Just got the following note from a new reader: Thank you for writing Practical Data Science with R. It’s challenging for me, but I am learning a lot by following your steps and entering the commands. Wow, this is exactly what Nina Zumel and I hoped for. We wish we could make everything easy, but … Continue reading Practical Data Science with R update

# Category: Opinion

## Advanced Data Reshaping in Python and R

This note is a simple data wrangling example worked using both the Python data_algebra package and the R cdata package. Both of these packages make data wrangling easy through he use of coordinatized data concepts (relying heavily on Codd’s “rule of access”). The advantages of data_algebra and cdata are: The user specifies their desired transform … Continue reading Advanced Data Reshaping in Python and R

## New Getting Started with vtreat Documentation

Win Vector LLC‘s Dr. Nina Zumel has just released some new vtreat documentation. vtreat is a an all-in one step data preparation system that helps defend your machine learning algorithms from: Missing values Large cardinality categorical variables Novel levels from categorical variables I hoped she could get the Python vtreat documentation up to parity with … Continue reading New Getting Started with vtreat Documentation

## Why R?

I was working with our copy editor on Appendix A of Practical Data Science with R, 2nd Edition; Zumel, Mount; Manning 2019, and ran into this little point (unfortunately) buried in the back of the book. In our opinion the R ecosystem is the fastest path to substantial data science, statistical, and machine learning accomplishment. … Continue reading Why R?

## It is Time for CRAN to Ban Package Ads

NPM (a popular Javascript package repository) just banned package advertisements. I feel the CRAN repository should do the same. Not all R-users are fully aware of package advertisements. But they clutter up work, interfere with reproducibility, and frankly are just wrong. For example, here is the advertisement code from ggplot2: .onAttach <- function(…) { withr::with_preserve_seed({ … Continue reading It is Time for CRAN to Ban Package Ads

## Introducing data_algebra

This article introduces the data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases. In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and rqdatatable). Introduction Parts of the … Continue reading Introducing data_algebra

## vtreat up on PyPi

I am excited to announce vtreat is now available for Python on PyPi, in addition for R on CRAN. vtreat is: A data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. vtreat prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. … Continue reading vtreat up on PyPi

## Florence Nightingale, Data Scientist

Florence Nightingale, Data Scientist.

In 1858 Florence Nightingale published her now famous “rose diagram” breaking down causes of mortality.

By w:Florence Nightingale (1820–1910). – http://www.royal.gov.uk/output/Page3943.asp [dea…

## Lord Kelvin, Data Scientist

In 1876 A. Légé & Co., 20 Cross Street, Hatton Gardens, London completed the first “tide calculating machine” for William Thomson (later Lord Kelvin) (ref). Thomson’s (Lord Kelvin) First Tide Predicting Machine, 1876 The results were plotted on the paper cylinders, and one literally “turned the crank” to perform the calculations. The tide calculating machine … Continue reading Lord Kelvin, Data Scientist

## PyCharm Video Review

My basic video review of the PyCharm integrated development environment for Python with Anaconda and Jupyter/iPython integration. I like the IDE extensions enough to pay for them early in my evaluation. Highly recommended for data science projects, at…

## Some Notes on GNU Licenses in R Packages

I was recently asked if Win-Vector LLC would move the R wrapr package from a GPL-3 license to an LGPL license. In the end I decided to move wrapr distribution to a “GPL-2 | GPL-3” license. This means the package is now available under both GPL-2 and GPL-3 licensing, allowing the user to pick which … Continue reading Some Notes on GNU Licenses in R Packages

## A Comment on Data Science Integrated Development Environments

A point that differs from our experience struck us in the recent note: A development environment specifically tailored to the data science sector on the level of RStudio, for example, does not (yet) exist. “What’s the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA” Amit Ghosh Actually, Python has a large … Continue reading A Comment on Data Science Integrated Development Environments

## A Kind Note That We Really Appreciate

The following really made my day. I tell every data scientist I know about vtreat and urge them to read the paper. Jason Wolosonovich Jason, thanks for your support and thank you so much for taking the time to say this (and for your permission to quote you on this). For those interested the R … Continue reading A Kind Note That We Really Appreciate

## R Books Discount!

We, the community of Manning R and data science authors, have talked Manning into offering a catalog-wide 40% discount on all books. Please take a look at some great deals on some great technical books here: http://mng.bz/adRj !

## Programming Over lm() in R

Here is simple modeling problem in R. We want to fit a linear model where the names of the data columns carrying the outcome to predict (y), the explanatory variables (x1, x2), and per-example row weights (wt) are given to us as strings. Lets start with our example data and parameters. The point is: we … Continue reading Programming Over lm() in R

## My Favorite data.table Feature

My favorite R data.table feature is the “by” grouping notation when combined with the := notation.

Let’s take a look at this powerful notation.

First, let’s build an example data.frame.

d <- wrapr::build_frame(

"gr…

## data.table is Much Better Than You Have Been Told

There is interest in converting relational query languages (that work both over SQL databases and on local data) into data.table commands, to take advantage of data.table‘s superior performance. Obviously if one wants to use data.table it is best to learn data.table. But if we want code that can run multiple places a translation layer may … Continue reading data.table is Much Better Than You Have Been Told

## Technical books are amazing opportunities

Nina and I have been sending out drafts of our book Practical Data Science with R 2nd Edition for technical review. A few of the reviews came back from reviewers that described themselves with variations of: Senior Business Analyst for COMPANYNAME. I have been involved in presenting graphs of data for many years. To us … Continue reading Technical books are amazing opportunities

## Timing Working With a Row or a Column from a data.frame

In this note we share a quick study timing how long it takes to perform some simple data manipulation tasks with R data.frames. We are interested in the time needed to select a column, alter a column, or select a row. Knowing what is fast and what is slow is critical in planning code, so … Continue reading Timing Working With a Row or a Column from a data.frame

## What is “Tidy Data”

I would like to write a bit on the meaning and history of the phrase “tidy data.” Hadley Wickham has been promoting the term “tidy data.” For example in an eponymous paper, he wrote: In tidy data: Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. … Continue reading What is “Tidy Data”