randyzwitch

julia> using ODBC

julia> ODBC.connect("MySQL")
Connection 1 to MySQL successful.

#Save query results into a DataFrame called 'results'
julia> results = query("Select * from a1987;");

julia> typeof(results)
DataFrame  (use methods(DataFrame) to see constructors)

randyzwitch

#Run until version > 1.0 on CRAN
library(devtools)
install_github("RSiteCatalyst", "randyzwitch", ref = "master")

#Run if version >= 1.1 on CRAN
library("RSiteCatalyst") 

#API Authentication
SCAuth(<username:company>, <shared_secret>)

randyzwitch

#Plot data using ggplot2
library(ggplot2)

#Combine year/month/day together into POSIX
pageviews_w_forecast$date <- ISOdate(pageviews_w_forecast$year, pageviews_w_forecast$month, pageviews_w_forecast$day)

#Convert columns to numeric
pageviews_w_forecast$pageviews <- as.numeric(pageviews_w_forecast$pageviews)
pageviews_w_forecast$pageviews_upper <- as.numeric(pageviews_w_forecast$pageviews_upper)
pageviews_w_forecast$pageviews_lower <- as.numeric(pageviews_w_forecast$pageviews_lower)

randyzwitch

-- Create table from yearly airline csv files
CREATE EXTERNAL TABLE airline (
  `Year` int,
  `Month` int,
  `DayofMonth` int,
  `DayOfWeek` int,
  `DepTime`  int,
  `CRSDepTime` int,
  `ArrTime` int,
  `CRSArrTime` int,

randyzwitch

-- Create view to "remove" 22 bad records from our table
create view vw_airline as
select * from airline
where uniquecarrier <> "UniqueCarrier";

randyzwitch

#Python/pypy
import math
def smallestdivisall(n):
    for i in xrange(1, math.factorial(n)+1):
        for j in xrange(1, n+1):
            if i % j != 0:
                break
            elif j == n:
                return i
                

randyzwitch

function smallestdivisall(n::Int64)
    for i = 1:factorial(n)
        for j = 1:n
            if i % j !=0
                break
            elseif j == n
                return i
            end
        end
    end

randyzwitch

library(ggplot2)
library(reshape2)

jit <- read.csv("~/Desktop/jit.csv")

#Melt data into proper format
jitm <- melt(jit, id.vars='numsearch')
jitm$value = log(jitm$value)

#Create Overall ggplot for ln performance

randyzwitch

library(ggplot2)

# This example uses the ChickWeight dataset, which comes with ggplot2
# First plot
p1 <- 
  ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet, group=Chick)) +
  geom_line() +
  ggtitle("Growth curve for individual chicks")

# Second plot

randyzwitch

#### 0. Setup
library("RSiteCatalyst")
library("RTextTools") #Loads many packages useful for text mining

#### 1. RSiteCatalyst code - Get Natural Search Keywords & Metrics

#Set credentials
SCAuth(<username:company>, <shared secret>)

#Get list of search engine terms