tomasgreif

GenerateCubeSQLite <- function(DataFrame,PrimaryKey,CubeDestination,DataSourceDestination,NumericAggregators=NA,DateAggregators=NA,MaxTime='2015-01-01',MinTime='2001-01-01') {
     
     # Function to clean names
     standardize_name <- function(string) {
          capped <- grep("^[^A-Z]*$", string, perl = TRUE)
          substr(string[capped], 1, 1) <- toupper(substr(string[capped], 1, 1))
          string <- gsub('_',' ',string, fixed=TRUE)
          return(string)
     }  
     

tomasgreif

standardize_name <- function(string) {
     capped <- grep("^[^A-Z]*$", string, perl = TRUE)
     substr(string[capped], 1, 1) <- toupper(substr(string[capped], 1, 1))
     string <- gsub('_',' ',string, fixed=TRUE)
     return(string)
}  


GenerateCube <- function(TableName,TableSchema,PrimaryKey,TimeTableName,TimeTableSchema,Destination) {

tomasgreif

#initialize
library(ggplot2) 

TestData <- data.frame(   a = rep(LETTERS[1:4],10),
                          b = rep(c('A','B'),20),
                          c = rep(LETTERS[1:5],each=8),
                          d = rep(c('A','B'),2,each=10),
                          m1 = rnorm(40),
                          m2 = rnorm(40),
                          m3 = rnorm(40),

tomasgreif

create or replace function gtd(sample text)
   returns varchar as $$
   declare outsql varchar;
begin
select tbl_definition into outsql from (

with 

source as (select $1::varchar instr)

tomasgreif

library(shiny)

  german_data <- read.table(file="http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data",
                            sep=" ", header=FALSE, stringsAsFactors=TRUE)
  names(german_data) <- c('ca_status','duration','credit_history','purpose','credit_amount','savings',
                          'present_employment_since','installment_rate_income','status_sex','other_debtors',
                          'present_residence_since','property','age','other_installment','housing','existing_credits',
                          'job','liable_maintenance_people','telephone','foreign_worker','gb')
  german_data$gb <- factor(german_data$gb, levels=c(2,1), labels=c("bad","good"))
  german_data <- german_data[,c("duration","age","credit_amount","gb")]

tomasgreif

#' Create SQL statement from rpart rules
#'
#' Rpart rules are changed to sql CASE statement.
#'
#' @param df data frame used for rpart model
#' @param model rpart model
#' @export
#' @examples
#' parse_tree(df=kyphosis,model=rpart(data=kyphosis,formula=Kyphosis~.))
#' parse_tree(df=mtcars,model=rpart(data=mtcars,formula=am~.))

tomasgreif

# www.analytikdat.cz
# www.analytikdat.cz/index.php/blog/entry/r-and-postgresql-using-rpostgresql-and-sqldf

# Load required libraries
library("RPostgreSQL")
library("sqldf")

# Establish connection
drv <- dbDriver("PostgreSQL")
# Simple version (localhost as default)