Diego Valle-Jones January 05, 2018
| Author: | Diego Valle-Jones |
| License: | MIT |
| Website: | https://github.com/diegovalle/mxmortalitydb |
This is a data only package containing all injury intent deaths (accidents, suicides, homicides, legal interventions, and deaths of unspecified intent) registered by the SSA/INEGI from 2004 to 2016. The data source for the database is the INEGI. In addition the data was coded with the Injury Mortality Matrix provided by the CDC. The code used to clean the database is available as a separate program
For the moment this package is only available from github. For the development version:
if (!require(devtools)) {
install.packages("devtools")
}
devtools::install_github('diegovalle/mxmortalitydb')library(mxmortalitydb)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
All deaths of unknown intent in Sinaloa (state code 25) where the injury mechanism was a firearm, by year of registration:
## The main data.frame in the package is called injury.intent
injury.intent %>%
filter(is.na(intent) &
mechanism == "Firearm" &
state_reg == 25 ) %>%
group_by(year_reg, intent) %>%
summarise(count = n())## # A tibble: 11 x 3
## # Groups: year_reg [?]
## year_reg intent count
## <int> <fctr> <int>
## 1 2004 <NA> 11
## 2 2005 <NA> 11
## 3 2006 <NA> 2
## 4 2009 <NA> 8
## 5 2010 <NA> 7
## 6 2011 <NA> 25
## 7 2012 <NA> 197
## 8 2013 <NA> 8
## 9 2014 <NA> 1
## 10 2015 <NA> 1
## 11 2016 <NA> 3
In addition to the injury.intent data.frame several other datasets are available:
- aggressor.relation.code (relationship between the aggressor and his victim, useful for merging aggressor_relationship_code, Spanish)
- geo.codes (names of states and municipios, useful for merging state_reg, state_occur_death and mun_reg, mun_occur_death codes)
- icd.103 (list of 103 deceases by the WHO, Spanish)
- metro.areas (2010 metro areas as defined by the CONAPO along with 2010 population counts)
- big.municipios (since metro areas are not statistical in nature this is a list of all municipios which are bigger than the smallest metro area but are not part of one)
- mex.list.group (groups of deceases, Spanish)
- mex.list (list of deceases, Spanish)
Homicides merged with the aggressor.relation.code table:
df <- injury.intent %>%
filter(intent == "Homicide") %>%
group_by(year_reg, aggressor_relation_code) %>%
summarise(count = n())
## A couple of other tables are included in the package to
## interpret some of the values in injury.intent
merge(df, aggressor.relation.code)## aggressor_relation_code year_reg count relationship
## 1 1 2012 25 Padre
## 2 1 2015 7 Padre
## 3 1 2016 13 Padre
## 4 1 2014 107 Padre
## 5 1 2013 18 Padre
## 6 2 2015 11 Madre
## 7 2 2016 9 Madre
## 8 2 2014 16 Madre
## 9 2 2013 17 Madre
## 10 2 2012 64 Madre
## 11 3 2015 9 Hermano
## 12 3 2014 13 Hermano
## 13 3 2013 16 Hermano
## 14 3 2012 14 Hermano
## 15 3 2016 11 Hermano
## 16 4 2015 2 Hermana
## 17 4 2012 7 Hermana
## 18 4 2013 6 Hermana
## 19 4 2014 9 Hermana
## 20 4 2016 4 Hermana
## 21 5 2012 10 Hijo
## 22 5 2013 14 Hijo
## 23 5 2015 10 Hijo
## 24 5 2016 13 Hijo
## 25 5 2014 20 Hijo
## 26 6 2012 1 Hija
## 27 6 2016 3 Hija
## 28 6 2013 2 Hija
## 29 6 2015 1 Hija
## 30 6 2014 2 Hija
## 31 7 2016 1 Abuelo
## 32 7 2015 1 Abuelo
## 33 7 2012 5 Abuelo
## 34 7 2013 4 Abuelo
## 35 7 2014 2 Abuelo
## 36 8 2014 8 Abuela
## 37 8 2012 3 Abuela
## 38 8 2013 5 Abuela
## 39 8 2016 1 Abuela
## 40 9 2013 29 Nieto
## 41 9 2015 3 Nieto
## 42 9 2014 19 Nieto
## 43 9 2016 4 Nieto
## 44 9 2012 5 Nieto
## 45 10 2013 2 Nieta
## 46 10 2012 1 Nieta
## 47 10 2014 1 Nieta
## 48 11 2012 27 Esposo, Cónyuge
## 49 11 2016 24 Esposo, Cónyuge
## 50 11 2013 29 Esposo, Cónyuge
## 51 11 2014 22 Esposo, Cónyuge
## 52 11 2015 30 Esposo, Cónyuge
## 53 12 2016 4 Esposa, Cónyuge
## 54 12 2014 10 Esposa, Cónyuge
## 55 12 2015 2 Esposa, Cónyuge
## 56 12 2012 3 Esposa, Cónyuge
## 57 12 2013 9 Esposa, Cónyuge
## 58 13 2014 6 Tío
## 59 13 2015 8 Tío
## 60 13 2013 9 Tío
## 61 13 2012 7 Tío
## 62 13 2016 13 Tío
## 63 14 2012 1 Tía
## 64 15 2012 19 Sobrino
## 65 15 2015 9 Sobrino
## 66 15 2014 10 Sobrino
## 67 15 2013 14 Sobrino
## 68 15 2016 4 Sobrino
## 69 16 2016 1 Sobrina
## 70 17 2013 15 Primo
## 71 17 2012 14 Primo
## 72 17 2015 11 Primo
## 73 17 2014 10 Primo
## 74 17 2016 9 Primo
## 75 18 2016 1 Prima
## 76 21 2012 1 Bisnieto
## 77 27 2016 1 Suegro
## 78 27 2015 2 Suegro
## 79 27 2012 2 Suegro
## 80 27 2013 1 Suegro
## 81 31 2012 4 Yerno
## 82 31 2014 1 Yerno
## 83 31 2016 3 Yerno
## 84 31 2015 4 Yerno
## 85 31 2013 5 Yerno
## 86 33 2015 9 Cuñado
## 87 33 2013 8 Cuñado
## 88 33 2012 9 Cuñado
## 89 33 2014 9 Cuñado
## 90 33 2016 3 Cuñado
## 91 34 2012 1 Cuñada
## 92 35 2014 2 Concuño
## 93 35 2012 1 Concuño
## 94 35 2013 1 Concuño
## 95 37 2015 6 Padrastro
## 96 37 2012 6 Padrastro
## 97 37 2016 6 Padrastro
## 98 37 2014 2 Padrastro
## 99 37 2013 7 Padrastro
## 100 38 2013 1 Madrastra
## 101 39 2013 2 Hijastro
## 102 39 2012 2 Hijastro
## 103 39 2016 1 Hijastro
## 104 39 2014 1 Hijastro
## 105 39 2015 2 Hijastro
## 106 41 2012 1 Hermanastro
## 107 41 2013 3 Hermanastro
## 108 41 2014 1 Hermanastro
## 109 45 2013 27 Concubino, compañero
## 110 45 2012 9 Concubino, compañero
## 111 45 2014 26 Concubino, compañero
## 112 45 2016 25 Concubino, compañero
## 113 45 2015 28 Concubino, compañero
## 114 46 2013 4 Concubina, compañera
## 115 46 2016 6 Concubina, compañera
## 116 46 2014 7 Concubina, compañera
## 117 46 2012 4 Concubina, compañera
## 118 46 2015 5 Concubina, compañera
## 119 47 2012 3 Amante, Amasio, Querido
## 120 47 2013 1 Amante, Amasio, Querido
## 121 47 2015 1 Amante, Amasio, Querido
## 122 47 2016 2 Amante, Amasio, Querido
## 123 48 2014 1 Amante, Amasia, Querida
## 124 49 2012 2 Novio
## 125 49 2014 2 Novio
## 126 49 2016 4 Novio
## 127 49 2015 2 Novio
## 128 49 2013 3 Novio
## 129 51 2014 5 Ex esposo
## 130 51 2015 3 Ex esposo
## 131 51 2012 3 Ex esposo
## 132 51 2016 4 Ex esposo
## 133 51 2013 2 Ex esposo
## 134 52 2016 2 Ex esposa
## 135 53 2015 1 Padrino
## 136 55 2014 1 Ahijado
## 137 57 2014 1 Compadre
## 138 57 2012 1 Compadre
## 139 57 2015 1 Compadre
## 140 61 2013 1 Trabajador doméstico
## 141 66 2012 5 Conocido
## 142 66 2016 15 Conocido
## 143 66 2013 6 Conocido
## 144 66 2015 6 Conocido
## 145 66 2014 4 Conocido
## 146 67 2012 5 Vecino
## 147 67 2014 4 Vecino
## 148 67 2016 5 Vecino
## 149 67 2013 3 Vecino
## 150 67 2015 5 Vecino
## 151 68 2012 6 Amigo
## 152 68 2016 5 Amigo
## 153 68 2015 2 Amigo
## 154 68 2014 1 Amigo
## 155 68 2013 4 Amigo
## 156 69 2012 1 Amiga
## 157 69 2013 1 Amiga
## 158 70 2015 1 Otro familiar
## 159 70 2016 3 Otro familiar
## 160 70 2012 3 Otro familiar
## 161 70 2014 7 Otro familiar
## 162 71 2015 12 Sin parentesco
## 163 71 2012 1 Sin parentesco
## 164 71 2013 5 Sin parentesco
## 165 71 2016 19 Sin parentesco
## 166 71 2014 7 Sin parentesco
## 167 72 2015 339 Ninguno
## 168 72 2014 324 Ninguno
## 169 72 2012 438 Ninguno
## 170 72 2013 358 Ninguno
## 171 72 2016 405 Ninguno
## 172 88 2015 25 No aplica
## 173 88 2016 2 No aplica
## 174 88 2014 3 No aplica
## 175 99 2015 20205 No especificado
## 176 99 2016 23934 No especificado
## 177 99 2013 22431 No especificado
## 178 99 2012 25253 No especificado
## 179 99 2014 19349 No especificado
A plot of female homicide counts (making sure to exclude those that occurred outside Mexico):
## make sure to only count deaths that occurred inside Mexico (codes 33 to 35 are USA, LATAM and Other)
df <- injury.intent %>%
filter(sex == "Female" &
intent == "Homicide" &
!state_occur_death %in% 33:35) %>%
group_by(year_reg, intent) %>%
summarise(count = n())
ggplot(df, aes(year_reg, count)) +
geom_line() +
labs(title = "Female homicides in Mexico, by year of registration")
Homicides in the Mexico City metro area (ZM Valle de México), by the state where the murder was registered
plotMetro <- function(metro.name) {
require(stringr)
## data.frame metro.areas contains the 2010 CONAPO metro areas
df <- merge(injury.intent,
metro.areas,
by.x = c('state_reg', 'mun_reg'),
by.y=c('state_code', 'mun_code'))
## Homicides in Mexico City, by state of registratio
df2 <- df %>%
filter(metro_area == metro.name &
intent == "Homicide") %>%
group_by(state_reg, year_reg) %>%
summarise(count = n())
## data.frame geo.codes contains the names of Mexican states (with mun_code 0) and municipios
df2 <- merge(df2, subset(geo.codes, mun_code ==0), by.x = 'state_reg', by.y = 'state_code')
ggplot(df2, aes(year_reg, count, group = state_reg, color = name)) +
geom_line() +
labs(title = str_c("Homicides in ", metro.name, ", by state of registration"))
}
plotMetro("Valle de México")## Loading required package: stringr
The drop in homicides in the State of Mexico looks weird, let's plot by where the murder occurred
plotMetro_occur <- function(metro.name) {
require(stringr)
## data.frame metro.areas contains the 2010 CONAPO metro areas
df <- merge(injury.intent,
metro.areas,
by.x = c('state_occur_death', 'mun_occur_death'),
by.y=c('state_code', 'mun_code'))
## Homicides in Mexico City, by state of registratio
df2 <- df %>%
filter(metro_area == metro.name &
intent == "Homicide") %>%
group_by(state_occur_death, year_reg) %>%
summarise(count = n())
## data.frame geo.codes contains the names of Mexican states (with mun_code 0) and municipios
df2 <- merge(df2, subset(geo.codes, mun_code ==0), by.x = 'state_occur_death', by.y = 'state_code')
ggplot(df2, aes(year_reg, count, group = state_occur_death, color = name)) +
geom_line() +
labs(title = str_c("Homicides in ", metro.name, ", by state of occurrence"))
}
plotMetro_occur("Valle de México")
So something changed in the way homicides were registered in the State of Mexico and you have to make sure to plot by where the homicide occurred.
I encourage you to get acquainted with the database since it may contain some errors (introduced at the source) and some fields may be difficult to interpret because of the large number of missing values (see the aggressor relation example). The field intent.imputed is the result of running a statistical model to impute the intent of deaths of unknown intent, and is mainly useful to the author of this package. Feel free to ignore the column.
Total Imputed Homicides in Mexico:
## make sure to only count deaths that occurred inside Mexico (codes 33 to 35 are USA, LATAM and Other)
injury.intent %>%
filter(intent.imputed == "Homicide" & !state_occur_death %in% 33:35) %>%
group_by(year_reg) %>%
summarise(count = n())## # A tibble: 13 x 2
## year_reg count
## <int> <int>
## 1 2004 10654
## 2 2005 11196
## 3 2006 11681
## 4 2007 10534
## 5 2008 15429
## 6 2009 21299
## 7 2010 27705
## 8 2011 30424
## 9 2012 28171
## 10 2013 25256
## 11 2014 22113
## 12 2015 22719
## 13 2016 26625
This package is free and open source software, licensed MIT.