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Test Number : 70-778
Test denomination : Analyzing and Visualizing Data with Power BI
Vendor denomination : Microsoft
: 127 Dumps Questions

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Analyzing and Visualizing Data with Power BI book

the utilize of vitality BI and SSRS for visualizing SQL Server and R records (part 4) | 70-778 Dumps and actual exam Questions with VCE rehearse Test

Visualizing statistics is vital half to realizing the dataset that they are attempting to analyses and a unique, pictoral view of the statistics. as a substitute of a tabular/matrix view or numerical views, a presentation in the kind of photographs, diagrams, and graphs can aid broaden facts insight. actually this brings the understanding of statistics to a brand new level.

to date, within the previous article they now gain mentioned how to analyze earnings facts. And now and again, visualization is much obligatory. in this article, they will focus on two techniques visualizing the data. specifically:

  • With power BI
  • With reporting functions (SSRS)
  • With R apparatus for visible Studio / R Studio
  • vigour BI

    For this matter, they can utilize again the WideWorldImportersDW demo database and they will utilize the visualization for clustering. let us engage here question:

    DECLARE @SQLStat NVARCHAR(4000) SET @SQLStat = 'select SUM(fs.[Profit]) AS earnings ,c.[Sales Territory] AS SalesTerritory ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN 4 WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''super Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a long artery West'' THEN 8 quit AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs exist a portion of dimension.metropolis AS c ON c.[City Key] = fs.[City Key] the place fs.[customer key] <> 0 AND c.[Sales Territory] not IN (''external'') neighborhood by means of c.[Sales Territory] ,fs.[Customer Key] ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN 4 WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''super Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a long artery West'' THEN 8 conclusion ;' DECLARE @RStat NVARCHAR(4000) SET @RStat = 'library(ggplot2) image_file <- tempfile() jpeg(filename = image_file, width = four hundred, top = 400) clusters <- hclust(dist(revenue[,c(1,3,5)]), system = ''average'') clusterCut <- cutree(clusters, 3) ggplot(income, aes(complete, amount, colour = revenue$SalesTerritory)) + geom_point(alpha = 0.four, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''red'', ''eco-friendly'',''yellow'',''blue'',''lightblue'',''magenta'',''brown'')) dev.off() OutputDataSet <- information.frame(statistics=readBin(file(image_file, "rb"), what=uncooked(), n=1e6))' EXECUTE sp_execute_external_script @language = N'R' ,@script = @RStat ,@input_data_1 = @SQLStat ,@input_data_1_name = N'earnings' WITH upshot units ((plot varbinary(max)))

    This might exist directly imported into vitality BI in a just a cramped distinctive means. On one hand the data might exist imported the usage of best a T-SQL query.

    opt for SUM(fs.[Profit]) AS earnings ,c.[Sales Territory] AS SalesTerritory ,CASE WHEN c.[Sales Territory] = 'Rocky Mountain' THEN 1 WHEN c.[Sales Territory] = 'Mideast' THEN 2 WHEN c.[Sales Territory] = 'New England' THEN 3 WHEN c.[Sales Territory] = 'Plains' THEN four WHEN c.[Sales Territory] = 'Southeast' THEN 5 WHEN c.[Sales Territory] = 'extraordinary Lakes' THEN 6 WHEN c.[Sales Territory] = 'Southwest' THEN 7 WHEN c.[Sales Territory] = 'a ways West' THEN 8 conclusion AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs combine dimension.city AS c ON c.[City Key] = fs.[City Key] where fs.[customer key] <> 0 AND c.[Sales Territory] now not IN ('external') community with the aid of c.[Sales Territory] ,fs.[Customer Key] ,CASE WHEN c.[Sales Territory] = 'Rocky Mountain' THEN 1 WHEN c.[Sales Territory] = 'Mideast' THEN 2 WHEN c.[Sales Territory] = 'New England' THEN 3 WHEN c.[Sales Territory] = 'Plains' THEN 4 WHEN c.[Sales Territory] = 'Southeast' THEN 5 WHEN c.[Sales Territory] = 'great Lakes' THEN 6 WHEN c.[Sales Territory] = 'Southwest' THEN 7 WHEN c.[Sales Territory] = 'far West' THEN eight conclusion

    And later one at a time the R code:

    clusters <- hclust(dist(sales[,c(1,3,5)]), system = ''common'') clusterCut <- cutree(clusters, 3) ggplot(sales, aes(total, amount, colour = earnings$SalesTerritory)) + geom_point(alpha = 0.4, size = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''red'', ''eco-friendly'',''yellow'',''blue'',''lightblue'',''magenta'',''brown''))

    After opening vigour BI, they choose net information -> SQL Server and insert All vital advice, as proven in the print screen beneath:

    After clicking adequate, statistics will exist imported into power BI. subsequent step is to opt for  “New visual” and on the visualizations record choose the R script visible.

    You may net a dialog window asking for enabling R visualization. After that, select the variables needed for the graph. in accordance with the R code, we're the utilize of columns income, quantity and SalesTerritoryID. All three columns will appear in a predefined dataset as an information.body that the R visualization is developing by means of default:

    within the R-script code they can paste the R code from the illustration above. starting with R code, they want some minor modifications – change the denomination of dataset and rename the columns. So from this:

    library(ggplot2) clusters <- hclust(dist(earnings[,c(1,3,5)]), formula = ''commonplace'') clusterCut <- cutree(clusters, 3) ggplot(income, aes(total, quantity, colour = sales$SalesTerritory)) + geom_point(alpha = 0.four, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''purple'', ''eco-friendly'',''yellow'',''blue'',''lightblue'',''magenta'',''brown''))

    into this:

    library(ggplot2) clusters <- hclust(dist(dataset[,c(1,2,3)]), components = 'commonplace') clusterCut <- cutree(clusters, 3) ggplot(dataset, aes(earnings, volume, colour = dataset$SalesTerritory)) + geom_point(alpha = 0.four, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c('black', 'crimson', 'green','yellow','blue','lightblue','magenta','brown'))

    also, acquire sure you check the costs or double quotes around the declared values in R code. After that, you'll net a visualization in energy BI:

    additionally, any variety of other R graph / visualization may likewise exist introduced. for this understanding I actually gain determined so as to add a boxplot on income per revenue territory, the utilize of R visualizer. So adding additional information to the cluster evaluation with boxplot will provide end-person further perception of how profit is spread among sales territories.

    The graph is got using privilege here R code:

    with the aid of doing so, what they necessity in the final step is so as to add some facts slicers, to exist able to dynamically refresh the data on both R visualizations. Of route, every other pre-prepared visualization accessible in energy BI can additionally utilize the capabilities of information slicers. I added two slicers:  volume and revenue Territory. i can select (or multi-choose) the values and according to my decisions All graphs which are in accordance with dataset the utilize of these slicers might exist automatically up to date.

    The quit product can resemble a dashboard or a document or a playground for data scientist, records wranglers or any one fervent to net insight on the statistics set:

    I brought the R Language emblem just to expose that your company brand will likewise exist added as neatly. The comprehensive vigour BI workbook is likewise obtainable for download.

    if you wish to Do extra superior analytics on the imported dataset, the area are always extra visuals available on the url handle: https://app.powerbi.com/visuals/ . the entire additional visuals can likewise exist downloaded and introduced into your vitality BI booklet. There are also  R-powered visuals to exist downloaded and ready to exist used. For this text, I actually gain determined to down load further visual Chord:

    And utilize it in present e-book, via importing it:

    And populate it with information with earnings and revenue Territory to behold the distribution of earnings by means of territory.

    again, the photograph tells greater than numbers, but acquire sure not to overdo on the visuals or create non-sense graphs which might cause people to exist stuck knowing the graph, as a substitute of assisting them exist aware.

    I actually gain delivered and made comparison for affiliation rules; really just assessment between the visualizations. i am simply including the ultimate comparison from energy BI ebook, the relaxation that you can comply with in the energy BI ebook. The R code is in reality the identical because the R code in dilapidated article, where I originally mentioned association suggestions. privilege here is the final define of the comparison of two visuals: R code visible and association guidelines visible.

    well-nigh, both are in accordance with equal R packages (arules, aRulesVisual), simply that graph on left hand aspect to exist configured through code and the outlook can likewise exist modified privilege down to final element, whereas, graph on privilege hand side is embedded visual, likewise in line with R, with All of the parameters construct in to exist installation in power BI.

    an additional very crucial factor and extremely positive system to import in energy BI are likewise R records.frames, which are desk presentation of the information. now and again, if you occur to are doing any superior analytics or visualizations, you may likewise want to add some additional info that can not exist study from the graph. as an instance, let’s engage the clustering graph; cluster businesses are very trait and visual, however a person who is extra into information would want to net some data to this clustering as neatly. in this case, they will utilize R visual once again, however maneuver and wrangle the outputted records into the dataframe and gain it characterize as a desk.

    For cluster evaluation, I gain added the variety of occurrences for each and every customer in income territory wherein cluster (1, 2 or three) is appearing. desk is just a divide presentation of graph.

    The table has been generated with here R code in vigour BI:

    library(gridExtra) clusters <- hclust(dist(dataset[,c(1,2,3)]), components = 'regular') clusterCut <- cutree(clusters, three) df <- records.body(table(clusterCut, dataset$SalesTerritory)) grid.desk(df)

    once more, this desk will automatically replace, based on chosen criteria within the slicers, which comes very convenient when analyzing facts.

    As you can see, I gain used the apparatus gridExtra, very effectual kit to serve the point of plotting the tables with records extracted from R datasets.

    Reporting capabilities (SSRS)

    Reporting functions has been round very lengthy, so i will not recede into the reporting details. but the entire R visualizations available in vitality BI can even exist implemented and utilized in SSRS. Reporting features brings additionally an further purposeful and valuable feature: choices. power BI offers you the slicers that may, in line with alternative, dynamically update the graphs, records and visualizations in R script, that may even exist achieved with choices in SSRS. but what SSRS makes it possible for is, to add any extra parameter into the code, making R Language through SSRS even more bendy to the quit consumer. i'll interpret this on an illustration with the clustering analysis from already mentioned case (and accustomed from dilapidated article).

    When growing new report and defining dataset, replica/pasting the R code into the question text:

    will return – every time document might exist achieved – an oversight and the textboxes to populate the values of the entire parameters mandatory in order to execute externa system sp_execute_external_script. So, the stronger (if not the most reliable) artery to advocate away from this, is conveniently to save the entire code as a stored system.

    CREATE system ClusterAnalysis_Plot AS DECLARE @SQLStat NVARCHAR(4000) SET @SQLStat = 'choose SUM(fs.[Profit]) AS earnings ,c.[Sales Territory] AS SalesTerritory ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN four WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''awesome Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a ways West'' THEN 8 quit AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS volume FROM [Fact].[Sale] AS fs exist portion of dimension.city AS c ON c.[City Key] = fs.[City Key] the place fs.[customer key] <> 0 AND c.[Sales Territory] now not IN (''exterior'') community by artery of c.[Sales Territory] ,fs.[Customer Key] ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN four WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''awesome Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a long artery West'' THEN 8 quit ;' DECLARE @RStat NVARCHAR(4000) SET @RStat = 'library(ggplot2) image_file <- tempfile() jpeg(filename = image_file, width = four hundred, top = 400) clusters <- hclust(dist(revenue[,c(1,3,5)]), formula = ''typical'') clusterCut <- cutree(clusters, three) ggplot(earnings, aes(profit, quantity, colour = earnings$SalesTerritory)) + geom_point(alpha = 0.four, dimension = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''crimson'', ''green'',''yellow'',''blue'',''lightblue'',''magenta'',''brown'')) dev.off() OutputDataSet <- data.frame(data=readBin(file(image_file, "rb"), what=uncooked(), n=1e6))' EXECUTE sp_execute_external_script @language = N'R' ,@script = @RStat ,@input_data_1 = @SQLStat ,@input_data_1_name = N'revenue' WITH influence units ((plot varbinary(max)));

    and then you effectively add the process.

    On record canvas, you add picture and Do the following homes:

    Now retailer, (install) and Execute the document  in Reporting provider. you should see a confidential graph in SSRS.

    using saved methods in SSRS is a extremely productive artery to visualize the information. using saved techniques that you would exist able to additionally implement the parameters that without laggard inject some values or chunk of codes to acquire your visualizations greater dynamic. moreover that, producing tables is plenty less difficult finished in evaluation to vitality BI, but if you are looking to extract information from R consequences, exist sure you All the time return the R consequences to a erudition body. information body is a data classification, that R and SQL Server can operate with each and every other.

    R apparatus for visual Studio / R Studio

    one artery to imagine the records is to net information into your RTVS or R Studio the area that you would exist able to visualize information. continually this can likewise exist finished by storing your records from SQL Server into any of those two courses. one artery to Do it's to acquire utilize of ODBC driver. Very advantageous and powerful is accessible in kit RODBC. The very straightforward syntax is:

    library(RODBC) myconn <-odbcDriverConnect("driver=SQL Server;Server=Srv_Tomaz;database=WideWorldImportersDW;trusted_connection=true") mydata <- sqlQuery(myconn, "opt for * FROM [Fact].[Order]") close(myconn)

    After working this R Code, you might net the total fact table into your R environment.  Now working on any sort of visualization is relatively easy.

    Please word, in case you can exist using larger dataset and importing records into R ambiance, with RODBC equipment, you may additionally adventure memory obstacles. In such situations, i would strongly recommend the usage of RevoScaleR kit, that solves memory hindrance issues!

    Conclusion

    records visualization is a crucial portion of information analysis, facts presentation and facts comprehension. All three apparatus are a system how to assist an information wrangled, information scientist or statistics analyst to address and to deal with plotting facts. there are lots of alternative ways, and third celebration equipment, that allows for you to deepen the information perception, just follow a simple rule, when using visuals: statistics visible necessity to clarify and broaden the statistics understanding. If this fundamental rule is not satisfied, definitely you're doing anything wrong. I didn't recede into the concept of visualization, given that this was now not my point, however I strongly recommend you to Do it. Very decent region to birth is statistics Visualization Catalogue.

    author: Tomaz Kastrun (tomaz.kastrun@gmail.com)

    Twitter: @tomaz_tsql

    weblog: http://tomaztsql.wordpress.com


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    References :


    Box.net : https://app.box.com/s/v4oew0iwpgid9xnvzxc7g42z1v1vrqob
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    Calameo : http://en.calameo.com/books/0049235263b14fbf4f712
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