1/16/2020

The purpose of todays presentation

How can Tracking leverage data visualization tools to communicate key messages to drive public health actions?

  • RStudio - ggplot, plotly
  • RMarkdown - flexdashboard
  • RShiny - shinydashboard
  • ArcGIS StoryMaps

Step 1: Load packages and data

# workspace
library(tidyverse)
library(tidycensus)
library(viridis)
library(tigris)
library(gganimate)
options(tigris_use_cache = TRUE)

# import 
data <- read_csv("./Static Maps and GIFs_EP/ExtremeHeatProjections_A2_90__Full.csv")

Step 2: Data wrangling

# wrangle
EH <- data %>%
   mutate(GEOID = as.character(countyFIPS)) %>%
   select(everything(), -c(stateFIPS, countyFIPS))


EH$GEOID <- ifelse(nchar(EH$GEOID) <5, 
            paste("0", EH$GEOID, sep=""), EH$GEOID)  
#glimpse(EH)

Step 2 Continued: Load and join census data

county_pop <- 
  get_acs(geography = "county", 
          variables = "B01003_001",  # total population 
          year = 2016, # 2010 decennial census
          geometry = TRUE, # load TIGER shapefiles
          keep_geo_vars = TRUE, 
          shift_geo = TRUE) %>% #shifts AK and HI
   rename(pop = estimate)

geo <- county_pop %>%
   select(GEOID, geometry) #remove extra variables 

EH_geo <- inner_join(geo, EH, by = 'GEOID')

Create static map

GA <- EH_geo %>% 
      filter(State == "Georgia" & Year == 2030)


g <- ggplot(GA, aes(fill = as.integer(Value))) + 
      geom_sf() +
      theme_minimal() +
      scale_fill_viridis() +
      coord_sf(datum = NA) +
      labs(title = "Future projections of extreme 
                    heat in Georgia. \nYear: 2030",
           caption = "Absolute Threshold: 90 degrees F 
                    \nEmissions Scenario: High Emissions (A2)",
           fill = "Extreme heat days")

g

Static Map Created

Create animated map

d <- c(2020, 2030, 2040, 2050, 2060, 2070, 2080)

GA_d <- EH_geo %>%
      filter(State == "Georgia" & Year %in% d) 

GAmap_d <- ggplot(GA_d, aes(fill = as.integer(Value))) + 
      geom_sf() +
      scale_fill_viridis_c() + scale_color_viridis_c() +
      theme_minimal() + coord_sf(datum = NA) +
      labs(title = "Title1", caption = "Caption1",
           fill = "Extreme heat days") +
      transition_states(Year, 
                        transition_length = 3, 
                        state_length = 10) +
      ease_aes('linear')
animate(GAmap_d)

#anim_save("GAmap_decade.gif", animation = GAmap_d)

Animated Map Created

Create animated chart

# workspace
library(tidyverse)
library(ggplot2)
library(viridis)
library(gganimate)
library(gifski)
library(png)

# import 
EH <- read_csv("./Static Maps and GIFs_EP/ExtremeHeatProjections_A2_90__Full.csv")

Selecting some counties in GA

GA <- EH %>%
      filter(State == "Georgia" &
                   County %in% c("Bartow", "Cherokee", 
                                 "Forsyth", "Cobb", 
                                 "Fulton", "Gwinnett",
                                 "Dekalb", "Rockdale", 
                                 "Clayton", "Douglas") &
             !is.na(Value))
#summary(GA)

Create the animated chart

g <- ggplot(GA,  aes(x = as.factor(County), 
                y = Value, fill = County)) +
      geom_col()+ coord_flip()+
      theme(legend.position = "none") +
      labs(title = "Some Title",
           caption = "Some Caption",
           x = "",  y = "Extreme Heat Days") +
      transition_states(Year,
                        transition_length = 1,
                        state_length = 1) +
      ease_aes('linear')+scale_fill_viridis_d() +
      geom_text(aes(y=Value, label=Value, hjust=0)) 
#anim_save("GA_EHdays_2020-2030.gif", animation = g)

View the animated chart