readme.md
In diegovalle/mxmortalitydb: Injury Intent Deaths 2004-2018 in Mexico
Injury Intent Deaths 2004-2022 in Mexico
Diego Valle-Jones
December 30, 2023
Injury Intent Deaths 2004-2022 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 2022. 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: 19 × 3
## # Groups: year_reg [19]
## 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
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: 16 × 3
## # Groups: year_reg [16]
## 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
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 2022 9 Padre
## 4 1 2016 13 Padre
## 5 1 2021 4 Padre
## 6 1 2017 7 Padre
## 7 1 2018 6 Padre
## 8 1 2019 4 Padre
## 9 1 2013 18 Padre
## 10 1 2015 7 Padre
## 11 1 2014 107 Padre
## 12 2 2016 9 Madre
## 13 2 2022 5 Madre
## 14 2 2019 6 Madre
## 15 2 2017 2 Madre
## 16 2 2015 11 Madre
## 17 2 2020 1 Madre
## 18 2 2013 17 Madre
## 19 2 2021 4 Madre
## 20 2 2018 8 Madre
## 21 2 2014 16 Madre
## 22 2 2012 64 Madre
## 23 3 2019 5 Hermano
## 24 3 2022 14 Hermano
## 25 3 2015 9 Hermano
## 26 3 2017 13 Hermano
## 27 3 2014 13 Hermano
## 28 3 2016 11 Hermano
## 29 3 2020 4 Hermano
## 30 3 2012 14 Hermano
## 31 3 2013 16 Hermano
## 32 3 2021 7 Hermano
## 33 3 2018 14 Hermano
## 34 4 2017 5 Hermana
## 35 4 2022 2 Hermana
## 36 4 2019 4 Hermana
## 37 4 2013 6 Hermana
## 38 4 2021 5 Hermana
## 39 4 2015 2 Hermana
## 40 4 2016 4 Hermana
## 41 4 2018 4 Hermana
## 42 4 2014 9 Hermana
## 43 4 2012 7 Hermana
## 44 5 2020 5 Hijo
## 45 5 2018 11 Hijo
## 46 5 2017 9 Hijo
## 47 5 2022 11 Hijo
## 48 5 2012 10 Hijo
## 49 5 2019 7 Hijo
## 50 5 2021 16 Hijo
## 51 5 2015 10 Hijo
## 52 5 2014 20 Hijo
## 53 5 2013 14 Hijo
## 54 5 2016 13 Hijo
## 55 6 2022 1 Hija
## 56 6 2019 1 Hija
## 57 6 2018 2 Hija
## 58 6 2015 1 Hija
## 59 6 2013 2 Hija
## 60 6 2012 1 Hija
## 61 6 2014 2 Hija
## 62 6 2016 3 Hija
## 63 7 2013 4 Abuelo
## 64 7 2019 1 Abuelo
## 65 7 2016 1 Abuelo
## 66 7 2015 1 Abuelo
## 67 7 2012 5 Abuelo
## 68 7 2014 2 Abuelo
## 69 8 2012 3 Abuela
## 70 8 2022 3 Abuela
## 71 8 2014 8 Abuela
## 72 8 2016 1 Abuela
## 73 8 2013 5 Abuela
## 74 9 2012 5 Nieto
## 75 9 2015 3 Nieto
## 76 9 2021 3 Nieto
## 77 9 2017 3 Nieto
## 78 9 2022 2 Nieto
## 79 9 2013 29 Nieto
## 80 9 2019 2 Nieto
## 81 9 2018 1 Nieto
## 82 9 2014 19 Nieto
## 83 9 2016 4 Nieto
## 84 10 2014 1 Nieta
## 85 10 2012 1 Nieta
## 86 10 2013 2 Nieta
## 87 10 2019 1 Nieta
## 88 11 2017 9 Esposo, Cónyuge
## 89 11 2012 27 Esposo, Cónyuge
## 90 11 2021 9 Esposo, Cónyuge
## 91 11 2018 15 Esposo, Cónyuge
## 92 11 2013 29 Esposo, Cónyuge
## 93 11 2016 24 Esposo, Cónyuge
## 94 11 2022 17 Esposo, Cónyuge
## 95 11 2019 7 Esposo, Cónyuge
## 96 11 2015 30 Esposo, Cónyuge
## 97 11 2020 9 Esposo, Cónyuge
## 98 11 2014 22 Esposo, Cónyuge
## 99 12 2017 2 Esposa, Cónyuge
## 100 12 2012 3 Esposa, Cónyuge
## 101 12 2013 9 Esposa, Cónyuge
## 102 12 2016 4 Esposa, Cónyuge
## 103 12 2021 26 Esposa, Cónyuge
## 104 12 2018 5 Esposa, Cónyuge
## 105 12 2015 2 Esposa, Cónyuge
## 106 12 2019 16 Esposa, Cónyuge
## 107 12 2014 10 Esposa, Cónyuge
## 108 12 2022 14 Esposa, Cónyuge
## 109 12 2020 8 Esposa, Cónyuge
## 110 13 2012 7 Tío
## 111 13 2019 2 Tío
## 112 13 2014 6 Tío
## 113 13 2013 9 Tío
## 114 13 2015 8 Tío
## 115 13 2022 2 Tío
## 116 13 2018 2 Tío
## 117 13 2017 6 Tío
## 118 13 2016 13 Tío
## 119 14 2018 1 Tía
## 120 14 2012 1 Tía
## 121 15 2013 14 Sobrino
## 122 15 2019 5 Sobrino
## 123 15 2021 6 Sobrino
## 124 15 2022 6 Sobrino
## 125 15 2012 19 Sobrino
## 126 15 2015 9 Sobrino
## 127 15 2016 4 Sobrino
## 128 15 2018 4 Sobrino
## 129 15 2017 9 Sobrino
## 130 15 2014 10 Sobrino
## 131 16 2016 1 Sobrina
## 132 17 2012 14 Primo
## 133 17 2015 11 Primo
## 134 17 2017 13 Primo
## 135 17 2014 10 Primo
## 136 17 2021 2 Primo
## 137 17 2020 4 Primo
## 138 17 2016 9 Primo
## 139 17 2022 4 Primo
## 140 17 2018 10 Primo
## 141 17 2013 15 Primo
## 142 17 2019 3 Primo
## 143 18 2016 1 Prima
## 144 21 2012 1 Bisnieto
## 145 27 2018 3 Suegro
## 146 27 2016 1 Suegro
## 147 27 2017 2 Suegro
## 148 27 2013 1 Suegro
## 149 27 2015 2 Suegro
## 150 27 2012 2 Suegro
## 151 31 2016 3 Yerno
## 152 31 2018 3 Yerno
## 153 31 2015 4 Yerno
## 154 31 2014 1 Yerno
## 155 31 2012 4 Yerno
## 156 31 2021 1 Yerno
## 157 31 2022 1 Yerno
## 158 31 2017 4 Yerno
## 159 31 2013 5 Yerno
## 160 32 2018 1 Nuera
## 161 33 2012 9 Cuñado
## 162 33 2016 3 Cuñado
## 163 33 2022 4 Cuñado
## 164 33 2014 9 Cuñado
## 165 33 2015 9 Cuñado
## 166 33 2017 6 Cuñado
## 167 33 2021 1 Cuñado
## 168 33 2018 7 Cuñado
## 169 33 2013 8 Cuñado
## 170 33 2019 1 Cuñado
## 171 34 2022 1 Cuñada
## 172 34 2012 1 Cuñada
## 173 35 2012 1 Concuño
## 174 35 2013 1 Concuño
## 175 35 2014 2 Concuño
## 176 35 2017 2 Concuño
## 177 37 2015 6 Padrastro
## 178 37 2016 6 Padrastro
## 179 37 2020 2 Padrastro
## 180 37 2013 7 Padrastro
## 181 37 2021 2 Padrastro
## 182 37 2022 5 Padrastro
## 183 37 2012 6 Padrastro
## 184 37 2017 2 Padrastro
## 185 37 2018 2 Padrastro
## 186 37 2014 2 Padrastro
## 187 37 2019 1 Padrastro
## 188 38 2013 1 Madrastra
## 189 39 2015 2 Hijastro
## 190 39 2021 1 Hijastro
## 191 39 2012 2 Hijastro
## 192 39 2017 1 Hijastro
## 193 39 2014 1 Hijastro
## 194 39 2016 1 Hijastro
## 195 39 2013 2 Hijastro
## 196 41 2020 1 Hermanastro
## 197 41 2012 1 Hermanastro
## 198 41 2013 3 Hermanastro
## 199 41 2014 1 Hermanastro
## 200 41 2018 1 Hermanastro
## 201 45 2017 21 Concubino, compañero
## 202 45 2012 9 Concubino, compañero
## 203 45 2013 27 Concubino, compañero
## 204 45 2019 5 Concubino, compañero
## 205 45 2021 8 Concubino, compañero
## 206 45 2020 7 Concubino, compañero
## 207 45 2014 26 Concubino, compañero
## 208 45 2018 10 Concubino, compañero
## 209 45 2022 15 Concubino, compañero
## 210 45 2016 25 Concubino, compañero
## 211 45 2015 28 Concubino, compañero
## 212 46 2017 4 Concubina, compañera
## 213 46 2013 4 Concubina, compañera
## 214 46 2022 1 Concubina, compañera
## 215 46 2012 4 Concubina, compañera
## 216 46 2014 7 Concubina, compañera
## 217 46 2015 5 Concubina, compañera
## 218 46 2016 6 Concubina, compañera
## 219 46 2021 1 Concubina, compañera
## 220 47 2016 2 Amante, Amasio, Querido
## 221 47 2013 1 Amante, Amasio, Querido
## 222 47 2015 1 Amante, Amasio, Querido
## 223 47 2019 1 Amante, Amasio, Querido
## 224 47 2017 1 Amante, Amasio, Querido
## 225 47 2012 3 Amante, Amasio, Querido
## 226 48 2014 1 Amante, Amasia, Querida
## 227 49 2019 1 Novio
## 228 49 2014 2 Novio
## 229 49 2022 1 Novio
## 230 49 2016 4 Novio
## 231 49 2017 1 Novio
## 232 49 2015 2 Novio
## 233 49 2013 3 Novio
## 234 49 2012 2 Novio
## 235 50 2018 1 Novia
## 236 51 2013 2 Ex esposo
## 237 51 2018 6 Ex esposo
## 238 51 2017 3 Ex esposo
## 239 51 2020 3 Ex esposo
## 240 51 2014 5 Ex esposo
## 241 51 2022 5 Ex esposo
## 242 51 2016 4 Ex esposo
## 243 51 2012 3 Ex esposo
## 244 51 2015 3 Ex esposo
## 245 52 2016 2 Ex esposa
## 246 53 2015 1 Padrino
## 247 55 2014 1 Ahijado
## 248 57 2019 1 Compadre
## 249 57 2014 1 Compadre
## 250 57 2012 1 Compadre
## [ reached 'max' / getOption("max.print") -- omitted 85 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: 19 × 2
## year_reg count
## <int> <int>
## 1 2004 10721
## 2 2005 11266
## 3 2006 11635
## 4 2007 10317
## 5 2008 15292
## 6 2009 21178
## 7 2010 27542
## 8 2011 30307
## 9 2012 28121
## 10 2013 25008
## 11 2014 21812
## 12 2015 22303
## 13 2016 26210
## 14 2017 34164
## 15 2018 38648
## 16 2019 38891
## 17 2020 38727
## 18 2021 37756
## 19 2022 35265
License
This package is free and open source software, licensed
MIT.
diegovalle/mxmortalitydb documentation built on Feb. 2, 2024, 5:28 p.m.
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Injury Intent Deaths 2004-2022 in Mexico
Diego Valle-Jones December 30, 2023
Injury Intent Deaths 2004-2022 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 2022. 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: 19 × 3
## # Groups: year_reg [19]
## 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
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: 16 × 3
## # Groups: year_reg [16]
## 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
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 2022 9 Padre
## 4 1 2016 13 Padre
## 5 1 2021 4 Padre
## 6 1 2017 7 Padre
## 7 1 2018 6 Padre
## 8 1 2019 4 Padre
## 9 1 2013 18 Padre
## 10 1 2015 7 Padre
## 11 1 2014 107 Padre
## 12 2 2016 9 Madre
## 13 2 2022 5 Madre
## 14 2 2019 6 Madre
## 15 2 2017 2 Madre
## 16 2 2015 11 Madre
## 17 2 2020 1 Madre
## 18 2 2013 17 Madre
## 19 2 2021 4 Madre
## 20 2 2018 8 Madre
## 21 2 2014 16 Madre
## 22 2 2012 64 Madre
## 23 3 2019 5 Hermano
## 24 3 2022 14 Hermano
## 25 3 2015 9 Hermano
## 26 3 2017 13 Hermano
## 27 3 2014 13 Hermano
## 28 3 2016 11 Hermano
## 29 3 2020 4 Hermano
## 30 3 2012 14 Hermano
## 31 3 2013 16 Hermano
## 32 3 2021 7 Hermano
## 33 3 2018 14 Hermano
## 34 4 2017 5 Hermana
## 35 4 2022 2 Hermana
## 36 4 2019 4 Hermana
## 37 4 2013 6 Hermana
## 38 4 2021 5 Hermana
## 39 4 2015 2 Hermana
## 40 4 2016 4 Hermana
## 41 4 2018 4 Hermana
## 42 4 2014 9 Hermana
## 43 4 2012 7 Hermana
## 44 5 2020 5 Hijo
## 45 5 2018 11 Hijo
## 46 5 2017 9 Hijo
## 47 5 2022 11 Hijo
## 48 5 2012 10 Hijo
## 49 5 2019 7 Hijo
## 50 5 2021 16 Hijo
## 51 5 2015 10 Hijo
## 52 5 2014 20 Hijo
## 53 5 2013 14 Hijo
## 54 5 2016 13 Hijo
## 55 6 2022 1 Hija
## 56 6 2019 1 Hija
## 57 6 2018 2 Hija
## 58 6 2015 1 Hija
## 59 6 2013 2 Hija
## 60 6 2012 1 Hija
## 61 6 2014 2 Hija
## 62 6 2016 3 Hija
## 63 7 2013 4 Abuelo
## 64 7 2019 1 Abuelo
## 65 7 2016 1 Abuelo
## 66 7 2015 1 Abuelo
## 67 7 2012 5 Abuelo
## 68 7 2014 2 Abuelo
## 69 8 2012 3 Abuela
## 70 8 2022 3 Abuela
## 71 8 2014 8 Abuela
## 72 8 2016 1 Abuela
## 73 8 2013 5 Abuela
## 74 9 2012 5 Nieto
## 75 9 2015 3 Nieto
## 76 9 2021 3 Nieto
## 77 9 2017 3 Nieto
## 78 9 2022 2 Nieto
## 79 9 2013 29 Nieto
## 80 9 2019 2 Nieto
## 81 9 2018 1 Nieto
## 82 9 2014 19 Nieto
## 83 9 2016 4 Nieto
## 84 10 2014 1 Nieta
## 85 10 2012 1 Nieta
## 86 10 2013 2 Nieta
## 87 10 2019 1 Nieta
## 88 11 2017 9 Esposo, Cónyuge
## 89 11 2012 27 Esposo, Cónyuge
## 90 11 2021 9 Esposo, Cónyuge
## 91 11 2018 15 Esposo, Cónyuge
## 92 11 2013 29 Esposo, Cónyuge
## 93 11 2016 24 Esposo, Cónyuge
## 94 11 2022 17 Esposo, Cónyuge
## 95 11 2019 7 Esposo, Cónyuge
## 96 11 2015 30 Esposo, Cónyuge
## 97 11 2020 9 Esposo, Cónyuge
## 98 11 2014 22 Esposo, Cónyuge
## 99 12 2017 2 Esposa, Cónyuge
## 100 12 2012 3 Esposa, Cónyuge
## 101 12 2013 9 Esposa, Cónyuge
## 102 12 2016 4 Esposa, Cónyuge
## 103 12 2021 26 Esposa, Cónyuge
## 104 12 2018 5 Esposa, Cónyuge
## 105 12 2015 2 Esposa, Cónyuge
## 106 12 2019 16 Esposa, Cónyuge
## 107 12 2014 10 Esposa, Cónyuge
## 108 12 2022 14 Esposa, Cónyuge
## 109 12 2020 8 Esposa, Cónyuge
## 110 13 2012 7 Tío
## 111 13 2019 2 Tío
## 112 13 2014 6 Tío
## 113 13 2013 9 Tío
## 114 13 2015 8 Tío
## 115 13 2022 2 Tío
## 116 13 2018 2 Tío
## 117 13 2017 6 Tío
## 118 13 2016 13 Tío
## 119 14 2018 1 Tía
## 120 14 2012 1 Tía
## 121 15 2013 14 Sobrino
## 122 15 2019 5 Sobrino
## 123 15 2021 6 Sobrino
## 124 15 2022 6 Sobrino
## 125 15 2012 19 Sobrino
## 126 15 2015 9 Sobrino
## 127 15 2016 4 Sobrino
## 128 15 2018 4 Sobrino
## 129 15 2017 9 Sobrino
## 130 15 2014 10 Sobrino
## 131 16 2016 1 Sobrina
## 132 17 2012 14 Primo
## 133 17 2015 11 Primo
## 134 17 2017 13 Primo
## 135 17 2014 10 Primo
## 136 17 2021 2 Primo
## 137 17 2020 4 Primo
## 138 17 2016 9 Primo
## 139 17 2022 4 Primo
## 140 17 2018 10 Primo
## 141 17 2013 15 Primo
## 142 17 2019 3 Primo
## 143 18 2016 1 Prima
## 144 21 2012 1 Bisnieto
## 145 27 2018 3 Suegro
## 146 27 2016 1 Suegro
## 147 27 2017 2 Suegro
## 148 27 2013 1 Suegro
## 149 27 2015 2 Suegro
## 150 27 2012 2 Suegro
## 151 31 2016 3 Yerno
## 152 31 2018 3 Yerno
## 153 31 2015 4 Yerno
## 154 31 2014 1 Yerno
## 155 31 2012 4 Yerno
## 156 31 2021 1 Yerno
## 157 31 2022 1 Yerno
## 158 31 2017 4 Yerno
## 159 31 2013 5 Yerno
## 160 32 2018 1 Nuera
## 161 33 2012 9 Cuñado
## 162 33 2016 3 Cuñado
## 163 33 2022 4 Cuñado
## 164 33 2014 9 Cuñado
## 165 33 2015 9 Cuñado
## 166 33 2017 6 Cuñado
## 167 33 2021 1 Cuñado
## 168 33 2018 7 Cuñado
## 169 33 2013 8 Cuñado
## 170 33 2019 1 Cuñado
## 171 34 2022 1 Cuñada
## 172 34 2012 1 Cuñada
## 173 35 2012 1 Concuño
## 174 35 2013 1 Concuño
## 175 35 2014 2 Concuño
## 176 35 2017 2 Concuño
## 177 37 2015 6 Padrastro
## 178 37 2016 6 Padrastro
## 179 37 2020 2 Padrastro
## 180 37 2013 7 Padrastro
## 181 37 2021 2 Padrastro
## 182 37 2022 5 Padrastro
## 183 37 2012 6 Padrastro
## 184 37 2017 2 Padrastro
## 185 37 2018 2 Padrastro
## 186 37 2014 2 Padrastro
## 187 37 2019 1 Padrastro
## 188 38 2013 1 Madrastra
## 189 39 2015 2 Hijastro
## 190 39 2021 1 Hijastro
## 191 39 2012 2 Hijastro
## 192 39 2017 1 Hijastro
## 193 39 2014 1 Hijastro
## 194 39 2016 1 Hijastro
## 195 39 2013 2 Hijastro
## 196 41 2020 1 Hermanastro
## 197 41 2012 1 Hermanastro
## 198 41 2013 3 Hermanastro
## 199 41 2014 1 Hermanastro
## 200 41 2018 1 Hermanastro
## 201 45 2017 21 Concubino, compañero
## 202 45 2012 9 Concubino, compañero
## 203 45 2013 27 Concubino, compañero
## 204 45 2019 5 Concubino, compañero
## 205 45 2021 8 Concubino, compañero
## 206 45 2020 7 Concubino, compañero
## 207 45 2014 26 Concubino, compañero
## 208 45 2018 10 Concubino, compañero
## 209 45 2022 15 Concubino, compañero
## 210 45 2016 25 Concubino, compañero
## 211 45 2015 28 Concubino, compañero
## 212 46 2017 4 Concubina, compañera
## 213 46 2013 4 Concubina, compañera
## 214 46 2022 1 Concubina, compañera
## 215 46 2012 4 Concubina, compañera
## 216 46 2014 7 Concubina, compañera
## 217 46 2015 5 Concubina, compañera
## 218 46 2016 6 Concubina, compañera
## 219 46 2021 1 Concubina, compañera
## 220 47 2016 2 Amante, Amasio, Querido
## 221 47 2013 1 Amante, Amasio, Querido
## 222 47 2015 1 Amante, Amasio, Querido
## 223 47 2019 1 Amante, Amasio, Querido
## 224 47 2017 1 Amante, Amasio, Querido
## 225 47 2012 3 Amante, Amasio, Querido
## 226 48 2014 1 Amante, Amasia, Querida
## 227 49 2019 1 Novio
## 228 49 2014 2 Novio
## 229 49 2022 1 Novio
## 230 49 2016 4 Novio
## 231 49 2017 1 Novio
## 232 49 2015 2 Novio
## 233 49 2013 3 Novio
## 234 49 2012 2 Novio
## 235 50 2018 1 Novia
## 236 51 2013 2 Ex esposo
## 237 51 2018 6 Ex esposo
## 238 51 2017 3 Ex esposo
## 239 51 2020 3 Ex esposo
## 240 51 2014 5 Ex esposo
## 241 51 2022 5 Ex esposo
## 242 51 2016 4 Ex esposo
## 243 51 2012 3 Ex esposo
## 244 51 2015 3 Ex esposo
## 245 52 2016 2 Ex esposa
## 246 53 2015 1 Padrino
## 247 55 2014 1 Ahijado
## 248 57 2019 1 Compadre
## 249 57 2014 1 Compadre
## 250 57 2012 1 Compadre
## [ reached 'max' / getOption("max.print") -- omitted 85 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: 19 × 2
## year_reg count
## <int> <int>
## 1 2004 10721
## 2 2005 11266
## 3 2006 11635
## 4 2007 10317
## 5 2008 15292
## 6 2009 21178
## 7 2010 27542
## 8 2011 30307
## 9 2012 28121
## 10 2013 25008
## 11 2014 21812
## 12 2015 22303
## 13 2016 26210
## 14 2017 34164
## 15 2018 38648
## 16 2019 38891
## 17 2020 38727
## 18 2021 37756
## 19 2022 35265
License
This package is free and open source software, licensed MIT.
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