541 lines
16 KiB
R
541 lines
16 KiB
R
#These first few lines run only when the file is run in RStudio, !!NOT when an Rmd/Rnw file calls it!!
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rm(list=ls(all=TRUE)) #Clear the variables from previous runs.
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cat("\f") # clear console
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# ---- load-packages --------------------------------------------------
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# Attach these packages so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
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library(magrittr) # enables piping : %>%
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library(dplyr) # data wrangling
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library(ggplot2) # graphs
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library(tidyr) # data tidying
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library(maps)
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library(mapdata)
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library(sf)
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library(readr)
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# ---- load-sources ---------------------------------------------------
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# ---- declare-globals ----------------------------------------------------
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#set ggplot theme
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ggplot2::theme_set(theme_bw())
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# ---- load-data ------------------------------------------------------
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# load the data, and have all column names in lowercase
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nc_diabetes_data_raw <- read_csv("https://raw.githubusercontent.com/mmmmtoasty19/nc-diabetes-epidemic-2020/62bdaa6971fbdff09214de7c013d40122abbe40d/data-public/derived/nc-diabetes-data.csv") %>%
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rename_all(tolower)
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us_diabetes_data_raw <- read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/62bdaa6971fbdff09214de7c013d40122abbe40d/data-public/raw/us_diabetes_totals.csv"
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,skip = 2)
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rural_counties <- read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/metadata/rural-counties.csv")
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county_centers_raw <- read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/raw/nc_county_centers.csv", col_names = c("county", "lat","long"))
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diabetes_atlas_data_raw <- read_csv("https://raw.githubusercontent.com/mmmmtoasty19/nc-diabetes-epidemic-2020/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/raw/DiabetesAtlasData.csv"
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,col_types = cols(LowerLimit = col_skip(),
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UpperLimit = col_skip(),
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Percentage = col_double()), skip = 2)
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# ---- load-map-data ----------------------------------------------------------
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# load in both US State Map and NC County Map
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nc_counties_map_raw <- st_as_sf(map("county",region = "north carolina", plot = FALSE,fill = TRUE)) %>%
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mutate_at("ID", ~stringr::str_remove(.,"north carolina,"))
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state_map_raw <- st_as_sf(map("state",plot = FALSE,fill = TRUE ))
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nc_cities <- st_as_sf(read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/metadata/nc_cities.csv"),
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coords = c("long", "lat")
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,remove = FALSE
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,agr = "constant"
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,crs = 4326)
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# ---- tweak-data --------------------------------------------------------------
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county_centers <- county_centers_raw %>%
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mutate_all(~stringr::str_replace_all(.,
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c("\\°" = ""
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,"\\+" = ""
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,"\\–" = "-"
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)
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)
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) %>%
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mutate(across(c("lat","long"), ~iconv(.,from = 'UTF-8', to = 'ASCII//TRANSLIT'))
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,across(c("lat","long"),~stringr::str_remove_all(.,"\\?"))) %>%
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mutate_at(c("lat","long"),as.numeric) %>%
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mutate(across("long", ~(. * -1))) %>%
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mutate_at("county", tolower)
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us_diabetes_data <- us_diabetes_data_raw %>%
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filter(Year >= 2000) %>%
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select( "Year","Total - Percentage") %>%
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rename(year = Year , us_pct = `Total - Percentage`)
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diabetes_atlas_data <- diabetes_atlas_data_raw %>%
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mutate_at("State", tolower) %>%
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filter(Year >= 2000)
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state_map_abb <- state_map_raw %>%
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left_join(read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/metadata/state-abb.csv") %>%
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mutate_at("state", tolower)
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,by = c("ID" = "state") )
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# ---- merge-data ---------------------------------------------------------
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#join US totals to NC data
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nc_diabetes_data <- nc_diabetes_data_raw %>%
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mutate_at("county", ~stringr::str_replace_all(.,"Mcdowell","McDowell")) %>%
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mutate(
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rural = county %in% rural_counties$rural_counties
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) %>%
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mutate_at("county",tolower) %>%
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left_join(us_diabetes_data)
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nc_counties_map <- nc_counties_map_raw %>%
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left_join(nc_diabetes_data, by = c("ID" = "county")) %>%
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left_join(county_centers, by = c("ID" = "county")) %>%
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rename(
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center_long = long
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,center_lat = lat)
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state_map <- state_map_abb %>%
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left_join(diabetes_atlas_data, by = c("ID" = "State")) %>%
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rename_all(tolower)
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# ---- o-g1 ------------------------------------------------------------------
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us_diabetes_data <- us_diabetes_data %>%
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mutate(
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change = lead(us_pct) - us_pct
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,change = if_else(change > 0, TRUE, FALSE)
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) %>%
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mutate_at("change", ~stringr::str_replace_na(.,"NA"))
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o_g1 <- us_diabetes_data %>%
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ggplot(aes(x = year, y = us_pct)) +
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geom_line(color= "#D95F02") +
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# geom_line(aes(color = change, group = 1)) +
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geom_point(shape = 21, size = 3,color= "#D95F02") +
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# geom_point(aes(color = change),shape = 21, size = 3) +
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scale_color_manual(values = c(
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"TRUE" = "#D95F02"
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,"FALSE" = "#7570B3"
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), guide = FALSE) +
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labs(
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title = "Percentage of Diagnosed Diabetes in Adults (18+), National Level"
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,x = NULL
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,y = NULL
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,caption = "Note: Data from the CDC's National Health Interview Survey (NHIS)"
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)
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o_g1
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# ---- s-g1 -----------------------------------------------------------------
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s_g1 <- state_map %>%
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st_drop_geometry() %>%
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ggplot(aes(x = year, y = percentage, color = region)) +
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geom_line(aes(group = id ),alpha = 0.3,na.rm = TRUE) +
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geom_smooth(method = "lm", se = FALSE) +
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ggpmisc::stat_poly_eq(formula = y ~ + x ,
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aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
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parse = TRUE) +
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geom_vline(xintercept = 2011, linetype = "dashed", color = "gray") +
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scale_color_brewer(palette = "Dark2"
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,direction = -1
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,labels = snakecase::to_title_case
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) +
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labs(
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title = "Percentage of Diagnosed Diabetes in Adults (18+) \nby State and Region"
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,x = NULL
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,y = NULL
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,color = "Region"
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,caption = "Regions from US Census Bureau"
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)
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s_g1
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# ---- s-g2 ---------------------------------------------------------------
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s_g2 <- state_map %>%
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st_drop_geometry() %>%
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filter(region == "south") %>%
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mutate_at("id", ~snakecase::to_title_case(.)) %>%
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ggplot(aes(x = year, y = percentage)) +
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geom_line(aes(group = id ),na.rm = TRUE, color= "#D95F02") +
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gghighlight::gghighlight(id == "North Carolina", label_params = list(vjust = 3)) +
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scale_y_continuous(breaks = seq(5,13,2)) +
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scale_x_continuous(minor_breaks = seq(2000,2016,1)) +
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labs(
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title = "Percentage of Diagnosed Diabetes in Adults (18+) \nSouth Region"
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,x = NULL
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,y = NULL
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,caption = "Regions from US Census Bureau"
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) +
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theme()
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s_g2
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# ---- nc-g1 ----------------------------------------------------------------------
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d1 <- nc_diabetes_data %>%
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group_by(year) %>%
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summarise(
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pct = mean(percentage)
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,us_pct = mean(us_pct)
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) %>%
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pivot_longer(
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cols = c("pct", "us_pct")
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,names_to = "metric"
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,values_to = "values"
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) %>%
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mutate(
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metric = factor(metric
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,levels = c("pct","us_pct")
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,labels = c("NC", "National"))
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)
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nc_g1 <- d1 %>%
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ggplot(aes(x = year, y = values, color = metric)) +
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geom_line() +
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geom_point(shape = 21, size = 3) +
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geom_vline(xintercept = 2011, linetype = "dashed", color = "gray") +
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scale_y_continuous(labels = function(x) paste0(x, "%")) +
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scale_color_brewer(palette = "Dark2") +
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labs(
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x = NULL
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,y = NULL
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,color = NULL
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,title = "Percent of Adults (20+) with Diagnosed Diabetes"
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)
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nc_g1
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# ---- nc-data-aberration ---------------------------------------------------
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nc_g1a <- nc_diabetes_data %>%
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ggplot(aes(x = year, y = percentage)) +
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geom_line(aes(group = county),alpha = 0.4) +
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labs(
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x = NULL
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,y = NULL
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,color = NULL
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)
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nc_g1a
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# ---- nc-g2 -----------------------------------------------------------------
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d2 <- nc_diabetes_data %>%
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select(-us_pct) %>%
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mutate(
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pct_rural = if_else(rural == TRUE, percentage, FALSE)
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,pct_urban = if_else(rural == FALSE, percentage, FALSE)
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) %>%
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select(-countyfips,-percentage) %>%
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group_by(year) %>%
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summarise(
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pct_rural = mean(pct_rural,na.rm = TRUE)
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,pct_urban = mean(pct_urban,na.rm = TRUE)
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) %>% left_join(us_diabetes_data) %>%
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pivot_longer(
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cols = c("us_pct", "pct_rural","pct_urban")
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,names_to = "metric"
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,values_to = "value"
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,values_drop_na = TRUE
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) %>%
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mutate(
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metric = factor(metric,
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levels = c("pct_rural","pct_urban","us_pct")
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,labels = c("Rural","Urban","US")
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)
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)
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nc_g2 <- d2 %>% ggplot(aes(x = year, y = value, color = metric)) +
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geom_line() +
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geom_point(shape = 21, size = 3) +
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geom_vline(xintercept = 2011, linetype = "dashed", color = "gray") +
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scale_y_continuous(labels = function(x) paste0(x, "%")) +
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scale_color_brewer(palette = "Dark2") +
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labs(
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x = NULL
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,y = NULL
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,color = NULL
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,title = "Percent of Adults (20+) with Diagnosed Diabetes \nDisplaying Rural vs Urban"
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)
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nc_g2
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# ---- spaghetti-plot ----------------------------------------------------
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g50 <- nc_diabetes_data %>%
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filter(year < 2015) %>%
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mutate(
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rural = factor(rural
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,levels = c(TRUE,FALSE)
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,labels = c("Rural", "Urban")
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)
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) %>%
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ggplot(aes(x = year, y = percentage, color = rural)) +
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geom_line(aes(group = county),alpha = 0.3) +
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geom_smooth(aes(group = rural), method = "loess", se= FALSE, size = 1.1) +
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scale_color_brewer(palette = "Dark2") +
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labs(
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title = "Percent of Adults (20+) with Diagnosed Diabetes \nAll North Carolina Counties"
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,x = NULL
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,y = NULL
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,color = NULL
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)
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g50
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# ---- c-g1 --------------------------------------------------------------
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nc_counties_map_binned <- nc_counties_map %>%
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filter(year < 2015) %>%
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mutate(
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bin = dlookr::binning(.$percentage, nbins = 6 ,type = "equal")
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,bin = forcats::fct_recode(bin
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,"6.5 - 7.9" = "[6.5,7.97]"
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,"8.0 - 9.4" = "(7.97,9.43]"
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,"9.5 - 10.9" = "(9.43,10.9]"
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,"11.0 - 12.4" = "(10.9,12.4]"
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,"12.5 - 13.8" = "(12.4,13.8]"
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,"13.9 - 15.3" = "(13.8,15.3]"
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)
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)
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c_g1 <- nc_counties_map_binned %>%
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filter(year %in% c(2006,2014)) %>%
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ggplot() +
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geom_sf() + #blank geom_sf keeps gridlines from overlapping map
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geom_sf(aes(fill = bin,color = rural)) +
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geom_sf(data = nc_cities) +
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ggrepel::geom_text_repel(data = nc_cities,
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aes(x = long, y = lat, label = city)
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,nudge_y = c(-1,1,1,-1,1)
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,nudge_x = c(0,0,0,-1,0)
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) +
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geom_text(data = . %>% filter(rural == TRUE)
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,aes(x = center_long, y = center_lat)
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,label = "R"
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,color = "#696969"
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) +
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coord_sf(xlim = c(-84.5,-75.5), ylim = c(33.75,37)) +
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facet_wrap(~year) +
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scale_fill_viridis_d(alpha = 0.6, direction = -1) +
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scale_color_manual(
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values = c(
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"FALSE" = "gray"
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,"TRUE" = "black"
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),guide = 'none') +
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labs(
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title = "Estimated Diabetes in Adults (20+) by County"
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,fill = "Percentage"
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,y = NULL
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,x = NULL
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) +
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theme(
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panel.background = element_rect(fill = "aliceblue")
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,panel.grid.major = element_line(color = "#D4D4D4", linetype = "dashed",
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size = 0.5)
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,legend.position = "bottom"
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,plot.title = element_text(hjust = 0.5)
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)
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c_g1
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# ---- county-distribution-histogram ------------------------------------
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# Not USED
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c_g1a <- nc_counties_map_binned %>%
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mutate(
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rural = factor(rural
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,levels = c(TRUE,FALSE)
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,labels = c("Rural", "Urban")
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)
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) %>%
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filter(year %in% c(2006,2014)) %>%
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ggplot(aes(x = bin, fill = rural)) +
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geom_bar(stat = "count"
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||
,position = "dodge"
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) +
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geom_text(aes(label=..count..)
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,position = position_dodge(width = 1)
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||
,stat = "count"
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,vjust = -0.1
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,size = 5) +
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facet_wrap(~year) +
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scale_fill_brewer(palette = "Dark2") +
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||
labs(
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||
fill = NULL
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,x = NULL
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||
,y = NULL
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)
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c_g1a
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||
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# ---- county-boxplot ----
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c_g1c <- nc_counties_map %>%
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mutate(
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rural = factor(rural
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,levels = c(TRUE,FALSE)
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,labels = c("Rural", "Urban")
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)) %>%
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filter(year < 2015) %>%
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ggplot(aes(x = year, y = percentage, group = interaction(year,rural), fill = rural)) +
|
||
geom_boxplot(alpha = 0.5) +
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||
scale_fill_brewer(palette = "Dark2") +
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||
scale_x_continuous(breaks = seq(2004,2014,2)) +
|
||
labs(
|
||
x = NULL
|
||
,y = NULL
|
||
,fill = NULL
|
||
,title = "Distribution of Estimated Cases by County 2006 - 2014"
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||
)
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||
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||
c_g1c
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||
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||
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# ---- c-g4 ---------------------------------------------------------------
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d3 <- nc_counties_map %>%
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st_drop_geometry() %>%
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filter(year %in% c(2006,2014)) %>%
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||
select(-countyfips,-us_pct) %>%
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||
pivot_wider(names_from = "year"
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||
,values_from = "percentage") %>%
|
||
mutate(
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||
pct_p = `2014` - `2006`
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||
,pct_c = ((`2014` - `2006`)/`2006`) * 100
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||
) %>%
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||
left_join(nc_counties_map_raw) %>%
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||
st_as_sf()
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||
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||
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||
c_g4 <- d3 %>%
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||
ggplot() +
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||
geom_sf() + #blank geom_sf keeps gridlines from overlapping map
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||
geom_sf(aes(fill = pct_c ,color = rural)) +
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||
geom_sf(data = nc_cities) +
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||
ggrepel::geom_text_repel(data = nc_cities,
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||
aes(x = long, y = lat, label = city)
|
||
,nudge_y = c(-1,1,1,-1,1)
|
||
,nudge_x = c(0,0,0,-1,0)
|
||
) +
|
||
geom_text(data = . %>% filter(rural == TRUE)
|
||
,aes(x = center_long, y = center_lat)
|
||
,label = "R"
|
||
,color = "#696969"
|
||
) +
|
||
# scale_fill_viridis_c(alpha = 0.6, direction = -1) +
|
||
scale_fill_gradient2(
|
||
low = "#d01c8b"
|
||
,mid = "#f7f7f7"
|
||
,high = "#4dac26"
|
||
,midpoint = 0
|
||
) +
|
||
scale_color_manual(
|
||
values = c(
|
||
"FALSE" = "gray"
|
||
,"TRUE" = "black"
|
||
),guide = 'none') +
|
||
labs(
|
||
title = "Percentage Change of Diagnosed Diabetes 2006-2014"
|
||
,fill = "Percentage"
|
||
,y = NULL
|
||
,x = NULL
|
||
) +
|
||
theme(
|
||
panel.background = element_rect(fill = "aliceblue")
|
||
,panel.grid.major = element_line(color = "#D4D4D4", linetype = "dashed",
|
||
size = 0.5)
|
||
)
|
||
|
||
c_g4
|
||
|
||
|
||
# ---- pct_p-histogram ----------------------------------------------------------
|
||
|
||
|
||
|
||
|
||
d4 <- d3 %>%
|
||
st_drop_geometry() %>%
|
||
mutate(
|
||
rural = factor(rural
|
||
,levels = c(TRUE,FALSE)
|
||
,labels = c("Rural", "Urban")
|
||
)
|
||
)
|
||
|
||
|
||
mean_d4 <- d4 %>%
|
||
group_by(rural) %>%
|
||
summarise(.groups = "keep"
|
||
,pct_c = mean(pct_c)
|
||
)
|
||
|
||
g51 <- d4 %>%
|
||
ggplot(aes(x = pct_c, fill = rural, y = ..density.., color = rural)) +
|
||
geom_histogram(binwidth = 5, position = "identity", alpha = 0.3) +
|
||
geom_density(alpha = 0.5) +
|
||
facet_wrap(~rural, ncol = 1) +
|
||
geom_vline(aes(xintercept = pct_c), data = mean_d4) +
|
||
geom_text(aes(x = pct_c, y = 0.038, label = round(pct_c, 2))
|
||
,data = mean_d4
|
||
,hjust = -0.15
|
||
,size = 5
|
||
,color = "#000000") +
|
||
geom_vline(xintercept = 0, linetype = "dashed", color = "#696969") +
|
||
scale_color_brewer(palette = "Dark2", guide = NULL) +
|
||
scale_fill_brewer(palette = "Dark2", guide = NULL) +
|
||
labs(
|
||
x = "Percentage Change"
|
||
,y = "Density"
|
||
,fill = NULL
|
||
)
|
||
g51
|
||
|
||
|
||
|
||
|
||
|