1. R Programming For Sql Developers ETL USING R
Kiran Math Consultant

2. Excel Data
ETL
Sql Server Table
Motivation

3. Motivation

4. Motivation

5. DEMO
MOTIVATion

6. Installation Comprehensive R Archive Network (CRAN)
R Studio
Installation

7. R <- Core && R <-packages
ggPlot2
sqldf
Base
Packages
rodbc
dplyr
stringR
reshape2
tidyR
lubridate
R <- Core && R <-packages

8. zillow

9. Visualize
Model
Transform
Get & Tidy
Transform
@hadleywickham

10. # Define a Variable a <- 25 # Call a Variable a ## [1] 25 # Do something to it a + 10 ## [1] 35
# Create a vector - Numeric x <- c(0.5, 0.6,0.7) ## call it x ## # Do something to the vector mean(x) ## [1] 0.6
Basics 1 - vector

11. Functions are blocks of code that allow R to be a modular and facilitate code reuse Funct_name <- function (arg1,arg2, ..){ ### do something }
## Compute the mean of the vector of numbers meanX <- function(a_vector) { s <- sum(a_vector) l <- length(a_vector) m <- s/l return(m) } ### create a vector v <- c(1,2,3,4,5) ### Find the mean meanX(v) ## [1] 3
Basics 2 - Functions

12. Data frame Variables To Preview the data frame head(dat) Tail(dat)
Observations
dat
A data frame is used for storing data tables. It is a list of vectors of equal length.
To retrieve data in a cell, we would enter its row and column coordinates in the single square bracket "[]" operator. The two coordinates are separated by a comma.
Number of Rows

13. R –Str()
Compactly display the internal structure of an R object, a diagnostic function
Str(object, ...)
tDat
If you need a quick overview of your dataset, use the R command str() and look at the structure.
tells you something about the classes of your variables and the number of observations.
dat$SaleDate <- as.Date(dat$SaleDate)
Change the class of column SaleDate

14. R – Summary() summary(object)
distribution of your variables in the dataset
tDat
Numerical variables: summary() gives you the range, quartiles, median, and mean.
Factor variables: summary() gives you a table with frequencies.

15. Reshaping Data - DPLYR
Select
Subset variables (Columns).
tDat
Dat

16. filter Data - DPLYR Filter()
allows you to select a subset of rows in a data frame.

17. piping- DPLYR
%>%
Passes object on LHS as first argument to function on RHS

18. Reshaping Data - tidyr Gather Gather columns into Rows
Spread ~ does the opposite
tDat
gDat

19. Make new variable (Column)
Mutate
Compute and appends or or more new columns
gDat

20. Reshaping Data - tidyr Separate Separate one column into several.
Spread ~ does the opposite
gDat
tDat

21. Visualize
Model
Transform
Get & Tidy
Transform
@hadleywickham

22. Data Visualization – ggplot2
Based of Grammar of Graphics
One can build every graph from same few components
Data set
Set of Geom – visual marks that represent the data
Coordinate system

23. Data Visualization – ggplot2
To display data values, map the variables in the dataset to aesthetic properties geom  color, size and x and y locations

24. Data Visualization – ggplot2
Qplot()
Creates a complete plot with given data, geom and mapping. Supplies many useful defaults

25. Data Visualization – ggplot2
Add Layer elements with +
Begin a plot that you can finish by adding layers to. No defaults but provides more control then qplot()

26. Data Visualization – ggplot2
Add Layer elements with +
Begin a plot that you can finish by adding layers to. No defaults but provides more control then qplot()

27. Data Visualization – ggplot2
Lm()
Begin a plot that you can finish by adding layers to. No defaults but provides more control then qplot()

28. Thank you