rpietro

#Load sqldf package, which will load all others necessary
#By default, SQLite runs in background to do processing, could use others DB engines if you wanted

library("sqldf")

#Import employees data

employees  <- structure(list(id = 1:20, lastname = structure(c(5L, 14L, 13L, 15L, 6L, 16L, 9L, 1L, 3L, 12L, 10L, 8L, 12L, 3L, 11L, 13L, 10L, 7L, 2L, 4L), .Label = c("a", "b", "c", "f", "g", "h", "i", "j", "n", "o", "p", "r", "s", "t", "w", "z"), class = "factor"), firstname = structure(c(12L, 6L, 5L, 12L, 11L, 15L, 9L, 18L, 17L, 7L, 8L, 10L, 4L, 14L, 19L, 16L, 1L, 13L, 2L, 3L), .Label = c("chris", "dima", "drew", "eric", "hila", "jason", "jeremy", "joe", "jon", "jowanza", "lashanda", "matt", "michael", "michelle", "randy", "rudi", "solon", "stewart", "tim"), class = "factor"), gender = structure(c(2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("f", "m"), class = "factor")), .Names = c("id", "lastname", "firstname", "gender"), class = "data.frame", row.names = c(NA, -20

shanebutler

# sql.export.gbm(): save a GBM model as SQL
# v0.11
# Copyright (c) 2013-2014 Shane Butler <shane dot butler at gmail dot com>
# 
# sql.export.gbm is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# sql.export.gbm is distributed in the hope that it will be useful, but

stucchio

from pylab import *
from numpy import *
from numpy.linalg import solve
from scipy.integrate import odeint
from scipy.stats import norm, uniform, beta
from scipy.special import jacobi



a = 0.0

stefanwalther

// '.\' is the current path
// Define the path here, if required ...
SET basePath = '.\'; 

TRACE ---------------------------------------------------------------;
TRACE Saving tables ... ;
TRACE ~~;

For i = 0 To NoOfTables() -1
     

coppeliaMLA

#First we are going to set up probaility distributions for our beliefs about the inputs
#We've been told ARPU is about £7 and it's very unlikely to be higher than £10 or lower than £4
#So we'll go for a normal distribution centred at 7 with 5% and 95% quantiles at 4 and 10

#Show how we get the variance

arpu.sd<-3/1.96
x<-seq(0, 15,by=0.5)
d<-dnorm(x, 7, arpu.sd)
plot(x, d, type='l')

stucchio

from matplotlib import use

from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

shanebutler

# sql.export.rf(): save a randomForest model as SQL
# v0.04
# Copyright (c) 2013-2014 Shane Butler <shane dot butler at gmail dot com>
# 
# sql.export.rf is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# sql.export.rf is distributed in the hope that it will be useful, but

pengelbrecht

require(RMySQL)
require(ggplot2)
require(scales)

myusername = "peter"
mypassword = "sekret"

system('ssh -f pe@192.168.0.10  -L 3306:localhost:3306 -N -o ExitOnForwardFailure=yes')
con <- dbConnect(MySQL(),
                 user=myusername, password=mypassword,

aronatkins

#!/bin/bash
#
# Installs the latest RStudio daily desktop build for OSX/macOS and Ubuntu(amd64)
#
# https://support.rstudio.com/hc/en-us/articles/203842428-Getting-the-newest-RStudio-builds

set -e

install_macos_daily() {
    REDIRECT_URL="https://www.rstudio.org/download/latest/daily/desktop/mac/RStudio-latest.dmg"