EpiPackage-OBrien.Rmd

---
title: "EpiPackage-OBrien"
output: html_document
---
Helpful Links:
https://cran.r-project.org/web/packages/usmap/vignettes/mapping.html
Uploading Packages to Use
```{r}
library(epiR)
library(epitools)
library(incidence)
library(Epi)
library(epiDisplay)
library(epicalc)
library(devtools)
library(roxygen2)
library(leaflet)
library(maps)
library(AER)
library(prevalence)
```

Maps- use geom polygon (?) 
```{r}
m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m

mapStates <- map("state", fill = TRUE, plot = FALSE)
leaflet(data = mapStates) %>% addTiles() %>%
  addPolygons(fillColor = topo.colors(10, alpha = NULL), stroke = FALSE)

```

```{r}
nycounties <- geojsonio::geojson_read("json/nycounties.geojson",
  what = "sp")
# Or use the rgdal equivalent:
nycounties <- rgdal::readOGR("json/nycounties.geojson", "OGRGeoJSON")

pal <- colorNumeric("viridis", NULL)

leaflet(nycounties) %>%
  addTiles() %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.3, fillOpacity = 1,
    fillColor = ~pal(log10(pop)),
    label = ~paste0(county, ": ", formatC(pop, big.mark = ","))) %>%
  addLegend(pal = pal, values = ~log10(pop), opacity = 1.0,
    labFormat = labelFormat(transform = function(x) round(10^x)))
```

```{r}
library(jsonlite)

# From http://data.okfn.org/data/datasets/geo-boundaries-world-110m
geojson <- readLines("json/countries.geojson", warn = FALSE) %>%
  paste(collapse = "\n") %>%
  fromJSON(simplifyVector = FALSE)

# Default styles for all features
geojson$style = list(
  weight = 1,
  color = "#555555",
  opacity = 1,
  fillOpacity = 0.8
)

# Gather GDP estimate from all countries
gdp_md_est <- sapply(geojson$features, function(feat) {
  feat$properties$gdp_md_est
})
# Gather population estimate from all countries
pop_est <- sapply(geojson$features, function(feat) {
  max(1, feat$properties$pop_est)
})

# Color by per-capita GDP using quantiles
pal <- colorQuantile("Greens", gdp_md_est / pop_est)
# Add a properties$style list to each feature
geojson$features <- lapply(geojson$features, function(feat) {
  feat$properties$style <- list(
    fillColor = pal(
      feat$properties$gdp_md_est / max(1, feat$properties$pop_est)
    )
  )
  feat
})

# Add the now-styled GeoJSON object to the map
leaflet() %>% addGeoJSON(geojson)
```

Prevalence Function 
```{r}
Rprev(Fatalities$fatal, Fatalities$pop)
datpack_prevalence <- function{data, existing_cases, population, state, time}{
data %>%
  group_by(data$state,data$year) %>%
  mutate(data["prevalence"] = data["existing_cases"]/ data["population"]) %>%
ggplot(data = states) + 
  geom_polygon(aes(x = long, y = lat, fill = region, group = group), color = "white") + 
  coord_fixed(1.3) +
  guides(fill=prevalence) 
  }
  # One Year apply across the state mcgrinr package tidyverse, r oxgen explaining argument merge and join and  preprocessing function 
#filter unique state , state refers to state column 

map_prevalence <- function(data,existing_cases,population,state,year) {
  data %>%
    group_by(!! sym(state), !! sym(year)) %>%
    mutate(prevalence = !! sym(existing_cases) / !!sym(population)) %>%
  geom_polygon(aes(x = long, y = lat, fill = region, group = group), color = "white") + 
  coord_fixed(1.3) +
  guides(fill=prevalence) 
ditch_the_axes <- theme(
  axis.text = element_blank(),
  axis.line = element_blank(),
  axis.ticks = element_blank(),
  panel.border = element_blank(),
  panel.grid = element_blank(),
  axis.title = element_blank()
  )
states <- map_data("state")
all_states <- ggplot(data = states, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray")
all_states

all_states_fatal <- all_states + 
      geom_polygon(data = Fatalities, aes(fill = fatal), color = "white") +
      geom_polygon(color = "black", fill = NA) +
      theme_bw() +
      ditch_the_axes

all_states_fatal
  
  eb2 <- all_states_fatal + 
    scale_fill_gradientn(colours = rev(rainbow(7)),
                         breaks = c(2, 4, 10, 100, 1000, 10000),
                         trans = "log10")
eb2
  }
map_prevalence(Fatalities,existing_cases= "fatal",population="pop",state="al",year="1982")

map_prevalence(Fatalities,existing_cases= "fatal",population="pop",state="state",year="year")
# Creating map 
  for (i in length(s)){
  if (s = "california"){
  plot_usmap(
    data = d, values = "prevalence", include = c("CA"), lines = "red"
  ) + 
  scale_fill_continuous(
    low = "white", high = "red", name = "Prevalence", label = scales::comma
  ) + 
  labs(title = "California", subtitle = "These are the states in the Pacific Timezone.") +
  theme(legend.position = "right")
  }
    else {print "none"}
  }
  return(prevalence)
}
```
truePrev(x, n, SE = 1, SP = 1, prior = c(1, 1),
nchains = 2, burnin = 10000, update = 10000,
verbose = FALSE)


EXAMPLE CODE:
```{r}
pal <- colorNumeric("viridis", NULL)

leaflet(mapStates) %>%
  addTiles() %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.3, fillOpacity = 1,
    fillColor = ~pal(log10()),
    label = ~paste0(county, ": ", formatC(pop, big.mark = ","))) %>%
  addLegend(pal = pal, values = ~log10(pop), opacity = 1.0,
    labFormat = labelFormat(transform = function(x) round(10^x)))
```

EXAMPLE CODE
```{r}
library(usmap)
library(ggplot2)

plot_usmap(include = c("CA", "ID", "NV", "OR", "WA")) +
  labs(title = "Western US States", subtitle = "These are the states in the Pacific Timezone.")

plot_usmap(data = Fatalities, values = "mormon", lines = "red") + 
  scale_fill_continuous(name = "Mormon", label = scales::comma) + 
  theme(legend.position = "right")

plot_usmap(
    data = Fatalities, values = "mormon", include = c("CA", "ID", "NV", "OR", "WA"), lines = "red"
  ) + 
  scale_fill_continuous(
    low = "white", high = "red", name = "Mormon", label = scales::comma
  ) + 
  labs(title = "Western US States", subtitle = "These are the states in the Pacific Timezone.") +
  theme(legend.position = "right")
```

Joining
```{r}
library(tidyverse)
library(fiftystater)
library(viridis)

tbl <- state.x77 %>%
  as_tibble(rownames = "state") %>%
  bind_cols(state_name = str_to_lower(state.abb)) %>%
  rename(value_x = Income) %>%
  select(state_name, value_x)

state_abbs <- tibble(state_full = str_to_lower(state.name), abb = str_to_lower(state.abb))
tbl_m <- left_join(tbl, state_abbs, by = c("state_name" = "abb")) %>%
  rename(id = state_full)

us_map <- map_data("state")
Fatalities %>%
  full_join(state_abbs, (by = "abb" = "state"))
    tbl_df() %>%
  mutate(state = rownames(votes.repub),
         state = tolower(state)) %>%
  right_join(us_map, by = c("state" = "region")) %>%
  ggplot(aes(x = long, y = lat, group = group, fill = `fatal`)) +
  geom_polygon(color = "black") + 
  theme_void() + 
  scale_fill_viridis(name = "Republican\fatal (%)")


ggplot(tbl_m) +
  geom_map(map = states, aes(map_id = region, fill = value_x)) + 
  expand_limits(x = states$long, y = states$lat) +
  coord_map() +
  geom_text(
    data = states %>%
      group_by(region) %>%
      summarise(lat = mean(c(max(lat), min(lat))),
                long = mean(c(max(long), min(long)))) %>%
      mutate(state = id) %>%
      left_join(tbl_m, by = c("state" = "id")),
    aes(x = long, y = lat, label = value_x )
    ) +
  scale_fill_viridis() +
  scale_x_continuous(breaks = NULL) +
  scale_y_continuous(breaks = NULL) +
  labs(x = "", y = "") + theme(legend.position = "bottom", 
                               panel.background = element_blank())
```