---
title: "Equity Dashboard"
author: "Shannon Coulter, SDCOE"
subtitle: "Source: Forcast 5"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    theme: lumen
params:
  district: "Carlsbad Unified"
editor_options: 
  markdown: 
    wrap: 80
date: "`r format(Sys.time(), '%b %d, %Y')`"
---

```{r setup, echo=FALSE, warning=FALSE, message=FALSE}
library(tidyverse)
library(plotly)
library(plyr)
library(flexdashboard)
library(here)
library(shiny)
library(crosstalk)
library(ggthemes)
library(htmltools)
library(ggrepel)
library(janitor)
```


```{r import-data}
# figure out how to source child/parent?
teach_stu_ratio <- read.csv(file = here("data", "district_ratios.csv")) #see script
susp <- read.csv(file = here("data", "all_districts_susp.csv"))
grad_rate <- read.csv(file = here("data", "all_districts_grad_rate2.csv"))
el_progress <- read.csv(file = here("data", "all_districts_el_progress.csv"))
achv_gap <- read.csv(file = here("data", "all_districts_achv_gap2.csv"))
chronic <- read.csv(file = here("data", "all_districts_chronic2.csv"))
drop <- read.csv(file = here("data", "all_districts_dropout_rate.csv"))
ag <- read.csv(file = here("data", 'uc_ag_rate_2.csv'))
goals <- read.csv(file = here("data", '2021 LCAP Goals by LEA.csv'))
```

```{r clean-data}
teach_stu_ratio <- teach_stu_ratio %>%
  dplyr::rename(race_eth = "X1", year = "X2", dist_name = "X3") 

teach_stu_ratio <- janitor::clean_names(teach_stu_ratio)
susp <- janitor::clean_names(susp)
grad_rate <- janitor::clean_names(grad_rate)
el_progress <- janitor::clean_names(el_progress)
achv_gap <- janitor::clean_names(achv_gap)
chronic <- janitor::clean_names(chronic)
drop <- janitor::clean_names(drop)
ag <- janitor::clean_names(ag)
goals <- janitor::clean_names(goals)

teach_stu_ratio <- teach_stu_ratio %>% 
  mutate(Student = as.numeric(sub("%","", teach_stu_ratio$student))/100,
         Teacher = as.numeric(sub("%","", teach_stu_ratio$teacher))/100)

teach_stu_ratio$race_eth[teach_stu_ratio$race_eth == "Caucasian"] <- "White"

susp <- susp %>% 
  mutate(status = as.numeric(sub("%","", susp$status))/100,
         year = as.factor(year),
         enrollment_type = as.factor(enrollment_type))

grad_rate <- grad_rate %>% 
  mutate(status = as.numeric(sub("%","", grad_rate$status))/100,
         year = as.factor(year),
         enrollment_type = as.factor(enrollment_type))

el_progress <- el_progress %>% 
  mutate(status = as.numeric(sub("%","", el_progress$status))/100,
         year = as.factor(year))

achv_gap <- achv_gap %>% 
  mutate(Disadvantaged = as.numeric(sub("%","", achv_gap$no_choices))/100,
         Non_Disadvantaged = as.numeric(sub("%","", achv_gap$pos_choices))/100,
         year = as.factor(year))

chronic <- chronic %>% 
  mutate(absenteeism_indicator = as.numeric(sub("%","", chronic$absenteeism_indicator))/100,
         change = as.numeric(sub("%","", chronic$change))/100,
         year = substr(chronic$data_set, 6, 9),
         year = as.factor(year))

drop <- drop %>% 
  mutate(status = as.numeric(sub("%","", drop$result_2))/100,
         year = as.factor(year),
         breakdown_type = as.factor(breakdown_type))

ag <- ag %>% 
  mutate(status = as.numeric(sub("%","", ag$status))/100,
         year = as.factor(year),
         enrollment_type = as.factor(enrollment_type))

goals <- goals %>%
  mutate(lea = gsub(" School District", "", goals$lea))
```

**`r params$district`** Equity  
================================================================================

## Row 1 
--------------------------------------------------------------------------------

### Teacher-Student Ratios  {data-width="500"}

```{r ratios, echo=FALSE, message=FALSE, warning=FALSE}
teach_stu_ratio_1 <- teach_stu_ratio %>% 
  pivot_longer(cols = c("Student", "Teacher"),
               names_to = "group", values_to = "percent") %>%
  mutate(race_eth = factor(race_eth, levels = c("American Indian or Alaskan Native", 
                                                "Asian", "Black or African American", 
                                                "Filipino", "Hispanic", "Multiracial", 
                                                "Native Hawaiian/Pacific Islander", 
                                                "White", "Unknown", "Unreported"))) %>%
  filter(dist_name == params$district)

ratio <- teach_stu_ratio_1 %>%
  ggplot(aes(x = race_eth, y = percent, fill = group)) +
  geom_col(position = "dodge") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_viridis_d(labels = c("Student", "Teacher")) +
  scale_x_discrete(limits = rev) +
  theme(panel.grid.major.y = element_blank()) + 
  theme(legend.position = "bottom", legend.direction = "horizontal") +
  ggtitle(teach_stu_ratio_1$dist_name) +
  labs(subtitle = "Teacher-Student Ratio",
       caption = "Source: California Department of Education",
       x = NULL, y = NULL, fill = NULL)

plotly::ggplotly(ratio) %>%
  layout(legend = list(orientation = "h", x = 0.4, y = -0.3))
```

### Suspension Rate

```{r suspension, echo=FALSE, message=FALSE, warning=FALSE}
susp_1 <- susp %>%
  filter(enrollment_type %in% c("All Students", "Amer Indian", "Black", "Hispanic", "SwD")) %>%
  filter(district == params$district)

sus <- susp_1 %>%
  ggplot(aes(x = year, y = status, group = enrollment_type, color = enrollment_type)) +
  geom_line(
    size = 1) +
  geom_point(size = 2.5) +
  geom_text_repel(aes(
    label = ifelse(year == "2019", scales::percent(status, accuracy = 2L), "")),
    force        = 0.5,
    nudge_x      = 0,
    direction    = "y",
    hjust        = 0,
    segment.size = 0.2,
    fontface     = "bold",
    show.legend = FALSE) +
  ggtitle(susp_1$district) +
  labs(subtitle = "Suspension Rate",
       caption = "Source: California Department of Education",
       x = NULL, y = NULL, fill = NULL) +
  scale_y_continuous(
    labels = scales::percent_format()) +
  scale_color_viridis_d() +
  theme(panel.grid.major.y = element_blank()) + 
  theme(legend.position = "bottom", 
        legend.direction = "horizontal",
        legend.title = element_blank(),
        fill = NULL)

ggplotly(sus) %>%
  layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
```

## Row 2
--------------------------------------------------------------------------------
### Graduation Rate {data-width="1000"}

```{r grad-rate, echo=FALSE, message=FALSE, warning=FALSE}
grad_rate_1 <- grad_rate %>%
  filter(enrollment_type %in% c("All Students", "Amer Indian", "Black", "Hispanic", "SwD")) %>%
  filter(district == params$district)

grad <- grad_rate_1 %>%
  {if(nrow(grad_rate_1) == 0) {
    cat("No data for this district")
  } else{
    ggplot(grad_rate_1, aes(x = year, y = status, group = enrollment_type, 
                            color = enrollment_type)) +
      geom_line(
        size = 1) +
      geom_point(size = 2.5) +
      geom_text_repel(aes(
        label = ifelse(year == "2019", scales::percent(status, accuracy = 2L), "")),
        force        = 0.5,
        nudge_x      = 0,
        direction    = "y",
        hjust        = 0,
        segment.size = 0.2,
        fontface     = "bold",
        show.legend = FALSE) +
      ggtitle(grad_rate_1$district) +
      labs(subtitle = "Graduation Rate",
           caption = "Source: California Department of Education",
           x = NULL, y = NULL, fill = NULL) +
      scale_y_continuous(
        labels = scales::percent_format(),
        limits = c(0,1)) +
      scale_color_viridis_d() +
      theme(panel.grid.major.y = element_blank()) + 
      theme(legend.position = "bottom", 
            legend.direction = "horizontal",
            legend.title = element_blank())
  }
  }

ggplotly(grad) %>%
  layout(legend = list(orientation = "h", x = 0.2, y = -0.2))
```

### EL Progress

```{r el-progress, echo=FALSE, message=FALSE, warning=FALSE}
el_progress_2 <- el_progress %>%
  filter(district == params$district)

el <- el_progress_2 %>%
  {if(nrow(el_progress_2) == 0) {
    cat("No data for this district")
  } else{
    ggplot(., aes(x = year, y = status, fill = year)) +
      geom_col() +
      scale_y_continuous(labels = scales::percent) +      
      scale_fill_viridis_d(option = "viridis") +
      theme(panel.grid.major.y = element_blank()) + 
      ggtitle(el_progress_2$district) +
      labs(subtitle = "English Learner Progress",
           caption = "Source: California Department of Education",
           x = NULL, y = NULL, fill = NULL) +
      theme(legend.position = "none")
  }
  }

ggplotly(el) %>%
  layout(legend = list(orientation = "h", x = 0.4, y = -0.2))
```

### Mathematics Achievement Gap

```{r achievement-gaps, echo=FALSE, message=FALSE, warning=FALSE}
achv_gap_2 <- achv_gap %>% 
  pivot_longer(cols = c("Disadvantaged", "Non_Disadvantaged"),
               names_to = "group", values_to = "percent") %>%
  filter(district == params$district)


gaps <- ggplot(achv_gap_2, aes(x = year, y = percent, fill = group)) +
  geom_col(position = "dodge") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_viridis_d(labels = c("Disadvantaged", "Non-Disadvantaged")) +
  theme(panel.grid.major.y = element_blank()) + 
  theme(legend.position = "bottom", legend.direction = "horizontal",
  ) +
  ggtitle(achv_gap_2$district) +
  labs(subtitle = "Achievement Gap Economically/Non-Economically Disadvantaged",
       caption = "Source: California Department of Education",
       x = NULL, y = NULL, fill = NULL)

ggplotly(gaps) %>%
  layout(legend = list(orientation = "h", x = 0.4, y = -0.2))
```
## Row 3 {data-height="10"}
--------------------------------------------------------------------------------
###### Source: Forcast 5

Engagement
================================================================================

## Row 1 
--------------------------------------------------------------------------------

### Chronic Absenteeism  {data-width="750"}

```{r chrn-abs, echo=FALSE, message=FALSE, warning=FALSE}
chronic_1 <- chronic %>%
  filter(district == params$district)

chrn <- chronic_1 %>% 
  {if(nrow(chronic_1) == 0) {
    cat("No data for this district")
  } else{
    ggplot(., aes(x = change, y = absenteeism_indicator, shape = enrollment_type,
                  color = enrollment_type)) +
      geom_point(size = 4) +
      theme(panel.grid.major.y = element_blank(),
            legend.position = "bottom", 
            legend.direction = "horizontal",
            legend.title = element_blank()) +
      scale_y_continuous(
        labels = scales::percent,
        limits = c(0, 0.4)) +
      scale_x_continuous(labels = scales::percent) +
      scale_color_viridis_d() +
      ggtitle(chronic_1$district) +
      labs(subtitle = "Chronic Abenteeism",
           caption = "Source: California Department of Education",
           x = 'Percent change last year', y = 'Percent chronically absent', fill = NULL)
  }
  }

plotly::ggplotly(chrn) %>%
  layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
```

### Dropout Rate

```{r drop-out, echo=FALSE, message=FALSE, warning=FALSE}
drop_1 <- drop %>%
  filter(district == params$district)

drp <- drop_1 %>%
  {if(nrow(drop_1) == 0) {
    cat("No data for this district")
  } else{
    ggplot(., aes(x = year, y = status, group = breakdown_type, color = breakdown_type)) +
      geom_line(
        size = 1) +
      geom_point(size = 2.5) +
      geom_text_repel(aes(
        label = ifelse(year == "2020", scales::percent(status, accuracy = 2L), "")),
        force        = 0.5,
        nudge_x      = 0.2,
        direction    = "y",
        hjust        = 0,
        segment.size = 0.2,
        fontface     = "bold",
        show.legend = FALSE) +
      ggtitle(drop_1$district) +
      labs(subtitle = "Dropout Rate",
           caption = "Source: California Department of Education",
           x = NULL, y = NULL, fill = NULL) +
      scale_y_continuous(
        labels = scales::percent_format(),
        limits = c(0, 0.4)) +
      scale_color_viridis_d() +
      theme(panel.grid.major.y = element_blank()) + 
      theme(legend.position = "bottom", 
            legend.direction = "horizontal",
            legend.title = element_blank())
  }
  }

plotly::ggplotly(drp) %>%
  layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
```

## Row 2 
--------------------------------------------------------------------------------

### A-G Completion Rate  {data-width="750"}

```{r ag-rate, echo=FALSE, message=FALSE, warning=FALSE}
ag_1 <- ag %>%
  filter(district == params$district)

agc <- ag_1 %>%
  {if(nrow(ag_1) == 0) {
    cat("No data for this district")
  } else{
    agc <- ggplot(., aes(x = year, y = status, group = enrollment_type, 
                         color = enrollment_type)) +
      geom_line(
        size = 1) +
      geom_point(size = 2.5) +
      geom_text_repel(aes(
        label = ifelse(year == "2019", scales::percent(status, accuracy = 2L), "")),
        force        = 0.5,
        nudge_x      = 0.2,
        direction    = "y",
        hjust        = 0,
        segment.size = 0.2,
        fontface     = "bold",
        show.legend = FALSE) +
      ggtitle(ag_1$district) +
      labs(subtitle = "A-G Completion Rate",
           caption = "Source: California Department of Education",
           x = NULL, y = NULL, fill = NULL) +
      scale_y_continuous(
        labels = scales::percent_format(),
        limits = c(0, 1)) +
      scale_color_viridis_d() +
      theme(panel.grid.major.y = element_blank()) + 
      theme(legend.position = "bottom", 
            legend.direction = "horizontal",
            legend.title = element_blank())
  }
  }

plotly::ggplotly(agc) %>%
  layout(legend = list(orientation = "h", x = 0.2, y = -0.2))
```

### LCAP Approval Status

**LCAP Approval Status**: Approved

Goals
================================================================================

## Row 1 
--------------------------------------------------------------------------------

### Apportionment

```{r}
goals1 <- goals %>%
  filter(lea == params$district)

# Values
apportion <- goals1$increased_apportionment_based_on_the_enrollment_of_foster_youth_english_learners_and_low_income_students

valueBox(apportion, caption = "Increased Apportionment", icon = "fa-money-check",
         color = "#482173")
```

### Apportionment

```{r}
goals1 <- goals %>%
  filter(lea == params$district)

# Values
apportion <- goals1$percentage_to_increase_or_improve_services

valueBox(apportion, caption = "Percent Apportionment Increase", icon = "fa-tags",
         color = "#482173")
```

## Row 2 
--------------------------------------------------------------------------------

### LCAP Goals

   


```{r}
goals1 <- goals %>%
  filter(lea == params$district)

g1 <- goals1$lcap_goal_1
g2 <- goals1$lcap_goal_2
g3 <- goals1$lcap_goal_3
g4 <- goals1$lcap_goal_4
g5 <- goals1$lcap_goal_5
g6 <- goals1$lcap_goal_6
g7 <- goals1$lcap_goal_7
```


`r if(TRUE){g1}`

`r if(TRUE){g2}`

`r if(TRUE){g3}`

`r if(TRUE){g4}`

`r if(TRUE){g5}`

`r if(TRUE){g6}`

`r if(TRUE){g7}`