| title | author | date | output | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Injury Intent Deaths 2004-2023 in Mexico |
Diego Valle-Jones |
January 05, 2024 |
|
| 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 2023. 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)
suppressPackageStartupMessages(library(dplyr))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: 20 × 3
## # Groups: year_reg [20]
## 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
## 18 2021 Homicide 35700
## 19 2022 Homicide 33291
## 20 2023 Homicide 32253
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: 17 × 3
## # Groups: year_reg [17]
## 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
## 15 2021 <NA> 20
## 16 2022 <NA> 4
## 17 2023 <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 2017 7 Padre
## 3 1 2016 13 Padre
## 4 1 2021 4 Padre
## 5 1 2018 6 Padre
## 6 1 2014 107 Padre
## 7 1 2015 7 Padre
## 8 1 2022 9 Padre
## 9 1 2020 6 Padre
## 10 1 2023 10 Padre
## 11 1 2013 18 Padre
## 12 1 2019 4 Padre
## 13 2 2016 9 Madre
## 14 2 2021 4 Madre
## 15 2 2017 2 Madre
## 16 2 2015 11 Madre
## 17 2 2022 5 Madre
## 18 2 2012 64 Madre
## 19 2 2023 3 Madre
## 20 2 2014 16 Madre
## 21 2 2020 1 Madre
## 22 2 2019 6 Madre
## 23 2 2018 8 Madre
## 24 2 2013 17 Madre
## 25 3 2015 9 Hermano
## 26 3 2022 14 Hermano
## 27 3 2012 14 Hermano
## 28 3 2017 13 Hermano
## 29 3 2014 13 Hermano
## 30 3 2019 5 Hermano
## 31 3 2023 15 Hermano
## 32 3 2013 16 Hermano
## 33 3 2020 4 Hermano
## 34 3 2021 7 Hermano
## 35 3 2018 14 Hermano
## 36 3 2016 11 Hermano
## 37 4 2012 7 Hermana
## 38 4 2017 5 Hermana
## 39 4 2019 4 Hermana
## 40 4 2015 2 Hermana
## 41 4 2013 6 Hermana
## 42 4 2018 4 Hermana
## 43 4 2016 4 Hermana
## 44 4 2021 5 Hermana
## 45 4 2014 9 Hermana
## 46 4 2022 2 Hermana
## 47 5 2017 9 Hijo
## 48 5 2019 7 Hijo
## 49 5 2016 13 Hijo
## 50 5 2021 16 Hijo
## 51 5 2018 11 Hijo
## 52 5 2023 9 Hijo
## 53 5 2012 10 Hijo
## 54 5 2013 14 Hijo
## 55 5 2022 11 Hijo
## 56 5 2015 10 Hijo
## 57 5 2020 5 Hijo
## 58 5 2014 20 Hijo
## 59 6 2013 2 Hija
## 60 6 2015 1 Hija
## 61 6 2022 1 Hija
## 62 6 2012 1 Hija
## 63 6 2014 2 Hija
## 64 6 2019 1 Hija
## 65 6 2018 2 Hija
## 66 6 2016 3 Hija
## 67 7 2013 4 Abuelo
## 68 7 2015 1 Abuelo
## 69 7 2012 5 Abuelo
## 70 7 2019 1 Abuelo
## 71 7 2016 1 Abuelo
## 72 7 2014 2 Abuelo
## 73 8 2022 3 Abuela
## 74 8 2013 5 Abuela
## 75 8 2016 1 Abuela
## 76 8 2012 3 Abuela
## 77 8 2014 8 Abuela
## 78 9 2017 3 Nieto
## 79 9 2012 5 Nieto
## 80 9 2018 1 Nieto
## 81 9 2013 29 Nieto
## 82 9 2019 2 Nieto
## 83 9 2021 3 Nieto
## 84 9 2014 19 Nieto
## 85 9 2022 2 Nieto
## 86 9 2016 4 Nieto
## 87 9 2015 3 Nieto
## 88 10 2012 1 Nieta
## 89 10 2013 2 Nieta
## 90 10 2019 1 Nieta
## 91 10 2014 1 Nieta
## 92 11 2014 22 Esposo, Cónyuge
## 93 11 2023 8 Esposo, Cónyuge
## 94 11 2017 9 Esposo, Cónyuge
## 95 11 2021 9 Esposo, Cónyuge
## 96 11 2022 17 Esposo, Cónyuge
## 97 11 2019 7 Esposo, Cónyuge
## 98 11 2020 9 Esposo, Cónyuge
## 99 11 2013 29 Esposo, Cónyuge
## 100 11 2018 15 Esposo, Cónyuge
## 101 11 2015 30 Esposo, Cónyuge
## 102 11 2012 27 Esposo, Cónyuge
## 103 11 2016 24 Esposo, Cónyuge
## 104 12 2013 9 Esposa, Cónyuge
## 105 12 2012 3 Esposa, Cónyuge
## 106 12 2014 10 Esposa, Cónyuge
## 107 12 2021 26 Esposa, Cónyuge
## 108 12 2022 14 Esposa, Cónyuge
## 109 12 2018 5 Esposa, Cónyuge
## 110 12 2023 1 Esposa, Cónyuge
## 111 12 2019 16 Esposa, Cónyuge
## 112 12 2016 4 Esposa, Cónyuge
## 113 12 2015 2 Esposa, Cónyuge
## 114 12 2020 8 Esposa, Cónyuge
## 115 12 2017 2 Esposa, Cónyuge
## 116 13 2013 9 Tío
## 117 13 2023 1 Tío
## 118 13 2014 6 Tío
## 119 13 2012 7 Tío
## 120 13 2016 13 Tío
## 121 13 2015 8 Tío
## 122 13 2018 2 Tío
## 123 13 2017 6 Tío
## 124 13 2019 2 Tío
## 125 13 2022 2 Tío
## 126 14 2012 1 Tía
## 127 14 2018 1 Tía
## 128 15 2021 6 Sobrino
## 129 15 2014 10 Sobrino
## 130 15 2016 4 Sobrino
## 131 15 2019 5 Sobrino
## 132 15 2017 9 Sobrino
## 133 15 2013 14 Sobrino
## 134 15 2018 4 Sobrino
## 135 15 2023 6 Sobrino
## 136 15 2015 9 Sobrino
## 137 15 2022 6 Sobrino
## 138 15 2012 19 Sobrino
## 139 16 2016 1 Sobrina
## 140 16 2023 1 Sobrina
## 141 17 2013 15 Primo
## 142 17 2017 13 Primo
## 143 17 2023 2 Primo
## 144 17 2022 4 Primo
## 145 17 2019 3 Primo
## 146 17 2018 10 Primo
## 147 17 2020 4 Primo
## 148 17 2016 9 Primo
## 149 17 2014 10 Primo
## 150 17 2012 14 Primo
## 151 17 2015 11 Primo
## 152 17 2021 2 Primo
## 153 18 2016 1 Prima
## 154 21 2012 1 Bisnieto
## 155 27 2017 2 Suegro
## 156 27 2018 3 Suegro
## 157 27 2012 2 Suegro
## 158 27 2013 1 Suegro
## 159 27 2015 2 Suegro
## 160 27 2016 1 Suegro
## 161 31 2023 1 Yerno
## 162 31 2018 3 Yerno
## 163 31 2016 3 Yerno
## 164 31 2021 1 Yerno
## 165 31 2012 4 Yerno
## 166 31 2013 5 Yerno
## 167 31 2014 1 Yerno
## 168 31 2015 4 Yerno
## 169 31 2017 4 Yerno
## 170 31 2022 1 Yerno
## 171 32 2018 1 Nuera
## 172 33 2015 9 Cuñado
## 173 33 2016 3 Cuñado
## 174 33 2018 7 Cuñado
## 175 33 2017 6 Cuñado
## 176 33 2013 8 Cuñado
## 177 33 2021 1 Cuñado
## 178 33 2019 1 Cuñado
## 179 33 2023 3 Cuñado
## 180 33 2012 9 Cuñado
## 181 33 2022 4 Cuñado
## 182 33 2014 9 Cuñado
## 183 34 2022 1 Cuñada
## 184 34 2012 1 Cuñada
## 185 35 2017 2 Concuño
## 186 35 2013 1 Concuño
## 187 35 2012 1 Concuño
## 188 35 2014 2 Concuño
## 189 37 2017 2 Padrastro
## 190 37 2015 6 Padrastro
## 191 37 2022 5 Padrastro
## 192 37 2012 6 Padrastro
## 193 37 2019 1 Padrastro
## 194 37 2016 6 Padrastro
## 195 37 2021 2 Padrastro
## 196 37 2018 2 Padrastro
## 197 37 2023 2 Padrastro
## 198 37 2020 2 Padrastro
## 199 37 2014 2 Padrastro
## 200 37 2013 7 Padrastro
## 201 38 2013 1 Madrastra
## 202 39 2016 1 Hijastro
## 203 39 2017 1 Hijastro
## 204 39 2012 2 Hijastro
## 205 39 2015 2 Hijastro
## 206 39 2013 2 Hijastro
## 207 39 2021 1 Hijastro
## 208 39 2014 1 Hijastro
## 209 41 2013 3 Hermanastro
## 210 41 2012 1 Hermanastro
## 211 41 2018 1 Hermanastro
## 212 41 2020 1 Hermanastro
## 213 41 2014 1 Hermanastro
## 214 45 2020 7 Concubino, compañero
## 215 45 2017 21 Concubino, compañero
## 216 45 2019 5 Concubino, compañero
## 217 45 2015 28 Concubino, compañero
## 218 45 2013 27 Concubino, compañero
## 219 45 2021 8 Concubino, compañero
## 220 45 2014 26 Concubino, compañero
## 221 45 2023 16 Concubino, compañero
## 222 45 2016 25 Concubino, compañero
## 223 45 2018 10 Concubino, compañero
## 224 45 2012 9 Concubino, compañero
## 225 45 2022 15 Concubino, compañero
## 226 46 2015 5 Concubina, compañera
## 227 46 2016 6 Concubina, compañera
## 228 46 2012 4 Concubina, compañera
## 229 46 2021 1 Concubina, compañera
## 230 46 2014 7 Concubina, compañera
## 231 46 2017 4 Concubina, compañera
## 232 46 2013 4 Concubina, compañera
## 233 46 2023 2 Concubina, compañera
## 234 46 2022 1 Concubina, compañera
## 235 47 2016 2 Amante, Amasio, Querido
## 236 47 2019 1 Amante, Amasio, Querido
## 237 47 2013 1 Amante, Amasio, Querido
## 238 47 2015 1 Amante, Amasio, Querido
## 239 47 2012 3 Amante, Amasio, Querido
## 240 47 2017 1 Amante, Amasio, Querido
## 241 48 2014 1 Amante, Amasia, Querida
## 242 49 2022 1 Novio
## 243 49 2014 2 Novio
## 244 49 2015 2 Novio
## 245 49 2012 2 Novio
## 246 49 2019 1 Novio
## 247 49 2016 4 Novio
## 248 49 2023 2 Novio
## 249 49 2013 3 Novio
## 250 49 2017 1 Novio
## [ reached 'max' / getOption("max.print") -- omitted 109 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.
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: 20 × 2
## year_reg count
## <int> <int>
## 1 2004 10445
## 2 2005 11046
## 3 2006 11490
## 4 2007 10362
## 5 2008 15315
## 6 2009 21138
## 7 2010 27638
## 8 2011 30146
## 9 2012 27922
## 10 2013 24819
## 11 2014 21748
## 12 2015 22305
## 13 2016 26274
## 14 2017 34254
## 15 2018 38923
## 16 2019 39068
## 17 2020 38621
## 18 2021 37573
## 19 2022 35308
## 20 2023 33319
This package is free and open source software, licensed MIT.