitive_cluster_score_post_injury_1)
```
### Cognitive Normalized
```{r, include=TRUE}
ggplotly(score_hist(simsimp, cognitive_test_1))
```
### Cognitive Summary Normalized
```{r, include=TRUE}
create_react(simsimp, cognitive_test_1)
```
### Sex Normalized
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, cognitive_test_1))
```
### Sex Summary Normalized
```{r, include=TRUE}
create_react_gender(simsimp, cognitive_test_1)
```
### Age Normalized
```{r, include=TRUE}
ggplotly(age_hist(simsimp, cognitive_test_1))
```
### Age Summary Normalized
```{r, include=TRUE}
create_react_age(simsimp, cognitive_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Anxiety-Mood
```{r, include=TRUE}
ggplotly(score_hist(simsimp, anxiety_mood_cluster_score_post_injury_1))
```
### Anxiety-Mood Summary
```{r, include=TRUE}
create_react(simsimp, anxiety_mood_cluster_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, anxiety_mood_cluster_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, anxiety_mood_cluster_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, anxiety_mood_cluster_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, anxiety_mood_cluster_score_post_injury_1)
```
### Anxiety-Mood Normalized
```{r, include=TRUE}
ggplotly(score_hist(simsimp, anxiety_mood_test_1))
```
### Anxiety-Mood Summary Normalized
```{r, include=TRUE}
create_react(simsimp, anxiety_mood_test_1)
```
### Sex Normalized
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, anxiety_mood_test_1))
```
### Sex Summary Normalized
```{r, include=TRUE}
create_react_gender(simsimp, anxiety_mood_test_1)
```
### Age Normalized
```{r, include=TRUE}
ggplotly(age_hist(simsimp, anxiety_mood_test_1))
```
### Age Summary Normalized
```{r, include=TRUE}
create_react_age(simsimp, anxiety_mood_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Ocular-Motor
```{r, include=TRUE}
ggplotly(score_hist(simsimp, ocular_motor_cluster_score_post_injury_1))
```
### Ocular-Motor Summary
```{r, include=TRUE}
create_react(simsimp, ocular_motor_cluster_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, ocular_motor_cluster_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, ocular_motor_cluster_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, ocular_motor_cluster_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, ocular_motor_cluster_score_post_injury_1)
```
### Ocular-Motor Normalized
```{r, include=TRUE}
ggplotly(score_hist(simsimp, ocular_motor_test_1))
```
### Ocular-Motor Summary Normalized
```{r, include=TRUE}
create_react(simsimp, ocular_motor_test_1)
```
### Sex Normalized
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, ocular_motor_test_1))
```
### Sex Summary Normalized
```{r, include=TRUE}
create_react_gender(simsimp, ocular_motor_test_1)
```
### Age Normalized
```{r, include=TRUE}
ggplotly(age_hist(simsimp, ocular_motor_test_1))
```
### Age Summary Normalized
```{r, include=TRUE}
create_react_age(simsimp, ocular_motor_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Vestibular
```{r, include=TRUE}
ggplotly(score_hist(simsimp, vestibular_cluster_score_post_injury_1))
```
### Vestibular Summary
```{r, include=TRUE}
create_react(simsimp, vestibular_cluster_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, vestibular_cluster_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, vestibular_cluster_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, vestibular_cluster_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, vestibular_cluster_score_post_injury_1)
```
### Vestibular Normalized
```{r, include=TRUE}
ggplotly(score_hist(simsimp, vestibular_test_1))
```
### Vestibular Summary Normalized
```{r, include=TRUE}
create_react(simsimp, vestibular_test_1)
```
### Sex Normalized
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, vestibular_test_1))
```
### Sex Summary Normalized
```{r, include=TRUE}
create_react_gender(simsimp, vestibular_test_1)
```
### Age Normalized
```{r, include=TRUE}
ggplotly(age_hist(simsimp, vestibular_test_1))
```
### Age Summary Normalized
```{r, include=TRUE}
create_react_age(simsimp, vestibular_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Sleep
```{r, include=TRUE}
ggplotly(score_hist(simsimp, sleep_cluster_score_post_injury_1))
```
### Sleep Summary
```{r, include=TRUE}
create_react(simsimp, sleep_cluster_score_post_injury_1)
```
### Sex
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, sleep_cluster_score_post_injury_1))
```
### Sex Summary
```{r, include=TRUE}
create_react_gender(simsimp, sleep_cluster_score_post_injury_1)
```
### Age
```{r, include=TRUE}
ggplotly(age_hist(simsimp, sleep_cluster_score_post_injury_1))
```
### Age Summary
```{r, include=TRUE}
create_react_age(simsimp, sleep_cluster_score_post_injury_1)
```
### Sleep Normalized
```{r, include=TRUE}
ggplotly(score_hist(simsimp, sleep_test_1))
```
### Sleep Summary Normalized
```{r, include=TRUE}
create_react(simsimp, sleep_test_1)
```
### Sex Normalized
```{r, include=TRUE}
ggplotly(gender_hist(simsimp, sleep_test_1))
```
### Sex Summary Normalized
```{r, include=TRUE}
create_react_gender(simsimp, sleep_test_1)
```
### Age Normalized
```{r, include=TRUE}
ggplotly(age_hist(simsimp, sleep_test_1))
```
### Age Summary Normalized
```{r, include=TRUE}
create_react_age(simsimp, sleep_test_1)
```
# Models
Sidebar {.sidebar}
------------
The interaction models are the strongest relative models, approaching adjusted *R-*squared values of 0.05; however, the amount of variance accounted for within these models is still very small. Considering the plots at the top of the page, it is evident there is not a linear relationship between the hypothesized predictor variables (age, sex, test 1 PCSS score) and RTL duration time.
Row {.tabset}
-----------------------------------------------------------------------
### Plot 1
```{r, include=FALSE}
p1 <- ggplot(simsimp, aes(dys_btwn_onset_rtp_3, total_symptom_score_post_injury_1)) +
geom_point(color = "gray70") +
geom_smooth() +
geom_smooth(method = "lm",
color = "magenta") +
labs(x = "Days to Complete RTL",
y = "Test 1 Total Symptom Severity Score")
```
```{r, include=TRUE}
ggplotly(p1)
```
### Plot 2
```{r, include=FALSE}
p2 <- ggplot(simsimp, aes(dys_btwn_onset_rtp_3, total_symptom_score_post_injury_1)) +
geom_point(color = "gray70") +
geom_smooth(aes(color = gender),
method = "lm") +
labs(x = "Days to Complete RTL",
y = "Test 1 Total Symptom Severity Score")
```
```{r, include=TRUE}
ggplotly(p2)
```
### Plot 3
```{r, include=FALSE}
p3 <- ggplot(simsimp, aes(dys_btwn_onset_rtp_3, total_symptom_score_post_injury_1)) +
geom_point(color = "gray70") +
geom_smooth(aes(color = age),
method = "lm") +
labs(x = "Days to Complete RTL",
y = "Test 1 Total Symptom Severity Score")
```
```{r, include=TRUE}
ggplotly(p3)
```
Row {.tabset}
-----------------------------------------------------------------------
### Test 1 Model
```{r, include=FALSE}
# modeling age:total symptom score model
names(simsimp)
sex_test_1_mod <- lm(dys_btwn_onset_rtp_3 ~ gender*total_symptom_score_post_injury_1,
data = simsimp)
summary(sex_test_1_mod)
confint(sex_test_1_mod)
sex_age_mod <- lm(dys_btwn_onset_rtp_3 ~ gender*age, data = simsimp)
summary(sex_age_mod)
sex_age_test_1_mod <- lm(dys_btwn_onset_rtp_3 ~
gender*age*total_symptom_score_post_injury_1,
data = simsimp)
summary(sex_age_test_1_mod)
# LM examples
test_1_sev_mod <- lm(dys_btwn_onset_rtp_3 ~ total_symptom_score_post_injury_1,
data = simsimp)
summary(test_1_sev_mod)
fitted(test_1_sev_mod)
```
```{r, include=TRUE}
summary(test_1_sev_mod)
```
### Sex Model
```{r, include=FALSE}
sex_mod <- lm(dys_btwn_onset_rtp_3 ~ gender, data = simsimp)
```
```{r, include=TRUE}
summary(sex_mod)
```
### Age Model
```{r, include=FALSE}
age_mod <- lm(dys_btwn_onset_rtp_3 ~ age, data = simsimp)
```
```{r, include=TRUE}
summary(age_mod)
```
Row {.tabset}
-----------------------------------------------------------------------
### Additive Model: Sex and Test 1 Severity
```{r, include=FALSE}
sex_test_add_mod <- lm(dys_btwn_onset_rtp_3 ~ gender + total_symptom_score_post_injury_1,
data = simsimp)
```
```{r, include=TRUE}
summary(sex_test_add_mod)
```
### Additive Model: Age and Test 1 Severity
```{r, include=FALSE}
age_test_add_mod <- lm(dys_btwn_onset_rtp_3 ~ age + total_symptom_score_post_injury_1,
data = simsimp)
```
```{r, include=TRUE}
summary(age_test_add_mod)
```
### Additive Model: Sex, Age and Test 1 Severity
```{r, include=FALSE}
sex_age_test_add_mod <- lm(dys_btwn_onset_rtp_3 ~ gender + age +
total_symptom_score_post_injury_1, data = simsimp)
```
```{r, include=TRUE}
summary(sex_age_test_add_mod)
```
Row {.tabset}
-----------------------------------------------------------------------
### Interaction Model: Sex, Age and Test 1 Severity
Adjusted *R-*squared of 0.058
```{r, include=FALSE}
sex_age_test_int_mod <- lm(dys_btwn_onset_rtp_3 ~ gender*age*total_symptom_score_post_injury_1, data = simsimp)
```
```{r, include=TRUE}
summary(sex_age_test_int_mod)
```
### Interaction Model: Sex:Age plus Test 1 Severity
Adjusted *R-*squared of 0.047
```{r, include=FALSE}
sex_age_int_plus_test <- lm(dys_btwn_onset_rtp_3 ~ gender*age + total_symptom_score_post_injury_1, data = simsimp)
```
```{r, include=TRUE}
summary(sex_age_int_plus_test)
```
```{r, include=FALSE}
#plot(sex_age_int_plus_test)
```
# Test 1 Severity ANOVA
Sidebar {.sidebar}
------------
The ANOVA generated results similar to the findings from the first iteration of the paper. Females report higher symptom severity than males across most clusters. The headache-migraine, sleep, and cognitive clusters are rated with the highest symptom severity.
```{r, include=FALSE}
simsimp_long <- simsimp %>%
pivot_longer(
cols = c(60:65),
names_to = "symptom_cluster",
values_to = "score_test_1",
names_pattern = "(.*)_test_1"
)
```
```{r, include=FALSE}
cluster_bxp <- function(df, x, y) {
ggplot(df, aes({{x}}, {{y}}, fill = gender)) +
geom_boxplot() +
scale_fill_OkabeIto() +
coord_flip() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "bottom")
}
cluster_bxp(simsimp_long, symptom_cluster, score_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Test 1 Boxplot
```{r, include=TRUE, fig.width=10}
cluster_bxp(simsimp_long, symptom_cluster, score_test_1)
```
Row {.tabset}
-----------------------------------------------------------------------
### Two-Way ANOVA: Sex and Test 1 Severity
```{r, include=FALSE}
anova_react <- function(df) {
df %>%
reactable(
defaultColDef = colDef(align = "center",
format = colFormat(digits = 2, separators = TRUE)),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
pwc_react <- function(df) {
df %>%
reactable(
defaultColDef = colDef(align = "center",
format = colFormat(digits = 3, separators = TRUE)),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
```
```{r, include=FALSE}
aov_res <- aov(score_test_1 ~ symptom_cluster * gender,
data = simsimp_long)
summary(aov_res)
aov_res
Anova(aov_res, type = "III")
TukeyHSD(aov_res)
aov_res2 <- Anova(aov_res, type = "III")
tukey_res <- TukeyHSD(aov_res)
```
```{r, include=TRUE}
anova_react(aov_res2)
```
### PWC
```{r, include=TRUE}
tidy(tukey_res) %>%
pwc_react()
```
