Following prerequisites and setup must be complete for successful completion of the exercise:

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2 Cntents Lab Prerequisites... 3 Dcument Structure... 4 Overview... 5 Intrductin... 5 Data Set... 5 What Is R?... 6 What Is R? Installing R... 6 What Is R? Installing RStudi... 7 What Is R? Basic Syntax... 8 What Is R? Data Frames What Is R? Creating Visauls in R (Part 1) What Is R? Creating Visuals in R (Part 2) Utilizing R in PwerBI Utilizing R in PwerBI Using What we Learned in R Studi Cnclusin: Cnclusin: Questins P a g e

3 Lab Prerequisites Fllwing prerequisites and setup must be cmplete fr successful cmpletin f the exercise: Yu must have an internet cnnectin. Signup fr Pwer BI: G t and sign up fr Pwer BI with a business address. If yu cannt sign up fr Pwer BI, let the instructr knw. If yu have an existing accunt, please g t and Sign in using yur Pwer BI Accunt. At minimum, a cmputer with 2-cres and 4GB RAM running ne f the fllwing versin f Windws: Windws 10, Windws 7, Windws 8 (64-bit preferred), Windws 8.1, Windws Server 2008 R2, Windws Server 2012, Windws Server 2012 R2. Micrsft Pwer BI Desktp requires Internet Explrer 9 r greater. Verify if yu have 32-bit r 64-bit perating system t decide if yu need t install the 32-bit r 64- bit applicatins. Search fr cmputer n yur PC, right click prperties fr yur cmputer. Yu will be able t identify if yur perating system is 64 r 32 bit based n system type as shwn belw. Dwnlad all ur sessin cntent here: Training-Pack Dwnlad and install Pwer BI Desktp using any ne f the ptins listed belw: If yu have Windws 10, use Micrsft App Stre t dwnlad and install Pwer BI Desktp app. Dwnlad and install Micrsft Pwer BI Desktp frm us/dwnlad/details.aspx?id= P a g e

4 Dcument Structure This dcument has tw main sectins: What is R?: This sectin is an intrductin t R: It ges thrugh sme examples f basic syntax, and building visuals in R. This in n way is a cmprehensive curse in R, but it will prvide yu with the mindset needed t g ut and explre n yur wn. Utilizing R Within Pwer BI : This sectin cvers hw t use R in PwerBI: This includes key tips and tricks, and general philsphy f hw t develp R Scripts in PwerBI. In the prcess here we g thrugh the prcess f implementing ur visual we created in RStudi within PwerBI, and then utilize R t add a bit mre value t ur visual. Thrughut this dcument yu will find all steps in a table n the left side. On the right will be screenshts r images t aid in fllwing alng. 4 P a g e

5 Overview Intrductin Tday we will be learning and explring the pwer that R adds t PwerBI. The challenge with this tpic is that, while R can be very simple and intuitive, there is s much that it can d that it can be very verwhelming. Additinally, many f the basics that R prvides can be dne slely with PwerBI. This causes a tricky dichtmy in which the easy stuff is t simple, but the useful stuff is t cmplicated. The gal f this wrk bk is t intrduce t yu the basics f R and examine sme relatively simple tls that R prvides that are nt easily accmplished in PwerBI. Data Set Fr the first prtin f this wrkshp we will be wrking with sets and arrays f ur creatin. The latter prtin here will use a dataset frm the public data catalg f the gvernment f Canada. Our surce was dwnlad frm here: After dwnlading, I trimmed ut sme f larger hspitals t have a mre cnsistent distributin f data. The riginal data was a little t varied t prvide useful insights it was. I additinally filtered dwn t a few surgical prcedures, again t eliminate sme clutter. Wrkshp Outline 1. What is R? 2. Utilizing R in PwerBI 3. Questins 5 P a g e

6 What Is R? R is an pen surce language and envirnment fr statistical cmputing and graphics. It prvides a wide variety f statistical and graphical techniques and is highly custmizable. This sectin examines sme f the basics f hw t use R and what it is capable f. What Is R? Installing R Installing R is as easy as We need t have it installed t wrk with it tday. What is R? Installing R We must begin with installing R. This is very simple. We ll navigate t R s page and dwnlad R frm there. If yu already have R installed n yur cmputer yu can skip this step. 1. Navigate t the fllwing link 2. Click Dwnlad R fr Windw 3. Open the dwnladed file and prceed as nrmal t install. It really is that easy. 6 P a g e

7 What Is R? Installing RStudi The default R GUI is nt very friendly t wrk in. We are ging t install RStudi which is way better t wrk and experiment with R in. It will be much easier t see what we are ding. This step is again ging t be extremely simple. What is R? Installing RStudi This will again be as easy as last step. If yu already have R Studi yu can skip this. 1. Navigate t the fllwing in yur web brwser studi/dwnlad/ 2. Click the dwnlad buttn fr RStudi Desktp, this is the free versin f RStudi 3. Once yur dwnlad finishes, g thrugh the nrmal prcess f installing. I prmise things get mre exciting after this 7 P a g e

8 What Is R? Basic Syntax One f the reasns R is s widely used is because it is very simple t get started. While yu can get very detailed, yu can als just explre the easily accessible packages that R has available. This sectin will start t g thrugh sme basics and allw us t build a fundatin fr what we can d nce we are in PwerBI. What is R? Basic Syntax Nw we are ging t review sme basics in R syntax. We are ging t talk abut variables, peratins and arrays. 1. Open RStudi. Yu will be presented with a simple wrkspace fr us t begin develping in. 2. Nw we are ging t assign sme variables. In the cnsle windw, type a = 6 and hit return. Observe the new item under values in ur envirnment. 3. Type b <- 12 and hit return. 4. Type 42 -> c and hit return. 5. Again, have a lk at ur envirnment and see these are just three ways f assigning new variables. 6. Type b <- FALSE hit return. 7. Our variable b which was an integer is nw verwritten as a Blean value. Nte the Blean values TRUE and FALSE must be represented in all capitals as shwn. 8. We can als create character variables. Type txt = Hell. 9. Let s make ne mre value, type true = TRUE. Nw we are ging t have a lk at peratins within R. 8 P a g e

9 S far, ur cnsle shuld lk smething like the screen sht t the right. Nw we are ging t use these variables in sme very simple cases. 10. In the cnsle type a + c. Ntice the result it returns. Als ntice that n variables have changed. This is a ne time result frm R. 11. Nw type result = a + c This time, R did nt give yu the result. But rather the result was stred as a variable. T see the result in the cnsle type result and hit return. 12. Type a^2 and hit return. R executed 6 squared and returned the result. 13. We can als use Blean expressins using the symbls & fr and, and fr r. Type b & true, then b true. Observe. 14. Nw we are ging t create sme arrays. In the cnsle type arr1 = c(1,2,3,4,5). We have created an array with the values 1 thrugh 5, entered manually. 15. Type arr2 = 6:10.This is a quick way f creating an array with numbers 6 thrugh Type arr1 and hit enter, then arr2 and hit enter t see the cntents f bth arrays. 17. Type arr1*arr2 ad bserve the dt prduct f the tw arrays. 18. Type arr2[3], this yields the 3 rd element f arr2. Yu can experiment with elements f arrays. Tyr arr2[2:4]. Arrays are the first step in a very imprtant cncept fr us. 9 P a g e

10 What Is R? Data Frames Nw that we have spent sme time explring syntax and peratins in R we can talk abut data frames. They are very simple t understand, but they are very imprtant fr wrking with R in PwerBI. Yu will see why later tday. What is R? Data Frames Let s create sme arrays t create ur data frame with. 1. Type id = 100:104 and hit return. This gives us an array with 5 entries. 2. Type name = c( Chris, Craig, Michael, Micah, Dmitry ), nw we have a array with 5 names. 3. Nw type frame = data.frame(id,name). Have a lk in ur envirnment nw. Rather than a data frame being a value it is nw in a different sectin labeled Data. It states that frame has 5 bjects f 2 variables. What exactly have we created? 4. Type frame and hit enter t see what we have created. Once we ve dne this it becmes very clear that a data frame is just a fancy wrd fr a table! The 5 bjects refer t the number f rws, and the tw variables refer t the number f clumns. Nw that we have seen what a data frame is, we have sme lgical restrictins. If we wish t create a data frame, all f the arrays we want t cmbine must have the same number f elements. 5. We can experiment by creating a new id array and adding a sixth element. We can d this by typing id2 = id, then id2[6] = P a g e

11 6. Next, we can attempt t create a new data frame with id2 and name and see what happens. Typing frame2 = data.frame(id2,name) yields the fllwing. What can we d with a Data Frame? Well the answer is a lt. But first we will just g ver sme basic peratins. 7. Type names(frame) and ntice this lists the names f the clumns in ur data frame. 8. Type ncl(frame), this returns the number f clumns ur data frame has. 9. Type nrw(frame), this (can yu guess?) returns the number f rws in ur data frame. 10. frame[2:4,] returns the 2 nd thrugh the 4 th rws. 11. frame[2:4,1] returns the values frm the first clumn fr rws 2 thrugh Lastly frame$id and frame$name return their respective clumn. Nw we ve seen what data frames are and sme basic peratins we can d with them. Our next step is t explre R visuals and what we have access t. 11 P a g e

12 What Is R? Creating Visauls in R (Part 1) Nw that we ve cvered basics f syntax and data frames, we can start t g ver visuals in R. This sectin is first ging t g thrugh sme basic default visuals in R, then we will have a lk at sme custmizatin and mre advanced visual packages. It is key t remember that nce we have a data frame ready, mst visuals are easy t implement. The hardest part is finding the right package and prcess fr what yu are trying t d. What is R? Creating Visuals in R We are ging t g thrugh sme simple examples first. Fr ur first tw visuals we will just be creating sme simple data t wrk with. After this we will imprt sme data t wrk with. Remember the idea behind these exercises is t get ur feet wet in sme f the simple analysis that R can enable. 1. First, we need t create sme new data. Yu can cpy and paste this int yur cnsle numbers = c(6, 6, 6, 5, 5, 8, 9, 3, 4, 2, 3, 4, 5, 8, 9, 0, 5, 2, 3, 4, 5, 1, 4, 6, 3) 2. Nw type hist(numbers). Observe hw a histgram was created in the bttm right f RSutdi. This displays the number f ccurrences f each number in ur array. Ntice hw in grups up numbers in pairs f tw. We can custmize this. 3. Type hist(numbers, breaks = 10). Nw ur histgram has a clumn fr each digit. Let s try a little mre custmizatin. 4. Nw we are ging t build a nrmal curve f best fit. Type xfit = seq(min(numbers),max(numbers),length =40) and yfit=dnrm(xfit,mean=mean(numbers),sd =sd(numbers)) and h = hist(numbers, breaks = 10) and lastly yfit=yfit*diff(h$mids[1:2])*length(numbers ) 12 P a g e

13 This prcess created the nrmal distributin f best fit and used this t apprximate a line f best fit. 5. Type lines(xfit,yfit, cl = blue ). Every visual in R has many parameters fr custmizatin. We cannt g thrugh all f them, hwever, they will always be just a Ggle search away. Next, we are ging t wrk with a scatter chart. Again, as anther example f hw we can extract infrmatin frm data using sme basic frmulas. 6. Enter the fllwing arrays int R. x = c(1,3,4,7,8,13,14,18,22,21,22,24,25,28,30,35) y = c(1,171, 16, 20, 124, 299, 135, 450, 244, 591, 424, 726, 595, 624, 1170, 1225) 7. Nw type plt(x,y). Observe the scatter plt. Nw we want t see what we can determine abut this plt. We are ging t experiment with creating lines f best fit. 8. Type lm = lm(y~x). In this statement lm stands fr linear mdel. We nw have an bject lm which represents the line f best fit fr this data. Type lm and hit return t see the details. Recall a line in the plane is represented as y = ax + b. a is the slpe, and b is the intercept. 9. Type abline(lm(y~x)). Have a lk at ur line f best fit! This is linear regressin. It lks like we can d a bit better thugh. 10. Our next step is t create a quadratic mdel. Recall a quadratic is in the frm y = ax^2 + bx + c. We need a new variable t represent x^2. Type x2 = x^ Nw we are ging t create ur mdel. Type q = lm(frmula = y~x+x2) This gives us a line f best fit in tw variables. 12. Nw we need t use this mdel t create a new line. Enter the fllwing int R and have a lk! xmdel = seq(0,35,0.5) ymdel = predict(q, list(x=xmdel,x2=xmdel^2)) lines(xmdel,ymdel) This is quadratic regressin! 13 P a g e

14 13. Yu can als use this mdel t predict a specific value. Let s predict fr x = 50. Type the fllwing. predict(q,list(x=50,x2=50^2)) 14. Befre we finish up with this example, I want t d ne mre thing. Type cr(x,y). This gives yu the crrelatin cefficient between x and y. This number shws yu hw psitively r negatively the data pints are assciated. These are the kinds f peratins and results that R ffer that are nt s easy t btain within PwerBI. Linear and quadratic regressin are very simple examples f machine learning and predictin. R can d far mre than just thse! The purpse f these exercises has been t see the prcess in which ne can explre data within R. The mst challenging part f building visuals within PwerBI is testing. It is very difficult t test much befre the final prduct This is where RStudi cmes in. 14 P a g e

15 What Is R? Creating Visuals in R (Part 2) Nw we are ging t develp a visual using a dataset that we will eventually prt int PwerBI. This ne is a ging t be mre advanced and use a much larger dataset. It will utilize the package ggplt2 which has an extensive visual library fr R. Once we have a visual that we are happy with here, we are ging int PwerBI. We will imprt the same dataset and use R in PwerBI t recreate ur visual and create sme interactive slicers t g alng side it. What is R? Creating Visuals in R We have cvered sme f the basic visuals available in R. Nw we are ging t explre sme ptins in ggplt2 t try and find a visual that we wuld want in ur PwerBI reprt. 1. Our first step in this explratin is t imprt sme data. S far, we have nly wrked with small arrays f ur creatin. This time we are ging t imprt ur prject dataset int R. T d this click File -> Imprt Dataset -> Frm Text (base). Frm here yu will navigate t ur cntent wrk bk and chse Surgeries.csv. Frm here click Imprt. 2. Have a lk this created a new data frame fr us. Type head(surgeries) t get the first 6 items f each clumn. 3. Nw we want t start utilizing sme f this data t create a visual using ggplt2. We first must install ggplt2. Type install.packages( ggplt2 ) and hit return. 4. Nw we need t lad the package int ur library. Type library(ggplt2). Any time yu want t use a package in R, yu simply must fllw thse tw steps t lad it up. 5. Nw we are ging t start t build ur visual. ggplt is an incredible visual package in R. It is ne f the mst used visual packages. It can be a bit strange t wrk with at first, but it is built t be 15 P a g e

16 flexible and reasnable t learn. There are many resurces t learn hw t use ggplt but here is ne, and anther, that I recmmend fr yu if yu wish t try mre utside f this tutrial. 6. T summarize quickly, a plt in ggplt cnsists f layers built upn a canvas. The first step we will take, is t create the canvas. T d this type (r cpy & paste) the fllwing: gg = ggplt(surgeries, aes(x = Surgeries$FISCAL_YEAR, y= Surgeries$WAITING)) This creates a canvas with aesthetic defining the x and y axis. Type gg and hit return and we shuld see a blank canvas. 7. Nw we are ging t start adding (literaly) sme gems, r gemetric representatins, t this canvas. There are many different gems that wrk in different aesthetic structures. Type: gg2 = gg + gem_pint() Nw type gg2 and yu shuld see a canvas with ur pints represented as dts. Nw ntice ur x axis is very static. All ur values are n integers. This is fine, but I think we can make a mre interesting viewing experience by shifting them all a little bit. 8. We are ging t recreate this canvas with a different gem. gg2 = gg + gem_jitter() Nw type gg2 and hit enter t see this effect. This time we used a different gem 16 P a g e

17 which is like pltting pints but avids verlap when it can by shuffling the x axis. This is interesting, but I think we can add sme mre t this! 9. Type gg2 = gg + gem_jitter(aes(clr = Surgeries$HEALTH_AUTHORITY)) and type gg2 t see the result! Nw we adjusted the aesthetic t include HEALTH_AUTHORITY as the clr f ur pints. 10. Nw let s add lines which represent mvement f the average as well. 11. Type gg2 = gg2 + gem_smth(aes(clr= Surgeries$HEALTH_AUTHORITY)) Type gg2 t see the result! Ntice here hw we added the lines directly t ur plt with the pints already included. We can iterate ver ur plt like this as much as we wish! 12. Next, just fr the sake f it, let s add the line fr the linear mdel f ur entire data set cmbined t this chart as well. Type this t create ur linear mdel: lm=lm(surgeries$waiting~surgeries$fi SCAL_YEAR) Nw unfrtunately we can t quite plug this mdel int a chart like befre. This time we must extract ur cefficients frm the mdel (remember y = ax + b). This is kind f crazy, but it turns ut that ur mdel is an bject pretty similar t a data frame. We can access different clumns which 17 P a g e

18 each have different data (type summary.lm(lm) if yu are interested.) T extract the slpe and intercept type: slpe = summary.lm(lm)$cefficients[2,1] And, int = summary.lm(lm)$cefficients[1,1] 13. Nw we get t add this line t ur plt. 14. Type the fllwing: gg2 = gg2 + gem_abline(intercept = int, slpe = slpe, clr = black ) And nw type gg2 t see the result. Here we have it. We ve created a visual using ggplt. While it isn t anything t fancy, it demnstrates the pwer f ggplt2. As ne final nte I d like t mentin that yu can change the scale in which yu are pltting extremely easy. T the right is an example f pltting this exact same visual except with ur y axis as sqrt(waiting). Our riginal canvas is mdified as such: gg = ggplt(surgeries, aes(x= Surgeries$FISCAL_YEAR, y=sqrt(surgeries$waiting))) While nt technically a rigrus way f viewing this data, this shws us the utility that this srt f pltting can prvide. Nte ur linear mdel is nt n this chart because we wuld have t recreate ur mdel in the square rt space fr it t fit in this frame. 18 P a g e

19 Utilizing R in PwerBI Alright, s far, we have explred sme basic syntax in R, used it t build sme basic visuals, and then tk a step frward and built a visual using the package ggplt2. Our next gal is t rebuild that visual using R Script in PwerBI. The key difference between wrking in RStudi and R Script is that we can t test each line individually. When PwerBI runs the script it ges thrugh everything we write and if there are any errrs it will spit back an errr. Hencefrth, we must always wrk carefully, ensuring that we can always recgnize where ur errrs are cming frm. Utilizing R in PwerBI Using What we Learned in R Studi. Let s get started. Utilizing R in PwerBI Using What we Learned in R Studi. Okay, s nw we need t get int PwerBI. Open a blank wrk bk. Our first step is ging t be t imprt ur surgeries data. 1. Click the drp dwn by get data, select text/csv. Then find and select ur Surgeries.csv flder in ur cntent pack. Click lad. 2. Next, we are ging create an R visual. Under the visualizatins tab click the R visual icn. Yu shuld see a visual appear alng with a new text bx dwn belw. Let s resize this visual s it s easier t see what we are ding. 3. Nw we need t drag ur data that we are ging t need int ur R visual. Recall we nly used WAITING, FISCAL_YEAR, and HEALTH_AUTHORITY. These names in the bxes under values are hw we are ging t address these items in ur script. Nw it is very imprt t set ur values t Dn t Summarize in ur values. D this nw by clicking the drp dwn beside the title. 19 P a g e

20 4. Nw have a lk at ur Script. Ntice the first few lines are cmments explaining ur situatin. We have a data frame named dataset. We will need t address ur data using this data frame name. 5. First thing is simple. We need t include ggplt2 in ur Script. Type library(ggplt2) int ur script. 6. Nw we need t create ur canvas, type: gg = ggplt(dataset, aes(x=fiscal_year,y=waiting)) Ntice hw we used dataset this time rather than Surgeries. That is because ur data is aliased differently this time. 7. Let s test ur script. Add gg t the end f ur script and the click the run buttn at the tp right f the script windw. 8. Nw lets add ur jitter and test this ut. Type: gg = gg + gem_jitter(aes(clr = dataset$health_authority)) Make sure this ccurs befre the final gg call, then hit run and check that this wrks. Our script shuld lk like the image t the right. 9. Nw t add ur average lines: gg = gg + gem_smth(aes(clr = dataset$health_authority)) Again, run this t make sure it wrks. This shuld be inserted between ur last gem, and ur final call f gg. Have a lk at the result! 20 P a g e

21 10. Lastly, we get t create ur regressin line. Insert ur steps at the beginning f ur script. mdel = lm( dataset$waiting ~ dataset$fiscal_year) int = summary.lm(mdel)$cefficients[1,1] slpe = summary.lm(mdel)$cefficients[2,1] and lastly insert ur last gem nt ur canvas. gg = gg + gem_abline(slpe = slpe, intercept = int, clur = black ) Our final script shuld lk smething like the ne t the right. 11. Our last step is t run this and see hw it lks. Nw the cl stuff cmes in the interactin! Let s add sme slicers and see what we can see. 12. I prefer the chiclet slicer frm the visual market place. S, let s g ahead and include that. Click n the three ellipses in the visualizatins selectin pane. Then select Imprt Frm Marketplace. Frm here use the search bar t find Chiclet Slicer and lad it int yur reprt by clicking Add. I m ging t create tw slicers next t my visual, ne fr hspital name and the ther fr prcedure. 13. With the white space selected, click the Chiclet slicer icn in the Visualizatins 21 P a g e

22 pane. Click and drag HOSPITAL_NAME int this ne. Repeat this prcess with PROCEDURE_GROUP. 14. Nw I am ging t d sme simple analysis. Lets create a Card with the average waiting fr Create a new measure by right clicking n ur table in the fields sectin, and then selecting New measure. Enter the fllwing: 2016 avg = CALCULATE(AVERAGE(Surgeries[WAI TING]),FILTER(Surgeries,Surgeries[FIS CAL_YEAR] = CALCULATE(MAX(Surgeries[FISCAL_Y EAR])))) Nw drag this measure nt ur canvas and then with the visual selected click the card icn in the Visualizatins pane. 15. Nw I want t predict the average waiting fr surgeries in 2020 using ur regressin line. There is a prblem hwever; we cannt pass a value ut f ur R Script. S, we are ging t have t get creative. In ur script lets add the fllwing. predict = slpe* int max = max(dataset$waiting,na.rm = TRUE) text = paste("2020 Predicted Average Waiting is", rund(predict,digits = 2)) Each f these three lines des smething 22 P a g e

23 imprtant fr us. First, we get ur predicted value, secnd, we find the max waiting value, third we need t create a text t display. Nw we are ging t display this in a text bx in ur visual. 16. We are ging t add this text t ur canvas again using the fllwing. gg = gg + gem_text(x=2011,y=max*0.95,label= text, size = 5) Our final script shuld lk like the ne t the right. When we run this, we get the fllwing. Cpied belw is ur final script as well. library(ggplt2) mdel = lm(dataset$waiting~dataset$fiscal_ YEAR) slpe = summary.lm(mdel)$cefficients[2,1] int = summary.lm(mdel)$cefficients[1,1] predict = slpe*2020+int max = max(dataset$waiting,na.rm = TRUE) label = paste("2020 Predicted Average Waiting is", rund(predict,digits = 2)) gg = ggplt(dataset, aes(x=fiscal_year,y=waiting)) gg = gg + gem_jitter(aes(clr = dataset$health_authority)) gg = gg + gem_smth(aes(clr = dataset$health_authority)) gg = gg + gem_abline(slpe = slpe, intercept = int, clur = "black") gg = gg + gem_text(x=2011,y=max*0.95,label= label,size = 5) gg 23 P a g e

24 Cnclusin: And this cncludes ur R in PwerBI training manual. I hpe yu ve fund this a helpful resurce in practicing and learning the fundatins f R and hw it can be used in PwerBI. The applicatins and techniques available t yu nw in R nw extend vastly in machine learning, visualizatins and all srts f ther mathematical and statistical cnstructs. It is nw up t yu t explre and figure ut what yu can d with it. Cnclusin: Limitatins There are a few limitatins with using R in PwerBI. There are a few key nes t be aware f that I will mentin here. The rest can be fund here. Data size limitatins data used by the R visual fr pltting is limited t 150,000 rws. If mre than 150,000 rws are selected, nly the tp 150,000 rws are used and a message is displayed n the image. Calculatin time limitatin if an R visual calculatin exceeds five minutes the executin times ut, resulting in an errr. Cnclusin: Questins Nw is the time fr yur questins and requests. Let me knw if yu have any prjects yu wish t wrk with while I am available. Or, let me knw if yu have any higher-level questins abut machine learning in general and where t start with that. 24 P a g e