SQLBits Module 2 RStats Introduction to R and Statistics. This is a 90 minute segment of a full preconference workshop, focusing on data analytics with R.

1. The Data Analyst’s
Toolkit
Introduction to R
Jen Stirrup | Data Relish Ltd| June, 2014
Jen.Stirrup@datarelish.com

2. Note
• This presentation was part of a full day workshop on Power BI and R,
held at SQLBits in 2014
• This is a sample, provided to help you see if my one day Business
Intelligence Masterclass is the right course for you.
• http://bit.ly/BusinessIntelligence2016Masterclass
• In that course, you’ll be given updated notes along with a hands-on
session, so why not join me?
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3. Course Outline
• Module 1: Setting up your data for R with Power Query
• Module 2: Introducing R
• Module 3: The Big Picture: Putting Power BI and R together
• Module 4: Visualising your data with Power View and Excel 2013
• Module 5: Power Map
• Module 6: Wrap up and Q and Q

4. What is R?
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• R is a powerful environment for statistical computing
• It is an overgrown calculator
• … which lets you save results in variables
x <- 3
y <- 5
z = 4
x + y + z

5. Vectors in R
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• create a vector (list) of elements, use the "c" operator
v = c("hello","world","welcome","to","the class.")
v = seq(1,100)
v[1]
v[1:10]
• Subscripting in R square brackets operators allow you to extract values:
• insert logical expressions in the square brackets to retrieve subsets of data from a vector or list. For
example:

6. Vectors in R
Microsoft Confidential 6
v = seq(1,100)
logi = v>95
logi
v[logi]
v[v<6]
v[105]=105
v[is.na(v)]

7. Save and Load RData
Data is saved in R as .Rdata files
Imported back again with load
a <- 1:10
save(a, file = "E:/MyData.Rdata")
rm(a)
load("E:/MyData.Rdata")
print(a)

8. Import From CSV Files
• A simple way to load in data is to read in a CSV.
• Read.csv()
• MyDataFrame <- read.csv(“filepath.csv")
• print(MyDataFrame)

9. Import From CSV Files
• Go to Tools in RStudio, and select Import
Dataset.
• Select the file CountryCodes.csv and select the
Import button.
• In RStudio, you will now see the data in the data
pane.

10. Import From CSV Files
The console window will show the following:
> #import dataset
> CountryCodes <- read.csv("C:/Program Files/R/R-
3.1.0/Working Directory/CountryCodes.csv", header=F)
> View(CountryCodes)
Once the data is imported, we can check the
data.
dim(CountryCodes)
head(CountryCodes)
tail(CountryCodes)

11. Import / Export via ODBC
• The Package RODBC provides R with a connection
to ODBC databases
• library(RODBC)
• myodbcConnect <-
odbcConnect(dsn="servername",uid="us
erid",pwd="******")

12. Import / Export via ODBC
• Query <- "SELECT * FROM lib.table WHERE
..."
• # or read query from file
myQuery <-
readChar("E:/MyQueries/myQuery.sql",
nchars=99999)
myData <- sqlQuery(myodbcConnect,
myQuery, errors=TRUE)
odbcCloseAll()

13. Import/Export from Excel Files
• RODBC also works for importing data from Excel
files
• library(RODBC)
• filename <- "E:/Rtmp/dummmyData.xls"
• myxlsFile <- odbcConnectExcel(filename, readOnly =
FALSE)
• sqlSave(myxlsFile, a, rownames = FALSE)
• b <- sqlFetch(myxlsFile, "a")
• odbcCloseAll()

14. Anscombe’s Quartet
Property Value
Mean of X 9
Variance of X 11
Mean of Y 7.50
Variance of Y 4.1
Correlation 0.816
Linear Regression Y = 3.00 + 0.5
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15. What does Anscombe’s Quartet look like?
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16. Looks good, doesn’t it?
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17. So, it is correct?
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18. Correlation r = 0.96
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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Number of
people
who died
by
becoming
tangled in
their
bedsheets
Deaths (US)
(CDC)
327 456 509 497 596 573 661 741 809 717
Total
revenue
generated
by skiing
facilities
(US)
Dollars in
millions
(US Census)
1,551 1,635 1,801 1,827 1,956 1,989 2,178 2,257 2,476 2,438

19. R and Power BI together
• Pivot Tables are not always enough
• Scaling Data (ScaleR)
• R is very good at static data visualisation
• Upworthy
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20. Why R?
• most widely used data analysis software - used by 2M + data
scientist, statisticians and analysts
• Most powerful statistical programming language
• flexible, extensible and comprehensive for productivity
• Create beautiful and unique data visualisations - as seen in New
York Times, Twitter and Flowing Data
• Thriving open-source community - leading edge of analytics
research
• Fills the talent gap - new graduates prefer R.
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21. Growth in Demand
• Rexer Data Mining survey, 2007 - 2013
• R is the highest paid IT skill Dice.com, Jan 2014
• R most used-data science language after SQL -
O'Reilly, Jan 2014
• R is used by 70% of data miners. Rexer, Sept 2013
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22. Growth in Demand
• R is #15 of all programming languages.
• RedMonk, Jan 2014
• R growing faster than any other data science
language.
• KDNuggets.
• R is in-memory and limited in the size of data that
you can process.
22

23. What are we testing?
• We have one or two samples and a hypothesis,
which may be true or false.
• The NULL hypothesis – nothing happened.
• The Alternative hypothesis – something did happen.
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24. Strategy
• We set out to prove that something did happen.
• We look at the distribution of the data.
• We choose a test statistic
• We look at the p value
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25. How small is too small?
• How do we know when the p-value is small?
• P => 0.05 – Null hypothesis is true
• P < 0.05 – alternative hypothesis is true
• it depends
• For high-risk, then perhaps we want 0.01 or even
0.001.
25

26. Confidence Intervals
• Basically, how confident are you that you can
extrapolate from your little data set to the larger
population?
• We can look at the mean
• To do this, we run a t.test
• t.test(vector)
26

27. Confidence Intervals
• Basically, how confident are you that you can
extrapolate from your little data set to the larger
population?
• We can look at the median
• To do this, we run a Wilcox test.
• t.test(vector)
27

28. Calculate the relative frequency
• How much is above, or below the mean?
• Mean(after > before)
• Mean(abs(x-mean)) < 2 *sd(s)
• This gives you the fraction of data that is greater
than two standard deviations from the mean.
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29. Testing Categorical Variables for
Independence
• Chi squares – are two variables independent? Are
they connected in some way?
• Summarise the data first: Summary(table(initial,
outcome))
• chisq.test
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30. How Statistics answers your question
• Is our model significant or insignificant? – The F Statistic
• What is the quality of the model? – R2 statistic
• How well do the data points fit the model? – R2 statistic

31. What do the values mean together?
The type of
analysis
Test statistic How can you tell if it is
significant?
What is the assumption you can make?
Regression analysis F Big F, Small p < 0.05 A general relationship between the
predictors and the response
Regression
Analysis
t Big t (> +2.0
or < -2.0), small p < 0.05
X is an important predictor
Difference of
means
t (two tailed) Big t (> +2.0
or < -2.0), small p < 0.05
Significant difference of means
Difference of
means
t (one tailed) Big t (> +2.0
or < -2.0), small p < 0.05
Significant difference of means
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32. What is Regression?
Using predictors to predict a response
Using independent variables to predict a dependent variable
Example: Credit score is a response, predicted by spend,
income, location and so on.

33. Linear Regression using World Bank data
We can look at predicting using World Bank data
Year <-
GDP <- (wdiData, )
Plot(wdiData,
Cor(year, wdiData)
Fit <- lm(cpi ~ year+quarter)
Fit

34. Examples of Data Mining in R
 cpi2011 <- fit$coefficients[[1]] + fit$coefficients[[2]]*2011 +
fit$coefficients[[3]]*(1:4)
attributes(fit)
fit$coefficients
Residuals(fit) – difference between observed and fitted values
Summary(fit)
Plot(fit)

35. What is Data Mining
Machine Learning
Statistics
Software Engineering and Programming with Data
Intuition
Fun!

36. The Why of Data Mining
to discover new knowledge
to improve business outcomes
to deliver better customised services

37. Examples of Data Mining in R
Logistic Regression (glm)
Decision Trees (rpart, wsrpart)
Random Forests (randomForest, wsrf)
Boosted Stumps (ada)
Neural Networks (nnet)
Support Vector Machines (kernlab)

38. Examples of Data Mining in R
• Packages: – fpc – cluster – pvclust – mclust
• Partitioning-based clustering: kmeans, pam, pamk,
clara
• Hierarchical clustering: hclust, pvclust, agnes, Diana
• Model-based clustering: mclust
• Density-based clustering: dbscan
• Plotting cluster solutions: plotcluster, plot.hclust
• Validating cluster solutions: cluster.stats

39. How can we make it easier?
• AzureML

40. The Data Mining Process
• Load data
• Choose your variables
• Sample the data into test and training sets (usually 70/30 split)
• Explore the distributions of the data
• Test some distributions
• Transform the data if required
• Build clusters with the data
• Build a model
• Evaluate the model
• Log the data process for auditing externally

41. Loading the Data
• Dsname is the name of our dataset
• Get(dsname)
• Dim(ds)
• Names(ds)

42. Explore the data
• Head(dataset)
• Tail(dataset)

43. Explore the data’s structure
• Str(dataset)
• Summary(dataset)

44. Pick out the Variables
• Id <- c(“Date”, “Location) target <- “RainTomorrow”
risk <- “RISK_MM”
• (ignore <-union(id, risk))
• (vars <- setdiff(names(ds), ignore))

45. Remove Missing Data
• dim(ds) ## [1] 366 24 sum(is.na(ds[vars]))
• ## [1] 47 ds <- ds[-attr(na.omit(ds[vars]),
"na.action"),]
• dim(ds) ## [1] 328 24 sum(is.na(ds[vars]))
• ## [1] 0

46. Clean Data Target as Categorical Data
• summary(ds[target])
• ## RainTomorrow ## Min. :0.000 ## 1st Qu.:0.000
• ## Median :0.000 ## Mean :0.183 ## 3rd Qu.:0.000
## Max. :1.000
• ....
• ds[target] <- as.factor(ds[[target]]) levels(ds[target])
<- c("No", "Yes")
• summary(ds[target])

47. Model Preparation
• (form <- formula(paste(target, "~ ."))) ##
RainTomorrow ~ .
• (nobs <- nrow(ds)) ## [1] 328
• train <- sample(nobs, 0.70*nobs) length(train) ## [1]
229
• test <- setdiff(1:nobs, train) length(test)
• ## [1] 99

48. Random Forest
• library(randomForest) model <- randomForest(form,
ds[train, vars], na.action=na.omit) model
• ##
• ## Call:
• ## randomForest(formula=form, data=ds[train,
vars], ...
• ## Type of random forest: classification
• ## Number of trees: 500
• ## No. of variables tried at each split: 4 ....

49. Evaluate the Model – Risk Chart
• pr <- predict(model, ds[test,], type="prob")[,2]
riskchart(pr, ds[test, target], ds[test, risk],
• title="Random Forest - Risk Chart", risk=risk,
recall=target, thresholds=c(0.35, 0.15))

50. Linear Regression
• X: predictor variable
• Y: response variable
• Lm( y ~ x, data= dataframe)

51. Multiple Linear Regression
• Lm is used again
• Lm( y ~ x + u + v, data frame)
• It is better to keep the data in one data
frame because it is easier to manage.

52. Getting Regression Statistics
• Save the model to a variable:
• M <- lm(y ~ x + u + v)
• Then use regression statistics to get the values that you need
from m.

53. Getting Regression Statistics
• Anova(m)
• Coefficients(m) / coef(m)
• Confint(m)
• Effects(m)
• Fitted(m)
• Residuals(m)

54. Getting regression statistics
• The most important one is summary(m). It shows:
• Estimated coefficients
• Critical statistics such as R2 and the F statistic
• The output is hard to read so we will write it out to Excel.

55. Understanding the Regression Summary
• The model summary gives you the information for
the most important regression statistics, such as the
residuals, coefficients and the significance codes.
• The most important one is the F statistic.
• You can check the residuals whether they are a
normal distribution or not. How can you tell this?

56. Understanding the Regression Summary
• The direction of the median is important e.g. a
negative direction will tell you if there is a skew to
the left.
• The quartiles will also help. Ideally Q1 and Q3 should
have the same magnitude. If not, a skew has
developed. This could be inconsistent with the
median result.
• It helps us to identify outliers.

57. Coefficients and R
• The Estimate column contains estimated regression
coefficients, calculated using the least squares
method. This is the most common method.
• How likely is it that the coefficients are zero? This
only shows estimates. This is the purpose of the
column t and p ( > ¦ t¦)

58. Coefficients and R
• The p value is a probability that this finding is
significant. The lower, the better. We can look at the
column signif. codes to help us to identify the most
appropriate level of p value.

59. Coefficients and R
• R2 is the coefficient of determination. How
successful is the model? We look at this value.
Bigger is better. It is the variance of y that is
explained by the regression model. The remaining
variance is not explained by the model. The adjusted
value takes into account the number of variables in
the model.

60. First Impressions
• Plotting the model can help you to investigate it
further.
• Library(car)
• Outlier.test(m)
• M <- lm(y ~ m)
• Plot(m, which=1)

61. First Impressions?
• How do you go about it?
• Check the plot first; how does it look?

62. The F Statistic
• Is the model significant or insignificant? This is the purpose of
the F statistic.
• Check the F statistic first because if it is not significant, then the
model doesn’t matter.

63. Significance Stars
The stars are shorthand for significance levels,
with the number of asterisks
displayed according to the p-value computed.
*** for high significance and * for low significance. In
this case, *** indicates that it's unlikely that no
relationship exists b/w heights of parents and heights of
their children.

64. Plot the Predicted Values
• data2011 <- data.frame(year=2011, quarter=1:4)
• > cpi2011 <- predict(fit, newdata=data2011) > style
<- c(rep(1,12), rep(2,4))
• > plot(c(cpi, cpi2011), xaxt="n", ylab="CPI", xlab="",
pch=style, col=style)
• > axis(1, at=1:16, las=3, +
labels=c(paste(year,quarter,sep="Q"), "2011Q1",
"2011Q2", "2011Q3", "2011Q4"))

65. How to get Help
Microsoft Confidential 65
example(rnorm)
Rseek.org

66. Resources
• Introductory Statistics with R by Peter Dalgaard. Good for beginners.
• The Art of R Programming
• http://www.r-project.org
• CRAN sites – Comprehensive R Archive Network