generated from KSUDS/p2_data
-
Notifications
You must be signed in to change notification settings - Fork 1
/
CDC_parser.R
208 lines (169 loc) · 9.36 KB
/
CDC_parser.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
# This code parses data from the CDC's Multiple Cause of Death datafile for FiveThirtyEight's
# "Gun Death in America" project.
# This code produces clean dataframes of firearm deaths and suicides (firearm and non-firearm).
# Code to further process this data for our interactive graphic can be found in the 'interactive_prep.R' file
# elsewhere in this repo.
# Questions/comments/corrections to ben.casselman@fivethirtyeight.com
# All data is from the CDC's Multiple Cause of Death datafile.
# Data: http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm#Mortality_Multiple
# Codebook: http://www.cdc.gov/nchs/data/dvs/Record_Layout_2014.pdf
# Most of these calculations can be checked through CDC's two web tools:
# Wonder search: http://wonder.cdc.gov/controller/datarequest/D76
# WISQARS search: http://webappa.cdc.gov/sasweb/ncipc/mortrate10_us.html (1999-2014)
library(readr)
library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
# The function below will download and parse each year of data.
# Note that older files may require coding tweaks to adapt to older file structures.
# This will save three files:
# 1. all_deaths_YR.RData: Full deaths file, with minimal cleaning
# 2. gun_deaths_YR.RData: Gun deaths only, with some basic additional variables
# 3. suicides_YR.RData: Suicides (firearm and non)
# NOTE THAT EACH FILE IS approx. 1gb
# Function for downloading and parsing data:
CDC_parser <- function(year, url) {
# Set up files
all_deaths_name <- paste0("deaths_", substr(year, 3, 4))
all_deaths_save <- paste0("all_deaths_", substr(year, 3, 4), ".RData")
gun_name <- paste0("guns_", substr(year, 3, 4))
gun_save <- paste0("gun_deaths_", substr(year, 3, 4), ".RData")
suicide_name <- paste0("suicide_", substr(year, 3, 4))
suicide_save <- paste0("suicide_", substr(year, 3, 4), ".RData")
# First download data. These are fixed-width files.
# Layout for recent years (need tweaks for earlier year)
layout <- fwf_widths(c(19,1,40,2,1,1,2,2,1,4,1,2,2,2,2,1,1,1,16,4,1,1,1,1,34,1,1,4,3,1,3,3,2,1,281,1,2,1,1,1,1,33,3,1,1),
col_names = c("drop1", "res_status", "drop2", "education_89", "education_03", "education_flag", "month",
"drop3", "sex", "detail_age", "age_flag", "age_recode", "age_recode2", "age_group",
"age_infant", "death_place", "marital", "day_of_week", "drop4", "data_year", "at_work",
"death_manner", "burial", "autopsy", "drop5", "activity", "injury_place",
"underlying_cause", "cause_recode358", "drop6", "cause_recode113", "cause_recode130",
"cause_recode39", "drop7", "multiple_causes", "drop8", "race", "race_bridged", "race_flag",
"race_recode", "race_recode2", "drop9", "hispanic", "drop10", "hispanic_recode"))
temp <- tempfile()
download.file(url, temp, quiet = T)
# Read in data
raw_file <- read_fwf(unzip(temp), layout)
# Drop empty fields
raw_file <- raw_file %>%
select(-contains("drop"))
# Save 'all_deaths' file
assign(eval(all_deaths_name), raw_file)
save(list = all_deaths_name, file = all_deaths_save)
# Subset suicides
# Suicide codes: X60 - X 84, U03, Y870
suicide_code <- list()
for (i in 1:24) {
suicide_code[[i]] <- paste0("X", i + 59)
}
suicide_code[length(suicide_code)+1] <- "U03"
suicide_code[length(suicide_code)+1] <- "Y870"
# Gun suicides
# X72 (Intentional self-harm by handgun discharge)
# X73 (Intentional self-harm by rifle, shotgun and larger firearm discharge)
# X74 (Intentional self-harm by other and unspecified firearm discharge)
suicide <- raw_file %>%
filter(underlying_cause %in% suicide_code) %>%
mutate(gun = ifelse(underlying_cause %in% c("X72", "X73", "X74"), 1, 0),
year = year)
assign(eval(suicide_name), suicide)
save(list = suicide_name, file = suicide_save)
rm(suicide)
rm(list = suicide_name)
# Subset firearm deaths
# Firearm death codes
# Accidental:
# W32 (Handgun discharge)
# W33 (Rifle, shotgun and larger firearm discharge)
# W34 (Discharge from other and unspecified firearms)
#
# Suicide:
# X72 (Intentional self-harm by handgun discharge)
# X73 (Intentional self-harm by rifle, shotgun and larger firearm discharge)
# X74 (Intentional self-harm by other and unspecified firearm discharge)
#
# Homicide:
# U01.4 (Terrorism involving firearms)
# X93 (Assault by handgun discharge)
# X94 (Assault by rifle, shotgun and larger firearm discharge)
# X95 (Assault by other and unspecified firearm discharge)
#
# Undetermined intent:
# Y22 (Handgun discharge, undetermined intent)
# Y23 (Rifle, shotgun and larger firearm discharge, undetermined intent)
# Y24 (Other and unspecified firearm discharge, undetermined intent)
#
# Legal intervention (Note that we code legal intervention deaths as homicides)
# Y35.0 (Legal intervention involving firearm discharge)
guns <- raw_file %>%
filter(underlying_cause %in% c("W32", "W33", "W34", "X72", "X73", "X74", "U014", "X93", "X94", "X95", "Y22", "Y23", "Y24", "Y350"))
rm(raw_file)
# Add categorical variable for intent, weapon, plus dummy for police shootings
guns <- guns %>%
mutate(intent = ifelse(underlying_cause %in% c("W32", "W33", "W34"), "Accidental",
ifelse(underlying_cause %in% c("X72", "X73", "X74"), "Suicide",
ifelse(underlying_cause %in% c("*U01.4", "X93", "X94", "X95", "Y350"), "Homicide",
ifelse(underlying_cause %in% c("Y22", "Y23", "Y24"), "Undetermined", NA)))),
police = ifelse(underlying_cause == "Y350", 1, 0),
weapon = ifelse(underlying_cause %in% c("W32", "X72", "X93", "Y22"), "Handgun",
ifelse(underlying_cause %in% c("W33", "X73", "X94", "Y23"), "Rifle etc",
"Other/unknown")),
year = year) # Dummy for young men (15-34)
# Create a cleaner age variable. Every age under 1 year will be coded as "0"
guns <- guns %>%
mutate(age = ifelse(substr(detail_age, 1, 1) == "1", as.numeric(substr(detail_age, 2, 4)), # Year
ifelse(detail_age == 9999, NA, 0)),
age = ifelse(age == 999, NA, age))
assign(eval(gun_name), guns)
save(list = gun_name, file = gun_save)
rm(guns)
rm(list = gun_name)
}
# Enter year and url (urls are inconsistent, so easier to enter them directly)
year <- 2013
url <- "ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2013us.zip"
# Now run the function for each year you want:
CDC_parser(year, url)
#########################################################################################################################
# The code below processes the data for FiveThirtyEight's Gun Deaths in America project
# For the project, we used the three most recent years available: 2012-14
# We'll combine these into a single data frame.
# In keeping with CDC practice, we'll eliminate deaths of non-U.S. residents
load("gun_deaths_14.RData")
load("gun_deaths_13.RData")
load("gun_deaths_12.RData")
all_guns <- rbind(guns_12, guns_13, guns_14)
all_guns <- all_guns %>%
filter(res_status != 4)
# Create new categorical variables for place of injury, educational status, and race/ethnicity.
# For race/ethnicity, we used five non-overlapping categories:
# Hispanic, non-Hispanic white, non-Hispanic black, non-Hispanic Asian/Pacific Islander, non-Hispanic Native American/Native Alaskan
all_guns <- all_guns %>%
mutate(place = factor(injury_place, labels = c("Home", "Residential institution", "School/instiution", "Sports", "Street",
"Trade/service area", "Industrial/construction", "Farm", "Other specified",
"Other unspecified")),
education = ifelse(education_flag == 1,
cut(as.numeric(education_03), breaks = c(0, 2, 3, 5, 8, 9)),
cut(as.numeric(education_89), breaks = c(0, 11, 12, 15, 17, 99))),
education = factor(education, labels = c("Less than HS", "HS/GED", "Some college", "BA+", NA)),
race = ifelse(hispanic > 199 & hispanic <996, "Hispanic",
ifelse(race == "01", "White",
ifelse(race == "02", "Black",
ifelse(as.numeric(race) >= 4 & as.numeric(race) <= 78, "Asian/Pacific Islander","Native American/Native Alaskan")))),
race = ifelse(is.na(race), "Unknown", race)) %>%
select(year, month, intent, police, sex, age, race, hispanic, place, education)
# This is the main data frame FiveThirtyEight used in its analysis.
# For example:
# Gun suicides by year:
all_guns %>%
filter(intent == "Suicide") %>%
group_by(year) %>%
summarize(suicides = length(year))
# Gun homicides of young men (15-34) by year:
all_guns %>%
filter(intent == "Homicide", age >= 15, age < 35, sex == "M") %>%
group_by(year) %>%
summarize(homicides = length(year))
save(all_guns, file = "all_guns.RData")
write.csv(all_guns, file = "full_data.csv")