We have our latest note on the theory of data wrangling up here. It discusses the roles of “block records” and “row records” in the `cdata`

data transform tool. With that and the theory of how to design transforms, we think we have a pretty complete description of the system.

# Tag: data wrangling

## Using a Column as a Column Index

We recently saw a great recurring R question: “how do you use one column to choose a different value for each row?” That is: how do you use a column as an index? Please read on for some idiomatic base R, data.table, and dplyr solutions.

## Wanted: `cdata`

Test Pilots

I need a few volunteers to please “test pilot” the development version of the `R`

package `cdata`

, please.

Jacqueline Cochran: at the time of her death, no other pilot held more speed, distance, or altitude records in aviation history than Cochran.

## rquery: Fast Data Manipulation in R

Win-Vector LLC recently announced the `rquery`

`R`

package, an operator based query generator.

In this note I want to share some exciting and favorable initial rquery benchmark timings.

## Getting started with seplyr

A big “thank you!!!” to Microsoft for hosting our new introduction to seplyr. If you are working R and big data I think the seplyr package can be a valuable tool.

## Data Wrangling at Scale

Just wrote a new `R`

article: “Data Wrangling at Scale” (using Dirk Eddelbuettel’s tint template).

Please check it out.

## Teaching pivot / un-pivot

## Authors: John Mount and Nina Zumel

## Introduction

In teaching thinking in terms of coordinatized data we find the hardest operations to teach are joins and pivot.

One thing we commented on is that moving data values into columns, or into a “thin” or entity/attribute/value form (often called “un-pivoting”, “stacking”, “melting” or “gathering“) is easy to explain, as the operation is a function that takes a single row and builds groups of new rows in an obvious manner. We commented that the inverse operation of moving data into rows, or the “widening” operation (often called “pivoting”, “unstacking”, “casting”, or “spreading”) is harder to explain as it takes a specific group of columns and maps them back to a single row. However, if we take extra care and factor the pivot operation into its essential operations we find pivoting can be usefully conceptualized as a simple single row to single row mapping followed by a grouped aggregation.

Please read on for our thoughts on teaching pivoting data. Continue reading Teaching pivot / un-pivot

## Coordinatized Data: A Fluid Data Specification

## Authors: John Mount and Nina Zumel.

## Introduction

It has been our experience when teaching the data wrangling part of data science that students often have difficulty understanding the conversion to and from row-oriented and column-oriented data formats (what is commonly called pivoting and un-pivoting).

Real trust and understanding of this concept doesn’t fully form until one realizes that rows and columns are *inessential* implementation details when *reasoning* about your data. Many *algorithms* are sensitive to how data is arranged in rows and columns, so there is a need to convert between representations. However, confusing representation with semantics slows down understanding.

In this article we will try to separate representation from semantics. We will advocate for thinking in terms of *coordinatized data*, and demonstrate advanced data wrangling in `R`

.

Continue reading Coordinatized Data: A Fluid Data Specification