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 2024. 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: 21 × 3
## # Groups: year_reg [21]
## 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
## # ℹ 11 more rows
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: 18 × 3
## # Groups: year_reg [18]
## 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
## 18 2024 <NA> 9
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 2024 2 Padre
## 9 1 2020 6 Padre
## 10 1 2023 10 Padre
## 11 1 2013 18 Padre
## 12 1 2022 9 Padre
## 13 1 2019 4 Padre
## 14 2 2021 4 Madre
## 15 2 2024 3 Madre
## 16 2 2015 11 Madre
## 17 2 2016 9 Madre
## 18 2 2012 64 Madre
## 19 2 2017 2 Madre
## 20 2 2014 16 Madre
## 21 2 2022 5 Madre
## 22 2 2019 6 Madre
## 23 2 2023 3 Madre
## 24 2 2013 17 Madre
## 25 2 2020 1 Madre
## 26 2 2018 8 Madre
## 27 3 2013 16 Hermano
## 28 3 2017 13 Hermano
## 29 3 2024 5 Hermano
## 30 3 2014 13 Hermano
## 31 3 2012 14 Hermano
## 32 3 2015 9 Hermano
## 33 3 2022 14 Hermano
## 34 3 2021 7 Hermano
## 35 3 2023 15 Hermano
## 36 3 2020 4 Hermano
## 37 3 2019 5 Hermano
## 38 3 2018 14 Hermano
## 39 3 2016 11 Hermano
## 40 4 2012 7 Hermana
## 41 4 2017 5 Hermana
## 42 4 2013 6 Hermana
## 43 4 2018 4 Hermana
## 44 4 2024 1 Hermana
## 45 4 2016 4 Hermana
## 46 4 2014 9 Hermana
## 47 4 2015 2 Hermana
## 48 4 2021 5 Hermana
## 49 4 2022 2 Hermana
## 50 4 2019 4 Hermana
## 51 5 2017 9 Hijo
## 52 5 2019 7 Hijo
## 53 5 2012 10 Hijo
## 54 5 2021 16 Hijo
## 55 5 2020 5 Hijo
## 56 5 2018 11 Hijo
## 57 5 2016 13 Hijo
## 58 5 2013 14 Hijo
## 59 5 2014 20 Hijo
## 60 5 2023 9 Hijo
## 61 5 2015 10 Hijo
## 62 5 2024 9 Hijo
## 63 5 2022 11 Hijo
## 64 6 2013 2 Hija
## 65 6 2015 1 Hija
## 66 6 2024 2 Hija
## 67 6 2012 1 Hija
## 68 6 2014 2 Hija
## 69 6 2019 1 Hija
## 70 6 2022 1 Hija
## 71 6 2016 3 Hija
## 72 6 2018 2 Hija
## 73 7 2019 1 Abuelo
## 74 7 2014 2 Abuelo
## 75 7 2013 4 Abuelo
## 76 7 2012 5 Abuelo
## 77 7 2015 1 Abuelo
## 78 7 2016 1 Abuelo
## 79 8 2013 5 Abuela
## 80 8 2012 3 Abuela
## 81 8 2014 8 Abuela
## 82 8 2022 3 Abuela
## 83 8 2016 1 Abuela
## 84 9 2021 3 Nieto
## 85 9 2013 29 Nieto
## 86 9 2012 5 Nieto
## 87 9 2024 5 Nieto
## 88 9 2017 3 Nieto
## 89 9 2018 1 Nieto
## 90 9 2019 2 Nieto
## 91 9 2016 4 Nieto
## 92 9 2014 19 Nieto
## 93 9 2022 2 Nieto
## 94 9 2015 3 Nieto
## 95 10 2012 1 Nieta
## 96 10 2013 2 Nieta
## 97 10 2019 1 Nieta
## 98 10 2014 1 Nieta
## 99 11 2017 9 Esposo, Cónyuge
## 100 11 2019 7 Esposo, Cónyuge
## 101 11 2014 22 Esposo, Cónyuge
## 102 11 2021 9 Esposo, Cónyuge
## 103 11 2013 29 Esposo, Cónyuge
## 104 11 2022 17 Esposo, Cónyuge
## 105 11 2023 8 Esposo, Cónyuge
## 106 11 2024 6 Esposo, Cónyuge
## 107 11 2012 27 Esposo, Cónyuge
## 108 11 2015 30 Esposo, Cónyuge
## 109 11 2016 24 Esposo, Cónyuge
## 110 11 2018 15 Esposo, Cónyuge
## 111 11 2020 9 Esposo, Cónyuge
## 112 12 2013 9 Esposa, Cónyuge
## 113 12 2019 16 Esposa, Cónyuge
## 114 12 2022 14 Esposa, Cónyuge
## 115 12 2021 26 Esposa, Cónyuge
## 116 12 2012 3 Esposa, Cónyuge
## 117 12 2015 2 Esposa, Cónyuge
## 118 12 2020 8 Esposa, Cónyuge
## 119 12 2014 10 Esposa, Cónyuge
## 120 12 2024 3 Esposa, Cónyuge
## 121 12 2023 1 Esposa, Cónyuge
## 122 12 2017 2 Esposa, Cónyuge
## 123 12 2016 4 Esposa, Cónyuge
## 124 12 2018 5 Esposa, Cónyuge
## 125 13 2013 9 Tío
## 126 13 2019 2 Tío
## 127 13 2017 6 Tío
## 128 13 2016 13 Tío
## 129 13 2012 7 Tío
## 130 13 2022 2 Tío
## 131 13 2015 8 Tío
## 132 13 2014 6 Tío
## 133 13 2023 1 Tío
## 134 13 2024 1 Tío
## 135 13 2018 2 Tío
## 136 14 2018 1 Tía
## 137 14 2012 1 Tía
## 138 15 2021 6 Sobrino
## 139 15 2015 9 Sobrino
## 140 15 2019 5 Sobrino
## 141 15 2014 10 Sobrino
## 142 15 2016 4 Sobrino
## 143 15 2013 14 Sobrino
## 144 15 2018 4 Sobrino
## 145 15 2017 9 Sobrino
## 146 15 2022 6 Sobrino
## 147 15 2024 5 Sobrino
## 148 15 2023 6 Sobrino
## 149 15 2012 19 Sobrino
## 150 16 2016 1 Sobrina
## 151 16 2023 1 Sobrina
## 152 17 2022 4 Primo
## 153 17 2017 13 Primo
## 154 17 2012 14 Primo
## 155 17 2013 15 Primo
## 156 17 2023 2 Primo
## 157 17 2015 11 Primo
## 158 17 2021 2 Primo
## 159 17 2018 10 Primo
## 160 17 2016 9 Primo
## 161 17 2019 3 Primo
## 162 17 2014 10 Primo
## 163 17 2024 4 Primo
## 164 17 2020 4 Primo
## 165 18 2016 1 Prima
## 166 21 2012 1 Bisnieto
## 167 27 2012 2 Suegro
## 168 27 2015 2 Suegro
## 169 27 2013 1 Suegro
## 170 27 2018 3 Suegro
## 171 27 2017 2 Suegro
## 172 27 2016 1 Suegro
## 173 31 2018 3 Yerno
## 174 31 2015 4 Yerno
## 175 31 2013 5 Yerno
## 176 31 2023 1 Yerno
## 177 31 2022 1 Yerno
## 178 31 2017 4 Yerno
## 179 31 2021 1 Yerno
## 180 31 2014 1 Yerno
## 181 31 2012 4 Yerno
## 182 31 2016 3 Yerno
## 183 31 2024 1 Yerno
## 184 32 2018 1 Nuera
## 185 33 2023 3 Cuñado
## 186 33 2017 6 Cuñado
## 187 33 2018 7 Cuñado
## 188 33 2015 9 Cuñado
## 189 33 2013 8 Cuñado
## 190 33 2022 4 Cuñado
## 191 33 2014 9 Cuñado
## 192 33 2012 9 Cuñado
## 193 33 2021 1 Cuñado
## 194 33 2016 3 Cuñado
## 195 33 2024 4 Cuñado
## 196 33 2019 1 Cuñado
## 197 34 2012 1 Cuñada
## 198 34 2022 1 Cuñada
## 199 35 2014 2 Concuño
## 200 35 2017 2 Concuño
## 201 35 2013 1 Concuño
## 202 35 2012 1 Concuño
## 203 37 2012 6 Padrastro
## 204 37 2017 2 Padrastro
## 205 37 2019 1 Padrastro
## 206 37 2018 2 Padrastro
## 207 37 2020 2 Padrastro
## 208 37 2015 6 Padrastro
## 209 37 2016 6 Padrastro
## 210 37 2024 2 Padrastro
## 211 37 2013 7 Padrastro
## 212 37 2021 2 Padrastro
## 213 37 2014 2 Padrastro
## 214 37 2022 5 Padrastro
## 215 37 2023 2 Padrastro
## 216 38 2013 1 Madrastra
## 217 39 2013 2 Hijastro
## 218 39 2016 1 Hijastro
## 219 39 2012 2 Hijastro
## 220 39 2015 2 Hijastro
## 221 39 2021 1 Hijastro
## 222 39 2017 1 Hijastro
## 223 39 2014 1 Hijastro
## 224 41 2018 1 Hermanastro
## 225 41 2013 3 Hermanastro
## 226 41 2014 1 Hermanastro
## 227 41 2012 1 Hermanastro
## 228 41 2020 1 Hermanastro
## 229 45 2020 7 Concubino, compañero
## 230 45 2023 16 Concubino, compañero
## 231 45 2014 26 Concubino, compañero
## 232 45 2017 21 Concubino, compañero
## 233 45 2018 10 Concubino, compañero
## 234 45 2016 25 Concubino, compañero
## 235 45 2013 27 Concubino, compañero
## 236 45 2019 5 Concubino, compañero
## 237 45 2021 8 Concubino, compañero
## 238 45 2024 7 Concubino, compañero
## 239 45 2012 9 Concubino, compañero
## 240 45 2022 15 Concubino, compañero
## 241 45 2015 28 Concubino, compañero
## 242 46 2016 6 Concubina, compañera
## 243 46 2021 1 Concubina, compañera
## 244 46 2017 4 Concubina, compañera
## 245 46 2012 4 Concubina, compañera
## 246 46 2013 4 Concubina, compañera
## 247 46 2022 1 Concubina, compañera
## 248 46 2015 5 Concubina, compañera
## 249 46 2014 7 Concubina, compañera
## 250 46 2023 2 Concubina, compañera
## [ reached 'max' / getOption("max.print") -- omitted 132 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: 21 × 2
## year_reg count
## <int> <int>
## 1 2004 10474
## 2 2005 11015
## 3 2006 11452
## 4 2007 10229
## 5 2008 15211
## 6 2009 21145
## 7 2010 27527
## 8 2011 30134
## 9 2012 27954
## 10 2013 24849
## # ℹ 11 more rows
This package is free and open source software, licensed MIT.