Recently I noticed that the
sparklyr had the following odd behavior:
#>  '0.7.2.9000'
#>  '0.6.2'
#>  '220.127.116.1100'
sc <- spark_connect(master = 'local')
#> * Using Spark: 2.1.0
d <- dplyr::copy_to(sc, data.frame(x = 1:2))
#>  NA
#>  NA
#>  NA
This means user code or user analyses that depend on one of
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: 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
dbplyr users. Continue reading Why to use the replyr R package
seplyr has a neat new feature: the function
seplyr::expand_expr() which implements what we call “the string algebra” or string expression interpolation. The function takes an expression of mixed terms, including: variables referring to names, quoted strings, and general expression terms. It then “de-quotes” all of the variables referring to quoted strings and “dereferences” variables thought to be referring to names. The entire expression is then returned as a single string.
This provides a powerful way to easily work complicated expressions into the
seplyr data manipulation methods. Continue reading Neat New seplyr Feature: String Interpolation
wrapr is an R package that supplies powerful tools for writing and debugging R code.
Continue reading wrapr: R Code Sweeteners
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?
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
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
I have some more thoughts on the topic: “the part-time
R-user.” Continue reading More on “The Part-Time R-User”
When I started writing about methods for better "parametric programming" interfaces for
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