Using R in power BI
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This document provides an overview of using R with Power BI. It discusses integrating R via the R Connector and Power Query, creating R visuals in Power BI reports, and some limitations. Key points include using R for data transformation, visualization, and advanced analytics. The R Connector allows loading R data frames into Power BI. R packages can be used in Power Query for tasks like web scraping, data wrangling, and missing value imputation. R visuals bring R plotting capabilities to Power BI reports.
Using R in power BI
- 1. Power BI with R Guruprasad Vijayarao 3/21/2018
- 2. Agenda • Why R in Power BI • R integration in Power BI • R Connector • R & Power Query • R Visuals • Q&A • Lab
- 3. What is R? • Used by 2m+ data scientists, statisticians and analysts • Flexible, extensible and comprehensive for productivity • As seen in New York Times, The Economist and FlowingData
- 4. CRAN: 7000+ add-on packages for R
- 5. Power BI Dashboard Creation
- 6. Power BI: R Integration Points R Connector R & Power Query R Visuals
- 7. R Connector • Install R - https://docs.microsoft.com/en-us/power-bi/desktop-r-visuals • Configure R Home Directory • Packages and dependencies must be explicitly loaded • Only Data Frames • No Complex Type, No user inputs • 30 minutes timeout • Setwd to full path • https://docs.microsoft.com/en-us/power-bi/service-r-packages- support
- 8. Data • Web Data Scrub - rvest, httr, rcurl, and others • Complicated Data Transformation: dplyr, tidyr, lubrdiate, stringr… • XML or JSON data: XML, jsonlite, and others • RegEx to Scrub the data • mice package to impute missing values • tidytext package to assign sentiment scores to text inputs • Export Data
- 9. Visuals • External IDE • Box Plot, Facet • Corelations • Regression • Prediction • Sentiment Analysis
- 10. R Visual Gallery
- 11. Data Refresh • Privacy levels • Personal Gateway
- 12. Limitations • Data size limitations: 150,000 rows • Calculation time limitation 5 minutes. • Relationships. • R visuals responses for cross-filtering but it cannot filter. • R visuals respond to highlighting other visuals, but you cannot click on elements in the R visual in order to cross filter other elements.
- 13. Resources • Documentation: • https://powerbi.microsoft.com/en-us/documentation/Powerbi-Service-R-Packages-Support • https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-r-scripts • R Visuals Gallery: https://app.powerbi.com/visuals/R-powered • R Script Showcase: https://community.powerbi.com/t5/R-Script-Showcase/bd-p/RVisuals
- 14. Related Conent • https://www.youtube.com/watch?v=SMaJf6UBKeI&t=591s • https://www.blue-granite.com/tutorials/power-bi-and-r
- 15. Lab • Sample https://www.linkedin.com/pulse/embedded-hr-analytics-using-r-power-bi-andy-lowrey • Source Code https://github.com/lowreya/workforce-analytics-demo
- 16. Demo Script – Cheet Sheet • Check the Notes below for scripts
Editor's Notes
- #7: https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-r-scripts/ https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-r-in-query-editor/ https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-r-visuals/
- #8: https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-r-in-query-editor/
- #9: Write.table (dataset, file = “[PATH OF FILE]”, sep = “[SEPARATOR]”, row.names = FALSE)
- #10: https://community.powerbi.com/t5/R-Script-Showcase/bd-p/RVisuals
- #11: https://community.powerbi.com/t5/R-Script-Showcase/bd-p/RVisuals
- #17: Use ‘data(package = .packages(all.available = TRUE))’ to list the data sets in all *available* packages. #------------------Visuals Density Chart plot(density(dat$StockValue)) abline(v = quantile(dat$StockValue, 0.05), col = "red") #------------------Visuals Correlation Chart data(mtcars) require("corrplot") library(corrplot) M <- cor(dataset) #Experiment different plotting parameters by uncommenting your preferred plotting instruction corrplot(M, method = "circle",type = "upper" , tl.srt = 0 ) #corrplot(M, method = "circle", order="hclust", addrect=2) #corrplot(M, method = "color", tl.cex=0.6, tl.srt = 45, tl.col = "black") #corrplot(M, method = "ellipse", type = "upper", tl.cex=0.6, tl.srt = 45, tl.col = "black") install.packages("corrplot") library(corrplot) M <- cor(mtcars) corrplot(M, method = "number") #------------------Venn Diagram install.package('VennDiagram') library(VennDiagram) v<-draw.triple.venn(dataset[1,1],dataset[1,2],dataset[1,3],dataset[1,4],dataset[1,5],dataset[1,6],dataset[1,7],c("First","Second","Third "),cex=1.2, cat.dist = c(0.1, 0.1, 0.1),cat.fontfamily = "plain", fontfamily = "plain")