Changes in homicide rates

library(mxmortalitydb)
library(stringr)
library(plyr)
library(ggplot2)
library(grid)  ## needed for arrow

plotMetro <- function(metro.name, metro.areas) {
    ## Plot the homicide counts in a metro area or municipio metro.name - name of
    ## the metro area to plot metro.areas - data frame containing a list of metro
    ## areas in the same format as the metro.area dataframe from mxmortalitydb

    ## 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"))
    ## Yearly homicides in Mexico City, by state of registration
    df2 <- ddply(subset(df, metro_area == metro.name & intent.imputed == "Homicide"), 
        .(year_reg), summarise, count = length(state_reg))

    ggplot(df2, aes(year_reg, count)) + geom_line() + labs(title = str_c("Homicides (plus deaths of unknown intent classified as homicide) in\n", 
        metro.name)) + ylim(0, max(df2$count)) + ylab("homicide count") + xlab("year of registration") + 
        theme_bw()
}

plotChanges <- function(df, metro.areas, country.rate, years) {
    ## Plot of rates and trends df - injury.intent dataframr metro.areas - data
    ## frame containing a list of metro areas in the same format as the
    ## metro.area dataframe from mxmortalitydb country.rate - rate to show as a
    ## gray dotted line years - start and end year to compare changes

    ## Where the municipio where the death occurred is unknown use the municipio
    ## where it was registered as place of occurrance  df[df$mun_occur_death == 999, ]$mun_occur_death <- df[df$mun_occur_death == 
        999, ]$mun_reg

    ## Counts of homicide by state and municipio
    df <- ddply(subset(df, year_reg %in% years & intent.imputed == "Homicide"), 
        .(state_occur_death, mun_occur_death, year_reg), summarise, count = length(state_reg))
    ## Merge the counts with our fake metro areas
    df <- merge(df, metro.areas, by.x = c("state_occur_death", "mun_occur_death"), 
        by.y = c("state_code", "mun_code"))
    ## Now get the counts by metro area (which may contain more than one
    ## municipio)
    df <- ddply(df, .(metro_area, year_reg), summarise, count = sum(count), 
        population = sum(mun_population_2010), rate = count/population * 10^5)
    ## We are only interesed if the metro area at some time had a homicide rate
    ## of at least 15
    df <- subset(df, metro_area %in% subset(df, rate > 15)$metro_area)
    ## Make sure the dataframe is ordered by metro and year
    df <- df[order(df$metro_area, df$year_reg), ]
    ## Order the chart by homicide rate in 2012
    df$metro_area <- reorder(df$metro_area, df$rate, function(x) x[[2]])
    ## Data frame for the arrow structure
    arrows <- ddply(df, .(metro_area), summarise, start = rate[1], end = rate[2], 
        metro_area = metro_area[1], change = ifelse(rate[1] >= rate[2], "decrease", 
            "increase"))

    ggplot(df, aes(rate, metro_area, group = as.factor(year_reg), color = as.factor(year_reg))) + 
        geom_point(aes(size = log(count))) + labs(title = "Homicide (plus deaths of unknown intent classified as homicide) rates and trends") + 
        scale_size("number\nof\nhomicides", breaks = c(log(50), log(500), log(3000)), 
            labels = c(50, 500, 3000)) + geom_segment(data = arrows, aes(x = start, 
        y = metro_area, xend = end, yend = metro_area, group = change, color = change), 
        arrow = arrow(length = unit(0.3, "cm")), alpha = 0.8) + scale_color_manual("year\nand\ntrend", 
        values = c("gray", "black", "blue", "red")) + ylab("metro area or municipio") + 
        xlab("homicide rate") + # scale_x_log10()+
    geom_vline(xintercept = country.rate, linetype = 2, color = "#666666") + 
        annotate("text", y = "Tapachula", x = 25, label = "country\naverage\n2012", 
            hjust = -0.1, size = 4, color = "#666666") + theme_bw()
}

## Let's treat the big municipalities which are not part of a metro area as
## if they were one rename big.municipios to merge with metro.areas
big.municipios2 <- big.municipios
names(big.municipios2) <- c("state_code", "mun_code", "mun_population_2010", 
    "metro_area")
metro.areas.fake <- rbind.fill(metro.areas, big.municipios2)

plotChanges(injury.intent, metro.areas.fake, 24.5, c(2011, 2012))

plotChanges(injury.intent, metro.areas.fake, 24.5, c(2006, 2012))
Interesting that Tijuana had about the same homicide rate in 2012 as in 2006. The rest of the violent metro areas/large municipios which saw decreases are in Michoacán. Sadly, it doesn’t look like pattern will hold in 2013 (according to crimenmexico Michoacán is experiencing a surge of violence and is at a maximum)

ll <- list("Acapulco", "Nuevo Laredo", "La Laguna", "Chihuahua", "Tecomán", 
    "Juárez", "Culiacán", "Victoria", "Hidalgo del Parral", "Zihuatanejo de Azueta", 
    "El Mante", "Ciudad Valles", "Ciudad Valles", "Durango", "Cuernavaca", "Zacatecas-Guadalupe", 
    "Monterrey", "Piedras Negras", "Mazatlán", "Veracruz", "Tijuana", "Guadalajara", 
    "Tepic", "Coatzacoalcos")
names(ll) <- ll  # make lapply print the names of the metro areas
lapply(ll, plotMetro, metro.areas.fake)

## $Acapulco

## 
## $`Nuevo Laredo`

## 
## $`La Laguna`

## 
## $Chihuahua

## 
## $Tecomán

## 
## $Juárez

## 
## $Culiacán

## 
## $Victoria

## 
## $`Hidalgo del Parral`

## 
## $`Zihuatanejo de Azueta`

## 
## $`El Mante`

## 
## $`Ciudad Valles`

## 
## $Durango

## 
## $Cuernavaca

## 
## $`Zacatecas-Guadalupe`

## 
## $Monterrey

## 
## $`Piedras Negras`

## 
## $Mazatlán

## 
## $Veracruz

## 
## $Tijuana

## 
## $Guadalajara

## 
## $Tepic

## 
## $Coatzacoalcos