Displaying 1 to 9 from 9 results

janitor - simple tools for data cleaning in R

  •    R

Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets. janitor has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.

SPSS .NET interop library

  •    DotNet

A .NET library for read/writing SPSS Data (.sav) files. This wraps the functionality exposed by the spssio32.dll native library that comes with SPSS.

SPSS & PSPP file library


Library for importing SPSS (now called PASW) & PSPP .sav datafiles, developed in C#. Support for writing and SPSS portable-files will be implemented in a later release.

haven - Read SPSS, Stata and SAS files from R

  •    C

SAS: read_sas() reads .sas7bdat + .sas7bcat files and read_xpt() reads SAS transport files (version 5 and version 8). write_sas() writes .sas7bdat files. SPSS: read_sav() reads .sav files and read_por() reads the older .por files. write_sav() writes .sav files.

rio - A Swiss-Army Knife for Data I/O

  •    R

Because rio is meant to streamline data I/O, the package is extremely easy to use. Here are some examples of reading, writing, and converting data files. Note: Because of inconsistencies across underlying packages, the data.frame returned by import might vary slightly (in variable classes and attributes) depending on file type.

SPSStoR - R package to convert SPSS syntax to R code

  •    R

A set of functions that takes SPSS syntax as input and outputs R commands to do the same analysis or data management tasks.

node-red-bluemix-nodes - A collection of node-red nodes for the IBM Bluemix platform

  •    HTML

A collection of nodes to be used with Node-RED in IBM Bluemix. Prior to version 1.0.1, this module also included nodes for the IBM Watson and Alchemy services. They have now been moved to node-red-node-watson.

watson-stock-market-predictor - A IBM Developer code pattern for Watson Studio: forecast the stock market with Python Notebooks, SPSS Modeler, Data Refinery, and other Watson Studio tools

  •    Jupyter

In this code pattern, we will demonstrate on how subject matter experts and data scientists can leverage IBM Watson Studio to automate data mining and the training of time series forecasters using open-source machine learning libraries, or the built-in graphical tool integrated into Watson Studio. It applies ARIMA algorithms (Auto-regressive Integrated Moving Average) and other advanced techniques to construct mathematical models capable of predicting trends based on data from the past. Using the IBM Watson Studio and other popular open-source Python libraries for data science, this code pattern provides an example of data science workflow which attempts to predict the end-of-day value of S&P 500 stocks based on historical data. It includes the data mining process, that uses the Quandl API – a marketplace for financial, economic and alternative data delivered in modern formats for today's analysts.

analyze-insights-on-startup-using-watson-studio - This code pattern will demonstrate a methodology to show how we can get analytical insights and visualisations from raw data on the Web

  •    Jupyter

The World Wide Web or the "Web" is the universe of network-accessible information. All this information present in a raw format on the Web. What if you want a way to ingest raw information on the web for any given topic and provide insights and visualiations for the same. This code pattern does, just that taking an example of performing analytics on Startups. Being in the age of start-ups. There is a rapid increase in a number of companies providing skilled services. We can scrape information about such companies and evaluate their success stories based on the number of articles or live use cases appeared in news portals.

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