pachevalier

library("dplyr")
set.seed(123)
N <- 100
df <- data.frame(id = 1:N, x = rnorm(N))
df$x[runif(N) < .1]  <- NA
table(is.na(tdf$x))
tdf  <- tbl_df(df)
out  <- arrange(tdf, desc(x))
out2  <- tdf[order(tdf$x, decreasing = TRUE),]

pachevalier

library("ggplot2")
set.seed(1234)
N  <- 100
df  <- data.frame(i = 1:N, x = rnorm(N))
df$y  <- 1 + df$x + rnorm(N)
df$z  <- (runif(N) < .3)

pdf("output/test.pdf")
ggplot(data = df, aes(x = x, y = y, shape = z)) + 
  geom_point(size = 3) + 

pachevalier

library(ggplot2)
library(maps)
world <- map_data("world")

ggplot() + 
  geom_map( data=world, aes(x=long, y=lat, group = group, map_id = region),colour="white", fill="grey10", map = world ) + 
  coord_map()

ggplot() + 
  geom_map( data=world, aes(x=long, y=lat, group = group, map_id = region),colour="white", fill="grey10", map = world ) 

pachevalier

"long","lat","order","hole","piece","group","id"
5.25559492,45.78458958,1,FALSE,"1","01.1","01"
5.23987135,45.77757781,2,FALSE,"1","01.1","01"
5.22391493,45.76913392,3,FALSE,"1","01.1","01"
5.19950932,45.77145701,4,FALSE,"1","01.1","01"
5.18966522,45.77258731,5,FALSE,"1","01.1","01"
5.18661122,45.78215759,6,FALSE,"1","01.1","01"
5.17711693,45.79331919,7,FALSE,"1","01.1","01"
5.16818192,45.79570866,8,FALSE,"1","01.1","01"
5.15757866,45.80378018,9,FALSE,"1","01.1","01"

pachevalier

library("dplyr")
library("tidyr")
set.seed(10)
messy <- data.frame(
  id = 1:4,
  trt = sample(rep(c('control', 'treatment'), each = 2)),
  work.T1 = runif(4),
  home.T1 = runif(4),
  work.T2 = runif(4),
  home.T2 = runif(4)

pachevalier

circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){
  r = diameter / 2
  tt <- seq(0,2*pi,length.out = npoints)
  xx <- center[1] + r * cos(tt)
  yy <- center[2] + r * sin(tt)
  return(data.frame(x = xx, y = yy))
}

library("venneuler")
library("ggplot2")

pachevalier

# https://www.data.gouv.fr/fr/datasets/contours-des-cantons-electoraux-departementaux-2015/
library("rgdal")
library("rgeos")
library("maptools")
library("ggplot2")
library("dplyr")
library("stringr")
library("mapproj")
library("ggthemes")
cantons <- readOGR("data/cantons2015/", layer= "CANTONS_2015_WGS84")

pachevalier

all: epci-20150303-5m.kml epci-20150303-50m.kml epci-20150303-100m.kml

epci-20150303-5m.kml: epci-20150303-5m.shp
	ogr2ogr -f KML  $@ $<

epci-20150303-5m.shp: epci-20150303-5m-shp.zip
	unzip $<

epci-20150303-5m-shp.zip:
	curl -o $@ "http://osm13.openstreetmap.fr/~cquest/openfla/export/epci-20150303-5m-shp.zip"

pachevalier

library(ggplot2)
library(magrittr)
library(ggthemes)
library(scales)
library(gridExtra)

set.seed(1234)
N <- 5
df <- data.frame(x = runif(N), y = letters[1:10])

pachevalier

library("gmm")
set.seed(1234567)
N  <- 1000
dd  <- data.frame(id = 1:N)
dd$u  <- rnorm(N)
dd$x  <- 1 + rnorm(N)
dd$y  <- 1 + dd$x + dd$u
m1  <- lm(y ~ x, data = dd)
m2  <- gmm(y ~ x, x = ~ x,  wmatrix = "ident", data = dd)
coefficients(m1)