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quarto-blog/posts/2020-06-25_diabetes-prevalence-in-nc/diabetes-1.R

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updated site
2023-10-12 10:33:35 -04:00
#These first few lines run only when the file is run in RStudio, !!NOT when an Rmd/Rnw file calls it!!
rm(list=ls(all=TRUE)) #Clear the variables from previous runs.
cat("\f") # clear console
# ---- load-packages --------------------------------------------------
# Attach these packages so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr) # enables piping : %>%
library(dplyr) # data wrangling
library(ggplot2) # graphs
library(tidyr) # data tidying
library(maps)
library(mapdata)
library(sf)
library(readr)
# ---- load-sources ---------------------------------------------------
# ---- declare-globals ----------------------------------------------------
#set ggplot theme
ggplot2::theme_set(theme_bw())
# ---- load-data ------------------------------------------------------
# load the data, and have all column names in lowercase
nc_diabetes_data_raw <- read_csv("https://raw.githubusercontent.com/mmmmtoasty19/nc-diabetes-epidemic-2020/62bdaa6971fbdff09214de7c013d40122abbe40d/data-public/derived/nc-diabetes-data.csv") %>%
rename_all(tolower)
us_diabetes_data_raw <- read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/62bdaa6971fbdff09214de7c013d40122abbe40d/data-public/raw/us_diabetes_totals.csv"
,skip = 2)
rural_counties <- read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/metadata/rural-counties.csv")
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"))
diabetes_atlas_data_raw <- read_csv("https://raw.githubusercontent.com/mmmmtoasty19/nc-diabetes-epidemic-2020/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/raw/DiabetesAtlasData.csv"
,col_types = cols(LowerLimit = col_skip(),
UpperLimit = col_skip(),
Percentage = col_double()), skip = 2)
# ---- load-map-data ----------------------------------------------------------
# load in both US State Map and NC County Map
nc_counties_map_raw <- st_as_sf(map("county",region = "north carolina", plot = FALSE,fill = TRUE)) %>%
mutate_at("ID", ~stringr::str_remove(.,"north carolina,"))
state_map_raw <- st_as_sf(map("state",plot = FALSE,fill = TRUE ))
nc_cities <- st_as_sf(read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/metadata/nc_cities.csv"),
coords = c("long", "lat")
,remove = FALSE
,agr = "constant"
,crs = 4326)
# ---- tweak-data --------------------------------------------------------------
county_centers <- county_centers_raw %>%
mutate_all(~stringr::str_replace_all(.,
c("\\°" = ""
,"\\+" = ""
,"\\–" = "-"
)
)
) %>%
mutate(across(c("lat","long"), ~iconv(.,from = 'UTF-8', to = 'ASCII//TRANSLIT'))
,across(c("lat","long"),~stringr::str_remove_all(.,"\\?"))) %>%
mutate_at(c("lat","long"),as.numeric) %>%
mutate(across("long", ~(. * -1))) %>%
mutate_at("county", tolower)
us_diabetes_data <- us_diabetes_data_raw %>%
filter(Year >= 2000) %>%
select( "Year","Total - Percentage") %>%
rename(year = Year , us_pct = `Total - Percentage`)
diabetes_atlas_data <- diabetes_atlas_data_raw %>%
mutate_at("State", tolower) %>%
filter(Year >= 2000)
state_map_abb <- state_map_raw %>%
left_join(read_csv("https://github.com/mmmmtoasty19/nc-diabetes-epidemic-2020/raw/b29bfd93b20b73a7000d349cb3b55fd0822afe76/data-public/metadata/state-abb.csv") %>%
mutate_at("state", tolower)
,by = c("ID" = "state") )
# ---- merge-data ---------------------------------------------------------
#join US totals to NC data
nc_diabetes_data <- nc_diabetes_data_raw %>%
mutate_at("county", ~stringr::str_replace_all(.,"Mcdowell","McDowell")) %>%
mutate(
rural = county %in% rural_counties$rural_counties
) %>%
mutate_at("county",tolower) %>%
left_join(us_diabetes_data)
nc_counties_map <- nc_counties_map_raw %>%
left_join(nc_diabetes_data, by = c("ID" = "county")) %>%
left_join(county_centers, by = c("ID" = "county")) %>%
rename(
center_long = long
,center_lat = lat)
state_map <- state_map_abb %>%
left_join(diabetes_atlas_data, by = c("ID" = "State")) %>%
rename_all(tolower)
# ---- o-g1 ------------------------------------------------------------------
us_diabetes_data <- us_diabetes_data %>%
mutate(
change = lead(us_pct) - us_pct
,change = if_else(change > 0, TRUE, FALSE)
) %>%
mutate_at("change", ~stringr::str_replace_na(.,"NA"))
o_g1 <- us_diabetes_data %>%
ggplot(aes(x = year, y = us_pct)) +
geom_line(color= "#D95F02") +
# geom_line(aes(color = change, group = 1)) +
geom_point(shape = 21, size = 3,color= "#D95F02") +
# geom_point(aes(color = change),shape = 21, size = 3) +
scale_color_manual(values = c(
"TRUE" = "#D95F02"
,"FALSE" = "#7570B3"
), guide = FALSE) +
labs(
title = "Percentage of Diagnosed Diabetes in Adults (18+), National Level"
,x = NULL
,y = NULL
,caption = "Note: Data from the CDC's National Health Interview Survey (NHIS)"
)
o_g1
# ---- s-g1 -----------------------------------------------------------------
s_g1 <- state_map %>%
st_drop_geometry() %>%
ggplot(aes(x = year, y = percentage, color = region)) +
geom_line(aes(group = id ),alpha = 0.3,na.rm = TRUE) +
geom_smooth(method = "lm", se = FALSE) +
ggpmisc::stat_poly_eq(formula = y ~ + x ,
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
parse = TRUE) +
geom_vline(xintercept = 2011, linetype = "dashed", color = "gray") +
scale_color_brewer(palette = "Dark2"
,direction = -1
,labels = snakecase::to_title_case
) +
labs(
title = "Percentage of Diagnosed Diabetes in Adults (18+) \nby State and Region"
,x = NULL
,y = NULL
,color = "Region"
,caption = "Regions from US Census Bureau"
)
s_g1
# ---- s-g2 ---------------------------------------------------------------
s_g2 <- state_map %>%
st_drop_geometry() %>%
filter(region == "south") %>%
mutate_at("id", ~snakecase::to_title_case(.)) %>%
ggplot(aes(x = year, y = percentage)) +
geom_line(aes(group = id ),na.rm = TRUE, color= "#D95F02") +
gghighlight::gghighlight(id == "North Carolina", label_params = list(vjust = 3)) +
scale_y_continuous(breaks = seq(5,13,2)) +
scale_x_continuous(minor_breaks = seq(2000,2016,1)) +
labs(
title = "Percentage of Diagnosed Diabetes in Adults (18+) \nSouth Region"
,x = NULL
,y = NULL
,caption = "Regions from US Census Bureau"
) +
theme()
s_g2
# ---- nc-g1 ----------------------------------------------------------------------
d1 <- nc_diabetes_data %>%
group_by(year) %>%
summarise(
pct = mean(percentage)
,us_pct = mean(us_pct)
) %>%
pivot_longer(
cols = c("pct", "us_pct")
,names_to = "metric"
,values_to = "values"
) %>%
mutate(
metric = factor(metric
,levels = c("pct","us_pct")
,labels = c("NC", "National"))
)
nc_g1 <- d1 %>%
ggplot(aes(x = year, y = values, color = metric)) +
geom_line() +
geom_point(shape = 21, size = 3) +
geom_vline(xintercept = 2011, linetype = "dashed", color = "gray") +
scale_y_continuous(labels = function(x) paste0(x, "%")) +
scale_color_brewer(palette = "Dark2") +
labs(
x = NULL
,y = NULL
,color = NULL
,title = "Percent of Adults (20+) with Diagnosed Diabetes"
)
nc_g1
# ---- nc-data-aberration ---------------------------------------------------
nc_g1a <- nc_diabetes_data %>%
ggplot(aes(x = year, y = percentage)) +
geom_line(aes(group = county),alpha = 0.4) +
labs(
x = NULL
,y = NULL
,color = NULL
)
nc_g1a
# ---- nc-g2 -----------------------------------------------------------------
d2 <- nc_diabetes_data %>%
select(-us_pct) %>%
mutate(
pct_rural = if_else(rural == TRUE, percentage, FALSE)
,pct_urban = if_else(rural == FALSE, percentage, FALSE)
) %>%
select(-countyfips,-percentage) %>%
group_by(year) %>%
summarise(
pct_rural = mean(pct_rural,na.rm = TRUE)
,pct_urban = mean(pct_urban,na.rm = TRUE)
) %>% left_join(us_diabetes_data) %>%
pivot_longer(
cols = c("us_pct", "pct_rural","pct_urban")
,names_to = "metric"
,values_to = "value"
,values_drop_na = TRUE
) %>%
mutate(
metric = factor(metric,
levels = c("pct_rural","pct_urban","us_pct")
,labels = c("Rural","Urban","US")
)
)
nc_g2 <- d2 %>% ggplot(aes(x = year, y = value, color = metric)) +
geom_line() +
geom_point(shape = 21, size = 3) +
geom_vline(xintercept = 2011, linetype = "dashed", color = "gray") +
scale_y_continuous(labels = function(x) paste0(x, "%")) +
scale_color_brewer(palette = "Dark2") +
labs(
x = NULL
,y = NULL
,color = NULL
,title = "Percent of Adults (20+) with Diagnosed Diabetes \nDisplaying Rural vs Urban"
)
nc_g2
# ---- spaghetti-plot ----------------------------------------------------
g50 <- nc_diabetes_data %>%
filter(year < 2015) %>%
mutate(
rural = factor(rural
,levels = c(TRUE,FALSE)
,labels = c("Rural", "Urban")
)
) %>%
ggplot(aes(x = year, y = percentage, color = rural)) +
geom_line(aes(group = county),alpha = 0.3) +
geom_smooth(aes(group = rural), method = "loess", se= FALSE, size = 1.1) +
scale_color_brewer(palette = "Dark2") +
labs(
title = "Percent of Adults (20+) with Diagnosed Diabetes \nAll North Carolina Counties"
,x = NULL
,y = NULL
,color = NULL
)
g50
# ---- c-g1 --------------------------------------------------------------
nc_counties_map_binned <- nc_counties_map %>%
filter(year < 2015) %>%
mutate(
bin = dlookr::binning(.$percentage, nbins = 6 ,type = "equal")
,bin = forcats::fct_recode(bin
,"6.5 - 7.9" = "[6.5,7.97]"
,"8.0 - 9.4" = "(7.97,9.43]"
,"9.5 - 10.9" = "(9.43,10.9]"
,"11.0 - 12.4" = "(10.9,12.4]"
,"12.5 - 13.8" = "(12.4,13.8]"
,"13.9 - 15.3" = "(13.8,15.3]"
)
)
c_g1 <- nc_counties_map_binned %>%
filter(year %in% c(2006,2014)) %>%
ggplot() +
geom_sf() + #blank geom_sf keeps gridlines from overlapping map
geom_sf(aes(fill = bin,color = rural)) +
geom_sf(data = nc_cities) +
ggrepel::geom_text_repel(data = nc_cities,
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"
) +
coord_sf(xlim = c(-84.5,-75.5), ylim = c(33.75,37)) +
facet_wrap(~year) +
scale_fill_viridis_d(alpha = 0.6, direction = -1) +
scale_color_manual(
values = c(
"FALSE" = "gray"
,"TRUE" = "black"
),guide = 'none') +
labs(
title = "Estimated Diabetes in Adults (20+) by County"
,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)
,legend.position = "bottom"
,plot.title = element_text(hjust = 0.5)
)
c_g1
# ---- county-distribution-histogram ------------------------------------
# Not USED
c_g1a <- nc_counties_map_binned %>%
mutate(
rural = factor(rural
,levels = c(TRUE,FALSE)
,labels = c("Rural", "Urban")
)
) %>%
filter(year %in% c(2006,2014)) %>%
ggplot(aes(x = bin, fill = rural)) +
geom_bar(stat = "count"
,position = "dodge"
) +
geom_text(aes(label=..count..)
,position = position_dodge(width = 1)
,stat = "count"
,vjust = -0.1
,size = 5) +
facet_wrap(~year) +
scale_fill_brewer(palette = "Dark2") +
labs(
fill = NULL
,x = NULL
,y = NULL
)
c_g1a
# ---- county-boxplot ----
c_g1c <- nc_counties_map %>%
mutate(
rural = factor(rural
,levels = c(TRUE,FALSE)
,labels = c("Rural", "Urban")
)) %>%
filter(year < 2015) %>%
ggplot(aes(x = year, y = percentage, group = interaction(year,rural), fill = rural)) +
geom_boxplot(alpha = 0.5) +
scale_fill_brewer(palette = "Dark2") +
scale_x_continuous(breaks = seq(2004,2014,2)) +
labs(
x = NULL
,y = NULL
,fill = NULL
,title = "Distribution of Estimated Cases by County 2006 - 2014"
)
c_g1c
# ---- c-g4 ---------------------------------------------------------------
d3 <- nc_counties_map %>%
st_drop_geometry() %>%
filter(year %in% c(2006,2014)) %>%
select(-countyfips,-us_pct) %>%
pivot_wider(names_from = "year"
,values_from = "percentage") %>%
mutate(
pct_p = `2014` - `2006`
,pct_c = ((`2014` - `2006`)/`2006`) * 100
) %>%
left_join(nc_counties_map_raw) %>%
st_as_sf()
c_g4 <- d3 %>%
ggplot() +
geom_sf() + #blank geom_sf keeps gridlines from overlapping map
geom_sf(aes(fill = pct_c ,color = rural)) +
geom_sf(data = nc_cities) +
ggrepel::geom_text_repel(data = nc_cities,
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
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