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# Postgis data type in my case is WSG84: geom | geometry(Point,4326) 
library(jsonlite)
library(RPostgreSQL)
source("dbConnect.R") # create some connection object called con

p <- dbGetQuery(con,
                "SELECT id,
                postal_code,
                st_asGeoJSON(geom)
                FROM sometable

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#!/usr/bin/env bash

# extend resources list and install R
sudo bash -c "echo 'deb http://stat.ethz.ch/CRAN/bin/linux/ubuntu trusty/' >> /etc/apt/sources.list"
sudo apt-get update
sudo apt-get install r-base

# install postgresql
sudo apt-get install postgresql

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library(microbenchmark)

# json test is a character vector of length 2 containing
# both of the json elements given in sample_data.json

microbenchmark(
  lite <- lapply(json_test,function(x){
    jsonlite::fromJSON(x,simplifyVector = F)
  }),
  SONIO <- lapply(json_test,function(x){

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\documentclass{article}

\begin{document}

<<regression,results='asis'>>=
library(texreg)
data(mtcars)
fit1 <- lm(mpg~wt+disp,data=mtcars)
texreg(fit1)

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library(openxlsx)
library(rvest)

journals <- read_html("https://www.aeaweb.org/econlit/journal_list.php")

j <- journals %>%
  html_nodes(".journal") %>%
  html_text()

dflist <- lapply(j,function(x){

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# # convenience package... 
# central vs. decentralized production discussion
# not part of this. 

# minimal introduction to R
# different editors and an interpreter
# How-to-R Studio
# # 4 Panes
# # the ctrl+1 ctrl+2 shortcut
# # don't store

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computeScores <- function(dframe,
                          maplist,
                          fct = sum,
                          na.rm = T){
  li <- lapply(maplist,function(x){
    out <- dframe[,grep(x,names(dframe),value=T)]
    apply(out,1,fct, na.rm = na.rm)
  })

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library(MASS)
set.seed(123)
m <- mvrnorm(10000, mu = c(20,2.6),
               Sigma = matrix(c(8,0.85,0.85,.57),
                              ncol = 2),
               empirical = TRUE)

disp <- m[,1]
wt <- m[,2]
am <- rbinom(10000,1,.4)

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• KOF Indicator Dashboard Shiny App
• Data Driven Documents Mike Bostock
• Highcharts Graphic Library
• dygraphs for R
• CRAN Task View (Disciplinary Package Overview
• Shiny Progress Bars
• Shiny CSS loaders (R package for spinners)
• drop R shiny app source Code

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dpl_gr <- function(ds,vars){
  ds %>% 
    group_by_(.dots = vars) %>% 
    summarise(n = n()) %>% 
    mutate(freq = n / sum(n))
}

dpl_gr(mtcars,c("am","cyl"))