---
title: "HTML Interactive Dashboard Example"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```

### Asset Map for Cincinatti MSA

```{r}
library(leaflet)
library(readxl)
library(htmltools)

assets <- read_excel("C:/Users/hvossler/OneDrive - Measurement Resources Company/Documents/Interactive Document Example/Healthcare Asset Locations.xlsx", sheet = 1)

# make palette
pal <- colorFactor(palette = 'Set1', domain = assets$Type)
assets$Label <- paste(assets$Name, assets$Type, sep = '\n')

assets %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(~Long, ~Lat, 
    popup = ~Label,
    label = ~lapply(paste(Name, Type, Address, sep = "<br>"), HTML),
    fillOpacity = 0.7,
    color = ~pal(Type))
```


***
Map assets using coordinate data (latitude, longitude)

-   We can adjust the labels to contain additional information
-   Colors are based on type of provider, again can be customized
-   Background map also customizable


### Barplot of population level data

```{r}
comdata <- read_excel("C:/Users/hvossler/OneDrive - Measurement Resources Company/Documents/Interactive Document Example/Healthcare Asset Locations.xlsx", sheet = 2)

library(ggplot2)
ggplot(comdata, aes(x=reorder(County, `% Severe Housing Cost Burden`), y=`% Severe Housing Cost Burden`, fill=MSA)) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(palette = "Set1") +
  scale_y_continuous(limits = c(0,16)) +
  coord_flip() +
  theme_minimal() +
  geom_text(aes(label=paste0(round(`% Severe Housing Cost Burden`, 2), "%")), 
            hjust = ifelse(comdata$`% Severe Housing Cost Burden`>0,0,1), 
            size=3) +
  xlab("County")
```

***
Single barplot here to visualize the % Housing Cost burden in each county, color 
coded by MSA type.


### Dygraphs provides rich facilities for charting time-series data in R and includes support for many interactive features.

```{r}
library(dygraphs)
lungDeaths <- cbind(mdeaths, fdeaths)
dygraph(lungDeaths, main = "Deaths from Lung Diseases in the UK") %>%
  dySeries("mdeaths", label = "Male") %>%
  dySeries("fdeaths", label = "Female") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20) %>% 
  dyRangeSelector()
```


<https://rstudio.github.io/dygraphs/>

-   Automatically plots xts time series objects (or any object convertible to xts).

-   Highly configurable axis and series display (including optional second Y-axis).

-   Rich interactive features including zoom/pan and series/point highlighting.

-   Display upper/lower bars (e.g. prediction intervals) around series.

-   Various graph overlays including shaded regions, event lines, and point annotations.


### MetricsGraphics enables easy creation of D3 scatterplots, line charts, and histograms.

```{r}
library(ggplot2)
library(plotly)

nona1 <- comdata %>%
  select(CountyFull, County, MSA, `Infant Mortality Rate`, `Life Expectancy`, TotalPopulation) %>%
  filter(`Infant Mortality Rate`!="NA") %>%
  filter(is.na('Life Expectancy')==FALSE) %>%
  filter(is.na(TotalPopulation)==FALSE)

ggplot(nona1, aes(x=`Life Expectancy`, y=round(as.numeric(`Infant Mortality Rate`),3), size=TotalPopulation, color=MSA, label=County)) +
  geom_point() +
  xlab("Life Expectancy (Years)") +
  ylab("Infant Mortality Rate (per 1000 live births)") +
  geom_text(hjust=.5, vjust=1.9, size=3)
```

------------------------------------------------------------------------

Another static figure showing infant mortality by life expectancy for each county. 

This is a figure that can be made interactive as well without too much hassle, if for isntance
you wanted to hover over the dots for additional information (actual number values)