<||> Interfaces to Popular R Functions for Data Science Pipelines, and More
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README.md

intubate <||> 1.4.0

Roberto Bertolusso
2016-07-26 - 2016-09-09 (GPL >= 2)

The aim of intubate (logo <||>) is to offer a painless way to add R functions that are non-pipe-aware to data science pipelines implemented by magrittr with the operator %>%, without having to rely on workarounds of varying complexity In addition, three extensions for pipelines, called 'intubOrders', 'intuEnv', and 'intuBags', are implemented.

Installation

  • the latest released version from CRAN (1.0.0) on 2016-08-28 with
install.packages("intubate")
  • the latest development version from github with
# install.packages("devtools")
devtools::install_github("rbertolusso/intubate")

In a nutshell

If you like magrittr pipelines (%>%) and you are looking for an alternative to performing a statistical analysis in the following way:

fit <- lm(sr ~ pop15, LifeCycleSavings)
summary(fit)

intubate let's you do it in these other ways:

library(intubate)
library(magrittr)

1) Using interface (provided by intubate or user defined)

ntbt_lm is the interface provided to lm, and one of the over 450 interfaces intubate currently implements (for the list of 88 packages currently containing interfaces see below).

LifeCycleSavings %>%
  ntbt_lm(sr ~ pop15) %>%    ## ntbt_lm is the interface to lm provided by intubate
  summary()

2) Calling the non-pipe-aware function directly with ntbt

You do not need to use interfaces. You can call non-pipe-aware functions directly using ntbt (even those that currently do not have an interface provided by intubate).

LifeCycleSavings %>%
  ntbt(lm, sr ~ pop15) %>%   ## ntbt calls lm without needing to use an interface
  summary()

The help for each interface contains examples of use.

Interfaces "on demand"

intubate allows you to create your own interfaces "on demand", right now, giving you full power of decision regarding which functions to interface.

The ability to amplify the scope of intubate may prove to be particularly welcome if you are related to a particular field that may, in the long run, continue to lack interfaces due to my unforgivable, but unavoidable, ignorance.

As an example of creating an interface "on demand", suppose the interface to cor.test was lacking in the current version of intubate and suppose (at least for a moment) that you want to create yours because you are searching for a pipeline-aware alternative to any of the following styles of coding:

data(USJudgeRatings)

## 1)
cor.test(USJudgeRatings$CONT, USJudgeRatings$INTG)

## 2)
attach(USJudgeRatings)
cor.test(CONT, INTG)
detach()

## 3)
with(USJudgeRatings, cor.test(CONT, INTG))
     
## 4)
USJudgeRatings %>%
   with(cor.test(CONT, INTG))

To be able to create an interface to cor.test "on demand", the only thing you need to do is to add the following line of code somewhere before its use in your pipeline:

ntbt_cor.test <- intubate          ## intubate is the helper function

Please note the lack of parentheses.

Nothing else is required.

The only thing you need to remember is that the names of an interface must start with ntbt_ followed by the name of the interfaced function (cor.test in this particular case), no matter which function you want to interface.

Now you can use your "just baked" interface in any pipeline. A pipeline alternative to the above code may look like this:

USJudgeRatings %>%
  ntbt_cor.test(CONT, INTG)           ## Use it right away

Calling non-pipe-aware functions directly with ntbt

As already stated, you do not have to create an interface if you do not want to. You can call the non-pipe-aware function directly with ntbt, in the following way:

USJudgeRatings %>%
  ntbt(cor.test, CONT, INTG)

You can potentially use ntbt with any function, also the ones without an interface provided by intubate. In principle, the functions you would like to call are the ones you cannot use directly in a pipeline (because data is not in first place in the definition of the function).

Example showing different techniques

The link below is to Dr. Sheather's website where the original code was extracted. In the link there is also information about the book. This code could be used to produce the plots in Figure 3.1 on page 46. Different strategies are illustrated.

http://www.stat.tamu.edu/~sheather/book/

1) As in the book (without using pipes and attaching data):

attach(anscombe)
plot(x1, y1, xlim = c(4, 20), ylim = c(3, 14), main = "Data Set 1")
abline(lsfit(x1, y1))
detach()

You needed to attach so variables are visible locally. If not, you should have used anscombe$x1 and anscombe$y1. You could also have used with. Spaces were added for clarity and better comparison with code below.

2) Using magrittr pipes (%>%) and intubate (1: provided interface and 2: ntbt):

anscombe %>%
  ntbt_plot(x2, y2, xlim = c(4, 20), ylim = c(3, 14), main = "Data Set 2") %>%
  ntbt(lsfit, x2, y2) %>%   # Call non-pipe-aware function directly with `ntbt`
  abline()                  # No need to interface 'abline'.
  • ntbt_plot is the interface to plot provided by intubate. As plot returns NULL, intubate forwards (invisibly) its input automatically without having to use %T>%, so lsfit gets the original data (what it needs) and everything is done in one pipeline.
  • ntbt let's you call the non-pipe-aware function lsfit directly. You can use ntbt always (you do not need to use ntbt_ interfaces if you do not want to), but ntbt is particularly useful to interface directly a non-pipe-aware function for which intubate does not provide an interface (as currently happens with lsfit).

3) Defining interface "on demand"

If intubate does not provide an interface to a given function and you prefer to use interfaces instead of ntbt, you can create your own interface "on demand" and use it right away in your pipeline. To create an interface, it suffices the following line of code before its use:

ntbt_lsfit <- intubate      # NOTE: we are *not* including parentheses.

That's it, you have created the interface to lsfit. Just remember that:

  1. intubate interfaces must start with ntbt_ followed by the name of the function to interface (lsfit in this case).
  2. Parentheses are not used in the definition of the interface.

You can now use ntbt_lsfit in your pipeline as any other interfaced function:

anscombe %>%
  ntbt_plot(x3, y3, xlim = c(4, 20), ylim = c(3, 14), main = "Data Set 3") %>%
  ntbt_lsfit(x3, y3) %>%    # Using just created "on demand" interface
  abline()

4) Using the formula variants:

Instead of the X Y approach, you can also use the formula variant. In this case, we will have to used lm as lsfit does not implement formulas.

anscombe %>%
  ntbt_plot(y4 ~ x4, xlim = c(4, 20), ylim = c(3, 14), main = "Data Set 4") %>%
  ntbt_lm(y4 ~ x4) %>%      # We use 'ntbt_lm' instead of 'ntbt_lmfit' 
  abline()

Extensions for pipelines provided by intubate

intubate implements three extensions:

  • intubOrders,
  • intuEnv, and
  • intuBags.

These experimental features are functional for you to use. Unless you do not mind having to potentially make some changes to your code while the architecture solidifies, they are not recommended (yet) for production code.

intubOrders

intubOrders allow, among other things, to:

  • run, in place, functions on the input (data) to the interfaced function, such as head, tail, dim, str, View, ...

  • run, in place, functions that use the result generated by the interfaced function, such as print, summary, anova, plot, ...

  • forward the input to the interfaced function without using %T>%

  • signal other modifications to the behavior of the interface

intubOrders are implemented by an intuBorder <||> (from where the logo of intubate originates).

The intuBorder contains 5 zones (intuZones?, maybe too much...):

zone 1 < zone 2 | zone 3 | zone 4 > zone 5

  • zone 1 and zone 5 will be explained later

  • zone 2 is used to indicate the functions that are to be applied to the input to the interfaced function

  • zone 3 to modify the behavior of the interface

  • zone 4 to indicate the functions that are to be applied to the result of the interfaced function

For example, instead of running the following sequence of function calls:

head(LifeCycleSavings)
tail(LifeCycleSavings, n = 3)
dim(LifeCycleSavings)
str(LifeCycleSavings)
summary(LifeCycleSavings)
result <- lm(sr ~ pop15 + pop75 + dpi + ddpi, LifeCycleSavings)
print(result)
summary(result)
anova(result)
plot(result, which = 1)

you could have run, using an intubOrder:

LifeCycleSavings %>%
  ntbt_lm(sr ~ pop15 + pop75 + dpi + ddpi,
          "< head; tail(#, n = 3); dim; str; summary
             |i|
             print; summary; anova; plot(#, which = 1) >")
  • Note:

    • i is used to force an invisible result
    • # is used as a placeholder either for the input or result in cases the call requires extra parameters.
  • intubOrders may prove to be of interest to non-pipeline oriented people too:

ntbt_lm(LifeCycleSavings, sr ~ pop15 + pop75 + dpi + ddpi,
        "< head; tail(#, n = 3); dim; str; summary
           |i|
           print; summary; anova; plot(#, which = 1) >")
  • Also using parameters in their preferred order. Please note that, in this case, data needs to be specified, at least for now:
ntbt_lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings,
        "< head; tail(#, n = 3); dim; str; summary
           |i|
           print; summary; anova; plot(#, which = 1) >")

\

Example demonstrating two strategies of using intubOrders

This example uses the code provided in the vignette of package survey, that can be found in:

https://cran.r-project.org/web/packages/survey/vignettes/survey.pdf

The original code on the vignette is:

library(survey)

data(api)

vars<-names(apiclus1)[c(12:13,16:23,27:37)] 

dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
summary(dclus1)
svymean(~api00, dclus1)
svyquantile(~api00, dclus1, quantile=c(0.25,0.5,0.75), ci=TRUE)
svytotal(~stype, dclus1)
svytotal(~enroll, dclus1)
svyratio(~api.stu,~enroll, dclus1)
svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H"))
svymean(make.formula(vars),dclus1,na.rm=TRUE)
svyby(~ell+meals, ~stype, design=dclus1, svymean)
regmodel <- svyglm(api00~ell+meals,design=dclus1)
logitmodel <- svyglm(I(sch.wide=="Yes")~ell+meals, design=dclus1, family=quasibinomial()) 
summary(regmodel)
summary(logitmodel)

\

Two strategies of using intubOrders are illustrated.

Strategy 1: long pipeline, light use of intubOrders:
apiclus1 %>%
  ntbt(svydesign, id = ~dnum, weights = ~ pw, fpc = ~ fpc, "<|| summary >") %>%
  ntbt(svymean, ~ api00, "<|f| print >") %>%
  ntbt(svyquantile, ~ api00, quantile = c(0.25,0.5,0.75), ci = TRUE, "<|f| print >") %>%
  ntbt(svytotal, ~ stype, "<|f| print >") %>%
  ntbt(svytotal, ~ enroll, "<|f| print >") %>%
  ntbt(svyratio, ~ api.stu, ~ enroll, "<|f| print >") %>%
  ntbt(svyratio, ~ api.stu, ~ enroll, design = subset("#", stype == "H"), "<|f| print >") %>%
  ntbt(svymean, make.formula(vars), na.rm = TRUE, "<|f| print >") %>%
  ntbt(svyby, ~ ell + meals, ~ stype, svymean, "<|f| print >") %>%
  ntbt(svyglm, api00 ~ ell + meals, "<|f| summary >") %>%
  ntbt(svyglm, I(sch.wide == "Yes") ~ ell + meals, family = quasibinomial(), "<|f| summary >")

\

Strategy 2: short pipeline, heavy use of one intubOrder:
apiclus1 %>%
  ntbt(svydesign, id = ~dnum, weights = ~pw, fpc = ~fpc,
       "<|f|
         summary;
         svymean(~api00, #);
         svyquantile(~api00, #, quantile = c(0.25, 0.5, 0.75), ci = TRUE);
         svytotal(~stype, #);
         svytotal(~enroll, #);
         svyratio(~api.stu,~enroll, #);
         svyratio(~api.stu, ~enroll, design = subset(#, stype == 'H'));
         svymean(make.formula(vars), #, na.rm = TRUE);
         svyby(~ell+meals, ~stype, #, svymean);
         summary(svyglm(api00~ell+meals, #));
         summary(svyglm(I(sch.wide == 'Yes')~ell+meals, #, family = quasibinomial())) >")

\

intubOrders with collections of inputs

When using pipelines, the receiving function has to deal with the whole object that receives as its input. Then, it produces a result that, again, needs to be consumed as a whole by the following function.

intubOrders allow you to work with a collection of objects of any kind in one pipeline, selecting at each step which input to use.

As an example suppose you want to perform the following statistical procedures in one pipeline:

CO2 %>%
  ntbt_lm(conc ~ uptake)

USJudgeRatings %>%
  ntbt_cor.test(CONT, INTG)

sleep %>%
  ntbt_t.test(extra ~ group)

We will first create a collection (a list in this case, but it could also be intuEnv or an intuBag, explained later) containing the three dataframes:

coll <- list(CO3 = CO2,
             USJudgeRatings1 = USJudgeRatings,
             sleep1 = sleep)
names(coll)

(We have changed the names to show we are not cheating...)

  • Note: the objects of the collection must be named.

We will now use as source the whole collection.

The intubOrder will need the following info:

  • zone 1, in each case, indicates which is the data.frame (or any other object) that we want to use as input in this particular function
  • zone 3 needs to include f to forward the input (if you want the next function to receive the whole collection, and not the result of this step)
  • zone 4 (optional) may contain a print (or summary) if you want something to be displayed
coll %>%
  ntbt_lm(conc ~ uptake, "CO3 <|f| print >") %>%
  ntbt_cor.test(CONT, INTG, "USJudgeRatings1 <|f| print >") %>%
  ntbt_t.test(extra ~ group, "sleep1 <|f| print >") %>%
  names()
  • Note: names() was added at the end to show that we have forwarded the original collection to the end of the pipeline.

What happens if you would like to save the results of the function calls (or intermediate results of data manipulations)?

\

intuEnv and intuBags

intuEnv and intuBags allow to save intermediate results without leaving the pipeline. They can also be used to contain the collections of objects.

Let us first consider

intuEnv

When intubate is loaded, it creates intuEnv, an empty environment that can be populated with results that you want to use later.

You can access the intuEnv as follows:

intuEnv()  ## intuEnv() returns invisibly, so nothing is output

You can verify that, initially, it is empty:

ls(intuEnv())

How can intuEnv be used?

Suppose that we want, instead of, or in addition to, displaying the results of interfaced functions, save the objects returned by them. One strategy is to save the results to intuEnv (the other is using intuBags).

How to save to intuEnv?

The intubOrder will need the following info:

  • zone 3 needs to include f to forward the input (if you want the next function to receive the whole collection, and not its result)
  • zone 5, in each case, indicates the name that the result will have in the intuEnv
coll %>%
  ntbt_lm(conc ~ uptake, "CO3 <|f|> lmfit") %>%
  ntbt_cor.test(CONT, INTG, "USJudgeRatings1 <|f|> ctres") %>%
  ntbt_t.test(extra ~ group, "sleep1 <|f|> ttres") %>%
  names()

As you can see, the collection stays unchanged, but look inside intuEnv

ls(intuEnv())

intuEnv has collected the results, that are ready for use.

Four strategies of using one of the collected results are shown below:

Strategy 1

intuEnv()$lmfit %>%
  summary()

Strategy 2

attach(intuEnv())
lmfit %>%
  summary()
detach()

Strategy 3

intuEnv() %>%
  ntbt(summary, "lmfit <||>")

Strategy 4

intuEnv() %>%
  ntbt(I, "lmfit <|i| summary >")

clear_intuEnv can be used to empty the contents of intuEnv.

clear_intuEnv()

ls(intuEnv())

Associating intuEnv with the Global Environment

If you want your results to be saved to the Global environment (it could be any environment), you can associate intuEnv to it, so you can have your results available as any other saved object.

First let's display the contents of the Global environment:

ls()

set_intuEnv let's you associate intuEnv to an environment. It takes an environment as parameter, and returns the current intuEnv, in case you want to save it to reinstate it later. If not, I think it will be just garbage collected (I may be wrong).

Let's associate intuEnv to the global environment (saving the current intuEnv):

saved_intuEnv <- set_intuEnv(globalenv())

Now, we re-run the pipeline:

coll %>%
  ntbt_lm(conc ~ uptake, "CO3 <|f|> lmfit") %>%
  ntbt_cor.test(CONT, INTG, "USJudgeRatings1 <|f|> ctres") %>%
  ntbt_t.test(extra ~ group, "sleep1 <|f|> ttres") %>%
  names()

Before forgetting, let's reinstate the original intuEnv:

set_intuEnv(saved_intuEnv)

And now, let's see if the results were saved to the global environment:

ls()

They were.

Now the results are at your disposal to use as any other variable (output not shown):

lmfit %>%
  summary()

Using intuEnv as source of the pipeline

You can use intuEnv (or any other environment) as the input of your pipeline.

We already cleared the contents of intuEnv, but let's do it again to get used to how to do it:

clear_intuEnv()

ls(intuEnv())

Let's populate intuEnv with the same objects as before:

intuEnv(CO3 = CO2,
        USJudgeRatings1 = USJudgeRatings,
        sleep1 = sleep)

ls(intuEnv())

When using an environment, such as intuEnv, as the source of your pipeline, there is no need to specify f in zone 3, as the environment is always forwarded (the same happens when the source is an intuBag).

Keep in mind that, if you are saving results and your source is an environment other than intuEnv, the results will be saved to intuEnv, and not to the source enviromnent. If the source is an intuBag, the results will be saved to the intuBag, and not to intuEnv.

We will run the same pipeline as before, but this time we will add subset and summary(called directly with ntbt) to illustrate how we can use a previously generated result (such as from data transformations) in the same pipeline in which it was generated, when using intuEnv (or an intuBag) as the source of the pipeline.

intuEnv() %>%
  ntbt(subset, Treatment == "nonchilled", "CO3 <||> CO3nc") %>%
  ntbt_lm(conc ~ uptake, "CO3nc <||> lmfit") %>%
  ntbt_cor.test(CONT, INTG, "USJudgeRatings1 <||> ctres") %>%
  ntbt_t.test(extra ~ group, "sleep1 <||> ttres") %>%
  ntbt(summary, "lmfit <||> lmsfit") %>%
  names()
  • Note that, as subset is already pipe-aware (data is its first parameter), you have two ways of proceeding. One is the one illustrated above (same strategy used on non-pipe-aware functions). The other, that works only when using pipe-aware functions, is:
intuEnv() %>%
  ntbt(subset, CO3, Treatment == "nonchilled", "<||> CO3nc")

\

intuBags

intuBags differ from intEnv in that they are based on lists, instead than on environments. Even if (with a little of care) you could keep track of several intuEnvs, it seems natural (to me) to deal with only one, while several intuBags (for example one for each database, or collection of objects) seem natural (to me). intuEnv (being a function call) can be called directly from inside functions (it always knows where the environment is), so you don't have to send it as an argument, as in the case of an intuBag.

Other than that, using an intuEnv or an intuBag is a matter of personal taste.

What you can do with one you can do with the other.

iBag <- intuBag(CO3 = CO2,
                USJudgeRatings1 = USJudgeRatings,
                sleep1 = sleep)
iBag %>%
  ntbt(subset, Treatment == "nonchilled", "CO3 <||> CO3nc") %>%
  ntbt_lm(conc ~ uptake, "CO3nc <||> lmfit") %>%
  ntbt_cor.test(CONT, INTG, "USJudgeRatings1 <||> ctres") %>%
  ntbt_t.test(extra ~ group, "sleep1 <||> ttres") %>%
  ntbt(summary, "lmfit <||> lmsfit") %>%
  names()

When using intuBags, it is possible to use %<>% if you want to save your results to the intuBag. This way, instead of a long pipeline, you could run several short ones.

iBag <- intuBag(CO3 = CO2,
                USJudgeRatings1 = USJudgeRatings,
                sleep1 = sleep)

iBag %<>%
  ntbt(subset, CO3, Treatment == "nonchilled", "<||> CO3nc") %>%
  ntbt_lm(conc ~ uptake, "CO3nc <||> lmfit")

iBag %<>%
  ntbt_cor.test(CONT, INTG, "USJudgeRatings1 <||> ctres")

iBag %<>%
  ntbt_t.test(extra ~ group, "sleep1 <||> ttres") %>%
  ntbt(summary, "lmfit <||> lmsfit")

names(iBag)

The intuBag will collect all your results, in any way you prefer to use it.

The same happens with intuEnv. Just remember that %<>% should not be used with intuEnv (you should always use %>%).

Using more than one source

Suppose you have a "database" containing the following two "tables":

iBag <- intuBag(members = data.frame(name=c("John", "Paul", "George",
                                            "Ringo", "Brian", NA),
                band=c("TRUE",  "TRUE", "TRUE", "TRUE", "FALSE", NA)),
           what_played = data.frame(name=c("John", "Paul", "Ringo",
                                           "George", "Stuart", "Pete"),
                instrument=c("guitar", "bass", "drums", "guitar", "bass", "drums")))
print(iBag)

and you want to perform an inner join. In these cases, the functions should receive the whole intuBag (or intuEnv, or collection), so zone 1 should be empty, and the names of the tables should be specified directly, in the function call, in their corresponding order (or by stating their parameter names).

iBag %>%
  ntbt(merge, members, what_played, by = "name", "<|| print >")

Example of an intuBag acting as a database

The following code has been extracted from chapter 13 of "R for data science", by Garrett Grolemund and Hadley Wickham (http://r4ds.had.co.nz/relational-data.html)

Original code:

library(dplyr)
library(nycflights13)

flights2 <- flights %>% 
  select(year:day, hour, origin, dest, tailnum, carrier)
flights2

flights2 %>%
  select(-origin, -dest) %>% 
  left_join(airlines, by = "carrier")

## 13.4.5 Defining the key columns

flights2 %>%
  left_join(weather)

flights2 %>%
  left_join(planes, by = "tailnum")

flights2 %>%
  left_join(airports, c("dest" = "faa"))

flights2 %>%
  left_join(airports, c("origin" = "faa"))

nycflights13 is a database. As such, we can deal with it using intuBags. The following code illustrates how all the above can be performed using an intuBag (or intuEnv) and one pipeline:

iBag <- intuBag(flightsIB = flights,
                airlinesIB = airlines,
                weatherIB = weather,
                planesIB = planes,
                airportsIB = airports)
## Note we are changing the names, to make sure we are not cheating
## (by reading from globalenv()).

iBag %<>%
  ntbt(select, flightsIB, year:day, hour, origin, dest, tailnum, carrier, "<|| head > flights2") %>%
  ntbt(select, flights2, -origin, -dest, "<|| print > flights3") %>% 
  ntbt(left_join, flights3, airlinesIB, by = "carrier", "<|| print >") %>%
  ntbt(left_join, flights2, weatherIB, "<|| print >") %>%
  ntbt(left_join, flights2, planesIB, by = "tailnum", "<|| print >") %>%
  ntbt(left_join, flights2, airportsIB, c("dest" = "faa"), "<|| print >") %>%
  ntbt(left_join, flights2, airportsIB, c("origin" = "faa"), "<|| print >")

names(iBag)

The same, using intuEnv, and avoiding creating flights3:

clear_intuEnv()

intuEnv(flightsIB = flights,
        airlinesIB = airlines,
        weatherIB = weather,
        planesIB = planes,
        airportsIB = airports) %>%
  ntbt(select, flightsIB, year:day, hour, origin, dest, tailnum, carrier,
       "<|D| head > flights2") %>%
  ntbt(left_join, select(flights2, -origin, -dest), airlinesIB, by = "carrier",
       "<|| print >") %>%
  ntbt(left_join, flights2, weatherIB, "<|| print >") %>%
  ntbt(left_join, flights2, planesIB, by = "tailnum", "<|| print >") %>%
  ntbt(left_join, flights2, airportsIB, c("dest" = "faa"), "<|| print >") %>%
  ntbt(left_join, flights2, airportsIB, c("origin" = "faa"), "<|| print >")

ls(intuEnv())
  • Note: the book is still not published (as of 8/27/16), so the examples in the chapter may have changed by the time you are reading this.

Saving results of function calls in zone 2 and zone 4 of the intubOrder:

Starting from version 1.2.0, you can save the results generated by functions included in zone 2 and zone 4 of the intubOrder. The results will be collected following the same rules as with a result specified in zone 5.

For example, the following case will be collected by intubOrder:

clear_intuEnv()

LifeCycleSavings %>%
  ntbt_lm(sr ~ pop15 + pop75 + dpi + ddpi,
          "< head; LCSt <- tail(#, n = 3); dim; str; LCSs <- summary
             |i|
             print; sfit <- summary; afit <- anova > fit")
ls(intuEnv())

By default, cases where you are saving results (on zone 2 and zone 4) will be printed.

If you want to only assign, but not print, you need to specify S in zone 3:

clear_intuEnv()

LifeCycleSavings %>%
  ntbt_lm(sr ~ pop15 + pop75 + dpi + ddpi,
          "< head; LCSt <- tail(#, n = 3); dim; str; LCSs <- summary
             |iS|
             print; sfit <- summary; afit <- anova > fit")
ls(intuEnv())

In the case the source is an intuBag, the result is collected, as expected, by the intuBag:

iBag <- intuBag(CO3 = CO2,
                USJudgeRatings1 = USJudgeRatings,
                sleep1 = sleep)
iBag %<>%
  ntbt_lm(conc ~ uptake, "CO3 < CO3h <- head |S| sfit <- summary; afit <- anova > fit")

names(iBag)

Packages containing interfaces

The 88 R packages that have interfaces implemented so far are:

  • adabag: Multiclass AdaBoost.M1, SAMME and Bagging
  • AER: Applied Econometrics with R
  • aod: Analysis of Overdispersed Data
  • ape: Analyses of Phylogenetics and Evolution
  • arm: Data Analysis Using Regression and Multilevel/Hierarchical Models
  • betareg: Beta Regression
  • brglm: Bias reduction in binomial-response generalized linear models
  • caper: Comparative Analyses of Phylogenetics and Evolution in R
  • car: Companion to Applied Regression
  • caret: Classification and Regression Training
  • coin: Conditional Inference Procedures in a Permutation Test Framework
  • CORElearn: Classification, Regression and Feature Evaluation
  • drc: Analysis of Dose-Response Curves
  • e1071: Support Vector Machines
  • earth: Multivariate Adaptive Regression Splines
  • EnvStats: Environmental Statistics, Including US EPA Guidance
  • fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
  • flexmix: Flexible Mixture Modeling
  • forecast: Forecasting Functions for Time Series and Linear Models
  • frontier: Stochastic Frontier Analysis
  • gam: Generalized Additive Models
  • gbm: Generalized Boosted Regression Models
  • gee: Generalized Estimation Equation Solver
  • glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
  • glmx: Generalized Linear Models Extended
  • gmnl: Multinomial Logit Models with Random Parameters
  • gplots: Various R Programming Tools for Plotting Data
  • gss: General Smoothing Splines
  • graphics: The R Graphics Package
  • hdm: High-Dimensional Metrics
  • Hmisc: Harrell Miscellaneous
  • ipred: Improved Predictors
  • iRegression: Regression Methods for Interval-Valued Variables
  • ivfixed: Instrumental fixed effect panel data model
  • kernlab: Kernel-Based Machine Learning Lab
  • kknn: Weighted k-Nearest Neighbors
  • klaR: Classification and Visualization
  • lars: Least Angle Regression, Lasso and Forward Stagewise
  • lattice: Trellis Graphics for R
  • latticeExtra: Extra Graphical Utilities Based on Lattice
  • leaps: Regression Subset Selection
  • lfe: Linear Group Fixed Effects
  • lme4: Linear Mixed-Effects Models using 'Eigen' and S4
  • lmtest: Testing Linear Regression Models
  • MASS: Robust Regression, Linear Discriminant Analysis, Ridge Regression, Probit Regression, ...
  • MCMCglmm: MCMC Generalised Linear Mixed Models
  • mda: Mixture and Flexible Discriminant Analysis
  • metafor: Meta-Analysis Package for R
  • mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation
  • minpack.lm: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds
  • mhurdle: Multiple Hurdle Tobit Models
  • mlogit: Multinomial logit model
  • mnlogit: Multinomial Logit Model
  • modeltools: Tools and Classes for Statistical Models
  • nlme: Linear and Nonlinear Mixed Effects Models
  • nlreg: Higher Order Inference for Nonlinear Heteroscedastic Models
  • nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models
  • ordinal: Regression Models for Ordinal Data
  • party: A Laboratory for Recursive Partytioning
  • partykit: A Toolkit for Recursive Partytioning
  • plotrix: Various Plotting Functions
  • pls: Partial Least Squares and Principal Component Regression
  • pROC: Display and Analyze ROC Curves
  • pscl: Political Science Computational Laboratory, Stanford University
  • psychomix: Psychometric Mixture Models
  • psychotools: Infrastructure for Psychometric Modeling
  • psychotree: Recursive Partitioning Based on Psychometric Models
  • quantreg: Quantile Regression
  • randomForest: Random Forests for Classification and Regression
  • Rchoice: Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters
  • rminer: Data Mining Classification and Regression Methods
  • rms: Regression Modeling Strategies
  • robustbase: Basic Robust Statistics
  • rpart: Recursive Partitioning and Regression Trees
  • RRF: Regularized Random Forest
  • RWeka: R/Weka Interface
  • sampleSelection: Sample Selection Models
  • sem: Structural Equation Models
  • spBayes: Univariate and Multivariate Spatial-temporal Modeling
  • stats: The R Stats Package (glm, lm, loess, lqs, nls, ...)
  • strucchange: Testing, Monitoring, and Dating Structural Changes
  • survey: Analysis of Complex Survey Samples
  • survival: Survival Analysis
  • SwarmSVM: Ensemble Learning Algorithms Based on Support Vector Machines
  • systemfit: Estimating Systems of Simultaneous Equations
  • tree: Classification and Regression Trees
  • vcd: Visualizing Categorical Data
  • vegan: Community Ecology Package

The aim is to continue adding interfaces to most methodologies used in data science or other disciplines.

For now the main focus is on interfacing non-pipe-aware functions having "formula" and "data" (in that order), but the non-formula variants should also work (even cases currently lacking interfaces). As a proof of concept, two libraries that contain non-formula variants only (glmnet and lars) have also been interfaced.

Also, only packages in CRAN (in addition to the ones provided in the base installation of R) have been implemented. Packages from, for example, bioconductor, could also be easily added, but I would need some help from the maintainers of those packages.

intubate core depends only on base, stats, and utils libraries. To keep it as lean as possible, and to be able to continue to include more interfaces without bloating your machine, starting from version 1.0.0 intubate will not install the packages that contain the functions that are interfaced. You will need to install them yourself, and load the corresponding libraries before using them in your pipelines. This also applies to magrittr (in case you want to use intubate without pipelines).

Then, if you are only interested in a given field, say: bio-statistics, bio-informatics, environmetrics, econometrics, finance, machine learning, meta-analysis, pharmacokinetics, phylogenetics, psychometrics, social sciences, surveys, survival analysis, ..., you will not have to install all the packages for which interfaces are provided if you intend to use only a subset of them. You only need to install the subset of packages you intend to use (which are probably already installed in your machine).

Moreover, there are cases where some packages are in conflict if loaded simultaneously, leading to a segmentation fault (for example, kernlab functions fail when testing the whole examples provided with intubate, but not when testing kernlab only examples in a clean environment. I ignore which is/are the other(s) package(s) conflicting with it. The only thing I know is that the package name is alphabetically ordered prior to kernlab)

I make no personal judgment (mostly due to personal ignorance) about the merit of any interfaced function. I have used only a subset of what is provided, and I am happy to include others, that I am currently unaware of, down the line. In principle I plan on including packages that are listed as reverse depends, imports, or suggest on package Formula (I am missing still some of them). Adding interfaces is easy (and can be boring...) so I will appreciate if you want to contribute (and you will be credited in the help of the interfaced package). Also is welcome the improvement of the provided examples (such as making sure the data used is correct for the statistical technique used).

I do not claim to be a data scientist (I am barely a statistician and I still have almost no clue of what a data scientist is or is not, and my confusion about the subject only increases with time), nor someone entitled to tell you what to use or not (I do not even feel entitled to tell you how you should use intubate, if you decide to use it).

As such, I am not capable of engaging in disputes of what is relevant or not, or, if there are competing packages, which to use. I will leave that to you to decide.

Please keep in mind that intubate will not install any packages corresponding to the interfaces that are provided. You can install only those that you need (or like) and disregard the rest. Also please remember that you can create your own interfaces (using helper function intubate), or call non-pipe-aware functions directly (using ntbt).

The original aim of intubate was to be able to include functions that have formula and data (in that order) in a magrittr pipeline using %>%. As such, my search so far has been concentrated in packages containing formulas and misplaced (from pipes point of view) data (with the exception of a couple of packages with non-formula variants interfaced as proofs of concept).

For example, this was the first implementation of ntbt_lm

ntbt_lm <- function(data, formula, ...)
  lm(formula, data, ...)

This approach was supposed to be repeated for each interface.

Soon after I realized that intubate could have just a few helper functions (that was version 0.99.2), later that only one helper function was enough (intubate), and later that you could call non-pipe-aware functions directly without defining interfaces (ntbt) and that the interfaces and ntbt could also be successfully used in cases where non-formula variants are implemented.

However, the starting point inevitably led the way. I did not see the big picture (well, what today I think the big picture is...), so the current version only addresses packages containing functions that use formula variant, even if in those cases you can also use the non-formula variants (you can see the examples corresponding to pROC, where both cases for formula and non-formula are demonstrated. You should be able to use that technique also for the rest of the packages).

I am brewing some ideas about a general approach to packages that do not use formula interface, but I leave it for a future iteration of intubate.

This means that there are three possibilities to the eventual lack of inclusion of your favorite package for the time being:

  1. The package only uses matrices or x- y- like notation (and not formulas)
  2. (more likely reason) I should know better, but I missed it (truth is that by implementing the supplied interfaces I realized how little I knew, and still know, about a field in which I am supposed to be an expert), and I apologize for that.
  3. I got to the point I need to take a rest (this reason is competing with 2. with increasing strength as time passes by)

Also, please keep in mind you can always create your own interfaces (with the helper function intubate), or call the non-pipe-aware functions directly (with ntbt).

Removing all interfaces in package:intubate environment

If you want to interface functions exclusively with ntbt, starting from version 1.3.0 you can remove all the supplied interfaces (functions starting with ntbt_) with the function intubate_rm_all_interfaces, that takes no arguments. This will not remove the interfaces created "on demand" by the user in the global environment (or any environment that is not the package:intubate environment).

intubate_rm_all_interfaces()

This makes the footprint of intubate even smaller, if you are interested in a minimalistic approach.

Creating interfaces "on demand" in package:intubate environment

If you want to create your own interfaces "on demand", but you do not want to pollute the global environment with the names, starting from version 1.4.0 there is a new approach.

Instead of using:

ntbt_fntointerface1 <- ntbt_fntointerface2 <- intubate

that creates the interfaces ntbt_fntointerface1 and ntbt_fntointerface2 in the global environment (or any other environment where you are creating it), you can do:

intubate(fntointerface1, "fntointerface2")

that creates the interfaces ntbt_fntointerface1 and ntbt_fntointerface2 in the package:intubate environment (where the interfaces provided by intubate are defined). This way your global environment will only contain variables related to your work, and not interfaces. As demonstrated above, you can supply either the names directly without quotations, or the strings containing the names.

Interfaces created this way are removed if you call intubate_rm_all_interfaces.

Bugs and Feature requests

The robustness and generality of the interfacing machinery still needs to be further verified (and very likely improved), as there are thousands of potential functions to interface and certainly some are bound to fail when interfaced. Some have already been addressed when implementing provided interfaces (as their examples failed).

The goal is to make intubate each time more robust by addressing the peculiarities of newly discovered failing functions.

For the time being, only cases where the interfaces provided with intubate fail will be considered as bugs.

Cases of failing user defined interfaces or when using ntbt to call functions directly that do not have interfaces provided with released versions of intubate, will be considered feature requests.

Of course, it will be greatly appreciated, if you have some coding skills and can follow the code of the interface, if you could provide the proposed solution, that shouldn't break anything else, together with the feature request.

Logo of intubate

The logo of intubate is: <||>. It corresponds to an intuBorder. I have not found it in a Google search as of 2016/08/08. I intend to use it as a visual identification of intubate. If you know of it having being in use before this date in any software related project, please let me know, and I will change it.

Names used

intuBorder(s) and intubOrder(s), as of 2016/08/08, only has been found, on Google, in a snippet of code for the name of a variable (intUBorder) (http://www.office-loesung.de/ftopic246897_0_0_asc.php) that would mean something like an "integer upper border". There is also an intLBorder for the lower border.

intuBag(s), as of 2016/08/08, seems to be used for a small bag for bikes (InTuBag, meaning Inner Tub Bag) (https://felvarrom.com/products/intubag-bike-tube-bag-medium-blue-inside?variant=18439367751), but not for anything software related. If intubate succeeds, they may end selling more InTuBags!

intubate, as of 2016/08/08, seems to be used related to the medical procedure, perhaps also by the oil pipeline industry (at least "entubar" in Spanish is more general than the medical procedure), but not for software related projects.

intuEnv, as of 2016/08/18, was found only in some Latin text.

I intend to use "intubate", "<||>", "intuBorder", "intubOrder(s)", "intuBag(s)", "intuEnv(s)"and other derivations starting with "intu", in relation to the use and promotion of "intubate" for software related activities.

At some point I intend to register the names and logo as trademarks.

See also

  • The setter package contains mutators to set attributes of variables, that work well in a pipe (much like stats::setNames()).

  • The srvyr package allows for analysis of complex surveys using the pipe-friendly syntax of dplyr.

Entries by date

At this point you may have an idea if intubate is or not for you. If you elect to continue reading, please be warned that my style or writing may or may not be of your liking. Most of what follows is for my personal amusement.

2016/08/05

  • Core of the interface function (now called intubate) should be finished. Please torture test with as many cases as possible to see how robust it is. I will be out for the rest of the week.

(I want to point out that I have delivered on my promise made on 2016/08/03 to further reduce the labor involved while defining interfaces. I am happy to report that using intubate instead of ntbt_function_data represents a 55.55% reduction of the amount of typing required, which will significantly increase the production of interfaces per minute. I am pleased with the results and consider this a huge success! Congratulations to Everybody!)

Please know that there is much more to come about which I will not comment for now, but that should potentially make intubate useful even if you do not want to use it in pipelines (remember that you should not be forced to use pipelines if you like other alternatives better). It should also take pipelines to a different dimension, in case you like them.

The Evil Plan

What I will (in fact, have to) publicly inform is that intubate features an Undisclosed Evil Plan of World Domination that will be unveiled in the time to come according to my Organization's Evil Master Plan.

Please also be notified that we are thinking about achieving our goals in three stages (aka The Stages):

  1. Set foundations needed for the ones to come later. This will be already World Domination but nobody will notice it.
  2. Using Deception, lay The Bait to attract you to The Trap.
  3. Convert you into our slave, without you being able to do anything about it.
Appendix: Legal aspects related to Evil Plans

If you find yourself at loss regarding the legality of the above and/or wonder why I am disclosing this information, please know that someone (I cannot tell the name but trust me that he/she is very well connected and knows everything about these things), informed me recently that: "to have an Undisclosed Evil Plan of World Domination" is OK (well... he/she really said "is acceptable") "as long as you disclose that you have one and clearly specify The Stages", which can be non-specific but need to include wording that "warns about the existence of The Bait, The Trap, Deception and Coercion in case any or all of these standard resources are planned to be utilized at some point".

The rationale is that "even if the Regulatory Agencies share the public's concern and are fully aware of the common misconception according to which they should protect, to the full extent of their abilities, the unaware citizen from being abused, full disclosure of The Evil Plan could prove unacceptably counterproductive to the successful fulfillment of World Domination by an Evil Organization that, we cannot forget, also pays taxes and thus is entitled to some level of protection to conduct its business in a satisfactory way".

This solution, albeit non perfect, is considered "a fair compromise that protects, to some degree, the rights of everybody involved".

You have been served!

Have fun (while you can...)

2016/08/03

  • Now all interfaces derive from only one helper function called, for now, ntbt_function_data.
### Create some non implemented interfaces
ntbt_legend <- ntbt_cat <-
  ntbt_function_data  ## One helper function only to create interfaces

(Two steps to create an interface seemed excessive, too much, no-way dude, especially with one of them requiring a rational decision. You need one step only now, so you can create your interfaces when you drink your coffee and text while driving to work.

This will be particularly appreciated down the line, when the technology of self-driving cars is definitely polished and established, and you will be forced to work while commuting to work after working at home... I mean you will have even more free time available to relax and enjoy.

Anyway, I am still not satisfied with the amount of labor required on the user's side and will put my best effort in trying to further reduce it.)

  • In addition to formula versions:
library(magrittr)
library(intubate)

USJudgeRatings %>%
  ntbt_cor.test(~ CONT + INTG)

you can also use, for example, the x y versions:

USJudgeRatings %>%
  ntbt_cor.test(CONT, INTG)

All the examples in the documentation run (but they are formula-only versions). Tests to see if this works as expected are welcome.

Of course it would be pure magic if it just simply works no matter what you throw at it, and I simply have no clue of all the possible behaviors of the interfaced functions.

My goal is that it works reasonably in cases you would normally use in data science pipelines.

I anticipate limitations. I will try to address the ones that can be solved in a general and easy way and that really represent a need, because the helper function machinery has to stay powerful yet simple.

  • If interfaced function returns NULL, the interface function forwards invisibly the input, so you can use the data downstream.
library(dplyr)
CO2 %>%
  mutate(color=sample(c("green", "red", "blue"),
                      length(conc), replace = TRUE))%>%
  ntbt_plot(conc, uptake, col = color) %>%  ## plot returns NULL
  ntbt_lm(conc ~ uptake) %>%  ## data passes through ntbt_plot
  summary()

within(warpbreaks, {
  time <- seq_along(breaks)
  W.T <- wool:tension
}) %>%
  ntbt_plot(breaks ~ time, type = "b") %>%
  ntbt_text(breaks ~ time, label = W.T,
            col = 1 + as.integer(wool)) %>%
  ntbt_cat("And now we write a legend.") %>%
  ntbt_legend("top",
              legend = levels(wool),
              col = 1 + as.integer(wool)) %>%
  invisible()
  • You can also do things like
ntbt_cat <- ntbt_print <- ntbt_View <-
  ntbt_function_data

CO2 %>%
  ntbt_cat("The first row has uptake -", uptake[1],
           "- and concentration", conc[1],"\n") %>%
  ntbt_cat("The mean uptake is -", mean(uptake),
           "- with standard deviation", sd(uptake),"\n") %>%
  ntbt_cat("uptake observations", uptake, sep="\n") %>%
  ntbt_print() %>%
  ntbt_View() %>%
  ntbt_lm(conc ~ .) %>%
  summary()

2016/07/30

Pipelines

Pipelines in R are made possible by the package magrittr, by Stefan Milton Bache and Hadley Wickham.

dplyr, by Hadley Wickham, Romain Francois, and RStudio, is used here to illustrate data transformation.

## Packages needed
library(dplyr)     ## Does data transformation
library(magrittr)  ## Implements pipelines

## Data used
# devtools::install_github("hadley/yrbss")
library(yrbss)
data(survey)

This machinery allows to perform data transformations using pipelines in the following way:

survey %>%
  group_by(year) %>%
  summarise(count = n(),
            countNA = sum(is.na(stheight)),
            propNA = mean(is.na(stheight))) %>%
  knitr::kable()

Pipelines seem to be the preferred way, these days, of doing data transformation. If you want an introduction about pipelines, and/or to learn more about them, please follow this link (http://r4ds.had.co.nz/transform.html) to the chapter on data transformation of the forthcoming book "R for Data Science" by Garrett Grolemund and Hadley Wickham.

R statistical functions and pipelines

Suppose you want to perform a regression analysis of the weight on height of males corresponding to the year 2013 (assuming for the sake of argument that it is a valid analysis to perform. See at the very end of the document for more on this).

As most R functions are not pipeline-aware, you should do something like the following.

First, you perform your data science transformations and save the result to a temporary object (tmp in this case).

survey %>%
  filter(!is.na(stheight) & !is.na(stweight) &
           year == 2013 & sex == "Male"
  ) %>%
  select(stheight, stweight) ->
tmp

Then, you perform your regression analysis on the transformed data stored in tmp.

fit <- lm(stweight ~ stheight, tmp)
summary(fit)

(There is nothing wrong in this approach. In fact it is good. Jolly good. Splendid! If you are absolutely happy with doing things this way then there is no need to continue to devote your efforts in reading this document. intubate is not for you. I am happy we could establish this in such little time.)

But what if, in addition to the data transformation, you would also like to perform your data modeling/analysis under the same pipeline paradigm (by adding lm to it), which would impart notation consistency and would avoid the need of creating the temporary object?

survey %>%
  filter(!is.na(stheight) & !is.na(stweight) &
           year == 2013 & sex == "Male"
  ) %>%
  select(stheight, stweight) %>%
  lm(stweight ~ stheight) %>%  ## Using the original function
  summary()

You get an error.

The reason of this failure is that pipeline-aware functions (such as the ones in dplyr that were specifically designed to work in pipelines) receive the data as the first parameter, and most statistical procedures that work with formulas to specify the model, such as lm and lots of other rock solid reliable functions that implement well established statistical procedures, receive the data as the second parameter.

This minor detail can make a difference, actually a huge one. In fact, it may create a division line of two clearly separated cultures, that I will call, for the lack of better names, the "traditionalists" and the "modernists".

(They could be the "modeldatas" and the "datamodels". Whichever you prefer that does not offend anyone)

The aim of intubate is to provide an easy alternative so nobody has to change the way they do things.

If you are a "traditionalist" and you want to create your new statistical package in the traditional way (first model and then data), you will not potentially find yourself at a crossroad if you think you need to decide which community to serve, when in fact you can serve both communities without having to do anything differently to what you have done so far. You can just keep doing it in the traditional way. In fact, (please...) keep doing it in the traditional way!

Why? Because Everybody will benefit.

 EXT. BUCOLIC PASTURE - EARLY MORNING
 
 Background music initially inaudible slowly increases in
 volume while the panning camera, starting from a small
 and fragile flower, reveals legions of smiling people
 holding hands. Half dress t-shirts with a capital T, the
 other half with a capital M.
 Everybody raises their arms to the sky - still holding hands -
 as if trying to embrace the universe.
 Camera slowly raises, zooming out and tilting down, making
 sure Everybody is included in the frame, while Everybody
 mantains eye contact with the camera.
 Sun rays break through heavy pure snow-white clouds.
 Everybody opens their mouth and slowly inhales while closing
 their eyes as if they really mean what comes next.
 (This is critical. Make sure it looks credible.)
 Music at full volume.
 Everybody sing.
                        Everybody
     We aaaaare the Woooorld - ta ta ta ta taaaa...
     We aaaare the chiildreeeen - ta ta ta ta taaaa...

For "traditionalist" users (as I was until a couple of months ago), nothing will have changed. In fact, they will be completely unaware of anything different happening at all. Just business as usual and another fantastic statistical procedure to add to their bag of resources.

For "modernist" users, intubate will do a couple of tricks behind the scenes so they will be able to run, right at the end of any required data transformation, your statistical procedures without any hassle using their preferred paradigm of pipelines.

There are alternatives that allow to include lm (and others) in the pipeline without errors and without intubate. They require workarounds of varying levels of complexity and are illustrated later.

If you choose intubate is because you do not want to bother about workarounds when working with pipelines that include statistical procedures.

intubate

The solution intubate proposes is to provide an interface to lm, called ntbt_lm, that can be used directly in the pipeline without error and without losing any of lm's capabilities.

library(intubate)
survey %>%
  filter(!is.na(stheight) & !is.na(stweight) &
           year == 2013 & sex == "Male"
         ) %>%
  select(stheight, stweight) %>%
  ntbt_lm(stweight ~ stheight) %>%  ## Using the interface function
  summary()

By using the interface the error vanishes, as the interface receives data as its first parameter and formula second, performs some function transformations, and then calls lm in the way it expects to receive the parameters (formula in first place, and data in second place). Now lm can continue to do all the good things we are used to.

(It is as if lm couldn't take anymore being accused by some of looking old. So it went to the beauty parlor, had a hair cut, and suddently looks "modern" and now is popular again.)

All the interfaces start with ntbt_ followed by the name of the interfaced function.

Just in case, worry not! The interfaces do not perform any statistical computations (you should be very suspicious if they would). The interfaced functions (those that are already well tested, or should be) are the ones performing the computations.

Interfaces "on demand"

(It used to be "on the fly", but "on demand" sounds more marketable. Right?)

What if you would like to have an interface to a non pipeline-aware function that is not currently implemented by intubate?

In a vast majority of cases of R functions (I would like to say all but I still do not know), you can create your own interface "on demand".

To help you in the process, intubate exposes one helper function, called intubate, to assist you.

(Well... something had to do this lazy package...)

Steps to create interfaces "on demand"

For the sake of argument, suppose ntbt_lm (the interface to lm) is not implemented by intubate, and you want to create an interface for it.

All you need is adding the following one line of code somewhere before the code that uses it:

ntbt_lm <- intubate

Hard?

(You see, it is not enough that intubate is a lazy package. It also promotes laziness).

Is it confusing?

No?

Really?

OK, let's see how you do in a quiz, under the Honor Code.

        Name:
        Honor Code Statement:
        
        Question: Which helper function would you use to construct
        your interface for the function `t.test`? [5]
        How should you name your interface? [5]

(Psst!... here... don't look!... I told you not to look!... yes, yes, play dumb... you don't have to worry... someone told me there is a solution manual somewhere in the net...)

Remember: names of interfaces must start with ntbt_ followed by the name of the function (lm in this case) you want to interface.

You can now use the interface function in any context in which you would use the original function. If you do not want to name the parameters, just remember to switch the order of formula and data arguments when using the interface (first data argument and then formula argument). As usual, you can put them in any order if you name the arguments (actually, there are cases in which this is not true. More on this later)

fit <- ntbt_lm(tmp, stweight ~ stheight)
summary(fit)

Of course you should want to use the interface in a pipeline context. Otherwise, intubate is virtually worthless.

tmp %>%
  ntbt_lm(stweight ~ stheight) %>%
  summary()

Adding interfaces to the intubate package also represent one-liners for me.

(Did you think I would work more than you?)

The time consuming part on my side is to prepare the documentation, that certainly needs improvement, and make sure the examples work.

Discussion

Disclaimer:

I have a vested interest in making intubate a success for egotistical purposes. As such, I may be overstating the strengths and understating the weaknesses (weaknesses?? which weaknesses??) of intubate. More than a discussion, this can be easily considered like a sales pitch for a product of dubious quality.

         You have been warned. Continue at your own risk.

What can you do if you do not want to use intubate and you still want to use these kind of functions in pipelines?

Example 1:

lm can still be added directly to the pipeline, without error, by specifying the name of the parameter associated with the model (formula in this case).

tmp %>%
  lm(formula = stweight ~ stheight)

(So what is the big fuss about intubate?)

The drawback of this approach is that not all functions use formula to specify the model.

So far I have encountered 5 variants:

  • formula
  • x
  • object
  • model, and
  • fixed

This means you will have to know and remember (yes, also some months from now) which name has been assigned to the model by each particular function.

(OK, OK, you don't need to remember. You can go back to the documentation... over and over again!)

In fact, the following are examples of functions using the other variants.

Example 2:

Using xyplot directly in a data pipeline will raise an error

library(lattice)
iris %>%
  xyplot(Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
         scales = "free", layout = c(2, 2),
         auto.key = list(x = .6, y = .7, corner = c(0, 0)))

unless x is specified.

iris %>%
  xyplot(x = Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
         scales = "free", layout = c(2, 2),
         auto.key = list(x = .6, y = .7, corner = c(0, 0)))

Example 3:

Using tmd (a different function in the same package) directly in a data pipeline will raise an error

library(lattice)
iris %>%
  tmd(Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
      scales = "free", layout = c(2, 2),
      auto.key = list(x = .6, y = .7, corner = c(0, 0)))

unless object is specified.

iris %>%
  tmd(object = Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
      scales = "free", layout = c(2, 2),
      auto.key = list(x = .6, y = .7, corner = c(0, 0)))

Example 4:

Using gls directly in a data pipeline will raise an error

library(nlme)
Ovary %>%
  gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
      correlation = corAR1(form = ~ 1 | Mare))

unless model is specified.

Ovary %>%
  gls(model = follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
      correlation = corAR1(form = ~ 1 | Mare))

Example 5:

Using lme directly in a data pipeline will raise an error

library(nlme)
try(Orthodont %>%
      lme(distance ~ age))
geterrmessage()

unless fixed(!) is specified.

Orthodont %>%
  lme(fixed = distance ~ age)
(Subliminal message 1:

there may be many much more different variants - possibly thousands - lurking around in the darkness. They may hunt you and hurt you... badly... intubate will keep you warm and safe, in a happy place where everybody loves you and nothing wrong can happen to you.)

I find that having to remember the name of the parameter associated to the model in each case is unfortunate, error prone, and gives an inconsistent look and feel to an otherwise elegant interface.

Moreover, it is consider good practice in R to not specify the name of the first two parameters, and name the remaining.

(Note the lack of citation to such categorical statement... ughh, sheer desperation).

Not having to specify the name of the model argument completely hides the heterogeneity of names that can be associated with it. You only write the model and completely forget which name has been assigned to it.

(Subliminal message 2:
  • not using intubate => uncool,
  • using intubate => extremely cool)

Other nightmares around the corner

If you are still not convinced (well, why should you?), be aware that there are functions that rely on the order of the parameters (such as aggregate, cor.test and other 28 I found so far) that will still raise an error even if you name the model.

Did you know that there are cases (and for very good reasons), where it is not true that if in a function call you name the parameters you can write them in any order you want?

You don't believe it? How about the following examples corresponding to cor.test?

1) Unnamed parameters in the natural order. Works

cor.test(~ CONT + INTG, USJudgeRatings)

2) Named parameters in the natural order. Works

cor.test(formula = ~ CONT + INTG, data = USJudgeRatings)

3) Named parameters with the order changed. Doesn't work!

cor.test(data = USJudgeRatings, formula = ~ CONT + INTG)

(Convinced?)

So let's see what happens if we want to add these cases to the %>% pipeline.

Example of error 1: cor.test

Using cor.test directly in a data pipeline will raise an error

USJudgeRatings %>%
  cor.test(~ CONT + INTG)

even when specifying formula (as it should be according to the documentation).

USJudgeRatings %>%
  cor.test(formula = ~ CONT + INTG)

Was it y then?

USJudgeRatings %>%
  cor.test(y = ~ CONT + INTG)

Nope...

Was it x then?

USJudgeRatings %>%
  cor.test(x = ~ CONT + INTG)

Nope...

Example of error 2: aggregate

Using aggregate directly in a data pipeline will raise an error

ToothGrowth %>%
  aggregate(len ~ ., mean)

even when specifying formula

ToothGrowth %>%
  aggregate(formula=len ~ ., mean)

or other variants.

Example of error 3: lda

Using lda directly in a data pipeline will raise an error

library(MASS)
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
                   Sp = rep(c("s","c","v"), rep(50,3)))
Iris %>%
  lda(Sp ~ .)

even when specifying formula.

Iris %>%
  lda(formula = Sp ~ .)

or other variants.

Let's try another strategy. Let's see if the %$% operator, that expands the names of the variables inside the data structure, can be of help.

Iris %$%
  lda(Sp ~ .)

Still no...

One last try, or I give up!

Iris %$%
  lda(Sp ~ Sepal.L. + Sepal.W. + Petal.L. + Petal.W.)

Finally! But... we had to specify all the variables (and they may be a lot), and use %$% instead of %>%.

There is still another workaround that allows these functions to be used directly in a pipeline. It requires the use of another function (with) encapsulating the offending function. Here it goes:

Iris %>%
  with(lda(Sp ~ ., .))

In the case of aggregate it goes like

ToothGrowth %>%
  with(aggregate(len ~ ., ., mean))

(Do you like it? Do you consider it safe for your children? Really? Come on! What kind of father are you??? You must be one of those that feed unpasteurized milk to them... Shame on you!)

In addition, there is the added complexity of interpreting the meaning of each of those . (unfortunately they do not mean the same) which may cause confusion, particularly at a future time when you may have to remember why you had to do this to yourself.

(Hint: the first is specifying to include in the rhs of the model all the variables in the data but len, the second is the name of the data structure passed by the pipe. Yes, it is called .!)

Undoubtedly, there may be more elegant workarounds that I am unaware of. But the point is that, no matter how elegant, they will be, well, still workarounds. You want to force unbehaving functions into something that is unnatural to them:

  • In one case you had to name the parameters,
  • in the other you had to use %$% instead of %>% and where not allowed to use . in your model definition,
  • if you wanted to use %>% you had to use also which and include . as the second parameter.

Does this sound right to you?

I certainly do not want to be distracted implementing workarounds when I am supposed to concentrate in producing the right statistical analysis.

The idea of avoiding such "hacks" motivated me to write intubate.

(That was low, please! Were you really that desperate that you had to use the word motivation to try to make a sell? Come on! What is the first thing they teach you in Trickery 101?)

Which was, again, the intubate alternative?

(Well... if you insist...)

For Example 1:

No need to specify formula.

tmp %>%
  ntbt_lm(stweight ~ stheight)

For Example 2:

No need to specify x.

iris %>%
  ntbt_xyplot(Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
              scales = "free", layout = c(2, 2),
              auto.key = list(x = .6, y = .7, corner = c(0, 0)))

For Example 3:

No need to specify object.

iris %>%
  ntbt_tmd(Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
           scales = "free", layout = c(2, 2),
           auto.key = list(x = .6, y = .7, corner = c(0, 0)))

For Example 4:

No need to specify model.

Ovary %>%
  ntbt_gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
           correlation = corAR1(form = ~ 1 | Mare))

For Example 5:

No need to specify fixed.

Orthodont %>%
  ntbt_lme(distance ~ age)

For Example of error 1:

It simply works.

USJudgeRatings %>%
  ntbt_cor.test(~ CONT + INTG)

For Example of error 2:

It simply works.

ToothGrowth %>%
  ntbt_aggregate(len ~ ., mean)

For Example of error 3:

It simply works.

Iris %>%
  ntbt_lda(Sp ~ .)

I think the approach intubate proposes looks consistent, elegant, simple and clean, less error prone, and easy to follow

(But, remember, I have a vested interest).

After all, the complication should be in the analysis you are performing, and not in how you are performing it.

Conclusions

(making a huge effort to seem unbiased):

  • You can use intubate to provide a consistent look and feel that allows to use non-pipe-aware functions in data science pipelines without having to rely on hacks of different levels of complexity.

  • You can use the machinery provided to create "on demand" interfaces that have not been implemented (and may never be).

  • The real thing is done by the interfaced function, not by the interface function. intubate simply sells air and takes all the credit by providing only a refined syntactic sugar (and let's not start on the potential consequences that the consumption of refined sugars may, or may not, have on your health...) that will be more or less palatable according to your individual taste.

  • Perhaps the fact that the documentation provides working examples of statistical and machine learning procedures suitable for data science is a plus.

Bottom line

At the end of the day, you can certainly be a very successful data scientist (yes, what in the past may have been referred to as a statistician with some computer skills) if you already feel confident with your ways and decide not to use intubate. After all, it is just another tool.

Final thoughts for new data scientists, unrelated to intubate.

(Please skip it if you still believe in Santa)

I am not stating that, in the particular case presented at the beginning of the document, doing a regression analysis is the correct thing to do. I am neither stating it is wrong. I simply didn't put any effort in establishing the merit or not of doing a regression analysis on that particular data. This would entail asking a lot of questions about how that data was collected. I just didn't do that. It was a little on purpose so I can have the discussion that follows.

That example and the rest provided in this document are only for illustration purposes related strictly to the computational techniques described, and have nothing to do with the validity or not of using any particular statistical methodology on any particular data.

(Well, the rest of the examples were taken from the help of the interfaced functions. I want to believe that the authors put some effort in establishing that they were doing what they were supposed to do.)

Sure, you may argue that, after all, in the case of linear regression the fitting procedure is no other thing than an optimization technique (minimization of the sum of squared residuals) completely unrelated to statistics, so why can't you just find the line of best fit for the sake of it, even if it doesn't make any sense and does not represent anything? Touché, you got me! (You are good!) You can, and nobody is going to throw you in jail for that.

But do you want to make population parameter estimation, statistical inferences such as testing if the population parameters are different than zero, or confidence intervals of the population parameters, or confidence bands of the population regression line, or prediction intervals for an observation?

Then you should probably start asking yourself, before doing anything, if the assumptions of the linear regression model are met or not, and if not, how bad is the violation of such assumptions (what about independence of the Y? Are you sure they don't form a time series? Are the errors iid normally distributed with mean zero and constant variance? Are you sure the error variance doesn't change when you change the values of the independent variable?).

The statistical function (every statistical function) will only perform calculations and spit out a collection of numerical values whose interpretation will only make sense provided the data - and you are responsible for the data you use on the statistical analysis - reasonably follows the assumptions of the particular statistical model you are entertaining. The more you depart from the assumptions, the less interpretable your results become.

Always remember that there is no provision coded in that black box that is the statistical function that will protect you from doing a statistical analysis on the wrong data. Nothing will be corrected or compensated on your behalf. For example, if the methodology is expecting a random sample (the vast majority do) and you are not providing one, no magical trick will convert your non-random-sample into one that is random and satisfies all the assumptions.

(Remember I warned you to skip this section if you still believed in Santa?)

Maybe you shouldn't run a statistical procedure just because you can, and then report results with interpretations that may make no meaning at all.

If you are not truly confident on what you are doing, perhaps your best move should be to consult first with your PCS (Primary Care Statistician) before doing something you may regret down the line.