R For Sql Developers. Kiran Math

Transcription

1 R For Sql Developers Kiran Math - kiranmath@outlook.com

2 R Figure 1: R

3 Download R CRAN The Comprehensive R Archive Network ~ OpenSource R MRAN Micrsoft R Open ~ Enhanced R Distribution

4 Microsoft R Microsoft R Server Microsoft R Client Microsoft R Open

5 Capability Comparision Capability R Open Microsoft R Open Microsoft R Server Data Size In - Memory In - Memory In - Memory + Disk Speed Single - Thread Multi - Thread, MKL Multi - Thread, Multi Node, MKL Support Community Community Community + Commercial Package Support CRAN CRAN + MRAN CRAN + MRAN + MSR Scale/ Performance Packages License Open Source Open Source Commercial

6 R Development Tools R Studio R Tools for Visual Studio (RTVS)

7 Data NHANES : The National Health and Nutrition Examination Survey SEQN GENDER AGEYR BMI WAIST Male Female Male Male Female Male NA

8 R - Objects Vectors Data Frames Factors Lists Matrices Arrays

9 Data Types Types Example Logical TRUE, FALSE Numeric 14.6, 50, 100 Integer 1L, 374L, 10L Character SQL, Saturday Complex 3 + 2i Raw c (SQL)

10 Variables Variables are reserved memory locations to store values. The variables are assigned with R-Objects. The data type of the R-object becomes the data type of the variable.

11 Vector A vector is a sequence of data elements of the same basic type. Members in a vector are called components.

12 Character Vector GENDER <- c ("Male", "Female", "Male", "Male", "Female", "Male") print(gender) ## [1] "Male" "Female" "Male" "Male" "Female" "Male" class(gender) ## [1] "character"

13 Numeric Vector AGEYR <- c(55l, 52L, 63L, 83L, 37L, NA) print(ageyr) ## [1] NA class(ageyr) ## [1] "integer"

14 Numeric Vector BMI <- c(31.26, 25.49, 19.60, 28.32, 19.34, 16.57) print(bmi) ## [1] class(bmi) ## [1] "numeric"

15 Numeric Vector SEQN < :21014 print(seqn) ## [1] class(seqn) ## [1] "integer"

16 Factor Variables that take limited number of different values. Categorical Variables Nominal Ordinal Data is stored as vector integer Unique Character value is stored once

17 Factor fac_gender <- factor(gender) str(fac_gender) ## Factor w/ 2 levels "Female","Male": print(fac_gender) ## [1] Male Female Male Male Female Male ## Levels: Female Male

18 BMI Classification Group BMI Range Underweight =< 18.4 Normal Overweight Obesity X Obesity bgroup <- c( "Under", "Normal", "Over", "Obese", "Extra") brange <- c(0, 18.4, 24.9, 29.9, 39.9, 100)

19 Ordered Factor fac_bmi <- cut(bmi, breaks = brange, labels = bgroup, ordered = TRUE) print(bmi) ## [1] print(fac_bmi) ## [1] Obese Over Normal Over Normal Under ## Levels: Under < Normal < Over < Obese < Extra

20 Data Frame dfbmi <- data.frame( id = SEQN, gender = fac_gender, age = AGEYR, bmi = fac_bmi ) id gender age bmi Male 55 Obese Female 52 Over Male 63 Normal Male 83 Over Female 37 Normal Male NA Under

21 Data Science Life Cycle Figure 2: R

22 Programming Paradigm Imperative Declarative Object-Oriented Procedural Functional Logical

23 Programming Paradigm Algorithm = Logic + Control Logic What Must Be Done Control How the desired solution is found Imperative Declarative Both are needed Only Logic is needed

24 Imperative Programming num <- c(1:10) print(num) ## [1] total <- 0 for (i in num) { total <- total + i } print(total) ## [1] 55

25 Declarative Programming num <- c(1:10) print(num) ## [1] total <- sum(num) print(total) ## [1] 55

26 Functional Programming R,at its heart, is a functional programming language. It Provides many tools to creation and manipulation of functions. Assign function to variables Store functions in list Pass function as argument to other functions Create function inside function Return function as a result of a function First Class Functions

27 R Function add <- function(x,y) { return (x + y) }

28 R Function total <- add(1, 2) print(total) ## [1] 3

29 Use Case Import CSV Data into R Using Open R

30 Import CSV file into R BMIFile <- "D:\\Presentation\\SpartanburgR2017\\Data\\BMI.c dfbmi <- read.csv(bmifile) SEQN GENDER AGEYR BMI WAIST Male Female Female Male Male Female

31 Filter Data Using Package DPLYR library(dplyr) df16plus <- dfbmi %>% filter(ageyr >=18 & is.na(bmi) == FALSE) SEQN GENDER AGEYR BMI WAIST Male Male Female Male Male Female

32 ## [1] Over Extra Normal Discretize BMI Function dbmi <- function(vbmi) { bgroup <- c( "Under", "Normal", "Over", "Obese", "E brange <- c(0, 18.4, 24.9, 29.9, 39.9, fac_bmi <- cut(vbmi, breaks = brange, labels = bgroup, ordered = TRUE) } return(fac_bmi) vsample <- c(25,40, 19) dbmi(vsample)

33 Add New Column to Data Frame df <- df16plus %>% mutate(bmigroup = dbmi(bmi)) SEQN GENDER AGEYR BMI WAIST BMIGroup Male Extra Male Obese Female Over Male Normal Male Over Female Normal

34 Visualize Data : Frequency hplot <- hist(df$bmi, xlab="bmi", border="blue", col="green" ) print(hplot, abline(v=24.9, col="red", lty = 3)) Histogram of df$bmi Frequency

35 Visualize Data : Frequency library(ggplot2) ggplot(df, aes(bmigroup)) + geom_bar() count 500 0

36 CSV To Sql Table : Variables library(revoscaler) sqldesttable <- "tblbmi" sqlrowsperread < sqlconnstring <- hidden BMIFile <- "D:\\Presentation\\SpartanburgR2017\\Data\\BMI.c datcol <- c( "SEQN" = "integer", "GENDER" = "factor", "AGEYR" = "integer", "BMI" = "numeric", "WAIST" = "numeric")

37 CSV To Sql Table des <- RxSqlServerData(connectionString = sqlconnstring, table = sqldesttable, rowsperread = sqlrowsperread) indat <- RxTextData( file = BMIFile, colclasses = datcol) rxdatastep(indata = indat, outfile = des, overwrite = TRUE) ## Rows Read: 9643, Total Rows Processed: 9643, Total Chunk ## ## Elapsed time to compute low/high values and/or factor le ## ## Rows Read: 9643, Total Rows Processed: 9643 ## Total Rows written: 9643, Total time: ##, Total Chunk Time: seconds

38 tblbmi SELECT TOP 6 * FROM tblbmi; SEQN GENDER AGEYR BMI WAIST Male Female Female Male Male Female

39 SQL Server Scripts With R EXEC = N = N R = N SQL = N = N = N Script Block Parameter WITH RESULT SETS (( Return Columns ))

40 Demo GetBMIGroup.StoredProcedure GetBMIGroupWithParameter.StoredProcedure GetBMIGroupWithParameterExternalFile.StoredProcedure

41 Thank you Figure 3: Thank you Kiran Math Greenville SC