Introduction

This is a quick start for using this Dataset.

Deaths given are per 100,000

library(choroplethr)
library(choroplethrMaps)

# Read our input
d <- read.csv("../input/mort.csv", sep=",")
t <- d[d$Category == "Neoplasms",c("FIPS","Mortality.Rate..2014.")]
t <- t[t$FIPS > 1000,] # We want counties, value > 1000

# Change to c("region","value") for mapping
colnames(t)<- c("region","value")


county_choropleth(t,
                 title      = "Mortality Rate 2014 (Neoplasms)",
                 legend     = "Deaths per 100,000")

# Quick Notes:
#  unique(d$Category)  # Easy way to list all
#  rownames(d)         # You can copy and paste

Pennsylvania, New Jersey and New York

# State Zoom
county_choropleth(t,
                 title      = "Mortality Rate 2014 (Neoplasms)",
                 legend     = "Deaths per 100,000",
                 num_colors = 1,
                 state_zoom = c("pennsylvania", "new jersey", "new york"))

Self-harm and interpersonal violence

See the article “How Americans Die May Depend On Where They Live” by Anna Maria Barry-Jester (FiveThirtyEight), which mentions the increasing death rate from suicide.

title <- "Mortality Rate 2014
                 (Self-harm and interpersonal violence)"
legend     = "Deaths per 100,000"



category = "Self-harm and interpersonal violence"
t <- d[d$Category == category,c("FIPS","Mortality.Rate..2014.")]
t <- t[t$FIPS > 1000,] # We want counties, value > 1000

# Change to c("region","value") for mapping
colnames(t)<- c("region","value")

county_choropleth(t,
                 title      = title,
                 legend     = legend)

Note below the legend change. In 2000 the highest scale is 25.19 to 71.50, but in 2014 the highest scale is 28.57 to 85.90.

Biggest Percent Increase (State Level)

Category % Change in Mortality Rate, 1980-2014 (mean)
Mental and substance use disorders 260.62
HIV/AIDS and tuberculosis 51.40
Maternal disorders 47.16
Neglected tropical diseases and malaria 33.46
Chronic respiratory diseases 33.09
Diabetes, urogenital, blood, and endocrine diseases 25.78
Neurological disorders 17.97



Biggest Percent *Decrease (State Level)

Leading this category is “Forces of nature, war, and legal intervention”, which is found by grouping % Changes by state for 1980-2014, averaging the values, then, taking the greatest decrease or negative value.

Shown on the map below, pink values (like Texas), show the greatest decrease.

Category *Values below are % Decrease in Mortality Rate, 1980-2014 (mean)
Forces of nature, war, and legal intervention 77.97
Neonatal disorders 63.07
Cardiovascular diseases 48.02
Transport injuries 45.11
Other non-communicable diseases 36.01
Nutritional deficiencies 31.39
Digestive diseases 24.42