DataWrangler @VGSOM
- 1. Amu Prabhjot Singh 10BM60011 Divya Hamirwasia 10BM60025
- 2. an interactive data transformation tool developed by the Stanford Visualization Group. allows direct manipulation of visual data provides automatic suggestions for relevant transformations used in activities like reformatting data values and formats, integrating data from multiple sources, missing values etc use of Wrangler reduces the specification time significantly
- 3. When the user selects any data, applicable transformations are suggested by the tool based on the current context of interaction Data wrangler uses a modeling technique to enumerate and rate the possible transformations This model combines user's inputs with diversity, frequency and specification difficulty of applicable transform types Wrangler provides short natural language descriptions of the transforms and also provides the visual previews of the transform results This helps analysts to assess the viable transforms quickly Wrangler's interactive history viewer records and shows the step of transforms applied on the data set so as to facilitate reuse. Wrangler scripts can be run in a web browser using JavaScript or Python code
- 4. underlying declarative data transformation language language consists of 8 classes of transformations ◦ Map One to zero One to One One to Many ◦ Look ups and Joins ◦ Reshape Fold unfold ◦ Positional Fill Lag ◦ Sorting ◦ Aggregation ◦ Key Generation ◦ Schema Transforms
- 5. This is the example data available with data wrangler. House crime data from the U.S. Bureau of Justice Statistics Csv format data
- 6. User interactions Inferring transform Current working parameters transform Generating candidate DATA WRANGLER transforms Data descriptions Ranking the results Corpus of historical usage statistics
- 7. GETTING STARTED ◦ Browser based tool: http://vis.stanford.edu/wrangler/ DATA ENTRY ◦ copy and paste the data to be wrangled into the input window. ◦ Input format : csv files, tsv files and manual entry TRANSFORMS • Cut • Merge • Delete • Promote • Drop • Split • Edit • Translate • Extract • Transpose • Fill • Unfold • Fold OUTPUT Two types of outputs: ◦ Data Output.xlsx Csv, tsv, row oriented JSON, column oriented JSON, look up tables ◦ Script Python, java script
- 8. helps to speed up the process of data manipulation helps managers to spend more time analyzing and learning from their data rather than spending much of the time just rearranging it allows interactive transformation of messy, real- world data and export data for use in Excel, R, Tableau, Protovis etc LIMITATION: data containing more than 40 columns and 1000 rows cannot be wrangled