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PAC 2019 virtual Srivalli Aparna

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‘R’ you ready for better results analysis?

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PAC 2019 virtual Srivalli Aparna

  1. 1. R you ready for better results analysis? By Srivalli Aparna
  2. 2. Introduction
  3. 3. Significanceoflog analysis
  4. 4. JMeter output
  5. 5. Exploratory Result Analysis
  6. 6. WhyR? • It’s Programming, so it’s fun and flexible • Developed specifically for data analysis • Supports exploratory analysis • Free • Originally developed at The University of Auckland • Comes with handy visualization libraries
  7. 7. More interesting stuff
  8. 8. R StudioR Studio
  9. 9. Sneak Peak
  10. 10. BaseR functions • nrow • ncol • head • tail • summary • str
  11. 11. BaseR functions aggregate(data, group_by_list, FUN, simplify = TRUE)
  12. 12. Data Manipulation
  13. 13. Addinganew column Dataframe$newcolumn <- Calculated values
  14. 14. Sub-settingdata subset <- Originaldataset[filter,]
  15. 15. Conditional datamanipulation ifelse(dataframe$colname==“condition", value if true, value if false)
  16. 16. Visual Data Exploration
  17. 17. Visual dataanalysis
  18. 18. Raw Scatter plot_ly(data = dataset, x = ~TimeStampColumn, y = ~ResponseTimeColumn’, mode = “markers”)
  19. 19. Scatter Plot
  20. 20. Adding a splash of colour
  21. 21. Usingcolor to addathird dimension
  22. 22. More visualisation
  23. 23. Rich visuals for reporting
  24. 24. Rich visuals for reporting
  25. 25. Interactive Dashboard
  26. 26. Interactive Dashboard
  27. 27. Thank You
  28. 28. • What is your approach for data analysis? • What is your choice of data analysis tool? • Building exploratory data analysis into load testing tools Discussion

‘R’ you ready for better results analysis?

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