---
title: "BPS SY23-24 Hiring Data"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    theme: cosmo
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)# interactive dashboard 
library(readxl) # import excel data set 
library(leaflet) #create interactive maps
library(tidyverse) # load core packages for data visualisation (dplyr:for data manipulation, tidyr:for data tidying, readr:for data import, purrr:for functional programming, tibble:for tibbles, a modern re-imagining of data frames, stringr:for strings, forcats:for factors)
library(DT) #Display data tables
library(crosstalk) # extends htmlwidgets
library(sf) #load shapefiles 
library(RColorBrewer) # color palaette
library(shiny)
library(plyr)
library(plotly)
library(lubridate)
library(ggplot2)
library(ggmap)
library(sp)
library(mapview)
library(emojifont)
library(geosphere)
library(moonBook) # For Donuts Chart
library(webr) # For Donuts Chart
library(shiny)
library(d3scatter)
library(summarywidget)
```

```{r data, include=FALSE}

# Load candidate information and teaching job postings data
candidates <- read.csv("Candidates.csv")
applications <-read.csv("Applications.csv")
vacancies <- read.csv("Vacancies.csv")
schools <- read.csv("School_Data.csv")

# Convert  the Application Date column from a character data type to a Date data type
applications <- applications %>%mutate(Application.Date = as.Date(Application.Date, format = "%m/%d/%Y"))

#Subset data to only keep needed columns
vacancies_sub <- subset(vacancies, select = c("PositionID", "Job_Title", "DeptID","Status"))
applications_sub <- subset(applications, select = c("ApplicantProfile.ID","Application.Date","Application.Status","Posting.ID"))

# Rename column to match school data
vacancies_sub <- vacancies_sub %>% rename(`Dept.ID` = DeptID) 
vacancies_sub <- vacancies_sub %>% rename(`Posting.ID` = PositionID)

# Rename Ethnicity for better visualization
candidates <-candidates %>%
  mutate(Ethnicity = ifelse(Ethnicity == "African-American/Black (Non Hispanic)", "African-American/Black", Ethnicity)) %>%
  mutate(Ethnicity = ifelse(Ethnicity == "White (Non Hispanic)", "White", Ethnicity))

# Combine school demographic data with teacher vacancies data
schools_vacancies <- merge(schools, vacancies_sub, by = "Dept.ID")

# Applications by School
school_apps <- merge(vacancies_sub, applications_sub, by = "Posting.ID") 

schools_apps <- left_join(school_apps, schools, by = "Dept.ID")

# Candidates by School
school_canidates <- merge(candidates, schools_apps, by = "ApplicantProfile.ID")

# Applications by School and Race
school_applications <- school_canidates

# Create Teacher of Color Category
school_applications <- school_applications %>%
  mutate(Category = case_when(
  Ethnicity %in% c("African-American/Black", "American Indian", "Asian", "Hispanic") ~ "Teacher of Color",
  Ethnicity == "White" ~ "White Teacher",
    TRUE ~ "Other"))

school_canidates <- school_canidates %>%
  mutate(Category = case_when(
  Ethnicity %in% c("African-American/Black", "American Indian", "Asian", "Hispanic") ~ "Teacher of Color",
  Ethnicity == "White" ~ "White Teacher",
    TRUE ~ "Other"))

# Hires by School
school_hired <- school_canidates %>%
  filter(Application.Status =="Hired")
school_hired
# Active Vacancies by School
school_vac <- schools_vacancies %>%
  filter(Status =="Active")

# Active Applications
school_apps_active <- school_applications %>%
filter(Application.Status!= "Position Filled/Closed") %>%
filter(Application.Status!= "Hired") %>%
filter(Application.Status!= "Candidate Hired for Another Position")

# Calculate total number of candidates in pool
Total_cands <- school_canidates %>%
  group_by(ApplicantProfile.ID) %>%
  summarise(
    num_apps_per_cand = n(),                         # Calculate the number of applications
    percentage_of_apps_per_cand = num_apps_per_cand / nrow(school_canidates) * 100  # Calculate the percentage
  )
# Vacancies by School

schools_vac  <- school_vac %>%
group_by(Dept.Name) %>%
  summarise(
    num_vac = n(),                         # Calculate the number of teachers
    percentage_of_apps = num_vac / nrow(school_vac) * 100  # Calculate the percentage of teachers of color
  ) %>%
    arrange(desc(num_vac))

school_vac <- left_join(school_vac, schools_vac, by ="Dept.Name")

# Candidates by School
school_canidates <- merge(school_canidates, Total_cands, by = "ApplicantProfile.ID")

# Calculate total number of candidates in pool
unique_cands <- school_canidates %>%
  group_by(ApplicantProfile.ID) %>%
  summarise(
    num_apps_per_cand = n(),                         # Calculate the number of applications
    percentage_of_apps_per_cand = num_apps_per_cand / nrow(school_canidates) * 100  # Calculate the percentage
  )

# Candidates by School
school_canidates <- merge(school_canidates, unique_cands, by = "ApplicantProfile.ID")

# Create unique points to plot candidates
school_canidates$Lat <- jitter(school_canidates$Lat, factor = 0.00001)
school_canidates$Long <- jitter(school_canidates$Long, factor = 0.00001)

school_hired$Lat <- jitter(school_hired$Lat, factor = 0.00001)
school_hired$Long <- jitter(school_hired$Long, factor = 0.00001)

school_cands_hired_sum <-school_hired %>%
  group_by(Dept.ID) %>%
  summarise(
    num_teachers_hired = n(),                         # Calculate the number of teachers
    percentage_of_teachers_hired = num_teachers_hired / nrow(school_hired) * 100  # Calculate the percentage of teachers of color
  ) %>%
  arrange(desc(num_teachers_hired))

school_hired <- left_join(school_hired,school_cands_hired_sum, by = "Dept.ID")

# Create shared Data Frames
Applications_sd <-SharedData$new(school_applications, group = "Applications")
Canidates_sd <-SharedData$new(school_canidates, group = "Canidates")
Hires_sd <-SharedData$new(school_hired, group = "Hires")
Vacancies_sd <- SharedData$new(school_vac, group = "Vacancies")

#Total
Total_Apps <- nrow(Applications_sd$data())
Total_Cands <- nrow(Canidates_sd$data())
Total_Hired <-nrow(Hires_sd$data())
Total_Vac <-nrow(Vacancies_sd$data())
Total_Active_Apps <- nrow(school_apps_active)

TOC_apps <- Applications_sd$data() %>%
  filter(Category == "Teacher of Color")
White_apps <- Applications_sd$data() %>%
  filter(Category == "White Teacher")
TOC_cands <- Canidates_sd$data() %>%
  filter(Category == "Teacher of Color")
White_cands <- Canidates_sd$data() %>%
  filter(Category == "White Teacher")
TOC_hires <- Hires_sd$data() %>%
  filter(Category == "Teacher of Color")
White_hires <- Hires_sd$data() %>%
  filter(Category == "White Teacher")

Total_TOC_apps <- nrow(TOC_apps)
Total_TOC_cands <- nrow(TOC_cands)
Total_TOC_hires <- nrow(TOC_hires)

unique_TOC_values <- unique(TOC_apps$ApplicantProfile.ID)
unique_TOC_values_num <- length(unique_TOC_values)

unique_Total_Cands <-unique(school_canidates$ApplicantProfile.ID)
unique_Total_Cands_num <- length(unique_Total_Cands)

## Schools Doing the Best
# Calculate the percentages
school_hired$Number_Teachers_of_Color <- (school_hired$Black.Teachers + school_hired$Asian.Teachers + school_hired$Hispanic.Teachers)
school_hired$Percent_Teachers_of_Color <- (school_hired$Black.Teachers + school_hired$Asian.Teachers + school_hired$Hispanic.Teachers) / school_hired$Total.Teachers * 100
school_hired$Percent_White <- school_hired$White.Teachers / school_hired$Total.Teachers * 100

schools$Number_Teachers_of_Color <- (schools$Black.Teachers + schools$Asian.Teachers + schools$Hispanic.Teachers)
schools$Percent_Teachers_of_Color <- (schools$Black.Teachers + schools$Asian.Teachers + schools$Hispanic.Teachers) / schools$Total.Teachers * 100
schools$Percent_White <- schools$White.Teachers / schools$Total.Teachers * 100

#Calculate school hiring data
school_coc_hired <- school_hired %>%
  filter(Ethnicity != "White") %>%
  filter(Ethnicity!= "Decline to Identify")

school_white_hired <- school_hired %>%
  filter(Ethnicity == "White")

school_toc_hired_sum <-school_coc_hired %>%
  group_by(Dept.ID) %>%
  summarise(
    num_toc_teachers = n(),                         # Calculate the number of teachers
    percentage_toc_hired = num_toc_teachers / nrow(school_coc_hired) * 100  # Calculate the percentage of teachers of color
  ) %>%
  arrange(desc(num_toc_teachers))

school_cands_hired_sum <-school_hired %>%
  group_by(Dept.ID) %>%
  summarise(
    num_teachers_hired = n(),                         # Calculate the number of teachers
    percentage_of_teachers_hired = num_teachers_hired / nrow(school_hired) * 100  # Calculate the percentage of teachers of color
  ) %>%
  arrange(desc(num_teachers_hired))

school_white_hired_sum <-school_white_hired %>%
  group_by(Dept.ID) %>%
  summarise(
    num_white_teachers_hired = n(),                         # Calculate the number of teachers
    percentage_of_white_teachers_hired = num_white_teachers_hired / nrow(school_white_hired) * 100  # Calculate the percentage of teachers of color
  ) %>%
  arrange(desc(num_white_teachers_hired))

#join school hiring data to school demographic data

best_schools <- left_join(schools,school_cands_hired_sum, by = "Dept.ID")
best_schools <- left_join(best_schools,school_toc_hired_sum, by = "Dept.ID")
best_schools <- left_join(best_schools,school_white_hired_sum, by = "Dept.ID")

# Calculate the number of teachers of color added for each school
best_schools$Added_Diversity <- (best_schools$Number_Teachers_of_Color + best_schools$num_toc_teachers )
# Calculate the percent of teachers of color added for each school
best_schools$Added_Percent_toc <- best_schools$Added_Diversity/best_schools$Total.Teachers * 100
# Calculate the change in percent for hired teachers of color by school 
best_schools$toc_percent_change <- best_schools$Added_Percent_toc - best_schools$Percent_Teachers_of_Color

best_schools%>%
  arrange(desc(toc_percent_change))

best_schools_top10 <- best_schools %>% 
  arrange(desc(toc_percent_change)) %>%
  head(10)

best_schools_top10_num <- sum(best_schools_top10$num_toc_teachers)
rec_schools <- left_join(best_schools,schools_vac, by ="Dept.Name")

rec_schools%>%
  arrange(desc(num_vac)) %>%
  head(10)

rec_schools$potiental_toc <- (rec_schools$num_vac + rec_schools$Added_Diversity)

rec_schools%>%
  arrange(desc(num_vac))

rec_schools$potiental_added_percent_toc <- rec_schools$potiental_toc/best_schools$Total.Teachers * 100

rec_schools%>%
  arrange(desc(num_vac))

rec_schools$potiental_toc_percent_change <- rec_schools$potiental_added_percent_toc - rec_schools$Added_Percent_toc

rec_schoolstop10 <-rec_schools %>%
  arrange(desc(potiental_toc_percent_change)) %>%
  head(10)
rec_schoolstop10_num <- sum (rec_schoolstop10$potiental_toc)

Needs_Improvement <- rec_schools %>%
  filter(percentage_toc_hired < 3.0) %>%
  head(10)

Needs_Impro_num <- sum(Needs_Improvement$num_toc_teachers)

# Create Function for Sunburst
as.sunburstDF <- function(DF, value_column = NULL, add_root = FALSE){
  require(data.table)
  
  colNamesDF <- names(DF)
  
  if(is.data.table(DF)){
    DT <- copy(DF)
  } else {
    DT <- data.table(DF, stringsAsFactors = FALSE)
  }
  
  if(add_root){
    DT[, root := "Total"]  
  }
  
  colNamesDT <- names(DT)
  hierarchy_columns <- setdiff(colNamesDT, value_column)
  DT[, (hierarchy_columns) := lapply(.SD, as.factor), .SDcols = hierarchy_columns]
  
  if(is.null(value_column) && add_root){
    setcolorder(DT, c("root", colNamesDF))
  } else if(!is.null(value_column) && !add_root) {
    setnames(DT, value_column, "values", skip_absent=TRUE)
    setcolorder(DT, c(setdiff(colNamesDF, value_column), "values"))
  } else if(!is.null(value_column) && add_root) {
    setnames(DT, value_column, "values", skip_absent=TRUE)
    setcolorder(DT, c("root", setdiff(colNamesDF, value_column), "values"))
  }
  
  hierarchyList <- list()
  
  for(i in seq_along(hierarchy_columns)){
    current_columns <- colNamesDT[1:i]
    if(is.null(value_column)){
      currentDT <- unique(DT[, ..current_columns][, values := .N, by = current_columns], by = current_columns)
    } else {
      currentDT <- DT[, lapply(.SD, sum, na.rm = TRUE), by=current_columns, .SDcols = "values"]
    }
    setnames(currentDT, length(current_columns), "labels")
    hierarchyList[[i]] <- currentDT
  }
  
  hierarchyDT <- rbindlist(hierarchyList, use.names = TRUE, fill = TRUE)
  
  parent_columns <- setdiff(names(hierarchyDT), c("labels", "values", value_column))
  hierarchyDT[, parents := apply(.SD, 1, function(x){fifelse(all(is.na(x)), yes = NA_character_, no = paste(x[!is.na(x)], sep = ":", collapse = " - "))}), .SDcols = parent_columns]
  hierarchyDT[, ids := apply(.SD, 1, function(x){paste(x[!is.na(x)], collapse = " - ")}), .SDcols = c("parents", "labels")]
  hierarchyDT[, c(parent_columns) := NULL]
  return(hierarchyDT)
}

school_vac_sun <- school_vac %>%
  select(Autonomy.Status, Dept.Name, num_vac)

sunburstDF_vac <- as.sunburstDF(school_vac_sun, value_column = "num_vac", add_root = TRUE)

school_app_sun <- school_canidates %>%
  select(Applicant.Type, Ethnicity, num_apps_per_cand.x)

sunburstDF_app <- as.sunburstDF(school_app_sun, value_column = "num_apps_per_cand.x", add_root = TRUE)

school_coc_hired_sun <- school_coc_hired %>%
  select(Applicant.Type, Ethnicity, num_teachers_hired)

sunburstDF_school_coc_hired <- as.sunburstDF(school_coc_hired_sun, value_column = "num_teachers_hired", add_root = TRUE)

school_TOC_cands_sun <- TOC_cands %>%
  select(Applicant.Type, Ethnicity, num_apps_per_cand.x)

sunburstDF_TOC_cands_sun <- as.sunburstDF(school_TOC_cands_sun, value_column = "num_apps_per_cand.x", add_root = TRUE)

best_schools_top10_sun <- best_schools_top10 %>% 
  select(Autonomy.Status, Dept.Name, num_toc_teachers)

sunburstDF_best_schools_top10_sun <- as.sunburstDF(best_schools_top10_sun, value_column = "num_toc_teachers", add_root = TRUE)

Needs_Improvement_schools_top10_sun <- Needs_Improvement %>% 
  select(Autonomy.Status, Dept.Name, num_toc_teachers)

sunburstDF_Needs_Improvement_schools_top10_sun <- as.sunburstDF(Needs_Improvement_schools_top10_sun, value_column = "num_toc_teachers", add_root = TRUE)

rec_schools_top10_sun <- rec_schoolstop10 %>% 
  select(Autonomy.Status, Dept.Name, potiental_toc)

sunburstDF_rec_schools_top10_sun <- as.sunburstDF(rec_schools_top10_sun, value_column = "potiental_toc", add_root = TRUE)
```

Background
=====================================

Column {data-width=650 }
-----------------------------------------------------------------------
**Data Pools**

### Active School Vacancies
```{r}
school_vac_num <- nrow(school_vac)
valueBox(school_vac_num, icon = "fa-school")
```

### Active Applicants
```{r}
valueBox(Total_Active_Apps, icon = "fa-file-circle-check")
```

### Candidates of Color
```{r}
gauge(round(100*(unique_TOC_values_num/unique_Total_Cands_num)), min = 0, max = 100, symbol = '%', 
  label = "Candidates of Color", gaugeSectors(success = c(60, 100), warning = c(31, 59), danger = c(0, 30)))
```


Column {data-width=650 }
-----------------------------------------------------------------------
**Hiring Data **

### Teachers Hired to date
```{r}
school_hired_num <- nrow(school_hired) 
  valueBox(school_hired_num, icon = "fa-chalkboard-user")
```

### Teachers of Color Hired to date
```{r}
school_TOC_hires_num <- nrow(TOC_hires) 
valueBox(school_TOC_hires_num, icon = "fa-users-line")
```

### Teachers of Color Hired
```{r}
gauge(round(100*(Total_TOC_hires/Total_Hired)), min = 0, max = 100, symbol = '%', 
  label = "Candidates of Color", gaugeSectors(success = c(60, 100), warning = c(31, 59), danger = c(0, 30)))
```


Row {data-width=650 }
-----------------------------------------------------------------------
**Recommendations**

### Teachers of Color Hired at Top Schools

```{r}
valueBox(best_schools_top10_num, icon = "fa-thumbs-up")
```

### Teachers of Color Hired at Needs Improvement Schools
```{r}
valueBox(Needs_Impro_num, icon = "fa-thumbs-down")
```

### Potential Teachers of Color
```{r}
gauge(round(100*(rec_schoolstop10_num/Total_Hired)), min = 0, max = 100, symbol = '%', 
  label = "% Teacher of Color", gaugeSectors(success = c(59, 100), warning = c(31, 58), danger = c(0, 30)))
```


Vacancies Pool
=====================================

Inputs {.sidebar data-width=200}
-----------------------------------------------------------------------
### Vacancies Filters

```{r vaca school filter}
filter_select("Dept.Name", "Select School", Vacancies_sd, ~Dept.Name)
```

```{r vaca job filter}
filter_select("Posting.ID", "Select Job Posting", Vacancies_sd, ~Posting.ID)
```

```{r vaca autonomy filter}
filter_select("Autonomy.Status", "Select Autonomy Status", Vacancies_sd, ~Autonomy.Status)
```

Column {data-width=650 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------
### School Vacancies

```{r}
plot_ly(data = sunburstDF_vac, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')
```

### Map

```{r vaca data}

color_palette <- colorFactor(
  palette = c("blue", "green", "orange", "black", "purple", "brown", "white"),
  domain = Vacancies_sd$Autonomy.Status
)


leaflet() %>%
  setView(lng = -71.0589, lat = 42.3601, zoom = 12) %>%
  addProviderTiles("CartoDB.DarkMatter", group = "Dark") %>%
  addProviderTiles("Esri.WorldGrayCanvas", group = "Grey") %>%
    addCircleMarkers(
    data = Vacancies_sd,
    lng = ~Long,
    lat = ~Lat,
    color = ~color_palette(Autonomy.Status),
    radius = 6,
    fillOpacity = 0.8,
    popup = ~Dept.Name,
    clusterOptions = markerClusterOptions(),
    group = "Vacancies") %>%
    addLayersControl(overlayGroups = c("Vacancies"), 
                     baseGroups = c("Dark","Grey"), 
                     options = layersControlOptions(collapsed = FALSE)) %>%
    addLegend("bottomleft", 
              pal = color_palette, 
              values = Vacancies_sd$data()$Autonomy.Status,
              title = "Ethnicity",
              opacity = 1)
```

Row {data-width=350 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------

### School
```{r vaca school pie}
plot_ly(Vacancies_sd,
        labels = ~Dept.Name,
        values = ~num_vac, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label',
        showlegend=T) %>%
  layout(title = "School Vacancies", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))

```


### Autonomy Status
```{r vaca type pie}
plot_ly(Vacancies_sd,
        labels = ~ Autonomy.Status,
        values = ~num_vac, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label',
        showlegend=T) %>%
  layout(title = "School Vacancies", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

Applicant Pool
=====================================

Inputs {.sidebar data-width=200}
-----------------------------------------------------------------------

### Applications Filters

```{r app school filter}
filter_select("Dept.Name", "Select School", Applications_sd, ~Dept.Name)
```

```{r app ethnicity filter}
filter_select("Ethnicity", "Select Ethnicity", Applications_sd, ~Ethnicity)
```

```{r app status filter}
filter_select("Status", "Select Application Status", Applications_sd, ~Status)
```

```{r app Type filter}
filter_select("Applicant.Type", "Select Type", Applications_sd, ~Applicant.Type)
```

```{r app candidate filter}
filter_select("ApplicantProfile.ID", "Select Canidate", Applications_sd, ~ApplicantProfile.ID)
```

```{r app job filter}
filter_select("Posting.ID", "Select Job Posting", Applications_sd, ~Posting.ID)
```

```{r app autonomy filter}
filter_select("Autonomy.Status", "Select Autonomy Status", Applications_sd, ~Autonomy.Status)
```

Column {data-width=650 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------

### Application Type

```{r}
plot_ly(data = sunburstDF_app, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')
```

### Data Table
```{r app data}
datatable(Applications_sd, extensions="Scroller", style="bootstrap", class="compact", width="100%",
            options=list(deferRender=TRUE, scrollY=300, scroller=TRUE))
```

Row {data-width=350 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------

### Category
```{r app category pie}
plot_ly(Applications_sd,
        labels = ~Category,
        values = ~Total_Apps, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Application Breakdown", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Ethnicity
```{r app ethnicity pie}
plot_ly(Applications_sd,
        labels = ~Ethnicity,
        values = ~Total_Apps, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Application Breakdown", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Type
```{r app type pie}
plot_ly(Applications_sd,
        labels = ~Applicant.Type,
        values = ~Total_Apps, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Application Breakdown", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Schools
```{r app school pie}
plot_ly(Applications_sd,
        labels = ~Dept.Name,
        values = ~Total_Apps, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Application Breakdown", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Autonomy Status
```{r app autonomy pie}
plot_ly(Applications_sd,
        labels = ~Autonomy.Status,
        values = ~Total_Apps, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Application Breakdown", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

###

Candidate Pool
=====================================

Inputs {.sidebar data-width=200}
-----------------------------------------------------------------------

### Canidate Filters

```{r can school filter}
filter_select("Dept.Name", "Select School", Canidates_sd, ~Dept.Name)
```

```{r can ethnicity filter}
filter_select("Ethnicity", "Select Ethnicity", Canidates_sd, ~Ethnicity)
```

```{r can status filter}
filter_select("Status", "Select Application Status", Canidates_sd, ~Status)
```

```{r can candidate filter}
filter_select("ApplicantProfile.ID", "Select Canidate", Canidates_sd, ~ApplicantProfile.ID)
```

```{r can job filter}
filter_select("Posting.ID", "Select Job Posting", Canidates_sd, ~Posting.ID)
```

```{r can autonomy filter}
filter_select("Autonomy.Status", "Select Autonomy Status", Canidates_sd, ~Autonomy.Status)
```

```{r can transformation filter}
filter_checkbox("Transformation.School", "Select Transformation", Canidates_sd, ~Transformation.School, allLevels = TRUE)
```

```{r can turnaround filter}
filter_checkbox("Turnaround.School", "Select Turnaround", Canidates_sd, ~Turnaround.School, allLevels = TRUE)
```

Column {data-width=650 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------

### Candidates of Color

```{r}
plot_ly(data = sunburstDF_TOC_cands_sun, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')
```


### Map 
```{r can map}

color_palette <- colorFactor(
  palette = c("blue", "green", "orange", "black", "purple", "brown", "white"),
  domain = Canidates_sd$Ethnicity
)


leaflet() %>%
  setView(lng = -71.0589, lat = 42.3601, zoom = 12) %>%
  addProviderTiles("CartoDB.DarkMatter", group = "Dark") %>%
  addProviderTiles("Esri.WorldGrayCanvas", group = "Grey") %>%
    addCircleMarkers(
    data = Canidates_sd,
    lng = ~Long,
    lat = ~Lat,
    color = ~color_palette(Ethnicity),
    radius = 6,
    fillOpacity = 0.8,
    popup = ~Dept.Name,
    clusterOptions = markerClusterOptions(),
    group = "Applications") %>%
    addCircleMarkers(data = Canidates_sd,
                    lng = ~Long,
                    lat = ~Lat,
                    color = ~color_palette(Ethnicity),
                    popup = ~Dept.Name,
                    fillOpacity = ifelse(test = Canidates_sd$data()$Ethnicity != "White", yes = 1, no = 0),
                    fillColor = "red",
                    group = "Canidates") %>%
    addLayersControl(overlayGroups = c("Applications","Canidates"), 
                     baseGroups = c("Dark","Grey"), 
                     options = layersControlOptions(collapsed = FALSE)) %>%
    addLegend("bottomleft", 
              pal = color_palette, 
              values = Canidates_sd$data()$Ethnicity,
              title = "Ethnicity",
              opacity = 1)
```

### Data Table

```{r can data}

datatable(Canidates_sd, extensions="Scroller", style="bootstrap", class="compact", width="100%",
            options=list(deferRender=TRUE, scrollY=300, scroller=TRUE))

```

Hire
=====================================

Inputs {.sidebar data-width=200}
-----------------------------------------------------------------------
### Hire Filters
```{r hires school filter}
filter_select("Dept.Name", "Select School", Hires_sd, ~Dept.Name)
```

```{r hires ethnicity filter}
filter_select("Ethnicity", "Select Ethnicity", Hires_sd, ~Ethnicity)
```

```{r hires candidate filter}
filter_select("ApplicantProfile.ID", "Select Canidate", Hires_sd, ~ApplicantProfile.ID)
```

```{r hires job filter}
filter_select("Posting.ID", "Select Job Posting", Hires_sd, ~Posting.ID)
```

```{r hires autonomy filter}
filter_select("Autonomy.Status", "Select Autonomy Status", Hires_sd, ~Autonomy.Status)
```

```{r hires transformation filter}
filter_checkbox("Transformation.School", "Select Transformation", Hires_sd, ~Transformation.School, allLevels = TRUE)
```

```{r hires turnaround filter}
filter_checkbox("Turnaround.School", "Select Turnaround", Hires_sd, ~Turnaround.School, allLevels = TRUE)
```

Column {data-width=650 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------

### Teacher of Color Hires

```{r}
plot_ly(data = sunburstDF_school_coc_hired, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')
```


### Hires Map 
```{r hires map}
color_palette <- colorFactor(
  palette = c("blue", "green", "orange", "black", "purple", "brown", "white"),
  domain = Canidates_sd$Ethnicity
)


leaflet() %>%
  setView(lng = -71.0589, lat = 42.3601, zoom = 12) %>%
  addProviderTiles("CartoDB.DarkMatter", group = "Dark") %>%
  addProviderTiles("Esri.WorldGrayCanvas", group = "Grey") %>%
  addCircleMarkers(data = Hires_sd,
                    lng = ~Long,
                    lat = ~Lat,
                    color = ~color_palette(Ethnicity),
                    popup = ~Dept.Name,
                    fillOpacity = ifelse(test = Hires_sd$data()$Ethnicity != "White", yes = 1, no = 0),
                    fillColor = "red",
                    group = "Hires") %>%
    addLayersControl(overlayGroups = c("Hires"), 
                     baseGroups = c("Dark","Grey"), 
                     options = layersControlOptions(collapsed = FALSE)) %>%
    addLegend("bottomleft", 
              pal = color_palette, 
              values = Hires_sd$data()$Ethnicity,
              title = "Ethnicity",
              opacity = 1)
```

### Data Table
```{r}
datatable(Hires_sd, extensions="Scroller", style="bootstrap", class="compact", width="100%",
            options=list(deferRender=TRUE, scrollY=300, scroller=TRUE))
```

Row {data-width=350 .tabset .tabset-fade .colored}
-----------------------------------------------------------------------

### Category
```{r hires category pie}
plot_ly(Hires_sd,
        labels = ~Category,
        values = ~num_teachers_hired, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Teacher Hire Breakdown", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Ethnicity
```{r hires ethnicity pie}
plot_ly(Hires_sd,
        labels = ~Ethnicity,
        values = ~num_teachers_hired, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Hires by Ethnicity", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Type
```{r hires type pie}
plot_ly(Hires_sd,
        labels = ~Applicant.Type,
        values = ~num_teachers_hired, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Hires By Applicate Type", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Schools
```{r hires school pie}
plot_ly(Hires_sd,
        labels = ~Dept.Name,
        values = ~num_teachers_hired, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Teachers Hired by School", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

### Autonomy Status
```{r hires autonomy pie}
plot_ly(Hires_sd,
        labels = ~Autonomy.Status,
        values = ~num_teachers_hired, 
        type = 'pie',
        marker = list(colors = c('#bcbd22', '#2ca02c')),
        textposition = 'inside',
        textinfo = 'label+percent',
        showlegend=T) %>%
  layout(title = "Hires by Autonomy Status", legend = list(orientation ="h",  xanchor = "center", x = 0.5, y = -0.1))
```

School Performance
=====================================

Row {data-width=350}
-----------------------------------------------------------------------
### Top Performers
**Schools with the highest number of Teacher of Color Hires**
```{r}
plot_ly(data = sunburstDF_best_schools_top10_sun, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')
```


Row {data-width=350}
-----------------------------------------------------------------------
### Needs Improvement 
**Schools with the lowest number of Teacher of Color Hires**
```{r}
plot_ly(data = sunburstDF_Needs_Improvement_schools_top10_sun, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')
```

Recommendations 
=====================================

### Teacher of Color Hires
**Potential number of teachers of color**
```{r}
plot_ly(data = sunburstDF_rec_schools_top10_sun, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')

```