Injury Intent Deaths 2004-2019 in Mexico
Diego Valle-Jones February 28, 2019
Injury Intent Deaths 2004-2019 in Mexico
| Author: | Diego Valle-Jones |
| License: | MIT |
| Website: | https://github.com/diegovalle/mxmortalitydb |
What does it do?
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 2019. 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
Installation
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)
suppressPackageStartupMessages(library(dplyr))Examples
Deaths by homicide in Mexico
injury.intent %>%
filter(intent == "Homicide") %>%
group_by(year_reg, intent) %>%
summarise(count = n())## `summarise()` has grouped output by 'year_reg'. You can override using the `.groups` argument.
## # A tibble: 17 × 3
## # Groups: year_reg [17]
## year_reg intent count
## <int> <fct> <int>
## 1 2004 Homicide 9330
## 2 2005 Homicide 9926
## 3 2006 Homicide 10454
## 4 2007 Homicide 8868
## 5 2008 Homicide 14007
## 6 2009 Homicide 19804
## 7 2010 Homicide 25757
## 8 2011 Homicide 27213
## 9 2012 Homicide 25967
## 10 2013 Homicide 23063
## 11 2014 Homicide 20013
## 12 2015 Homicide 20763
## 13 2016 Homicide 24560
## 14 2017 Homicide 32082
## 15 2018 Homicide 36687
## 16 2019 Homicide 36662
## 17 2020 Homicide 36773
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())## `summarise()` has grouped output by 'year_reg'. You can override using the `.groups` argument.
## # A tibble: 14 × 3
## # Groups: year_reg [14]
## year_reg intent count
## <int> <fct> <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
## 12 2017 <NA> 1
## 13 2019 <NA> 2
## 14 2020 <NA> 2
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())## `summarise()` has grouped output by 'year_reg'. You can override using the `.groups` argument.
## 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 2020 6 Padre
## 3 1 2017 7 Padre
## 4 1 2016 13 Padre
## 5 1 2015 7 Padre
## 6 1 2019 4 Padre
## 7 1 2018 6 Padre
## 8 1 2014 107 Padre
## 9 1 2013 18 Padre
## 10 2 2016 9 Madre
## 11 2 2017 2 Madre
## 12 2 2013 17 Madre
## 13 2 2019 6 Madre
## 14 2 2020 1 Madre
## 15 2 2015 11 Madre
## 16 2 2018 8 Madre
## 17 2 2014 16 Madre
## 18 2 2012 64 Madre
## 19 3 2017 13 Hermano
## 20 3 2019 5 Hermano
## 21 3 2014 13 Hermano
## 22 3 2015 9 Hermano
## 23 3 2013 16 Hermano
## 24 3 2016 11 Hermano
## 25 3 2018 14 Hermano
## 26 3 2020 4 Hermano
## 27 3 2012 14 Hermano
## 28 4 2015 2 Hermana
## 29 4 2014 9 Hermana
## 30 4 2018 4 Hermana
## 31 4 2013 6 Hermana
## 32 4 2017 5 Hermana
## 33 4 2019 4 Hermana
## 34 4 2012 7 Hermana
## 35 4 2016 4 Hermana
## 36 5 2018 11 Hijo
## 37 5 2017 9 Hijo
## 38 5 2012 10 Hijo
## 39 5 2019 7 Hijo
## 40 5 2013 14 Hijo
## 41 5 2015 10 Hijo
## 42 5 2020 5 Hijo
## 43 5 2016 13 Hijo
## 44 5 2014 20 Hijo
## 45 6 2019 1 Hija
## 46 6 2013 2 Hija
## 47 6 2018 2 Hija
## 48 6 2015 1 Hija
## 49 6 2016 3 Hija
## 50 6 2012 1 Hija
## 51 6 2014 2 Hija
## 52 7 2019 1 Abuelo
## 53 7 2013 4 Abuelo
## 54 7 2012 5 Abuelo
## 55 7 2015 1 Abuelo
## 56 7 2016 1 Abuelo
## 57 7 2014 2 Abuelo
## 58 8 2012 3 Abuela
## 59 8 2014 8 Abuela
## 60 8 2013 5 Abuela
## 61 8 2016 1 Abuela
## 62 9 2015 3 Nieto
## 63 9 2019 2 Nieto
## 64 9 2012 5 Nieto
## 65 9 2017 3 Nieto
## 66 9 2013 29 Nieto
## 67 9 2016 4 Nieto
## 68 9 2018 1 Nieto
## 69 9 2014 19 Nieto
## 70 10 2019 1 Nieta
## 71 10 2012 1 Nieta
## 72 10 2014 1 Nieta
## 73 10 2013 2 Nieta
## 74 11 2014 22 Esposo, Cónyuge
## 75 11 2012 27 Esposo, Cónyuge
## 76 11 2017 9 Esposo, Cónyuge
## 77 11 2015 30 Esposo, Cónyuge
## 78 11 2019 7 Esposo, Cónyuge
## 79 11 2016 24 Esposo, Cónyuge
## 80 11 2020 9 Esposo, Cónyuge
## 81 11 2018 15 Esposo, Cónyuge
## 82 11 2013 29 Esposo, Cónyuge
## 83 12 2014 10 Esposa, Cónyuge
## 84 12 2017 2 Esposa, Cónyuge
## 85 12 2015 2 Esposa, Cónyuge
## 86 12 2012 3 Esposa, Cónyuge
## 87 12 2016 4 Esposa, Cónyuge
## 88 12 2020 8 Esposa, Cónyuge
## 89 12 2018 5 Esposa, Cónyuge
## 90 12 2019 16 Esposa, Cónyuge
## 91 12 2013 9 Esposa, Cónyuge
## 92 13 2014 6 Tío
## 93 13 2016 13 Tío
## 94 13 2015 8 Tío
## 95 13 2018 2 Tío
## 96 13 2019 2 Tío
## 97 13 2012 7 Tío
## 98 13 2013 9 Tío
## 99 13 2017 6 Tío
## 100 14 2012 1 Tía
## 101 14 2018 1 Tía
## 102 15 2012 19 Sobrino
## 103 15 2015 9 Sobrino
## 104 15 2013 14 Sobrino
## 105 15 2019 5 Sobrino
## 106 15 2014 10 Sobrino
## 107 15 2017 9 Sobrino
## 108 15 2018 4 Sobrino
## 109 15 2016 4 Sobrino
## 110 16 2016 1 Sobrina
## 111 17 2017 13 Primo
## 112 17 2013 15 Primo
## 113 17 2020 4 Primo
## 114 17 2016 9 Primo
## 115 17 2014 10 Primo
## 116 17 2019 3 Primo
## 117 17 2018 10 Primo
## 118 17 2012 14 Primo
## 119 17 2015 11 Primo
## 120 18 2016 1 Prima
## 121 21 2012 1 Bisnieto
## 122 27 2017 2 Suegro
## 123 27 2012 2 Suegro
## 124 27 2016 1 Suegro
## 125 27 2018 3 Suegro
## 126 27 2015 2 Suegro
## 127 27 2013 1 Suegro
## 128 31 2015 4 Yerno
## 129 31 2018 3 Yerno
## 130 31 2013 5 Yerno
## 131 31 2012 4 Yerno
## 132 31 2016 3 Yerno
## 133 31 2017 4 Yerno
## 134 31 2014 1 Yerno
## 135 32 2018 1 Nuera
## 136 33 2014 9 Cuñado
## 137 33 2016 3 Cuñado
## 138 33 2015 9 Cuñado
## 139 33 2017 6 Cuñado
## 140 33 2018 7 Cuñado
## 141 33 2019 1 Cuñado
## 142 33 2012 9 Cuñado
## 143 33 2013 8 Cuñado
## 144 34 2012 1 Cuñada
## 145 35 2013 1 Concuño
## 146 35 2012 1 Concuño
## 147 35 2014 2 Concuño
## 148 35 2017 2 Concuño
## 149 37 2020 2 Padrastro
## 150 37 2013 7 Padrastro
## 151 37 2018 2 Padrastro
## 152 37 2015 6 Padrastro
## 153 37 2017 2 Padrastro
## 154 37 2019 1 Padrastro
## 155 37 2012 6 Padrastro
## 156 37 2014 2 Padrastro
## 157 37 2016 6 Padrastro
## 158 38 2013 1 Madrastra
## 159 39 2017 1 Hijastro
## 160 39 2012 2 Hijastro
## 161 39 2013 2 Hijastro
## 162 39 2015 2 Hijastro
## 163 39 2014 1 Hijastro
## 164 39 2016 1 Hijastro
## 165 41 2012 1 Hermanastro
## 166 41 2020 1 Hermanastro
## 167 41 2014 1 Hermanastro
## 168 41 2013 3 Hermanastro
## 169 41 2018 1 Hermanastro
## 170 45 2017 21 Concubino, compañero
## 171 45 2012 9 Concubino, compañero
## 172 45 2019 5 Concubino, compañero
## 173 45 2013 27 Concubino, compañero
## 174 45 2020 7 Concubino, compañero
## 175 45 2014 26 Concubino, compañero
## 176 45 2016 25 Concubino, compañero
## 177 45 2015 28 Concubino, compañero
## 178 45 2018 10 Concubino, compañero
## 179 46 2012 4 Concubina, compañera
## 180 46 2017 4 Concubina, compañera
## 181 46 2016 6 Concubina, compañera
## 182 46 2013 4 Concubina, compañera
## 183 46 2014 7 Concubina, compañera
## 184 46 2015 5 Concubina, compañera
## 185 47 2016 2 Amante, Amasio, Querido
## 186 47 2019 1 Amante, Amasio, Querido
## 187 47 2015 1 Amante, Amasio, Querido
## 188 47 2017 1 Amante, Amasio, Querido
## 189 47 2013 1 Amante, Amasio, Querido
## 190 47 2012 3 Amante, Amasio, Querido
## 191 48 2014 1 Amante, Amasia, Querida
## 192 49 2014 2 Novio
## 193 49 2019 1 Novio
## 194 49 2017 1 Novio
## 195 49 2013 3 Novio
## 196 49 2016 4 Novio
## 197 49 2012 2 Novio
## 198 49 2015 2 Novio
## 199 50 2018 1 Novia
## 200 51 2018 6 Ex esposo
## 201 51 2014 5 Ex esposo
## 202 51 2017 3 Ex esposo
## 203 51 2020 3 Ex esposo
## 204 51 2016 4 Ex esposo
## 205 51 2013 2 Ex esposo
## 206 51 2012 3 Ex esposo
## 207 51 2015 3 Ex esposo
## 208 52 2016 2 Ex esposa
## 209 53 2015 1 Padrino
## 210 55 2014 1 Ahijado
## 211 57 2019 1 Compadre
## 212 57 2012 1 Compadre
## 213 57 2015 1 Compadre
## 214 57 2014 1 Compadre
## 215 61 2013 1 Trabajador doméstico
## 216 66 2012 5 Conocido
## 217 66 2020 7 Conocido
## 218 66 2019 5 Conocido
## 219 66 2018 7 Conocido
## 220 66 2013 6 Conocido
## 221 66 2015 6 Conocido
## 222 66 2014 4 Conocido
## 223 66 2017 11 Conocido
## 224 66 2016 15 Conocido
## 225 67 2019 4 Vecino
## 226 67 2018 5 Vecino
## 227 67 2014 4 Vecino
## 228 67 2016 5 Vecino
## 229 67 2012 5 Vecino
## 230 67 2015 5 Vecino
## 231 67 2017 8 Vecino
## 232 67 2020 5 Vecino
## 233 67 2013 3 Vecino
## 234 68 2019 2 Amigo
## 235 68 2012 6 Amigo
## 236 68 2018 3 Amigo
## 237 68 2013 4 Amigo
## 238 68 2016 5 Amigo
## 239 68 2015 2 Amigo
## 240 68 2014 1 Amigo
## 241 68 2017 3 Amigo
## 242 68 2020 1 Amigo
## 243 69 2013 1 Amiga
## 244 69 2012 1 Amiga
## 245 70 2016 3 Otro familiar
## 246 70 2017 1 Otro familiar
## 247 70 2014 7 Otro familiar
## 248 70 2018 10 Otro familiar
## 249 70 2019 1 Otro familiar
## 250 70 2012 3 Otro familiar
## [ reached 'max' / getOption("max.print") -- omitted 35 rows ]
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())## `summarise()` has grouped output by 'year_reg'. You can override using the `.groups` argument.
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
## `summarise()` has grouped output by 'state_reg'. You can override using the `.groups` argument.
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")## `summarise()` has grouped output by 'state_occur_death'. You can override using the `.groups` argument.
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.
Warning
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: 17 × 2
## year_reg count
## <int> <int>
## 1 2004 10608
## 2 2005 11197
## 3 2006 11664
## 4 2007 10620
## 5 2008 15540
## 6 2009 21319
## 7 2010 27716
## 8 2011 30410
## 9 2012 28212
## 10 2013 25242
## 11 2014 22132
## 12 2015 22776
## 13 2016 26738
## 14 2017 34857
## 15 2018 39642
## 16 2019 39929
## 17 2020 39287
License
This package is free and open source software, licensed MIT.