index.qmd

---
title: "Dashboarding with Quarto"
author: "Joshua J. Cook, Kirk Paul Lafler"
format: 
  dashboard:
    theme: united
    scrolling: true
    logo: images/logo.png
    nav-buttons:
      - website
      - icon: person-raised-hand
        href: https://joshua-j-cook-portfolios.netlify.app/ 
      - li
      - icon: linkedin
        href: https://www.linkedin.com/in/KirkPaulLafler/
      - github
embed-resources: true
---

```{r}
#| include: false

packages <- c("tidyverse", "gt", "quarto")

if (any(!sapply(packages, requireNamespace, quietly = TRUE))) {
  install.packages(packages[!sapply(packages, requireNamespace, quietly = TRUE)])
}

library(tidyverse)
library(RColorBrewer)
library(plotly)
library(gt)
library(quarto)

clinical_trials_fl <- readRDS("query_results_clean_final.rds")
str(clinical_trials_fl)

clinical_trials_fl %>%
  select(nct_id, overall_status) 

clinical_trials_fl_completed <- clinical_trials_fl %>%
  filter(overall_status == "Completed") %>%
  summarise(total_unique_completed = n_distinct(nct_id)) %>%
  pull(1)

clinical_trials_fl_recruiting <- clinical_trials_fl %>%
  filter(overall_status == "Recruiting") %>%
  summarise(total_unique_completed = n_distinct(nct_id)) %>%
  pull(1)

clinical_trials_fl_terminated <- clinical_trials_fl %>%
  filter(overall_status == "Terminated") %>%
  summarise(total_unique_completed = n_distinct(nct_id)) %>%
  pull(1) 

unique_study_types <- unique(clinical_trials_fl$study_type)
```

## Row 1 - Value Boxes

```{r}
#| content: valuebox
#| title: "Total Trials Completed"
list(
  icon = "person-check-fill",
  color = "success",
  value = clinical_trials_fl_completed
)
```

```{r}
#| content: valuebox
#| title: "Total Trials Recruiting"
list(
  icon = "person-fill-add",
  color = "warning",
  value = clinical_trials_fl_recruiting
)
```

```{r}
#| content: valuebox
#| title: "Total Trials Terminated"
list(
  icon = "person-fill-x",
  color = "danger",
  value = clinical_trials_fl_terminated
)
```

## Row 2 - Big Line Plot

::: {.card title="Annual Clinical Trial Completion in Florida"}
```{r}
#| title: "Annual Clinical Trial Completion in Florida"
#| padding: 10px

clinical_trials_fl_yearly_summary <- clinical_trials_fl %>%
  mutate(year = year(completion_date)) %>%
  group_by(year) %>%
  summarise(count = n_distinct(nct_id)) %>%
  filter(year >= 2014 & year <= 2023)

# Extract the 7th color from the "Oranges" palette
oranges_palette <- brewer.pal(8, "Oranges")
dark_orange <- oranges_palette[7]

# Line chart
gg <- ggplot(clinical_trials_fl_yearly_summary, aes(x = year, y = count)) +
  geom_line(color = dark_orange, size = 1) +  # Specify the color and size of the line
  geom_point(color = dark_orange, size = 2) +  # Specify the color and size of the points
  scale_x_continuous(breaks = 2014:2023) +  # Ensure all years are shown
  labs(x = "Year",
       y = "Successfully Completed Clinical Trials") +
  theme_minimal()  # Use a minimal theme for aesthetics

# Convert the ggplot object to a plotly object
ggplotly(gg)
```

These figures are only for the last 10 years. It is also important to consider the dramatic regulatory changes that occured in 2004/2005, and then again in 2015/2017, which potentially led to major shifts in registration and results submission compliance for ClinicalTrials.gov. <https://aact.ctti-clinicaltrials.org/points_to_consider/>
:::

## Row 3 - Two small bar and pie plots

```{r}
#| title: "Phases of Clinical Trials Completed in Florida"
#| padding: 10px

clinical_trials_fl_phase_summary <- clinical_trials_fl %>%
  distinct(nct_id, .keep_all = TRUE) %>%  # Keep only unique NCT_IDs
  count(phase)  # Count the number of NCT_IDs for each phase

clinical_trials_fl_phase_summary <- clinical_trials_fl_phase_summary %>%
  mutate(across(where(is.character), ~na_if(., "Not Applicable")))

# Bar chart; ggplotly() didn't work because of scales, reverted to full plotly

plot_ly(data = clinical_trials_fl_phase_summary, 
        x = ~phase, 
        y = ~n, 
        type = 'bar', 
        color = ~phase, 
        colors = "Oranges") %>%
  layout(xaxis = list(title = "Phase"),
         yaxis = list(title = "Successfully Completed Clinical Trials"))
```

```{r}
#| title: "Top 10 Conditions of Clinical Trials Completed in Florida"
#| padding: 10px

clinical_trials_fl_condition_summary <- clinical_trials_fl %>%
  group_by(condition_name) %>%
  summarise(n = n_distinct(nct_id)) %>%
  ungroup() %>%
  arrange(desc(n)) %>%
  top_n(10, n)  # Adjust this to include more or fewer conditions

# 2. Create a pie chart; same as above
plot_ly(clinical_trials_fl_condition_summary, 
                 labels = ~condition_name, 
                 values = ~n, 
                 type = 'pie', 
                 textinfo = 'label+percent',
                 marker = list(colors = RColorBrewer::brewer.pal(n = 8, name = "Oranges")),
                 hole = 0.0) %>%
  layout(title = 'Condition Distribution in Clinical Trials',
         showlegend = TRUE,
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
```