infrahumano’s gists

infrahumano / suicide_rates.csv

Created May 2, 2019 20:25

From this paper: https://www.jaacap.org/article/S0890-8567(19)30288-6/abstract

year	index	rate
2013	1	0.29263404
2013	2	0.268499255
2013	3	0.359004647
2013	4	0.371072024
2013	5	0.352970958
2013	6	0.235313967
2013	7	0.247381344
2013	8	0.307718247
2013	9	0.349954098

infrahumano / collisions_injuries.csv

Last active March 2, 2019 15:38

Number of pedestrian fatalities in Canada 1999-2016

We can make this file beautiful and searchable if this error is corrected: It looks like row 9 should actually have 21 columns, instead of 18 in line 8.

	"Injury Severity","Person Position", "Total","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016"
	"Total","Total",44167,2980,2904,2758,2921,2777,2735,2898,2871,2753,2431,2216,2238,2023,2075,1951,1846,1895,1895
	"Total","Driver/Operator",26745,1760,1685,1669,1750,1650,1644,1787,1800,1660,1525,1378,1350,1240,1239,1188,1129,1154,1137
	"Total","Front row, center",598,58,43,45,54,49,34,52,23,34,32,28,34,26,22,16,19,19,10
	"Total","Front row, right outboard, including motorcycle passenger in sidecar",5862,440,465,391,446,404,390,421,380,355,298,267,295,245,259,193,221,186,206
	"Total","Second row, left outboard, including motorcycle passenger",1344,93,108,87,85,104,84,77,94,90,72,51,68,49,54,52,55,68,53
	"Total","Second row, center",501,42,37,36,33,35,35,25,23,37,30,23,20,27,35,23,9,16,15
	"Total","Second row, right outboard",1293,97,90,85,94,81,92,97,87,84,57,66,74,49,60,50,42,47,41
	"Total","Third row, left outboard",35,0,1,2,2,3,3,1,1,4,0,1,2,0,1,

infrahumano / flower_generator.py

Created February 3, 2019 23:15

Trying to immitate in python this: https://twitter.com/lorenschmidt/status/1091794209554792448

	import numpy as np

	side = '\({[<'
	other_side = '/)}]>'
	centered = '"*:\|oOwWvVXx!^+=-08TYUuMA'
	transformer = dict(list(zip(side, other_side)) + list(zip(other_side, side)) + list(zip(centered, centered)))
	total = list(side + other_side + centered)

	def row(centered_flower, size):
	result = ''

infrahumano / matriz_de_transicion.py

Last active March 23, 2018 13:22

Votos al senado a votos por presidente (vía una encuesta)

	import numpy as np

	columnas = ['partido cambio radical',
	'partido polo democrático alternativo',
	'partido social de unidad nacional partido de la u',
	'partido liberal colombiano',
	'partido alianza verde',
	'partido conservador colombiano',
	'partido centro democrático',
	'coalición lista de la decencia (asi,up,mais)']

infrahumano / participacion_versus_fraccion_votos_partido_senado.py

Created March 17, 2018 21:53

Gráfico: participacion versus fracción de votos por partido a nivel municipal (senado).

	import pandas as pd
	import seaborn as sns

	sns.set(style='ticks', color_codes=True)
	sns.set_palette(sns.color_palette('RdGy', 10)[::-1])
	senado = pd.read_csv('datos/senado.csv.gz')
	participacion = pd.read_csv('datos/participacion.csv')

	partidos =['coalición lista de la decencia (asi,up,mais)',
	'partido alianza verde',

infrahumano / farc_versus_el_patriota.table

Created March 16, 2018 01:32

FARC versus El Patriota

	municipio departamento farc el patriota
	0 Abejorral Antioquia 4 0
	1 Abriaqui Antioquia 0 1
	2 Alejandria Antioquia 1 1
	3 Amaga Antioquia 7 2
	4 Amalfi Antioquia 5 0
	5 Andes Antioquia 13 10
	6 Angelopolis Antioquia 1 0
	7 Angostura Antioquia 18 0
	8 Anori Antioquia 107 0

infrahumano / el_patriota_versus_voto_en_blanco.table

Created March 15, 2018 00:09

El Patriota contra el Voto en Blanco

	el patriota voto en blanco
	municipio
	Abejorral 0 88
	Abriaqui 1 10
	Alejandria 1 42
	Amaga 2 314
	Amalfi 0 156
	Andes 10 361
	Angelopolis 0 51
	Angostura 0 57

infrahumano / petro_gana_a_duque.table

Created March 14, 2018 10:38

¿Dónde le gana Petro a Duque?

	candidato_nombre Gustavo Petro Iván Duque diferencia
	departamento municipio
	Amazonas El Encanto 12.0 9.0 3.0
	La Chorrera 302.0 15.0 287.0
	La Pedrera 29.0 9.0 20.0
	La Victoria 13.0 1.0 12.0
	Miriti Parana 29.0 3.0 26.0
	Puerto Arica 16.0 5.0 11.0
	Puerto Nariño 290.0 123.0 167.0
	Puerto Santander 23.0 10.0 13.0

infrahumano / data_frame_explosion.R

Last active November 5, 2017 10:47

Is there a way of doing this within the tidyverse? I do not want to reinvent gunpowder.

	library(dplyr)

	explode <- function(df, f) {
	df_indexed <- df %>% mutate (explosion_index=1:n())
	f_indexed <- function(row_indexed) {
	return(f(row_indexed) %>% mutate(explosion_index=row_indexed['explosion_index']))
	}
	expansion <- df_indexed %>% apply(1, f_indexed) %>% do.call(rbind, .)
	explosion <- df_indexed %>%
	left_join(expansion, by='explosion_index') %>%

Javier Moreno infrahumano