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.

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From Hamilton’s Lectures on metaphysics and logic (1871).
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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").



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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 :).

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Posted on Categories Opinion, Programming, StatisticsTags , , , , , 10 Comments on Let’s Have Some Sympathy For The Part-time R User

Let’s Have Some Sympathy For The Part-time R User

When I started writing about methods for better "parametric programming" interfaces for dplyr for R dplyr users in December of 2016 I encountered three divisions in the audience:

  • dplyr users who had such a need, and wanted such extensions.
  • dplyr users who did not have such a need ("we always know the column names").
  • dplyr users who found the then-current fairly complex "underscore" and lazyeval system sufficient for the task.

Needing name substitution is a problem an advanced full-time R user can solve on their own. However a part-time R would greatly benefit from a simple, reliable, readable, documented, and comprehensible packaged solution. Continue reading Let’s Have Some Sympathy For The Part-time R User

Posted on Categories Coding, data science, Opinion, Programming, Statistics, TutorialsTags , , , , 13 Comments on Tutorial: Using seplyr to Program Over dplyr

Tutorial: Using seplyr to Program Over dplyr

seplyr is an R package that makes it easy to program over dplyr 0.7.*.

To illustrate this we will work an example.

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Posted on Categories data science, Opinion, Programming, Statistics, TutorialsTags , , , , 12 Comments on dplyr 0.7 Made Simpler

dplyr 0.7 Made Simpler

I have been writing a lot (too much) on the R topics dplyr/rlang/tidyeval lately. The reason is: major changes were recently announced. If you are going to use dplyr well and correctly going forward you may need to understand some of the new issues (if you don’t use dplyr you can safely skip all of this). I am trying to work out (publicly) how to best incorporate the new methods into:

  • real world analyses,
  • reusable packages,
  • and teaching materials.

I think some of the apparent discomfort on my part comes from my feeling that dplyr never really gave standard evaluation (SE) a fair chance. In my opinion: dplyr is based strongly on non-standard evaluation (NSE, originally through lazyeval and now through rlang/tidyeval) more by the taste and choice than by actual analyst benefit or need. dplyr isn’t my package, so it isn’t my choice to make; but I can still have an informed opinion, which I will discuss below.

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Posted on Categories Opinion, Programming, Statistics, TutorialsTags , , , 8 Comments on In praise of syntactic sugar

In praise of syntactic sugar

There has been some talk of adding native pipe notation to R (for example here, here, and here). And even a tidyeval/rlang pipe here.

I think a critical aspect of such an extension would be to treat such a notation as syntactic sugar and not insist such a pipe match magrittr semantics, or worse yet give a platform for authors to insert their own preferred ad-hoc semantics. Continue reading In praise of syntactic sugar