R vs. Python vs. SQL

• Name
• What do you do for work?
  
• Which programming language do you already know?
  
• Why take this class?
• Fun fact!

• Build basic understanding on the purposes of the 3 tools
• Remove barriers & frustration
• Get a taste for R, Python, and SQL
• Name 2-3 similarities among the tools
• Name 2-3 differences

• Invented at University of Auckland, New Zealand
• Statistical computing

R : RStudio, RGui
Packages : ggplot2, reshape

Python : Jupyter Notebook (iPython), Mac Terminal
Libraries : Pandas, NumPy

SQL : PostgreSQL, SQLite, Toad for Oracle, much more…

#This is R
print("Hello World")

#This is Python
print "Hello World"

same arithmatic symbols for adding, subtracting, multiplication, and division

+ - * /

Note: Python output number type = input data type

x to the power of y

#This is R
x^y

#This is Python
pow(x, y)

x ** y

Find a partner. Take turns writing in R & Python.

1) Print Hello World

2) 6 to the power of 4

3) Make x=3, m=8, b=6, and y=mx+b.

What is y?

4) What is 8 divided by 3?

The answer should be in decimal form.

Know the path to your data

#define path to file
dataFile <- "C:/Users/lynnaj/Documents/girldevelopit/
fun_data/vdeOilData.csv"

#load data
vdeOilDF <- read.csv(dataFile)

#view the first 5 lines of the data
head(vdeOilDF)

#https://stat.ethz.ch/R-manual/R-devel/library/utils/html/read.table.html

#You can also double click on the dataframe in Environment tab

import pandas as pd

datafile = 'C:\\Users\\lynnaj\\Documents\\girldevelopit\\fun_data\\vdeOilData.csv'

vdeOilDF = pd.read_csv(datafile)

vdeOilDF.head()

Create an empty table:

CREATE TABLE VDEandOil (vde_prices double, week_number double, month varchar, year double, oil_prices double);

Stuff the data into the table:

COPY VDEandOil FROM '/path/to/VDEandOil.txt' DELIMITER ',' CSV;

http://stackoverflow.com/questions/2987433/how-to-import-csv-file-data-into-a-postgresql-table

1) Read vdeOilData.csv or sensorsData.csv onto your RStudio Software

2) Read the same file onto your iPython Notebook

3) Import the file onto your PostgreSQL DB

vdeOilDF$Date <- as.Date(vdeOilDF$Date, format="%Y-%m-%d")
vdeOilDF$weekNumber <- as.factor(vdeOilDF$weekNumber)
vdeOilDF$vdeClose <- as.numeric(vdeOilDF$vdeClose)

vdeOilDF$pricePerBarrel <- as.numeric(vdeOilDF$pricePerBarrel)
vdeOilDF$year <- as.numeric(vdeOilDF$year)
vdeOilDF$month <- as.factor(vdeOilDF$month)

summary(vdeOilDF)

mean(vdeOilDF$pricePerBarrel)

max()
min()
sd()

summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  

http://pandas.pydata.org/pandas-docs/stable/