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

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Analyzing and Visualizing Data with Power BI book

the use of vigor BI and SSRS for visualizing SQL Server and R records (part 4) | 70-778 Dumps and Real exam Questions with VCE Practice 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 have 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 equipment for visible Studio / R Studio
  • vigour BI

    For this matter, they can use again the WideWorldImportersDW demo database and they will use the visualization for clustering. let us take 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 way West'' THEN 8 end AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs be a part 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 way 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)]), method = ''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 effect units ((plot varbinary(max)))

    This might be directly imported into vigor BI in a just a little distinctive means. On one hand the data might be 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 join 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 Get information -> SQL Server and insert all vital advice, as proven in the print screen beneath:

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

    You may get 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 use 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 name 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, make certain you check the costs or double quotes around the declared values in R code. After that, you'll get a visualization in energy BI:

    additionally, any variety of other R graph / visualization may also be introduced. for this reason I actually have determined so as to add a boxplot on income per revenue territory, the use 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 right 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 be able to dynamically refresh the data on both R visualizations. Of route, every other pre-prepared visualization accessible in energy BI can additionally use 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 use of these slicers might be automatically up to date.

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

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

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

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

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

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

    I actually have 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 rest that you can comply with in the energy BI ebook. The R code is in reality the identical because the R code in old article, where I originally mentioned association suggestions. right 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 be configured through code and the outlook can also be modified right down to final element, whereas, graph on right hand side is embedded visual, also in line with R, with all of the parameters construct in to be installation in power BI.

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

    For cluster evaluation, I have 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 distinct 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 have used the equipment gridExtra, very effective kit to serve the aim of plotting the tables with records extracted from R datasets.

    Reporting capabilities (SSRS)

    Reporting functions has been round very lengthy, so i will not go into the reporting details. but the entire R visualizations available in vigor BI can even be 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 be 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 end consumer. i'll explain this on an example with the clustering analysis from already mentioned case (and usual from old article).

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

    will return – every time document might be achieved – an error and the textboxes to populate the values of the entire parameters mandatory in order to execute externa method sp_execute_external_script. So, the stronger (if not the most reliable) way to keep 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 end AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS volume FROM [Fact].[Sale] AS fs be part 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 way 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 way West'' THEN 8 end ;' 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 familiar graph in SSRS.

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

    R equipment for visual Studio / R Studio

    one way to imagine the records is to get information into your RTVS or R Studio the place that you would be able to visualize information. continually this can also be finished by storing your records from SQL Server into any of those two courses. one way to do it's to make use 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 get the total fact table into your R environment.  Now working on any sort of visualization is relatively easy.

    Please word, in case you can be 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 part of information analysis, facts presentation and facts comprehension. All three equipment are a method 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 go 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
    zoho.com : https://docs.zoho.com/file/67jzb1e92f64080914144ad5c7bb21981d8fb
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