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

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Analyzing and Visualizing Data with Power BI book

the consume of verve BI and SSRS for visualizing SQL Server and R records (part 4) | 70-778 Dumps and existent 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 kindly 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 possess 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 paraphernalia for visible Studio / R Studio
  • vigour BI

    For this matter, they can consume again the WideWorldImportersDW demo database and they will consume the visualization for clustering. let us pick 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 pass West'' THEN 8 linger AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs live a fraction 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 pass 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)]), fashion = ''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 live directly imported into verve BI in a just a runt distinctive means. On one hand the data might live 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 associate 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 pick secure information -> SQL Server and insert bar None vital advice, as proven in the print screen beneath:

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

    You may secure 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 consume of columns income, quantity and SalesTerritoryID. bar None three columns will issue 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 cognomen 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, gain certain you check the costs or double quotes around the declared values in R code. After that, you'll secure a visualization in energy BI:

    additionally, any variety of other R graph / visualization may moreover live introduced. for this reason I actually possess determined so as to add a boxplot on income per revenue territory, the consume 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 birthright here R code:

    with the aid of doing so, what they need in the final step is so as to add some facts slicers, to live able to dynamically refresh the data on both R visualizations. Of route, every other pre-prepared visualization accessible in energy BI can additionally consume 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 bar None graphs which are in accordance with dataset the consume of these slicers might live automatically up to date.

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

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

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

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

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

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

    I actually possess 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 comfort that you can comply with in the energy BI ebook. The R code is in reality the identical because the R code in archaic article, where I originally mentioned association suggestions. birthright here is the last 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 live configured through code and the outlook can moreover live modified birthright down to final element, whereas, graph on birthright hand side is embedded visual, moreover in line with R, with bar None of the parameters construct in to live installation in power BI.

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

    For cluster evaluation, I possess 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 discrete 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 possess used the paraphernalia gridExtra, very effectual kit to serve the flush of plotting the tables with records extracted from R datasets.

    Reporting capabilities (SSRS)

    Reporting functions has been round very lengthy, so i will not depart into the reporting details. but the entire R visualizations available in verve BI can even live 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 live achieved with choices in SSRS. but what SSRS makes it practicable for is, to add any extra parameter into the code, making R Language through SSRS even more bendy to the linger consumer. i'll expound this on an example with the clustering analysis from already mentioned case (and accustomed from archaic article).

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

    will recur – every time document might live achieved – an oversight and the textboxes to populate the values of the entire parameters mandatory in order to execute externa fashion sp_execute_external_script. So, the stronger (if not the most reliable) pass to maintain away from this, is conveniently to reclaim 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 linger AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS volume FROM [Fact].[Sale] AS fs live fraction 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 pass 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 pass West'' THEN 8 linger ;' 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 finish the following homes:

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

    using saved methods in SSRS is a extremely productive pass to visualize the information. using saved techniques that you would live able to additionally implement the parameters that without delay inject some values or chunk of codes to gain your visualizations greater dynamic. moreover that, producing tables is plenty less difficult finished in evaluation to verve BI, but if you are looking to extract information from R consequences, live confident you bar None the time recur the R consequences to a erudition body. information carcass is a data classification, that R and SQL Server can operate with each and every other.

    R paraphernalia for visual Studio / R Studio

    one pass to imagine the records is to secure information into your RTVS or R Studio the station that you would live able to visualize information. continually this can moreover live finished by storing your records from SQL Server into any of those two courses. one pass to finish it's to gain consume 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 secure the total fact table into your R environment.  Now working on any sort of visualization is relatively easy.

    Please word, in case you can live 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 fraction of information analysis, facts presentation and facts comprehension. bar None three paraphernalia are a fashion 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 need to clarify and broaden the statistics understanding. If this fundamental rule is not satisfied, definitely you're doing anything wrong. I didn't depart into the concept of visualization, given that this was now not my point, however I strongly recommend you to finish 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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