## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup, message=FALSE, warning=FALSE--------------------------------------
library(ggdibbler)
library(ggplot2)
library(dplyr)
library(sf)
library(distributional)

## -----------------------------------------------------------------------------
glimpse(toy_temp)

## -----------------------------------------------------------------------------
# Plot Raw Data
ggplot(toy_temp) +
  geom_sf(aes(geometry=county_geometry)) +
  geom_jitter(aes(x=county_longitude, y=county_latitude, colour=recorded_temp), 
              width=5000, height =5000, alpha=0.7)


## -----------------------------------------------------------------------------
# Mean data
toy_temp_mean <- toy_temp |> 
  group_by(county_name) |>
  summarise(temp_mean = mean(recorded_temp)) 
  
# Plot Mean Data
ggplot(toy_temp_mean) +
  geom_sf(aes(geometry=county_geometry, fill=temp_mean))

## -----------------------------------------------------------------------------
# Mean and variance data
toy_temp_est <- toy_temp |> 
  group_by(county_name) |>
  summarise(temp_mean = mean(recorded_temp),
            temp_se = sd(recorded_temp)/sqrt(n())) 

## -----------------------------------------------------------------------------
# Distribution
toy_temp_dist <- toy_temp_est |> 
  mutate(temp_dist = dist_normal(temp_mean, temp_se)) |>
  select(county_name, temp_dist) 

# Plot Distribution Data
ggplot(toy_temp_dist) +
  geom_sf_sample(aes(geometry=county_geometry, fill=temp_dist)) 

## -----------------------------------------------------------------------------
ggplot(toy_temp_dist) + 
  geom_sf_sample(aes(geometry = county_geometry, fill=temp_dist), linewidth=0.1) + 
  geom_sf(aes(geometry = county_geometry), fill=NA, linewidth=1)