Posted on Categories StatisticsTags , , Leave a comment on Does replyr::let work with data.table?

Does replyr::let work with data.table?

I’ve been asked if the adapter “let” from our R package replyr works with data.table.

My answer is: it does work. I am not a data.table user so I am not the one to ask if data.table benefits a from a non-standard evaluation to standard evaluation adapter such as replyr::let. Continue reading Does replyr::let work with data.table?

Posted on Categories Coding, Opinion, Programming, Statistics, TutorialsTags , , , 7 Comments on Comparative examples using replyr::let

Comparative examples using replyr::let

Consider the problem of “parametric programming” in R. That is: simply writing correct code before knowing some details, such as the names of the columns your procedure will have to be applied to in the future. Our latest version of replyr::let makes such programming easier.


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Archie’s Mechanics #2 (1954) copyright Archie Publications

(edit: great news! CRAN just accepted our replyr 0.2.0 fix release!)

Please read on for examples comparing standard notations and replyr::let. Continue reading Comparative examples using replyr::let

Posted on Categories Coding, Computer Science, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Programming, StatisticsTags , Leave a comment on A Simple Example of Using replyr::gapply

A Simple Example of Using replyr::gapply

It’s a common situation to have data from multiple processes in a “long” data format, for example a table with columns measurement and process_that_produced_measurement. It’s also natural to split that data apart to analyze or transform it, per-process — and then to bring the results of that data processing together, for comparison. Such a work pattern is called “Split-Apply-Combine,” and we discuss several R implementations of this pattern here. In this article we show a simple example of one such implementation, replyr::gapply, from our latest package, replyr.


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Illustration by Boris Artzybasheff. Image: James Vaughn, some rights reserved.

The example task is to evaluate how several different models perform on the same classification problem, in terms of deviance, accuracy, precision and recall. We will use the “default of credit card clients” data set from the UCI Machine Learning Repository.

Continue reading A Simple Example of Using replyr::gapply

Posted on Categories Opinion, StatisticsTags , , , , , , 3 Comments on Organize your data manipulation in terms of “grouped ordered apply”

Organize your data manipulation in terms of “grouped ordered apply”

Consider the common following problem: compute for a data set (say the infamous iris example data set) per-group ranks. Suppose we want the rank of iris Sepal.Lengths on a per-Species basis. Frankly this is an “ugh” problem for many analysts: it involves all at the same time grouping, ordering, and window functions. It also is not likely ever the analyst’s end goal but a sub-step needed to transform data on the way to the prediction, modeling, analysis, or presentation they actually wish to get back to.


Iris germanica Purple bearded Iris Wakehurst Place UK DiliffIris, by DiliffOwn work, CC BY-SA 3.0, Link

In our previous article in this series we discussed the general ideas of “row-ID independent data manipulation” and “Split-Apply-Combine”. Here, continuing with our example, we will specialize to a data analysis pattern I call: “Grouped-Ordered-Apply”. Continue reading Organize your data manipulation in terms of “grouped ordered apply”

Posted on Categories Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , , , 2 Comments on The case for index-free data manipulation

The case for index-free data manipulation

Statisticians and data scientists want a neat world where data is arranged in a table such that every row is an observation or instance, and every column is a variable or measurement. Getting to this state of “ready to model format” (often called a denormalized form by relational algebra types) often requires quite a bit of data manipulation. This is how R data.frames describe themselves (try “str(data.frame(x=1:2))” in an R-console to see this) and is part of the tidy data manifesto.

Tools like SQL (structured query language) and dplyr can make the data arrangement process less burdensome, but using them effectively requires “index free thinking” where the data are not thought of in terms of row indices. We will explain and motivate this idea below. Continue reading The case for index-free data manipulation

Posted on Categories Coding, Computer Science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Programming, StatisticsTags , , 2 Comments on Using replyr::let to Parameterize dplyr Expressions

Using replyr::let to Parameterize dplyr Expressions

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Imagine that in the course of your analysis, you regularly require summaries of numerical values. For some applications you want the mean of that quantity, plus/minus a standard deviation; for other applications you want the median, and perhaps an interval around the median based on the interquartile range (IQR). In either case, you may want the summary broken down with respect to groupings in the data. In other words, you want a table of values, something like this:

dist_intervals(iris, "Sepal.Length", "Species")

# A tibble: 3 × 7
     Species  sdlower  mean  sdupper iqrlower median iqrupper
                         
1     setosa 4.653510 5.006 5.358490   4.8000    5.0   5.2000
2 versicolor 5.419829 5.936 6.452171   5.5500    5.9   6.2500
3  virginica 5.952120 6.588 7.223880   6.1625    6.5   6.8375

For a specific data frame, with known column names, such a table is easy to construct using dplyr::group_by and dplyr::summarize. But what if you want a function to calculate this table on an arbitrary data frame, with arbitrary quantity and grouping columns? To write such a function in dplyr can get quite hairy, quite quickly. Try it yourself, and see.

Enter let, from our new package replyr.

Continue reading Using replyr::let to Parameterize dplyr Expressions

Posted on Categories OpinionTags , , 13 Comments on Parametric variable names and dplyr

Parametric variable names and dplyr

When writing reusable code or packages you often do not know the names of the columns or variables you need to work over. This is what I call “parametric treatment of variables.” This can be a problem when using R libraries that assume you know the variable names. The R data manipulation library dplyr currently supports parametric treatment of variables through “underbar forms” (methods of the form dplyr::*_), but their use can get tricky.


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Rube Goldberg machine 1931 (public domain).

Better support for parametric treatment of variable names would be a boon to dplyr users. To this end the replyr package now has a method designed to re-map parametric variable names to known concrete variable names. This allows concrete dplyr code to be used as if it was parametric. Continue reading Parametric variable names and dplyr

Posted on Categories Coding, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, ProgrammingTags , , , , , 2 Comments on New R package: replyr (get a grip on remote dplyr data services)

New R package: replyr (get a grip on remote dplyr data services)

It is a bit of a shock when R dplyr users switch from using a tbl implementation based on R in-memory data.frames to one based on a remote database or service. A lot of the power and convenience of the dplyr notation is hard to maintain with these more restricted data service providers. Things that work locally can’t always be used remotely at scale. It is emphatically not yet the case that one can practice with dplyr in one modality and hope to move to another back-end without significant debugging and work-arounds. replyr attempts to provide a few helpful work-arounds.

Our new package replyr supplies methods to get a grip on working with remote tbl sources (SQL databases, Spark) through dplyr. The idea is to add convenience functions to make such tasks more like working with an in-memory data.frame. Results still do depend on which dplyr service you use, but with replyr you have fairly uniform access to some useful functions.

Continue reading New R package: replyr (get a grip on remote dplyr data services)