josep2

#load necessary packages
library(ggplot2)

#Get the data in here
Mobile<-read.csv('Mobile.csv')

#Clean up the dates in my data set 
Mobile$Check <-strptime(as.character(Mobile$Day), "%m/%d/%Y")

#Check the format

josep2

py_dict = readLines('python_dictionary.txt')
# e.g.
#{"cat_name": "Ella", "dwell_status": "tree_dweller", "coat_color": "gray, white, orange", "is_from_hell": "Y"}
#{"cat_name": "Billie", "dwell_status": "bush_dweller", "coat_color": "gray, white", "is_from_hell": "N"}

dict_to_df = function(dict) {
  
  require(plyr)
  store = list()
  

josep2

#The only library we need for this

library(rCharts)

#Load the dataset
data<-read.csv('Bonds_Sosa_Mcgwire.csv')

#First we look at tge relationship between HR's and Age for the three players
Basic<-nPlot(HR~Age, 
             group='Player',

josep2

Year,Age,Tm,Lg,G,PA,AB,R,H,2B,3B,HR,RBI,SB,CS,BB,SO,BA,OBP,SLG,OPS,OPS+,TB,GDP,HBP,SH,SF,IBB,Player
1986,21,PIT,NL,113,484,413,72,92,26,3,16,48,36,7,65,102,0.223,0.33,0.416,0.746,103,172,4,2,2,2,2,Bonds
1987,22,PIT,NL,150,611,551,99,144,34,9,25,59,32,10,54,88,0.261,0.329,0.492,0.821,114,271,4,3,0,3,3,Bonds
1988,23,PIT,NL,144,614,538,97,152,30,5,24,58,17,11,72,82,0.283,0.368,0.491,0.859,148,264,3,2,0,2,14,Bonds
1989,24,PIT,NL,159,679,580,96,144,34,6,19,58,32,10,93,93,0.248,0.351,0.426,0.777,126,247,9,1,1,4,22,Bonds
1990,25,PIT,NL,151,621,519,104,156,32,3,33,114,52,13,93,83,0.301,0.406,0.565,0.97,170,293,8,3,0,6,15,Bonds
1991,26,PIT,NL,153,634,510,95,149,28,5,25,116,43,13,107,73,0.292,0.41,0.514,0.924,160,262,8,4,0,13,25,Bonds
1992,27,PIT,NL,140,612,473,109,147,36,5,34,103,39,8,127,69,0.311,0.456,0.624,1.08,204,295,9,5,0,7,32,Bonds
1993,28,SFG,NL,159,674,539,129,181,38,4,46,123,29,12,126,79,0.336,0.458,0.677,1.136,206,365,11,2,0,7,43,Bonds
1994,29,SFG,NL,112,474,391,89,122,18,1,37,81,29,9,74,43,0.312,0.426,0.64

josep2

library(plotly)

#Load Data
BabeRuth<-read.csv('babe.csv')

## Enter API Credentials
p <- plotly(username="Plotly UserName", key="Plotly Key")

##Give it a check with a random graph

josep2

#Load Necssary packages

library(plotly)

#Upload the CSV

pitchfork<-read.csv('pitchfork_review_data.csv')

##Make a Best New Music Set

josep2

## My first attempt at using Julia

## Load Packages we want to use

using Gadfly, RDatasets, GLM

## Now Load some data in

neuro = dataset("boot", "neuro")

josep2

##Load data

Data<-read.csv('twitter_followers.csv')

##Load necessary packages

library(plotly)

##Plotly credentials

josep2

##Load Necessary Packages

library(maps)
require(RCurl)
library(RgoogleMaps)
library(stringr)
library(plotly)
library(tm)
library(rJava)
library(Snowball)

josep2

##Necessary Packages
library(ggplot2)
library(scales)
library(ggthemes)
library(RCurl)
library(repmis)

#Load Data

FinURL <-"https://www.dropbox.com/s/jayd5vksbo1gf3w/freemason.csv"