View temp-cdmx.R
library(viridis)
library(ggridges)
library(aire.zmvm)
library(lubridate)
library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)
library(lubridate)
View january-temps.R
#based on https://cran.r-project.org/web/packages/ggjoy/vignettes/gallery.html
library(viridis)
library(ggridges)
library(aire.zmvm)
library(lubridate)
library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)
library(lubridate)
View homicide-northamerica.R
library(directlabels)
library(ggplot2)
library(scales)
library(tidyr)
df = data.frame(Canada = c(522, 609, 611),
US = c(15872, 17793, 19362),
Mexico = c(20010, 20762, 24559))
df = data.frame(apply(df, 2, function(x) x = x/x[1]))
df$year = 2014:2016
View his.R
pop <- read.csv("../downloader/data/pop_muns.csv") %>% filter(date == '2010-06-01 00:00:00')
mex <- injury.intent %>%
filter(year_reg == 2016 & intent %in% c("Homicide", "Legal Intervention")) %>%
group_by(state_occur_death, mun_occur_death) %>%
rename(state_code = state_occur_death, mun_code = mun_occur_death) %>%
summarise(count = n()) %>%
left_join(read.csv("../downloader/data/pop_muns.csv") %>% filter(date == '2016-06-01 00:00:00'),
by = c("state_code", "mun_code")) %>%
mutate(rate = count / population * 10^5) %>%
View temp_cdmx.R
#based on https://cran.r-project.org/web/packages/ggjoy/vignettes/gallery.html
library(viridis)
library(ggridges)
#devtools::install_github('diegovalle/aire.zmvm')
library(aire.zmvm)
library(lubridate)
library(ggplot2)
library(dplyr)
library(tidyr)
View mx_geofacet.R
library(dplyr)
library(readr)
library(ggplot2)
library(geofacet)
read_csv("https://elcri.men/data/victimas.csv.gz") %>%
filter(modalidad == "HOMICIDIOS" & tipo == "DOLOSOS") %>%
mutate(date = as.Date(paste0(date, "-01"))) %>%
mutate(rate = count / population * 10^5 * 12) %>%
View mx_grid.R
library(dplyr)
library(stringr)
library(readr)
library(ggplot2)
library(geofacet)
options(stringsAsFactors = FALSE)
mx_grid <- data.frame(
code = c(2L, 8L, 26L, 3L, 5L, 10L, 19L, 25L, 28L, 1L, 18L, 24L, 32L,
11L, 13L, 14L, 22L, 30L, 6L, 15L, 29L, 9L, 17L, 21L, 31L, 4L,
View carto_electoral.sh
#!/bin/bash
# Author: Diego Valle-Jones
# Web: http://www.diegovalle.net
# Purpose: Script to download electoral shapefiles
# from http://cartografia.ife.org.mx/sige7/?distritacion=federal
set -euo pipefail
# Every six months or so the INE updates the shapefiles
# be sure to update this variable
DATE="15mar2017"
View secciones_edomex.sh
#!/bin/bash
#From http://dorganizacion.ieem.org.mx/SIGE/#
mkdir kml
for i in {1..45}
do
curl -Lo kml/seccion_$i.kml "https://www.google.com/fusiontables/exporttable?query=select+col27+from+1DDGrYAoLQ6dTDRz0sUDP3RLxIk8AfI45syz7kxrT+where+col12+%3D+$i&o=kml&g=col27&styleId=2&templateId=2"
done
View leaflet_municipios.R
library("dplyr")
library("mxmaps")
library("geojsonio")
library("jsonlite")
library("leaflet")
# Convert the topoJSON to spatial object
tmpdir <- tempdir()
# have to use RJSONIO or else the topojson isn't valid
write(RJSONIO::toJSON(mxmunicipio.topoJSON), file.path(tmpdir, "mun.topojson"))