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sicarul / testgeojson.json
Last active November 30, 2022 16:53
Test geojson
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@sicarul
sicarul / ggplot_density_prob.R
Created March 14, 2021 05:19
GGplot test grafico densidad
library(ggplot2)
library(dplyr)
data_normal = rnorm(10000, mean=1000, sd=10)
df = data.frame(Datos=data_normal)
ggplot(df, aes(Datos)) +
theme_minimal() +
geom_area(data = filter(df, Datos>=980), stat="density", fill="#990000", alpha=0.3) +
geom_density(stat="density") +
@sicarul
sicarul / distracted_bf.Rmd
Last active April 23, 2020 15:38
Distracted BF
``` r
library(tidyverse)
dbf = data.frame(color=character(0), x=numeric(0), y=numeric(0), stringsAsFactors = F)
## Distractive woman
# Left arm
dbf = bind_rows(dbf, crossing(color='skin', x=seq(4,5), y=seq(1,9)))
dbf = bind_rows(dbf, data.frame(color='skin', x=5, y=10))
@sicarul
sicarul / pbi_vs_libertad.R
Created April 28, 2019 03:55
Pbi Vs Libertad
library(tidyverse)
library(countrycode)
library(gganimate)
library(ggrepel)
gapminder <- read_csv("https://raw.githubusercontent.com/cienciadedatos/datos-de-miercoles/master/datos/2019/2019-04-24/gapminder_es.csv")
# freedom = read_csv('freedom.csv') # - Obtenido de https://www.fraserinstitute.org/economic-freedom/dataset?geozone=world&page=dataset&min-year=2&max-year=0&filter=0
# datapasta::tribble_paste(spread(freedom, anio, libertad_economica))
freedom = tibble::tribble(
@sicarul
sicarul / futbol.R
Created April 11, 2019 03:00
Distribucion de goles por torneo
library(tidyverse)
library(gganimate)
library(ggimage)
partidos <- readr::read_delim("https://raw.githubusercontent.com/cienciadedatos/datos-de-miercoles/master/datos/2019/2019-04-10/partidos.txt",delim = "\t")
# unique(partidos$anfitrion)
mapping_paises = tribble(
~codigo, ~anfitrion,
@sicarul
sicarul / importaciones.R
Created April 8, 2019 02:47
Importaciones sobre PBI Argentina - 1980 a 2017
library(tidyverse)
library(ggplot2)
# Fuente: https://www.indec.gob.ar/bajarCuadroEstadistico.asp?idc=2EDA403F02C233A1B46A148394F2F5F71E0DB8DCC4501DEBE022A3C1D226244BA5A822D58E3F32B1
importaciones = read_csv('importaciones.csv')
importaciones_cat = gather(importaciones, `Año`, Dolares, -Uso) %>%
mutate(Dolares=Dolares*1000, `Año`=as.integer(`Año`))
#Fuente: https://data.worldbank.org/indicator/NY.GDP.MKTP.CN?locations=AR

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