Posted on Categories Opinion, Programming, StatisticsTags , , 3 Comments on Please inspect your dplyr+database code

Please inspect your dplyr+database code

A note to dplyr with database users: you may benefit from inspecting/re-factoring your code to eliminate value re-use inside dplyr::mutate() statements. Continue reading Please inspect your dplyr+database code

Posted on Categories Coding, Opinion, Statistics, TutorialsTags , , , , Leave a comment on Let X=X in R

Let X=X in R

Our article "Let’s Have Some Sympathy For The Part-time R User" includes two points:

  • Sometimes you have to write parameterized or re-usable code.
  • The methods for doing this should be easy and legible.

The first point feels abstract, until you find yourself wanting to re-use code on new projects. As for the second point: I feel the wrapr package is the easiest, safest, most consistent, and most legible way to achieve maintainable code re-use in R.

In this article we will show how wrapr makes code-rewriting even easier with its new let x=x automation.


411gJqs4qlL

Let X=X

Continue reading Let X=X in R

Posted on Categories Administrativia, Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , , , 3 Comments on Upcoming data preparation and modeling article series

Upcoming data preparation and modeling article series

I am pleased to announce that vtreat version 0.6.0 is now available to R users on CRAN.


Vtreat

vtreat is an excellent way to prepare data for machine learning, statistical inference, and predictive analytic projects. If you are an R user we strongly suggest you incorporate vtreat into your projects. Continue reading Upcoming data preparation and modeling article series

Posted on Categories Opinion, Programming, TutorialsTags , Leave a comment on On debugging

On debugging

My favorite advice on debugging is from Professor Norman Matloff:

Finding your bug is a process of confirming the many things that you believe are true – until you find one that is not true.


LeafInsect

Continue reading On debugging

Posted on Categories Opinion, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , , 3 Comments on My advice on dplyr::mutate()

My advice on dplyr::mutate()

There are substantial differences between ad-hoc analyses (be they: machine learning research, data science contests, or other demonstrations) and production worthy systems. Roughly: ad-hoc analyses have to be correct only at the moment they are run (and often once they are correct, that is the last time they are run; obviously the idea of reproducible research is an attempt to raise this standard). Production systems have to be durable: they have to remain correct as models, data, packages, users, and environments change over time.

Demonstration systems need merely glow in bright light among friends; production systems must be correct, even alone in the dark.


Vlcsnap 00887

“Character is what you are in the dark.”

John Whorfin quoting Dwight L. Moody.

I have found: to deliver production worthy data science and predictive analytic systems, one has to develop per-team and per-project field tested recommendations and best practices. This is necessary even when, or especially when, these procedures differ from official doctrine.

What I want to do is share a single small piece of Win-Vector LLC‘s current guidance on using the R package dplyr. Continue reading My advice on dplyr::mutate()

Posted on Categories Opinion, Statistics, TutorialsTags , , , 1 Comment on Remember: p-values Are Not Effect Sizes

Remember: p-values Are Not Effect Sizes

Authors: John Mount and Nina Zumel.

The p-value is a valid frequentist statistical concept that is much abused and mis-used in practice. In this article I would like to call out a few features of p-values that can cause problems in evaluating summaries.

Keep in mind: p-values are useful and routinely taught correctly in statistics, but very often mis-remembered or abused in practice.

14582827568 7a2f83011f
From Hamilton’s Lectures on metaphysics and logic (1871).
Internet Archive Book Images

Continue reading Remember: p-values Are Not Effect Sizes

Posted on Categories Opinion, Programming, StatisticsTags , , , , 2 Comments on It is Needlessly Difficult to Count Rows Using dplyr

It is Needlessly Difficult to Count Rows Using dplyr

  • Question: how hard is it to count rows using the R package dplyr?
  • Answer: surprisingly difficult.

When trying to count rows using dplyr or dplyr controlled data-structures (remote tbls such as Sparklyr or dbplyr structures) one is sailing between Scylla and Charybdis. The task being to avoid dplyr corner-cases and irregularities (a few of which I attempt to document in this "dplyr inferno").



800px Johann Heinrich F├╝ssli 054

Continue reading It is Needlessly Difficult to Count Rows Using dplyr

Posted on Categories Coding, Opinion, Statistics, TutorialsTags , , , ,

Why to use the replyr R package

Recently I noticed that the R package sparklyr had the following odd behavior:

suppressPackageStartupMessages(library("dplyr"))
library("sparklyr")
packageVersion("dplyr")
#> [1] '0.7.2.9000'
packageVersion("sparklyr")
#> [1] '0.6.2'
packageVersion("dbplyr")
#> [1] '1.1.0.9000'

sc <- spark_connect(master = 'local')
#> * Using Spark: 2.1.0
d <- dplyr::copy_to(sc, data.frame(x = 1:2))

dim(d)
#> [1] NA
ncol(d)
#> [1] NA
nrow(d)
#> [1] NA

This means user code or user analyses that depend on one of dim(), ncol() or nrow() possibly breaks. nrow() used to return something other than NA, so older work may not be reproducible.

In fact: where I actually noticed this was deep in debugging a client project (not in a trivial example, such as above).


Tron
Tron: fights for the users.

In my opinion: this choice is going to be a great source of surprises, unexpected behavior, and bugs going forward for both sparklyr and dbplyr users. Continue reading Why to use the replyr R package

Posted on Categories Opinion, Programming, Rants, StatisticsTags , , 2 Comments on Is dplyr Easily Comprehensible?

Is dplyr Easily Comprehensible?

dplyr is one of the most popular R packages. It is powerful and important. But is it in fact easily comprehensible? Continue reading Is dplyr Easily Comprehensible?

Posted on Categories Administrativia, Opinion, StatisticsTags , ,

Thank You For The Very Nice Comment

Somebody nice reached out and gave us this wonderful feedback on our new Supervised Learning in R: Regression (paid) video course.

Thanks for a wonderful course on DataCamp on XGBoost and Random forest. I was struggling with Xgboost earlier and Vtreat has made my life easy now :).

Continue reading Thank You For The Very Nice Comment