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require(stats) | |
library(ggplot2) | |
x <- read.csv("tabla31.csv", header=FALSE) | |
plot(x$V3, x$V2, xlim=c(0,200), ylim=c(0, | |
70), | |
ylab="Déficit calórico", | |
xlab="Presupuesto de Gasto por niño (PIM)", | |
main="Hay correlación entre gasto y déficit calórico?\nPrimicia: NO hay!") | |
reg_lineal <- lm(x$V3 ~ x$V2) | |
abline(lsfit(x$V3, x$V2)) |
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Amazonas,32.86,104 | |
Áncash,32.65,134 | |
Apurímac,50.49,139 | |
Arequipa,38.41,118 | |
Ayacucho,45.88,129 | |
Cajamarca,53.93,120 | |
Cusco,32.08,118 | |
Huancavelica,35.60,125 | |
Huánuco,42.40,125 | |
Ica,20.06,152 |
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maxima | lejos de ti | |
---|---|---|
1 | El baile del caballo | |
1 | Opam kay kasja | |
2 | donde esta el amor | |
3 | ya no sere tu panuelito | |
4 | ai se eu te pego | |
5 | tu ausencia | |
6 | hoy he vuelto a ver el amor | |
7 | intentalo | |
8 | linda wawita |
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# -*- coding: utf-8 -*- | |
import prettyplotlib as ppl | |
import numpy as np | |
from prettyplotlib import plt | |
import csv | |
from array import array | |
import collections | |
""" Top 10 ranking """ |
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# -*- coding: utf-8 -*- | |
import prettyplotlib as ppl | |
import numpy as np | |
from prettyplotlib import plt | |
import csv | |
from array import array | |
""" Top 20 ranking """ | |
x = [] |
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Moquegua | 24.8 | 1949 | |
---|---|---|---|
Tumbes | 28.4 | 1839 | |
Ayacucho | 56.6 | 1779 | |
Pasco | 55.8 | 1769 | |
Huancavelica | 68.5 | 1750 | |
Apurímac | 60.9 | 1725 | |
Madre de Dios | 27.1 | 1584 | |
Cusco | 38.2 | 1574 | |
Amazonas | 61.7 | 1404 | |
Tacna | 26.5 | 1399 |
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Loreto | 2013-10-25 | 80 | http://bit.ly/18YdMbA | |
---|---|---|---|---|
Junin | 2013-09-28 | 35 | http://bit.ly/18qqNpT | |
Curahuasi | 2013-09-18 | 50 | http://bit.ly/18qqEmx | |
Huanuco | 2013-04-18 | 17 | http://bit.ly/18qqzPL | |
Urubamba | 2013-04-15 | 100 | http://bit.ly/18qqlYX | |
Ucayali | 2013-10-05 | 15 | http://bit.ly/18qqfAm | |
Satipo | 2013-09-27 | 24 | http://bit.ly/18YeiGx |
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library(ggplot2) | |
y <- read.csv("datos.txt", header=FALSE) | |
y <- as.vector(y[,1]) | |
x <- 2005:2012 | |
int <- lsfit(x,y)$coefficients[1] | |
slope <- lsfit(x,y)$coefficients[2] | |
p <- ggplot(,aes(x,y)) | |
p + geom_point() + geom_abline(intercept=int, slope=slope) + | |
labs(title = "Perú: Número total de delitos por año") |
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# -*- coding: utf-8 -*- | |
import prettyplotlib as ppl | |
from prettyplotlib import plt | |
import sys | |
import pymc as pm | |
import numpy as np | |
datos = np.loadtxt("datos.txt") | |
alpha = 1.0/datos.mean() | |
print alpha |
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152516 | |
153055 | |
144205 | |
151560 | |
160848 | |
181866 | |
206610 | |
254405 |