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Using R Machine Learning Script as a Power BI Desktop Data Source. You can expand the analytical potential of a BI tool by embedding, or providing a seamless interface to advanced analytics engines such as R. It is not a new idea: some BI vendors such as Tableau have already tried to boost the analytical capabilities of their BI tools through integration with R, but the way Tableau has done it is far from perfect because they connect to R through calculated fields. The R calculations have to be wrapped into some Tableau functions that are designed to support the integration and which, in turn, pass values to R via the Rserve package. To put it simply, Tableau Desktop relies on having an extra software layer between it and R. This article demonstrates a contrasting approach. It shows how to develop a Power BI Dashboard that uses an R machine- learning script as its data source. Despite of the simplicity of the example and of the data sources, it shows most of the typical phases of the BI dashboard development. It connects to off- premises data sources over the Internet, does data cleansing, transformation and enrichment through the use of analytical datasets returned by the R script and also develops a dashboard design that allows you to share the dashboard with your colleagues. It is fairly simple and straightforward to use R as a Power BI Desktop data source. The R code should first be developed and tested in R Studio, or another R IDE, and then copied into the Power BI Desktop. Power BI Desktop (formerly known as Power BI Designer) was released in the first half of 2. In spite of its relative infancy, Power BI Desktop is rapidly gaining the attention of BI and analytic professionals. It was developed by Microsoft in collaboration with Pyramid Analytics – an international company headquartered in The Netherlands with offices across USA, UK and EMEA. Although Power BI Desktop is positioned as . By combining the online Power BI Designer, developer APIs, integration with R, direct connectivity to on- premise data sources, a native mobile BI app and custom visuals it becomes a BI platform. Currently Power BI Desktop supports the almost all popular data source sources, as well as R, MS Azure HD Insight, Apache Spark, and Apache HDInsight Spark (as of today R data source the R data source has pass its Beta testing). Here is the complete list. Figure 1. Current Power BI Desktop data sources. Some of the above sources, such as R, Spark, ODBC, can deliver data that was processed by the corresponding analytical engines (I call these datasets – analytical datasets). G2 Crowd is a platform for business software reviews. They replace analyst quadrants with crowd sourced data and real- time, algorithmic ratings. This capability significantly increases the analytical potential of Power BI Desktop and does not require bringing in the analytical engines visualisation although it can be done. According to the recent review of G2 Crowd . Power BI Desktop rating. Microsoft Power BI and Tableau are running neck to neck already. See the detailed review including pricing of both products here. Setting up R Script as a Data Source. Let’s assume that our goal is to develop an active dashboard, shown below, thatcategories/clusters US States into two of more groups based on their cost of living, crime rate, health care quality, state and local tax burden, personal well- being for seniors and weather,allows to compare two or more clusters,visualises the above measures on the map,displays clusters’ medoids. Figure 3. Dashboard of the US Retirement Statistics. The selection of this data source was inspired by the “Getting Started with Power BI Desktop” tutorial. That data is located in two HTML tables. The first one, “Rank of States for Retirement”, is provided by the Bankrate, Inc. To learn more on how they ranked the states read here. The second table, “List of U. S. We get it from this Wikipedia page. The R code that does all we need to produce the dashboard we’ve just shown is displayed in Figure 3. I will walk you through the code to explain what the blocks of code do. The zero block loads the required libraries and defines the fact. To. Numeric function that is used to convert from factor to numeric type. Block 1 defines the URLs of those two tables and validates them. R script that does the web scraping, clustering and returns the data frame. Here is the last six records from the first data source. Some values in the Health care quality column have a ” (tie)” tail. The last statement in the block 1 cuts that tail off and converts the column’s character values to factors. The code block 3 scrapes the second table from the HTML page, removes unwanted columns and rows, renames the remaining columns, joins this data source to the previously processed first table on State column, reorders columns of the merged data frame, renames them and converts the factor values to numbers. The code in block 4 groups the combined data frame into seven clusters using the k- medoid method, a partitioning technique of clustering that is more robust to noise and outliers than k- means. It minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances as the k- mean does. Note that the optimal number of clusters is also calculated by the pamk() function and saved in the fpc$nc variable. The Last two statements of the block reorder the dataframe columns and sort its rows by the cluster number and the state code. This block also calculates the cluster medoids, dataframe elements whose dissimilarity to the members of the corresponding cluster is minimal. They are similar to centroids, but are always members of the dataset. The set of the medoids returned by the pamk() as a matrix that has to be converted into a dataframe to make it accessible from Power BI Desktop. In order to link the medoids to the dataset of the retirement statistics, a cluster id column is added to the medoids dataframe. The last statement of the code in Figure 4 removes the dataframes that are not used for reporting. Building the Dashboard. We have prepared the R script that delivers two dataframes which are required to build the dashboard in Figure 3. If you want to follow along you need to install the Power BI Desktop. After the installation, check that the Power BI Desktop R Scripting configuration option points to the correct R installation location. Open the Power BI Desktop and on the Welcome Screen . Click on the Load button. After the Loading progress window goes away you should see the Power BI Desktop window as in Figure 5. Click on the “Manage Relationships” button (select the Modelling ribbon, if you don’t see the button). On the Manage Relationships screen click on the Edit button to replace the auto detected relationship (Figure 6). Choose the Cluster column on both sides of the join (Figure 7), hit the OK button and close the “Manage Relationship” window. Our data source is ready, so let’s build the dashboard tiles. To create the dashboard slicer click on the last icon in the 4th row of the Visualisations panel and set the slicer properties as shown in Figure 8. After that the slicer control should look like the one in Figure 3 (without the image on the left of the control). Setting up the table controls is quite simple and I leave it for you to do it independently. Figure 5. Power BI Desktop with two R dataframes loaded. The Table Heatmap control in the right top corner of the dashboard is a custom visual that should be downloaded from https: //app. To import the downloaded “Table Heatmap” visual, click on the last button (ellipses) in the Visualisation panel and navigate to where you saved the Table. Heat. Map. pbiviz file. Next click on the Table Heatmap button in the Visualization panel and set up the control’s properties as shown on the Figure 9. You can find all the possible values for the Colorbrewer attribute here. Configuration of the Filled Map visual is shown in Figure 1. After adjusting position of the controls on the page canvas and, if you like, adding images, you should be able to see a dashboard similar to the one in Figure 3. Change the page name from Page 1 to State Clusters, save the file as Best and Worst US States to Retire and click on the Publish button. After the publishing process is complete click on the Open Best and Worst US States to Retire. Power BI link. Figure 6. Click on the Edit button to replace the autodetected relationship. Figure 7. Edit the relationship between the dataframes. It will take you to the on- line version of the newly developed States Clusters page at (in our case) https: //app. Report. Section. The end of the URL indicates that it is a report section. Next we add this section to the dashboard by clicking on the toolbar’s Pin Live Page button. Select the “New Dashboard” option (see Figure 1. Using R Machine Learning Scripts” and click on the Pin Live button to view the dashboard. The created dashboard is fully interactive. You can filter the reports on the page using the slicer, map legend, by selecting state(s) on the map and sort tables’ rows by clicking on the columns headers. Figure 8. Dashboard slicer configuration. Figure 9. Configuration of the Table Heatmap. Figure 1. 0. Configuration of the Filled Map control. R Scripts: Recommendations and Limitation. 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Sql Server Express Manual Uninstall Ie9 From VistaMany of the files did not originate at Deep Root, but are instead the aggregate of outside data firms and Republican super PACs, shedding light onto the increasingly advanced data ecosystem that helped propel President Donald Trump’s slim margins in key swing states. Although files possessed by Deep Root would be typical in any campaign, Republican or Democratic, experts say its exposure in a single open database raises significant privacy concerns. Data Trust received over $6. RNC during the 2. Open. Secrets. org, and its president, Johnny De. Stefano, now serves as Trump’s director of presidential personnel. The Koch brothers’ political group Americans for Prosperity, which had a data- swapping agreement with Data Trust during the 2. Target. Point, whose co- founder previously served as director of Mitt Romney’s strategy team. 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The details of voters’ likely preferences for issues like stem cell research and gun control were likely drawn from a variety of sources according to a Democratic strategist who spoke with Gizmodo.“Data like that would be a combination of polling data, real world data from door- knocking and phone- calling and other canvassing activities, coupled with modeling using the data we already have to extrapolate what the voters we don’t know about would think,” the strategist said. Deep Root has retained Stroz Friedberg, a cybersecurity and digital forensics firm, to investigate. He used the same process last month to detect sensitive files tied to a US Defense Department project and exposed by an employee of a top defense contractor. This is not the first leak of voter files uncovered by Vickery, who told Gizmodo that he was alarmed over how the data was apparently being used—some states, for instance, prohibit the commercial use of voter records. Moreover, it was not immediately clear to whom the data belonged. In an email to Gizmodo, Deep Root said that its voter models are used to enhance the understanding of TV viewership for political ad buyers. Deep Root also works primarily with GOP customers (although similar vendors, such as Nation. Builder, service the Democrats as well). Deep Root is one of three data firms hired by the Republican National Committee in the run- up to the 2. Founded by Lundry, a data scientist on the Jeb Bush and Mitt Romney campaigns, the firm was one of three analytics teams that worked on the Trump campaign following the party’s national convention in the summer of 2. Lundry’s work brought him into Trump’s campaign war room, according to a post- election Ad. Age article that charted the GOP’s 2. Deep Root was hand- picked by the RNC’s then- chief of staff, Katie Walsh, in September of last year and joined two other data shops—Target. 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Data Trust acquires voter rolls from state officials and then standardizes the voter data to create a clean, manageable record of all registered US voters, a source familiar with the firm’s operations told Gizmodo. Voter data itself is public record and therefore not particularly sensitive, the source added, but the tools Data Trust uses to standardize that data are considered proprietary. That data is then provided to political clients, including analytics firms like Deep Root. While Data Trust requires its clients to protect the data, it has to take clients at their word that industry- standard encryption and security protocols are in place. Target. Point and Causeway, the two firms employed by the RNC in addition to Deep Root, apparently layered their own analytics atop the information provided by Data Trust. Target. Point conducted thousands of surveys per week in 2. Ad. Age, gauging voter sentiment on a variety of topics. While Causeway helped manage the data, Deep Root used it to perfect its TV advertising targets—producing voter turnout estimates by county and using that intelligence to target its ad buys. A source with years of experience working on political campaign data operations told Gizmodo that the data exposed by Deep Root appeared to be customized for the RNC and had apparently been used to create models for turnout and voter preferences. Metadata in the files suggested that the database wasn’t Deep Root’s working copy, but rather a post- election version of its data, the source said, adding that it was somewhat surprising the files hadn’t been discarded. Because the data from the 2. Even the 2. 01. 6 data is quickly becoming stale. Many of filenames indicate they potentially contain market research on Democratic candidates and the independent expenditure committees that support them. Divided by state, the files include the voters’ names and addresses, along with a unique RNC identification number assigned to every US citizen registered to vote. Each row indicates where voters likely fall on issues of interest to Exxon. Mobil, the country’s biggest natural gas producer. The data evaluates, for example, whether or not a specific voter believes drilling for fossil fuels is vital to US security. It also predicts if the voter thinks the US should be moving away from fossil- fuel use. The Exxon. Mobil “national score” document alone contains data on 1. Americans spread across 1. Reddit analysis. Some of the data included in Deep Root’s dataset veers into downright bizarre territory. A folder titled simply . Other subreddits that appear to have been scraped by Deep Root or a partner organization focused on more benign topics, like mountain biking and the Spanish language. The Reddit data could’ve been used as training data for an artificial intelligence algorithm focused on natural language processing, or it might have been harvested as part of an effort to match up Reddit users with their voter registration records. During the 2. 01. Barack Obama’s campaign data team relied on information gleaned from Facebook profiles and matched profiles to voter records. During the 2. 01. Reddit played host to a legion of Trump supporters who gathered in subreddits like r/The. Trump himself participated in an “Ask Me Anything” session on r/The. Five. Thirty. Eight found that, when Redditors weren’t commenting in political subreddits, they most often frequented r/fatpeoplehate. It’s possible that Deep Root intended to use data from r/fatpeoplehate to build a more comprehensive profile of Trump voters. However, campaigns have attempted to match voter files with social media profiles in the past. Such an effort by Deep Root wouldn’t be entirely surprising, and would likely yield rich data on the small portion of users it was able to match with their voter profiles, the source explained. Data exposes sensitive voter info. The Deep Root incident represents the largest known leak of Americans’ voter records, outstripping past exposures by several million records. Five voter- file leaks over the past 1.
Dual channel function / waveform / signal generator for the speaker / headphone audio output. This app puts a waveform on the audio output at 44.1 kHz.Sine wave - Wikipedia. The graphs of the sine and cosine functions are sinusoids of different phases. A sine wave or sinusoid is a mathematical curve that describes a smooth repetitive oscillation. A sine wave is a continuous wave. It is named after the function sine, of which it is the graph. It occurs often in pure and applied mathematics, as well as physics, engineering, signal processing and many other fields. Its most basic form as a function of time (t) is: y(t)=Asin. Find the right Tektronix Signal Generator and learn why they are the world's fastest and most versatile signal generator for today's complex signals. It is the only periodic waveform that has this property. This property leads to its importance in Fourier analysis and makes it acoustically unique. General form. This could, for example, be considered the value of a wave along a wire. In two or three spatial dimensions, the same equation describes a travelling plane wave if position x and wavenumber k are interpreted as vectors, and their product as a dot product. For more complex waves such as the height of a water wave in a pond after a stone has been dropped in, more complex equations are needed. Occurrences. Because of this . Presence of higher harmonics in addition to the fundamental causes variation in the timbre, which is the reason why the same musical note (the same frequency) played on different instruments sounds different. On the other hand, if the sound contains aperiodic waves along with sine waves (which are periodic), then the sound will be perceived . Fourier used it as an analytical tool in the study of waves and heat flow. It is frequently used in signal processing and the statistical analysis of time series. Traveling and standing waves. Sine waves traveling in two directions in space can be represented asu(t,x)=Asin. Note that, on a plucked string, the interfering waves are the waves reflected from the fixed end points of the string. Therefore, standing waves occur only at certain frequencies, which are referred to as resonant frequencies and are composed of a fundamental frequency and its higher harmonics. The resonant frequencies of a string are determined by the length between the fixed ends and the tension of the string. See also. Encyclopedia of Mathematics. Retrieved December 8, 2. Function Generator - DS3. DS3. 45 Function/ARB Generator. The DS3. 45 Function Generator is a full- featured 3. MHz synthesized function generator. Direct Digital Synthesis (DDS) architecture. It also generates arbitrary. Msample/s update rate. Functions and Outputs. The DS3. 45 generates sine waves and square waves. MHz, and triangle and ramp waveforms up. Hz. The frequency resolution for all functions is 1 . Both outputs have 5. The amplitude of all function outputs is adjustable. Vpp to 1. 0 Vpp with 3- digit resolution, and can be displayed. Vp, Vpp, Vrms or d. Bm. In addition, standard TTL and ECL output. Additional useful connectors are provided on the rear panel. A. trigger input is used to trigger arbitrary waveforms, modulation. TTL trigger output is provided. The sweep marker outputs allow specified portions of a frequency. A 1. 0 MHz rear- panel input allows the DS3. A 1. 0 MHz rear- panel output allows multiple. DS3. 45's to be phase locked. Modulation. The DS3. Function Generator offers a wide variety of modulation. It contains an internal modulation generator which can. The modulation. waveform can be a sine, square, triangle, ramp or an arbitrary waveform. When using. AM, modulation depths of . Negative values of modulation correspond to Double Sideband. Suppressed Carrier (DSBSC) modulation. FM spans can be selected. External Amplitude Modulation. In addition to the internal modulation generator, the output waveform. AM input. This input is always active—even when. Burst Modulation. You can generate tone bursts of any output function except noise. By adjusting the phase. While using. the burst mode, the maximum frequency for sine waves and square. MHz, while triangles and ramps are limited to 1. Hz. Frequency Sweeps. The DS3. 45 Function Generator can frequency sweep any of its function. You can sweep up or down in frequency using. Unlike conventional function generators, there. The DS3. 45's DDS architecture. Two sweep marker frequencies can be specified. When the sweep crosses. TTL transition is generated. MARKER output to allow synchronization of external. Arbitrary Waveform Capability. The DS3. 45 isn't just a function generator. It's also a full- featured. Output waveforms have 1. Msamples/s. Since composing complex arbitrary waveforms at the keyboard can. Arbitrary Waveform Composer (AWC) software is. AWC is a menu- based program which lets you. DS3. 45. Click. here for the latest version of the Windows AWC software. DS3. 45 Function/ARB Generator. Frequency range. Max. Frequency. Resolution. Sine. 30. 2 MHz. 1 . All instrument. functions are controllable over the interfaces. Non- volatile memory. Nine sets of instrument settings can be saved and recalled Dimensions. |