License: GPL >= 2

The aim of intubate (logo <||>) is to offer a painless way to add R functions that are not pipe-aware to data science pipelines implemented by magrittr with the operator %>%, without having to rely on workarounds of varying complexity. It also implements three extensions called intubOrders, intuEnv, and intuBags.

Installation

install.packages("intubate")
# 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)
## 
## Call:
## lm(formula = sr ~ pop15, data = LifeCycleSavings)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.637 -2.374  0.349  2.022 11.155 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.49660    2.27972   7.675 6.85e-10 ***
## pop15       -0.22302    0.06291  -3.545 0.000887 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.03 on 48 degrees of freedom
## Multiple R-squared:  0.2075, Adjusted R-squared:  0.191 
## F-statistic: 12.57 on 1 and 48 DF,  p-value: 0.0008866

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()
## 
## Call:
## lm(formula = sr ~ pop15)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.637 -2.374  0.349  2.022 11.155 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.49660    2.27972   7.675 6.85e-10 ***
## pop15       -0.22302    0.06291  -3.545 0.000887 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.03 on 48 degrees of freedom
## Multiple R-squared:  0.2075, Adjusted R-squared:  0.191 
## F-statistic: 12.57 on 1 and 48 DF,  p-value: 0.0008866

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()
## 
## Call:
## lm(formula = sr ~ pop15)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.637 -2.374  0.349  2.022 11.155 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.49660    2.27972   7.675 6.85e-10 ***
## pop15       -0.22302    0.06291  -3.545 0.000887 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.03 on 48 degrees of freedom
## Multiple R-squared:  0.2075, Adjusted R-squared:  0.191 
## F-statistic: 12.57 on 1 and 48 DF,  p-value: 0.0008866

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 (results not shown):

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
## 
##  Pearson's product-moment correlation
## 
## data:  CONT and INTG
## t = -0.8605, df = 41, p-value = 0.3945
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4168591  0.1741182
## sample estimates:
##        cor 
## -0.1331909

Calling non-pipe-aware functions directly with ntbt

Of course, 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)
## 
##  Pearson's product-moment correlation
## 
## data:  CONT and INTG
## t = -0.8605, df = 41, p-value = 0.3945
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4168591  0.1741182
## sample estimates:
##        cor 
## -0.1331909

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 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 you interface. 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:

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:

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

For example, instead of running the following sequence of function calls (only plot shown):

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) >")
## 
## ntbt_lm(data = ., sr ~ pop15 + pop75 + dpi + ddpi)
## 
## * head <||> input *
##              sr pop15 pop75     dpi ddpi
## Australia 11.43 29.35  2.87 2329.68 2.87
## Austria   12.07 23.32  4.41 1507.99 3.93
## Belgium   13.17 23.80  4.43 2108.47 3.82
## Bolivia    5.75 41.89  1.67  189.13 0.22
## Brazil    12.88 42.19  0.83  728.47 4.56
## Canada     8.79 31.72  2.85 2982.88 2.43
## 
## * tail(#, n = 3) <||> input *
##            sr pop15 pop75    dpi  ddpi
## Uruguay  9.24 28.13  2.72 766.54  1.88
## Libya    8.89 43.69  2.07 123.58 16.71
## Malaysia 4.71 47.20  0.66 242.69  5.08
## 
## * dim <||> input *
## [1] 50  5
## 
## * str <||> input *
## 'data.frame':    50 obs. of  5 variables:
##  $ sr   : num  11.43 12.07 13.17 5.75 12.88 ...
##  $ pop15: num  29.4 23.3 23.8 41.9 42.2 ...
##  $ pop75: num  2.87 4.41 4.43 1.67 0.83 2.85 1.34 0.67 1.06 1.14 ...
##  $ dpi  : num  2330 1508 2108 189 728 ...
##  $ ddpi : num  2.87 3.93 3.82 0.22 4.56 2.43 2.67 6.51 3.08 2.8 ...
## 
## * summary <||> input *
##        sr             pop15           pop75            dpi         
##  Min.   : 0.600   Min.   :21.44   Min.   :0.560   Min.   :  88.94  
##  1st Qu.: 6.970   1st Qu.:26.21   1st Qu.:1.125   1st Qu.: 288.21  
##  Median :10.510   Median :32.58   Median :2.175   Median : 695.66  
##  Mean   : 9.671   Mean   :35.09   Mean   :2.293   Mean   :1106.76  
##  3rd Qu.:12.617   3rd Qu.:44.06   3rd Qu.:3.325   3rd Qu.:1795.62  
##  Max.   :21.100   Max.   :47.64   Max.   :4.700   Max.   :4001.89  
##       ddpi       
##  Min.   : 0.220  
##  1st Qu.: 2.002  
##  Median : 3.000  
##  Mean   : 3.758  
##  3rd Qu.: 4.478  
##  Max.   :16.710  
## 
## * print <||> result *
## 
## Call:
## lm(formula = sr ~ pop15 + pop75 + dpi + ddpi)
## 
## Coefficients:
## (Intercept)        pop15        pop75          dpi         ddpi  
##  28.5660865   -0.4611931   -1.6914977   -0.0003369    0.4096949  
## 
## 
## * summary <||> result *
## 
## Call:
## lm(formula = sr ~ pop15 + pop75 + dpi + ddpi)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.2422 -2.6857 -0.2488  2.4280  9.7509 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28.5660865  7.3545161   3.884 0.000334 ***
## pop15       -0.4611931  0.1446422  -3.189 0.002603 ** 
## pop75       -1.6914977  1.0835989  -1.561 0.125530    
## dpi         -0.0003369  0.0009311  -0.362 0.719173    
## ddpi         0.4096949  0.1961971   2.088 0.042471 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.803 on 45 degrees of freedom
## Multiple R-squared:  0.3385, Adjusted R-squared:  0.2797 
## F-statistic: 5.756 on 4 and 45 DF,  p-value: 0.0007904
## 
## 
## * anova <||> result *
## Analysis of Variance Table
## 
## Response: sr
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## pop15      1 204.12 204.118 14.1157 0.0004922 ***
## pop75      1  53.34  53.343  3.6889 0.0611255 .  
## dpi        1  12.40  12.401  0.8576 0.3593551    
## ddpi       1  63.05  63.054  4.3605 0.0424711 *  
## Residuals 45 650.71  14.460                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

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

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 (results not shown).

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)
## [1] "CO3"             "USJudgeRatings1" "sleep1"

(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 if 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()
## 
## ntbt_lm(data = ., conc ~ uptake)
## 
## * print <||> result *
## 
## Call:
## lm(formula = conc ~ uptake)
## 
## Coefficients:
## (Intercept)       uptake  
##       73.71        13.28  
## 
## 
## ntbt_cor.test(data = ., CONT, INTG)
## 
## * print <||> result *
## 
##  Pearson's product-moment correlation
## 
## data:  CONT and INTG
## t = -0.8605, df = 41, p-value = 0.3945
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4168591  0.1741182
## sample estimates:
##        cor 
## -0.1331909 
## 
## 
## ntbt_t.test(data = ., extra ~ group)
## 
## * print <||> result *
## 
##  Welch Two Sample t-test
## 
## data:  extra by group
## t = -1.8608, df = 17.776, p-value = 0.07939
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.3654832  0.2054832
## sample estimates:
## mean in group 1 mean in group 2 
##            0.75            2.33
## [1] "CO3"             "USJudgeRatings1" "sleep1"
  • 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 invisible, so nothing is output

You can verify that, initially, it is empty:

ls(intuEnv())
## character(0)

How can intuEnv be used?

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

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()
## [1] "CO3"             "USJudgeRatings1" "sleep1"

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

ls(intuEnv())
## [1] "ctres" "lmfit" "ttres"

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

Four strategies of using one of the collected results are shown below (output not shown):

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())
## character(0)

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()
## [1] "USJudgeRatings" "coll"           "fit"            "ntbt_cor.test" 
## [5] "ntbt_lsfit"     "result"

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()
## [1] "CO3"             "USJudgeRatings1" "sleep1"

Before forgetting, let’s reinstate the original intuEnv:

set_intuEnv(saved_intuEnv)
## <environment: R_GlobalEnv>

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

ls()
##  [1] "USJudgeRatings" "coll"           "ctres"          "fit"           
##  [5] "lmfit"          "ntbt_cor.test"  "ntbt_lsfit"     "result"        
##  [9] "saved_intuEnv"  "ttres"

They were.

Now the results are at your disposal to use as any other variable (result 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())
## character(0)

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

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

ls(intuEnv())
## [1] "CO3"             "USJudgeRatings1" "sleep1"

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. We will use intuEnv 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()
## [1] "USJudgeRatings1" "ttres"           "CO3nc"           "ctres"          
## [5] "lmsfit"          "lmfit"           "sleep1"          "CO3"
  • 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).

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()
## [1] "CO3"             "USJudgeRatings1" "sleep1"          "CO3nc"          
## [5] "lmfit"           "ctres"           "ttres"           "lmsfit"

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)
## [1] "CO3"             "USJudgeRatings1" "sleep1"          "CO3nc"          
## [5] "lmfit"           "ctres"           "ttres"           "lmsfit"

The intuBag will keep 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 consisting in 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)
## $members
##     name  band
## 1   John  TRUE
## 2   Paul  TRUE
## 3 George  TRUE
## 4  Ringo  TRUE
## 5  Brian FALSE
## 6   <NA>  <NA>
## 
## $what_played
##     name instrument
## 1   John     guitar
## 2