flexdashboard_demo.Rmd

---
title: "MassCEC Offshore Wind Employment Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: cerulean
---

```{r setup, include=FALSE}
'
MIT License

Copyright (c) 2022 Salvador Balkus

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'

library(tidyverse)
library(rjson)
library(leaflet)
library(maps)
library(sp)
library(tidycensus)
library(blsAPI)
library(RColorBrewer) # Needed for choropleth mapping
library(htmlwidgets)


options(tigris_use_cache = TRUE)

### Occupation Definitions ###

jobs <- read_tsv(r"{C:\Users\rdbpo\Documents\BigDataClub\MassCEC_Project\MassCEC-Dashboard\occupations.txt}")
jobs$SOC <- as.character(jobs$SOC)

### Getting Metropolitan Area Data from Census ###
# Usage: https://walker-data.com/tidycensus/articles/basic-usage.html

census_api_key("9b5b6a5de8bad8c597d0eae0d350bb76595e9029")
# Note that the BLS reports data for NECTAS in New England instead of normal Metropolitan Areas
NE_areas <- get_decennial(geography = "new england city and town area", variables = "METDIV", year = 2020, geometry = TRUE)

# The function below is used to generate a map for a single component for a single category

generate_employment_map <- function(category, component, jobs, NE_areas){

  ### Getting BLS Data ###
  # See how to construct the code here: https://download.bls.gov/pub/time.series/oe/
  survey <- 'OE'
  seasonal <- 'U'
  areatype <- 'M'
  industry <- '000000'
  
  all_occupations <- list()
  jobs_filter <- jobs %>% filter(Category == category) %>% filter(Component == component)
  
  
  for(occupation in jobs_filter$SOC){
  
    # Met Areas
    met_areas <- lapply(as.list(NE_areas$GEOID), function(x){paste("00",x,sep="")})
    
    ids_employ <- lapply(met_areas, function(area){as.character(paste(survey, seasonal, areatype, area, industry, occupation, '01',sep=""))})
    ids_locquotient <- lapply(met_areas, function(area){as.character(paste(survey, seasonal, areatype, area, industry, occupation, '17',sep=""))})
    
    # Specify which list of series you want to call
    id_list <- c(ids_locquotient)
    
    payload <- list(
      'seriesid'=id_list,
      'startyear'=2020,
      'endyear'=2020,
      'registrationkey'='981268fe81cd404c94ba4991183c5325')
    
    response <- blsAPI(payload)
    json <- fromJSON(response)
    
    # Turn all returned values into one list
    bls_values <- lapply(json$Results$series, function(x){
      if(length(x$data) != 0 && x$data[[1]]$value != "-" ){return(as.numeric(x$data[[1]]$value))}
      else{return(NA)}
      
      })
    
    
    bls_data <- tibble(GEOID = NE_areas$GEOID, code = unlist(met_areas), estimate = unlist(bls_values)) %>% filter(!is.na(estimate))
    
    ### Merge the Data ###
    
    areas <- merge(NE_areas, bls_data, by="GEOID")
    all_occupations[[occupation]] <- areas
  }
  
  ### Setting Up Map with Palette and Legend ###
  
  # PROBLEM Data is derived from the data frame put into the leaflet command...
  
  m <- leaflet(data = areas) %>% addTiles()
  titles <- jobs_filter$Title
  
  for(job_title in titles){
    # Select the occupation that we want. Let's use Industrial Engineer as an example
    occ <- unique((jobs_filter %>% filter(Title == job_title))$SOC)
    new_areas <- all_occupations[[occ]]
    
    # Set up the color palette
    pal <- colorNumeric(
      palette = "Blues",
      domain = new_areas$estimate)
    
    # Prepare the text for tooltips 
    labels <- paste(new_areas$NAME,"<br/>", "Location Quotient: ", new_areas$estimate, "<br/>",
                    sep="") %>% lapply(htmltools::HTML)
    
    ### Create the map ###
    m <- m %>%
      addPolygons(data = new_areas, fillOpacity = 0.8, fillColor = ~pal(estimate), color = "gray", dashArray = "3", weight = 2, opacity = 1, highlightOptions = highlightOptions(
        weight = 5,
        color = "white",
        dashArray = "",
        fillOpacity = 0.7,
        bringToFront = TRUE),
        label = labels,
        labelOptions = labelOptions(
          style = list("font-weight" = "normal", padding = "3px 8px"),
          textsize = "15px",
          direction = "auto"),
        group = job_title
      ) 
  }
  
  m <- m %>%
    addLegend(pal = pal, values = ~estimate, opacity = 0.7, title = NULL, position = "bottomright", group = job_title) %>% 
    addLayersControl(
    baseGroups = titles,
    options = layersControlOptions(collapsed = FALSE)
  )
  
  return(m)
}
```


Towers
=======================================

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

### General

```{r}

generate_employment_map("Towers", "General", jobs, NE_areas)

```

### Tower Internals

```{r}

generate_employment_map("Towers", "Tower Internals: Stairway, Ladder, Door, Platform (concrete and steel rebar), Railing, Cables", jobs, NE_areas)

```

### Cables Only

```{r}

generate_employment_map("Towers", "Specific for Cables only", jobs, NE_areas)

```

### Coatings

```{r}

generate_employment_map("Towers", "Coatings", jobs, NE_areas)

```

### Tower Segments

```{r}

generate_employment_map("Towers", "Steel Tower Segments", jobs, NE_areas)

```

### Lighting

```{r}

generate_employment_map("Towers", "Lighting", jobs, NE_areas)

```

### Pumps & Equipment

```{r}

generate_employment_map("Towers", "Pumps/Equipment", jobs, NE_areas)

```

### Flange & Forgings

```{r}

generate_employment_map("Towers", "Flange/Forgings", jobs, NE_areas)

```

### Yaw System

```{r}

generate_employment_map("Towers", "Yaw System (Bearings, Brake (discs), Motor, Drive (Steel))", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different tower components and associated occupations!






© 2022 Salvador Balkus

Blades
===================================

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

### General

```{r}
generate_employment_map("Blades", "General", jobs, NE_areas)

```

### Turbine Operation

```{r}

generate_employment_map("Blades", "Turbine Operation", jobs, NE_areas)

```

### Blade Production

```{r}

generate_employment_map("Blades", "Blade Production", jobs, NE_areas)

```

### Pitch Bearings

```{r}

generate_employment_map("Blades", "Pitch Bearings", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different blade components and associated occupations!






© 2022 Salvador Balkus

Foundation
===================================

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

### General

```{r}
generate_employment_map("Foundation", "General", jobs, NE_areas)

```

### Jacket

```{r}

generate_employment_map("Foundation", "Jacket ( Steel Lattice, Transition section, Legs, Braces, Nodes, Pins, Integrated upper deck, Heavy wall sections)", jobs, NE_areas)

```

### Monopile

```{r}

generate_employment_map("Foundation", "Monopile", jobs, NE_areas)

```

### Gravity Based

```{r}

generate_employment_map("Foundation", "Gravity Based", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different foundation components and associated occupations!






© 2022 Salvador Balkus

Submarine Cables
===================================

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

### General

```{r}
generate_employment_map("Submarine Cables", "General", jobs, NE_areas)

```

### Conductor

```{r}

generate_employment_map("Submarine Cables", "Conductor", jobs, NE_areas)

```

### Insulation Layer

```{r}

generate_employment_map("Submarine Cables", "Insulation Layer", jobs, NE_areas)

```

### Armor Layer

```{r}

generate_employment_map("Submarine Cables", "Armor Layer", jobs, NE_areas)

```

### Outer Layer

```{r}

generate_employment_map("Submarine Cables", "Outer Layer", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different cable components and associated occupations!






© 2022 Salvador Balkus

Nacelle
===================================

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

### General

```{r}
generate_employment_map("Nacelle", "General", jobs, NE_areas)

```

### Nacelle Cover

```{r}

generate_employment_map("Nacelle", "Covers", jobs, NE_areas)

```

### Nacelle Cover (Steel)

```{r}

generate_employment_map("Nacelle", "Cover Steel", jobs, NE_areas)

```

### Rotor Hub

```{r}

generate_employment_map("Nacelle", "Rotor Hub", jobs, NE_areas)

```

### Speed Shafts

```{r}

generate_employment_map("Nacelle", "Shafts", jobs, NE_areas)

```

### Hydraulic Pitch System

```{r}

generate_employment_map("Nacelle", "Hydraulic Pitch", jobs, NE_areas)

```

### Electric Pitch System

```{r}

generate_employment_map("Nacelle", "Electric Pitch", jobs, NE_areas)

```

### General Generators

```{r}

generate_employment_map("Nacelle", "Generators", jobs, NE_areas)

```

### Armature System

```{r}

generate_employment_map("Nacelle", "Armature", jobs, NE_areas)

```

### Rotor & Stator

```{r}

generate_employment_map("Nacelle", "Rotors", jobs, NE_areas)

```

### Electric Generator

```{r}

generate_employment_map("Nacelle", "Electric Generator", jobs, NE_areas)

```

### Castings & Forgings

```{r}

generate_employment_map("Nacelle", "Castings", jobs, NE_areas)

```

### Bearings

```{r}

generate_employment_map("Nacelle", "Bearings", jobs, NE_areas)

```

### Gears

```{r}

generate_employment_map("Nacelle", "Gears", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different cable components and associated occupations!






© 2022 Salvador Balkus

Substations
===================================

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

### General

```{r}
generate_employment_map("Substations", "General", jobs, NE_areas)

```

### Jacket Foundation

```{r}

generate_employment_map("Substations", "Jacket Foundation", jobs, NE_areas)

```

### Jacket Coating

```{r}

generate_employment_map("Substations", "Jacket Coating", jobs, NE_areas)

```

### Transition Pieces

```{r}

generate_employment_map("Substations", "Pieces", jobs, NE_areas)

```

### Transition Coating

```{r}

generate_employment_map("Substations", "Transition Coating", jobs, NE_areas)

```

### Transition Electrical System

```{r}

generate_employment_map("Substations", "Transition Electrical", jobs, NE_areas)

```

### Structual Steel Decks

```{r}

generate_employment_map("Substations", "Decks", jobs, NE_areas)

```

### Structual Steel Coating

```{r}

generate_employment_map("Substations", "Steel Coating", jobs, NE_areas)

```

### Structual Steel Sensors

```{r}

generate_employment_map("Substations", "Sensors", jobs, NE_areas)

```

### Equipment Deck Structure

```{r}

generate_employment_map("Substations", "Structure", jobs, NE_areas)

```

### Equipment Deck Electrical System

```{r}

generate_employment_map("Substations", "Deck Electrical", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different cable components and associated occupations!






© 2022 Salvador Balkus

Vessels
===================================

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

### All Vessels

```{r}

generate_employment_map("Vessels", "All", jobs, NE_areas)

```

Column {data-width=350}
-----------------------------------------------------------------------

### Welcome to the MassCEC Offshore Wind Employment Dashboard! 

The map on the left depicts the location quotient for individual occupations in the offshore wind sector across New England. Location quotient measures the concentration of workers in a given occupation compared to the nation as a whole. For example, if an area has a location quotient of 2 for industrial engineers, then that area employs two times as many engineers per capita as the United States does as a whole. Each tab lists the occupations necessary to manufacture specific wind farm components.

Click between tabs to explore different cable components and associated occupations!






© 2022 Salvador Balkus